Composite Counterpropagation Neural Networks for Solving Power Flow Problem

Abstract

Power flow study is performed to determine the power systems static states at each bus to find the steady state operating conditions of the systems. Power flow study is the most frequently carried out study performed by power utilities and it is required in almost all the stages of power systems planning, operation and control. In this paper, two modules of counterpropagation neural networks (CPNN) are proposed to solve power flow problem under different loading/contingency conditions for computing bus voltage magnitudes and angles of the given power systems. It implements a pattern mapping task. Due to its fast training, the proposed CPNN will be particularly useful for power systems planning studies, as a number of combinations can be tried using it within a small time frame. The mathematical model of power flow comprises a set of non-linear algebraic equations conventionally solved with the Newton-Raphson method. The effectiveness of the proposed CPNN based approach for solving power flow is demonstrated by computation of bus voltage magnitudes and voltage angles for different loading conditions and single line-outage contingencies in IEEE 14-bus systems.