Fault distance estimation‐based protection scheme for DC microgrids

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 article proposes a localized protection scheme for dc microgrids with radial configuration under the impact of constant power loads (CPLs) to determine the location of faults accurately. The proposed fault location scheme is primarily designed for fault location of CPLs in dc microgrids. First, a local protection relay for CPL is designed based on the transient behavior of the current and voltage in the main distribution line. Then, the estimation of the fault resistance is formulated based on the power sharing in the system to improve the accuracy of the protection system. To realize a robust protection scheme considering the variation of fault resistance, a fault resistance estimation procedure is employed to design a system that locates both low- and high-impedance faults. Finally, the effectiveness of the proposed strategy is evaluated based on offline digital time-domain simulations in Digsilent PowerFactory software environment and experimentally verified by implementing on a laboratory scale hardware setup. The obtained simulation and experimental test results, and comparison with other methods prove that the proposed scheme is immune against these disturbances and can efficiently and reliably estimate the location and resistance of faults with high accuracy and acceptable error margin.

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

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.

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.

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 differential protection scheme for the ring microgrids based on the power change rate of the bus is proposed. By setting the power change rate on both sides of the bus as the differential amount, the power change rate is maximized at the moment of short-circuit, which can immediately indicate whether a fault has occurred. Compared to the protection scheme that uses the current for fault identification, the proposed scheme reduces the time for the fault current to increase to the action threshold, saves the time required for fault detection. Moreover, it is proportional to the square of the current, and the higher orders increase the speed of the change rate, meanwhile, it is proportional to the short-circuit impedance which broadens the fault identification range and improves the protection sensitivity. Finally, the results of simulations prove that the proposed protection scheme has improved speed, sensitivity, and interference resistance while maintaining good selectivity for differential protection.

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.

DC microgrid provides the horizontal infrastructures to integrate distributed generation (DG) and loads. Unlike traditional AC systems, DC systems cannot survive or sustain high magnitude fault currents. It makes locating faults very difficult. The conventional protection techniques completely de-energies the DC link in the DC microgrid. A new protection scheme for multi-terminal DC microgrid against line-to-line fault and the low resistance earth fault is presented in this study. The scheme isolates the faulted section from the DC microgrid. Healthy sections are operated without any disturbance and supply continuity is maintained in a ring main DC bus system. The current sensors are mounted at DC bus segments to monitor the entering and outgoing current at different nodes. Further, the current sensors are also mounted at both ends of service mains to monitor their current difference at both ends of the service mains. The controller detects this current difference and opens circuit breakers. To meet the requirement of fast interrupting time and high short-circuit current withstanding capability, insulated-gate bipolar transistors used as circuit breakers. The fault location scheme gives the fault location in various sections (service mains) and faults resistance in the microgrid. The proposed concepts have been verified by computer simulation.

This report presents a novel fault detection, characterization, and fault current control algorithm for a standalone solar-photovoltaic (PV) based dc microgrids. The protection scheme is based on the current derivative algorithm. The overcurrent and current directional/differential comparison based protection schemes are incorporated for the dc microgrid fault characterization. For a low impedance fault, the fault current is controlled based on the current/voltage thresholds and current direction. Generally, the droop method is used to control the power-sharing between the converters by controlling the reference voltage. In this article, an adaptive droop scheme is also proposed to control the fault current by calculating a virtual resistance R droop , and to control the converter output reference voltage. For a high impedance fault, differential comparison method is used to characterize the fault. These algorithms effectively control the converter pulsewidth and reduce the flow of source current from a particular converter, which helps to increase the fault clearing time. Additionally, a trip signal is sent to the corresponding dc circuit breaker (DCCB), to isolate the faulted converter, feeder or a dc bus. The dc microgrid protection design procedure is detailed, and the performance of the proposed method is verified by simulation analysis.

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

To provide more clean energy and satisfy the increasing power demand, microgrids using renewable energy sources (RES) are designed as additional power supplies. Recently, DC microgrids (DCMGs) have gained the attention for their higher power efficiency and simpler configuration compared to AC microgrids (ACMGs). Although DCMGs seem better than ACMGs, lack of protection standard is a critical problem when operating DCMGs. Since DC fault response is completely different as AC fault, AC protection methods cannot be used for DCMGs. In this paper, a ring bus DCMG model is simulated using computer software PSCAD. A combined protection scheme based on cable current derivatives is introduced. This protection scheme not only detects and localizes low resistance DC faults very fast, but also accurately handles high resistance DC faults. The reliability of this scheme is proved by the simulation results.

A Novel Adaptive Protection Technique Based on Rate-of-Rise of Fault Current in DC Microgrid

Lowvoltage DC microgrids represent a highly adaptable approach to bringing renewable power generation closer to consumers, however their large, bidirectional fault currents and quick settling times complicate traditional protection methods. Therefore, an effective DC protection scheme must incorporate sufficient fault current limiting as well as an alternative method to coordinate protection relay tripping decisions. This article proposes a harmonic-based directional overcurrent protection scheme that limits fault currents through inductive fault current limiters and uses injectedAC voltages to coordinate protection relays. The injected harmonic quantities are measured by each relay and thus optimal protection coordination (OPC) can be performed. The OPC is completed through time-domain simulations and a non-linear constrained optimization program to determine relay settings. A 9-bus test system was used to evaluate the overall performance of the proposed protection scheme. The results demonstrate the scheme’s ability to quickly control and isolate faults of varying resistances. Significant improvements in relay operation times are made by exploring objective functions of varying parameter complexity. The proposed scheme offers a reliable and practical solution to the present challenges in DC microgrids.

DC microgrid is a compact framework comprising interconnected nearby sources and loads. The renewable energy source used in DC microgrids being intermittent leads to the change in the power availability as well as the fault current levels. In such situations, detecting and clearing the faults is very important to protect the DC microgrid without compromising on fault clearing time and interruption of the load. This paper proposes a hybrid Cumulative Sum (CumSum) and Wavelet transform-based approach to detect the fault. The CumSum value raises the amplitude by averaging the fault current. Wavelet transforms obtain important fault current features by decomposing the current signal. The hybrid method of CumSum and Wavelet analysis proposed here enables the detection of the fault and differentiates the fault condition from sudden load variation. Additionally, it helps to recognize the location of the fault by the wavelet energy difference. The proposed scheme is tested with a developed ring-type low voltage DC (LVDC) microgrid hardware model under various fault conditions. The scheme is implemented using TMS320F28069 digital signal processors (DSP) of Texas Instruments. The hardware results are validated using MATLAB simulation. The proposed method performance is also compared with the existing methods used for DC microgrid protection. The outcome shows that the proposed method has a high accuracy of 98.72%, selectivity of 96.08%, and reliability of 99.01%. The execution time required by the proposed method is also less.