Conventional Islanding Detection Methods Research Articles

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

The assimilation of Distributed Generation (DG) into the electric power system (EPS) has become more attractive as the world is following a trend to reduce greenhouse gas emissions by introducing more renewable energy forms resulting in high penetration scenarios. This high penetration of DGs brings several challenges to the protection philosophy of the EPS which compromises its reliability, availability, and efficiency. Under high DG penetration scenarios, conventional islanding detection methods (Idms) fail to detect an island as the grid loses its inertia to leverage a significant frequency and voltage mismatch necessary for Idms to effectively detect an islanding event. This has given rise to the birth of Artificial Intelligent (AI) methods that are found to perform better in islanding detection. AI Idms are computationally intensive and require a lot of data to operate accurately. Because the computational burden of these methods requires fast computing hardware, the current trend of AI Idms are integrated with Wide Area Monitoring, Protection, and Control (WAMPAC) system. This paper aims at reviewing all these Idms and the WAMPAC’s system latency when hosting AI Idms which are currently the best in islanding detection. This is done to determine if the WAMPAC system latency plus Idms computational time meet the islanding detection time specified by the IEEE Standard 1547 framework.

Reactive Power P&O Anti-Islanding Method for a Grid-Connected Inverter With Critical Load

A grid-connected inverter with critical loads should be able to supply a stable voltage to critical loads at the instant of a mode change as well as during clearing time while quickly detecting unintentional islanding. The indirect current control has been proposed to provide not only a stable voltage to critical loads during clearing time but also a seamless transfer of inverters at the instant of a mode change. However, the conventional islanding detection methods cannot be applied to the indirect current control based inverter since the magnitude and frequency of voltage do not vary when an islanding occurs. This paper proposes a reactive power P&O anti-islanding method for the indirect current control. The proposed islanding detection method generates a small reactive power perturbation at the output of the inverter and detects the islanding by observing reactive power mismatch during the islanding. Using the proposed method, the inverter can supply high quality power to critical loads without distorting the output current during the clearing time. The proposed anti-islanding method is validated through simulation and experimental results.

Islanding detection method for microgrid based on extracted features from differential transient rate of change of frequency

One of the most important challenges in microgrid operation is the unintentional islanding occurrence. Unintentional islanding can cause serious safety hazards and technical issues. Islanding detection methods can be classified into active and passive methods. The main disadvantages of the passive methods are large non-detection zone as well as determination of suitable threshold value to avoid unwanted distributed generations tripping in normal network events. In order to overcome these drawbacks, this study proposes a novel, fast, and reliable method to identify islanding conditions. The proposed method calculates different transient states in the rate of change of frequency signal in two consecutive cycles. Various features of the differential signal are extracted. The extracted feature vectors associated with different operation conditions such as islanding and non-islanding events are used to train artificial neural networks (ANNs). The performances of different structures of ANNs and also other machine learning methods such as support vector machine and adaptive neuro fuzzy inference system are evaluated for islanding detection purposes. The simulation results indicate that the proposed method provides more accurate and faster responses compared with other conventional islanding detection methods.