Islanding Detection Research Articles

Frequency-Locked Loop Based New Automatic Phase-Shift Method for Active Islanding Detection of Three-Phase MicroGrid

This paper proposes an active Islanding Detection Method (IDM) integrating Frequency Locked-Loop (FLL) for the detection of voltage phase and frequency at the point of common coupling (PCC) and a modified Auto Phase Shift (APS) method for three-phase Distributed Energy Resource (DER) serving a local RLC load. The FLL is composed of a double second-order generalized integrator (DSOGI) to attenuate the harmonics and pass the fundamental positive sequence components in a frequency adaptive manner. Different from traditional APS methods, a new variable frequency term with an exponential factor is injected into the phase shift algorithm to reduce the region of Non-Detection Zone (NDZ). This modified approach enables the decentralized power generation system to detect the islanding phenomenon more quickly. Simulations and experimental tests conducted on a DG system on OPALRT (OP5600) and hardware test-bed demonstrate the behaviour of the proposed model within IEEE Standards. Additionally, a comparative evaluation was performed on the seamless transition of a microgrid (MG) from Grid-connected mode to Islanded mode and vice versa, using two different MGs, each having a single voltage source converter (VSC) and a local load but with control circuits incorporating DSOGI-FLL and ROGI-FLL respectively. Hardware testing results indicated that the control circuit based on ROGI-FLL demonstrated superior performance.

Island detection for grid connected photovoltaic distributed generations via integrated signal processing and machine learning approach

The applications of distributed generators in distribution networks have proven to be highly reliable and cost-effective. Accidental islanding is currently regarded as an undesirable operational mode in utility practice because it can harm individuals and connected systems. Therefore, the occurrence of islanding must be detected, and then distributed generators must be disconnected from the main network. In this article, a fast and accurate island detection method is proposed for photovoltaic distributed generations with a near-zero non-detection zone. A new island detection approach is developed by combining signal processing and machine learning techniques. Variational mode decomposition is used as a signal processing technique. Whereas the ensemble bagged-trees method is used as a machine learning technique. Variational mode decomposition is used to process positive- and negative-sequence component voltage signals along with power signal measurements acquired from the point of common coupling in order to identify intrinsic-mode functions. Next, the ensemble bagged-trees method is utilized to detect islanding during active and reactive power mismatch events and inconvenient quality factors. The results demonstrate that the suggested technique is able to discriminate between islanding and non-islanding events such as capacitive switching, fault emulation, and distribution generation cut-off. Besides, it has a minimum non-detection zone of less than 4% and a 4.8 ms detection time. Therefore, it is a reliable and reasonable solution for the distribution grid.

Islanding detection and classification of non-islanding disturbance in multi-distributed generation power system using deep neural networks

This paper proposes a statistical feature-based deep neural network (S-DNN) islanding detection technique and classification of non-islanding disturbances for hybrid systems with both synchronous and inverter-based distributed generators. The proposed technique extracts five statistical features from the point of common coupling (PCC)'s three-phase voltage. Several islanding and non-islanding scenarios for the test system were developed in the MATLAB environment. The S-DNN model is employed to effectively detect islanding and classify non-islanding disturbances using extracted features. In the detection phase of this study, the model is designed to identify events that occur in both islanded and non-islanding modes, providing the capability to accurately detect islanding conditions. In the classification phase, the model focuses specifically on non-islanding disturbances, such as line faults, load switching, and capacitor switching disturbances, enabling precise categorization of these distinct types of events. By leveraging the S-DNN model's capabilities, this study aims to enhance both islanding detection and non-islanding disturbance classification. By introducing white Gaussian noise with varying signal-to-noise ratios (30 dB and 40 dB) to the data, we aimed to evaluate the method's ability to handle and mitigate the effects of noisy and uncertain input signals. The inclusion of noise was intended to simulate realistic conditions where measurement inaccuracies or disturbances can be present. The decision tree (DT), multilayer perceptron (MLP), and support vector machine (SVM) are put up against the proposed technique, and it performs better than them in terms of accuracy, precision, recall, and F1 score.

An island detection approach in 6G paradigm for an active distribution network – A future perspective for next generation smart grids

Aiming ultra-reliable and mountable connectivity of tremendously high data rates in the Tb/s era with zero-perceived latency of 6G systems, next generation smart grids will need to captivate the advantages of breakthrough novel technology perceptions, incorporating THz wireless links, broadband and machine learning-based models designs, protocols and management practices. Motivated by the transformation potential of 6G systems, this paper deals with an island detection approach in 6G paradigm for an active distribution network. The proposed island detection method is tailored to adopt the breakthrough technologies of 6G, particularly, ultra-low latency, high volume data transmission and machine learned intelligent system. This methodology calculates the angular sum of the positive and the zero-sequence phase angle of the voltage at point of common coupling combined with random forest (RF) machine learning to identify islanding conditions. The technique has a robust resistance to noise due to the inherent nature of RF, has no consequences on power quality and exhibits a zero non-detection zone. It has the ability to differentiate between islanding and non-islanding events precisely even during active and reactive power discrepancy and transients similar to islanding. The method depicts a high testing accuracy of 98.46% with an island detection time of 52 ms.

Automatic Detection and Dynamic Analysis of Urban Heat Islands Based on Landsat Images

Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote sensing approach. Therefore, we developed a method to extract the spatial extent of the UHI during the period from 2000 to 2021 in Nanjing, China, and explored the impact of urban two- and three-dimensional expansion on UHI spatial extent and UHI intensity. After cropland effects (i.e., bare soil) were eliminated, our proposed method combines the Getis-Ord-Gi* and the standard deviation of the normalized difference vegetation index (NDVI STD) to extract the UHI area from Landsat 5 and Landsat 8 images using land surface temperature (LST) spatial autocorrelation characteristics and the seasonal variation of vegetation. Our results show the following: (1) Bare farmland has a large influence on the extraction results of UHI—combined with the seasonal variation characteristics of NDVI STD, the impact of bare soil on UHI extraction was highly reduced, strongly improving the accuracy of UHI extraction. (2) The dynamics of the UHI area are consistent with the changes in the built-up area in Nanjing at both spatial and temporal scales, but with the increase of the urban green ratio, the UHI area of mature urban areas trends to decrease due to the cooling effect of green space. (3) The accumulation of population and GDP promote the vertical expansion of urban buildings. When the two-dimensional expansion of the city reaches saturation, the UHI intensity is primarily affected by three-dimensional urban expansion.

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.