Power quality monitoring by advanced mathematical tools: A survey

Gupta, Gunjan; Fritz, Wilfred

doi:10.1109/icpeices.2016.7853716

Cited by 3 publications

(1 citation statement)

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“…One of the most important power quality disturbances is voltage sags, which due to their random nature, make it difficult to monitor the system [6]. They also inflict significant financial losses on the electricity industry and customers due to equipment failure or industrial process interruptions [7].…”

Section: Introductionmentioning

confidence: 99%

A novel approach for single and combined power quality disturbances detection using fundamental and harmonic phasors estimation model

Samadinasab

Jalilian

2022

IET Generation Trans & Dist

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The growing use of non‐linear loads is one of the main reasons for harmonic distortions in distribution systems. Power quality disturbances have caused improper performance of electrical equipment and have created many technical and economic problems for industries and customers. One approach to overcome these problems and also the correct detection of PQ disturbances is power quality monitoring (PQM). Measurement and estimation are two essential tools to understand and investigate PQ problems in distribution networks. Here, a new estimation model is used to simultaneously extract the fundamental and harmonic phasors information of the system. In other words, the proposed estimation model has been used to access system‐level data. Also, the recursive variational mode extraction (RVME) approach efficiency has been investigated for detecting power quality disturbances (PQDs). Finally, to validate the effectiveness of the proposed PQDs detection method, six different scenarios of disturbances are investigated. The IEEE 13‐bus and IEEE 34‐bus standard distribution test networks are used to evaluate the efficiency of the proposed method. Also, the real‐time PQ disturbances are used for validating the proposed PQDs detection method. Results obtained demonstrate the efficiency and high accuracy of the proposed approach in correctly diagnosing real‐time PQ disturbances.

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Section: Introductionmentioning

confidence: 99%