Abstract
Voltage and frequency stability are highly important for reliable performance of smart grids. In grid-connected mode, the utility controls these parameters, but when islanding occurs these parameters exceed their limits, which may result in irreparable damage to the system. This paper presents a time-domain approach which uses basic mathematical morphology (MM) operators, dilation and erosion filters, for microgrid islanding detection. The proposed method applies a dilation-erosion differential filter (DED) of the RMS signal (DEDFOR) at the point of common coupling (PCC) in a micro-grid connected to distributed generations (DGs). To evaluate the performance of the proposed approach, it is tested and compared with existing techniques in the literature under various conditions such as capacitor bank switching and motor starting. The results verify the accuracy and efficiency of the proposed technique for islanding detection under different operating conditions and various power mismatches.
Highlights
The rapid integration of smart grid (SG) technology opens up the possibility to aggregate microgrids into the electrical power system and to improve power quality in a safe and reliable manner [1,2]
According to the U.S Department of Energy [4], a microgrid is a group of interconnected loads that is often composed of distributed energy resources (DERs) [5], distributed generations (DGs), flexible loads, and energy storage systems (ESSs) with the ability to connect and disconnect from the grid to operate in both grid-connected and islanded mode [6,7]
From the results reported in this work, which are collected under different operation conditions and scenarios, it can be seen that the proposed threshold works properly
Summary
The rapid integration of smart grid (SG) technology opens up the possibility to aggregate microgrids into the electrical power system and to improve power quality in a safe and reliable manner [1,2]. To reduce the non-detection zone (NDZ) in passive techniques spectral decomposition and advanced filtering techniques including wavelet singular entropy [40], artificial neural network [41], Goertzel algorithm based on discrete Fourier transform [27], pattern recognition [42], and data mining [43] were developed These methods require time-consuming data-training processes as opposed to an analytic model that characterizes the actual physical interconnection topology. Simulation results on the various loads with different active and reactive power mismatches, compared with other islanding detection techniques, demonstrate the efficiency and accuracy of the proposed methods under different operation conditions with reduced NDZ.
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