Abstract
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.
Highlights
There has been an increasing and ongoing transition towards renewable energy resources (RERs) for power generation for several years
In some cases, depending upon the benefits provided by the electric utility in terms of power selling and, incentives for supplied power during peak hours, favorable selling prices etc., the distributed power generation (DPG) systems are linked to the electric power grid, which has led to considerable progress in DPG-linked electrical power grids in different countries
Our study is focused on improving the resilience of DPG through early detection of islanding and allowing local facilities to respond very quickly
Summary
There has been an increasing and ongoing transition towards renewable energy resources (RERs) for power generation for several years. Using this four phases approach, a previous study on power resilience enhancement of PV based DPG for the New York location [15] has been carried out Based on their resilience assessment, they suggested the development of effective grid disturbance detection techniques as a critical area for research. Recently in photovoltaic based DPG (PV-DPG) networks, techniques such as hybrid WT and multi-resolution spectroscopy along with a deep learning approach were applied [30,31] Another method of islanding sensing is the implementation of an adaptive neuro-fuzzy inference device (ANFIS) and a discrete WT [32]. SP&IC techniques are being applied to further improve islanding detection in PV-DPGs. 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 a photovoltaic-based distributed power generation (PV-DPG) system.
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