Bidirectional Power Flow in DC Microgrid and its Islanding Detection Using Support Vector Machine

Roy, Soumadeep; Nayar, Saurabh; Kumar, Saurabh; Alam, Aftab; Ghose, T. K.

doi:10.1109/iccs45141.2019.9065548

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Power flow is unidirectional in conventional distribution circuits. However, with the help of distributed energy sources, the flow can be made bi-directional [36,37,38,39,40,41]. The flow of the fault current relies on the place where fault occurs.…”

Section: Protectionmentioning

confidence: 99%

Comprehensive Analysis of Planning Operation and Protection of Microgrid Systems

Muzzammel

2020

JENRR

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Microgrid is a decentralized system in which electrical sources and loads are connected in a way that operates normally in connection with and in synchronous with conventional grid. This system has the ability to work autonomously by disconnecting from conventional grid in island mode. Microgrid offers large number of advantages in power system. Among them, integration of renewable energy sources, reliability, two-way power flow and cost effectiveness are the significant merits of microgrids. Besides its advantages, different challenges are associated with the planning, operation and protection of microgrid systems. Safe two-way power flow, reliable switching of renewable energy, optimal power flow, quick isolation of faulty part from healthy system and incorporation of energy mix modeling are the major challenges behind its real time implementation. In this research, comprehensive analysis is carried out on the planning, operation and protection of microgrid model. A test system is developed in Matlab/ Simulink in which solar power source, wind power source and diesel power source are modelled and connected to residential, commercial and industrial loads. Optimal power flow is achieved through Newton’s method. In addition to this, operation of microgrid test system is ensured by the observation of voltage, current and power under steady state and transient conditions of wind based power generation, DC and AC parameters for solar power generation and generation and demand of power in the case of diesel based power generation within acceptable limits. Circuit breakers are installed to ensure protection against faults. Priority is made on the basis of promotion of renewable energy sources. Moreover, realization of microgrid is depicted with its hardware prototype.

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Section: Protectionmentioning

confidence: 99%

Comprehensive Analysis of Planning Operation and Protection of Microgrid Systems

Muzzammel

2020

JENRR

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“…The Markov model is another approach that is used by researchers in [42] to differentiate the fault condition from transients in a DC microgrid. The authors in [43,44] have used SVMs to identify fault conditions in DC systems. A variety of faults are taken into account and utilized to train the model parameters in [45], employing deep reinforcement learning as the foundation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids

Salehimehr,

Miraftabzadeh,

Brenna

2024

Sustainability

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DC microgrids have gained significant attention in recent years due to their potential to enhance energy efficiency, integrate renewable energy sources, and improve the resilience of power distribution systems. However, the reliable operation of DC microgrids relies on the early detection and location of faults to ensure an uninterrupted power supply. This paper aims to develop fast and reliable fault detection and location mechanisms for DC microgrids, thereby enhancing operational efficiency, minimizing environmental impact, and contributing to resource conservation and sustainability goals. The fault detection method is based on compressed sensing (CS) and Regression Tree (RT) techniques. Besides, an accurate fault location method using the feature matrix and long short-term memory (LSTM) model combination has been provided. To implement the proposed fault detection and location method, a DC microgrid equipped with photovoltaic (PV) panels, the vehicle-to-grid (V2G) charging station, and a hybrid energy storage system (ESS) are used. The simulation results represent the proposed methods’ superiority over the recent studies. The fault occurrence in the studied DC microgrid is detected in 1 ms, and the proposed fault location method locates the fault with an accuracy of more than 93%. The presented techniques enhance DC microgrid reliability while conserving renewable resources, vital to promoting a greener and more sustainable power grid.

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“…In addition, they need a predefined mother wavelet and thus it is very difficult for fault location in DC microgrid. 14,[17][18][19] Extreme learning machine (ELM) and online sequential ELM (OS-ELM) algorithms are good for efficient training, superior classification, and accurate fault location estimation. But the major problem with these algorithms is that they cannot encode more than one layer and they exhibit slow convergence and low accuracy.…”

Section: Introductionmentioning

confidence: 99%

Effective fault distance estimation and diagnosis in the PV based DC‐ring network using Hilbert Huang transform and weighted online sequential random vector functional network

Anjaiah

Dash

2021

Int Trans Electr Energ Syst

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This paper proposes a novel approach for fault detection, classification, and distance estimation of five kinds of faults (ie, cable and Arc faults) in the PV-based DC-ring microgrid. The proposed approach is based on the Hilbert Huang Transform (HHT) and online sequential random vector functional link network (OS-RVFLN). Initially, differential current signals are passed through empirical mode decomposition (EMD) to yield intrinsic mode functions (IMFs). Among the series of IMFs, more efficient IMFs are selected based on the threshold of maximum weighted kurtosis index (M-WKI) value for fault diagnosis. The selected IMFs are passed through Hilbert transform (HT) for determining the instantaneous frequency, magnitude spectrum, and Hilbert energy. Then, the Teager energy operator (TEO) is applied to the more effective IMF among the selected IMFs of each DC fault for fault detection. The most suitable target features are extracted from the selected efficient IMFs. This feature set data is initialized to the proposed HHT-OSRVFLN algorithm for five kinds of faults classification and its location estimation. The performance of the proposed algorithm in the proposed microgrid is proved in terms of low fault detection time, superior classification accuracy, and low relative error through different case studies by consuming minimum computation time in MATLAB/Simulink environment. Finally, the digital architecture of the proposed method is designed and simulated in Xilinx ISE 14.5 software environment to estimate the fault location. The Chip scope hardware debugging output proves the correctness, simplicity, feasibility, practicability, and robustness of the proposed method for online fault diagnosis.List of Symbols and Abbreviations: R f , fault resistance; Idc 1 , Idc 2 , DC currents; Vdc 1 , Vdc 2 , DC voltages; α 1 , α 2 , attenuation constants; ωd 1 , ωd 2 , damping frequencies; ω n1 , ω n2 , natural frequencies; I 3 , differential current; I avg , average current; SD (k), squared difference; M e (t), mean envelope; E [C(t)], Teager energy of the signal; ω(t), instantaneous frequency; θ(t), phase angle; I b , best IMF; H, hidden layers; β 0 , output weight matrix; Z, output of the signal; d pfd , predicted fault distance; d actl , actual fault distance; t c , computation time; CC, correlation coefficient; EMD, empirical mode decomposition; FPGA, field programmable gate array; HHT, Hilbert Haung transform; ICON, integrator controller; ILA, integrated logic analyzer; IMF, intrinsic mode functions; KI, Kurtosis index; MAC, multiply and accumulate unit; OS-RVFLN, online sequential random vector functional link; TEO, Teager energy operator; VHDL, hardware description language; WKI, weighted kurtosis index.

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Bidirectional Power Flow in DC Microgrid and its Islanding Detection Using Support Vector Machine

Cited by 6 publications

References 13 publications

Comprehensive Analysis of Planning Operation and Protection of Microgrid Systems

Comprehensive Analysis of Planning Operation and Protection of Microgrid Systems

A Novel Machine Learning-Based Approach for Fault Detection and Location in Low-Voltage DC Microgrids

Effective fault distance estimation and diagnosis in the PV based DC‐ring network using Hilbert Huang transform and weighted online sequential random vector functional network

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