Comparative assessment of passive islanding detection techniques for microgrid

An active islanding detection technique for an inverter-based distributed generator (DG) is presented. Two control strategies are studied: one in which the DG only supplies active power and one in which the DG supplies both active and reactive power to improve the power factor. The nondetection zone (NDZ) of the over/under voltage and frequency protection (OVP/UVP and OFP/UFP) method for each control strategy is determined. For the presented interface control, it is observed that the NDZ of the OVP/UVP and OFP/UFP is larger when the DG interface is designed to improve the power factor. In order to decrease the NDZ, an islanding detection method is designed that is based on creating a mismatch between the load and DG reactive power, thus forcing the frequency to deviate outside the OFP/UFP limits. It is shown that the proposed islanding detection technique is robust and has a negligible NDZ.

Background: The growth of renewable energy sources is increasing in the world to meet the energy consumption demand. The major problem after the integration of renewable sources is islanding. The islanding is not safe for equipment and customers. As per Distributed Generation (DG) interconnection standards, it should be detected within 2 seconds. Objective: This paper presents the review of various islanding detection methods for increasing the stability of islanded DG. This will help future researchers for selecting the best islanding detection method with zero NDZ. Methods & Results: The islanding detection methods are classified as local and remote techniques. The local techniques are again classified as active, passive and hybrid methods. Each method is presented with their islanding detection time, power quality issues, Non Detection Zone (NDZ), advantages and disadvantages. Conclusion: The fuzzy based artificial intelligence with Particle Swarm Optimization (PSO) passive methods have been reduced the NDZ to zero and increase the stability of DG without degrading the power quality as active and passive methods.

This article presents a Radial Basis Neural Network (RBNN) based islanding detection technique. Islanding detection and prevention is a mandatory requirement for grid-connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. While passive schemes have a large non detection zone (NDZ), concern has been raised on active method due to its degrading power quality effect. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. The proposed algorithm is compared with the widely used rate of change of frequency relays (ROCOF) and found working effectively in the situations where ROCOF fails. This approach utilizes rate of change of frequency at the target distributed generation location and fed to the radial basis neural network for intelligent islanding detection. Hence a better reliability is provided. This approach utilizes the artificial neural network (ANN) as a machine learning technology for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loading, switching operation, and network conditions. Simulation studies showed that the RBNN-based algorithm detects islanding situation accurate than other islanding detection algorithms. Moreover, for those regions which are in need of a better visualization, the proposed approach would serve as an efficient aid such that the mains power disconnection can be better distinguished

This paper provides an analytical survey of the islanding detection techniques for the distributed generation systems. Islanding phenomena on takes place when the power supply from the main utility is intermittent due to numerous reasons, but the distributed generation keeps supplying power into the distribution networks. Islanding can be dangerous to workers and electrical equipment, even the power grid. Therefore, islanding detection is the priority among priorities. Islanding detection is a precondition of two working modes switching for distributed generation. In this paper, a detailed description followed by the classification of the islanding detection techniques has been made based on features, such as detection time, size of non-detection zone, power quality disturbances, system cost and operation under multiple distributed generation units. The merits and demerits of islanding detection methods are analyzed and summarized in this paper. In this manner, the greatest detection outcome can be achieved by exploiting the merits of each method. This paper is aimed to serve as a convenient reference and guidance value for deciding the islanding detection method for future islanding users in distributed generation system.

An Islanding Fault Detection Method with CFDF-SVM Based RPV Approach under Pseudo Islanding Phenomenon

Distributed generators (DGs) require detection of islanding condition due to personnel safety hazards and equipment damages. Anti-islanding methods are divided into passive and active methods. The passive methods are based on measurement of the natural effects of islanding. The active methods use the effects by intentional transients or harmonics. When the power generated by the DG matches power consumption by load, passive methods fail due to small natural effect of islanding. Therefore, the passive methods have a non-detection zone (NDZ). The active methods can reduce the NDZ size. But these methods reduce the power quality of the grid. In this paper, a novel anti-islanding method (AIM) is proposed. The single phase DG using the proposed AIM injects output current included a little harmonic current into the grid and monitors the harmonic component of voltage at the point of common coupling (PCC) by using Goertzel algorithm. The Goertzel algorithm is a kind of discrete Fourier transform (DFT). It extracts magnitude and phase of the desired frequency from the input signal, with minimum computation. The proposed islanding detection algorithm does not only have NDZ but also bad effect on the power quality of the grid due to injecting harmonic components qualified the interconnection standard. The proposed islanding detection method (IDM) is verified by the PSIM simulations and the experimental results.

In the area of renewable energy technologies, islanding of distributed generation system needs to be prevented due to safety reasons and to ensure quality of power supplied to the customers. Several islanding detection methods based on passive and active schemes have been proposed in the literature. Passive methods have a large non detection zone (NDZ), While active schemes degrade power quality. This paper proposed a new passive islanding detection method which combines the Wavelet-transform and neural network techniques. This method can reduce the NDZ to zero without any perturbation that deteriorates the power quality. The simulation results show that the proposed islanding detection method is effective and robust in all kind of conditions.

One of the facing problems in distributed generation is being unintentionally islanded which the detection of this phenomenon is of great importance. Common methods are active and passive islanding detection. These two methods are based on changing in parameters such as frequency, voltage and current harmonics. If, after the islanding occur, this changes are revealing and visible, islanding phenomenon is demonstrated which is detected by passive methods. If the generated power of inverter is equal to local loads consumption and assuming of the generated reactive power of grid connected inverter equal to zero, it should be detected by active methods but these two methods have some challenges such as reduction in power quality or wide Non Detection Zone (NDZ). In this paper, in proposed method, change of a new parameter will be studied for islanding detection diagnosis. With the difference that the studied system is a three phase system and unbalanced load is studied which is more consistent to reality. Because of small changes in parameters, we have to find the right approach toward active methods. Generally there are two views about the islanding performance. At first view, this phenomenon is seems negatively but in fact this is an opportunity. The first view suggests that all generators in the island should bring out of grid within a maximum of two seconds. In this view the concept of anti-islanding protection is proposed to tackle the task of creating such islands. The second view expresses the concept of micro grid. In this case as far as possible, power required by Inner Island loads were provided by distributed generators. The studied system was similar to standard IEEE-1547 and UL-1741. With the difference that we used three phase and local loads are assumed unbalanced. Conventional active detection methods are not able to identify the island with the aim to help keep it stable. They usually try to bring out the electrical parameters such as voltage and frequency from nominal limits. So in micro grids the island should be diagnosed using passive or remote techniques. Note that detection time of 2 seconds which is referred to on standards is not good and the detection time must be lower than this value. However, the proposed method that is one of the active detection methods is capable for use in micro grids. Simulation results using PSIM software on standard network shows the effectiveness of the proposed method. Keywords: Islanding, Unbalanced Loads, dq-frame, Phase Locked Loop (PLL), Non Detection Zone (NDZ), Micro-grid.

ABSTRACTImproved efficiency, enhanced integration with distributed generations (DGs), and the proliferation of DC loads in power systems have driven a surge in the adoption of DC microgrids (DCMGs). However, unintentional islanding poses a significant threat to these microgrids, necessitating robust islanding detection techniques to safeguard personnel, equipment and system stability. This paper proposes two novel passive islanding detection methods for DCMGs, explicitly addressing the non‐detection zone (NDZ). The first proposed method involves collecting voltage samples at the DG terminals and computing their empirical mode decomposition (EMD). Subsequently, intrinsic mode functions (IMFs) are extracted, and the energy in each window is calculated to establish a diagnosis. To effectively distinguish islanding from other disturbances and faults, various scenarios are investigated. The second method is predicated on ensemble empirical mode decomposition (EEMD). Following the collection of voltage samples from DG terminals, white noise is introduced to these samples. The method subsequently extracts IMFs through averaging and quantifies the energy content within each window. The proposed methods are validated through simulations in Simulink/MATLAB. The obtained results demonstrate the methods’ effectiveness in achieving fast and accurate islanding detection.

Nowadays, distributed generation (DG) has become an indispensable part for meeting the growing power demand in electrical power generation and distribution. However, one of the drawbacks of DG is unintentional islanding phenomena, which has become a safety issue for both human and equipment connected to the system. To prevent this hazardous condition, according to IEEE 1547 standards, this islanding condition must be detected within 2 s. This paper proposed an approach to develop a hybrid islanding detection method (IDM) to prevent the damages caused by this islanding condition. The proposed hybrid IDM is a combination of three different IDMs, two passive and one active; that is, rate of change of active power (ROCOAP) and rate of change of reactive power (ROCORP) are passive IDMs where load connecting strategy (LCS) is active. To differentiate islanding conditions with similar occurrences, different case studies are taken into account with photovoltaic (PV) and synchronous generator (SG) working as distributed generators on PSCAD/EMTDC platform. The simulation results confirm that proposed IDM is more favorable compared to other IDMs due to simplicity and fast islanding detection time when its performance is tested on the 11‐kV Malaysian distribution system for various cases.

This paper has introduced an algorithm for the identification of islanding events in the remotely located distribution grid with renewable energy (RE) sources using the voltage signals. Voltage signal is processed using Stockwell transform (ST) to compute the median-based islanding recognition factor (MIRF). The rate of change in the root mean square (RMS) voltage is computed by differentiating the RMS voltage with respect to time to compute the voltage rate of change in islanding recognition factor (VRCIRF). The proposed voltage-based islanding recognition factor (IRFV) is computed by multiplying the MIRF and VRCIRF element to element. The islanding event is discriminated from the faulty and operational events using the simple decision rules using the peak magnitude of IRFV by comparing peak magnitude of IRFV with pre-set threshold values. The proposed islanding detection method (IDM) effectively identified the islanding events in the presence of solar energy, wind energy and simultaneous presence of both wind and solar energy at a fast rate in a time period of less than 0.05 cycles compared to the voltage change rate (ROCOV) and frequency change rate (ROCOF) IDM that detects the islanding event in a time period of 0.25 to 0.5 cycles. This IDM provides a minimum non-detection zone (NDZ). This IDM efficiently discriminated the islanding events from the faulty and switching events. The proposed study is performed on an IEEE-13 bus test system interfaced with renewable energy (RE) generators in a MATLAB/Simulink environment. The performance of the proposed IDM is better compared to methods based on the use of ROCOV, ROCOF and discrete wavelet transform (DWT).

Multi grid-connected inverters islanding detection methods include two categories: passive islanding detection and active islanding detection. Firstly, an active islanding detection method based on negative sequence current disturbance is analyzed. Aiming at the shortcomings of this method, a novel passive islanding detection method is proposed in this paper. The proposed islanding detection method uses wavelet transform to extract signal features, and then according to the extracted signal features adopts the neural network to judge whether or not the islanding occurs. The simulation results show that the passive multi grid-connected inverters islanding detection method has good islanding detection performance, and does not affect the stability and power quality of the system.

Islanding detection is of great importance for reliable operation of smart grids. Islanding detection methods can be classified into three different groups: active, passive and communication based methods. Active methods inject a disturbing signal (typically a voltage) and analyze the system response (typically in the current). These techniques have a low non-detection zone but present the inconvenience inherent to injecting a disturbing signal. Passive methods monitor the grid condition from the grid variables. These techniques are easy to implement, but present a large non-detection zone. Communications methods have the inconvenient of relying on communications, currently being of limited use. This paper proposes a new active islanding detection method, based on measurement of the grid high frequency impedance by means of the injection of a high frequency voltage. Advantages of the method are almost negligible adverse effects due to the injected high frequency voltage and accurate and fast islanding detection -in the rage of few ms-. Furthermore, the estimated high frequency impedance can be used for adaptive control of the power converter.1

Grid converters are the key player in renewable energy integration. The high penetration of renewable energy systems is calling for new more stringent grid requirements. As a consequence, the grid converters should be able to exhibit advanced functions like: dynamic control of active and reactive power, operation within a wide range of voltage and frequency, voltage ride-through capability, reactive current injection during faults, grid services support. This book explains the topologies, modulation and control of grid converters for both photovoltaic and wind power applications. In addition to power electronics, this book focuses on the specific applications in photovoltaic wind power systems where grid condition is an essential factor. With a review of the most recent grid requirements for photovoltaic and wind power systems, the book discusses these other relevant issues: Modern grid inverter topologies for photovoltaic and wind turbines Islanding detection methods for photovoltaic systems Synchronization techniques based on second order generalized integrators (SOGI) Advanced synchronization techniques with robust operation under grid unbalance condition grid filter design and active damping techniques Power control under grid fault conditions, considering both positive and negative sequences Grid Converters for Photovoltaic and Wind Power Systems is intended as a coursebook for graduated students with a background in electrical engineering and also for professionals in the evolving renewable energy industry.

Many techniques used and still in usage for solving the problem of islanding detection are intrinsically passive, active, or hybrid of both. Each one of them has its own benefits and drawbacks. In this paper, we propose a method, which takes the advantage of a machine learning (ML)-based algorithm, namely, support vector machine (SVM), in order to produce the results more efficiently. The results of the simulations based on the model and experimentally measured parameters of a real-life practical photovoltaic (PV) plant give much better output than the traditional reported methods. During the tests and simulations, an additional problem, namely, grid fault, emerged, posing new challenges for the proposed method. Occurrences of islanding and grid fault are grouped together with the same kernel dimension and no custom hyperplane bordering. Discrimination between islanding and grid fault events is an essential dilemma, which is handled by the proposed SVM-based algorithm to achieve more precision in islanding detection and simultaneously detect the grid faults authentically. Nondetection zones (NDZs) and detection time (DT) are tested using two dimensions, namely, the generated active energy from PV plant (0%–110% of $P_{n}$ ) and distribution network voltage levels (±10% of $U_{n}$ ). Simulations based on the model and parameters of a real-life practical PV power plant are performed in MATLAB/Simulink environment, and several tests are executed for several scenarios. Finally, comparisons with previously reported techniques prove the effectiveness, authenticity, selectivity, accuracy, and precision of the proposed islanding and grid fault detection strategy with allowable impact on power quality according to UL1741 and its superiority over other methods.