Identification of Islanding Events in Utility Grid With Renewable Energy Penetration Using Current Based Passive Method

Abstract

Renewable energy (RE) generation levels are increasing in modern power systems at a fast rate due to their advantages of clean and non-exhaustible nature of energy. However, this type of generation creates technical challenges in terms of operation and control due to uncertain and un-predictable nature of generation. Islanding is an operational scenario where there is a loss of grid and RE generators continue to feed power to the local load. This has harmful effects on the RE generators and operating personal. Hence, it is expected that islanding scenario is identified in minimum time and RE generators are disconnected within 2 s duration after island formation. This paper designed an islanding identification scheme (IDS) by designing a current islanding detection indicator (CIDI) that combines the features computed by processing the current signals, negative sequence current (NSC) and negative sequence voltage (NSV) using the Stockwell transform (ST) and the Hilbert transform (HT). Information contained by the total harmonic distortions of voltage ( THD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v</sub> ) and current ( THD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> ) is also used while designing the CIDI. Islanding and non-islanding events of category-I & II are identified and discriminated from each other by comparison of peak magnitude of CIDI with the first threshold value (FTV) and second threshold value (STV). This IDS effectively recognizes the islanding events even in the noisy environment with minimum non-detection zone (NDZ) and minimum time. The efficiency is greater than 98% even with the noise of 20dB SNR (signal to noise ratio). The performance of proposed IDS is better compared to IDS using discrete wavelet transform (DWT), Empirical mode decomposition (EMD), Slantlet transform & Ridgelet probabilistic neural network (RPNN), and artificial neural network (ANN). The effectiveness of IDS is validated on IEEE-13 nodes test system using MATLAB software, practical distribution network and in real time scenario by use of real time digital simulator (RTDS).