封面圖片

Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence

This paper considers data-based real-time adaptive Fault Detection (FD) in Grid-connected PV (GPV) systems under Power Point Tracking (PPT) modes during large variations. Faults under PPT modes remain undetected for longer periods introducing new protection challenges and threats to the system. An i...

全面介紹

Saved in:
書目詳細資料
Main Authors: Bakdi, Azzeddine, Bounoua, Wahiba, Guichi, Amar, Mekhilef, Saad
格式: Article
出版: Elsevier 2021
主題:
TK Electrical engineering. Electronics Nuclear engineering
在線閱讀:http://eprints.um.edu.my/26636/
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
id my.um.eprints.26636
record_format eprints
spelling my.um.eprints.266362022-04-01T03:26:46Z http://eprints.um.edu.my/26636/ Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence Bakdi, Azzeddine Bounoua, Wahiba Guichi, Amar Mekhilef, Saad TK Electrical engineering. Electronics Nuclear engineering This paper considers data-based real-time adaptive Fault Detection (FD) in Grid-connected PV (GPV) systems under Power Point Tracking (PPT) modes during large variations. Faults under PPT modes remain undetected for longer periods introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual estimations from Micro Phasor Measurement Unit (Micro-PMU). The high-dimensional high-frequency multivariate characteristics are non linear time-varying where computational efficiency becomes crucial to realize online adaptive FD. The adaptive assumption-free method is developed through Principal Component Analysis (PCA) for dimension reduction and feature extraction with reduced complexity. Novel fault indicators D-x(t) and discrimination index AD(t) are developed using Kullback-Leibler Divergence (KLD) for an accurate evaluation of Transformed Components (TCs) through recursive Smooth Kernel Density Estimation (KDE). The algorithm is developed through extensive data with 2.2 x 10(6) measurements from a GPV system under Maximum PPT (MPPT) and Intermediate PPT (IPPT) switching modes. The validation scenarios include seven faults: open circuit, voltage sags, partial shading, inverter, current feedback sensor, and MPPT/IPPT controller in boost converter faults. The adaptive algorithm is proved computationally efficient and very accurate for successful FD under large temperature and irradiance variations with noisy measurements. Elsevier 2021-02 Article PeerReviewed Bakdi, Azzeddine and Bounoua, Wahiba and Guichi, Amar and Mekhilef, Saad (2021) Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence. International Journal of Electrical Power and Energy Systems, 125. ISSN 0142-0615, DOI https://doi.org/10.1016/j.ijepes.2020.106457 <https://doi.org/10.1016/j.ijepes.2020.106457>. 10.1016/j.ijepes.2020.106457
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Bakdi, Azzeddine
Bounoua, Wahiba
Guichi, Amar
Mekhilef, Saad
Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
description This paper considers data-based real-time adaptive Fault Detection (FD) in Grid-connected PV (GPV) systems under Power Point Tracking (PPT) modes during large variations. Faults under PPT modes remain undetected for longer periods introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual estimations from Micro Phasor Measurement Unit (Micro-PMU). The high-dimensional high-frequency multivariate characteristics are non linear time-varying where computational efficiency becomes crucial to realize online adaptive FD. The adaptive assumption-free method is developed through Principal Component Analysis (PCA) for dimension reduction and feature extraction with reduced complexity. Novel fault indicators D-x(t) and discrimination index AD(t) are developed using Kullback-Leibler Divergence (KLD) for an accurate evaluation of Transformed Components (TCs) through recursive Smooth Kernel Density Estimation (KDE). The algorithm is developed through extensive data with 2.2 x 10(6) measurements from a GPV system under Maximum PPT (MPPT) and Intermediate PPT (IPPT) switching modes. The validation scenarios include seven faults: open circuit, voltage sags, partial shading, inverter, current feedback sensor, and MPPT/IPPT controller in boost converter faults. The adaptive algorithm is proved computationally efficient and very accurate for successful FD under large temperature and irradiance variations with noisy measurements.
format Article
author Bakdi, Azzeddine
Bounoua, Wahiba
Guichi, Amar
Mekhilef, Saad
author_facet Bakdi, Azzeddine
Bounoua, Wahiba
Guichi, Amar
Mekhilef, Saad
author_sort Bakdi, Azzeddine
title Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
title_short Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
title_full Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
title_fullStr Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
title_full_unstemmed Real-time fault detection in PV systems under MPPT using PMU and high-frequency multi-sensor data through online PCA-KDE-based multivariate KL divergence
title_sort real-time fault detection in pv systems under mppt using pmu and high-frequency multi-sensor data through online pca-kde-based multivariate kl divergence
publisher Elsevier
publishDate 2021
url http://eprints.um.edu.my/26636/
_version_ 1735409438137778176
score 13.251813

相似書籍