In the past decades, Energy Management Systems (EMS) have provided electric utilities with On-line Static Security Assessment tools to provide coverage for steady state operating conditions of the system. Currently, the power system industry's attention is focused upon providing On-line Dynamic Security Assessment. With the accelerated growth of computing power and recently introduced analytical methods, the possibility of have On-line Dynamic Security Assessment (DSA) is becoming a reality.
Dynamic Security Assessment's function is to determine which contingencies may cause power system limit violations or system instability. A brute force approach would be to conduct detail stability analysis for each credible contingency followed by a check for violations. However this approach is slow. The existing DSA practice is to carry out off-line studies with the most stressed operating conditions in deriving guidelines for the operators. However, during actual system operations, conditions seldom match the situations studied off-line. Consequently, the guidelines and limits produced are too conservative with significant financial consequences to the utilities. A more effective approach is to assess only those contingencies likely to cause dynamic violations in real-time.
There are a number of areas in the pre and post processing of DSA whose performance need enhancements. In post processing, significant improvements in performance may be realized if a package can screen the immense quantities of output data from a stability run, perform analysis and make high speed decisions, and finally control the computational process in reducing the number of studies required. Neural networks and artificial intelligence may hold the key to substantially enhancing the performance and streamlining some of the DSA tasks. Aritifical neural networks (ANN) have been studied for many years with the hope of achieving human-like performance in solving certain problems in speech and image processing. In the recent years, there has been a resurgence in the field of neural networks due to the introduction of new network topologies, training algorithms and VLSI techniques. The potential benefits of neural networks; such as parallel distributed processing, high computation rates, fault tolerance, and adaptive capability have lured reseachers from fields such as power systems, controls, signal processing and robotics to seek neural network solutions to some of their more challenging problems. The purpose of this research is to evaluate the applications of neural networks to Dynamic Security Assessment (DSA). The project gives a working design for an on-line DSA from a Canadian utility to provide understanding into the various problems associated with On-line DSA.
For more information, please contact M. A. El-Sharkawi