Real-Time Voltage Stability Monitoring Using Machine Learning-Based PMU Measurements

Abstract

Recently, due to the increasing demand with scarcity in installed production capacities, power systems are being operated closer to voltage stability limits resulting in a higher eventuality of voltage collapse. Thus, fast and accurate monitoring of voltage stability has become an important factor in the efficient operation of modern power systems. In this chapter, two approaches based on the combination of multi-layer perceptron (MLP) neural network and adaptive neuro-fuzzy inference system (ANFIS) with moth swarm algorithm (MSA) have been proposed to monitor voltage stability of power systems using phasor measurement units (PMUs) data. In the proposed hybrid MLP–MSA and ANFIS–MSA models, the MSA algorithm is adopted to optimize the connection weights and biases of the MLP network and to determine the tuning parameter in ANFIS model. To evaluate the prediction capability and efficiency of the proposed models, several statistical indicators such as root mean square error (RMSE), correlation coefficient (R) and root mean square percentage error (RMSPE) are used. Numerical studies are carried out on two standard power systems. The obtained results indicate that the proposed ANFIS–MSA model has the most reliable and accurate prediction ability and deemed to be the effective method to estimate the voltage stability margin of the power system based on measurements from PMU devices.