FIELD

This disclosure relates generally to power quality issues, and more particularly, to systems and methods related to managing voltage event alarms associated with power quality issues in an electrical system.

BACKGROUND

As is known, power quality issues are one of the most significant and costly impacts on electrical systems (also sometimes referred to as “electrical networks”). Poor power quality is estimated to cost the European economy up to €150 billion annually, according to the Leonardo Power Quality Initiative.1Additionally, the U.S. economy experiences losses ranging from $119 billion to $188 billion annually, according to research by the Electric Power Research Institute (EPRI).2Perhaps the most important statistic is the EPRI finding that 80 percent of power-quality disturbances are generated within a facility. One exemplary economic model summarizes the total cost associated with power quality events as follows:
Total losses=production losses+restart losses+product/material losses+equipment losses+third-party costs+other miscellaneous costs3

Other miscellaneous costs associated with power quality issues may include intangible losses such as a damaged reputation with customers and suppliers or more direct losses such as the devaluation of credit ratings and stock prices.

SUMMARY

Described herein are systems and methods related to managing voltage event alarms associated with power quality issues or events in an electrical system. The electrical system may be associated with at least one load, process, building, facility, watercraft, aircraft, or other type of structure, for example. The electrical system may be monitored and/or controlled by one or more electrical/power monitoring systems, for example.

In one aspect of this disclosure, a method for managing voltage event alarms in an electrical system includes processing electrical measurement data from, or derived from, energy-related signals captured by at least one IED of a plurality of IEDs to identify an anomalous voltage condition (e.g., a power quality issue or event) at a point of installation of a respective one of the plurality of IEDs in the electrical system. In embodiments, the anomalous voltage condition corresponds to a measured IED voltage being above or greater than one or more upper voltage event alarm thresholds or below or less than one or more lower voltage event alarm thresholds, for example, as shown inFIGS. 49 and 50. The method also includes determining if the electrical system is affected (or “impacted,” as sometimes referred to herein) by the identified anomalous voltage condition. In embodiments, in response to determining that the electrical system is not affected by the identified anomalous voltage condition, at least one of the one or more upper alarm thresholds or at least one of the one or more lower alarm threshold is adjusted to at least a magnitude of the measured IED voltage captured during the identified anomalous voltage condition. In some embodiments, the at least one of the one or more upper alarm thresholds or the at least one of the one or more lower alarm thresholds is also adjusted to a duration of the measured IED voltage.

In some embodiments, the above-discussed method (and/or other systems and/or methods discussed herein) may be implemented on the at least one IED called for in the above-discussed method. Additionally, in some embodiments the above-discussed method (and/or other systems and/or methods discussed herein) may be implemented partially or fully remote from the at least one IED, for example, on other IEDs of the plurality of IEDs and/or a control system or device coupled to the at least one IED and/or the other IEDs. The above-discussed method (and/or other systems and/or methods discussed herein) may also be implemented partially or fully in a gateway, a cloud-based system, on-site software, a remote server, etc. (which may alternatively be referred to as a “head-end” or “Edge” system herein). Examples of the plurality of IEDs (including the at least one IED) may include a smart utility meter, a power quality meter, and/or another measurement device (or devices). The plurality of IEDs may include breakers, relays, power quality correction devices, uninterruptible power supplies (UPSs), filters, and/or variable speed drives (VSDs), for example. Additionally, the plurality of IEDs may include at least one virtual meter in some embodiments. In embodiments, the plurality of IEDs may also incorporate analog and/or digital I/O capabilities from equipment directly or indirectly connected to the electrical system. For example, ambient temperature readings (e.g., ° F., ° C.) from outside a facility and connected to the at least one IED would be considered as an analog input connected to the electrical system. Additionally, a breaker status (e.g., off/on, open/closed) derived from a breaker located inside switchgear within the facility and brought into the at least one IED would be considered as a digital input connected to the electrical system.

In embodiments in which the above-discussed method (and/or other systems and/or methods discussed herein) is implemented on the at least one IED, for example, the at least one IED may be coupled to measure energy-related signals, receive the electrical measurement data at an input, and configured to generate at least one or more outputs. The outputs may be used, for example, to manage voltage event alarms and alarm thresholds associated with anomalous voltage conditions in the electrical system.

It is understood that an anomalous voltage condition may refer to any type of electrical occurrence of interest. What is considered an anomalous voltage condition for one installation, for example, may not be considered as an anomalous voltage condition for another installation. Accordingly, a “power quality” event, for example, is an electrical occurrence of interest that is generally recognized as an anomalous voltage condition that may adversely impact the operation and/or equipment installed on an electrical system.

The above-discussed method (and/or other systems and/or methods discussed herein) may include one or more of the following features either individually or in combination with other features in some embodiments. For example, in some embodiments the energy-related signals captured by the at least one IED may include at least one of a voltage signal, a current signal, input/output (I/O) data, and a derived or extracted value. In some embodiments, the I/O data includes at least one of a digital signal (e.g., two discrete states) and an analog signal (e.g., continuously variable). The digital signal may include, for example, at least one of on/off status(es), open/closed status(es), high/low status(es), synchronizing pulse and any other representative bi-stable signal. Additionally, the analog signal may include, for example, at least one of temperature, pressure, volume, spatial, rate, humidity, and any other physically or user/usage representative signal.

In accordance with some embodiments of this disclosure, the derived or extracted value includes at least one of a calculated, computed, estimated, derived, developed, interpolated, extrapolated, evaluated, and otherwise determined additional energy-related value from at least one of the measured voltage signal and/or the measured current signal. In some embodiments, the derived value additionally or alternatively includes at least one of active power(s), apparent power(s), reactive power(s), energy(ies), harmonic distortion(s), power factor(s), magnitude/direction of harmonic power(s), harmonic voltage(s), harmonic current(s), interharmonic current(s), interharmonic voltage(s), magnitude/direction of interharmonic power(s), magnitude/direction of sub-harmonic power(s), individual phase current(s), phase angle(s), impedance(s), sequence component(s), total voltage harmonic distortion(s), total current harmonic distortion(s), three-phase current(s), phase voltage(s), line voltage(s), spectral analysis and/or other similar/related parameters. In some embodiments, the derived value additionally or alternatively includes at least one energy-related characteristic, the energy-related characteristic including magnitude, direction, phase angle, percentage, ratio, level, duration, associated frequency components, energy-related parameter shape, and/or decay rate. In accordance with some embodiments of this disclosure, the derived or extracted value may be linked to at least one process, load(s) identification, etc., for example.

It is understood that the energy-related signals captured by the at least one IED may include (or leverage) substantially any electrical parameter derived from at least one of the voltage and current signals (including the voltages and currents themselves), for example. It is also understood that the energy-related signals may be continuously or semi-continuously/periodically captured/recorded and/or transmitted and/or logged by the at least one IED, and anomalous voltage conditions detected based on the energy-related signals, and associated alarm thresholds, may be updated (e.g., evaluated/re-evaluated, prioritized/re-prioritized, tracked, etc.) in response thereto. For example, anomalous voltage conditions may initially be detected and characterized (e.g., as particular types of power quality events) from energy-related signals captured at a first time, and may be updated or revised (e.g., recharacterized) in response to variation(s)/change(s) identified from energy-related signals captured at a second time. Associated alarm thresholds (e.g., the recited upper alarm thresholds and lower alarm thresholds) may also be updated or revised.

In some embodiments, the at least one IED capturing the energy-related signals includes at least one metering device. The at least one metering device may correspond, for example, to at least one metering device in the electrical system for which the energy-related signals are monitored.

As used herein, an IED is a computational electronic device optimized to perform a particular function or set of functions. Examples of IEDs may include smart utility meters, power quality meters, microprocessor relays, digital fault recorders, and other metering devices. IEDs may also be imbedded in VSDs, uninterruptible power supplies (UPSs), circuit breakers, relays, transformers, or any other electrical apparatus. IEDs may be used to perform measurement/monitoring and control functions in a wide variety of installations. The installations may include utility systems, industrial facilities, warehouses, office buildings or other commercial complexes, campus facilities, computing co-location centers, data centers, power distribution networks, or any other structure, process or load that uses electrical energy. For example, where the IED is an electrical power monitoring device, it may be coupled to (or be installed in) an electrical power transmission or distribution system and configured to sense/measure and store data (e.g., waveform data, logged data, I/O data, etc.) as electrical parameters representing operating characteristics (e.g., voltage, current, waveform distortion, power, etc.) of the electrical distribution system. These parameters and characteristics may be analyzed by a user to evaluate potential performance, reliability and/or power quality-related issues, for example. The IED may include at least a controller (which in certain IEDs can be configured to run one or more applications simultaneously, serially, or both), firmware, a memory, a communications interface, and connectors that connect the IED to external systems, devices, and/or components at any voltage level, configuration, and/or type (e.g., AC, DC). At least certain aspects of the monitoring and control functionality of an IED may be embodied in a computer program that is accessible by the IED.

In some embodiments, the term “IED” as used herein may refer to a hierarchy of IEDs operating in parallel and/or tandem/series. For example, an IED may correspond to a hierarchy of energy meters, power meters, and/or other types of resource meters. The hierarchy may comprise a tree-based hierarchy, such a binary tree, a tree having one or more child nodes descending from each parent node or nodes, or combinations thereof, wherein each node represents a specific IED. In some instances, the hierarchy of IEDs may share data or hardware resources and may execute shared software. It is understood that hierarchies may be non-spatial such as billing hierarchies where IEDs grouped together may be physically unrelated.

It is understood that an input is data that a processor and/or IED (e.g., the above-discussed plurality of IEDs) receives, and an output is data that a processor and/or IED sends. Inputs and outputs may either be digital or analog. The digital and analog signals may be both discrete variables (e.g., two states such as high/low, one/zero, on/off, etc. If digital this may be a value. If analog, the presence of a voltage/current may be considered by the system/IED as an equivalent signal) or continuous variables (e.g., continuously variable such as spatial position, temperature, pressure voltage, etc.). They may be digital signals (e.g., measurements in an IED coming from a sensor producing digital information/values) and/or analog signals (e.g., measurements in an IED coming from a sensor producing analog information/values). These digital and/or analog signals may include any processing step within the IED (e.g., derive a Power Factor, a magnitude, among all the derived calculations).

Processors and/or IEDs may convert/reconvert digital and analog input signals to a digital representation for internal processing. Processors and/or IEDs may also be used to convert/reconvert internally processed digital signals to digital and/or analog output signals to provide some indication, action, or other response (such as an input for another processor/IED). Typical uses of digital outputs may include opening or closing breakers or switches, starting or stopping motors and/or other equipment, and operating other devices and equipment that are able to directly interface with digital signals. Digital inputs are often used to determine the operational status/position of equipment (e.g., is a breaker open or closed, etc.) or read an input synchronous signal from a utility pulsed output. Analog outputs may be used to provide variable control of valves, motors, heaters, or other loads/processes in energy management systems. Finally, analog inputs may be used to gather variable operational data and/or in proportional control schemes.

A few more examples where digital and analog I/O data are leveraged may include (but not be limited to): turbine controls, plating equipment, fermenting equipment, chemical processing equipment, telecommunications, equipment, precision scaling equipment, elevators and moving sidewalks, compression equipment, waste water treatment equipment, sorting and handling equipment, plating equipment temperature/pressure data logging, electrical generation/transmission/distribution, robotics, alarm monitoring and control equipment, as a few examples.

As noted earlier in this disclosure, the energy-related signals measured by the plurality of IEDs may include I/O data. It is understood that the I/O data may take the form of digital I/O data, analog I/O data, or a combination digital and analog I/O data. The I/O data may convey status information, for example, and many other types of information, as will be apparent to one of ordinary skill in the art from discussions above and below.

It is understood that the terms “processor” and “controller” are sometimes used interchangeably herein. For example, a processor may be used to describe a controller. Additionally, a controller may be used to describe a processor.

In some embodiments, the above-discussed method (and/or other systems and/or methods discussed herein) is generally applicable to non-periodic power quality issues or events such as transients, short-duration root-mean-square (rms) variations (e.g., sags, swells, momentary interruptions, temporary interruptions, etc.), and some long-duration rms variations (e.g., that may be as long as about 1-5 minute(s)).

Examples of electrical measurement data processed from, or derived from, energy-related signals that may be captured by the at least one IED to identify the anomalous voltage condition may include at least one of continuously measured voltage and current signals and their derived parameters and characteristics. Electrical parameters and events may be derived, for example, from analyzing energy-related signals (e.g., real power, reactive power, apparent power, harmonic distortion, phase imbalance, frequency, voltage/current transients, voltage sags, voltage swells, etc.). More particularly, the at least one IED may evaluate a power quality event's magnitude, duration, load impact, recovery time from impact, unproductive recovery energy consumed, CO2emissions from recovery energy, costs associated with the event, and so forth.

It is understood there are types of power quality events and there are certain characteristics of these types of power quality events, as described further below in connection with the table from IEEE Standard 1159-2019 (known art) provided below, for example. A voltage sag is one example type of power quality event. The characteristics of voltage sag events are the magnitude of the voltage sag and its duration, for example. The above-discussed method (and/or other systems and/or methods discussed herein) may evaluate and adjust voltage event alarms based on the affect (or impact) of voltage sag events (and other events) on the electrical system. In embodiments, voltage event alarm thresholds are adjusted based on the voltage sag's magnitude and duration. In some embodiments, an anomalous voltage condition (i.e., voltage perturbation) is indicative of a power quality event (e.g., a voltage sag). As used herein, examples of power quality events may include voltage and/or current events on phase conductors, neutral conductors, and/or ground paths. As illustrated inFIG. 3, for example, as will be described further below, is some embodiments there will be multiple alarm thresholds, for example, depending on the duration of an event. Additionally, there may be a threshold “above the nominal” (e.g., transients, swells, overvoltages), and a threshold “below the nominal” (e.g., sags, interruptions, undervoltages).

In accordance with some embodiments of this disclosure, the at least one IED responsible for capturing the energy-related signals (which are used to identify the anomalous voltage condition) is configured to monitor one or more loads in the electrical system (e.g., at the point of installation of the respective one of the plurality of IEDs monitored by the at least one IED). In embodiments, each of the loads has a rated voltage and recommended operational range, for example, as shown inFIGS. 49 and 50. The rated voltage corresponds to a desired voltage magnitude/level for optimal load operation. Additionally, the recommended operational range is the area above and below the rated voltage where the loads may still operate continuously, although not necessarily optimally. In some embodiments, the upper and lower alarm thresholds may be configured inside (or within) the recommended operational range of the loads to ensure the loads retain their operational integrity and are not overly stressed, and to ensure the end-user knows if/when they are getting close to the recommended boundaries. In other embodiments, the upper and the lower alarm thresholds align with the recommended operational range of the loads so that excursions beyond the recommended operational range may be measured, captured and stored (e.g., on a memory device associated with the IED or electrical system). Additional aspects of rated voltages and recommended operational ranges of loads, and their relationship to the upper and lower alarm thresholds, are described further in connection withFIGS. 49 and 50, for example.

In some embodiments, determining if the electrical system is affected by the identified anomalous voltage condition includes determining if one or more loads monitored by the respective one of the plurality of IEDs are affected by the identified anomalous voltage condition based on a measured magnitude and/or duration of excursions beyond the recommended voltage range for the one or more loads. The recommended operational range may be substantially equal to or within the bounds of the upper and lower alarm thresholds, for example. In embodiments, each of the one or more upper alarm thresholds and each of the one or more lower alarm thresholds has an associated magnitude and duration. The one or more upper alarm thresholds may include a threshold above a nominal or expected voltage at the point of installation of the respective one of the plurality of IEDs in the electrical system. The threshold above the nominal voltage may be indicative of transients, swells, or overvoltages, for example. The one or more lower alarm thresholds may include a threshold below a nominal or expected voltage at the point of installation of the respective one of the plurality of IEDs in the electrical system. The threshold below the nominal voltage may be indicative of sags, interruptions, or undervoltages, for example.

In embodiments, determining if the electrical system is affected by the identified anomalous voltage condition includes identifying loss of load in the electrical system associated with the identified anomalous voltage condition, and generating and/or initiating a load loss alarm indicating the identified loss of load. In some embodiments, the load loss alarm may be communicated via at least one of: a report, a text, an email, audibly, and an interface of a screen/display. The screen/display may be a screen/display of user device, for example, of a user responsible for monitoring the electrical system. Additionally, in embodiments the load loss alarm may be indicated or displayed on a system for managing voltage event alarms in the electrical system. In embodiments, the electrical system includes one or more loads installed at respective locations in the electrical system, and the loads are monitored by one or more of the IEDs in the electrical system. Loss of load may be associated with the monitored loads, for example. In embodiments, the loss of load may also be determined using virtual meters, status inputs, etc.

As used herein, a load loss (sometimes also referred to as a “loss of load”) is the unexpected, unplanned and/or unintentional removal of one or more loads from the electrical system. In this application, a voltage perturbation or event, and the subsequent load loss, is likely a result of one or more external influences on the electrical system (e.g., a fault, etc.), or the normal or abnormal operation of loads, protective devices, mitigation devices, and/or other equipment intentionally connected to the electrical system. Load losses may be indicated by measured parameters such as voltage, current, power, energy, harmonic distortion, imbalance, etc., or they may be indicated by discrete (digital) and/or analog input-output (I/O) signals originating from equipment directly and/or indirectly connected to the electrical system. For example, breakers often provide an output indication on their present position (e.g., open/closed, off/on, etc.) to communicate their operational status.

In embodiments, determining if the electrical system is affected by the identified anomalous voltage condition includes determining if one or more portions or zones of the electrical system proximate to the respective one of the plurality of IEDs is affected by the identified anomalous voltage condition. In some embodiments, the at least one IED of the plurality of IEDs includes the respective one of the plurality of IEDs. Additionally, in some embodiments the respective one of the plurality of IEDs is installed at a first point of installation in the electrical system, and at least one IED of the plurality of IEDs is installed in at least the first point of installation in the electrical system and a second point of installation in the electrical system. In embodiments, the first and second points of installation in the electrical system correspond to first and second hierarchical zones of the electrical system.

Additionally, in embodiments determining if the electrical system is affected by the identified anomalous voltage condition includes measuring one or more first parameters associated with the electrical system at a first time, measuring one or more second parameters associated with the electrical system at a second time, and comparing the first parameters to the second parameters to determine if the electrical system is affected by the identified anomalous voltage condition. In embodiments, comparing the first parameters to the second parameters to determine if the electrical system is affected by the identified anomalous voltage condition includes comparing the first parameters to the second parameters to determine if a meaningful discrepancy exists between the first parameters and the second parameters. In response to determining that a meaningful discrepancy exists between the first parameters and the second parameters, it may be determined that the electrical system is affected by the identified anomalous voltage condition. Additionally, in response to determining that a meaningful discrepancy does not exist between the first parameters and the second parameters, it may be determined that the electrical system is not affected by the identified anomalous voltage condition. In other words, the existence of a meaningful discrepancy (or lack thereof) between the first and second parameters may be used to determine if an event affected or did not affect the operation of an electrical system.

In some embodiments, the first parameters are substantially the same as the second parameters. The first and second parameters may include energy-related parameters, for example. In embodiments, the energy-related parameters may include at least one of voltage, current, energy, active power, apparent power, reactive power, harmonic voltages, harmonic currents, total voltage harmonic distortion, total current harmonic distortion, harmonic power, individual phase currents, three-phase currents, phase voltages, and line voltages. In embodiments, the energy-related parameters may include (or leverage) substantially any electrical parameter derived from the voltage and current signals (including the voltages and currents themselves). In embodiments, the first time corresponds to a time prior to the identified voltage event. Additionally, in embodiments the second time corresponds to a time after the identified voltage event.

In embodiments, in response to determining that the electrical system is not affected by the identified anomalous voltage condition, voltage condition data associated with the identified anomalous voltage condition may be stored in a memory device associated with the IED. The identified anomalous voltage condition may be classified as non-impactful to the electrical system. In embodiments, in response to determining that the electrical system is affected by the identified anomalous voltage condition, voltage condition data associated with the identified anomalous voltage condition may be stored in the memory device associated with the IED. The identified anomalous voltage condition may be classified as impactful (i.e., impacting the operation of one or more loads and/or processes) to the electrical system.

In embodiments, the upper alarm thresholds and the lower alarm thresholds are indicated in a dynamic tolerance curve associated with one or more loads monitored by the respective one of the plurality of IEDs in the electrical system. In some embodiments, the dynamic tolerance curve characterizes a response of the one or more loads to power quality events including the anomalous voltage condition. Additionally, in some embodiments the dynamic tolerance curve characterizes an impact of the power quality events on the electrical system. In embodiments, the dynamic tolerance curve includes at least one magnitude threshold above the nominal voltage associated with a duration and/or at least one magnitude threshold below the nominal voltage associated with a duration. In embodiments, the upper alarm thresholds and/or the lower alarm thresholds are adjusted to the measured IED voltage to more accurately represent true voltage event (i.e., perturbation) sensitivity at the respective one of the plurality of IEDs point of installation in the electrical system.

In some embodiments, the identified anomalous voltage condition includes a plurality of anomalous voltage conditions. Additionally, in some embodiments the plurality of anomalous voltage conditions are prioritized based on one or more parameters associated with the plurality of anomalous voltage conditions. The parameters may include magnitude and/or duration of the plurality of anomalous voltage conditions, for example. In embodiments, the magnitude and/or duration of the plurality of anomalous voltage conditions are indicative of anomalous voltage condition type(s).

In some embodiments, in response to determining that the electrical system is affected by the identified anomalous voltage condition, at least one source of the identified anomalous voltage condition may be identified. In some embodiments, it may be determined if the at least one identified source is upstream or downstream from a metering point in the electrical system associated with the respective one of the plurality of IEDs.

In embodiments, the identified anomalous voltage condition is indicative of a voltage event. The voltage event may include, for example, one of a voltage sag, a voltage swell, a voltage transient, an instantaneous interruption, a momentary interruption, a temporary interruption, and a long-duration root-mean-square (rms) variation.

In another aspect of this disclosure, a method for managing voltage event alarms in an electrical system includes processing electrical measurement data from, or derived from, energy-related signals captured by at least one IED of a plurality of IEDs to identify an anomalous voltage condition at a point of installation of a respective one of the plurality of IEDs in the electrical system. In embodiments, the anomalous voltage condition corresponding to a magnitude and/or duration of a measured IED voltage being above or greater than one or more upper alarm thresholds or below or less than one or more lower alarm thresholds. The method also includes determining if the electrical system is affected by the identified anomalous voltage condition. In embodiments, in response to determining that the electrical system is not affected by the identified anomalous voltage condition, at least one of the one or more upper alarm thresholds or at least one of the one or more lower alarm thresholds is adjusted to the magnitude and duration of the measured IED voltage. In some embodiments, the at least one of the one or more upper alarm thresholds or the at least one of the one or more lower alarm thresholds is adjusted to the magnitude and duration of the measured IED voltage such that the magnitude of the measured IED voltage corresponds to a voltage magnitude setpoint of the alarm thresholds, and the duration of the measured IED voltage corresponds to a voltage event duration setpoint of the alarm thresholds.

In some embodiments, determining if the electrical system is affected by the identified anomalous voltage condition includes identifying loss of load in the electrical system associated with the identified anomalous voltage condition, and initiating a load loss alarm indicating the identified loss of load. In some embodiments, the load loss alarm may be generated, communicated, indicated, stored, analyzed, managed and/or utilized in at least one or more component (e.g., IED, gateway) and/or system (e.g., on-site, cloud-based) associated with the electrical system, control system, and/or power monitoring system.

In a further aspect of this disclosure, a method for managing voltage event alarms in an electrical system includes processing electrical measurement data from, or derived from, energy-related signals captured by at least one intelligent electronic device (IED) of a plurality of IEDs to identify an anomalous voltage condition (e.g., a power quality issue or event) at a point of installation of a respective one of the plurality of IEDs in the electrical system. The anomalous voltage condition may correspond, for example, to a measured IED voltage being above or greater than one or more upper alarm thresholds or below or less than one or more lower alarm thresholds, for example, as shown inFIGS. 49 and 50. The method also includes determining if the electrical system is affected (or “impacted,” as sometimes referred to herein) by the identified anomalous voltage condition, for example, based, at least in part, on an evaluation of at least one of a magnitude and a duration of the identified anomalous voltage condition. In response to determining that the electrical system is affected by the identified anomalous voltage condition, at least one of a plurality of criteria may be chosen to adjust at least one of the upper alarm thresholds and/or at least one of the lower alarm thresholds.

In accordance with some embodiments of this disclosure, the at least one of the magnitude and the duration of the identified anomalous voltage condition (e.g., voltage sag) used in the evaluation to determine if the electrical system is affected by the identified anomalous voltage condition corresponds to a measured magnitude and a measured duration of the identified anomalous voltage condition. In accordance with some embodiments of this disclosure, at least one of the measured magnitude and the measured duration of the identified anomalous voltage condition is compared to at least one of a reference magnitude and a reference duration to determine if the electrical system is affected by the identified anomalous voltage condition. In accordance with some embodiments of this disclosure, the reference magnitude and the reference duration are used to determine impact of the identified anomalous voltage condition on the electrical system. In response to determining that the impact of identified anomalous voltage condition exceeds an impact threshold, for example, it may be determined that part and/or all of the electrical system and/or its loads are affected by the identified anomalous voltage condition.

In accordance with some embodiments of this disclosure, the chosen at least one of the plurality of criteria includes recovery time. In accordance with some embodiments of this disclosure, the recovery time is associated with impact of the anomalous voltage condition on the electrical system. Additionally, in accordance with some embodiments of this disclosure, the recovery time corresponds to accumulated recovery time, the accumulated recovery time associated with recurrence of the anomalous voltage condition over one or more periods of time (e.g., days, weeks, months, seasons, years, etc.). The accumulated recovery time may be reset, for example, in response to one or more conditions (e.g., user specified conditions).

In accordance with some embodiments of this disclosure, the chosen at least one of the plurality of criteria includes the response of one or more load types to the anomalous voltage condition. Additionally, in accordance with some embodiments of this disclosure, the chosen at least one of the plurality of criteria includes input/output (I/O) data. The I/O data may include, for example, at least one of a digital signal (e.g., two discrete states) and an analog signal (e.g., continuously variable). The digital signal may include, for example, at least one of on/off status(es), open/closed status(es), high/low status(es), synchronizing pulse and any other representative bi-stable signal. Additionally, the analog signal may include, for example, at least one of temperature, pressure, volume, spatial, rate, humidity, and any other representative physically representative signal.

In accordance with some embodiments of this disclosure, the chosen at least one of the plurality of criteria includes time(s) of occurrence of the anomalous voltage condition. In accordance with some embodiments of this disclosure, a voltage event alarm or alarms associated with the one or more upper alarm thresholds and/or the one or more lower alarm thresholds may be configured to be turned on, off, or adjusted (e.g., increased or decreased) in response to the time(s) of occurrence of the anomalous voltage condition meeting certain criteria. The certain criteria may include, for example, a time or times at which the voltage event alarm or alarms are configured to be silenced or muted (e.g., for a given time period).

In accordance with some embodiments of this disclosure, the chosen at least one of the plurality of criteria includes user configured criteria. Additionally, in accordance with some embodiments of this disclosure, the chosen at least one of the plurality of criteria includes or is based on historical data. The historical data may include, for example, at least one of non-impactful event data and impactful event data from historical events. The historical events may be associated with one or more aspects, portions, zones, processes, etc. of the electrical system, for example. In one aspect of this disclosure, the historical events may be grouped and/or archived based on an end-user segment under consideration (i.e., end-user segment associated with the electrical system for which voltage event alarms are being managed). The end-user segment under consideration may include, for example, at least one of: retail, offices, hotels, hospitals, data centers, food and beverages, oil and gas, industrial, automotive, utility, manufacturing, educational, governmental, residential and commercial. It is understood that the above-mentioned example end-user segments are but a few of many possible end-user segments that may utilize the systems and methods disclosed herein.

In accordance with some embodiments of this disclosure, the chosen at least one of the plurality of criteria includes a combination of criteria (i.e., two or more of the plurality of criteria). The combination of criteria may be selected, for example, based on one or more factors (e.g., user configured or learned factors). The factors may include affiliation of the criteria and determined or learned effectiveness of the criteria for evaluating and adjusting thresholds, for example. For example, recovery time, which is one example type of criteria, is often related to impact, which is another example type of criteria. Accordingly, in accordance with some embodiments of this disclosure, it may make most sense to choose recovery time and impacts as the at least one of the plurality of criteria. Additionally, with respect to determined or learned effectiveness of the criteria for evaluating and adjusting thresholds, it may be determined or learned over time which combinations of criteria are more effective for evaluating and adjusting thresholds, and which combinations of criteria are less effective for evaluating and adjusting thresholds. Example combinations of criteria may include, for example, at least two of: segment context, recovery energy, emissions volume (anticipated CO2emissions), product losses, cost(s), load type(s), and any other number of criteria as disclosed herein and apparent to one of ordinary skill in the art.

In accordance with some embodiments of this disclosure, the amount(s) and/or level(s) by which the at least one of the upper alarm thresholds and/or the at least one of the lower alarm thresholds is/are adjusted is/are based on an evaluation of at least one characteristic associated with the identified anomalous voltage condition with respect to at least one of the plurality of criteria (e.g., impact as a first criteria, recovery time as a second criteria, production losses as a third criteria, time as a fourth criteria, etc.). The at least one characteristic associated with the identified anomalous voltage condition may include, for example, at least one of the magnitude and the duration of the identified anomalous voltage condition.

In accordance with some embodiments of this disclosure, the upper alarm thresholds and the lower alarm thresholds are indicated in a dynamic tolerance curve associated with one or more loads monitored by the respective one of the plurality of IEDs in the electrical system. In accordance with some embodiments of this disclosure, the dynamic tolerance curve characterizes response of the one or more loads to power quality events including the identified anomalous voltage condition, and an impact of the power quality events on the electrical system, based on and/or responsive to the at least one of a plurality of criteria. The dynamic tolerance curve may include, for example, a magnitude threshold above the nominal voltage associated with a duration and a magnitude threshold below the nominal voltage associated with a duration. The magnitude threshold above the nominal voltage and the magnitude threshold below the nominal voltage may be based on and/or responsive to the at least one of a plurality of criteria, for example. In accordance with some embodiments of this disclosure, the at least one dynamic tolerance curve includes a plurality of dynamic tolerance curves, the plurality of dynamic tolerance curves including at least a first dynamic tolerance curve for a first day or time period and a second dynamic tolerance curve for a second day or time period.

In accordance with some embodiments of this disclosure, the aforesaid method (e.g., further illustrated byFIGS. 55-57, as will be discussed further below) may be used to initially set or adjust the at least one of the upper alarm thresholds and/or the at least one of the lower alarm thresholds (e.g., magnitude, duration). Additionally, in accordance with some embodiments of this disclosure, the aforesaid method may be used to refine or readjust or further constrain the at least one of the upper alarm thresholds and/or the at least one of the lower alarm thresholds after these thresholds are initially set or adjusted using at least one of the previously discussed methods (e.g., further illustrated byFIGS. 51-54, as will be discussed further below), for example. In accordance with some embodiments of this disclosure, this may further reduce the number of alarms a system operator, etc. may see.

Systems for managing voltage event alarms in an electrical system are also disclosed herein. In one aspect of this disclosure, a system for managing voltage event alarms in an electrical system includes at least one processor and at least one memory device coupled to the at least one processor. The at least one processor and the at least one memory device may be configured to process electrical measurement data from, or derived from, energy-related signals captured by at least one IED of a plurality of IEDs to identify an anomalous voltage condition at a point of installation of a respective one of the plurality of IEDs in the electrical system. The anomalous voltage condition may correspond, for example, to a measured IED voltage being above or greater than one or more upper alarm thresholds or below or less than one or more lower alarm thresholds. The at least one processor and the at least one memory device may also be configured to determine if the electrical system is affected by the identified anomalous voltage condition, for example, based, at least in part, on an evaluation of at least one of a magnitude and a duration of the identified anomalous voltage. In response to determining that the electrical system is affected by the identified anomalous voltage condition, at least one of a plurality of criteria may be chosen to adjust at least one of the upper alarm thresholds and/or at least one of the lower alarm thresholds.

In another aspect of this disclosure, a system for managing voltage event alarms in an electrical system includes at least one IED including at least one processor and at least one memory device coupled to the processor. The at least one processor and the at least one memory device are configured to process electrical measurement data from, or derived from, energy-related signals captured by the at least one IED to identify an anomalous voltage condition at a point of installation of the at least one IED in the electrical system. In embodiments, the anomalous voltage condition corresponds to a measured IED voltage being above or greater than one or more upper alarm thresholds or below or less than one or more lower alarm thresholds. The at least one processor and the at least one memory device are also configured to determine if the electrical system is affected by the identified anomalous voltage condition. In embodiments, in response to determining that the electrical system is not affected by the identified anomalous voltage condition, at least one of the one or more upper alarm thresholds or at least one of the one or more lower alarm thresholds is adjusted to the measured IED voltage.

In some embodiments, the at least one processor and the at least one memory device of the at least one IED are further configured to identify loss of load in the electrical system associated with the identified anomalous voltage condition, and initiate a load loss alarm indicating the identified loss of load. In some embodiments, the load loss alarm may be generated, communicated, indicated, stored, analyzed, managed and/or utilized in at least one or more component (e.g., IED, gateway) and/or system (e.g., on-site, cloud-based) associated with the electrical system, control system, and/or power monitoring system.

Alternatively, the at least IED may capture voltage and/or current signals (or waveforms) and forward the waveforms to another processor to evaluate for impact. The other processor may be located on-site (gateway, on-site software, IED, etc.) or off-site (cloud-based, remote software, etc.).

As will be further understood from discussions below, an important advantage of characterizing the electrical system's voltage event tolerance at a metering/IED point is to customize alarm thresholds at the meter's point of installation. Using dynamic voltage event characterization to manage alarms provides several benefits including ensuring1) relevant events are captured, 2) excessive alarms are prevented, 3) appropriate alarms are configured, and 4) important alarms are prioritized.

As is known, existing approaches to alarm configuration and management typically require manual configuration by an end-user based on standards (e.g., IEEE standards), recommendations, or guessing. As is also known, existing approaches to alarm configuration and management typically require some form of setpoint learning that necessitates a configuration “learning period” to determine what is normal. Unfortunately, however, if an event occurs during the learning period, it is considered normal behavior unless the end-user caught it or suggested otherwise. Moreover, setpoint learning is typically based on a voltage event's magnitude alone, and doesn't include any form of event impact analysis. Existing approaches may also use a “capture everything” approach that requires the end-user to apply filters to distinguish which alarms are important and which are not. Again, a “capture everything” approach does not generally disaggregate an impactful event from a non-impactful event. In short, the end-user (which may not be an expert) is required to actively discriminate which event alarms/thresholds are important, either before or after the event alarms are captured.

In some embodiments, the at least one IED and the loads of the above and below discussed systems and methods are installed at a same respective location or metering points in the electrical system. Additionally, in some embodiments the at least one IED and the loads of the above-method (and below described systems and methods) are installed at different respective locations (i.e., a plurality of locations) or metering points in the electrical system. In embodiments in which the electrical system includes more than one load, for example, a specific IED may be upstream from one load in the electrical system while being downstream from another load in the electrical system.

As used herein, the terms “upstream” and “downstream” are used to refer to electrical locations within an electrical system. More particularly, the electrical locations “upstream” and “downstream” are relative to an electrical location of an IED collecting data and providing this information. For example, in an electrical system including a plurality of IEDs, one or more IEDs may be positioned (or installed) at an electrical location that is upstream relative to one or more other IEDs in the electrical system, and the one or more IEDs may be positioned (or installed) at an electrical location that is downstream relative to one or more further IEDs in the electrical system. A first IED or load that is positioned on an electrical circuit upstream from a second IED or load may, for example, be positioned electrically closer to an input or source of the electrical system (e.g., a utility feed) than the second IED or load. Conversely, a first IED or load that is positioned on an electrical circuit downstream from a second IED or load may be positioned electrically closer to an end or terminus of the electrical system than the other IED.

A first IED or load that is electrically connected in parallel (e.g., on an electrical circuit) with a second IED or load may be considered to be “electrically” upstream from said second IED or load in embodiments, and vice versa. In embodiments, algorithm(s) used for determining a direction of a power quality event (i.e., upstream or downstream) is/are located (or stored) in the IED, cloud, on-site software, gateway, etc. As one example, the IED can record an electrical event's voltage and current phase information (e.g., by sampling the respective signals) and communicatively transmit this information to a cloud-based system. The cloud-based system may then analyze the voltage and current phase information (e.g., instantaneous, root-mean-square (rms), waveforms and/or other electrical characteristic) to determine if the source of the voltage event was electrically upstream or downstream from where the IED is electrically coupled to the electrical system (or network).

In some embodiments, the electrical measurement data from, or derived from, energy-related signals captured by the at least one IED may be processed on the at least one IED, as in the above-described system for managing voltage event alarms in an electrical system, or be processed in on-site software, in a cloud-based application, or in a gateway, etc., to manage voltage event alarms. Additionally, in some embodiments the electrical measurement data may be processed on a control system associated with the electrical system to manage voltage event alarms. The control system may be used for controlling one or more parameters associated with the electrical system, for example. In embodiments, identifying the anomalous voltage condition may include identifying: (a) a type of power quality event associated with the anomalous voltage condition, (b) a magnitude of the anomalous voltage condition, (c) a duration of the anomalous voltage condition, and/or (d) a location of the anomalous voltage condition in the electrical system. In embodiments, the power quality event type may include one of a voltage sag, a voltage swell, and a voltage transient. Additionally, in embodiments the location of the anomalous voltage condition may be derived from voltage and current signals as measured by the IEDs and associated with the anomalous voltage condition.

As discussed above, an anomalous voltage condition may be indicative of a voltage event. As also discussed above, a voltage event is one example type of power quality event. A power quality event may include at least one of a voltage sag, a voltage swell, and a voltage transient, for example. According to IEEE Standard 1159-2019, for example, a voltage sag is a decrease to between 0.1 and 0.9 per unit (pu) in rms voltage or current at the power frequency for durations of 0.5 cycle to 1 min. Typical values are 0.1 to 0.9 pu. Additionally, according to IEEE Standard 1159-2019, a voltage swell is an increase in rms voltage or current at the power frequency for durations from 0.5 cycles to 1 min. Below is a table from IEEE Standard 1159-2019 (known art), which defines various categories and characteristics of power system electromagnetic phenomena.

TypicalTypicalTypicalCategoriesspectral contentdurationvoltage magnitude1.0 Transients1.1 Impulsive1.1.1 Nanosecond5ns rise<50ns1.1.2 Microsecond1μs rise50 ns-1 ms1.1.3 Millisecond0.1ms rise>1ms1.2 Oscillatory1.2.1 Low frequency<5kHz0.3-50ms0-4pua1.2.2 Medium frquency5-500kHz20μs0-8pu1.2.3 High frequency0.5-5MHz5μs0-4pu2.0 Short-duration root-mean-square (rms) variations2.1 Instantaneous2.1.1 Sag0.5-30cycles0.1-0.9pu2.1.2 Swell0.5-30cycles1.1-1.8pu2.2 Momentary2.2.1 Interruption0.5 cycles-3 s<0.1pu2.2.2 Sag30 cycles-3 s0.1-0.9pu2.2.3 Swell30 cycles-3 s1.1-1.4pu2.2.4 Voltage Imbalance30 cycles-3 s2%-15%2.3 Temporary2.3.1 Interruption>3 s-1 min<0.1pu2.3.2 Sag>3 s-1 min0.1-0.9pu2.3.3 Swell>3 s-1 min1.1-1.2pu2.3.4 Voltage Imbalance>3 s-1 min2%-15%3.0 Long duration rms variations3.1 Interruption, sustained>1min0.0pu3.2 Undervoltages>1min0.8-0.9pu3.3 Overvoltages>1min1.1-1.2pu3.4 Current overload>1min4.0 Imbalance4.1 Voltagesteady state0.5-5%4.2 Currentsteady state1.0-3.0%5.0 Waveform distortion5.1 DC offsetsteady state0-0.1%5.2 Harmonics0-9kHzsteady state0-20%5.3 Interharmonics0-9kHzsteady state0-2%5.4 Notchingsteady state5.5 Noisebroadbandsteady state0-1%6.0 Voltage flucuations<25Hzintermittent0.1-7%0.2-2Pstb7.0 Power frequency variations<10s±0.10HzNOTE-These terms and categories apply to power quality measurements and are not to be confused with similar terms defined in IEEE Std 1366 ™-2012 [B30] and other reliability-related standards, recommended practices, and guides.aThe quanity pu refers to per unit, which is demensionless. The quanity 1.0 pu corresponds to 100%. The nominal condition is often considered to be 1.0 pu. In this table, the nominal peak value is used as the base for transients and nominal rms value is used as the base for rms variations.bFlicker severity index Pstas defined in IEC 61000-4-15-2010 [B17] and IEEE Std 1453 ™ [B31].

It is understood that the above table is one standards body's (IEEE in this case) way of defining/characterizing power quality events. It is understood there are other standards that define power quality categories/events as well, such as the International Electrotechnical Commission (IEC), American National Standards Institute (ANSI), etc., which may have different descriptions or power quality event types, characteristics, and terminology. In embodiments, power quality events may be customized power quality events (e.g., defined by a user).

In some embodiments, the electrical measurement data processed to identify the anomalous voltage condition (and power quality events) may be continuously or semi-continuously captured by the at least one IED, and the tolerance curve may be dynamically updated in response to anomalous voltage conditions (or power quality events) detected (or identified) from the electrical measurement data. For example, the tolerance curve may initially be generated in response to power quality events identified from electrical measurement data captured at a first time, and may be updated or revised in response to (e.g., to include or incorporate) power quality events identified from electrical measurement data captured at a second time. As events are captured, the tolerance curve (also sometimes referred to herein as “a dynamic tolerance curve”) may be continuously (e.g., dynamically) updated according to the unique response of the electrical system.

In some embodiments, the tolerance curve may be displayed in a graphical user interface (GUI) of the at least one IED, or the GUI of a control system used for monitoring or controlling one or more parameters associated with the electrical system. In embodiments, the control system may be a meter, an IED, on-site/head-end software (i.e., a software system), a cloud-based control system, a gateway, a system in which data is routed over the Ethernet or some other communications system, etc. A warning may be displayed in the GUI of the IED, the monitoring system or the control system, for example, in response to a determined impact (or severity) of the anomalous voltage condition (or power quality event) being outside of a range or threshold (e.g., voltage event alarm threshold). In some embodiments, the range is a predetermined range, for example, a user configured range. Additionally, in some embodiments the range is automatic, for example, using standards-based thresholds. Further, in some embodiments the range is “learned,” for example, by starting with a nominal voltage and pushing out the thresholds as non-impactful events occur in the natural course of the electrical network's operation.

The GUI may be configured to display factors contributing to the anomalous voltage condition (or power quality event). Additionally, the GUI may be configured to indicate a location of the anomalous voltage condition (or power quality event) in the electrical system. Further, the GUI may be configured to indicate how the loads (or another specific system or piece of equipment in the electrical system) will respond to the anomalous voltage condition (or power quality event). It is understood that any number of information may be displayed in the GUI. As part of this invention, any electrical parameter, impact to a parameter, I/O status input, I/O output, process impact, recovery time, time of impact, phases impacted, potentially discrete loads impacted beneath a single IED, etc. may be displayed in the GUI.FIG. 20, for example, as will be discussed further below, shows a simple example of incorporating percent load impacted with an indication of recovery time.

In embodiments, the tolerance curve displayed in the GUI does not have fixed scaling but, rather, can (and needs to) auto-scale, for example, to capture or display a plurality of power quality events. In accordance with various aspects of the disclosure, the beauty of having a dynamic tolerance curve is not being constrained to a static curve or curves (e.g., with fixed scaling). For example, referring briefly toFIG. 2(which will be discussed further below), while the y-axis is shown as a percent of nominal inFIG. 2, it can also be shown as an absolute nominal value (e.g., 120 volts, 208 volts, 240 volts, 277 volts, 480 volts, 2400 volts, 4160 volts, 7.2 kV, 12.47 kV, etc.). In this case, auto-scaling would be required because different voltage ranges would require different scaling for the y-axis. Additionally, the x-axis may be scaled in different units (e.g., cycles, seconds, etc.) and/or may have a variable maximum terminus point (e.g., 10 seconds, 1 minute, 5 minutes, 600 cycles, 3600 cycles, 18,000 cycles, etc.). In other words, in some embodiments there is no reason for the GUI to show more than it has to.

In embodiments, a goal of the invention claimed herein is to build a customized tolerance curve for a discrete location within a customer's power system (e.g., at a given IED) based on a perceived impact to downstream loads. Additionally, in embodiments a goal of the invention claimed herein is to quantify the time it takes to recover from a power quality event. In short, aspects of the invention claimed herein are directed toward describing the impact of a power quality event, which allows a customer to understand their operational parameters and constraints, accordingly.

The features proposed in this disclosure evaluate specific power quality events to quantify their impact on loads of an electrical system, recovery time, and other useful or interesting parameters. Its scope may include discrete metered points, network zones, and/or the aggregated electrical system in total. Novel ideas to display these concepts are also discussed, allowing the energy consumer to more efficiently and cost-effectively identify, analyze, mitigate, and manage their electrical networks.

Of the seven recognized power quality categories defined by IEEE 1159-2019, short-duration root mean square (rms) variations are generally the most disruptive and have the largest universal economic impact on energy consumers. Short-duration rms variations include voltage sags/dips, swells, instantaneous interruptions, momentary interruptions and temporary interruptions. One example study by the Electric Power Research Institute (EPRI) estimates an average of about 66 voltage sags are experienced by industrial customers each year. As the trend of industries becoming more dependent on sag-sensitive equipment has increased, so has the impact of these events.

The prevalence of voltage sags and the consequences of a growing install base of sag-sensitive equipment present many additional opportunities for electric solutions and services providers. The table below illustrates several example opportunities:

OpportunitiesBenefitsSolutionsIncreased monitoring systems componentsMore suitable sag-immunity equipmentTargeted sag mitigation equipmentServicesEngineering and consultingRemote monitoring and diagnosticsEquipment installation

As is known, a primary purpose of metering system alarms is to indicate when a threshold has been exceeded. Exceeding alarm thresholds are often the result of an anomalous electrical system condition, and the alarm acts as a warning of this condition. Metering systems have a history of providing excessive alarms, especially when the alarm thresholds are not configured properly. These excessive alarms (sometimes referred to as nuisance alarms or “alarm avalanche”) inundate users with warnings regarding their system and can lead to confusion (best case scenario) and simply disregarding feedback from their power monitoring system altogether (worst case scenario).

As further discussed in the Detailed Description section of this disclosure, it is possible/preferable to make alarms more productive by configuring them based on voltage events that actually impact the end-user's systems. In accordance with some embodiments of this invention, if an alarm is generated and the system has not been impacted (determined based on a pre-event vs. post-event change in the energy/power consumption), at least one alarm threshold is tightened to match the characteristics of said voltage event. This continues until a voltage event occurs with a coincident impact to the energy/power consumption.

Because each end-user has different tolerances for a system impact due to voltage events, it is possible to use other relevant characteristics and repercussions associated with said voltage events to fine-tune the alarm thresholds so that nuisance alarms will be reduced. This is true even in the case where a voltage event produces an energy/power impact. For example, some end-users may experience “brief” impactful voltage events that they wish to suppress. For instance, a commercial office building may experience a brief voltage sag event from the utility resulting in an interruption to the building. The equipment and systems within the building may automatically reset upon reapplying the source voltage with little to no impact to their operation. The facility manager for the building may want to ignore and/or reduce the priority of the impact associated with the voltage event.

The principal element of this feature is to allow end-users to better configure alarm thresholds to filter impactful events based on additional criteria (i.e., a subset of impactful voltage events). There are numerous event criteria that can be used/leveraged to help configure alarm thresholds. For example, a voltage event producing an impact to the electrical system at a given location within the system may have some recovery time (recovery period), ‘tr’. The end-user may wish to ignore events where tr≤5 minutes. By analyzing non-impactful and impactful event data from historical events, it is possible to determine the optimal alarm thresholds and configure the appropriate IEDs to ignore (or alternatively, provide at a lower priority) voltage events with a recovery time less than or equal to 5 minutes. An appropriate dynamic tolerance curve (DTC) may be created and/or updated to reflect the optimal alarm thresholds for voltage events to satisfy the trconstraint.

It is important to note that absolute magnitude (amplitude), relative percent (of some nominal value), and/or duration (time) may be considered, configured and/or adjusted to provide the optimal alarm threshold(s). Tolerance curves may be dynamically created/adjusted/readjusted/etc. to reflect the optimal alarm thresholds and/or priorities to achieve the desired alarm suppression (or conversely, the desired alarm expression). This DTC will likely exhibit altered, more moderate (e.g., looser) threshold settings than a dynamic tolerance curve based only on energy/power impacts due to the reasons mentioned above. It is possible to illustrate two overlapping DTCs to end-users: the first to describe only the impact sensitivity characteristics due to voltage events at the measuring IED's location, and the second to describe to describe the more liberal characteristics desired by the end-user as measured at said IED's location. Moreover, it is possible to have any number of overlapping DTCs to reflect multiple considerations describing the other alarm threshold constraints.

For example, it is possible to set alarm thresholds to describe the following:a particular amount of load impacted, either by absolute value (e.g., ≥10 kW) or relative; percentage of nominal (e.g., ≥10% of the average load);a particular zone, process and/or IED is impacted;a particular level of production losses;a particular location of the event (upstream/upline or downstream/downline);a particular cost (e.g., manufacturing, business, or intangible);a particular change in some parameter (i.e., electrical, mechanical, I/O, etc.); andOr by some combination or amalgamation of the aforementioned conditions.

Configuration/reconfiguration of alarm thresholds to reflect the ideas herein may also be limited to events either above or below the nominal voltage. For example, the feature may be configured to only change alarm thresholds above the nominal voltage to reflect concepts described in this document while having alarm thresholds below the nominal voltage reflect any impact associated with a voltage event (and vice versa).

As will be further appreciated from discussions below, the disclosed systems and methods provide versatility to automatically configure/reconfigure alarm thresholds that best mirror the end-user's intentions and limit/constrain alarms from their metering/monitoring system. Because all end-users have different ‘pain points’, different needs/usages, and different benefits derived from their metering/monitoring system, it is important to allow autonomy to choose how alarms are suppressed with respect to the specific system being monitored.

It is understood that there are many other features and advantages associated with the disclosed invention, as will be appreciated from the discussions below.

