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    A comprehensive review of potential protection methods for VSC multi-terminal HVDC systems
    (Pergamon-Elsevier Science Ltd, 2024) Farkhani, Jalal Sahebkar; Celik, Ozgur; Ma, Kaiqi; Bak, Claus Leth; Chen, Zhe
    High voltage direct current (HVDC) transmission systems represent a significant development for future power systems due to presenting promising solutions for long-distance power transmission. However, the protection of HVDC systems is becoming one of the most significant challenges due to the extremely high short-circuit current within a short time span and the lack of zero crossing in the DC systems. DC protection schemes must detect and isolate the fault within 4-6 ms, while AC protection systems operate with considerably longer response times. Therefore, the techniques utilized for HVDC protection systems require more attention to enhance their performance in the future. This paper provides a comprehensive review of various protection techniques for HVDC systems, highlights recent advances, and analyzes the pros and cons of each method. Initially, different challenges of HVDC protection systems are presented, and then various HVDC protection schemes are discussed and classified into two groups based on the operation time. After that existing protection schemes are investigated in detail by including practical examples. The review aims to close the gap between the presented HVDC protection methods and technical challenges in order to overcome future issues of power system protection. Moreover, signal processing methods and artificial intelligence (AI) techniques, which play a key role in the future of protection systems, are extensively investigated to highlight the possible solutions. Finally, various recommendations, key challenges, and future trends with respect to the development of HVDC protection schemes have been presented for researchers and engineers studying in this field.
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    A Deep GMDH Neural-Network-Based Robust Fault Detection Method for Active Distribution Networks
    (Mdpi, 2023) Celik, Ozgur; Farkhani, Jalal Sahebkar; Lashab, Abderezak; Guerrero, Josep M.; Vasquez, Juan C.; Chen, Zhe; Bak, Claus Leth
    The increasing penetration of distributed generation (DG) to power distribution networks mainly induces weaknesses in the sensitivity and selectivity of protection systems. In this manner, conventional protection systems often fail to protect active distribution networks (ADN) in the case of short-circuit faults. To overcome these challenges, the accurate detection of faults in a reasonable fraction of time appears as a critical issue in distribution networks. Machine learning techniques are capable of generating efficient analytical expressions that can be strong candidates in terms of reliable and robust fault detection for several operating scenarios of ADNs. This paper proposes a deep group method of data handling (GMDH) neural network based on a non-pilot protection method for the protection of an ADN. The developed method is independent of the DG capacity and achieves accurate fault detection under load variations, disturbances, and different high-impedance faults (HIFs). To verify the improvements, a test system based on a real distribution network that includes three generators with a capacity of 6 MW is utilized. The extensive simulations of the power network are performed using DIgSILENT Power Factory and MATLAB software. The obtained results reveal that a mean absolute percentage error (MAPE) of 3.51% for the GMDH-network-based protection system is accomplished thanks to formulation via optimized algorithms, without requiring the utilization of any feature selection techniques. The proposed method has a high-speed operation of around 20 ms for the detection of faults, while the conventional OC relay performance is in the blinding mode in the worst situations for faults with HIFs.
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    Fault Detection, Classification, and Location Based on Empirical Wavelet Transform-Teager Energy Operator and ANN for Hybrid Transmission Lines in VSC-HVDC Systems
    (State Grid Electric Power Research Inst, 2025) Farkhani, Jalal Sahebkar; Celik, Ozgur; Ma, Kaiqi; Bak, Claus Leth; Chen, Zhe
    Traditional protection methods are not suitable for hybrid (cable and overhead) transmission lines in voltage source converter based high-voltage direct current (VSC-HVDC) systems. Accordingly, this paper presents the robust fault detection, classification, and location based on the empirical wavelet transform-Teager energy operator (EWT-TEO) and artificial neural network (ANN) for hybrid transmission lines in VSC-HVDC systems. The operational scheme of the proposed protection method consists of two loops: (1)an EWT-TEO based feature extraction loop, (2) and an ANN-based fault detection, classification, and location loop. Under the proposed protection method, the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform (EWT) method. The energy content extracted by the EWT is fed into the ANN for fault detection, classification, and location. Various faul(t) cases, including the high-impedance fault (HIF) as well as noises, are performed to train the ANN with two hidden layers. The test system and signal decomposition are conducted by PSCAD/EMT-DC and MATLAB, respectively. The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave (TW) based protection method. The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems, where a mean percentage error of approximately 0.1% is achieved.
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    Trade-Offs in Modelling Accuracy and Complexity of DC Circuit Breakers: A Comparative Aggregated Approach
    (MDPI, 2025) Farkhani, Jalal Sahebkar; Celik, Ozgur; Randewijk, Peter Jan; Gomez, Jonathan Cervantes; Bak, Claus Leth; Chen, Zhe
    The growing interest in high-voltage direct current (HVDC) technology and multi-terminal HVDCs (MTDCs) has motivated the evaluation of DC circuit breakers (DCCBs) for increased operational flexibility. While modeling DCCBs remains essential, their complex structures and modeling techniques require careful consideration. In this context, trade-offs in modeling accuracy and complexity of DCCBs are of paramount importance, and hence, benchmarking-based modeling methodology for hybrid and non-hybrid DCCBs is performed in this study. To this end, the performance of different aggregated DCCB technologies, namely hybrid DCCBs, simple DCCBs, and voltage-source DCCBs, is benchmarked for MTDC applications, with the full representation of hybrid DCCBs taken as the baseline for comparison. First, it is shown that the aggregated hybrid DCCB provides an accurate representation of the full hybrid DCCB's performance. This is followed by an analysis of the parameters for the simple DCCB and voltage-source DCCB (VSCB) that enable their performance to closely match that of the aggregated hybrid DCCB. Finally, the impact of aggregated DCCB models on voltage transients within a test system is analyzed, demonstrating the effectiveness of aggregated modeling across different DCCB technologies. Simulation-based analyses are conducted in PSCAD/EMTDC to compare the performance of different aggregated DCCB models.