Optimization of solutions on expansion of electric power systems (EPS) and their control plays a crucial part in ensuring efficiency of the power industry, reliability of electric power supply to consumers and power quality. Until recently, this goal was accomplished by applying classical and modern methods of linear and nonlinear programming. In some complicated cases, however, these methods turn out to be rather inefficient. Meta-heuristic optimization algorithms often make it possible to successfully cope with arising difficulties. State estimation (SE) is used to calculate current operating conditions of EPS using the SCADA measurements of state variables (voltages, currents etc.). To solve the SE problem, the Energy Systems Institute of Siberian Branch of Russian Academy of Sciences (ESI of SB RAS) has devised a method based on test equations (TE), i.e. on the steady state equations that contain only measured parameters. Here, a technique for EPS SE using genetic algorithms (GA) is suggested. SE is the main tool for EPS monitoring. The quality of SE results determines largely the EPS control efficiency. An algorithm for exclusion of wrong SE calculations is described. The algorithm using artificial neural networks (ANN) is based on the analysis of results of the calculation performed solving the SE problem with different combinations of constants. The proposed procedure is checked on real data.
The paper addresses the methods for identification of random error variances in measurements. Consideration is given to the algorithms of identifying variances using a numerical method and ANN. The numerical methods based on calculation of discrepancy correlation in a pair of test equations with a common measurement are presented. Application of ANN allows identification of variances early at the stages of implementing the algorithms of state estimation for the larger part of the existing measurements as well as identification of measurements variances involved in one test equation only. Recommendations on application of the two offered algorithms for EPS state estimation in real conditions are given based on the their study and comparison. Index Termselectric power system, real time, identification of error variances, test equations, Kohonen maps.
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