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Application of Computational Intelligence Methods for Power Quality Disturbance Detection, Classification and Mitigation in Microgrids

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Power Quality in Microgrids: Issues, Challenges and Mitigation Techniques

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1039))

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Abstract

Power quality (PQ) is defined as the ability to maintain a pure sinusoidal voltage waveform with the specified amplitude and frequency within the required limit with no changes in shape or magnitude. Both the supply and demand sides are noticing an increase in the importance of power quality. Switching devices are increasingly being used, which inevitably leads to a decline in power quality. At the same time, these devices are also more likely to break down as a result of bad power quality. The primary research focus corresponding to a microgrid is on techniques for detecting and mitigating power quality disturbances. The classification of disturbances can be done in a variety of ways, including by using Artificial Neural Networks (ANN), fuzzy logic, machine learning, and deep learning. Numerous compensation-based methods, controller-based methods, and optimization-based methods have all been reported for the mitigation of power quality issues. The significant portions of the main developments are techniques based on computational intelligence. In this work, an effort has been made to thoroughly review computational methods for power quality disturbance detection and power quality enhancement using computational intelligence methods.

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Abbreviations

ANN:

Artificial Neural Networks

MG:

Microgrid

DER:

Distributed Energy Resources

RER:

Renewable Energy Resources

PSO:

Particle Swarm Optimization

ACO:

Ant Colony Optimization

SLDV:

Short and Long Duration Variations

VUF:

Voltage Unbalance Factor

PQD:

Power Quality Disturbances

FFT:

Fast Fourier Transform

PQDC:

Power Quality Detection and Classification

DFT:

Discrete Fourier Transform

STET:

Short-Time Fourier Transform

DTFT:

Discrete Time Fourier Transform

WT:

Wavelet Transform

SPT:

Signal Processing Techniques

ST:

Stockwell Transform

HHT:

Hilbert Huang Transform

HT:

Hilbert Transform

EMD:

Empirical Mode Decomposition

IMF:

Intrinsic Mode Functions

TFR:

Time Frequency Representation

GA:

Genetic Algorithm

SVM:

Support Vector Machine

FL:

Fuzzy Logic

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SOET, CMR University, Bengaluru, India

    Abhishek Kumar

  2. GreyB Services, Sahibzada Ajit Singh Nagar, Punjab, 160062, India

    Ishan Srivastava

  3. Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa

    Arvind R. Singh

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  1. Abhishek Kumar
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  2. Ishan Srivastava
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  3. Arvind R. Singh
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Correspondence to Arvind R. Singh .

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Editors and Affiliations

  1. Department of Railroad and Electrical Engineering, Woosong University, Daejeon, Korea (Republic of)

    Surender Reddy Salkuti

  2. Department of Electrical Engineering, Veer Surendra Sai University of Technology (VSSUT), Burla, Odisha, India

    Papia Ray

  3. Department of EECE, University of South Africa, Pretoria, Gauteng, South Africa

    Arvind R. Singh

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Kumar, A., Srivastava, I., Singh, A.R. (2023). Application of Computational Intelligence Methods for Power Quality Disturbance Detection, Classification and Mitigation in Microgrids. In: Salkuti, S.R., Ray, P., Singh, A.R. (eds) Power Quality in Microgrids: Issues, Challenges and Mitigation Techniques. Lecture Notes in Electrical Engineering, vol 1039. Springer, Singapore. https://doi.org/10.1007/978-981-99-2066-2_2

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  • DOI: https://doi.org/10.1007/978-981-99-2066-2_2

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