An Effective Islanding Detection Method for Distributed Generation Integrated Power Systems Using Gabor Transform Technique and Artificial Intelligence Techniques

Incorporating distributed generation (DG) technology into modern power systems heralds a multitude of technological, economic, and environmental advantages. These encompass enhanced reliability, reduction in power losses, heightened efficiency, low initial investment requirements, abundant availability, and a minimized environmental footprint. However, the rapid detection and disconnection of DG units during islanding events are paramount to circumvent safety hazards and potential equipment damage. Although passive techniques are predominantly utilized for islanding protection due to their minimal systemic interference, their susceptibility to substantial nondetection zones (NDZ) instigates a transition toward more innovative techniques. Addressing this crucial need, the study employs a novel amalgamation of signal processing methodologies and a suite of intelligent classifiers to augment the detection of islanding events in power systems. Concealed features from a variety of signals are extracted by the methodology and utilized as robust inputs to the intelligent classifiers. This empowers these classifiers to make a reliable distinction between islanding events and other types of disturbances. The breadth of the study is expanded to evaluate a range of sophisticated models including gradient boosting, decision tree–based models, and multilayer perceptrons (MLPs). These models are thoroughly tested on a radial distribution system integrated with two DG units and subjected to rigorous simulations and comparative analysis using the DIgSILENT Power Factory software. The findings underscore the efficacy of the proposed method, showcasing a significant improvement over conventional techniques in terms of efficiency and resilience. Above all, the methodology exhibits an exceptional ability to discriminate between islanding events and other system disturbances. Specifically, the islanding detection method demonstrated a detection time of less than one cycle (20 ms), ensuring rapid response. Moreover, the proposed algorithm demonstrated the capability to detect all ranges of power mismatch, including zero-power mismatch, thereby eliminating the NDZ. These results illuminate the benefits of integrating signal processing methodologies with intelligent classifiers, marking a significant stride forward in the realm of islanding event detection.