Fault Calculation Method of Distribution Network Based on Deep Learning

Zhang, Cong; Peng, Ke; Li, Huan; Xu, Bingyin; Chen, Yu

doi:10.3390/sym13061086

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…Based on Eqs. (4) and (5), we can calculate the displacement of the horizontal plane produced by the submarine after a certain tiny moment before it loses power: 𝑥𝑥 𝑥𝑥𝑥𝑥 = 𝑣𝑣 0 𝑑𝑑𝑡𝑡 + 0.5(𝑎𝑎 0 + 𝑎𝑎 1 ) • (𝑑𝑑𝑡𝑡) 2 (5)…”

Section: Prediction Of Submarine Positionsmentioning

confidence: 99%

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Submarine Search and Rescue Study Based on Digital Elevation Model (DEM) and Probabilistic Grids

2024

AJETS

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This article explores a comprehensive strategy to enhance safety measures for undersea tourism and exploration, particularly focusing on addressing submarine safety concerns in emergencies or malfunctions. The research aims to gain regulatory approval and trust by proposing strategies to ensure the safety of both tourists and submarine operators within various constraints. The study begins by predicting the submarine's position after power loss through an analysis of seawater density, ocean currents, and the creation of a digital elevation model of the Ionian Sea to pinpoint where the submarine could potentially strand on the seabed. Efforts are also dedicated to optimizing search and rescue equipment selection by synthesizing relevant parameters, developing utility formulas via the entropy weight method, and determining cost-effectiveness ratios to maximize utility within set budget limits. Moreover, a gridded probabilistic search model is introduced that leverages predictions of the submarine's potential stranding location in the ocean. This model assigns probabilities to possible submarine sightings within each grid, facilitating efficient search operations by continuously updating the search and rescue location with the highest cumulative probability. The approach extends its reach by adapting the model to different oceanic regions and incorporating multiple submarine interaction dynamics to significantly enhance search and rescue efforts.

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Section: Prediction Of Submarine Positionsmentioning

confidence: 99%

“…Combined with Digital Elevation (DEM) Data Map and the model's calculation of the submarine's 𝑧𝑧-axis coordinates [2], we can determine when:…”

Section: Prediction Of Submarine Positionsmentioning

confidence: 99%

Submarine Search and Rescue Study Based on Digital Elevation Model (DEM) and Probabilistic Grids

2024

AJETS

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“…Refs. [20][21][22] propose the utilization of artificial intelligence algorithms for distribution network fault location. These approaches integrate traditional methods with artificial intelligence algorithms, utilizing AI to predict fault location, streamline the complex steps of traditional fault location algorithms, and demonstrate certain resistance to interference.…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [22] utilizes a backpropagation (BP) neural network for fault localization; nevertheless, the BP neural network is susceptible to local minima, gradient vanishing or exploding, overfitting, and other issues. Additionally, the BP neural network is sensitive to input data, limiting its generalization ability in processing noise or unseen data.…”

Section: Introductionmentioning

confidence: 99%

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

Wang,

Guo,

Shi

2024

Mathematics

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The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks.

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“…Certain scholars have introduced a calculation approach founded on machine learning techniques to address the tradeoff between speed and accuracy in current distribution calculations within ADNs [16][17][18]. The ML method tackles the accuracy issue in electric current calculations by learning the mapping between electrical characteristics and shortcircuit currents.…”

Section: Introductionmentioning

confidence: 99%

GCN-Based Short-Circuit Current Calculation Method for Active Distribution Networks

Ye,

Wang,

Zhang

2023

IEEE Access

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The increasing integration of Inverter Interfaced Distributed Generators (IIDGs) into distribution networks has led to a more active nature of the grid. However, the accuracy-speed trade-off caused by iterative algorithms and simplified models in the short-circuit current calculation method for active distribution networks (ADNs) is becoming more prominent. To address this issue, we propose a novel approach that utilizes Graph Convolutional Neural Networks (GCNs) for short-circuit current calculation in ADNs. Our study explores the characteristics of short-circuit current in different network structures and evaluates the feasibility of representing electrical quantities using a graph data format. The proposed method employs the GCN model to calculate multi-output ADN short-circuit current and investigates the block construction of the GCN model. Algorithm analysis demonstrates that our method effectively calculates network-wide short-circuit current under various network structures, IIDG penetration rates, and fault conditions, meeting both accuracy and computational speed requirements. The proposed method offers several advantages, including superior precision, rapid computation, minimal hardware resource utilization, and robust resistance to interference.

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Fault Calculation Method of Distribution Network Based on Deep Learning

Cited by 5 publications

References 16 publications

Submarine Search and Rescue Study Based on Digital Elevation Model (DEM) and Probabilistic Grids

Submarine Search and Rescue Study Based on Digital Elevation Model (DEM) and Probabilistic Grids

Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19

GCN-Based Short-Circuit Current Calculation Method for Active Distribution Networks

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