Abstract
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.
Highlights
Distribution systems have presented several changes in the last years
This paper proposed a fault detection system for microgrids based on machine learning (ML)
2.3, their performance was studied using using a test database, which corresponded to of the database that was not considered in the a test database, which corresponded to 15% of the database that was not considered in the training training process. process
Summary
Distribution systems have presented several changes in the last years. Among the most significant ones, there is the integration of distributed energy resources (DER), which has been motivated by advances in power electronics and increased environmental awareness [1]. The authors in [31,32] present a fault detector based on morphological techniques, transient content, and zero sequence current for adaptive overcurrent protection in distribution networks with increasing photovoltaic penetration as well as changing load conditions. With the proposed FD approach, we addressed some weaknesses previously presented in state-of-the-art FD methods, such as network imbalance, synchronization of the measurements, changes in topology, non-robust communication systems, and on-grid/off-grid modes of operation.
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