Power quality disturbance identification using morphological pattern spectrum and probabilistic neural network

Chen, Z. M.; Li, M. S.; Ji, Tianyao; Wu, Qiuxia

doi:10.1109/pesgm.2015.7285920

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…In addition to the techniques mentioned in Sections 3.1-3.7, there are several other special techniques, including transforming the PQS directly through a neural network, making it easy to extract features [107]. The article [108] improve PCA to extract feature.…”

Section: Miscellaneous Feature Extraction Techniquesmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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Section: Miscellaneous Feature Extraction Techniquesmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…Whereas, a new morphological filter for DFIG wind farm based microgrid has been proposed in [115]. Morphological pattern spectrum (MPS) and PNN is proposed in [116]. In [117] authors proposed novel method morphology singular entropy (MSE), which consists of three techniques, i.e., MM, singular value decomposition (SVD) and entropy theory.…”

Section: ) Mathematical Morphology Based Pqds Detection Techniquementioning

confidence: 99%

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

et al. 2020

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The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This paper presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area.

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“…Other techniques such as sparse signal decomposition (SSD) [16], radial basis function neural network [17], and probabilistic neural network techniques in [18,19] have been used to identify and detect PQ problems. In [20], the phasor measurement units based on the fuzzy technique have been used to categorise the PQ disturbances.…”

Section: Introductionmentioning

confidence: 99%

Categorisation of power quality problems using long short‐term memory networks

Abdelsalam

Hassanin

Hasanien

2021

IET Generation Trans & Dist

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Recognition of power quality (PQ) troubles is a critical task in the electrical power system. All previous works solve this problem using two‐step methodology: Feature extraction and classification steps with each step using its own techniques, and this consumes a computation time. The purpose of this study is to utilise a novel artificial intelligence (AI) technique for recognition of PQ events. The proposed AI technique is the long short‐term memory (LSTM) network, which detects and classifies the PQ events in one step. This technique extracts amplitude, disturbance duration and total harmonic distortion from the captured waveform, and the LSTM uses its own rules to classify the PQ events. Many simple PQ events such as interruption, sag, flicker, swell and surge or complex PQ events such as sag plus harmonics and swell plus harmonics are generated using MATLAB programming environment to evaluate the performance of LSTM. Also, real‐time measurements are collected from an industrial substation and are used to ensure the effectiveness of the proposed LSTM technique. A comparison with other techniques is conducted and the results verify the good performance of LSTM in classifying the PQ problems.

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Power quality disturbance identification using morphological pattern spectrum and probabilistic neural network

Cited by 7 publications

References 14 publications

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

Categorisation of power quality problems using long short‐term memory networks

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