Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

Cui, Yiqian; Shi, Junyou; Wang, Zili

doi:10.1109/tpwrd.2015.2409376

Cited by 23 publications

(5 citation statements)

References 34 publications

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“…Power gird requests accurate and robust fault diagnosis methods to guarantee its safe and stable operation (Sun et al, 2004), (Cui et al, 2016). Fast and clear fault diagnosis methods can help dispatchers make quick decisions to stop the further development of outage events.…”

Section: Introductionmentioning

confidence: 99%

A novel fault diagnosis method for power grid based on graph Fourier transform

Liao

Yu²,

Zhang³

et al. 2023

Front. Energy Res.

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The fast and robust identification of fault elements is essential for the security and continuous operation of the power grid. The existing methods might be maloperation for bad data disturbance and require strict and exact synchronization. To address the challenge, this paper uses the conflict graph to propose a new sensitivity graph signal model for the power grid fault diagnosis. Next, a novel graph Fourier transform (GFT)-based method is proposed to diagnose the fault branch. Firstly, the measurement sensitivity graph signals are constructed by the conflict graph model, where the data is from activated recorders and protection devices. Next, the eigenvalue and GFT coefficient are used to extract the frequency characteristics of the signals. The fault branches provide the maximum contribution rate to the high-frequency coefficient of GFT. Then, for each node, the importance degree of the measurement sensitivity conflict graph signal is defined. The high-frequency importance degree-based method is proposed to discriminate the fault branch. Finally, simulations and practical cases verify the correctness and effectiveness of the proposed method. The proposed method owns fast faults diagnosis and good practicability. Additionally, the identification accuracy is high and the method is robust to bad data interference, due to considering measured data from whole activated fault recorders and protection devices.

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Section: Introductionmentioning

confidence: 99%

A novel fault diagnosis method for power grid based on graph Fourier transform

Liao

Yu²,

Zhang³

et al. 2023

Front. Energy Res.

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show abstract

“…In the past decades, many studies have been proposed in the power system faults prediction area [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ], such as expert systems [ 15 , 16 ], rough set [ 17 ], neural networks [ 18 , 19 , 20 , 21 , 22 ], etc. In general, it is very meaningful to make adequate good use of those electrical measurement data collected by the power station or state grid; this is valuable and first-hand information for improving the fault prediction performance and for ensuring the reliability and stability of power systems.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network

Guo

Jiang

2021

Sensors

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The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

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“…Besides, protection devices may also fail leading to incomplete action information. All these cases can increase the uncertainty and incompleteness of fault alarm messages, making fault diagnosis more di cult [4][5][6][7]. erefore, improving the fault information processing ability of fault diagnosis methods is signi cant for faulty equipment identi cation.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

et al. 2020

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Power transmission networks play an important role in smart girds. Fast and accurate faulty-equipment identification is critical for fault diagnosis of power systems; however, it is rather difficult due to uncertain and incomplete fault alarm messages in fault events. This paper proposes a new fault diagnosis method of transmission networks in the framework of membrane computing. We first propose a class of spiking neural P systems with self-updating rules (srSNPS) considering biological apoptosis mechanism and its self-updating matrix reasoning algorithm. The srSNPS, for the first time, effectively unitizes the attribute reduction ability of rough sets and the apoptosis mechanism of biological neurons in a P system, where the apoptosis algorithm for condition neurons is devised to delete redundant information in fault messages. This simplifies the complexity of the srSNPS model and allows us to deal with the uncertainty and incompleteness of fault information in an objective way without using historical statistics and expertise. Then, the srSNPS-based fault diagnosis method is proposed. It is composed of the transmission network partition, the SNPS model establishment, the pulse value correction and computing, and the protection device behavior evaluation, where the first two components can be finished before failures to save diagnosis time. Finally, case studies based on the IEEE 14- and IEEE 118-bus systems verify the effectiveness and superiority of the proposed method.

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Power System Fault Reasoning and Diagnosis Based on the Improved Temporal Constraint Network

Cited by 23 publications

References 34 publications

A novel fault diagnosis method for power grid based on graph Fourier transform

A novel fault diagnosis method for power grid based on graph Fourier transform

A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network

A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism

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