An Improved Machine Learning‐Based Model for Detecting and Classifying PQDs with High Noise Immunity in Renewable‐Integrated Microgrids

An Improved Machine Learning‐Based Model for Detecting and Classifying PQDs with High Noise Immunity in Renewable‐Integrated Microgrids

Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. Power quality disturbances (PQDs) seriously affect the performance of MG networks and reduce the lifecycle of numerous sensitive d...

Full description

Saved in:
Bibliographic Details
Published in:International transactions on electrical energy systems Vol. 2024; no. 1
Main Authors: Channa, Irfan Ali, Li, Dazi, Koondhar, Mohsin Ali, Dahri, Fida Hussain, Mahariq, Ibrahim
Format: Journal Article
Language:English
Published: Hoboken Hindawi Limited 01-01-2024
Hindawi-Wiley
Subjects:
Accuracy
Algorithms
Classification
Deep learning
Density
Discrete Wavelet Transform
Distributed generation
Fourier transforms
Kernel functions
Machine learning
Multiresolution analysis
Networks
Neural networks
Power supply
Renewable energy sources
Signal processing
Support vector machines
Wavelet analysis
Wavelet transforms
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. Power quality disturbances (PQDs) seriously affect the performance of MG networks and reduce the lifecycle of numerous sensitive devices in MG networks. Hence, this paper presents a new approach to detect and classify the PQDs using discrete wavelet transform, multiresolution analysis, and optimized‐kernel support vector machine. The obtained unique features from DWT‐MRA are fed to train the well‐known intelligent classifiers. In the optimized‐kernel SVM model, computing power is enhanced for classifying multiple PQ events based on the local density and leave‐one‐out (LOO) algorithm. To get higher separation in feature space, the kernel width of each sample is estimated based on the local density. By using the LOO method, an improved grid search strategy is implemented to get the penalty parameter to achieve satisfactory results. Moreover, a typical MG network is simulated in MATLAB software considering the validation of the proposed technique to address the power quality issues in MG networks, and the results of the proposed method are compared with other conventional ML classifiers. The simulation results confirm that the proposed method is more effective and accurate than other intelligent classifiers.
ISSN:2050-7038
2050-7038
DOI:10.1155/2024/9118811