Online monitoring, evaluation and improvement of steady state voltage stability for electric power systems using artificial neural networks techniques

Abstract EN

This paper presents a method for monitoring, evaluation and improving of electrical power system voltage stability that is based on Artificial neural networks (ANN).

The training data is obtained by solving several normal and abnormal conditions using the Linear Programming technique.

The selected objective function gives minimum deviation of the reactive power control variables, which leads to the maximization of minimum Eigen value of load flow Jacobian.

The considered reactive power control variables are switchable VAR compensators, OLTC transformers and excitation of generators.

The method has been implemented on a modified IEEE 30-bus test system.

The results obtain from the test clearly show that the trained neural network is capable of improving the voltage stability in power system with a high level of precision and speed.

Thus, the method can be used as a guide by the operator in Energy Control Center (ECC) for power system control.