A Proposed PMU-based Voltage Stability and Critical Bus Detection Method using Artificial Neural Network

Voltage stability detection is currently still becoming the main issue in the modern power system which renewable energy integrated. Classical method to assess the voltage stability require complete model of power system, power system analysis and long computation time. In the data driven analysis and synchronized measurement era, the model can be proposed based on the historical data to deal with the complexity. For that reason, the voltage stability and critical bus detection based on ANN algorithm with the instantaneous PMU measurement is proposed. Seven hidden layers consisted of one normalization, four rectifier linear unit, one softmax and one sigmoid layer are formulated to do the detection. To measure the accuracy, the k-fold cross-validation is used. The proposed model is simulated on modified IEEE 14 test system which consider different loading scenario, line contingency, number of PMU and PV integration. To mimic the actual historical data, the synthetic data is generated and labelled. The result shows that the proposed method can represent the complete power system model by giving high accuracy which for voltage stability detection is > 97% and critical buses detection is > 96% for all scenarios. Moreover, the required computation time is between 16–18 second per detection which make the scalability to the real time detection is reasonable.