Non-detection Zone Research Articles

Local Parameter-Based Islanding Detection in DC Microgrid With Zero Non-Detection Zone

Local Parameter-Based Islanding Detection in DC Microgrid With Zero Non-Detection Zone

Passive Islanding Detection of Inverter-Based Resources in a Noisy Environment

Islanding occurs when a load is energized solely by local generators and can result in frequency and voltage instability, changes in current, and poor power quality. Poor power quality can interrupt industrial operations, damage sensitive electrical equipment, and induce outages upon the resynchronization of the island with the grid. This study proposes an islanding detection method employing a Duffing oscillator to analyze voltage fluctuations at the point of common coupling (PCC) under a high-noise environment. Unlike existing methods, which overlook the noise effect, this paper mitigates noise impact on islanding detection. Power system noise in PCC measurements arises from switching transients, harmonics, grounding issues, voltage sags and swells, electromagnetic interference, and power quality issues that affect islanding detection. Transient events like lightning-induced traveling waves to the PCC can also introduce noise levels exceeding the voltage amplitude by more than seven times, thus disturbing conventional detection techniques. The noise interferes with measurements and increases the nondetection zone (NDZ), causing failed or delayed islanding detection. The Duffing oscillator nonlinear dynamics enable detection capabilities at a high noise level. The proposed method is designed to detect the PCC voltage fluctuations based on the IEEE standard 1547 through the Duffing oscillator. For the voltages beyond the threshold, the Duffing oscillator phase trajectory changes from periodic to chaotic mode and sends an islanded operation command to the inverter. The proposed islanding detection method distinguishes switching transients and faults from an islanded operation. Experimental validation of the method is conducted using a 3.6 kW PV setup.

A Novel Two-Stage Hybrid Anti-Islanding Protection Scheme and Nondetection Zones Analysis of Inverter-Based Generations

A Novel Two-Stage Hybrid Anti-Islanding Protection Scheme and Nondetection Zones Analysis of Inverter-Based Generations

Probabilistic method for risk analysis of unintentional islanding of distributed generators

Unintentional islanding can be a harmful event, requiring the fast actuation of the anti-islanding protection to disconnect the distributed generators. Although passive techniques are mostly used, they have non-detection zones and fail to detect islanding under specific conditions. Therefore, knowing in advance such operative conditions and the risks associated with the islanding occurrence can be a strategic information to support grid operators in the decision-making process regarding the effectiveness of distributed generators interconnection protection. In this context, this paper proposes a probabilistic method to assess the risk of energized island formation by combining the probability of breaker (or recloser) opening with the probability of anti-islanding protection failure. The method employs probabilistic short circuit associated with overcurrent protection actuation by using Monte Carlo simulation and time-domain analysis to obtain the cases of unintentional islanding and to calculate the risk of distributed generator islanded operation. The results drove more realistic and less conservative conclusions than previous literature. Additionally, it is shown that faults as the cause of breaker opening do play a relevant role in the performance of the anti-islanding protection.

Triple‐indexed passive islanding detection strategy for grid‐connected distributed generation networks using an extended Kalman filter

AbstractIslanding detection is a challenging issue in modern grid‐connected distributed generation networks (GCDGN). Generally, islanding detection has two categories local and remote, local schemes can be categorized into active, passive, and hybrid schemes. This article proposes a triple‐indexed passive islanding detection (TIPID) scheme using an extended Kalman filter (EKF). Initially, the EKF algorithm is applied on voltage signal at the point of the common coupling to estimate the desired fundamental and non‐fundamental features. The first index, known as the cumulative voltage logarithmic index, is computed by taking the natural logarithm of the fundamental voltage features to detect any variations in the GCDGN. The second index, known as the voltage differentiation index (VDI), is calculated from the fundamental voltage features, while the third index, known as the odd‐order harmonic distortion index (OOHDI), is obtained from the non‐fundamental odd‐order harmonics of the PCC voltage. Then, the VDI and OOHDI are compared to pre‐defined threshold to detect/distinguish islanding events. The proposed TIPID method is validated through extensive simulations on the IEEE 13‐bus test bed via MATLAB/Simulink 2022b. Results show that under both balanced/unbalanced load & generation, the proposed TIPID approach detects islanding occurrences with reduced non‐detection zone (NDZ) in less than 5 ms.

A hybrid islanding detection method using wavelet transform for hybrid systems with zero non-detection zone

Purpose Islanding detection has become a serious concern due to the extensive integration of renewable energy sources. The non-detection zone (NDZ) and system-specific applicability, which are the two major issues with the islanding detection methods, are addressed here. The purpose of this paper is to devise an islanding detection method with zero NDZ and, which will be applicable to all types of renewable energy sources using the sequence components of the point of common coupling voltage. Design/methodology/approach Here, a parameter using the sequence components is derived to devise an islanding detection method. The parameter derived from the sequence components of point of common coupling voltage is analysed using wavelet transform. Various operating conditions, such as islanding and non-islanding, are considered for several test systems to evaluate the performance of the proposed method. All the simulations are carried out in Simulink/MATLAB environment. Findings The results showed that the proposed method has zero NDZ for both inverter- and synchronous generator-based renewable energy sources. In addition, the proposed method works satisfactorily as per the IEEE 1547 standards requirement. Originality/value Performance of the proposed method has been tested in several test systems and is found to be better than some conventional methods.

Voltage Frequency Differential Protection Algorithm

Advancements in new technologies, a reduction in CO2 emissions, and the rising demand for energy are causing a growth in the share of renewable energy sources. In distribution networks, an increasing number of distributed generators (DGs) makes the utility grid’s protection complex and demanding. Vector surge and rate-of-change-of-frequency are the established anti-islanding protection methods, recognizing that the standard paradigm for protection, involving distributed generation, cannot be set only once but has to be continuously updated following the requirements and changes in the system. One of the requirements is active participation in the preservation of system frequency and voltage, which can be interrupted if the DG trips and disconnects from the utility grid. Anti-islanding protection and spurious tripping can be avoided by implementing new algorithms and techniques. This paper presents a novel protection scheme based on a voltage frequency differential. The proposed algorithm employs remote and local frequency measurements in such a manner that, for the occurrence of a frequency difference, it is assumed that the DG is in an islanding state. In this article, we demonstrate the feasibility of the algorithm through numerical analysis of grid events and laboratory testing emulating real grid-measured values. The test results show that the algorithm is resilient to false tripping for non-islanding events and more reliable than conventional methods in islanding detection. The algorithm can be set to low-frequency differential values, drastically reducing the non-detection zone in any DG type, regardless of its size and voltage level at the point of common coupling. Unlike standard anti-islanding methods, the algorithm supports the ability of the DG to fault-ride through demand.

An Islanding Detection Method Based on the Reactive Power Disturbance for Multiple Inverter-Based DG Systems

In this paper, an islanding detection method for multiple inverter-based distributed generation systems is proposed, which is based on perturbing reactive power output. An asymmetric form of positive and negative reactive power disturbance is presented so that there always exists a certain total amount of disturbance in the system. To decrease the disturbance amplitude without expanding the non-detection zone, a positive feedback of frequency difference is added to the disturbance. A reactive power disturbance synchronization method without communication among inverters is proposed so that the averaging effect caused by unsynchronized reactive disturbances can be eliminated and the island can be detected in a multiple-DG system. Additionally, the islanding detection criterion is improved by establishing the relationship between the direction of disturbance and the numerical symbol of frequency difference, so there is no misjudgment of islanding detection in a weak grid. Finally, an experimental platform consisting of three 6kW three-phase grid-connected inverters and a 99.9kVA islanding detection test device is established. The experimental and simulation results indicate that the proposed method performs fast and precise islanding detection for the multiple-DG system with small NDZ and little impact on power quality. Additionally, no misjudgment happens in a weak grid.

Islanding Detection and Power Quality Diagnosis of Wind Power Integrated Microgrid with Reduced Feature Trained Novel Optimized Random Decision Forest

Distributed generations (DGs) have been increasingly addressing the ongoing power deficit in the electricity market. However, a significant concern in DG-integrated microgrids is the detection of accidental islanding. To tackle this issue, this article proposes a cost-friendly, novel data-driven passive islanding detection scheme named EEMD-HOBRC, combining noise-assisted ensemble empirical mode decomposition (EEMD) and a hybrid optimization-based random forest classifier (HOBRFC). The detection scheme employs a diverse set of features extracted from both raw and EEMD decomposed signals. Essential features are selected using the binary grey wolf optimizer (BGWO) to reduce computational burden. To further improve classification accuracy, the parameters of the random forest classifier are optimized through a hybrid particle swarm and reformed grey wolf optimization (PSRGWO) technique with Cohen’s kappa index as the cost function. The proposed technique is rigorously validated in two different multi-DG environments, encompassing islanding and various nonislanding events. The results demonstrate the effectiveness of the approach in terms of enhanced accuracy, detection time, and performance under both noisy and noise-free conditions. The accuracy of detection under ideal and high noise scenarios is found to be 99.88% and 99.2%, respectively, with maximum detection time of 34.27 ms. Comparative analysis with other algorithms also supports the superiority of the proposed technique. Finally, the method is successfully applied to shrink the nondetection zone (NDZ) with minimal power mismatch, further enhancing its utility in practical applications.

Fast hybrid islanding detection for DGs with inverters using maximum likelihood-based ROCOF and SFS

The present study introduces an unconventional and fast hybrid strategy for islanding detection in Distributed Generators (DGs) based on inverters. The active method utilized in this work is Sandia Frequency Shift (SFS), while the passive method is the Rate Of Change Of Frequency (ROCOF) Relay. The key contribution of this paper is the use of Maximum Likelihood to estimate the ROCOF as opposed to the conventional Phase-Locked-Loop (PLL) method. The objective of this paper is to improve the responsiveness of detection across various conditions by providing fast detection without compromising accuracy. Several simulation investigations, adhering to the anti-islanding test standards of IEEE 1547 and UL1741, have been carried out on a system integrated with the suggested hybrid method. These studies aim to showcase the high sensitivity and specificity of the proposed technique for detecting islanding. In addition, pertinent comparisons are made between the proposed technique and the PLL-based method. The simulation results illustrate that the proposed control system effectively complies with the DG islanding protection requirements. In Furthermore, compared to the PLL, the proposed technique mitigates the common drawbacks associated with other islanding detection techniques by reducing the Non-Detection Zone, enhancing the power quality of the system, and, most significantly, producing fast detection under different conditions. The proposed approach exhibits a response time nearly three times faster than the PLL method, while in less demanding conditions, it provides an instantaneous response.

Power Equations for Non-Detection Zone of Islanding Detection in Renewable-Energy-based Microgrids with Multiple Connection Points to Micro-Grids

Power Equations for Non-Detection Zone of Islanding Detection in Renewable-Energy-based Microgrids with Multiple Connection Points to Micro-Grids

Comparison of a Novel AFPSO-SFS & PSO-SFS Approaches for Islanding Protection of Inverter-Based DGs

The most significant of the active islanding detection techniques is “Sandia Frequency Shift” (SFS), and this employs frequency drifting to identify extra Nondetection Zone. Inappropriate tuning parameters, such as the acceleration factor and chopping factor, result in frequency drifting, which degrades the quality of the power and causes the continuous power mode to fail. The failure can be overcome by considering optimized parameters to identify the nondetection zone (NDZ) in small DG system (120 kVA) at constant power/current mode. This article discusses, a new Adaptive Fuzzy Particle Swarm Optimization-SFS (AFPSO-SFS) technique is recommended to attain optimal parameters of SFS anti-islanding detection technique for validation of the proposed algorithm Particle Swarm Optimization–SFS (PSO-SFS) is also implemented. Furthermore, the ability of this method is to identify an islanding situation which deteriorates the major scientific issues for small DG system. Thus, the optimized parameter helps to attain an anti-islanding as well as less impact on power quality. Moreover, this article concerns serious issue of the grid interconnected DG model in an islanding condition, when the utility line would be unintentionally isolated. The grid detachment is generally considered as a fault. When an islanding occurs, the DG protection system must be inactive to detect the fault. For the investigation of power mismatches, the output of considered system using the proposed methods is contrast with results obtained from PSO-SFS and conventional SFS. The recommended strategy analyzes islands more effectively and diagnoses nondetection zones more accurately. The recommended method’s reliability is calculated quantitatively and emulated with MATLAB/Simulink.

State of the Art Review of Islanding Detection Methods for Integrated Distributed Generation System

Renewable energy technologies (RET) are expanding over the globe to fulfill the world’s overall energy requirement. The main issue following the adoption of RET into power system network is an electrical islanding. It causes voltage, frequency, current, phase angle, power and harmonic content deviation outside the allowable limits, which may be hazardous to both connected apparatus and customers. It should be noticed within 2 s, according to Distributed Generation (DG) IEEE 1547 interconnection specifications. This research examines several islanding recognition strategies for improving the solidity of utility-connected DG. It will assist potential scholars in choosing the optimum islanding recognition technique with less non-detection zone (NDZ). The comparative assessment of various detection parameters analyzed in terms of NDZ, power quality issues and number of DG networks used. Finally, the sensitivity analysis of twenty passive methods carried out for low power mismatch cases like load switching and different fault cases, and the best methods for future islanding detection are recommended.

A hybrid IDM using wavelet transform for a synchronous generator-based RES with zero non-detection zone

<abstract> <p>Increased use of renewable energy sources in distribution grids has led to the growing concern over unintentional islanding. Among the available islanding detection methods, the passive methods have larger non-detection zones and are susceptible to system faults. However, they have no effect on power quality like active methods. We present a hybrid method for the islanding detection of synchronous generator-based renewable energy sources. Here, the larger non-detection zone issue of the passive islanding detection method was reduced by combining it with a wavelet transform. The rate of change of a voltage unbalanced factor was computed from the sequence components of the renewable energy source terminal voltage. Then, the factor was analyzed using a wavelet transform, and a threshold for the factor was found. The robustness of the method was tested for several islanding as well as non-islanding situations. The efficacy of the method was also tested in multi-source test systems. From the analysis, it was observed that the proposed method was fast, reliable, and had a zero non-detection zone.</p> </abstract>

An Edge Computing-Oriented Islanding Detection Using Differential Entropy and Multi-Support Vector Machines

This paper presents an edge computing-oriented islanding detection paradigm using multi-support vector machines (SVM) and differential entropy. The proposed methodology can detect islanding events and locate which microgrid was disconnected in a distribution system with microgrid cluster. The proposed method employs multi-SVMs in the cloud to detect and locate potential islanding events in power distribution systems. The accuracy and reliability of the results of SVMs are enhanced by introducing differential entropy for quantitative evaluation. Additionally, the method decomposes the SVM inference process at the edge, enabling edge devices with low computational power and limited resources to support data-driven algorithms without compromising accuracy and speed. An extensive study is performed on the modified IEEE123 distribution system established in real-time digital simulation. Simulation results elucidate that the proposed method can accurately and promptly detect system islanding operations with zero non-detection zone. Comparison with other methods illustrates the superiority of the proposed method in terms of discrimination accuracy, detection time, and reliability.

Application of Artificial Neural Networks to Islanding Detection in Distribution Grids: A Literature Review

Active distribution grids that contain energy sources (so-called distributed generation or DG) are nowadays a reality. Besides the many benefits DGs bring to the distribution grid, some challenges are associated with their integration. Since there are DGs now in the distribution grid, the occurrence of islanding operation is possible. Since an islanding operation can be dangerous, it is necessary to have an effective method to detect it. In the last decade, scientists have made a great effort to develop and test various islanding detection methods (IDMs). Many approaches have been tested, and the methods based on computational intelligence (CI) have shown great potential. Among them, artificial neural networks (ANNs) gained most of the research attention. This paper focuses on ANN application for islanding detection. It gives an exhaustive review of the ANN types used for islanding detection, the types of input data, and their transformation to fit the ANNs. Furthermore, various applications based on specific input data, preprocessing types, different learning algorithms, real-time implementation, and various distribution models used for ANN are reviewed. This paper investigates the potential of ANNs to enhance islanding detection accuracy, reduce non-detection zone (NDZ), and contribute to an overall efficient detection method.

Nondetection Zone Elimination and Detection Speed Improvement for DC Microgrids Islanding Detection With Adaptive Resonant Frequency

Nondetection Zone Elimination and Detection Speed Improvement for DC Microgrids Islanding Detection With Adaptive Resonant Frequency

Islanding Event Detection in Grid-Connected Distributed Generation Systems Using Unscented Kalman Filter

A pressing concern in modern smart grid systems revolves around islanding, leading to unpredictable system parameters and a decline in power quality. In response to this concern, we introduce a novel passive method for identifying islanding in grid-connected distributed generation units. This method utilizes the unscented Kalman filter (UKF) to assess the voltage signal captured at the DG position. The triphase voltage signal observed at the point of common coupling (PCC) is used as the test signal. The UKF extracts and filters the harmonic content of the voltage signal to produce a residual signal, which detects changes in the power system. The estimation of total harmonic distortion (THD) follows, and its fluctuations help discern between islanding and typical events. This suggested approach undergoes assessment through a test system simulated in MATLAB/Simulink across different situations. Outcome findings underscore the efficacy of the suggested approach in distinguishing between islanding and regular occurrences, ensuring enhanced reliability and resilience against incorrect operations by removing the zone of nondetection. In our detailed experiments, we found that the proposed unscented Kalman filter (UKF) technique improved islanding detection accuracy by approximately 90% over traditional methods, under varied conditions. Specifically, the nondetection zone (NDZ) was reduced by 95% when compared to the most commonly used passive methods. Furthermore, in scenarios with high harmonic content and noise, the UKF showcased a 90% improvement in reliability over conventional techniques.

An Anti-Islanding Protection Method Based on Voltage-Synchronous Impedance Angle Measurements

Grid-tied distributed generators (DGs) need to be equipped with anti-islanding protection to avoid the impact of unplanned islanding, which would affect system stability, auto-reclosing, and personal safety. Among the active anti-islanding protections, impedance measurements based on signal injection have the advantages of a low non-detection zone (NDZ) and are less prone to maloperation during grid disturbances; however, there are problems with signal interference in multi-DG systems. Hence, the impedance angle measurement method with signals injected synchronously is proposed. In this method, each DG injects phase-coherent signals to detect islanding using zero-cross points of the voltage (ZCPV) to avoid the protection failures caused by signal interference. An islanding identification criterion based on the measured impedance angle is proposed by analyzing the impedance characteristics of grid connection and islanding, which avoids the influence of variation of the DG operation state on islanding detection. Finally, we present a signal injection strategy and performance analysis in combination with an existing DG control platform, avoiding additional hardware investment. RTDS-based simulation verification shows that the proposed method can 100% avoid DG maloperation due to voltage and frequency disturbances during grid-connected operation and exit operation within 2 s when islanding occurs.

Zero power mismatch islanding detection algorithm for hybrid distributed generating system

Distributed generation is essential for both keeping up with the rising power demand and reducing the amount of money spent on fossil fuels. There is widespread agreement that the world should prioritize the development of renewable energy systems such as wind and solar energy. This study describes the design and utility-grid integration of a hybrid distributed generating system that utilizes photovoltaic and wind-driven permanent magnet synchronous generators (hybrid PMSG-PV systems). To prevent damage to the grid, hybrid distributed generation systems, consumer devices, and line workers must be protected from islanding. Detection of islanding in hybrid DG systems has been suggested using passive islanding and time-spectral analysis. Measuring and amplifying the ripple content present in voltage at point of common coupling (PCC) about 0.4 seconds after the permissible delay time after the circuit breaker opens on the utility grid side is how islanding is discovered using this method. Compared to other methods, the proposed method has smoother islanding detection waveforms owing to increases in both the window size and threshold limit. The suggested method detects islanding in 40 ms and is verified in a variety of non-islanding scenarios, such as fault occurrence, parallel feeder loss, and load shift. In addition, the cost is reduced, the response time is rapid, and there is no non-detection zone (NDZ) when using these methods. Unlike active islanding detection methods, their function is unaffected by the size, quantity, or type of distributed generators linked to the utility grid; hence, there are no power quality concerns.