Status and Prospect of Islanding Detection in Active Distribution Network

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.

The rapid growth of inverter-based resources (IBRs) has created a need for new islanding detection methodologies to determine whether an IBR has been disconnected from the transmission grid in some manner (islanded) or remains connected to the transmission grid (grid-connected). Active islanding detection methods inject a signal into the power system to achieve detection. Existing schemes frequently limit consideration to a single node system with one IBR. Schemes tested on multiple IBRs often see interference, with the signals from one IBR disturbing the others, or require intricate communication. Further, several methods destabilize an islanded grid to detect it, preventing a prospective microgrid from remaining in operation while islanded. This work develops an active islanding detection scheme using Pulse Compression Probing (PCP) that is microgrid-compatible and can be used with multiple IBRs without requirement for communication. This active islanding detection scheme can be implemented on existing inverter switching sequences and has a detection time of 167–223 ms, well within the detection time specified by existing standards. The method is verified via electromagnetic transient (EMT) simulation on a modified version of a 34-bus test system.

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

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

In the future, functionalities like islanding detection must also operate during intended island operation of (MV+MV or MV+LV) nested microgrids. In this paper, healthy and faulty islanding detection of LV network connected generation unit during nested (MV+LV) microgrid islanded operation were studied by simulations with PSCAD model from real-life smart grid pilot. Main focus in the simulations was on the study and comparison of usage possibilities of combined (high-speed communication based transfer trip & fault detection/direction + voltage vector shift) and multi-criteria (voltage total harmonic distortion & voltage unbalance) based islanding detection schemes also during intended island operation of nested microgrid consisting only from inverter based generation units.

A large number of renewable distributed generation (DG) systems connect to the distribution network, affecting the structure of the traditional distribution network and forming an active distribution network (ADN). Although the accelerating grid penetration of DGs brings significant challenges to the distribution network operation, the islanded operation capability of DGs provides a flexible solution to the self‐healing of ADNs from faults. To ensure that ADN can quickly recover and reconfigure in the event of a fault and continue to maintain safe, economical, and reliable operation, this paper proposes a dynamic microgrid formation method for ADNs combined with the dynamic network reconfiguration and intentional islanding operation of DGs. An optimization model is designed to represent the proposed self‐healing method, maximizing the load restoration and minimizing the DG cost, line network loss, and voltage excursion simultaneously. A binary hybrid optimization solver is applied to pursue the optimal self‐healing schedules from the optimization model. The self‐healing method is evaluated on the Institute of Electrical and Electronics Engineers (IEEE) 33‐node system and the IEEE 123‐node system, which indicate its rationality and effectiveness fully verified. By optimizing the on and off states of the normally open switches and the on‐grid and off‐grid operation states of DGs, ADNs not only get healed with minimum load curtailment, but also achieve minimal DG generation cost, network loss, and node voltage deviation. In addition, compared with traditional solvers, the proposed solver has a slightly higher computational time than the corresponding solver.

Background: Introducing pilot protection in active distribution networks containing PV can improve the reliability and selectivity of protection. However, the basic communication facilities of the existing distribution network make it difficult to meet the requirements of data synchronization, and the PV T-connection to the network leads to sudden changes in the impedance angle. Methods: Therefore, pilot protection of a negative sequence and additional network considering PV is proposed. The scheme is based on the feature that the PV model only outputs positive sequence components after a fault. For asymmetrical faults, the negative sequence impedance detected at both ends of the protection is utilized to construct a comparative negative sequence impedance protection criterion. For symmetrical faults, the voltage characteristics of the faulty additional network are utilized to construct a protection criterion. Results: Finally, an actual distribution network model with PV is constructed in PSCAD to verify the effectiveness and reliability of the protection method. Conclusion: The protection method is selective and reliable and is not affected by high penetration rate and PV fault characteristics.

That large-scale wind farm connects to the power grid has affected the safe and stable operation of the grid. Low Voltage ride through (LVRT) capability is required by new power system operating codes. At the same time, the sudden change of the grid voltage is a shock for wind turbine. Fast and accurate detection of grid voltage sudden change fault is an important condition to achieve safe operation of the power grid and LVRT of wind power system. This paper fully analyzes the existing voltage sudden change detection method and presents a method of comparing the instantaneous value of test signals with the instantaneous value of “standard sinusoidal digital signal”. It generates the standard signal with the same frequency and phase as the test signal by Phase Locked Loop (PLL) technology. It gets values of test signal at some point in a cycle by the digital signal processing technology. Then, the difference between each present value of test signal and its corresponding “standard sinusoidal digital signal” values is used to reflect the real-time status of the signal under test. This instantaneous value comparison method can accurately and without delay detect the sudden change of voltage (sudden drop or a sudden jump in) and the computing is small. In order to ensure the accuracy of the detection without changes in grid frequency, but also from the impact of voltage sag depth, we improve the PLL signal input circuit. The fast and accurate detection of the grid voltage sudden change supplies the timely and accurate condition for wind turbine to through the low voltage.

At present, the commonly used active and passive islanding detection methods have their own shortcomings, and the islanding detection effect is difficult to meet the requirements. Therefore, a new detection method based on wavelet signal processing and artificial intelligence to identify islanding is proposed in this paper. In this method, the feature quantities required for islanding detection are obtained by wavelet transform and signal processing, and then the feature quantities are identified by neural network to determine whether the islanding is generated in distributed generation system. Wavelet transformation has a strong ability of signal feature extraction, while the neural network has strong learning and identification abilities, the combination of the both is beneficial to improve the success rate of islanding detection. Simulation verification shows that the new islanding detection method proposed in this paper can detect islanding quickly and accurately, and the performance of islanding detection has been significantly improved.

The quick detection of islanding in active distribution networks is the main challenge for power engineers. The late detection of islanding may have hazardous impacts on the power systems. Due to the advancement in photovoltaic (PV) technology and their eco-friendly nature, these systems are widely in demand. The islanding methods in the power system are classified into active, passive, remote, and hybrid. The passive methods depend on the measurement of electrical parameters such as voltage, frequency, and phase angle at the point of common coupling (PCC) during islanding condition. The purpose of this paper is to analyze the performance of different passive methods in 100 kW grid-connected PV array. This research paper compares over/under frequency, over/under voltage and rate of change of frequency (ROCOF)-based islanding detection methods (IDMs). The performance of these methods has been analyzed at different conditions. Computer simulations are performed using MATLAB/Simulink.

Grid synchronization algorithm is used to track the fundamental phase angle and frequency of the grid voltages/ currents accurately, to generate the reference currents for control of grid-interactive power converters. Phase-locked loop (PLL) is vital in the grid synchronization technique. It is mandatory to track the phase angle and filter out the harmonics and interharmonics during distorted grid conditions. In this article, a modified sliding Goertzel discrete Fourier transform (SGDFT)-based PLL is proposed to extract the fundamental component during interharmonics conditions. The proposed PLL grants three degrees of freedom (DOFs), i.e., vigorous against several distorted grid conditions, namely, voltage sag, high-frequency harmonics, interharmonics, and dc offset. Furthermore, the proposed PLL precisely extracts the fundamental frequency and phase angle with the faster transient response and attains superior system stability. The detailed mathematical modeling of recommended PLL is analyzed with existing PLLs, such as harmonic, interharmonic, and dc offset PLL (HIHDO PLL), hybrid prefiltering stage PLL (HPFS PLL), and SGDFT-based PLL. The numerical simulations for the proposed PLL are carried out using MATLAB <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> /Simulink environment and the performance of the recommended PLL is tested experimentally through the dSPACE 1104 DSP platform.

A prospective study on the radiation therapy associated changes in body weight and bioelectrical standardized phase angle

For proper work of grid-connected converters it is important to have accurate detection of phase angle, frequency and amplitude of grid voltage. Estimation of these grid parameters can be achieved using PLL (phase locked loop) based on SRF (synchronous reference frame) theory. This PLL should have robustness against noise and offset introduced by measurement and data conversion. This paper proposes an improved PLL algorithm that has excellent noise and offset rejection property. The main part of proposed PLL is a novel two-phase generator which is used to obtain two quadrature signals for SRF block and PI regulator in closed control loop. Performances of proposed PLL are verified through experimental results given in this paper.

The performance requirements of Grid-Tied Inverters (GTI) are regularly updated as the penetration of Distributed Generation (DG) increases. In view of improved reliability, the GTIs are required to add on the feature of low or high voltage ride-through (VRT). However, few studies are available to assess the impact of VRT methods on the existing control of the GTI, especially in terms of the implementation of islanding detection (ID). It is observed that the requirements for low VRT and ID are contradictory i.e., for certain low voltage levels, while low VRT assumes fault in the grid and demands continued operation, ID assumes the system to be islanded and tries to trip or initiate transfer to intentional islanded operation. Hence, it is important that the ID method differentiates between events corresponding to fault in the grid that demands VRT and actual islanding. In this paper, a previously proposed method of ID that makes use of the constancy of a voltage to current ratio in response to a pattern of step changes in active power reference, is adapted to operate with low VRT successfully. It is shown that the method can effectively differentiate between a fault condition that demands VRT and an islanding phenomenon. Also, a method for balanced current injection even during unbalanced operation with current limiting action is devised and implemented. The method is validated through MATLAB/Simulink simulation and the results thereof are presented.