A Novel Islanding Detection Technique for a Resilient Photovoltaic-Based Distributed Power Generation System Using a Tunable-Q Wavelet Transform and an Artificial Neural Network

Energies

EasterS. Suviseshamuthu¹  S.Thomas George²  M.S. P. Subathra³  S.Ananda Kumar³  N.J. Sairamya⁴  Maria Malvoni⁵  ShauhratS. Chopra⁶  NallapaneniManoj Kumar⁶ 

[1] Center for Mobility and Rehabilitation Engineering Research, Kessler Foundation, West Orange, NJ 07052, USA;Department of Biomedical Engineering, School of Engineering and Technology, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India;Department of Electrical and Electronics Engineering, School of Engineering and Technology, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India;Department of Electronics and Communication Engineering, School of Engineering and Technology, Karunya Institute of Technology and Sciences, Coimbatore 641114, Tamil Nadu, India;School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece;School of Energy and Environment, City University of Hong Kong, Hong Kong, China;

关键词: photovoltaics;    distributed generation;    grid faults;    islanding detection;    islanding issues in power system;    resilient photovoltaic system;   

DOI  :  10.3390/en13164238

来源: DOAJ

【 摘 要 】

Finding an appropriate technique to detect an islanding issue is one of the major challenges associated with the design of a resilient grid-linked photovoltaic-based distributed power generation (PV-DPG) system. In general, the technique used for islanding detection must be able to sense the disruptions from the electric grid and quickly disconnect PV-DPG from the grid. The quick disconnection of PV-DPG mostly avoids power quality problems, damage to power assets, voltage stability issues, and frequency instability. In this paper, a new islanding detection technique that is based on tunable Q-factor wavelet transform (TQWT) and an artificial neural network (ANN) is proposed for PV-DPG. The proposed approach consists of two steps: in the first step, the vital detection parameters are computed by performing simulations considering all possible switching transients, islanding events, and faults from the grid side. Then, the decomposition of obtained signals is done using TQWT on different levels. Using the obtained coefficients, at each level, features such as range, minimum, mean, standard deviation, maximum, energy, and log energy entropy are computed. The optimal feature set was selected as the input for the second step. The classification of the non-islanding and islanding states for PV-DPG is made using the ANN classifier in the second step, which achieved an accuracy of 98%. The results representing the efficiency of the proposed approach in noisy and non-noisy environments are also explained. Overall, it is understood that the proposed islanding detection technique would provide suitable insights to detect an islanding issue.

【 授权许可】

Unknown