Non-islanding Conditions Research Articles

Application of Dual-Tree Complex Wavelet Transform in Islanding Detection for a Hybrid AC/DC Microgrid with Multiple Distributed Generators

This paper presents the design and validation of a novel adaptive islanding detection method (AIDM) for a hybrid AC/DC microgrid network using a combination of Artificial Intelligence (AI) and Signal Processing (SP) approaches. The proposed AIDM is aimed to detect and discriminate between the different fault/disturbance conditions that result in islanding and/or non-islanding conditions in a hybrid microgrid. For the islanding and non-islanding conditions detection by the AIDM, firstly, fault/disturbance signals are obtained from a test microgrid. Secondly, these signals are decomposed using Dual-Tree Complex Wavelet Transform. Thirdly, a Synthetic Minority Oversampling Technique (SMOTE) is applied for data preprocessing to increase the accuracy of the classifier. Finally, an artificial neural network (ANN) is used as the classifier for training and testing the proposed AIDM for different microgrid configurations and event scenarios. The proposed method is tested with different data categories from three different microgrid test systems with different scenarios. All modeling and simulations are executed in MATLAB Simulink Version 2023a. Results indicate that the proposed scheme could detect and discriminate between islanding and non-islanding conditions accurately in terms of dependability, precision, and accuracy. An average accuracy of 99–100% could be achieved when tested and validated with microgrid networks adapted from IEEE 13-bus systems.

Superimposed Positive Sequence Impedance for Detecting Unintentional Islanding in Microgrid

Incorporation of environmentally friendly energy sources (RESs) into the electricity grid has many benefits, including economic, technological, and environmental. However, excessive renewable energy sources (RES) in the power grid provide technical problems, including equipment protection, DG operation, and islanding detection. One of the most serious challenges is the islanding phenomenon. Islanding can cause several problems, such as frequency instability and voltage fluctuations resulting in damage to electrical equipment or threatening utility workers who may be working/accessing the equipment. This research proposes an efficient islanding detection algorithm to lessen the impact of such threats. This novel passive islanding detection scheme is based on superimposed positive sequence impedance (SPSI). For calculating the superimposed positive sequence impedance (SPSI), the voltage and current signals are obtained from targeted DG points. The scheme’s performance is tested on multiple bus systems across islanding and non-islanding conditions using a MATLAB/Simulink environment. It is shown that even in the presence of noise, the algorithm can determine an islanding decision with high accuracy and a short detection time of 84 ms. In comparison to other algorithms, it operates at zero power mismatch (ZPM) and does not affect power quality.

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.

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.

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.

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 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.

Multivariable passive method for detection of islanding events in renewable energy based power grids

Penetration of distributed generation resources (DGR) in power grid is rapidly increasing to meet future energy demand efficiently. It helps in mitigating the problems of high carbon emission, green house effect, and increased cost of oil and natural gases. Island formation of a part of the renewable energy based power grid (REPG) creates hazards to operating personnel and equipments. Hence, efficient identification of islanding condition and disconnection of DGR immediately become essential for avoiding damage to equipment, personnel safety hazards and grid protection. This paper investigated a signal processing based passive islanding detection method (IDM) which is effective with the availability of multi-DGR. A islanding detection factor (MIDF) using multi-variables is proposed which is computed using features extracted by processing the voltage signal, negative sequence current (NSC) and negative sequence voltage (NSV) using Stockwell transform (ST) and Hilbert transform (HT). Total harmonic distortions of voltage ( T H D v $THD_{v}$ ) and current ( T H D i $THD_{i}$ ) are also considered to compute MIDF. Islanding event is recognized and differentiated from non-islanding conditions by comparing the MIDF with two threshold values. This method effectively detects the islanding event and discriminates it from the operational and faulty events. IDM has small non-detection zone (NDZ) and low computational time. Performance of IDM is not affected by noisy environment. Effectiveness of the IDM is established by testing for different event incidence angles, various fault impedances, different values of loads and capacitors, and different penetration levels of RE. Efficacy of IDM is established by testing the IDM through an elaborated study on IEEE-13 bus distribution grid interfaced with wind power plant (WPP) and solar power plant (SPP). Further, IDM is effectively implemented to detect the islanding event created on a practical distribution network. Effectiveness of IDM is better in comparison to IDMs designed by applications of discrete wavelet transform (DWT), empirical mode decomposition (EMD), Slantlet Transform and Ridgelet probabilistic neural network (RPNN), artificial neural network (ANN), change in rate of active and reactive power, rate of change of frequency, and rate of change of voltage angle.

Islanding Detection in Microgrid Compromising Missing Values Using NI Sensors

Islanding detection has become an antiquated topic in microgrid, but during the shabby network condition, the conventional islanding detection methodologies become impractical. This article develops a new islanding detection technique considering interrupted communication during islanding detection. This article has experimented a new predictive model (PM)-based method for islanding detection in the laboratory-based multiple distributed generation (DG) system (system with more than one DG). Microphasor measurement units ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu $</tex-math></inline-formula> PMUs) are incorporated for measuring phasors from the real-time system. The measurements are analyzed by the artificial neural network (ANN) for initiating PM, which specifies the occurrence of islanding in terms of probability. The following two subalgorithms: evaluation and decoding are simultaneously working to estimate probability of the islanding during sound network condition. The posterior probability evaluation comes into action when communication is interrupted. The accuracy of the algorithm is tested under UL 1741 loading conditions. The performance of the algorithm is analyzed under the simultaneous manifestation of communication interruption and several islanding or nonislanding condition. Nondetection zone has been evaluated for different scenarios of missing frames. The algorithm has also been compared with existing methodologies.

Islanding Detection Using a Micro-Synchrophasor for Distribution Systems with Distributed Generation

Distributed Generation (DG) has changed the power generation system to small-scale instead of large-scale generation. The demanding issue with the interconnection of DG is the detection of unintended islanding in a network. Several methods proposed in the literature show drawbacks such as high non-detection zones (NDZ) and higher tripping time. In this paper, the IEEE 13 bus distribution network with DGs like wind and solar power plants is integrated at two buses. Islanding is detected by utilizing data from a micro-synchrophasor located at the distribution grid and the DG. The micro-synchrophasor-based unintended islanding detection algorithm is based on parameters such as voltage, rate of change of voltage, frequency, rate of change of frequency, voltage phase angle difference and the rate of change of the voltage phase angle difference between the utility and the islanded grid. The proposed islanding detection algorithm discriminates between islanding and non-islanding conditions and is highly efficient under zero power mismatch conditions. The proposed method has null NDZ and satisfies the IEEE 1547 standard for DG tripping time. The effectiveness of the proposed IDM was verified when there are multiple DGs in the islanded grid. Also, the proposed method does not require additional hardware as it can be incorporated in digital relays with synchrophasor functionality.

An Advanced Signal Decomposition Technique for Islanding Detection in DG System

This article presents a new passive islanding detection method by using an advanced signal decomposition technique, i.e., time-varying filter-based empirical mode decomposition (TVF-EMD). In the TVF-EMD, the adaptively tuned and predefined parameters and variation of the cutoff filter frequencies with respect to time make it more desirable over other decomposition techniques for enhanced resolution-based time-frequency analysis of nonstationary signals. The voltage signal measured at the distributed generation is processed through the TVF-EMD to decompose the signal into various intrinsic mode functions (IMF). The energy of the IMF is further extracted through the Teager energy operator. The computed energy is then used to identify the islanding scenario. In order to assess the performance of the proposed TVF-EMD, a number of simulation studies are performed on two standard test systems under varying islanding (i.e., different power mismatches and load quality factors) and critical nonislanding (i.e., capacitor switching and nonlinear load switching) conditions. The obtained results prove the efficacy of the proposed method.

Decentralized Unintentional Islanding Identification for Converter-Interfaced Multiple DGs

The unrelieved developments in the field of power electronics have cemented a strong base to distributed generation, exploiting various renewable and nonrenewable energy resources. However, such a power generation is denied permission to supply during accidental isolation owing to copious whys and wherefores. It is vital to perceive such isolation and eventually shut down the distributed generators under such circumstances. To do so, an active islanding detection based on disturbance injection through q-axis current controller is adopted, and an analyzing method is proposed in this article for converter-interfaced multiple DGs (including solar, PMSG-based wind energy conversion system and battery) to distinguish between the islanded and nonislanded situations. The proposed method is competent to detect the island formation, within 160 ms, in a decentralized manner, with zero nondetection zone for the load quality factor ≤ 2.5 and also distinguishes the nonislanding conditions from the islanding conditions accurately. The proposed analyzing methodology is corroborated for several operating conditions by simulation using MATLAB 2017b/Simulink platform.

Analysis of power system parameters for islanding detection using wavelet transform

&lt;p&gt;&lt;span&gt;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.&lt;/span&gt;&lt;/p&gt;

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.

Auto‐correlation‐based Islanding detection technique verified through hardware‐in‐loop testing

This study describes a novel Islanding detection technique based on a discrimination factor (DF) which is derived from an auto-correlation factor (ACF) of the voltage signals acquired from the terminal of the distributed generator. The value of DF is calculated from the most affected lags of the ACF during various Islanding and non-Islanding conditions. Further, the validation of the presented scheme has been carried out by developing a hardware-in-loop laboratory setup. In this setup, a power distribution network has been modelled on a real-time digital simulator (RTDS/RSCAD) which is physically connected to the digital signal processor controller. Various non-Islanding and Islanding test scenarios have been generated. The proposed ACF-based technique is able to differentiate Islanding state with non-Islanding states even for perfect power equilibrium situation. The proposed approach can determine the Islanding state in a period of three cycles from the inception of Islanding. Finally, a relative assessment of the above algorithm with other existing methods shows its supremacy in terms of better stability during critical non-Islanding situations and lower non-detection zone in case of Islanding states.

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.

Preventing cascading tripping of distributed generators during non-islanding conditions using thermostatic loads

Integration of large amounts of asynchronous renewable energy sources (RES) would reduce the effective inertia of the future Great Britain (GB) network. As a result, after a large infeed loss, containing the grid frequency and limiting its rate of change (RoCoF) above certain thresholds would be a challenge for the system operator. In particular, large RoCoFs could activate RoCoF-sensitive loss-of-mains (LoM) protections of distributed generators (DGs) and trigger cascading disconnections. In this context, thermostatic loads (TCLs) can be controlled to collectively provide support and contribute to the overall system inertial and frequency response. This paper focuses on the transient response period after a frequency disturbance and the fast protection events occurring in this time frame. In particular, this works evaluates the interplay between the local activation settings of LoM protection of DGs and those used to enable the TCL support. This interaction is analysed through a case study on a 36-bus dynamic equivalent of the GB network which shows how local post-fault frequency dynamics drive the overall system response. Results show that TCLs are able to prevent RoCoF-driven DG tripping and reduce the need to adopt high LoM settings, decreasing the risks of maloperation associated with desensitised protections.

Kriging Empirical Mode Decomposition via support vector machine learning technique for autonomous operation diagnosing of CHP in microgrid

Combined Heat and Power (CHP) is the one of new energy resources which has been added to power system in recent years. High efficiency, Loss reducing of power system and etc, are the main advantages of CHP as same as other distributed generations. But, unwanted islanding is one of the main problems for this generation. This article presents a novel technique for CHP unit islanding detection using Kriging Empirical Mode Decomposition (KEMD) and Support Vector Machine (SVM) pattern learning technique. In this technique the variation of Intrinsic Mode Functions (IMF) of local signals in two-dimensional mode is utilized as input data of relay. An optimal signal selection model is applied to the proposed relay in order to Non-Detection Zone (NDZ) and fails detection reducing. The best signal selection is introduces based on mean square value between islanding and non-islanding conditions. Also, by considering Optimal SVM model for the proposed relay as a pattern recognizing and weighing it using shark smell optimization, this technique has overcome the threshold selection problem. This relay is applied to CHP system in a microgrid system contains various types of DGs. Many islanding and non-islanding situation in various operation conditions in the studied microgrid are simulated. The results of simulation results are show that the proposed relay is suitable for microgrid application. Negligible NDZ, high detection time, zero fail detection and low cost of this relay are the main advantages of the proposed technique.

Islanding detection method based on S transform and ANFIS

In distributed generation systems, islands of power generation will disturb grid recovery and power quality. Therefore, various islanding detection technologies have been proposed, and most are remote detection, local active, and passive methods. Most islanding detection technologies are currently implemented with high computational complexity, inaccurate detection, affecting power quality (active methods), and other problems. This paper proposes an islanding detection method based on S transform (ST) and Adaptive-Network-based Fuzzy Inference System (ANFIS). It uses ST for electrical signal processing and then extracts features which are used as the inputs of the ANFIS system. ANFIS starts training and learning to obtain classification capabilities and accurately determines the islanding events and outputs the results. The innovation of this method is the combination of ST and ANFIS structure. ST has better time-frequency characteristics and signal processing capability. ANFIS can automatically start training and learning in order to enhance the capability. The combination of these two methods can greatly improve the accuracy of islanding detection. The ST and ANFIS based islanding detection method proposed in this paper are simulated in MATLAB Simulink. According to the different characteristics of islanding and non-islanding conditions, full consideration is given to the comprehensiveness of the condition design, and 20 sets of data are designed. Through the training of 12 sets of data, and the testing of 8 sets of data, with equal quantity for the islanding and non-islanding cases, the output of this method is completely correct. Also, the proposed method is compared to ANFIS based and artificial neural network based islanding detection methods. The results show that the proposed ST-ANFIS based islanding detection method can accurately detect the islanding and non-islanding conditions with higher accuracy.

Standalone condition diagnosing of fuel cell in microgrid composed of wind turbine/fuel cell/combined heat & power using Variational Mode Decomposition analysis model

With increasing load in the power system, several new energy resources such as Fuel Cell (FC) have been added into the systems which increased the systems complexity and uncertainty. Unwanted islanding is one of the main problems for this generation. This article presents a novel technique for fuel cell system islanding detection using Variational Mode Decomposition and Radial Bases Function pattern learning technique. In this technique the state changes of Intrinsic Mode Functions energy of THD signals in two-dimensional mode is utilized as input data of relay. An optimal signal selection model is applied to the proposed relay in order to Non-Detection Zone and fails detection reducing. The best signal selection is introduces based on mean square value between islanding and non-islanding conditions. Also, by considering Optimal Radial Bases Function model for the proposed relay as a pattern recognizing and weighing it using shark smell optimization, this technique has overcome the threshold selection problem. This relay is applied to FC system in a microgrid system contains various types of DGs. Many islanding and non-islanding situation in various operation conditions in the studied microgrid are simulated. The results of simulation results are show that the proposed relay is suitable for microgrid application. Negligible NDZ, high detection time, zero fail detection and low cost of this relay are the main advantages of the proposed technique.