Islanding Detection In Microgrids Research Articles

DC Microgrid Islanding Detection New Approach Based on Multi-Scale Standard Deviation and Optimize Deep Belief Network

DC Microgrid Islanding Detection New Approach Based on Multi-Scale Standard Deviation and Optimize Deep Belief Network

Retraction Note: Passive islanding detection in microgrids using artificial neural networks

Retraction Note: Passive islanding detection in microgrids using artificial neural networks

Absolute Positive Sequence Voltage Difference Based Passive Islanding Detection in Micro Grids

Absolute Positive Sequence Voltage Difference Based Passive Islanding Detection in Micro Grids

Integrated Microgrid Islanding Detection with Phase Angle Difference for Reduced Nondetection Zone

Microgrids with distributed generation (DG) are rapidly coming into distribution networks to supply load demand in order to preserve ecological balance and reduce greenhouse gas emissions. Power electronic advancements are making renewables dispatchable to loads while turning passive networks to active with bidirectional power flow. The IEEE-1547-2018 regulations enforced certain standards on microgrids, including the ability to detect unintended failures, island the microgrid in less than 2 seconds, and feed connected loads while maintaining voltage, frequency, and power quality. There are numerous islanding techniques accessible, including passive, active, hybrid, and communication techniques. The active techniques degrade power quality due to injection of deviations, passive type leaves larger nondetection zone (NDZ), and communication type are costly. The hybrid type combines both passive and active methods. To get away from all these issues, a passive technique is formulated in this paper, which measures the differential phase angle of voltage and current at DG output, to detect islanding. This approach reliably identifies islanding in 20 ms at nearly zero NDZ. This approach is also stable during transient conditions such as load switching and throwing off. There is also no power quality issue because there are no injections during testing. This method is tested in MATLAB/Simulink and evaluated using the differential frequency technique to get performance indices in accordance with UL-1741 testing standards.

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.

Robust Islanding Detection in Microgrids Employing Rate of Change of Kinetic Energy Over Reactive Power

This study tries to outline a comprehensive approach established on the kinetic energy of the distributed generations (DGs) for islanding mode detection in micro-grids. More specifically, the proposed index calculates the rate of change of kinetic energy over reactive power (ROCOKORP) for each DG at the point of common coupling (PCC). Despite the islanding detection being the most challenging issue during complete match between the load and generation of DGs, the proposed method can swiftly deal with such an issue, therefore confirming its superiority. Other challenging conditions where islanding detection can either be difficult or confused with other events such as loads inside the non-detection zone (NDZ), various fault types, and loads at various power factors have also been considered to investigate the effectivity of the proposed approach. The proposed method’s performance has been verified and evaluated in comparison to the state-of-the-art via a simulation testbed.

Islanding Detection Methods for Microgrids: A Comprehensive Review

Microgrids that are integrated with distributed energy resources (DERs) provide many benefits, including high power quality, energy efficiency and low carbon emissions, to the power grid. Microgrids are operated either in grid-connected or island modes running on different strategies. However, one of the major technical issues in a microgrid is unintentional islanding, where failure to trip the microgrid may lead to serious consequences in terms of protection, security, voltage and frequency stability, and safety. Therefore, fast and efficient islanding detection is necessary for reliable microgrid operations. This paper provides an overview of microgrid islanding detection methods, which are classified as local and remote. Various detection methods in each class are studied, and the advantages and disadvantages of each method are discussed based on performance evaluation indices such as non-detection zone (NDZ), detection time, error detection ratio, power quality and effectiveness in multiple inverter cases. Recent modifications on islanding methods using signal processing techniques and intelligent classifiers are also discussed. Modified passive methods with signal processing and intelligent classifiers are addressing the drawbacks of passive methods and are getting more attention in the recently published works. This comprehensive review of islanding methods will provide power utilities and researchers a reference and guideline to select the best islanding detection method based on their effectiveness and economic feasibility.

Evaluation of Passive Islanding Detection Methods for Line to Ground Unsymmetrical Fault in Three Phase Microgrid Systems: Microgrid Islanding Detection Method

Distributed Generators (DGs) are incorporated in the power distribution systems to develop green energies in microgrids. Islanding is a challenging task in a microgrid. Different types of islanding methods, e.g. local and remote methods, have been developed for handling this task, with local methods being easier to implement, while remote methods are communication-based and costly. The local methods are classified as passive, active, and hybrid, out of which the passive methods are more simple and economical. In this paper, a passive islanding detection method is proposed to detect single line to ground fault. This fault is considered to represent the 60 to 70% of the total un-intentional faults of this category. The available passive methods cannot detect islanding at lower power mismatches as the variations in voltage and frequency fall within thresholding values. In this method, the voltage signals are first retrieved at the targeted DG output and then the phase angle is estimated. Finally, the phase angle is differentiated to get Rate Of Change Of Voltage Phase Angle (ROCOVPA) to detect islanding, and then it is compared with the Rate Of Change Of Frequency (ROCOF) at zero percent power mismatch. Simulation results depict that the ROCOVPA is more effective than ROCOF. The proposed method not only reduces detection time and Non-Detection Zone (NDZ) but is also stable during non-islanding cases like load connection and disconnection to avoid nuisance tripping.

Harmonic filter‐based improved islanding detection technique for microgrid

Islanding detection in microgrid using harmonic filters connected at the distributed generation units (DGs) end has been described. Traditional islanding techniques related to impedance estimation suffer from poor detection accuracy due to false detection during load switching. In the proposed method, tuned filters connected at DG terminals are utilised for islanding detection in the microgrid. These filters can be connected at the DG end to reduce harmonic injection to the source end, besides helping the islanding detection through the proposed technique. The measured grid-side harmonic impedances at tuned frequency after the filter are used for islanding detection instead of the direct input harmonic impedance calculated in traditional techniques. The proposed method can overcome the difficulties of the existing impedance-based islanding detection techniques like larger non-detection zone (NDZ), false detection during load switching etc. The effectiveness of the proposed method is tested in a six bus microgrid system and comparison with other existing islanding detection techniques reveals the fact that the proposed method has much better performance in terms of improved detection accuracy, detection speed, and it also significantly reduces the NDZ.

Microgrid Islanding Detection Based on Mathematical Morphology

Voltage and frequency stability are highly important for reliable performance of smart grids. In grid-connected mode, the utility controls these parameters, but when islanding occurs these parameters exceed their limits, which may result in irreparable damage to the system. This paper presents a time-domain approach which uses basic mathematical morphology (MM) operators, dilation and erosion filters, for microgrid islanding detection. The proposed method applies a dilation-erosion differential filter (DED) of the RMS signal (DEDFOR) at the point of common coupling (PCC) in a micro-grid connected to distributed generations (DGs). To evaluate the performance of the proposed approach, it is tested and compared with existing techniques in the literature under various conditions such as capacitor bank switching and motor starting. The results verify the accuracy and efficiency of the proposed technique for islanding detection under different operating conditions and various power mismatches.

Hybrid Islanding Detection in Microgrid With Multiple Connection Points to Smart Grids Using Fuzzy-Neural Network

This paper presents a new hybrid islanding detection approach for microgrids (MGs) with multiple connection points to smart grids (SGs) which is based on the probability of islanding (PoI) calculated at the SG side and sent to the central control for microgrid (CCMG). The PoI values are determined using a combination of passive, active, and communication islanding detection approaches based on the utility signals measured at the SGs sides which are processed by discrete wavelet transform using an artificial neural network (ANN). If ${\text{PoI}}_{{\rm{ANN}}}$ is larger than the threshold value (indicating high possibility of islanding) then a more accurate approach based on fuzzy network is used to recompute it ( ${\text{PoI}}_{{\rm{FUZZY}}}$ ) where the fuzzy parameters are determined by an adaptive neuro-fuzzy inference system. In the proposed technique, an active islanding is only performed when PoI is high and the amplitudes of the disturb signals are proportional to ${\text{PoI}}_{{\rm{FUZZY}}}$ . Furthermore, if the PoI is not correctly received by CCMG, two auxiliary tests will be performed in the MG side to detect islanding. These tests include an intentional passive islanding detection in a short preset time and an active islanding detection with disturb signals proportional to the calculated PoI. Detailed simulations are performed and analyzed to evaluate the performance of the proposed method.

A Novel Passive Method for Islanding Detection in Microgrids

A Novel Passive Method for Islanding Detection in Microgrids

Microgrid Islanding Detection Method based on Frequency - Reactive Power Feedback

Islanding detection is one of the essential features for photovoltaic grid-connected systems; detection performance is directly related to safe operation of the equipment. Summarize existing islanding detection, then an active method based on frequency – reactive power feedback islanding detection is proposed. Once an islanding condition occurs, the method introducing a load frequency - reactive power characteristics associated component as input of reactive power perturbation, breaks the existing islands of stability point in the case of the power balance. Then it detects the system frequency deviation by real time, and draws into the function of reactive perturbation to form a positive feedback loop. So the frequency drifting is accelerated and the frequency of the output voltage of the photovoltaic grid-connected inverter exceeds a preset threshold value, and then the islanding will be detected. Simulation shows that the proposed method has no detection dead zone and it is fast and has little influence on power quality.