An intelligent method for online voltage stability margin assessment using optimized ANFIS and associated rules technique.

This paper puts forward an intelligent method for online voltage stability margin (VSM) assessment based on optimal fuzzy system and feature selection technique, which has excellent performance for large power systems. The proposed VSM estimation method includes three key parts: feature extraction and selection part, estimator part and training part. In this method, power system's loading parameters are used as the main input of adaptive neuro-fuzzy inference system (ANFIS) and association rules (AR) technique is used to select the most effective loading parameters. In the training part, we used Harris hawks optimization algorithm (HHOA) to train the ANFIS efficiently. Using the proposed method, the VSM can be monitored online with high precision for both small and large systems and appropriate control measures can be applied if necessary. Knowing the exact amount of VSM and applying precautionary measures can prevent from voltage collapse, heavy financial losses and power supply interruption. The proposed method tested on 39-bus, 118-bus and 300-bus IEEE test system and the MATLAB simulation results demonstrated that the propounded method has much better performance than other recently introduced VSM assessment approaches. Providing a VSM estimation method, which is effective for large power systems, selecting the most informative loading parameters and improving the ANFIS's performance using HHOA are the main contributions of this paper.