Frequency and Voltage Data Processing Based Feeder Protection in Medium Voltage Microgrid

Srivastava, Adhishree; Parida, S. K.

doi:10.1109/isgteurope.2019.8905705

Cited by 3 publications

(2 citation statements)

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“…Protection systems inside the distribution grid are particularly affected because they are based on the principle of overcurrent and unidirectional power flow [9]. Thus, traditional overcurrent protection schemes do not adequately protect microgrids [10].Several approaches have been proposed in the literature to deal with microgrid protection [11]. These protection schemes can be classified into three classes: external protection, adaptive protection, and fault detection.…”

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Intelligent Fault Detection System for Microgrids

et al. 2020

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The dynamic features of microgrid operation, such as on-grid/off-grid operation mode, the intermittency of distributed generators, and its dynamic topology due to its ability to reconfigure itself, cause misfiring of conventional protection schemes. To solve this issue, adaptive protection schemes that use robust communication systems have been proposed for the protection of microgrids. However, the cost of this solution is significantly high. This paper presented an intelligent fault detection (FD) system for microgrids on the basis of local measurements and machine learning (ML) techniques. This proposed FD system provided a smart level to intelligent electronic devices (IED) installed on the microgrid through the integration of ML models. This allowed each IED to autonomously determine if a fault occurred on the microgrid, eliminating the requirement of robust communication infrastructure between IEDs for microgrid protection. Additionally, the proposed system presented a methodology composed of four stages, which allowed its implementation in any microgrid. In addition, each stage provided important recommendations for the proper use of ML techniques on the protection problem. The proposed FD system was validated on the modified IEEE 13-nodes test feeder. This took into consideration typical features of microgrids such as the load imbalance, reconfiguration, and off-grid/on-grid operation modes. The results demonstrated the flexibility and simplicity of the FD system in determining the best accuracy performance among several ML models. The ease of design's implementation, formulation of parameters, and promising test results indicated the potential for real-life applications.Energies 2020, 13, 1223 2 of 21 as improved reliability, integration of renewable energies, reduction of network losses, and better voltage profile [7]. Nevertheless, microgrids present new challenges such as bidirectional power flow, considerable variations on fault currents levels, as well as power intermittency and quality issues, and additionally possible network reconfiguration, due to the presence of renewable energy sources [8]. Protection systems inside the distribution grid are particularly affected because they are based on the principle of overcurrent and unidirectional power flow [9]. Thus, traditional overcurrent protection schemes do not adequately protect microgrids [10].Several approaches have been proposed in the literature to deal with microgrid protection [11]. These protection schemes can be classified into three classes: external protection, adaptive protection, and fault detection. The external protection (EP) approach uses additional equipment such as reactances, super-capacitors, or fault current limiters (FCL) for preventing misfiring of the protection devices [12][13][14]. However, these solutions lack flexibility and, therefore, are not suitable for microgrids, where topology changes and DER connection/disconnection is possible [15]. The adaptive protection (AP) approach works online to dynamically modify protec...

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confidence: 99%

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confidence: 99%