I. Technical Field
The embodiments described herein relate generally to computer modeling and management of electrical power systems and, more particularly, to a simplified way to model a topology of an electrical power system.
II. Background
Computer models of complex systems enable improved system design, development, and implementation through techniques for off-line simulation of system operation. That is, system models can be created on computers and then “operated” in a virtual environment to assist in the determination of system design parameters. All manner of systems can be modeled, designed, and operated in this way, including machinery, factories, electrical power and distribution systems, processing plants, devices, chemical processes, biological systems, and the like. Such simulation techniques have resulted in reduced development costs and superior operation.
Design and production processes have benefited greatly from such computer simulation techniques, and such techniques are relatively well developed, but they have not been applied in real-time, e.g., for real-time operational monitoring and management. In addition, predictive failure analysis techniques do not generally use real-time data that reflect actual system operation. Greater efforts at real-time operational monitoring and management would provide more accurate and timely suggestions for operational decisions, and such techniques applied to failure analysis would provide improved predictions of system problems before they occur.
That is, an electrical network model that can age and synchronize itself in real-time with the actual facility's operating conditions is critical to obtaining predictions that are reflective of the system's reliability, availability, health and performance in relation to the life cycle of the system. Static systems simply cannot adjust to the many daily changes to the electrical system that occur at a facility (e.g., motors and pumps switching on or off, changes to on-site generation status, changes to utility electrical feed . . . etc.) nor can they age with the facility to accurately predict the required indices. Without a synchronization or aging ability, reliability indices and predictions are of little value as they are not reflective of the actual operational status of the facility and may lead to false conclusions. With such improved techniques, operational costs and risks can be greatly reduced.
For example, mission critical electrical systems, e.g., for data centers or nuclear power facilities, must be designed to ensure that power is always available. Thus, the systems must be as failure proof as possible, and many layers of redundancy must be designed in to ensure that there is always a backup in case of a failure. It will be understood that such systems are highly complex, a complexity made even greater as a result of the required redundancy. Computer design and modeling programs allow for the design of such systems by allowing a designer to model the system and simulate its operation. Thus, the designer can ensure that the system will operate as intended before the facility is constructed.
As with all analytical tools, predictive or otherwise, the manner in which data and results are communicated to the user is often as important as the choice of analytical tool itself. Ideally, the data and results are communicated in a fashion that is simple to understand while also painting a comprehensive and accurate picture for the user. For example, graphical displays (e.g., two-dimensional and three-dimensional views) of the operational aspects of an electrical system greatly enhances the ability of a system operator, owner and/or executive to understand the health and predicted performance of the electrical system.
Moreover, the ability to predict, and understand the health and stability of an electrical network (the capability of a power system to maintain stability and/or recover from events and disturbances without violating system operational constraints) in both static and in real-time, is important in order to insure that the power distribution system can meet the power demands and maintain sufficient active and reactive power reserves to hand the ongoing changes in demand and disturbances to the system due to various contingencies. Traditional transient stability programs are capable of accurately computing the trajectories of power system quantities, such as voltages, frequencies, power flow, etc., following disturbances. However, programs leave the understanding of these trajectories, i.e., severity of these disturbances, and their relevance to the power system security largely to an engineer's judgment.
Conventional approaches to modeling complex network topologies, their interconnectivity, interdependencies and relationships are limited to the application of diagrammatic sketches, computer aided design (CAD), or other forms of design technologies that require extensive training and know-how by the user in order to design realistic and error free networks, such as electrical one-line diagrams for power system simulation. As such, there is a need for novel methods that provide intuitive modeling paradigms that reduce the need for end user training or know-how for properly modeling, connecting and defining electrical power transmission, or distribution networks.