A new real time optimal under frequency load shedding method by using power system security indices and artificial neural networks

Today modern power systems are operated in lower security level due to power system deregulation and increasing the power transfer capacity. Extensive power systems blackouts in recent years show the remarkable increase of power system vulnerability in contingency situations. Load shedding is one of the last corrective actions for keeping power system stability. In this paper a real time optimal under frequency load shedding by using artificial neural network is presented. This structure contains two offline and online studies. In offline studies according to the values of vulnerability and security margin indices of total power system، minimum frequency، reduction rate of equivalent inertial center frequency (dfc/dt) for N-K contingency scenarios، the power system security is determined and the ANN inputs data base will be established. In each scenario، the necessary active and reactive load shedding value for preserving power system stability is determined by solving an offline optimization problem by using intelligent hybrid CPCE algorithm. The values of the active and reactive load shedding in each load shedding step in each contingency scenario are considered as the ANN outputs. Genetic algorithm is employed for optimizing the ANN training process. The trained ANN will be used for online application in power system by using real time operation information that is collected by wide area monitoring system (WAMS) and phasor measurement units (PMU). Simulation results for IEEE 118-bus test system shows the effectiveness of the proposed method.