Improved Active Islanding Detection Technique for Multi-Inverter Power System

Due to the increased penetration of multi-inverter distributed generation(DG)systems,different DG technologies,inverter control methods,and other inverter functions are challenging the capabilities of islanding detection.In addition,multi-inverter networks connecting the distribution system point of common coupling(PCC)create islanding at paralleling inverters,which adds the vulnerability of islanding detection.Furthermore,available islanding detection must overcome more challenges from non-detection zones(NDZs)under reduced power mismatches.Therefore,in this study,a new method called parameter self-adapting active islanding detection was utilized to minimize the dilution effect and reduce NDZs in multi-inverter power systems.The method utilizes an active frequency drift(AFD)method and applies a positive feedback gain of adoption parameters,which significantly minimizes NDZs at parallel inverters.The simulation and experimental outcomes indicate that the proposed method can effectively weaken the dilution effect in multi-inverter networks connecting the distribution system PCC.

Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources(DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method(IIDM) using an intrinsic mode function(IMF)feature-based grey wolf optimized artificial neural network(GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

Power Quality Assessment of Voltage Positive Feedback Based Islanding Detection Algorithm

Islanding refers to a condition where distributed generators(DGs)inject power solely to the local load after electrical separation from power grid.Several islanding detection methods(IDMs)categorized into remote,active,and passive groups have been reported to detect this undesirable state.In active techniques,a disturbance is injected into the DG’s controller to drift a local yardstick out of the permissible range.Although this disturbance leads to more effective detections even in well-balanced island,it raises the total harmonic distortion(THD)of the output current under the normal operation conditions.This paper analyzes the power quality aspect of the modified sliding mode controller as a new active IDM for grid-connected photovoltaic system(GCPVS)with a string inverter.Its performance is compared with the voltage positive feedback(VPF)method,a well-known active IDM.This evaluation is carried out for a 1 k Wp GCPVS in MATLAB/Simulink platform by measuring the output current harmonics and THD as well as the efficiency under various penetration and disturbance levels.The output results demonstrate that since the proposed disturbance changes the amplitude of the output current,it does not generate harmonics/subharmonics.Thereby,it has a negligible adverse effect on power quality.It is finally concluded that the performance of the sliding mode-based IDM is reliable from the standpoints of islanding detection and power quality.