Multivariable passive method for detection of islanding events in renewable energy based power grids

Penetration of distributed generation resources (DGR) in power grid is rapidly increasing to meet future energy demand efficiently. It helps in mitigating the problems of high carbon emission, green house effect, and increased cost of oil and natural gases. Island formation of a part of the renewable energy based power grid (REPG) creates hazards to operating personnel and equipments. Hence, efficient identification of islanding condition and disconnection of DGR immediately become essential for avoiding damage to equipment, personnel safety hazards and grid protection. This paper investigated a signal processing based passive islanding detection method (IDM) which is effective with the availability of multi-DGR. A islanding detection factor (MIDF) using multi-variables is proposed which is computed using features extracted by processing the voltage signal, negative sequence current (NSC) and negative sequence voltage (NSV) using Stockwell transform (ST) and Hilbert transform (HT). Total harmonic distortions of voltage ((Formula presented.)) and current ((Formula presented.)) are also considered to compute MIDF. Islanding event is recognized and differentiated from non-islanding conditions by comparing the MIDF with two threshold values. This method effectively detects the islanding event and discriminates it from the operational and faulty events. IDM has small non-detection zone (NDZ) and low computational time. Performance of IDM is not affected by noisy environment. Effectiveness of the IDM is established by testing for different event incidence angles, various fault impedances, different values of loads and capacitors, and different penetration levels of RE. Efficacy of IDM is established by testing the IDM through an elaborated study on IEEE-13 bus distribution grid interfaced with wind power plant (WPP) and solar power plant (SPP). Further, IDM is effectively implemented to detect the islanding event created on a practical distribution network. Effectiveness of IDM is better in comparison to IDMs designed by applications of discrete wavelet transform (DWT), empirical mode decomposition (EMD), Slantlet Transform and Ridgelet probabilistic neural network (RPNN), artificial neural network (ANN), change in rate of active and reactive power, rate of change of frequency, and rate of change of voltage angle.