Abstract:Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and differen… Show more
This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.
This paper provides a comprehensive and systematic review of fault localization methods based on artificial intelligence (AI) in power distribution networks described in the literature. The review is organized into several sections that cover different aspects of the methods proposed. It first discusses the advantages and disadvantages of various techniques used, including neural networks, fuzzy logic, and reinforcement learning. The paper then compares the types of input and output data generated by these algorithms. The review also analyzes the data-gathering systems, including the sensors and measurement equipment used to collect data for fault diagnosis. In addition, it discusses fault type and DG considerations, which, together with the data-gathering systems, determine the applicability range of the methods. Finally, the paper concludes with a discussion of future trends and research gaps in the field of AI-based fault location methods. Highlighting the advantages, limitations, and requirements of current AI-based methods, this review can serve the researchers working in the field of fault location in power systems to select the most appropriate method based on their distribution system and requirements, and to identify the key areas for future research.
“…Different short circuit levels and bidirectional power flows are produced when multiple DERs are connected to various grid nodes. These variations depend on the DER's type, size, location, and operation mode [8][9][10]. In addition, selectivity losses, false tripping, or blinding protection are caused by coordination problems and compromised protection schemes adjust settings in the distribution grid [11,12].…”
This article introduces a new approach for validating directional overcurrent protection schemes in ring-topology electrical distribution systems with distributed energy resources (DERs). The proposed protection scheme incorporates overcurrent and directional functions and addresses DER-induced challenges such as variable short circuit levels. This study employs real-time and offline simulations to evaluate the performance of the protection coordination scheme using a digital twin under DER-supplied loads. The utilization of digital twins offers the possibility to simulate different scenarios, providing real-time responses to dynamic changes and allowing for informed decision-making in response to disturbances or faults. This study aims to present a new approach to validate the performance of the proposed protection scheme when the load is entirely supplied by DERs, highlighting issues such as false trips and protection system blindness resulting from changes in short circuit currents. The results show a breakdown in the coordination of the protection scheme during the fault conditions, demonstrating the effectiveness of digital twins in validating the protection scheme’s performance. Performing an analysis in the electromagnetic transient (EMT) domain improves the validation and refines the results.
“…By studying these factors, researchers aim to gain insights into the mechanisms that govern fault propagation in hybrid microgrids and develop strategies to mitigate their effects. Advanced modeling and simulation techniques have been employed to analyze fault propagation dynamics and assess the effectiveness of different fault detection and isolation methods [9][10][11].…”
Due to their efficient renewable energy consumption performance, AC/DC hybrid microgrids have become an important development form for future power grids. However, the fault response will be more complex due to the interconnected structure of AC/DC hybrid microgrids, which may have a serious influence on the safe operation of the system. Based on an AC/DC hybrid microgrid with an integrated bidirectional power converter, research on the interaction impact of faults was carried out with the purpose of enhancing the safe operation capability of the microgrid. The typical fault types of the DC sub-grid were selected to analyze the transient processes of fault circuits. Then, AC current expressions under the consideration of system interconnection structure were derived and, on this basis, we obtained the response results of non-fault subnets under the fault process, in order to reveal the mechanism of DC fault propagation. Subsequently, a current limitation control strategy based on virtual impedance control is proposed to address the rapid increase in the DC fault current. On the basis of constant DC voltage control in AC/DC hybrid microgrids, a virtual impedance control link was added. The proposed control strategy only needs to activate the control based on the change rate of the DC current, without additional fault detection systems. During normal operations, virtual impedance has a relatively small impact on the steady-state characteristics of the system. In the case of a fault, the virtual impedance resistance value is automatically adjusted to limit the change rate and amplitude of the fault current. Finally, a DC fault model of the AC/DC hybrid microgrid was built on the RTDS platform. The simulation and experimental results show that the control strategy proposed in this paper can reduce the instantaneous change rate of the fault state current from 19.1 kA/s to 2.73 kA/s, and the error between the calculated results of equivalent modeling and simulation results was within 5%. The obtained results verify the accuracy of the mathematical equivalent model and the effectiveness of the proposed current limitation control strategy.
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