Detection In Microgrids Research Articles

A protection scheme based on ensemble of linear discriminant analysis for fault detection in microgrid considering varying operational conditions

A protection scheme based on ensemble of linear discriminant analysis for fault detection in microgrid considering varying operational conditions

Fault analysis in clustered microgrids utilizing SVM-CNN and differential protection

The integration of distributed generation, microgrids, and renewable energy sources has significantly enhanced the resilience of modern electrical grids. However, this transition presents challenges in control, stability, safety, and protection due to low fault currents from renewables. This paper addresses these challenges by proposing novel methodologies to enhance fault detection, classification, and localization in microgrids. The literature review highlights a shift towards intelligent learning methods in microgrid protection systems, improving fault response times and identifying electrical faults, including high impedance faults. Nonetheless, existing methods often neglect high impedance fault detection and the integration of differential protection in clustered microgrids. To fill these gaps, this study presents a methodology combining support vector machines and convolutional neural networks for fault detection in microgrids, integrating differential protection for high impedance fault detection. The paper also proposes approaches to optimize protection in clustered microgrid systems. The effectiveness of the methodology is validated using Opal-RT through comparative analyses of signal decomposition techniques, performance and accuracy of support vector machines and convolutional neural networks, K-Fold validation, and sensitivity analysis. Results demonstrate robustness and high performance, achieving up to 100 % accuracy in fault detection and classification.

Protection strategy for fault detection in AC microgrid based on MVMD & differential CUSUM

Abstract In the era of smart grids and microgrids, the transformation of the traditional grid system brings many operational, technical, and economic benefits. However, the complexity of the network due to the integration of various distributed generations (DGs), continuous change of topology, and non-linear load make fault detection a major issue that forces power engineers to focus on. In this paper, a novel fault detection scheme is suggested based on the multivariate variational mode decomposition mode (MVMD) and differential cumulative sum (DCUSUM). As a generalized extension of the original variational mode decomposition (VMD) algorithm for multivariate data residing in multidimensional spaces, the main goal of MVMD is to decompose the input signal into different band-limited intrinsic mode functions (IMFs). Due to the inherent characteristics of being insensitive to noise and very effective in decomposing the local features even with similar frequencies, it is very effective for fault detection in microgrid distribution systems. The proposed DCUSUM algorithm computes the differential cumulative energy for the remaining significant modes. A fault detection index is considered in this approach and applied for fault detection by adaptively through the threshold setting to accurately result in fault detection. To justify the proposed approach, a standard AC microgrid test system is considered and the approach is verified for fault detection under various fault conditions and resistances. The obtained results and the comparative analysis with other methods reflect the better accuracy, robustness, and reliability of the proposed approach.

A novel LF-TLBO-based optimisation scheme for islanding detection in microgrids

A novel LF-TLBO-based optimisation scheme for islanding detection in microgrids

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

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

A New Lissajous-Based Technique for Islanding Detection in Microgrid

A New Lissajous-Based Technique for Islanding Detection in Microgrid

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

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

A wavelet based PSD approach for fault detection and classification in grid connected inverter interfaced microgrid

The recent progress in the area of microgrid protection has led to the application of data-driven and signal processing techniques for fault detection in microgrids. The deployment of inverter interfaced distributed generators makes the traditional protection schemes ineffective. In this paper a wavelet based power spectral density (PSD) has been utilized to detect fault, identify faulty phases and locate fault in the microgrid. Wavelet based PSD decouples the frequency information from the time information and it is the optimal technique to reveal the change of distribution of harmonics in non-stationary faulty signals as for fault analysis it can consider the faulty frequency band instead of one particular harmonic. The proposed technique can detect fault and identify faulty phases by analyzing current signals from the substation only. The normalized value of PSD of three phase current signals has been considered for fault detection and probabilistic distribution of PSD has been considered for faulty phase identification. The normalized value of PSD of the current signals collected from both ends of the main feeders and one end of the non-DG sub-feeder have been used to locate the faulty feeder and sub-feeder. Appropriate threshold values have been considered to detect fault and to locate the faulty feeder. To locate faults, the technique does not require synchronized signals and thus provides a promising and economical solution for inverter interfaced microgrid. It does not require huge amount of data set for training and testing as it is essentially a threshold based technique and also does not require collection of data at high sampling rate. The proposed scheme has been extensively tested for high impedance fault (HIF), unbalanced loading, load switching, variation of DG parameters, measurement uncertainty and presence of noise in the signal with signal to noise ratio up to 10 dB. This technique provides accurate results for fault resistance from 0 Ω to 300 Ω and up to 10% measurement error. The performance of the proposed technique has been verified for feeder length variation up to 20%. Half cycle of current signal only after fault is required and three levels of wavelet decomposition has been considered. The simulation results and comparative assessment demonstrate the efficacy of the proposed scheme.

Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids

Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy inverters. To address these challenges, this paper proposes a new methodology for fault detection and classification in a renewable microgrid. The main contributions encompass two key aspects. Firstly, it enhances fault detection performance in microgrids characterized by nonlinear relationships, including photovoltaic, hydrokinetic, and variable electric load systems. Secondly, the combination of the discrete wavelet transform with various types of neural networks and supervised learning techniques provides a robust methodology for fault detection and classification. The proposed approach is evaluated using an IEEE-5 feeder test bed representing a realistic ring network configuration. The results show that the radial basis function neural network model exhibited promising outcomes, yielding a low prediction error of 1.31 e-31, highlighting its practical potential for enhancing system reliability and performance. Furthermore, various test cases were conducted by altering the ground resistance to train the neural networks, demonstrating the effectiveness of this neural network in accurately identifying fault conditions. Additionally, this research achieved promising outcomes with other models, including support vector machine and nonlinear autoregressive with external input, emphasizing the adaptability of these models in fault detection.

Deep Neural Network with Hilbert–Huang Transform for Smart Fault Detection in Microgrid

The fault detection method (FDM) plays a crucial role in controlling and operating microgrids (MGs), because it allows for systems to rapidly isolate and restore faults. Due to the fact that MGs use inverter-interfaced distributed production, conventional FDMs are no longer appropriate because they are dependent on substantial fault currents. This study presents a smart FDM for MGs based on the Hilbert–Huang transform (HHT) and deep neural networks (DNNs). The suggested layout aims to prepare the fast detection of fault kind, phase, and place data to protect MGs and restore services. The HHT pre-processes the branch current measurements obtained from the protective relays to extract the characteristics, and singular value decomposition (SVD) is used to extract some features from intrinsic mode functions (IMFs) that are obtained from HHT to use as input of DNNs. As part of the fault data development, all the information eventually enters the DNNs. Compared with prior studies, this suggested method provides considerably superior fault-type identification accuracy. It is also possible to determine new fault locations. A detailed assessment analysis of this suggested FDM was conducted on IEEE 34-bus and MG systems to demonstrate its effectiveness. The simulations indicated that the proposed method is effective for detecting precision, computing time, and robustness to measurement uncertainties.

Practical Challenges of Attack Detection in Microgrids Using Machine Learning

The move towards renewable energy and technological advancements in the generation, distribution and transmission of electricity have increased the popularity of microgrids. The popularity of these decentralised applications has coincided with advancements in the field of telecommunications allowing for the efficient implementation of these applications. This convenience has, however, also coincided with an increase in the attack surface of these systems, resulting in an increase in the number of cyber-attacks against them. Preventative network security mechanisms alone are not enough to protect these systems as a critical design feature is system resilience, so intrusion detection and prevention system are required. The practical consideration for the implementation of the proposed schemes in practice is, however, neglected in the literature. This paper attempts to address this by generalising these considerations and using the lessons learned from water distribution systems as a case study. It was found that the considerations are similar irrespective of the application environment even though context-specific information is a requirement for effective deployment.

Harmonic Signature-Based One-Class Classifier for Islanding Detection in Microgrids

This article presents a new passive islanding detection technique in MGs that uses locally measured voltage signals at the PoC of DERs. The proposed method distinguishes islanding events from normal/non-islanding conditions by utilizing superimposed harmonic spectra extracted through a full-cycle discrete Fourier transform. Our solution utilizes a machine-learning-based one-class classifier to define and adjust thresholds for full harmonic spectra. Unlike other methods, our approach does not require data synchronization or communication infrastructure, nor does it suffer from common errors that often arise in current transformers. Moreover, our design is compatible with distributed and decentralized control strategies, as it relies solely on local voltage measurements at the PoC. Another advantage of this method is its low sampling frequency requirement, in the range of 1 kHz, making it cost-effective and implementable in most existing systems. In a comprehensive evaluation of a typical MG test system that included synchronous and inverter-based DERs, the proposed scheme demonstrated exceptional performance. Specifically, the scheme was able to detect 99.06% of different islanding events within the training range, with a detection time of just 10 to 21 ms. Additionally, the scheme remained 100% stable during various normal conditions, short-circuit faults, load changes, voltage changes, capacitor switching, and frequency changes.

Islanding detection in microgrid using deep learning based on 1D CNN and CNN-LSTM networks

Islanding detection is a critical task due to safety hazards and technical issues for the operation of microgrids. Deep learning (DL) has been applied for islanding detection and achieved good results due to the ability of automatic feature learning in recent years. Long short term memory (LSTM) and two dimensional (2D) convolutional neural networks (CNN) based DL techniques are implemented and demonstrated well performance on islanding detection. However, one dimensional (1D) CNN is more suitable for real-time implementations since it has relatively low complexity and cost-effective than 2D CNN. In this paper, for the first time, the 1D CNN and the combination of 1D CNN-LSTM are proposed for islanding detection to better exploit the global information of islanding data samples using the strengths of both networks. The proposed methods utilize only voltage and current harmonic measurements as input at the point of common coupling (PCC). About 4000 cases under the modified CERTS microgrid model are simulated to evaluate the performance of the proposed architectures. The simulation results and the presented analysis show that the proposed networks have achieved the maximum accuracy of 100% on the task of islanding detection; especially the proposed CNN-LSTM model outperforms the other approaches. Furthermore, the robustness of the proposed methods is demonstrated with unseen samples under low none detection zone and the expansion of microgrid topology. • This paper presents a novel 1D CNN-LSTM based deep learning method for islanding detection by using only harmonic measurements at the PCC. • The proposed method is robust to power quality disturbances and sudden changes. • The robustness of the proposed method is verified under the expansion of DGs with the six hundred unseen islanding and non-islanding samples. • The effectiveness of the proposed methods is proven under small power mistmaches, by testing with two hundred unseen events.

A Memory Based Current Algorithm for Pole-to-Pole Fault Detection in Microgrids

Abstract: The world’s growing attention to sustainable energy and development can be causal to the recently observed disrupt in the existing global power system networks. Moreover, an additional incentive towards this change are the challenges associated with the traditional power grid, including its rigid structure, aging architecture, and ecologically profligate nature. Modern power systems have observed a rapidly growing trend of decentralized energy generation in the recent past. A prominent structure incorporating decentralized energy generation and renewable energy are microgrids. While microgrids promote on-site generation and distributed energy resources (DERs), their unique characteristics of bidirectional flow of power, renewable generational intermittency, and varying levels of current causes challenges uncommon to the traditional grid. One of the vital challenges associated with microgrids is the protection of microgrids against faults and disturbances that can cause impairment to life and property. Conventional protection algorithms are ineffective in protecting the system from faults due to the unconventional topology of the microgrid. This paper attempts to contribute to work in the sector related to the protection of microgrids. This paper presents the pole-to-pole fault analysis of a hybrid photovoltaic system and presents a current based algorithm for the detection of DC pole-to-pole faults in the system under study. The protection algorithm is further verified on a hybrid Photovoltaic-Wind-Battery microgrid. Keywords: DC Faults, Hybrid Microgrids, Power Systems, Protection Algorithm, DERs

Microgrid fault detection technique using phase change of Positive sequence current

ABSTRACT This article presents a new algorithm for fault detection in grid-tied microgrids with inverter-interfaced distributed generators (DGs). To support the Grid codes, the DGs require a low voltage ride through (LVRT) capability. The control strategy used in the DGs results in large changes in the fault characteristics of the microgrid. Hence, it is required to study the fault characteristics of the DGs in a microgrid under various operating conditions. The study presents fault detection for microgrids with PQ-controlled DGs having LVRT capability under different DG voltage and different fault conditions (high impedance and low impedance). The fault location has been identified using the phase change in the positive sequence current at specific DG voltages. The reliability of the proposed scheme has been validated under diverse fault conditions through extensive simulations in the Matlab/ Simulink environment, and the comparison with other fault detection techniques proves the efficacy of the proposed scheme for fault detection in microgrids.

Resilient Consensus Control Design for DC Microgrids against False Data Injection Attacks Using a Distributed Bank of Sliding Mode Observers

This paper investigates the problem of false data injection attack (FDIA) detection in microgrids. The grid under study is a DC microgrid with distributed boost converters, where the false data are injected into the voltage data so as to investigate the effect of attacks. The proposed algorithm uses a bank of sliding mode observers that estimates the states of the neighbor agents. Each agent estimates the neighboring states and, according to the estimation and communication data, the detection mechanism reveals the presence of FDIA. The proposed control scheme provides resiliency to the system by replacing the conventional consensus rule with attack-resilient ones. In order to evaluate the efficiency of the proposed method, a real-time simulation with eight agents has been performed. Moreover, a verification experimental test with three boost converters has been utilized to confirm the simulation results. It is shown that the proposed algorithm is able to detect FDI attacks and it protects the consensus deviation against FDI attacks.

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.

DPMU-based multiple event detection in a microgrid considering measurement anomalies

A microgrid may be subjected to various unexpected events, such as sudden tripping of a generator/load, line outages due to faults, sudden switching of large capacitor banks, etc. Detection and classification of events play an essential role in the reliable operation, control, and restoration of microgrids. Due to low observability in microgrids, anomalies in measurements pose a challenge for reliable detection of events using conventional methods. This work proposes a data-driven approach for event detection in microgrids using phasor measurements collected from distribution-level phasor measurement units (DPMUs). The proposed method can determine whether the change in phasor measurement is caused by an event or due to any measurement anomalies. The model explores the interdependency among all phasor measurements collected from an unobservable microgrid. This method can identify and replace anomalies in DPMU measurements using a Rauch–Tung–Striebel (RTS) smoother. The proposed algorithm performance is validated on a test system developed in an OPAL-RT/HYPERSIM real-time simulator and phasor measurements from DPMUs.

Empirical mode decomposition based algorithm for islanding detection in micro-grids

Owning to extensively enhancement of renewable energy resources in the distribution grids, the employment of such sources is also associated with various issues such as the islanding problem. In this paper, an effective method has been proposed for detection of the islanding in the micro-grids comprised of inverter or direct fed types of distributed generations. The proposed method is designed based on the intrinsic modes of the voltage signal measured at the PCC point. More specifically, through the calculation of the positive sequence of the voltage signal variation (PSVSV) and extracting the signal energy of PSVSV's intrinsic modes, the islanding can be detected. The superiority of the proposed islanding detection method is manifested in the condition where the generation of distributed resources is in balance with the loading consumption. The performance of the proposed method has been evaluated considering the conditions where islanding is difficult to detect or might be mistaken with other phenomena given by loads within the NDZ region, different fault types, and loads with different power factors. The performance evaluation has been carried out through simulations, and furthermore has been compared with the state-of-the-art algorithms.

A Hilbert–Huang Transform-Based Adaptive Fault Detection and Classification Method for Microgrids

Fault detection in microgrids is of great significance for power systems’ safety and stability. Due to the high penetration of distributed generations, fault characteristics become different from those of traditional fault detection. Thus, we propose a new fault detection and classification method for microgrids. Only current information is needed for the method. Hilbert–Huang Transform and sliding window strategy are used in fault characteristic extraction. The instantaneous phase difference of current high-frequency component is obtained as the fault characteristic. A self-adaptive threshold is set to increase the detection sensitivity. A fault can be detected by comparing the fault characteristic and the threshold. Furthermore, the fault type is identified by the utilization of zero-sequence current. Simulations for both section and system have been completed. The instantaneous phase difference of the current high-frequency component is an effective fault characteristic for detecting ten kinds of faults. Using the proposed method, the maximum fault detection time is 13.8 ms and the maximum fault type identification time is 14.8 ms. No misjudgement happens under non-fault disturbance conditions. The simulations indicate that the proposed method can achieve fault detection and classification rapidly, accurately, and reliably.