Power System Voltage Stability Assessment Using a Hybrid Approach Combining Dragonfly Optimization Algorithm and Support Vector Regression

In this paper, an efficient approach based on the combination of dragonfly optimization (DFO) algorithm and support vector regression (SVR) has been proposed for online voltage stability assessment. As the performance of the SVR model extremely depends on careful selection of its parameters, the DFO algorithm involves SVR parameters setting, which significantly ameliorates their performance. In the proposed approach, the voltage magnitudes of the phasor measurement unit (PMU) buses are adopted as the input data for the hybrid DFO-SVR model, while the minimum values of voltage stability index (VSI) are taken as the output vector. Using the data provided by PMUs as the input variables makes the proposed model capable of assessing the voltage stability in a real-time manner, which helps the operators to adopt the required measures to avert large blackouts. The predictive ability of the proposed hybrid model was investigated and compared with the adaptive neuro-fuzzy inference system (ANFIS) through the IEEE 30-bus and the Algerian 59-bus systems. According to the obtained results, the proposed DFO-SVR model can successfully predict the VSI. Moreover, it provides a better performance than the ANFIS model.