Islanding Detection Algorithm Research Articles

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.

Seamless Transition Between Microgrid Operation Modes Using ADRC Without an Islanding Detection Algorithm nor PLL

Seamless Transition Between Microgrid Operation Modes Using ADRC Without an Islanding Detection Algorithm nor PLL

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.

RFMFA Technique for Islanding Detection Scheme in Integrated DG System

EEE 1547 specifies time limits for the completion of DG interconnection tasks, and one of these tasks is the detection of islanding, which must be completed within two seconds of the task's start. This thesis provides new and effective islanding detection algorithms for a hybrid distributed generating system connected to the grid. The suggested technique is a hybrid of two different optimization algorithms: Random Forest (RF) and Moth Flame Optimization (MFO). At the objective stage, this technique employs the ROCOF. The suggested methods are distinguished by their ease of use due to the lack of a mandatory minimum. Furthermore, they are unaffected by the capacity or kind of Distributed Generation linked to the utility grid and not affected by system factors. In addition, the NDZ is not present after applying these processes. When compared to the standard approaches, these methods show a marked improvement in their ability to classify events into islanding and non-islanding categories. Several elements, including varying load situations, switching operations, and network conditions, must be considered before determining the proposed solutions' viability. The MATLAB/Simulink platform provides a setting where the suggested procedures may be implemented.

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.

Intelligent Island detection method of DC microgrid based on Adaboost algorithm

Aiming at the problems of large detection blind areas and difficult threshold settings in traditional passive islanding detection methods, this paper proposes an intelligent passive islanding detection method based on the adaptive boosting (Adaboost) algorithm. This method first uses feature screening technology to obtain the key feature electrical quantities of DC microgrid islands and form a sample set, and then rely on the Adaboost algorithm to generate a high-precision island classification model to realize the detection and classification of DC microgrid operation status. It has the advantages of automatic setting threshold, small detection blind area, and high accuracy. In addition, aiming at the problem that the classification results of the intelligent islanding detection algorithm based on machine learning cannot be directly used as the islanding discrimination results, an islanding criterion method based on the sliding window algorithm is added on the basis of the classification algorithm. This method realizes the accurate detection of the DC microgrid islanding operation state by the secondary analysis and judgment of the classification results of the islanding classification model. Finally, a DC microgrid model is built to simulate and verify the proposed method. The results show that the method can quickly and accurately complete the islanding detection of the DC microgrid.

Seamless Operation of Master–Slave Organized AC Microgrid With Robust Control, Islanding Detection, and Grid Synchronization

In this article, islanding detection, control, grid synchronization, and power share techniques have been considered for the seamless operation of ac microgrid in grid-connected mode, islanded mode, and during the transition between both modes. A master–slave organization coordinates the power distribution among microgrid sources. The control scheme of each source is designed with a nested loop robust linear quadratic regulator and mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{2}/H_{\infty }$</tex-math></inline-formula> optimal controller. The islanding detection algorithm is developed with synchro-extraction transform and optimal support vector machine (OSVM). An optimal linear Kalman filter-based synchronous reference frame phase locked loop (OLKF-SRFPLL) method is considered for the reconnecting of the microgrid to the utility grid. For optimal parameter tuning in the OSVM and LKF-SRFPLL methods, a particle swarm optimization technique is used. The seamless operation of the proposed ac microgrid system is verified in MATLAB/SIMULINK, and the practical feasibility is verified with an experimental photo-type setup and dSPACE 1202 control kit. Simulation and experimental results are presented to illustrate the seamless operation of the proposed ac microgrid system.

MACHINE LEARNING BASED ISLANDING DETECTION DURING BLACKOUT

In modern era, demand of electricity is increasing rapidly due to fast development in industrial sector, communication technologies, commercial and domestic usage. The need of the hour is to protect the power system from blackouts during abnormal conditions, like voltage collapse and frequency disturbance etc between the generation and consumption. The sudden change in weather conditions, loading of power system, and over ageing of transmission and distribution lines becomes the cause of blackouts. Islanding detection and protection is one of the best possible techniques to improve the power quality, reliability, system security, transmission congestion relief and efficient energy utilization. Distribution generators (DGs), if properly placed have many positive impacts on the operation of power system. The islanding can improve the reliability by counting the operation of electrical power to keep the balance between consumption and generation. The main problem is sustained islanding with fast timing. Plant should sustain its island mode condition when fault occurs in transmission lines. Most islanding detection algorithms suffer from large NDZ and Non detection zone must be minimized to protect system properly and avoid blackouts. This study presents a technique to develop a reliable system to prevent complete blackouts, while maintaining the stability of the power system with high variability and uncertainty under the abnormal conditions. It is also needed to present the period all the DGs need to connect and disconnect immediately when the island occurs. For this purpose, each DG must be equipped with islanding detection devices. In this research work the presented system is a two-step process, based on the collection of some of the key characteristics in the current and voltage signals to analyze second harmonic by the Fourier transform (FT).

A Novel Microgrid Islanding Detection Algorithm Based on a Multi-Feature Improved LSTM

Islanding detection is one of the conditions necessary for the safe operation of the microgrid. The detection technology should provide the ability to differentiate islanded operations from power grid disturbances effectively. Given that it is difficult to set the fault threshold using the passive detection method, and because the traditional active detection method affects the output power quality, a microgrid islanding detection method based on the Sliding Window Discrete Fourier Transform (SDFT)-Empirical Mode Decomposition (EMD) and Long Short-Term Memory (LSTM) network optimized by an attention mechanism is proposed. In this paper, the inverter output current and voltage at the point of common coupling (PCC) are transformed by the SDFT. The positive sequence, zero sequence, and negative sequence components of voltage and current harmonics are calculated and reconstructed by adopting the symmetrical component method (SCM). Meanwhile, the current and voltage are decomposed into a mono intrinsic mode function (IMF). The symmetric components of voltage, current, and IMFs are used as inputs to the deep learning algorithm. An LSTM with the features extracted to classify islanding and grid disturbance is proposed. By using the attention mechanism to set the weight values of the features of hidden states obtained by the LSTM network, the proportion of important features increases, which improves the classification effect. MATLAB/Simulink simulation results indicate that the proposed method can effectively classify the islanding state under different working conditions with an accuracy level of 98.4% and a loss value of 0.0725 with a maximal detection time of 66.94 ms. It can also reduce the non-detection zone (NDZ) and detection time and has a certain level of noise resistance. Meanwhile, the problem whereby the active method affects the microgrid power quality is avoided without disturbing the current or power of the microgrid.

Fast Islanding Detection of Nested Grids Including Multiple Resources Based on Phase Criteria

Smart grid enables integration of distributed generations (DGs) to contribute to support the network. In the islanding condition, a section of the system and at least one DG become separated from the main grid and can continue to operate autonomously. This paper proposes a passive islanding detection algorithm using just the DG power factor angle to extract an indicator for islanding detection with a negligible non-detection zone (NDZ). The proposed algorithm guarantees reliable detection of islanding condition even where the system is composed of multiple DGs including different types. In this paper, islanding and non-islanding events are discriminated using the proposed algorithm even in a nested microgrid which is operated autonomously. Moreover, the proposed algorithm provides secure detection in the noisy condition. Also, the proposed algorithm allows reaching the frequency and voltage ride-through requirements quickly by correctly discriminating grid-connected disturbances from islanding condition. The performance of the proposed algorithm is validated by performing extensive simulations and scenarios in different test systems. The results of the IEEE 34 bus test system have been presented in this paper.

A new passive islanding detection technique for different zones in utility grid

Distributed generation (DG) has reformed the meaning of traditional generation of power from large-scale to small-scale generation. The main issue of connecting the DG to the utility grid is the detection of unintended islanding. This paper shows the impact of the islanding phenomenon in the case of grid-connected photovoltaic arrays and how to develop a convenient technique to detect this phenomenon. A passive islanding detection algorithm is proposed for all types of DGs by varying and analyzing the DC-link voltage for voltage source converter in the photovoltaic inverter. The proposed algorithm is applied on the low and medium voltage scales. Furthermore, a comparison for applying the proposed technique with resistance load on the two scales is presented. In addition, the proposed technique for anti-islanding protection is performed and compared with a lot of techniques such as underfrequency, overfrequency, and rate of change of frequency according to the detection time of islanding. The simulation results using MATLAB/ SIMULINK platform illustrate the effectiveness of the proposed method.

A Novel Synchronous DGs Islanding Detection Method based on Online Dynamic Features Extraction

Reliable and fast state discrimination are among the most important requirements in islanding detection methods (IDMs) to avoid islanded operation problems of distributed generation (DG) systems. This paper proposes a novel, IDM based on the dynamic response characteristics of synchronous DGs. The proposed method is based on four features of the dynamic behavior of synchronous DGs, namely, participation matrix; zero mode; oscillation frequency; and damping factor. This allows to distinguish between islanding conditions and grid-connected disturbances, even when demand and generation are closely matched. These features are extracted in practice from local measurements using Hilbert-Huang transform (HHT) technique. Moreover, a new method for online observation of the participation matrix based on online power system measurements is proposed. The impacts of synchronous DG controllers and motor loads are considered in the proposed method. Different scenarios such as load shedding, load restoration, capacitor switching, and short circuit faults have been simulated to assess the performance of the proposed method. Simulations are performed on a typical distribution system with real data for different scenarios to verify the high speed performance, reliability, robustness, and applicability of the proposed islanding detection algorithm. Comparison with alternative islanding detection methods prove the superiority of the proposed approach.

An improved islanding detection algorithm based on AFDPF

Unplanned islanding is a harmful phenomenon that will cause a series of adverse effects on the power grid. Therefore, islanding detection is a necessary feature of a microgrid with distributed power generation. Based on the Active Frequency Drift with Positive Feedback (AFDPF) algorithm, this paper proposes an improved frequency offset method.The method uses the frequency difference between two cycles to determine the direction of the disturbance signal. The value of the third power is superimposed on the point of common coupling (PCC)to improve detection speed.And check the Nondetection zone of this method based on the Qf0×Cnom coordinate system. Finally, through MATLAB/simulink simulation, the effectiveness and superiority of the improved frequency shift islanding detection algorithm are verified.

Fast Islanding Detection for Distribution System including PV using Multi-Model Decision Tree Algorithm

Modern distribution system including Distributed Generation (DG) requires reliable and fast islanding detection algorithms in order to determine the grid status. In this paper, a new multi-model classification-based method is proposed, in order to detect islanding condition for photovoltaic units. Decision tree is chosen as the classification algorithm to classify input feature vectors. The final result is based on voting among three decision tree algorithms. First order derivatives of electrical parameters are employed to construct feature vectors. To cover intermittent nature of renewable sources, different generating states for PV unit are assumed. Probable events are simulated under different system operating states to generate classification data set. The pro­po­sed method is tested on typical distribution system including the PV unit, different loads, and synchronous generator. This study sh­o­wed that this method succeeds in highly fast islanding det­ec­tion. This quick response can be used in micro-grid application as well as anti-islanding strategy. The results revealed that the proposed vot­ing-base algorithm could classify instances with very high acc­ur­a­cy which leads to reliable operation of distributed gene­rat­i­on units.

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.

A new time domain passive islanding detection algorithm for hybrid distributed generation systems

A new time domain passive islanding detection algorithm for hybrid distributed generation systems

Determining Islanding Operation Using Micro Grid Phasor Measurement Unit Parameters

For improving the reliability of the micro grid network, a robust islanding detection algorithm is required. For creating a vigorous islanding detection algorithm voltage angle and current angle should be detected appropriately from the grid. The main objective of this research is to determine and compare the voltage angle and current angle with normal operation and islanding operationof the micro grid to develop multilevel islanding detection algorithm to ensure the higher reliability. In this work, IEEE 30 bus network phasor measurement unit parameters have been determined in PowerWorldSimulator. The result shows that the proposed methodology can appropriately determine the islanding event and the comparative study shows an incredible change in phasor measurement unit parameters when grid goes on islanding operation. © 2020, World Academy of Research in Science and Engineering. All rights reserved.

A Passive Islanding Detection Algorithm Based on Modal Current and Adaptive Boosting

An effective and timely detection of islanding is a task of paramount importance to nullify the potential threat to field personnel and equipment damage. A passive islanding detection method based on modal current and adaptive boosting is proposed in the present work. The three-phase currents of the target distributed generation unit are converted into modal signals reducing the dataset. These modal currents are subsequently decomposed into mono-frequency components by employing empirical mode decomposition tool. The authentic mono-frequency components identified using correlation are transformed with the help of Hilbert transform. Various features like entropy, skewness, power, kurtosis, signal-to-noise ratio, and total harmonic distortion are obtained from Hilbert transform. Subsequently, these features act as the input for the adaptive boosting technique to categorize islanding and non-islanding classes. The results obtained explicitly demonstrate that the proposed methodology is highly accurate with reduced non-detection zone.

An islanding detection based on droop characteristic for virtual synchronous generator

This paper proposes an islanding detection and control strategy to realize the stable and autonomous operation of microgrids using the virtual synchronous generator (VSG) concept under unplanned grid reconfigurations (e.g., islanding operation). Instead of a design-oriented method, this paper presents a detailed and comprehensive theoretical analysis and draws a brief conclusion, which is easy to implement and still effective under complex situations. Based on the results of mutation sequence and voltage oscillation, a rapider and more reliable islanding detection algorithm is proposed, which hardly needs parameter design. Comparing with frequency measured passive detection method, the proposed algorithm has higher detection speed and reliability. The feasibility of the proposed islanding detection and control scheme is verified by extensive simulations.

Power Quality Assessment of Voltage Positive Feedback Based Islanding Detection Algorithm

Islanding refers to a condition where distributed generators (DGs) inject power solely to the local load after electrical separation from power grid. Several islanding detection methods (IDMs) categorized into remote, active, and passive groups have been reported to detect this undesirable state. In active techniques, a disturbance is injected into the DG's controller to drift a local yardstick out of the permissible range. Although this disturbance leads to more effective detections even in well-balanced island, it raises the total harmonic distortion (THD) of the output current under the normal operation conditions. This paper analyzes the power quality aspect of the modified sliding mode controller as a new active IDM for grid-connected photovoltaic system (GCPVS) with a string inverter. Its performance is compared with the voltage positive feedback (VPF) method, a well-known active IDM. This evaluation is carried out for a 1 kWp GCPVS in MATLAB/Simulink platform by measuring the output current harmonics and THD as well as the efficiency under various penetration and disturbance levels. The output results demonstrate that since the proposed disturbance changes the amplitude of the output current, it does not generate harmonics/subharmonics. Thereby, it has a negligible adverse effect on power quality. It is finally concluded that the performance of the sliding mode-based IDM is reliable from the standpoints of islanding detection and power quality.