Islanding Detection Research Articles

Analysis of an Anti-Islanding Prevention Technique in a Grid-Connected PV System

In the present power system with distributed generators (DGs), there are some issues that are not resolved yet. One of these issues involves a fast and very accurate detection of islanding. The number of DGs introduced into electricity distribution systems is increasing day by day, and it is the most challenging concept in modern power system scenarios, since DG islanding can cause degraded proficiency, excellence, reliability and quality of supply. Hence, anti-islanding with fast response time is essential for a DG connected grid system. To overcome the challenges from islanding DGs, researchers proposed a numerous model that deals with the consequences of intentional islanding, and clears it as fast as possible.

Islanding Issues, Consequences, and a Robust Detection Method for Hybrid Distributed Generation Based Power Systems

Islanding refers to the situation where a Distributed Energy Resource (DER) remains as the sole power supply for a specific section of a power system, even after the main utility grid has been cut off. Suitable islanding detection is crucial to maintain the stability and dependability of a power distribution system that includes DERs. Islanding detection using easy-to-implement passive techniques exhibits a cost-effective response. The purpose of this study was to examine the causes and effects of islanding that a system can experience and propose a passive islanding detection method that uses ROCOPAD. The effectiveness of the proposed method was assessed using a MATLAB Simulink-based power system integrated with multiple Distributed Generations (DGs). The results showed that the proposed ROCOPAD-based islanding detection provided the best results. Evaluation metrics, including detection accuracy, false operation, and detection time, highlighted the effectiveness of the proposed approach.

Passive Island Detection Method Based on Sequence Impedance Component and Load-Shedding Implementation

Active islanding detection techniques majorly affect power quality due to injected harmonic signals, whereas passive methods have a large non-detection zone (NDZ). This article presents a new method based on the resultant sequential impedance component (RSIC) as a new approach to island detection with zero NDZs. The abrupt variable in the conventional impedance approach was replaced by the RSIC of the inverter in this method. When the measured value exceeds the threshold range, islanding is detected by monitoring the variations in the RSIC at the point of common coupling (PCC). For proper power utilization in the identified islands, a priority-based load-shedding strategy is also recommended and implemented in this article. Its efficacy was verified in a wide range of real-world settings. It offers superior stability in various non-islanding (NIS) scenarios to prevent accidental tripping. The proposed method advantages include a cheap cost, the simplicity of implementation, independence from the number and type of distributed generation (DG) units connected, and no power quality effects. Compared to other methods reported in the literature, the obtained detection times illustrate that the proposed method is superior.

Islanding Detection in Distribution Generation using Active Method

There are two techniques to ensure that renewable energy systems run continuously: on-grid and off-grid. In the first case the system can be managed in a network, in the second case it can be managed in a micro grid or island mode. Islanding means when a distributed generator (DG) keeps running even after there is no longer any external electricity. This situation can be dangerous for utility systems because it prevents equipment from connecting properly. Therefore, this paper proposes an active method for detecting island patterns at a given time. The use of DG installations such as photovoltaics can cause islanding detection issues. In addition, the number of DG sets in the power distribution system will affect the accuracy of island detection (ISD). On a test example DC power supply linked to a 415 V, 50 Hz network, the suggested solution was put into practice. In the MATLAB Simulink environment, the proposed method's efficacy was assessed. The proposed method is compared with existing active methods and shortens the detection time.

Passive Island Detection Method Based on Positive Sequence Components for Grid-Connected Solar–Wind Hybrid Distributed Generation System

This paper presents the development and practical implementation of a new passive island detection method for grid-connected hybrid distributed generating systems. The method employs positive sequence voltage and current components at the point of common coupling. The product of the difference in positive sequence voltage and the difference in positive sequence current is used to produce the Islanding Detection Index (IDI). Islanding is identified when the IDI magnitude surpasses a predefined threshold over a predefined delay time. Even in the case of a zero power mismatch condition, the proposed method can identify the islanding condition in under 70 msec. Its effectiveness is validated under various operating circumstances. It provides excellent stability against various non-islanding situations, avoiding unwanted tripping. The suggested method also has the advantages of being simple and inexpensive to implement, having no adverse effects on output power quality, not requiring a classifier, not being system-dependent, and working regardless of the quantity and type of distributed generation connected to the utility grid. The obtained detection timings were compared to those previously reported in the literature, confirming the proposed approach’s superiority. The suggested work utilizes the OPAL-RT real-time hardware-in-loop environment and MATLAB/Simulink.

A New Variational Mode Decomposition-Based Passive Islanding Detection Strategy for Hybrid Distributed Renewable Generations

A New Variational Mode Decomposition-Based Passive Islanding Detection Strategy for Hybrid Distributed Renewable Generations

An improved active islanding detection method for grid-connected solar inverters with a wide range of load conditions and reactive power

Islanding occurs when a utility-interactive distributed generator (DG) is disconnected from the utility grid and continues to supply power to local loads. This condition has several consequences including damage to the equipment and personnel safety risks. Therefore, it is required by utility interconnection standards for the DG to stop energizing local loads in the case of islanding. In this paper, an active islanding detection method (IDM) based on injecting a disturbance into the phase-locked loop (PLL) of a grid-connected photovoltaic (PV) inverter and monitoring the harmonic components of the point of common coupling (PCC) is proposed. A modified Goertzel algorithm is utilized to detect the injected harmonic which is effective for a wide range of load conditions in terms of load quality factor, resonant frequency, and reactive power. The simulation results in MATLAB/Simulink show that the proposed technique is fast with minimum side effects on power quality features such as output current total harmonic distortion (THD). This method is implemented on a single phase 5 kW grid-tied PV inverter and is tested experimentally. The experimental results verify that the proposed technique is fast with zero non-detection zone (NDZ) which is effective for a variety of load conditions.

Islanding Detection in Grid-Connected Urban Community Multi-Microgrid Clusters Using Decision-Tree-Based Fuzzy Logic Controller for Improved Transient Response

The development of renewable-energy-based microgrids is being considered as a potential solution to lessen the unrelenting burden on the centralized utility grid. Furthermore, recent studies reveal that integrated multi-microgrid cluster systems developed in urban communities maximize the effectiveness of microgrids and greatly decrease the utility grid dependence. However, due to the uncertain nature of renewable energy sources and frequent load variations, these systems face issues with unintentional islanding operations. This can create severe damage to the microgrid’s performance in its stable operating condition and lead to undesired transient responses. Hence, islanding must be identified rapidly to take preventive measures to address the issue. This requires the development of a suitable anti-islanding technique that is faster in terms of accuracy and timely detection. With this intention, this paper proposes a decision-tree-based fuzzy logic (DT-FL) controller for the rapid identification of islands in an urban community multi-microgrid cluster. The DT-FL controller’s operation includes two steps. First, the decision tree extracts the electrical parameters at the point of common coupling of the multi-microgrid system. Second, these extracted parameters are utilized for the online tuning of the fuzzy logic controller, for the fast detection of islanding. The multi-microgrid cluster under study, along with the proposed islanding technique, is implemented in the MATLAB-2021a software. The efficacy of the proposed DT-FL controller is validated by comparing its performance with that of the conventional fuzzy logic controller under different test scenarios. From the results, it is observed that the proposed DT-FL controller shows superior performance in terms of the islanding detection time as well as the transient response of the system when compared with the conventional controller.

Improved Active Islanding Detection Technique with Different Current Injection Waveform

The active frequency drift (AFD) method is an effective method to detect islanding in grid-connected photovoltaic systems. However, it has some inherent drawbacks, such as generating higher harmonics. In order to reduce the harmonics and non-detection zone (NDZ), various improved AFD methods have been proposed, but they still suffer from high harmonics and reduced detection speed. To overcome these limitations, this paper proposes an innovative islanding detection technique based on AFD. Analysis reveals that the proposed method reduces harmonics by 68% compared to conventional AFD and has a larger chopping factor. Therefore, this technique offers several distinct advantages, including accelerated detection speed, reduced NDZ and harm caused by disturbances, and improved power quality. Furthermore, to verify the harmonic impact of this proposed islanding detection method, simulations and analyses are conducted using simulation software of Matlab/Simulink. An experimental prototype is set up in Laboratory. The simulation and experimental results demonstrate the superiority of the proposed method.

A Hybrid Islanding Detection Method Based on Lissajous Pattern Having Robust Performance Under Various Power Quality Scenarios

A fast, accurate, robust, and two-staged islanding detection technique (IDT) is proposed. It is the hybridization of second-order general integrator-frequency locked loop (SOGI-FLL), Lissajous pattern (LP), and active power absorption-cum-reactive power injection via dc-bus connected battery unit-based inverter. The LP is used to identify islanding condition on the basis of frequency variation of fundamental voltage. Here, the measured ac voltage signal is preprocessed by SOGI-FLL to obtain fundamental bus voltage under various power quality scenarios. This assures robust islanding detection by removing uncertainty, and thus, reliability is improved. The uncertainty effects due to the partial shading condition of the PV module (source-side disturbance) and weak grid-connected condition are avoided by dc-bus connected battery storage unit. Thus, it removes above-said limitations of the existing scheme. The LP-based detection within the nondetection zone is expedited due to active and reactive powers variation via controlling of the inverter and charging operation of the dc-bus connected battery unit. During the reactive power injection by solar inverter, active power is absorbed by dc-bus connected battery unit. In effect, an active power output of the inverter is reduced. Here, the reactive power injection at the reduced active power output condition of the inverter accelerates frequency variation allowing rapid islanding detection. The real-time experiments are carried out using Typhoon-HIL tools to compare the proposed method with other works reported in the literature. Improved performances under diversified scenarios are found in the proposed case.

Voltage and frequency stabilization by fuzzy integrated droop control of a multi renewable source micro grid

A multi renewable source micro-grid system has many power quality issues when it is operated in grid islanding condition. During grid connected mode the voltage and frequency of the system will remain stable for small disturbances like load changes or environmental changes. During these sudden variations in the system, the grid supports the load maintaining the system stable. In this paper a droop control is introduced for the renewable source module 3-ph inverter during standalone mode supporting the load. For detection of grid islanding, an active islanding detection control is adopted which makes the 3-ph inverter to shift from synchronous reference frame (SRF) control to droop control. The performance of the droop control is improvised with fuzzy interference structure (FIS) module replacing the conventional proportional integral and derivative (PID) control. A comparative analysis is done in this paper with different controllers in droop control scheme using matrix laboratory (MATLAB) Simulink software. Stability analysis is carried out on the system determining the voltage and frequency fluctuations during grid disturbances. There is an improvement in load compensation, voltage ripple, voltage magnitude, frequency ripple and settling time with the introduced fuzzy droop controller.

Optimized Hybrid CNN-LSTM Based Islanding Detection of Solar-Wind Power System

The rising prevalence of dispersed energy-generating resources like wind and solar power systems degrades the quality of electricity. For the safety of maintenance staff and the equipment in such situations, detecting the power quality troubles in the power systems is highly significant. In order to cater for these issues, the stationary discrete wavelet transform (SDWT) and hybrid long short-term memory (LSTM) convolution neural network (CNN) based classifier is proposed in this work. Besides, the African vulture optimization algorithm (AVOA) is adopted for optimizing the weights of layers in hybrid LSTM-CNN (HLC). This work aims to detect the islanding operation under different faulty conditions in the network. The implementation results are analyzed for various conditions, showing the fidelity of a proposed method by detecting the islanding faults. Moreover, the proposed method is validated under different noise levels and shows better performance in noisy signal to noise ratio (SNR). The accuracy obtained for the proposed SDWT-HLC method without noise is 99.92%. The accuracy with 20, 30 and 40 dB noise for the proposed method is obtained as 99.84%, 99.87% and 99.89%, respectively. Furthermore, the accuracy, precision, recall and F1-score are 0.999, 0.995, 0.994 and 0.994, which shows the efficacy of the proposed method.

Preserving microgrid sustainability through robust islanding detection scheme ensuring cyber-situational awareness

Along with the numerous environmental and operational benefits of renewable energy sources (RESs) integration into modern distribution networks, maintaining system security in these networks during challenging phenomena, especially unintentional islanding can still be problematic in some cases. This paper puts forward a sensitivity-based islanding detection algorithm that utilizes the transient kinetic energy of the RESs to identify the islanding condition in microgrids and also considers cyber-attacks, which may lead to operators making inappropriate operational choices. To be specific, the proposed algorithm finds a generalized index that considers simultaneously the impact of volatility of parameters during islanding and non-islanding conditions and is also robust against data integrity attacks. The generalized index, the so-called sensitivity islanding detection (SID) index, is designed based on the sensitivity of transient kinetic energy over transient influential parameters. Furthermore, a statistical index for islanding detection is introduced based on the standard variation that effectively monitors the volatilities. An unsupervised anomaly detection method that uses kernel density estimation technology and statistical calculations are proposed as a way to find cyber-attacks. Following that, an outlier detection method is used to identify anomalies. The effectiveness of the proposed algorithm is evaluated under various conditions using simulation environments.

Islanding detection and power quality disturbance classification in multi DG based microgrid using down sampling empirical mode decomposition and multilayer neural network

Abstract Power Quality, Equipment and Personnel safety of any distributed generation (DG) system connected to utility Grid merely depends on accurate detection of Islanding and non-islanding Power quality disturbances. The main objective of the proposed research is to detect islanding events with very narrow non-detection zone (NDZ) and classification of power quality disturbances with higher accuracy using signal processing and intelligent method together. A noise robust down sampling empirical mode decomposition (DEMD) is used to extract signature of islanding and power quality (PQ) disturbance features from the collected voltage signals and multilayer perceptron neural network (MLNN) is proposed to classify islanding and non-islanding (PQ) events. The performance of the proposed (DEMD-MLNN) technique is verified with IEEE-9 bus distributed generation system dominated by solar &wind energy penetration. The simulation work is carried out in MATLAB/Simulink platform. The efficacy of the proposed DEMD-MLNN is verified by large number of numerical experimentations with and without noise and comparing with existing competitive well-known techniques.

A statistics-based review on island detection methods in microgrids: Overall investigation and state-of-the-art

AbstractIn this paper, a comprehensive statistics-based review of islanding detection methods (IDMs) in microgrids (MGs) is presented. Islanding detection is the situation of isolating the MG from the main grid whether programmed as a result of load managing purposes or un-programmed due to the occurrence of faults. Islanding detection is a vital issue in MG's analyses due to the prevention of subsequent protection problems in the power system. In other words, when the MG's operation mode changes, the current passing through the protective devices changes subsequently and the protection system should be able to adapt the new settings to the protective devices. So, IDMs are vital for electrical engineers to overcome the abovementioned protection issue. This review paper surveys the existing literature in IDMs by concentration on total publications, type of publications (journal, conference paper, or book), five authors with the highest number of publications (including the affiliations), and five most published sources. Also, the five most cited publications and state-of-the-art IDMs are investigated in detail, utilizing some known and novel categorizations. This paper will be useful for the MG's researchers to know the most desirable IDMs, especially in recent years, and provides an insightful overview for future studies.

An Islanding Detection Method Using Synchronized Small-AC-Signal Injection for Grid-Forming Inverters in Microgrids

Islanding detection is of great importance for the microgrid in order to change the control objectives and to avoid unsynchronized reclosure. Active islanding detection methods based on impedance measurement have very small non-detection zone (NDZ) in the single-inverter system, but require the coordination of the perturbation current injected by each inverter in the microgrid consisting of multiple inverters. The conventional solution by injecting a harmonic current synchronized with the fundamental output voltage is affected by different winding connections in grid-side transformers of inverters and degrades the voltage quality in stand-alone mode. This paper proposes an islanding detection method based on impedance measurement for the microgrid comprised of grid-forming (GFM) inverters with seamless transfer capabilities. The proposed method achieves synchronized injection of small-AC-signal (SACS) current using the SACS current droop control and the virtual impedance. The synchronized SACS current injection in the proposed method is independent of fundamental components and is not influenced by different winding connections in grid-side transformers of inverters. Moreover, the SACS current limiting control is introduced to mitigate the impact of injected SACS current on the grid current quality. In addition, the control scheme is peer-to-peer and enables plug-and-play functionality. The effectiveness and advantages of the proposed method are validated through simulation and experimental results.

An Extensive Review and Analysis of Islanding Detection Techniques in DG Systems Connected to Power Grids

Nowadays, the integration of distributed generators with the main utility grid is highly increasing due to the benefits which can be obtained, such as increasing the system efficiency and reliability. Apart from that, many technical and safety issues appear in the system due to this integration. One of these issues is the islanding condition, which has to be detected effectively and quickly before having any detrimental effects on the protection, stability, and security of the system. This study provides a detailed overview of several islanding detection approaches, which are divided into traditional methods, including local and remote methods, and modern methods, including methods based on signal processing and computational intelligence. Moreover, a comparison between each method based on various criteria, such as non-detected zone, quality factor, response time, implementation cost, degrading power quality, reliability, suitability for the type of distributed generators, suitability for multi-distributed generators system, and sensitivity to cyber-attacks, is carried out. Therefore, this review will offer a solid background in order to help researchers interested in this field distinguish between islanding detection methods and their relative advantages and disadvantages, as well as to be able to choose the most suitable islanding detection method among the others to be implemented in the network.

Intelligent Island detection method of DC microgrid based on Adaboost algorithm

Aiming at the problems of large detection blind areas and difficult threshold settings in traditional passive islanding detection methods, this paper proposes an intelligent passive islanding detection method based on the adaptive boosting (Adaboost) algorithm. This method first uses feature screening technology to obtain the key feature electrical quantities of DC microgrid islands and form a sample set, and then rely on the Adaboost algorithm to generate a high-precision island classification model to realize the detection and classification of DC microgrid operation status. It has the advantages of automatic setting threshold, small detection blind area, and high accuracy. In addition, aiming at the problem that the classification results of the intelligent islanding detection algorithm based on machine learning cannot be directly used as the islanding discrimination results, an islanding criterion method based on the sliding window algorithm is added on the basis of the classification algorithm. This method realizes the accurate detection of the DC microgrid islanding operation state by the secondary analysis and judgment of the classification results of the islanding classification model. Finally, a DC microgrid model is built to simulate and verify the proposed method. The results show that the method can quickly and accurately complete the islanding detection of the DC microgrid.

μPMU-based islanding detection method in power distribution systems

This paper focuses on the use of micro-phasor measurement units (μPMUs) for the prompt detection of unplanned islanding conditions. The investigated method is based on the monitoring of voltage and current phasors at distributed generation (DG) buses equipped by μPMUs. The difference between two consecutive phasors (voltage phasors and current phasors) are monitored until a perturbation occurs. When a perturbation is detected, the first phasor, called pre-disturbance phasor, is kept fixed and the second one, called post-disturbance phasor, is continuously updated with each new sample arrival. The differences between the pre-disturbance and post-disturbance phasors are calculated and summed up for a predefined time window. When this exceeds a predefined threshold, the island condition of the related bus is detected. The presented procedure can distinguish the islanding condition from other perturbations, such as motor starting, faults, and capacitor switching. The performance of the proposed procedure, as well as its sensitivity to the system conditions and predefined values, are analyzed considering a 7-bus distribution system and the IEEE 33-bus test system, both with synchronous generators and inverter-based DGs.

A Novel Passive Islanding Detection Method for a Microgrid Using Instantaneous Energy

A Novel Passive Islanding Detection Method for a Microgrid Using Instantaneous Energy