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The purpose of this paper is to analyze the frequent power quality (PQ) issues happening in distributed generation, the outcomes of the PQ harmonics, the methods used to assess the quantity of harmonic distortion which occurs in the power system (PS), and, in the end, classification of these disturbances using recent advance artificial intelligent techniques like a neural network, fuzzy logic, and the genetic algorithm has been further stated. To protect the PS detection and classification of voltage (V) and current (I) issues are essential tasks and due to increasing interest in a distributed generation, it is becoming more popular. Most PQ disturbances are unstable and ephemeral especially in a distributed generation; therefore, the call for detection and classification of voltage and current disruptions are essential tasks to protect the PS. Many disturbances of (PQ are unpredictable and transient. By using wavelet transforms, expert systems, and artificial neural networks, some intelligent system technologies control fault analysis precisely saying it can help to detect the fault locations. The most important part of the generalized classification system of PQ events is the extraction and classification of features for PQ event classification.

Feature Extraction and Classification Techniques for Power Quality Disturbances in Distributed Generation: A Review