Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method, which takes the advantage of a machine learning (ML)-based algorithm, namely, support vector machine (SVM), in order to produce the results more efficiently. The results of the simulations based on the model and experimentally measured parameters of a real-life practical photovoltaic (PV) plant give much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely, grid fault, emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with the same kernel dimension and no custom hyperplane bordering. Discrimination between islanding and grid fault events is an essential dilemma, which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously detect the grid faults authentically. Nondetection zones (NDZs) and detection time (DT) are tested using two dimensions, namely, the generated active energy from PV plant (0%–110% of $P_{n}$ ) and distribution network voltage levels (±10% of $U_{n}$ ). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed for several scenarios. Finally, comparisons with previously reported techniques prove the effectiveness, authenticity, selectivity, accuracy, and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.

Support Vector Machine-Based Islanding and Grid Fault Detection in Active Distribution Networks

Future distribution systems are faced with more challenges on islanding detection due to the increasing penetration level of inverter-based distributed generators (DGs). Different DG technologies, inverter control as well as other advanced inverter functions, such as fault ride through are challenging the capability of islanding detection schemes. On the other hand, for multiple feeders network, topological change of feeders and islanding at adjacent feeder increase the vulnerability of islanding detection devices. Furthermore, available islanding detection schemes are suffering from notable non-detection zones (NDZs) under reduced power mismatches. Therefore, to mitigate these issues, this paper proposes an effective methodology for building decision trees-based intelligent relay (IR). This methodology utilizes the NDZ boundaries of existing standard relays and applies a comprehensive training/testing strategy, which effectively reduces the NDZ while maintaining a superior dependability and security performance. To validate the applicability of the proposed methodology, the hardware-in-the-loop simulations are realized by programming the generated IR logics into a real commercial relay.

Islanding Detection of Hybrid Distributed Generation Under Reduced Non-Detection Zone

Fault detection and location in a microgrid using mathematical morphology and recursive least square methods

Distributed generation (DG) offers solution to the ever increasing energy needs by generating energy at the consumer end, in most cases, by means of renewable energy sources. Islanding detection is an important aspect of interconnecting a DG to the utility. This study presents comprehensive review of various islanding detection techniques along with their relative advantages and disadvantages. A broad classification of islanding detection methods (IDMs) is laid out as classical methods, signal processing (SP)-based methods, and computational intelligence-based methods with a focus on SP-based methods and computational intelligence-based methods. The evolution of SP techniques used for islanding detection is presented along with the merits and shortcomings of each technique. Furthermore, the advent of computational intelligence methods based IDMs are discussed along with their merits and demerits. An insight into various islanding methods based on quantitative measures of performance indices such as detection time, detection accuracy, and efficiency are tabulated and presented. Finally, the prospective direction of research for IDMs is also presented.

Comprehensive review of IDMs in DG systems

Taxonomy of Islanding detection techniques for distributed generation in microgrid

This study proposes a novel method based on mathematical morphology (MM) for islanding detection in micro-grid integrated with distributed generation (DG). The method uses basic MM operators like dilate erode difference filter (DEDF) to operate on three-phase voltage and current signals on target DG location. These filter output values are fed to a new operator defined as the average of the difference between maximum and minimum value of DEDF at each structuring element called K V and K I . Accordingly, a new operator called the MM ratio index (MM RI) of K I to K V is derived. The MM RI computed is used to track the islanding condition from non-islanding condition. Additionally, to further aid the islanding detection, a mathematical morphological operator close open difference filter on negative sequence voltage is computed, which is passed through a difference operator D. The proposed method is analysed extensively to efficiently detect islanding in electrical power distribution network integrated with DG.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2015.0910

Musliyarakath Aneesa Farhan

Papers

Mathematical morphology-based islanding detection for distributed generation