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 2s 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 (T HD v ) and current (T HD i ) 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 & Rdigelet 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). INDEX TERMSHilbert transform; Islanding; Renewable Energy; Stockwell transform; utility grid network.

Enhancing grid resiliency in distributed energy systems through a comprehensive review and comparative analysis of islanding detection methods

“…Assuming a signal-to-noise ratio of 10 dB, the results show selectivity for non-IS events and precise identification of IS. In 33 , a technique is put forth to extract negative sequence data from voltage and current signals and construct an indicator for current IS detection by combining the ST with the HT. This method, which was tested with an IEEE-13 nodes model, achieves an efficiency of more than 98% and can successfully identify IS events even in noisy surroundings with a signal-to-noise ratio of 20 dB.…”

“…Comparing hybrid systems to passive approaches, their NDZ will be less, but compared to active solutions, it will be greater. The use of ANNs 33 , DL 34 , DT 35 , FL 36 , SVM 37 , and ANFIS 38 have drawn the attention of many researchers in recent years. These methods have the significant benefit of not requiring communication channels.…”

A Comprehensive Survey on Islanding Detection Methods of Synchronous Generator-Based Microgrids: Issues, Solutions and Future Works

“…The local methods operate based on local measurement of proper signals on the SGBMGs side without using any communication link. These methods are traditionally classified into active and passive methods [13]- [16]. Active methods operate based on injecting an intentional disturbance to SGBMG which pushes the voltage and frequency to become lower or greater than a permissible threshold after the islanding occurrence.…”

“…Active methods operate based on injecting an intentional disturbance to SGBMG which pushes the voltage and frequency to become lower or greater than a permissible threshold after the islanding occurrence. It should be noted that this disturbance can be applied to the SSSG voltage control system, and/or the SSSG frequency control system or by switching some loads in the SGBMGs feeders [13]- [14], [16]. However, passive methods only monitor some predefined indices or extract various hidden features to distinguish islanding condition without injecting any disturbance to the SGBMG.…”

“…In such a condition, the rate of change of frequency depends on the SSSG inertia. However, for the inverter-based microgrids (IBMGs), the frequency may not change significantly after a long duration, and to detect islanding condition an intentional disturbance may be required [12]- [13]. This perturbation is mainly employed to create an intentional power mismatch which leads to voltage and frequency drifts from their nominal values.…”