PRIORITY CLAIM

This U.S. patent application claims priority under 35 U.S.C. § 119 to: Indian Patent Application No. 201921041963, filed on 16 Oct. 2019. The entire contents of the aforementioned application are incorporated herein by reference.

TECHNICAL FIELD

The disclosure herein generally relates to the field of testing and monitoring of solar panel networks, and, more particularly, to methods and systems for fault detection, diagnosis and localization in a solar panel network.

BACKGROUND

Solar photovoltaic (PV) segment is growing very rapidly owing to decline in capital costs including technology costs and manufacturing costs. While capital costs are declining, maintenance and operational costs still prohibit solar panel networks or solar plants from achieving optimum profitability. It is estimated that about 19% of the annual energy in the solar panel networks may be lost due to faults occurring in the solar panel networks, from studies conducted for domestic solar generation in United Kingdom. Although many types of faults exist, some of the faults such as a line-line fault, a line-ground fault, an arc fault and a hot spot fault are especially critical, as they not only cause hefty energy loss, but are also hazardous that may cause severe damage to the solar panel networks. Conventional fault detection and diagnosis (FDD) techniques for the solar panel networks are limited and confined to identifying faults either at broader classification level (voltage level or current level) or to studying one specific fault type at a time.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor-implemented method for fault detection, diagnosis and localization in a solar panel network under test is provided. The method comprising the steps of: obtaining a schema of the solar panel network under test, using a schema algorithm, wherein the solar panel network under test comprises a plurality of solar panels and a plurality of circuit elements, the plurality of solar panels arranged in one or more strings, one or more sub-arrays and one or more arrays, along with one or more circuit elements of the plurality of circuit elements and one or more inverters; determining a set of solar panel parameters for each solar panel present in the solar panel network under test, based on a name-plate dataset of each solar panel and a time-stamped operational log dataset of the solar panel network under test, wherein the set of solar panel parameters for each solar panel comprises a photo (light) current (IL), a diode reverse saturation current (IO), a series resistance (RS), a shunt resistance (RSH), and an ideality factor (n); generating a network simulation model for the solar panel network under test, from the obtained schema and the set of solar panel parameters for each solar panel, using a modelling technique; randomly generating a plurality of fault feature tensors (FFTs) and a plurality of no-fault feature tensors (NFFTs), for the generated network simulation model, wherein each fault feature tensor (FFT) of the plurality of fault feature tensors (FFTs) is associated with a fault scenario of a plurality of fault scenarios and each no-fault feature tensor (NFFT) of the plurality of no-fault feature tensors (NFFTs) is associated with a no-fault scenario of a plurality of no-fault scenarios, wherein each fault scenario is defined based on one or more fault types occurring at one or more fault locations in the solar panel network under test, and one or more environmental parameters comprising an operating temperature of an environment and a solar irradiance value of the environment, and each no-fault scenario is defined based on the one or more environmental parameters comprising the operating temperature of the environment and the solar irradiance value of the environment; determining (i) a no-fault simulated dataset for each no-fault feature tensor (NFFT) of the plurality of no-fault feature tensors (NFFTs) by simulating the corresponding no-fault feature tensor (NFFT) using the generated network simulation model, and (ii) a fault simulated dataset for each fault feature tensor (FFT) of the plurality of fault feature tensors (FFTs) by simulating the corresponding fault feature tensor (FFT), using the generated network simulation model; randomly dividing the plurality of fault feature tensors (FFTs) and the plurality of no-fault feature tensors (NFFTs) into a training dataset and a testing dataset, based on a predefined ratio, wherein the training dataset comprises one or more training FFTs along with corresponding fault simulated datasets and one or more training NFFTs along with corresponding no-fault simulated datasets, and the testing dataset comprises one or more testing FFTs along with corresponding fault simulated datasets and one or more testing NFFTs along with corresponding no-fault simulated datasets; and generating, via the one or more hardware processors, a fault detection, diagnosis and localization (FDDL) model for fault detection, diagnosis and localization in the solar panel network under test, by training a convolutional neural network (CNN) model with the training dataset, wherein (i) each training FFT of the one or more training FFTs present in the training dataset, is provided as label and the corresponding fault simulated dataset is provided as an input to the CNN model, and (ii) each training NFFT of the one or more training NFFTs present in the training dataset, is provided as label and the corresponding no-fault simulated dataset is provided as the input to the CNN model.

In another aspect, a system for fault detection, diagnosis and localization in a solar panel network under test is provided. The system comprising: a memory storing instructions; one or more Input/Output (I/O) interfaces; and one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to: obtain a schema of the solar panel network under test, using a schema algorithm, wherein the solar panel network under test comprises a plurality of solar panels and a plurality of circuit elements, the plurality of solar panels arranged in one or more strings, one or more sub-arrays and one or more arrays, along with one or more circuit elements of the plurality of circuit elements and one or more inverters; determine a set of solar panel parameters for each solar panel present in the solar panel network under test, based on a name-plate dataset of each solar panel and a time-stamped operational log dataset of the solar panel network under test, wherein the set of solar panel parameters for each solar panel comprises a photo (light) current (IL), a diode reverse saturation current (IO), a series resistance (RS), a shunt resistance (RSH), and an ideality factor (n); generate a network simulation model for the solar panel network under test, from the obtained schema and the set of solar panel parameters for each solar panel, using a modelling technique; randomly generate a plurality of fault feature tensors (FFTs) and a plurality of no-fault feature tensors (NFFTs), for the generated network simulation model, wherein each fault feature tensor (FFT) of the plurality of fault feature tensors (FFTs) is associated with a fault scenario of a plurality of fault scenarios and each no-fault feature tensor (NFFT) of the plurality of no-fault feature tensors (NFFTs) is associated with a no-fault scenario of a plurality of no-fault scenarios, wherein each fault scenario is defined based on one or more fault types occurring at one or more fault locations in the solar panel network under test, and one or more environmental parameters comprising an operating temperature of an environment and a solar irradiance value of the environment, and each no-fault scenario is defined based on the one or more environmental parameters comprising the operating temperature of the environment and the solar irradiance value of the environment; determine (i) a no-fault simulated dataset for each no-fault feature tensor (NFFT) of the plurality of no-fault feature tensors (NFFTs) by simulating the corresponding no-fault feature tensor (NFFT) using the generated network simulation model, and (ii) a fault simulated dataset for each fault feature tensor (FFT) of the plurality of fault feature tensors (FFTs) by simulating the corresponding fault feature tensor (FFT), using the generated network simulation model; randomly divide the plurality of fault feature tensors (FFTs) and the plurality of no-fault feature tensors (NFFTs) into a training dataset and a testing dataset, based on a predefined ratio, wherein the training dataset comprises one or more training FFTs along with corresponding fault simulated datasets and one or more training NFFTs along with corresponding no-fault simulated datasets, and the testing dataset comprises one or more testing FFTs along with corresponding fault simulated datasets and one or more testing NFFTs along with corresponding no-fault simulated datasets; and generate a fault detection, diagnosis and localization (FDDL) model for fault detection, diagnosis and localization in the solar panel network under test, by training a convolutional neural network (CNN) model with the training dataset, wherein (i) each training FFT of the one or more training FFTs present in the training dataset, is provided as label and the corresponding fault simulated dataset is provided as an input to the CNN model, and (ii) each training NFFT of the one or more training NFFTs present in the training dataset, is provided as label and the corresponding no-fault simulated dataset is provided as the input to the CNN model.

In yet another aspect, there is provided a computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: obtain a schema of the solar panel network under test, using a schema algorithm, wherein the solar panel network under test comprises a plurality of solar panels and a plurality of circuit elements, the plurality of solar panels arranged in one or more strings, one or more sub-arrays and one or more arrays, along with one or more circuit elements of the plurality of circuit elements and one or more inverters; determine a set of solar panel parameters for each solar panel present in the solar panel network under test, based on a name-plate dataset of each solar panel and a time-stamped operational log dataset of the solar panel network under test, wherein the set of solar panel parameters for each solar panel comprises a photo (light) current (IL), a diode reverse saturation current (IO), a series resistance (RS), a shunt resistance (RSH), and an ideality factor (n); generate a network simulation model for the solar panel network under test, from the obtained schema and the set of solar panel parameters for each solar panel, using a modelling technique; randomly generate a plurality of fault feature tensors (FFTs) and a plurality of no-fault feature tensors (NFFTs), for the generated network simulation model, wherein each fault feature tensor (FFT) of the plurality of fault feature tensors (FFTs) is associated with a fault scenario of a plurality of fault scenarios and each no-fault feature tensor (NFFT) of the plurality of no-fault feature tensors (NFFTs) is associated with a no-fault scenario of a plurality of no-fault scenarios, wherein each fault scenario is defined based on one or more fault types occurring at one or more fault locations in the solar panel network under test, and one or more environmental parameters comprising an operating temperature of an environment and a solar irradiance value of the environment, and each no-fault scenario is defined based on the one or more environmental parameters comprising the operating temperature of the environment and the solar irradiance value of the environment; determine (i) a no-fault simulated dataset for each no-fault feature tensor (NFFT) of the plurality of no-fault feature tensors (NFFTs) by simulating the corresponding no-fault feature tensor (NFFT) using the generated network simulation model, and (ii) a fault simulated dataset for each fault feature tensor (FFT) of the plurality of fault feature tensors (FFTs) by simulating the corresponding fault feature tensor (FFT), using the generated network simulation model; randomly divide the plurality of fault feature tensors (FFTs) and the plurality of no-fault feature tensors (NFFTs) into a training dataset and a testing dataset, based on a predefined ratio, wherein the training dataset comprises one or more training FFTs along with corresponding fault simulated datasets and one or more training NFFTs along with corresponding no-fault simulated datasets, and the testing dataset comprises one or more testing FFTs along with corresponding fault simulated datasets and one or more testing NFFTs along with corresponding no-fault simulated datasets; and generate a fault detection, diagnosis and localization (FDDL) model for fault detection, diagnosis and localization in the solar panel network under test, by training a convolutional neural network (CNN) model with the training dataset, wherein (i) each training FFT of the one or more training FFTs present in the training dataset, is provided as label and the corresponding fault simulated dataset is provided as an input to the CNN model, and (ii) each training NFFT of the one or more training NFFTs present in the training dataset, is provided as label and the corresponding no-fault simulated dataset is provided as the input to the CNN model.

In an embodiment, the generated FDDL model is tested with the testing dataset, wherein (i) each testing FFT of the one or more testing FFTs present in the testing dataset, is provided as the label and the corresponding fault simulated dataset is provided as the input to the generated FDDL model, and each testing NFFT of the one or more testing NFFTs present in the testing dataset, is provided as the label and the corresponding no-fault simulated dataset is provided as the input to the CNN model.

In an embodiment, the CNN model comprises three 2-dimensional convolutional layers each connected with a max-pooling layer and a dropout layer, and two fully connected layers connected with the dropout layer.

In an embodiment, the name-plate dataset of each solar panel present in the solar panel network under test, comprises a number of PV cells (NS) present in the corresponding solar panel, an open circuit voltage (VOC-SRC), a short circuit current (ISC-SRC), a maximum power point voltage (VMP-SRC), and a maximum power point current (IMP-SRC), at standard rating conditions, and the time-stamped operational log dataset of the solar panel network under test, comprises one or more of: a solar panel-wise current and a solar panel-wise voltage of each solar panel, a string-wise current and a string-wise voltage of each string, a sub-array-wise current and a sub-array-wise voltage of each sub-array, an array-wise current and an array-wise voltage of each array, a set of voltages of each inverter (DC and AC) of the one or more inverters, a power output of each inverter, a solar irradiation value (G), a cloudiness value (C), a rainfall value (r), a solar panel temperature of each solar panel, a temperature of each inverter, and a set of fault currents.

In an embodiment, the no-fault simulated dataset for each no-fault feature tensor (NFFT) is determined based on the set of solar panel parameters for each solar panel and the one or more environmental parameters, and comprises a solar panel level no-fault simulated Current-Voltage-Power dataset (SCp, SVp, SPp) of each solar panel, a string level no-fault simulated Current-Voltage-Power dataset (SCs, SVs, SPs) of each string, a sub-array level no-fault simulated Current-Voltage-Power dataset (SCsa, SVsa, SPsa) of each sub-array, an array level no-fault simulated Current-Voltage-Power dataset (SCa, SVa, SPa) of each array, and an inverter level no-fault simulated Current-Voltage-Power dataset (SCi, SVi, SPi) of each inverter.

In an embodiment, the solar panel level no-fault simulated Current-Voltage-Power dataset (SCp, SVp, SPp) of each solar panel is determined based on the set of parameters of the corresponding solar panel and the one or more environmental parameters, the string level no-fault simulated Current-Voltage-Power dataset (SCs, SVs, SPs) of each string is determined based on the solar panel level Current-Voltage-Power dataset (SCp, SVp, SPp) of each solar panel present in the corresponding string, the sub-array level no-fault simulated Current-Voltage-Power dataset (SCsa, SVsa, SPsa) of each sub-array is determined based on the string level no-fault simulated Current-Voltage-Power dataset (SCs, SVs, SPs) of each string present in the corresponding sub-array, the array level no-fault simulated Current-Voltage-Power dataset (SCa, SVa, SPa) of each array is determined based on the sub-array level no-fault simulated Current-Voltage-Power dataset (SCsa, SVsa, SPsa) of each sub-array present in the corresponding array, and the inverter level no-fault simulated Current-Voltage-Power dataset (SCi, SVi, SPi) of each inverter is determined based on the corresponding array level no-fault simulated Current-Voltage-Power dataset (SCa, SVa, SPa) and the one or more environmental parameters.

In an embodiment, the fault simulated dataset for each fault feature tensor (FFT) is determined based on the set of solar panel parameters for each solar panel and the one or more environmental parameters, and comprises a solar panel level fault simulated Current-Voltage-Power dataset (FCp, FVp, FPp) of each solar panel, a string level fault simulated Current-Voltage-Power dataset (FCs, FVs, FPs) of each string, a sub-array level fault simulated Current-Voltage-Power dataset (FCsa, FVsa, FPsa) of each sub-array, an array level fault simulated Current-Voltage-Power dataset (FCa, FVa, FPa) of each array, and an inverter level fault simulated Current-Voltage-Power dataset (FCi, FVi, FPi) of each inverter.

In an embodiment, the solar panel level fault simulated Current-Voltage-Power dataset (FCp, FVp, FPp) of each solar panel is determined based on the set of solar panel parameters of the corresponding solar panel and the one or more environmental parameters, the string level fault simulated Current-Voltage-Power dataset (FCs, FVs, FPs) of each string is determined based on the solar panel level fault simulated Current-Voltage-Power dataset (FCp, FVp, FPp) of each solar panel present in the corresponding string, the sub-array level fault simulated Current-Voltage-Power dataset (FCsa, FVsa, FPsa) of each sub-array is determined based on the string level fault simulated Current-Voltage-Power dataset (FCs, FVs, FPs) of each string present in the corresponding sub-array, the array level fault simulated Current-Voltage-Power dataset (FCa, FVa, FPa) of each array is determined based on the sub-array level fault simulated Current-Voltage-Power dataset (FCsa, FVsa, FPsa) of each sub-array present in the corresponding array, and the inverter level fault simulated Current-Voltage-Power dataset (FCi, FVi, FPi) of each inverter is determined based on the corresponding array level fault simulated Current-Voltage-Power dataset (FCa, FVa, FPa) and the one or more environmental parameters.

In an embodiment, the one or more fault types are selected from a group comprising of: a short circuit fault, an open circuit fault, a shading fault, a soiling fault, a hot-spot fault, an arc fault, a degradation fault, and a clipping fault, the short circuit fault comprises a line-line fault, and a line-ground fault, and the one or more fault locations comprises one or more solar panel level fault locations, one or more string level fault locations, one or more sub-array level fault locations, one or more array level fault locations, one or more inverter level fault locations, one or more circuit elements level fault locations, and one or more line level fault locations.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

