Novel Protection Scheme for Ring DC Microgrids

The low-voltage dc (LVDC) microgrid possesses numerous benefits and their penetration in the power system has increased rapidly in recent years. However, the detection of faults in the LVDC microgrid is a challenging issue due to the large magnitude of fault currents and fault-level variation in the microgrid. The performance of the recent current and its derivative-based protection scheme is limited in case of faults in the islanding mode of operation, different microgrid topologies, varying distributed generations (DGs) penetration, and against the measurement noise. This article presents an enhanced differential protection scheme for LVDC microgrid integrated with multiple DGs and storage. The differential current and its first derivative are processed through the decision tree (DT) algorithm for fault detection and the K-nearest neighbor (KNN) technique is utilized for fault classification. The robustness of the proposed protection scheme is tested for different fault types and fault conditions with variation in microgrid topology and operating conditions. The impact of the intermittent and volatile nature of the DGs, the presence of measurement noise, and assessment during external faults and critical no-fault cases have been investigated. The proposed scheme is tested on the MATLAB/ SIMULINK and validated on the Typhoon HIL platform for the assessment of real-time performance. The test results show that the proposed scheme can detect and classify faults with high accuracy and faster response time and, thus, can be a potential candidate for providing dependable protection measures for LVDC microgrids.

A novel protection scheme for low voltage DC microgrid using inductance estimation

This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation (SST or simply the sparse S-Transform). The average and differential current components are passed through a change detection filter, which senses the instant of fault inception and registers a change detection point (CDP). Subsequently, if CDP is registered for one or more phases, then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique, which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault. The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location. The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification, which is least effected from bias setting, has a faster relay trip response (less than one cycle from fault incipient) and a better accuracy in fault location. The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system, subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications.

The integration of DC networks including DC microgrids (DCMGs) into power systems is rapidly increasing. This is notably attributed to the distinctive fault current characteristics arising from inverter-based distributed generation resources in DCMGs, which differentiate them from conventional networks. As a result, the protection of DCMGs presents considerable challenges. Leveraging the recent strides in artificial intelligence, this paper introduces a novel multi-agent-based protection scheme for DC microgrids. Subsequently, three fault classification approaches are proposed in an intelligent protection scheme platform, employing diverse machine learning-based methods as a backup protection for fault detection and the main protection for fault location. The proposed protection scheme uses three main protection layers—namely, equipment, substation, and system—each endowed with specialized agents. In this way, the first and second fault classification approaches employ classifiers based on machine learning algorithms, such as Support Vector Machine (SVM) and Decision Tree (DT), to ascertain the microgrid status. Subsequent fault location is accomplished through various neural networks dedicated to the fault location. In the third approach, three Deep Neural Networks (DNNs) are proposed for fault classification, prompting the exclusion of classifiers from the substation layer due to the heightened training proficiency of DNNs. Intelligent Electronic Devices (IEDs) are placed at the beginning of the lines and send voltage and current information to the substation layer. Communication among agents and layers is performed by the IEC-61850 protocol. Comprehensive simulations and analyses are conducted using DIgSILENT, MATLAB, and Python (TensorFlow platform and Keras library) software. The findings underscore the efficacy of the proposed scheme and Fault Detection and Location (FDL) approaches, affirming their capability for precise fault classification and location determination.

This paper presents a differential energy-based protection scheme for microgrid using Ensemble Empirical Mode Decomposition (EEMD). Current signal from both ends of the line is retrieved and processed through EEMD and first Intrinsic Mode Function (IMF) is selected for energy calculation. Differential energy is calculated for each line and a threshold is set for fault detection. IEEE 34 bus distribution system is modified as a microgrid for fault analysis and simulated on PSCAD/EMTDC platform. LG and LLLG faults are simulated at different fault location for island and grid connected mode. The results obtained shows that the proposed method can detect the fault successfully in the system.

Microgrids are small networks of loads and sources that can operate either connected to the grid or islanded. In order to operate in islanded mode, the microgrid must have a new protection scheme as classical distribution protection is inadequate. This work develops a differential zone protection scheme that works by comparing current measurements from several devices on the boarders of the zones. This method is as effective at detecting microgrid faults as digital relay differential protection schemes at significantly lower costs. The optimal placement of zones and devices is formulated. A case study shows that the optimal placement solution can reduce costs by more than 50% when compared to having a relay at each line segment.

In this paper, a protection scheme for a DC micro-grid is proposed. The proposed protection scheme’s novelty lies in its usage of concepts like K-means clustering, wavelet analysis, and machine learning techniques for fault detection. Current sample data is considered for different fault events. Using the K-means clustering, a low impedance fault is detected. For the discrimination of high impedance fault and sudden change in load condition, the data is processed with discrete wavelet transform and then fed into machine learning classifiers to get the fault information. The datasets are trained using an SVM classifier to obtain the results. The speed of operation of the classifier suits the requirement. A comparative analysis is done using other classifiers like KNN and NB for the best accuracy results. The proposed algorithm is tested on 5 bus ring-type DC microgrid using MATLAB/SIMULINK.

Implementation of micro grid concept creates several issues to the existing protection scheme in the radial distribution network. Depending on the location and capacity of the distributed generators, the short-circuit current from the main grid and distributed generations causes variations in the fault current levels inside the micro grid. The changes cause effect of sympathetic tripping, blinding of protection, relay over-reaching, relay under-reaching and loss of selectivity of the overcurrent protection scheme. Such issues must be managed carefully to ensure full benefit from micro grid adaptation. Conventional protection schemes are unable to detect fault current levels common in micro grid. Several methods have been proposed in various research paper. Micro grid protection is substantial to provide reliable and continuous power supply to the customers. The main aim of proposed work is to detect the fault for fast restoration of the system back to the normal operation. The proposed protection scheme for micro grid uses intelligent electronics device for detecting the fault

A new differential protection scheme based on Synchro-squeezing transform applied to AC Micro-Grid

The integration of renewable sources into a DC microgrid offers clear advantages such as high efficiency and effective simple control. Despite this, the high penetration of distributed generators can cause problems such as safety, and islands in the microgrid can affect the reliability of the power system. Because the nature of the fault current in the DC, microgrid is different from that of AC, which brings a great challenge for fault detection and diagnosis. To address this issue, this paper presents an intelligent differential protection scheme for microgrids that employ the machine learning (discrete wavelet transform and ANN) method. The proposed differential protection scheme employs DWT for fault detection and ANN for event classification. The discrete Fourier transform is used in this study to preprocess the voltage and current signals in order to calculate the microgrid fault formation. Subsequently, available ANN-based classifiers are presented to evaluate the proposed scheme’s efficiency in terms of defect detection, identification, and classification. This study collects data from various aspects by simulating different fault and no fault cases for the microgrid configuration in grid-tied and island modes of operation. The simulation is performed on a standard medium-voltage microgrid employing MATLAB/Simulink. The results also show that the proposed method can detect the faults in grid-tied and islanded microgrids.KeywordsDistributed energy resourcesDC microgridsArtificial neural networksDiscrete wavelet transform (DWT)Fault detectionIslanding detection techniquesMicrogrid protection

By simulating the inertia and damping characteristics of the synchronous generator, the virtual synchronous generator (VSG) control enables the inverters to provide inertia and damping support for the system, which can effectively improve the frequency stability of the high-scale new energy power system. However, in this control strategy, if the inertia link is set too small, it has no significant effect on the frequency change. If the inertia is set too large, it will lead to system oscillation. Fixed inertia coefficients in traditional VSG control can’t meet the requirements of stable operation of inverters under different working conditions. For this reason, an adaptive inertia VSG control method with power and power change rate is proposed, which limits the frequency change rate of inverters to a certain range and improves the stability of self-organizing power grid. Finally, the effectiveness of this control method is verified by simulation.

The high-level proliferation of inverter-interfaced distributed generators (IIDGs) in modern distribution networks (DNs) has changed system topologies and fault current signatures, which compromises the protective relays in DNs. Investigating IIDG fault behaviors-based protection scheme will benefit the grid’s safety and stability. This paper proposes a novel current differential protection (CDP) scheme that considers the delay behaviors of positive- and negative-sequence component extractors for IIDGs in DNs. A frequency-domain analytical model of the fault current for a grid-connected IIDG with the PQ control strategy and a low-voltage ride-through (LVRT) capability is investigated. The dynamic behavior of the IIDGs considering the sequence-component extractor based on the Pade approximation is presented, where the T/4 delay extractor of the IIDGs causes a two-stage behavior in the fault transient process. It is found that a 5 ms error between the measured and actual values after the fault will affect the transient characteristics of the IIDGs. The transient current generated by the IIDGs during grid faults contains a large number of low-order harmonic components within the range of 0–200 Hz, which is significantly different to the current provided by the power grid. Therefore, the proposed CDP scheme uses protective relays at both terminals to obtain the required transient electric quantity using the Prony method. By constructing the frequency-characteristics ratio (FCR) and the exchanging FCR between two terminal relays, the developed protection criteria are implemented. The accuracy of the fault analysis method, whose maximum computational error is below 0.1%, and the feasibility of the proposed protection scheme are demonstrated by using a 10 kV DN in a PSCAD/EMTDC simulation, which can be applied to various fault conditions and traditional DNs without IIDGs.

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To design a perfect protection scheme is a big challenge for microgrid (MG) network implementation because it can operate either grid connected mode neither islanding mode. Due to distributed generation (DGs) resources the fault current always varied steadily hence the system parameters. This research focus on two vital properties of microgrid: peer-to-peer and plug-and-play to avoid sophisticated communication network for microgrid adaptive protection schemes. A microgrid model has been proposed by conventional protection scheme and addressing all possible drawbacks along with distributed energy resources. Whole microgrid is divided into different protection zone to identify perfect fault detection techniques by analyzing corresponding system parameters. Moreover, transformer protection also considered in new proposed protection scheme.

Overcurrent and earth fault protective equipment employing time grading and directional detection cannot provide correct discrimination on all power networks and in many cases clearing times for some faults would not be acceptable. Differential protection is an alternative overcurrent protective scheme, which is used to protect individual sections of networks or pieces of equipment, such as transformers, generators, e.t.c. Thus, where protection co-ordination is difficult using time delayed over current and earth fault protection, or where fast fault clearance is critical, then differential protection may be used. Kirchhoff’s first law, which states that the sum of the currents flowing to a node must be equal to the sum of the currents flowing out from it is the basic principle of the differential protection scheme. It detects the difference between the current entering a section and that leaving it. Under normal operating conditions, the current leaving the protected unit would be equal to that entering it at every instant. If the current flowing into the protected unit is the same as the current leaving, then the fault is not in the protected unit and the protective equipment or relay should not operate. If there is a difference in either the phase or magnitude between input and output, then the fault is in the protected unit and the protection should operate. This paper investigates how power transformers can be protected using the current-differential protection schemes.