Genetic algorithms for bad data detection at decomposition of state estimation problem

The accuracy of data used to construct calculation models of the network is an important factor that affects the reliability indices of electric power systems (EPS) at their operation. Along with random errors the measurements of EPS state variables contain quite often bad data. The paper addresses the algorithms of bad data detection by the test equation method at decomposition of a state estimation problem. The authors outline the concept of test equations. The decomposition algorithm includes structural and functional decomposition of the state estimation problem. The structural decomposition is carried out by dividing the calculated scheme into subsystems with respect to voltage levels. The functional decomposition is performed in accordance with the problems solved during the state estimation process: bad data detection, state estimation on the basis of the quadratic and robust criteria. The authors present the methods for detecting bad data a priori and solving the state estimation problem on the basis of robust criterion using test equations. Consideration is given to the applicability of genetic algorithms to these problems. The experimental studies have confirmed the efficiency of the suggested approaches.