Islanding Detection Technique Research Articles

Teager-Kaiser Energy Operator (TKEO) Based Islanding Detection Technique for Microgrid

Microgrid with local DGs suffers from unintentional islanding and protection issues. Improper detection of islanding leads to equipment damage, maloperation of circuit breakers, equipment damage, and serious injuries to operating personnel. Therefore, islanding events should be detected and DGs should be isolated from the utility within time. In this work, a new passive islanding scheme based on the Teager-Kaiser energy operator (ψ) is proposed. Three-phase current and voltage samples are collected at PCC and used to calculate the ψ ( v ) / ψ ( i ) . This ψ ( v ) / ψ ( i ) is used to calculate the index (N). If the index (N) is less than the threshold, the proposed IDT detects it as an islanding event (IE) event otherwise it detects as an NIE. The proposed IDT has the ability to discriminate between the non-islanding event (NIE) and islanding event (IE) during zero RPM and, zero APM different types of load switching, and the inception of different faults at different locations. The IEEE 13 bus system has been taken as a test system to test the efficacy of the proposed IDT and the test system was designed and executed in MATLAB/Simulink.

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This paper presents a new islanding detection technique based on an artificial neural network (ANN) for a doubly fed induction wind turbine (DFIG). This technique takes advantage of ANN as pattern classifiers. Five different ANN systems are presented in this paper based on various inputs: three phase power, phase voltage, phase current, neutral voltage, and neutral current. An ANN structure is trained for each input, and the comparison between the different structures is presented. Feedforward ANN structures are used for the five systems. Three different learning algorithms are used: backpropagation and two artificial optimization techniques: Genetic Algorithm (GA) and Cuckoo optimization algorithm. For each method in each training technique, the results and the cost function are presented. The comparison of different inputs different algorithms is conducted. MATLAB 2020a is used to simulate the ANN structure and code the training algorithms. A detailed discussion of the input sample rate has also been manipulated to make the computational burden a factor in assessing the performance.

An optimal deep learning based Islanding power quality detection technique for distributed generation systems

Power quality disturbance (PQD) defines the presence of inconsistencies that occur in the usual wave shapes of voltage and current signals. Power quality is considered the main challenge for power industry with the increase in dynamic load and highly subtle electronic devices. Besides, the islanding events, particularly unintended islanding, grasp significant challenges and it needs to be identified at the early stage. Islanding is an anomalousstate in the power system, where the distributed generators (DGs) are placed on supplying electrical energy to the local load even after the shortage of the major grid. Therefore, it is essential to identify and differentiate the PQ events and islanding events in ensuring pollution-free power, equipment, and labor safety. With this motivation, this paper presents an automated optimal deep learning based islanding detection (AODL-ID) technique. The proposed AODL-ID technique involves three major stages namely decomposition, classification, and hyperparameter tuning. Firstly, an empirical mode decomposition (EMD) approach is utilized to decompose the basic signals from the polluted signals. In addition, bidirectional gated recurrent neural network (BiGRNN) technique is employed for the classification of islanding and non-islanding PQ events in the wind energy penetrated DG systems by means of features (Voltage and current (RMS, half-cycle, peak and fundamental) Frequency. Power Factor / Cos Phi. Power and energy (active, reactive, harmonic, apparent)). Since the hyperparameters play a significant role in overall classification performance, the hyperparameter tuning of the BiGRNN model takes place using chaotic crow search algorithm (CCSA). To examine the enhanced classification outcome of the AODL-ID technique, a set of experimental analyses is carried out and the outcomes are investigated interms of various evaluation metrics. The simulation outcomes highlighted the supremacy of the AODL-ID technique over the compared techniques.

A Novel Islanding Detection Technique Based on Positive-Feedback Negative Sequence Current Injection

This article proposes a new active anti-islanding technique to detect unintentional islanding with high speed and reliability. In grid-connected mode, the controller has no influence on the power system. When the grid is disconnected, the inverter injects negative sequence current in a positive feedback loop, and causes the output negative sequence voltage to increase to a threshold value that causes the inverter to stop operating. The proposed method is nonintrusive and does not cause disturbance to the system in grid-connected mode. The method is tested using PSCAD simulation software, and using controller hardware-in-the-loop (C-HIL) using the physical control board of the inverter. The inverter considered in this article is a photovoltaic inverter with 840-kW rated power. Optimization of the parameters of the proposed control, as well as small signal stability analysis in grid-connected mode are presented. Verification results show that the controller can detect unintentional islands and stop the inverter within 500 ms.

Analysis of Passive Islanding Detection Techniques for Double Line Fault in Three Phase Microgrid System

Microgrids are able to dispatch power to distribution systems with the advancement of power electronics-based inverters. As per IEEE-1547-2018 standards, Microgrid has to maintain voltage of 0.88 < V < 1.1 p.u (per unit) and frequency of 58.8< f_0 < 61.2 Hz and detect un-intentional faults in less than 2 seconds to bring Microgrid into islanding mode seamlessly. Unless these faults are detected and Microgrid is islanded, the system stability cannot be maintained and Microgrid cannot feed the connected loads. To detect these unsymmetrical faults, to bring the Microgrid to islanding mode and to be stable during non-islanding cases like loads switch on and throw off at Point of Common Coupling (PCC), a passive islanding detection method, Rate Of Change Of Voltage Phase Angle (ROCOVPA) is proposed. The methodology is simple. First, the voltage phase angle of generator bus and the grid is monitored. Then, absolute value is found and finally differentiated to get ROCOVPA and detect islanding. Also, this technique is compared with the widely used method of Rate Of Change Of Frequency (ROCOF) at different percentage active and reactive power mismatches. It also avoids nuisance tripping so that Microgrid's stability is maintained. This method is tested for un-symmetrical double line fault, for islanding cases and switch on or throw off, for non-islanding cases with linear and non-linear loads. In this method, the power quality is also not affected because of no perturbations during testing and the Non-Detection Zone (NDZ) is almost zero. The proposed method is verified by simulating islanding and non-islanding conditions in MATLAB/Simulink and by comparing with ROCOF method and found effective.

A simplified passive islanding detection technique based on susceptible power indice with zero NDZ

Distributed energy resources (DER’s) incorporation in power system brings economical, technical and environmental benefits. Besides these benefits, excess penetration of DER’s elevates technical concerns. Islanding detection is one of main issue and is essential for personnel safety and protection of equipment. This work introduces a passive islanding detection scheme based on phase angle of positive sequence voltage (PAOPSV). The PAOPSV was selected on the basis of rigorous investigation of 13 different indices. Comparative analysis demonstrates that PAOPSV has the best sensitivity and accuracy for islanding detection among all other parameters. An extensive case study considering worst-case scenarios is accomplished in order to check the working efficacy of the suggested scheme that easily discriminates islanding events from non-islanding events such as load switching, different fault types switching, capacitor and motor switching. The suggested scheme is simple with fast execution and easy to implement that is established on IEEE 1547 generic test system in MATLAB/SIMULINK environment. Islanding is detected within 0.10 s even with a zero non-detection zone.

Novel Passive Islanding Detection Technique by Monitoring Reverse Power at PCC

Power System of the age has included remarkable contribution of distributed energy resources (DERs). This inclusion has opened new era of research and studies. Islanding detection has become one of the important events in power system that is to be detected precisely. This study introduces a single-parameter-based passive technique for islanding detection which has zero nondetection zone without involvement of any signal processing tool. The proposed technique is tested for various islanding and nonislanding events. The simulation results prove that the suggested technique detects islanding event accurately and it is capable of distinguishing islanding and nonislanding events by an economical and simpler method.

A new passive islanding detection technique for a microgrid based on transient energy

A new passive islanding detection technique based on transient energy that is measured and analysed at targeted distributed generation (DG) or the point of common coupling (PCC) is presented. The proposed technique measures the superimposed components and then calculates the transient active energy (TAE) and the transient reactive energy (TRE). Using the relation between TAE and the TRE, the relay can determine the islanding situation even in a worst-case operational scenario, and this is a notably shorter response time according to the current standards. The performance of the proposed technique is simulated in the MATLAB environment. Islanding and non-islanding scenarios for different power match conditions and types of DGs are simulated. The evaluation of the simulation results shows that the proposed technique identifies the islanding with a less than zero power mismatch, operates accurately under the condition of load variation, and does not conflict with the power system operation during normal conditions. It removes the passive technique drawbacks by having a tiny non-detection zone (NDZ) and a faster response.

Effective ROCOF-Based Islanding Detection Technique for Different Types of Microgrid

This article presents an adaptive rate of change of frequency (ROCOF)-based passive islanding detection technique (IDT) to overcome the non-detection zone (NDZ) issue and limitation of existing IDTs. The proposed ROCOF-based passive IDT relies on the settings of the phase-locked loop (PLL) closed-loop control system for frequency, ROCOF, and threshold ROCOF value for the PCC relay-setting calculations. So, the PLL results in alternate frequency and ROCOF output value with respect to the PLL settings modification. The PLL settings can be different in different types of microgrid systems. Therefore, the proposed adaptive ROCOF-based IDT provides a generic solution for islanding detection applications for the safe and reliable operation of different types of microgrid systems. The adaptive ROCOF-based IDT is developed and implemented on two different types of the microgrid systems such as i) generator-based microgrid ii) generator, PV, and storage-based hybrid microgrid in Matlab/Simpower system. Results showed that the proposed IDT successfully detected the islanding condition even with zero power mismatch and it is fully capable to distinguish between islanding (loss of mains and external fault) and nonislanding (internal fault, load switching, motor starting, etc.) conditions with a diverse range of operating conditions.

Islanding Detection for DC Microgrids Based on Episode of Care Severity Index

Lack of an effective protection scheme is a major barrier to increased integration of renewable energy sources. Despite the system instability and personnel safety concerns due to unintentional islanding, detection of this abnormal condition has not been adequately addressed in the DC microgrids. To overcome the large non-detection zone (NDZ) and power quality degradation of previous islanding detection techniques, this paper proposes a two-stage hybrid islanding detection technique for DC microgrids. In the first stage, a disturbance is detected by quantifying the severity of voltage change using the proposed superimposed voltage-based episode of care severity index. Then, a superimposed voltage-based current perturbation is injected to distinguish an islanding condition from other disturbances. The proposed technique uses the available distributed generation measurements. It has a near-zero NDZ and does not degrade the power quality during normal operation. The effective performance of the proposed islanding detection technique is verified through several case studies in a ±750 V DC microgrid test system.

Analysis of active islanding detection techniques for grid-connected inverters systems

&lt;span&gt;An island is a section of the electrical grid that contains producing assets and loads that are separated from the main grid and powered by these generators, such as solar systems, with voltage and frequency maintained at nominal levels. It's worth noting that the concept of islanding is linked to time. When the inverter detects an isolated grid activity for a particular period of time, the inverter is compelled to decouple from the general grid, according to the criteria that dictate the working principle of a photovoltaic (PV) systemThis paper presents research and comparisons of the main islanding detection techniques for single-phase systems based on various structures, as well as a comparison of the improvement of the traditional islanding detection method using three different methods (active frequency drift (AFD), slip mode phase shift (SMS) and Sandia frequency shift (SFS)). Under normal and abnormal operating conditions, a comparison of these three examined improvements was made. Additionally, physical security information management (PSIM) software simulation results are generated to test the performance and effectiveness of the effective technique plan.&lt;/span&gt;

A technique for detection of islanding in a microgrid on the basis of rate of change of superimposed impedance (ROCSI)

Improper detection of islanding situation of a microgrid leads to undesirable mode of operation which causes power quality issues, reclosing failures, and serious equipment damage. Therefore, detection of islanding is a crucial challenge for protection schemes in the microgrid. In this work, a new islanding detection technique based on rate of change of superimposed impedance (ROCSI) is proposed. The voltage and current signals measured at PCC are used to estimate the impedance (Z). This estimated impedance is used to calculate the value of index (M), which is used to discriminate between islanding and non islanding events. To test the efficacy of proposed islanding detection technique, different islanding events (IE) and non islanding events (NIE) are created on modified IEEE 13 bus system integrated with two 2 MW solar photo voltaic distributed generations (PVDGs). The entire system is modelled and simulated on MATLAB/SIMULINK environment. Proposed islanding detection technique clearly identifies the IE during different active power mismatch, reactive power mismatch conditions and different quality factor loads. It also clearly identifies the NIE during load switching, single pole tripping, feeder disconnection, faults, and adjacent DG disconnection.

Implementation of Islanding Recognizing Technique for Wind Distributed Generations Considering Insignificant NDZ

The incorporation of distributed energy resources (DERs) into the electricity grid yields environmental, technical, and economic benefits. However, in addition to the benefits, the widespread use of DERs causes technical issues. Islanding is a big concern in terms of equipment protection and personnel safety, and it should be detected as soon as possible. The proposed approach employs a passive islanding detection technique based on reactive power (Q). The Q was chosen following a comparison of five other indices. Comparative analysis reveals that Q has the highest sensitivity and accuracy for islanding recognition when compared to all other observed parameters. Different case studies have been performed considering the worst-case scenario to check the working efficiency of the proposed scheme that simply distinguishes the islanding conditions from non-islanding conditions, which include load, motor, and capacitor switching, various types of fault switching, DG tripping cases, and weak grid contribution. The proposed strategy is straightforward, with quick execution and simple implementation in the MATLAB/SIMULINK environment on the IEEE 1547-2018 generic test system. With a small non-detection zone, islanding is detected in 0.038 s.

A Survey of Islanding Detection Methods for Microgrids and Assessment of Non-Detection Zones in Comparison with Grid Codes

Detection of unintentional islanding is critical in microgrids in order to guarantee personal safety and avoid equipment damage. Most islanding detection techniques are based on monitoring and detecting abnormalities in magnitudes such as frequency, voltage, current and power. However, in normal operation, the utility grid has fluctuations in voltage and frequency, and grid codes establish that local generators must remain connected if deviations from the nominal values do not exceed the defined thresholds and ramps. This means that islanding detection methods could not detect islanding if there are fluctuations that do not exceed the grid code requirements, known as the non-detection zone (NDZ). A survey on the benefits of islanding detection techniques is provided, showing the advantages and disadvantages of each one. NDZs size of the most common passive islanding detection methods are calculated and obtained by simulation and compared with the limits obtained by ENTSO-E and islanding standards in the function of grid codes requirements in order to compare the effectiveness of different techniques and the suitability of each one.

A Comprehensive Survey on Islanding Detection Methods of Synchronous Generator-Based Microgrids: Issues, Solutions and Future Works

Nowadays, the penetration of distributed generations (DGs) and microgrids (MGs) has been significantly increased due to the technical, social, environmental, and financial reasons. One of the most common types of DGs which is widely used in different networks is small-scale synchronous generators (SSSGs). Synchronous-based microgrids (SGBMGs) and SSSGs can operate under both grid-connected and islanded conditions. To guarantee a stable, uninterruptible, and safe autonomous operation, the unintentional islanding events should be quickly detected and then, appropriate control and precaution actions should be performed. Various islanding detection techniques have been proposed in the two past decades including remote and local methods. Meanwhile, local methods can be divided into two major groups, i.e. passive and active methods. To select the most suitable islanding detection scheme (IDS), various factors including the DG type and its technology should be considered. Therefore, the IDS for SGBMGs may be completely different from that for inverter-based microgrids. In this paper, the suitable IDS which can be practically applied to the SGBMGs are comprehensively investigated. To do so, at first, various suitable indices are introduced which can be properly employed by the system engineers and researchers to compare various IDS and select the most appropriate one. Then, various types of existing IDS are carefully investigated using these indices. Based on these investigations, it is concluded that local passive methods are more appropriate for SGBMGs due to their technical and economic benefits. Moreover, to enhance the existing IDS, some ideas are provided in this paper which can be considered in the future research works.

Battery Storage-Based Novel Hybrid Islanding Detection Technique Using Lissajous Pattern Estimation

The reliability, uncertainty, and sensitivity of local islanding detection method for distributed generation (DG) system under diversified real-time scenarios and nonlinear loading conditions are not greatly explored. In this article, a new type hybrid islanding detection technique (IDT) is suggested, which reduces uncertainty and simultaneously improves reliability and sensitivity under different situations. In the technique, one detection parameter is defined, which is calculated from estimation of major and minor axes of Lissajous pattern (LP). Here, the evaluation of the parameter is done in two stages to avoid erroneous detection. In the first stage, the initiation of voltage transient is detected by LP. In the second stage, the active control via battery energy storage system (BESS) distinguishes nuisance tripping case from the islanding state. The extra active power injection/absorption via BESS also helps to identify islanding state at very near to zero power condition, i.e., sensitivity is enhanced. The detection time is found within 160 ms for different test cases, which is well below that of the acceptable upper value (2 s) specified in standard. The uncertainties in detection and threshold setting due to variations of harmonics and other operating conditions are removed in the proposed case, which is a distinct advantage. The Typhoon-HIL tool is used to carry out various case studies in real-time. Here, superior performances of proposed approach are observed in comparison with other state of the arts reported in the literature.

A Robust Passive Islanding Detection Technique With Zero-Non-Detection Zone for Inverter-Interfaced Distributed Generation

In this paper, a new and efficient passive islanding detection technique for a grid-connected hybrid distributed generating system has been proposed using Second Generation Wavelet Transform (SGWT) and Maximum Overlap Discrete Wavelet Transform (MODWT). In this technique, the ripple content of the voltage signal is measured at the Point of Common Coupling (PCC) and it is monitored through the decomposition of the signal by applying SGWT and MODWT technique up to suitable finer levels. This algorithm able to detects all kinds of islanding at nearly 0.3 sec, even with zero power mismatch. It is verified on a wide range of operational conditions and is also validated in various non-islanding conditions. Furthermore, the proposed technique is simple, there is no need for a classifier requirement, not depends on system parameters, zero non-detection zone (NDZ), and its operation is independent of the capacity and nature of distributed generation (DG) connected to the utility grid.

An Intelligent Deep Convolutional Neural Networks-Based Islanding Detection for Multi-DG Systems

Unintentional islanding is a problem in electrical distribution networks; it happens when the central utility is unintentionally separated from the rest of the distributed power system. The islanding detection problem becomes severe in non-detection zones. We propose an intelligent islanding detection technique with zero non-detection zone for a hybrid distributed generation system. It is based on the computation of frequency spectrum variations over time using short-term Fourier transform and convolutional neural networks. For various islanding and non-islanding occurrences, the three-phase voltage at the point of common coupling is monitored, and time-series data is collected. Then computations for a set of multiple frequencies on scaled time-series data are carried out, and complex numbers are split into magnitude and phase values. To detect islanding and non-islanding occurrences, a modified convolutional neural network with forward propagation was utilized. For the IEC 61850-7-420 test system, several islanding and non-islanding scenarios are created and deployed to train the convolutional neural network for the proposed approach. The efficacy of the proposed islanding detection learning model is assessed using 5-fold cross-validation. The findings reveal that under normal and noisy conditions, the proposed methodology has zero non-detection zone with original dataset, excellent accuracy, selectivity, and sensitivity.

Hybrid parametric islanding detection technique for microgrid system

Hybrid parametric islanding detection technique for microgrid system

RES Based Islanded DC Microgrid with Enhanced Electrical Network Islanding Detection

An electrical islanding detection method for DC microgrid (MG) is proposed in this paper. Unlikely conventional AC MG system protection has been challenging for the DC MG system. The goal of the proposed scheme is to detect the islanding intelligently within the agent nodes. The proposed islanding detection technique detects the electrical link failure intelligently, and if any electrical link failure occurs, then the proposed technique maintains the MG operation and load sharing. Islanding detection is carried out using an injection signal and utilizes the existing communication network to check network connectivity. After checking the network connectivity, all the nodes maintain the look-up table of the connected network. This research work illustrates that the proposed electrical islanding detection technique is effective in maintaining the DC MG operation in the case of an electrical islanding/link failure scenario. The proposed scheme’s performance is checked through MATLAB/Simulink for the detection of islanding and maintaining the operation.