Mohd Faris Abdullah

In modern distribution networks, addressing the issue of unintentional islanding—characterized as the inadvertent disconnection of distributed generation sources from the utility grid—continues to present a significant challenge. This phenomenon raises concerns that warrant further investigation due to its implications for system reliability and operational safety. The identification of islanding events is particularly complicated when local generation closely aligns with local demand, making detection difficult. The development of precise, rapid, and dependable methodologies for the detection of islanding in renewable and distributed generation systems requires compliance with rigorous standards. The current body of literature delineates an array of strategies for islanding detection, which can be systematically categorized into three primary approaches: ( i ) remote detection techniques, ( ii ) local detection methodologies, and ( iii ) machine learning-based classification-enabled intelligent classifiers. Recent advancements have garnered significant attention regarding the enhanced characteristics and benefits of intelligent methodologies in contrast to traditional approaches. Accurate and timely detection of islanding prevents hazardous back-feeding to the utility, protects inverter and downstream equipment from out-of-spec voltages and frequencies, preserves system stability during transition and reconnection, reduces the risk of large-scale protection miscoordination, and enables compliance with interconnection standards (e.g., IEEE 1547) that prescribe strict detection and ride-through requirements. However, achieving high detection accuracy introduces trade-offs: many high-performance techniques demand significant computational and communications resources, active methods may degrade power quality or increase nuisance trips, passive schemes suffer from large non-detection zones under near-balanced conditions, and machine-learning approaches require representative training data and careful feature engineering to avoid overfitting and poor generalization in realistic, noisy environments. This research provides a comprehensive overview of the transition from traditional techniques to intelligent islanding detection methodologies. Moreover, it elucidates the primary challenges, benefits, limitations, and prospective directions for research in intelligent detection schemes. Furthermore, this study provides a comprehensive and impartial analysis of intelligent classifier-based strategies for islanding detection that have been developed over the last decade. This research further examines various feature selection techniques and identifies the parameters most employed for efficient islanding detection. In conclusion, this comprehensive study presents a discussion of the findings obtained, along with strategic recommendations for future research initiatives within this field.

Islanding detection needs are becoming a pivotal constituent of the power system, since the penetration of distributed generators in the utility power system is continually increasing. Accurate threshold setting is an integral part of the island detection scheme since an inappropriate threshold might cause a hazardous situation. This study looked at the islanding conditions as well as two transient faults, such as a single line to ground fault and a three-phase balance fault, to assess the event distinguishing ability of the proposed method. Therefore, the goal of this research was to determine the threshold of the island if the distributed generator (DG) capacity is greater than the connected feeder load, which is the over-frequency island condition, and if the DG capacity is less than the connected feeder load, which is the under-frequency island condition. The significance of this research work is to propose a new island detection threshold setting method using the slip angle and acceleration angle that comes from phasor measurement unit (PMU) voltage angle data. The proposed threshold setting method was simulated in the PowerWorld simulator on a modified IEEE 30 bus system equipped with DG. There are three different interconnection scenarios in the test system and the performance of the proposed method shows that getting the island threshold for all the scenarios requires a single time step or 20 mile seconds after incepting an island into the network. In addition, it can distinguish between the real islanding threshold and the transient faults threshold.

Salient pole synchronous generator is one of harmonic sources that causes various power system harmonic problem especially related to neutral. This paper aims to study the characteristics of third harmonic voltage and current from generator when connected to balanced load via cable, generator load connection with different generator neutral grounding resistor and generator load connection shunted by zig-zag transformer. Lab scale experiments have been conducted to vary all parameters related to cable capacitor and generator neutral grounding resistor under various combined resistive and inductive load. Cable capacitance which depends on cable length, size and number of parallel cable, combined with load impedance determines the third harmonic current magnitude. The presence of cable capacitance that interact with generator inductance resulted to series harmonic resonance at 9^th and 15^th harmonic order that causes high 9^th and 15^th harmonic current flowing between generator and cable capacitor. Generator neutral grounding resistor can effectively reduce the third harmonic in the phase and neutral. However care should be taken not to use very high generator neutral grounding resistor that can also restrict earth fault current that may pose problem to the sensitivity of earth fault protection system. Zig-zag transformer efficiently divert third harmonic current from generator to enter the load but high circulating third harmonic current between generator and zig-zag transformer may pose heating problem to the equipments.