A comprehensive review on intelligent islanding detection techniques for renewable energy integrated power system

Abstract

Summary Renewable sources of energy are used extensively in the distribution grid to meet the need for growing electricity demand and to resolve the global heating crisis caused by traditional energy sources. They offer many advantages like better efficiency, power quality, and lower air pollution. One of the vital issues with these integrations is islanding condition arising due to the sudden disconnection of grid because of some abnormal situations; however, the distributed generation (DG) retains the power supply to the local loads. Thus, efficient detection of islanding and quick detachment of DG are vital to prevent failure of equipments, grid safety disruption, and risks to personnel safety. In this context, this article represents a comprehensive review of islanding issues and different islanding detection methods (IDMs) that provide a detailed assessment for filling the research gap and thereby highlights the various issues related to island detection. A broad classification of IDMs is established based on the conventional and modern techniques such as local (Active, Passive & Hybrid) and remote IDMs with their detailed illustrations including both intelligent and signal processing‐based IDMs. Comparison of various IDMs based on their merits, demerits, and various performance analysis including the non‐detection zone, detection time, quality factor, power quality, effect on a microgrid, implementation cost, and applicability in multi‐DG system is provided. Hence, this review will provide an insight for the new researchers interested in the area highlighting the different islanding detection techniques with their relative merits and demerits.

Adaptive Control Strategy of Multiemergency Power Supply Network System Connected to New Energy

“…Combined with the connotation of CD, a set of comprehensive evaluation index system that can reflect the CD of NE and regional power grids is constructed by defining and analyzing the coordinated planning mode of NE sources and regional power grids. e corresponding weights are constructed from the comprehensive evaluation model [11] and using this model to evaluate the CD degree of NE and regional power grids in the whole country [12] and draw relevant conclusions, in order to provide decisionmaking reference for the coordinated planning of NE and regional power grids [13].…”

Fault Classification in a DG Connected Power System using Artificial Neural Network

“…Machine learning (ML) techniques are widely used for fault classification in DG interconnected power systems because of its ability to process large sets of data [25]. In addition to this, machine learning techniques also remove the threshold calculations.…”

Hybrid islanding detection technique for distribution network considering the dynamic behavior of power and load

“…The drawback of these methods is that their NDZ is very large in which the detection of islanding is impossible since passive IDMs cannot recognize any islanding occurrence disturbed signal. 17 Some of the passive IDMs used for islanding detection are rate of change of frequency (ROCOF), 18,19 voltage unbalance and harmonic distortion, 20 rate of change of output power (ROCOP) of DG, [21][22][23] transient index value (TIV), 24 intrinsic time decomposition (ITD), 25 and rate of change of phase angle difference (ROCPAD). 26 To overcome the drawbacks of active and passive IDMs, another method known as hybrid IDM have been developed by researchers by combining different active and passive IDMs or sometimes combined these two methods with artificial intelligence and signal classifier based IDMs.…”

“…These methods can be applied to any network consisted of DG because they make no disturbance in the network and their detection speed is faster than active ones. The drawback of these methods is that their NDZ is very large in which the detection of islanding is impossible since passive IDMs cannot recognize any islanding occurrence disturbed signal 17 . Some of the passive IDMs used for islanding detection are rate of change of frequency (ROCOF), 18,19 voltage unbalance and harmonic distortion, 20 rate of change of output power (ROCOP) of DG, 21–23 transient index value (TIV), 24 intrinsic time decomposition (ITD), 25 and rate of change of phase angle difference (ROCPAD) 26…”