A novel method of high impedance fault detection and fault resistance calculation based on damping rate double-ended measurement for distribution network

Yu, Kun; Yu, Quan Yu; Zhang, Xiangjun; Zeng, Jupeng; Ni, Yanru; Zeng, Hao; Liu, Feng

doi:10.1016/j.ijepes.2021.107686

Cited by 12 publications

(3 citation statements)

References 29 publications

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“…Overcurrent Relaying (OCR) principle is the cornerstone of Power Distribution Protective Relaying (PDPR). Although overcurrent relays are accurate in sensing and discriminating faults with lower impedances, their sensitivity to vegetation faults is inadequate [1]. Less than 20% of the total HIFs could be detected in conventional overcurrent relaying [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Operational Machine Learning Approach for Detection of High Impedance Faults in Distribution Power Networks

Khond,

Kale,

Ballal

2023

Preprint

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Due to insufficient quantitative and indistinguishable qualitative disturbance caused by the High Impedance Faults (HIFs), the detection of the same has been challenging and a topic of active research for decades. The article presents the novel Operational Machine Learning (MLops) concept for HIF detection. The proposed MLops algorithm is combined with the conventional current thresholding-based principles to reduce the ML models’ search space, resulting in improved detection accuracy. Moreover, due to the inherent adaptability of the MLops technique, the proposed method is found to be better capable of generalization as the fault current signature varies. In addition, a novel cumulative sequential ML ensemble method of prediction is used to eliminate the problem of false positive HIF detection without compromising the sensitivity of the HIF detection. The article also describes the method to accommodate for the maximum variance caused by the HIF by using multiple models of HIFs to generate the synthetic data. The synthetic data is obtained using minimal data from the experimental hardware set-up for validation purposes hence improving the utility of the proposed ML-based method in practical scenarios. The ML models such as Support Vector Machines (SVM), Decision Tree (DT), and K-nearest neighbors (KNN) are trained on the synthetic data. The proposed MLops algorithm is tested on the experimental set-up operating at 400V (L-L). The results for the detection accuracy in noisy environments and adaptability due to the parameter update confirm the desired objectives of achieving high sensitivity and robustness for HIF detection.

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Section: Introductionmentioning

confidence: 99%

Operational Machine Learning Approach for Detection of High Impedance Faults in Distribution Power Networks

Khond,

Kale,

Ballal

2023

Preprint

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“…In the field of distribution network fault location, scholars at home and abroad have proposed various methods such as fault routing, fault ranging and fault zone location [5]. Among them, the fault ranging method mainly includes the injection method [6,7], the impedance method [8][9][10] and the traveling wave method [11][12][13][14]. Fault zone location methods mainly include the zero sequence current comparison method [15,16], the harmonic component method [17,18], the medium resistance method [19,20], the matrix method [21][22][23][24] and the artificial intelligence method [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Distribution Network Regionalized Fault Location Based on an Improved Manta Ray Foraging Optimization Algorithm

Zhang

Liu

2022

Electronics

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To address the problem that the accuracy of traditional intelligent algorithms in distribution network fault location decreases with the expansion of distribution network scale, a regionalized fault location method for distribution networks containing distributed power sources based on the improved manta ray foraging optimization (IMRFO) algorithm is proposed. First, the global convergence property of the basic manta ray foraging optimization (MRFO) algorithm is improved by fusing the restart strategy and the opposition-based learning strategy. Then, based on the two-port equivalence principle, a topological model for regionalized fault hierarchical localization in distribution networks is constructed. Finally, the algorithm is improved by binary discretization using the Sigmoid function to output the fault vector and complete the fault location of the distribution network. Simulation experiments are conducted using MATLAB for IEEE-33 node distribution networks and the simulation results show that the IMRFO algorithm combined with the regionalization of complex distribution networks has a better effect of dimensionality reduction. Compared with the traditional distribution network simulation model, the fault location fault tolerance is greatly improved and its accuracy rate is increased by 1.8% and the location speed is improved by 15.537 ms.

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“…Based on the measured zero-sequence parameters and fault phase voltage, the calculation method of transition resistance was deduced in [31]. Reference [32] accurately calculated the transition resistance based on the network damping rate and network insulation parameters measured before and after the fault. e existing researches focus mainly on the calculation method of transition resistance and fault determination, and there are few in-depth studies on the development tracking and identification technology of SPGFs.…”

Section: Introductionmentioning

confidence: 99%

Single-Phase Grounding Fault Identification Based on Transition Conductance Tracking Method

Fang

Cai

et al. 2022

Mathematical Problems in Engineering

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The instability of single-phase grounding faults makes it difficult to track the fault development process accurately. In this paper, a transition conductance tracking method for single-phase grounding fault identification is established to perceive the health state of the distribution network. To solve the situation that the insulation parameters of the neutral ungrounded system are difficult to obtain, a phase-shift method is proposed to measure the insulation parameters accurately. Based on the measurement results of insulation parameters, a transition conductance tracking model is constructed to monitor the changes of transition conductance in real time. Since the transition conductance varies steadily, it can be used as an important parameter to evaluate the development of a single-phase grounding fault. The innovative tracking method is verified by the simulation model, and the error analysis shows that the tracking error of transition conductance is less than 9%. The simulation results demonstrate that the proposed method can track the development of grounding faults effectively and give a quick warning to the operator. The fault identification theory adopted in this paper is not limited by the neutral grounding mode and especially can be applied to situations where the neutral point parameters are not adjusted.

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A novel method of high impedance fault detection and fault resistance calculation based on damping rate double-ended measurement for distribution network

Cited by 12 publications

References 29 publications

Operational Machine Learning Approach for Detection of High Impedance Faults in Distribution Power Networks

Operational Machine Learning Approach for Detection of High Impedance Faults in Distribution Power Networks

Distribution Network Regionalized Fault Location Based on an Improved Manta Ray Foraging Optimization Algorithm

Single-Phase Grounding Fault Identification Based on Transition Conductance Tracking Method

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