Signal processing and AI based diagnosis of power quality disturbances: A review

2015 International Conference on Energy Economics and Environment (ICEEE) Pub Date : 2015-03-27 DOI:10.1109/ENERGYECONOMICS.2015.7235071

Padmanabh Thakur, Ashutosh Kumar Singh

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引用次数: 7

Abstract

The precise diagnosis of power quality disturbances (PQDs) has now become a significant concern among the utility engineers as well as consumers due to the high cost of downtimes associated with it. Numerous methods, such as, Artificial Intelligence (AI), Signal Processing (SP), Space Vector Representation, Symmetrical Component, have been evaluated for the precise diagnosis of PQDs. Among these methods, AI and SP based techniques have received extensive attention by the researchers and industry engineers. This paper discusses the various AI and SP based methodologies currently used for the diagnosis of PQDs. Existing AI and SP based methods are critically reviewed to highlight their applications, merits, and shortfalls. It is revealed that, besides the numerous applications and merits of these methodologies, none of them is found proficient for the precise diagnosis of PQDs. Accurate recognition of PQDs is still a challenging task. Therefore, the need of incorporation of new techniques for the accurate estimation of PQDs has been asserted.

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信号处理与基于人工智能的电能质量干扰诊断研究进展

由于与之相关的停机成本高，电能质量干扰(PQDs)的精确诊断现在已成为公用事业工程师和消费者关注的一个重要问题。许多方法，如人工智能(AI)，信号处理(SP)，空间向量表示，对称分量，已被评估用于精确诊断pqd。在这些方法中，人工智能和基于SP的技术受到了研究人员和行业工程师的广泛关注。本文讨论了目前用于PQDs诊断的各种基于AI和SP的方法。现有的基于人工智能和SP的方法进行了严格的审查，以突出其应用，优点和不足。结果表明，除了这些方法的众多应用和优点外，没有一种方法能够准确诊断PQDs。pqd的准确识别仍然是一项具有挑战性的任务。因此，需要结合新技术来准确估计PQDs。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 International Conference on Energy Economics and Environment (ICEEE)

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