Non-islanding Events Research Articles

Islanding Detection Scheme of Distributed Generation Systems using Hybrid FAT-SGO Approach

In this paper an effective hybrid FAT-SGO approach is proposed for islanding detection of distributed generation (DG) system. The proposed approach is the joint implementation of Feedback Artificial Tree (FAT) and Shell Game Optimization (SGO) named as FAT-SGO technique. Reducing the non-detection zone (NDZ) as near as possible and keep the output power quality unmovable is main contribution of this paper. Furthermore, this method solves the issue of establishing detection thresholds inherent in existing methods. The proposed strategy uses the rate of change of frequency (ROCOF) in DG destination location is utilized as input sets of FAT system for intelligent islanding detection. Here, FAT is trained by SGO, which extracts the different intrinsic characteristics among islanding and grid disturbance. With the extracted characteristics, the FAT method is used for classifying the disturbances in islanding and grid. For authenticating the feasibility of this strategy is authorized through various conditions and different conditions of load, switching operation, and network. The simulation of the proposal is done in MATLAB SIMULINK and the performance in islanding and non-islanding events was studied. Statistic analysis of proposed and existing methods of mean, median and standard deviation is analyzed. DG performance is assessed by comparative analysis with current techniques.

A New Adaptive Multiscale Morphological Filter and Robust RVFLN Classifier for Distributed Generation Systems during Islanding and Non-Islanding Events

A new method for islanding and non-islanding disturbances detection and classification is proposed for a multiple PV based distributed generation (DG) system utilizing adaptive multi-scale morphological filter (AMF) and random vector functional link network (RVFLN) classifier. In comparison to different signal analysis techniques, the mathematical MF, that has wide application in power signals, EEG signal analysis, image processing, pattern recognition, etc. posses the benefit of easy execution, fast processing, and minimal computations. Further it is well known that a single scale morphological filter has limited noise filtering capacity and may also filter useful signal disturbance components resulting in erroneous detection of disturbance signals in microgrid. Therefore, an adaptive multiscale combined morphological filter is presented in this paper built on the concept of multiscale overall filtering which has better denoising effect and can retain useful signals better than the traditional filter. The proposed technique is built upon the measurement of voltage signal samples and the processing of these signals through AMF has been done for feature extraction. The extracted features are then employed as inputs to an efficient, fast, and easily implementable randomized network based classifier (RVFLN) which is made robust to reject the presence noise and outliers in the signal data. The outputs exhibited from the suggested technique concludes that it is s very fast and accurate technique for the detection and classification of islanding and non-islanding events in comparison to the widely used approaches.

Pearson's Correlation Coefficient for Islanding Detection Using Micro-PMU Measurements

An islanding detection scheme (IDS) based on using frequency and phase angle information obtained from the micro-phasor measurement units (μPMU) is proposed in this article. From the μPMU data of reference substation and distributed generator terminals, two parameters are formulated; a cumulative sum of frequency difference and a phase angle difference. In the event of islanding, a linear correlation exists between the two parameters. The linear correlation between the two parameters is evaluated in real time using Pearson's correlation coefficient formula. A time bound threshold is considered to differentiate islanding events from other non-islanding events like load switching and faults. The advantage of the proposed IDS is it does not require the establishment of any frequency and phase angle based thresholds. Also the islanding detection time is independent of power mismatch conditions. Real-time performance of the algorithm is accessed on a test bench comprised of dSPACE DS1104 real-time controller and a real-time digital simulator (RTDS).

An efficient learning based RFMFA technique for islanding detection scheme in distributed generation systems

This paper proposes an effective hybrid system for islanding detection of distributed generation (DG). The proposed control scheme is a united execution of both the Random Forest (RF) and Moth–Flame Optimization (MFO) named as RFMFO. The fundamental objective of the proposed work is to diminish the Non-Detective Zone (NDZ) to as close as could be allowed and to keep the output power quality unaltered. Moreover, the issue of setting the discovery thresholds in the current methods is overwhelmed by this strategy. For intelligent islanding detection, Rate of Change of Frequency (ROCOF) is used; by this methodology at the target DG location is utilized as the input sets for an RF system. So as to extricate various features among islanding and grid disturbance, the precision of the RF is prepared by MFO algorithm. In the proposed work, the RF is utilized to classify the islanding and non-islanding events subject to the extracted features. A few conditions and diverse loading, switching operation, and network conditions are resolved to approve the practicality of the proposed method. The proposed technique is implemented in MATLAB/Simulink working platform. The sampling point of voltage swell is 0–1.4×104 N and 2.7 ×104- 4×104 N, voltage sag has the sampling point of 1.4×104– 2.7 ×104 N and the PCC has the sampling point of 2.1 ×104- 4 ×104 N. The performance of the DG is evaluated by using comparative analysis with the current methods.

Active Unintentional Islanding Detection Method for Multiple-PMSG-Based DGs

Penetration of small distributed generators (DGs) has been made possible in distribution systems owing to the advances in power electronics. However, it is not desired to energize the loads during an unintentional island formation. Hence, in this article, an active unintentional islanding detection method based on disturbance injection through a q -axis current controller is presented for converter interfaced multiple permanent-magnet-synchronous-generator (PMSG)-based DGs. The injected disturbance forces the frequency of the system to change meaningfully on the postislanding condition. Subsequently, a simple analyzing method is proposed based on the blend of a second-order bandpass filter and mean of absolute frequency variation to detect and also to differentiate the islanding from the nonislanding events. The proposed method detects the island formation within 178 ms for the most crucial islanding condition for PMSG-based DGs, whereas the minimum detection time among several international standards is 200 ms. And, also it distinguishes the nonislanding events from the islanding events precisely without destabilizing the system. The MATLAB R2019b/Simulink platform is used to validate the results in simulations.

Data Description Technique-Based Islanding Classification for Single-Phase Grid-Connected Photovoltaic System.

This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.

Improving the islanding detection performance of passive protection by using the undervoltage block function

Conventionally, passive techniques are used to protect distributed generators against islanding events. These protections must disconnect the generators as soon as possible, either to de-energize the island or to commute control modes and so that the generators operate isolated as a microgrid. The parametrization of pick-up and time delay of frequency-based and voltage-based protections must be done based on dynamic simulations. Moreover, adjusting the undervoltage block function of frequency-based protection is commonly neglected and either a default value is used, or it is disabled. Thus, this paper proposes a practical methodology to parametrize the undervoltage block function of frequency-based anti-islanding protections. This methodology is a direct method to adjust the undervoltage block and its use resulted in a better performance of the anti-islanding protection. Based on 2149 islanding and 2457 non-islanding events and considering ride-through requirements from IEEE 1547-2018, the voltage block methodology associated with the automatic setting map methodology presented 97.39% of correct islanding detection and 0.16% of incorrect trips.

FPGA implementation of an Islanding detection technique for microgrid using periodic maxima of superimposed voltage components

In this study, a novel islanding detection technique (IDT) based on the islanding discrimination factor using the periodic maxima of superimposed voltage components is proposed. The voltage profile of the target distributed generator is monitored continuously and its sequence components are extracted using half-angle theorem. A modular approach is used to implement the IDT algorithm, which is targeted on the reconfigurable hardware, i.e. field-programmable gate array. Hardware description language Verilog has been used to optimise the hardware resources and minimise the computational complexity. Hardware-in-loop verification of the proposed IDT has been done for islanding and non-islanding events with a microgrid (MG) test system developed on real-time digital simulator. Various cases for both islanding and non-islanding events, namely different faults are realised on the developed MG system. The results suggest that the proposed scheme successfully differentiates islanding events from a non-islanding event, and also detects the islanding event accurately under perfect power balance condition within half a cycle.

Artificial Intelligence-Based Cyber–Physical Events Classification for Islanding Detection in Power Inverters

Along with the rapid integration of distributed generation units (DGUs) into the power grids is the rise in unconventional and unpredictable patterns of the undesirable cyber–physical intrusions and faults; this drastically increases the risk of islanding possibilities and threatens the sustainability of the energy delivery infrastructure. Classification of cyber–physical events and developing solutions to mitigate their impacts before rising to an islanding situation is a critical monitoring task in DGUs. Passive islanding detection has been widely applied to studying the behavior of voltage signals at the point of common coupling, which is a sophisticated challenge due to cross similarity among fault (event) patterns and their fast dynamics. In this article, a novel quadratic time–frequency decomposition, namely HSS-transform, is applied over an alternative complex representation of three-phase signal defined by the synchronous reference frame transformation. We further exploit the principles of informative sparse representation-based classification (TISC) to develop a comprehensive artificial intelligence framework for fast and reliable classification of DGU islanding and nonislanding events with the focus on practical limitations and requirements of a smart power electronics inviter as the desirable observational site. Different from the state-of-the-art techniques, TISC does not need any training procedure, while due to its linear mathematical formulation acts inherently fast with low computational burden on the inverter processing unit. Moreover, the simultaneous three-phase feature extraction strategy ensures preservation of the between-phase information.

Analysis of power system parameters for islanding detection using wavelet transform

<p><span>This paper classifies the basic power system parameters based on sensitivity and performance capability. This is the basic step in selecting the suitable parameter for intelligent islanding detection technique. The behaviour of basic power system parameters is analyzed using wavelet transform under all possible islanding and non-islanding conditions. In wavelet transform, the mother wavelet plays a significant role in the extraction of parameter features. The db4 mother wavelet is selected after an extensive analysis on different mother wavelets. As a result, it has been found that the extracted feature of reactive power shows the highest capability to distinguish islanding from non-islanding events.</span></p>

A novel phaselet-based approach for islanding detection in inverter-based distributed generation systems

In this paper, a novel signal processing approach based on phaselet algorithm is proposed to detect the islanding phenomenon. Phaselets can effectively compute the phasors over data windows that are not limited to an integer multiplication of a half-cycle. Thereby, they can obtain features of the islanded and non-islanded situations without injecting any disturbance or high frequency signal into the power system, which makes the proposed method more accurate and reliable because there are no power quality reduction problems. In the proposed approach, phaselet algorithm uses the variable filtering window capability in order to phasor estimation of the negative sequence components of the voltage and current waveforms. To evaluate the performance of the proposed technique, various islanding and non-islanding events have been implemented using a grid-connected PV system modeled in a MATLAB/Simulink environment, which are based on the UL-1741 and IEEE-1547 standards. The simulation results show that our approach is able to accurately distinct islanding incident from other power quality disturbances. The ability to detect islanding occurrence in less than two cycles even in the case of perfect power match, the zero non-detection zone (NDZ) and no power quality degradation are the main advantages of the proposed method.

Intelligent Islanding Detection of Multi-distributed Generation Using Artificial Neural Network Based on Intrinsic Mode Function Feature

The integration of distributed energy resources (DERs) into distribution networks is becoming increasingly important, as it supports the continued adoption of renewable power generation, combined heat and power plants, and storage systems. Nevertheless, inadvertent islanding operation is one of the major protection issues in distribution networks connected to DERs. This study proposes an intelligent islanding detection method (IIDM) using an intrinsic mode function (IMF) feature-based grey wolf optimized artificial neural network (GWO-ANN). In the proposed IIDM, the modal voltage signal is pre-processed by variational mode decomposition followed by Hilbert transform on each IMF to derive highly involved features. Then, the energy and standard deviation of IMFs are employed to train/test the GWO-ANN model for identifying the islanding operations from other non-islanding events. To evaluate the performance of the proposed IIDM, various islanding and non-islanding conditions such as faults, voltage sag, linear and nonlinear load and switching, are considered as the training and testing datasets. Moreover, the proposed IIDM is evaluated under noise conditions for the measured voltage signal. The simulation results demonstrate that the proposed IIDM is capable of differentiating between islanding and non-islanding events without any sensitivity under noise conditions in the test signal.

A Proposed Passive Islanding Detection Approach for Improving Protection Systems

Nowadays, distributed generation (DG) is commonly used in networks. In spite of the numerous benefits of DG units in these networks, several challenges are introduced such as unintentional islanding, reverse power flow, protection concerns, etc. In this paper, a new passive islanding detection approach is proposed depends on the production of voltage sequence components at every relay location not only at the DG point of common coupling. The proposed approach uses only one threshold value for all relays in the distribution network. The performance of the proposed approach is not influenced by changes in type, capacity or location of DG. The proposed approach is evaluated under different transient conditions such as capacitor switching, load switching and DG switching. Also, the performance of the suggested islanding approach has been compared with most prevalent islanding detection techniques. The performance of the proposed algorithm is stable during balanced and unbalanced conditions. Moreover, detecting the islanding case at all relays provides the ability to improve the protective relays performance by updating the protection setting for all relays based on local measurement only without the need for fault current limiters (FCLs) or a communication network. The proposed approach is evaluated using the Canadian distribution system embedded with DG units in MATLAB/Simulink simulation. The achieved results demonstrate that the proposed approach is robust during all non-islanding events. Also, it succeeds to detect islanding condition with high confidence without non detection zone (NDZ).

Adaptive Frequency Estimation Method for ROCOF Islanding Detection Relay

Rate-of-change-of-frequency (ROCOF) is one of the most popular local islanding detection methods due to its simplicity and low cost. However, the method suffers from significant non-detection zone (NDZ) and experiences security issues due to non-islanding events. In this article, an enhancement of the ROCOF relay, improving frequency estimation is proposed and compared to standard methods. Results are obtained through real-time hardware-in-the-loop (RT-HIL) simulations using an IEC61850 compliant STM32F746NG microprocessor acting as a ROCOF relay. Feasibility of the method has been proven experimentally. As demonstrated, it has an ability to significantly mitigate a number of nuisance trippings for non-islanding events. For the CIGRE medium voltage benchmark distribution network, the proposed method provided full security at the setting of 0.5 Hz/s for adjacent feeder faults, whereas the standard methods happened to fail even at the setting of 1 Hz/s. Additionally, as shown, an improvement of the relay security can lead to reduction of NDZ.

Use of Matrix-Pencil Method for Efficient Islanding Detection in Static DG and a Parallel Comparison With DWT Method

Islanding or nonislanding events in grid-connected distributed generation bring along a typical distinguishable transient signature in its frequency profile. This demarcation leads to the development of a new islanding protection approach, which is based on the estimation of frequency waveform parameter (transient's frequency) by Matrix pencil (MP) method. To demonstrate the efficacy of the proposed MP method, four critical scenarios are considered in this paper for covering all possible disturbance events. These events are also compared along with a discrete wavelet transform (DWT) based islanding detection method in simulations as well as in RT-LAB-based real-time environment. It is noteworthy to mention that the proposed MP method has been found to have a positive edge over the DWT-based method in terms of robustness and chances of misidentification.

A New Islanding Detection Method Based on Wavelet-transform and ANN for Micro-grid Including Inverter Assisted Distributed Generator

Abstract This paper presents a new composite approach based on wavelet-transform and ANN for islanding detection of distributed generation (DG). The proposed method first uses wavelet-transform to detect the occurrence of events, and then uses artificial neural network (ANN) to classify islanding and non-islanding events. Total harmonic distortion and voltage unbalance are extracted as feature inputs for ANN classifier. The performance of the proposed method is tested by simulations for two typical distribution networks based on MATLAB/Simulink. The results show that the developed method can effectively detect islanding with low misclassification. The method has the advantages of small non-detection zone and robustness against noises.

Islanding Detection in Distributed Generation Integrated Thimi – Sallaghari Distribution Feeder Using Wavelet Transform and Artificial Neural Network

In response to the problem of increased load demand, efforts have been made to decentralize the power utility through the use of distributed generation (DG). Despite the advantages of DG integration, un-intentional islanding remains a big challenge and has to be addressed in the integration of DG to the power system. Islanding condition occurs when the DG continues to power a part of the grid system even after the connection to the rest of the system has been lost, either intentionally or un-intentionally. The unintentional islanding mode of operation is not desirable as it poses a threat to the line workers’ safety and power quality issues. There are many methods which may be used to detect the islanding situation. Passive methods such as under/over voltage and under/over frequency work well when there is an imbalance of power between the loads and the DG present in the power island. However, these methods has larger Non Detection Zone (NDZ) and fail to detect the islanding condition if there is a balance of power supplied and consumed in the island. Remote technique of islanding detection is reliable but is not economical in small network area. Active technique of islanding detection distorts the power quality of the system as it introduces external signal in the system. This paper uses the Wavelet Transform (WT) to extract the features of voltage signal at PCC (Point of Common Coupling) and these features have been used to train Artificial Neural Network (ANN). The ANN model trained by these WT features, which understands the pattern of input feature vector, have been used to classify the islanding and non-islanding events. In this proposed method, NDZ has been efficiently eliminated which is created due to difference between active and reactive power during islanding condition. No power quality problem exists in this method as there is no disturbance injection. Hence, this proposed method is better than conventional passive and active methods.

Islanding Detection of Distributed Generation Using Electrical Variables in Space Vector Domain

This paper presents a new approach for islanding detection in distribution systems incorporating synchronous generator-based distributed generations. The proposed method first utilizes the dynamic equations of three electrical variables under islanding situations. The variables are: change of frequency; rate of change of frequency; and rotor angle deviation. Second, transformation of these variables is conducted in the space vector domain, which gives rise to three parameters, namely, semi-major axis, semi-minor axis, and inclination angle. Finally, exploiting these parameters, a decision boundary is established to classify grid events resulting in the formation of an island or not. The proposed method is comprehensively investigated under different operating conditions, which include excess/deficit of reactive/active power imbalance scenarios in the formed island containing different load-types (constant impedance, constant power, and constant current) with varying quality factor. A large variety of islanding and non-islanding events are generated using a practical distribution network simulated in real time on OPAL-RT digital simulation platform under the aforementioned operating conditions. A detailed evaluation of the proposed approach is conducted and it demonstrates the effectiveness of the method.

A novel hybrid islanding detection method using dynamic characteristics of synchronous generator and signal processing technique

Integration of the distributed generators (DGs) to power system is growing thanks to their technical, economic and environment benefits. The high penetration of DGs however raises a number of technical problems. One of these problems is islanding, which occurs when one or more DGs continue to energize a portion of the power system which has been isolated from the main grid. Timely islanding detection and discrimination between islanding and non-islanding disturbance conditions are very important for the safety of the personnel and the safe operation of equipment. This paper proposes a hybrid islanding detection method for distribution systems containing synchronous distributed generation (SDG) based on two active and reactive power control loops and a signal processing technique. The operation of the control loops accelerates islanding detection at early stages of instability by processing the terminal voltage of SDG. The proposed method is validated by extensive simulations. The results demonstrate that the proposed method is able to timely and precisely detect islanding situations and can clearly distinguish between islanding and non-islanding events, even if active and reactive power between generation and demand are very close. In addition, the proposed method has negligible impacts on power quality and is capable to allow the islanded system to operate in autonomous mode after separation from the main grid.

A novel islanding detection technique using modified Slantlet transform in multi-distributed generation

In this paper, a new hybrid islanding detection scheme based on a combination of a modified Slantlet Transform (MSLT) and machine learning is applied to a passive time frequency islanding detection of multiple distributed generation units. A Harmony Search Algorithm (HSA) is used to optimally specify suitable scales of Slantlet transform and Slantlet decomposition levels for accurate islanding classification. Slantlet transform is utilized to derive the features in the required detection parameters measured from islanding and non-islanding events using identified Slantlet scales. In order to automate classification process, machine learning classifiers are utilized to detect islanding and non-islanding conditions with an objective of increasing the detection rate and avoiding nuisance distributed generation tripping during non-islanding situations. Islanding and non-islanding events are simulated for a multi-distributed generations system and used to assess the performance of the proposed anti-islanding protection method. The numerical results showing the efficiency of the proposed islanding detection technique are explained and conclusions are drawn.