The issue of power quality (PQ) has become more prominent over the last few decades as the demand of clean and high quality power is increasing around the globe. The effect of power quality disturbances on the equipment most of the time is very destructive, usually generates disruptions, which consecutively affects the other load connected to the power systems. The main purpose of this article is to present a comprehensive review of various power quality analysis techniques such as heuristic optimization, signal processing, machine learning, neural networks, artificial intelligence, and hardware implementation, so that a brief overview will be presented to the researcher and power engineers working in the field of power quality. Additionally, a comparative analysis is also reported on various methods based on several criteria including timing and accuracy, use of industrial and non‐industrial data set, noisy and noiseless conditions, and finally single and multiple power quality events for analysis. More than 200 research publications are included for analysis and listed in reference so that it will be easy for the researcher in the domain of power quality to explore the possibility of further improvement in this field.

中文翻译：

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电能质量分析的信号处理技术和机器学习算法综述

在过去的几十年中，随着全球对清洁和高质量电源的需求不断增长，电源质量（PQ）问题变得更加突出。电能质量扰动在大多数情况下对设备的影响是非常有害的，通常会产生干扰，从而连续影响与电源系统连接的其他负载。本文的主要目的是全面介绍各种电能质量分析技术，例如启发式优化，信号处理，机器学习，神经网络，人工智能和硬件实现，以便向研究人员提供简要概述。和从事电能质量领域的电力工程师。另外，还基于多种标准对各种方法进行了比较分析，这些标准包括时序和准确性，使用工业和非工业数据集，噪声和无噪声条件，最后是一次和多次电能质量事件进行分析。包括200多个研究出版物供分析，并在参考文献中列出，因此对于电能质量领域的研究人员而言，探索该领域进一步改进的可能性将非常容易。