Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

Zhang, Tong; Huang, Yu; Zeng, Peng; Sun, Langxiang; Song, Chunhe; Liu, Jianchang

doi:10.1016/j.ijepes.2019.105572

Cited by 28 publications

(19 citation statements)

References 35 publications

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“…Each layer of PF components is segmented into s segments, denoted as pf. To calculate the energy value E of pf and its proportion p in the total energy of the corresponding PF component ∑ s g=1 E, a very small positive constant σ is introduced into the energy entropy formula S, as shown in Equation (10).…”

Section: Energy Entropy Feature Extractionmentioning

confidence: 99%

“…However, when decomposing actual noisy signals, this method encounters mode mixing, thereby diminishing the accuracy of line selection. Guo et al [9] utilized the variational mode decomposition (VMD) method to decompose fault signals and then employed support vector machines (SVM) [10] for feature extraction from the time-frequency spectrum to achieve fault line selection. Nevertheless, when using VMD for signal decomposition, it requires the preconfiguration of the number of decomposition layers, making it susceptible to under-decomposition or over-decomposition.…”

Section: Introductionmentioning

confidence: 99%

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Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Lu,

Hou,

2023

IET Generation Trans & Dist

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When a grounding fault occurs in the distribution network with distributed generation, the network topology becomes intricate, making it challenging to extract fault characteristics, resulting in a decrease in the precision of fault discrimination. To address this issue, a graph isomorphism network (GIN) approach based on the parameterized adaptive graph learning (AdapGL) module is proposed, transforming the distribution network fault selection problem into a graph classification task. First, the adjacency matrix of the distribution network's topology graph is initialized. This matrix, combined with feature vectors from the robust local mean decomposition energy entropy and transient dielectric loss angle of line, will be input into the GIN. Then, the AdapGL module is integrated into the GIN, dynamically learning and updating the one‐way relationships between actual network nodes to complete the graph classification task. Finally, a radial distribution network model (RDNM) and an improved IEEE 34 nodes model are established, and the fault selection results of the AdapGL‐GIN method are compared with those of other methods. The results indicate that the proposed method achieves higher accuracy than other methods, demonstrating significant practical importance in engineering applications.

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Section: Energy Entropy Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Lu,

Hou,

2023

IET Generation Trans & Dist

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“…e image analysis process is complex, resulting in the reduction of fault identification efficiency. Reference [15] proposes a method based on feature model for single-phase grounding fault in active distribution network system, which transforms the solution of nonlinear feature model into single-objective optimization of feature entropy, which can well identify single-phase fault, but the identification effect of equipment with feature type is not ideal.…”

Section: Related Researchmentioning

confidence: 99%

Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning

Shan

Ma²,

Ye³

et al. 2022

Journal of Robotics

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In view of the poor effect of most fault diagnosis methods on the intelligent recognition of equipment images, a fault diagnosis method of distribution equipment based on the hybrid model of robot and deep learning is proposed to reduce the dependence on manpower and realize efficient intelligent diagnosis. Firstly, the robot is used to collect the on-site state images of distribution equipment to build the image information database of distribution equipment. At the same time, the robot background is used as the comprehensive database data analysis platform to optimize the sample quality of the database. Then, the massive infrared images are segmented based on chroma saturation brightness space to distinguish the defective equipment images, and the defective equipment areas are extracted from the images by OTSU method. Finally, the residual network is used to improve the region-based fully convolutional networks (R-FCN) algorithm, and the improved R-FCN algorithm trained by the online hard example mining method is used for fault feature learning. The fault type, grade, and location of distribution equipment are obtained through fault criterion analysis. The experimental analysis of the proposed method based on PyTorch platform shows that the fault diagnosis time and accuracy are about 5.5 s and 92.06%, respectively, which are better than other comparison methods and provide a certain theoretical basis for the automatic diagnosis of power grid equipment.

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“…The detection speed and accuracy have been raised using heuristic and intelligent methods. These techniques often use the high frequency component of the voltage and current and require large databases or smart devices [7][8][9][10][11]. Jiang establishes a mixed integer linear programming (MILP) method for estimating the faulty line section using the incomplete and incorrect smart fault indicators (SFIs) statuses [7].…”

Section: Introductionmentioning

confidence: 99%

“…The presented method requires impedance matrix and the information of voltage sag measured by a large number of intelligent electronic devices, installed at the substation and some of the network's end nodes. A fault location method based on Fibonacci search algorithm and minimum entropy theory that can be applied to DNs with any DG types has been adopted in [11]. This scheme uses the information collected by the phasor measurement units (PMUs) and can be only applied to recognize the location of the single to the ground (SLG) faults.…”

Section: Introductionmentioning

confidence: 99%

Accurate fault location method for distribution network in presence of DG using distributed time‐domain line model

Mohajer

Sadeh

2021

IET Generation Trans & Dist

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In this paper a novel technique is presented for fault location in distribution networks in the presence of distributed generators (DGs). The proposed approach uses recorded voltage and current at the beginning of feeder and DG terminals. This method is based on transient state data, and due to presence of high frequencies in this state, to achieve high accuracy it utilizes the distributed time-domain (DTD) line model. The fault location problem is formulated as an optimization problem and is solved using Genetic Algorithm (GA). Through modal transformation, in addition to locate the symmetrical three-phase to ground (3LG) faults, the location of asymmetrical faults including single line to ground (SLG) and double line (LL) faults can also be determined. The effects of fault distance, fault resistance and fault inception angle on the accuracy of the proposed algorithm have also been investigated. A solution is also proposed to overcome the multi-response problem. The simulations are conducted and the results are obtained via PSCAD and MATLAB softwares, respectively. The effectiveness of the proposed approach has been verified using the modified IEEE 34-node test feeder under different fault conditions. The simulation results demonstrate the high efficiency and accuracy of the proposed method.

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Single phase fault diagnosis and location in active distribution network using synchronized voltage measurement

Cited by 28 publications

References 35 publications

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Fault line selection algorithm for distribution networks based on AdapGL‐GIN network

Fault Diagnosis Method of Distribution Equipment Based on Hybrid Model of Robot and Deep Learning

Accurate fault location method for distribution network in presence of DG using distributed time‐domain line model

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