Online Voltage Stability Margin Assessment Using Optimized Adaptive ANFIS and Wavelet Transform Based on Principal Component Analysis

Abstract

This paper presents an intelligent method for online voltage stability margin (VSM) assessment using optimized adaptive ANFIS. Harris Hawks Optimization Algorithm (HHOA) is used to train the ANFIS and conventional wavelet transform (WT) is also applied as a feature extraction technique on the network voltage profile. The network voltage profile is used as the main data to estimate VSM because it contains the necessary information about the network structure, load levels, production pattern, and control system performance in the network. Using wavelet transform technique with high resolution, the necessary features for entering the ANFIS block are extracted, but due to the variety and multiplicity of these features, especially for large networks, the Principal Component Analysis (PCA) method is used to select the appropriate features and remove additional data. The characteristic of this hybrid algorithm is that it can be used both in dynamic and static conditions of the network. Finally, the proposed VSM estimation algorithm is applied to the 39-bus and 118-bus IEEE test systems, and its results are evaluated. The comparison of the results with other VSM methods shows that the proposed algorithm is effective for large power grids.

Keywords

• online voltage stability assessment
• adaptive neuro-fuzzy inference system
• harris hawks optimization algorithm
• wavelet transform based on principal component analysis