The quality of power in modern-day power system is polluted with increased penetration of converter-based distributed generations such as wind farm, solar PV system. In such scenarios detection of islanding and power quality disturbances as well as the removal of these from the system is quite crucial for equipment and maintenance personnel safety. Here, a down-sampling empirical mode decomposition (DEMD) and optimized random forest (RF) machine learning approach are hybridized to detect islanding conditions with reduced non-detection zone (NDZ) and classify non-islanding power quality events in a highly wind energy penetrated distribution generation system. DEMD has a special ability to filter out the fundamental signal from the polluted signal and random forest is quite an unbiased non-linear machine learning approach. Moreover, an improved grey wolf optimization technique is proposed to optimize the parameter of RF. The proposed technique is simulated in MATLAB/Simulink with IEEE 13-Bus test grid. The efficacy of the proposed method is evaluated through comparative analysis with existing machine learning techniques under normal and noisy environments as well as validated in narrow NDZ with lesser detection time.

Power Quality, Equipment and Personnel safety of any distributed generation (DG) system connected to utility Grid merely depends on accurate detection of Islanding and non-islanding Power quality disturbances. The main objective of the proposed research is to detect islanding events with very narrow non-detection zone (NDZ) and classification of power quality disturbances with higher accuracy using signal processing and intelligent method together. A noise robust down sampling empirical mode decomposition (DEMD) is used to extract signature of islanding and power quality (PQ) disturbance features from the collected voltage signals and multilayer perceptron neural network (MLNN) is proposed to classify islanding and non-islanding (PQ) events. The performance of the proposed (DEMD-MLNN) technique is verified with IEEE-9 bus distributed generation system dominated by solar &wind energy penetration. The simulation work is carried out in MATLAB/Simulink platform. The efficacy of the proposed DEMD-MLNN is verified by large number of numerical experimentations with and without noise and comparing with existing competitive well-known techniques.

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