A hybrid AI framework for identification of power quality disturbances in electrical network

Sustaining power quality is an utmost priority for energy distributors in a modern power system integrated with distributed generation and nonlinear loads. The power quality disturbances (PQDs) comprising of multiple PQD events creates complexities in accurate detection and classification. Therefore, this study presents an effective, resilient and automated hybrid classifier for detection and classification of complex power quality disturbances for single occurrence as well as combinations of double, triple, and quadruple occurrences. To develop an effective and cohesive classifier, it is vital to identify the applicable features from the disturbance signal that can enhance data efficiency while capturing the signal’s fundamental qualities. Consequently, a set of features is retrieved utilizing the Stockwell Transform (ST), and the significant features are identified by the Chi-Square Test (CST). These features are utilized to train the Long Short-Term Memory (LSTM) network. The method has been validated on the signals generated using Power System Computer Aided Design (PSCAD) and OPAL-Real Time (OPAL-RT) for the IEEE 9-bus and 33-bus systems. The novelty of proposed hybrid method lies in the real time validation under noisy conditions. Additionally, to signify the superiority of the suggested method, the performance of the classifier has been compared with a multi-layer feedforward neural network and existing approaches documented in the manuscript. The proposed hybrid method given 99.11% accuracy which is higher in comparison of existing methods. The results confirm the superiority of the suggested hybrid technique for the real-time detection and classification of PQD signals.