The utilization of low-voltage direct current (LVdc) distribution system is gaining popularity in recent years due to its numerous advantages. However, the development of efficient faultdetection scheme for an LVdc m...
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The utilization of low-voltage direct current (LVdc) distribution system is gaining popularity in recent years due to its numerous advantages. However, the development of efficient faultdetection scheme for an LVdc microgrid is a challenging problem. This article proposes a fast and effective variational mode decomposition (VMD)-based faultdetection technique for the LVdc distribution system with penetration of renewable sources using the local end current measurements only. This scheme processes the rate of change of current signal of the distribution lines through the VMD algorithm that decomposes it to different modes. The cumulative energy of the most significant mode is being computed and used as a faultdetection index. The proposed scheme is tested for pole to pole and pole to ground fault with broad variation in fault location, fault resistance, and distributed generations penetration in grid-connected and islanding mode of microgrid operation. The performance of the proposed technique is compared with the existing differential current and overcurrent relaying scheme as well as other existing schemes. The testing has been done in MATLAB/Simulink and Typhoon hardware-in-loop testbed. The proposed scheme detects all types of fault with wide variation in fault and operating conditions of the microgrid with faster response time. The test results indicate that the proposed protection scheme is a potential candidate for providing effective protection measure for LVdc microgrid.
This study presents a novel micro-grid protection scheme based on Hilbert-Huang transform (HHT) and machine learning techniques. Initialisation of the proposed approach is done by extracting the three-phase current si...
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This study presents a novel micro-grid protection scheme based on Hilbert-Huang transform (HHT) and machine learning techniques. Initialisation of the proposed approach is done by extracting the three-phase current signals at the targeted buses of different feeders. The obtained non-stationary signals are passed through the empirical mode decomposition method to extract different intrinsic mode functions (IMFs). In the next step using HHT to the selected IMFs component, different needful differential features are computed. The extracted features are further used as an input vector to the machine learning models to classify the fault events. The proposed micro-grid protection scheme is tested for different protection scenarios, such as the type of fault (symmetrical, asymmetrical and high impedance fault), micro-grid structure (radial and mesh) and mode of operation (islanded and grid connected) and so on. Three different machine learning models are tested and compared in this framework: Naive Bayes classifier, support vector machine and extreme learning machine. The extensive simulated results from a standard IEC micro-grid model prove the effectiveness and reliability of the proposed micro-grid protection scheme.
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