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Abstract
This paper has introduced an algorithm for the identification of islanding events in the remotely located distribution grid with renewable energy (RE) sources using the voltage signals. Voltage signal is processed using Stockwell transform (ST) to compute the median-based islanding recognition factor (MIRF). The rate of change in the root mean square (RMS) voltage is computed by differentiating the RMS voltage with respect to time to compute the voltage rate of change in islanding recognition factor (VRCIRF). The proposed voltage-based islanding recognition factor (IRFV) is computed by multiplying the MIRF and VRCIRF element to element. The islanding event is discriminated from the faulty and operational events using the simple decision rules using the peak magnitude of IRFV by comparing peak magnitude of IRFV with pre-set threshold values. The proposed islanding detection method (IDM) effectively identified the islanding events in the presence of solar energy, wind energy and simultaneous presence of both wind and solar energy at a fast rate in a time period of less than 0.05 cycles compared to the voltage change rate (ROCOV) and frequency change rate (ROCOF) IDM that detects the islanding event in a time period of 0.25 to 0.5 cycles. This IDM provides a minimum non-detection zone (NDZ). This IDM efficiently discriminated the islanding events from the faulty and switching events. The proposed study is performed on an IEEE-13 bus test system interfaced with renewable energy (RE) generators in a MATLAB/Simulink environment. The performance of the proposed IDM is better compared to methods based on the use of ROCOV, ROCOF and discrete wavelet transform (DWT).
Highlights
Renewable energy (RE) provides clean energy to the consumers and reduces transmission losses when integrated to the grid in large quantum near-load centers
In [9], the authors proposed a passive islanding detection method (IDM) using a hybridization of modified slantlet transform (MSLT) and machine learning for islanding detection in the presence of multiple distributed generation (DG) plants
The results to classify the islanding events, fault events and operational events in different categories using the decision rules supported by peak magnitude of the voltage-based islanding recognition factor (IRF) are illustrated in Section 6, and Section 7 includes the study to compare the performance of the proposed algorithm with the algorithms reported in literature
Summary
Renewable energy (RE) provides clean energy to the consumers and reduces transmission losses when integrated to the grid in large quantum near-load centers. In [9], the authors proposed a passive IDM using a hybridization of modified slantlet transform (MSLT) and machine learning for islanding detection in the presence of multiple distributed generation (DG) plants. A method for identification of islanding events for a grid-connected solar photovoltaic (PV) system using slantlet transform, and differentiating these events from fault and switching events using ridgelet probabilistic neural network (EPNN) is investigated in [10] This approach is insensitive to external grid disturbances and effectively detects islanding with good accuracy. An islanding detection method (IDM) based on the use of voltage signals is proposed in this paper. The results to classify the islanding events, fault events and operational events in different categories using the decision rules supported by peak magnitude of the voltage-based IRF are illustrated, and Section 7 includes the study to compare the performance of the proposed algorithm with the algorithms reported in literature.
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