A logic based expert system (LBES) for fault diagnosis of power system

Park, Young Moon; Kim, Gwang-Won; Sohn, Jin-Man

doi:10.1109/59.574960

Cited by 92 publications

(4 citation statements)

References 2 publications

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“…When using such a system, it is able to diagnose faults in the fastest time, and the results are generally true and valid. [1,2] The expert system power fault diagnosis system has a high degree match with human beings, which brings advantages to a certain extent. Such system appears early, application time is relatively long, though continuous research and improvement of relevant professionals, fault diagnosis expert system has been very big step forward, but also to the appropriate and efficient completion of fault diagnosis work.…”

Section: Expert Systemmentioning

confidence: 99%

Research on Power System Fault Diagnosis Technology

Gao

Tang

2014

AMR

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The current proposed technologies for fault diagnosis of power system were investigated, including expert system, artificial neural network, optimization methods, and rough set theory. The applicability and characteristics of these technologies for power system fault diagnosis were briefly analyzed and also. The existing flaws and improvements are given. Finally, the recently research status were reviewed and discussed, the key technology issues on and main development trends of the issues are pointed out to motivate the developments.

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Section: Expert Systemmentioning

confidence: 99%

Research on Power System Fault Diagnosis Technology

Gao

Tang

2014

AMR

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“…When a fault event occurs in a power grid, rapid and accurate fault diagnosis is important for efficient fault processing and ensuring reliable operation of the power grid. Generally, the existing methods for power grid fault diagnosis include expert systems, artificial neural networks, Petri nets, and analytical models [1][2][3][4][5]. The advantages and disadvantages of these methods are as follows.…”

Section: Introductionmentioning

confidence: 99%

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

Lin,

Wang,

et al. 2024

IEEE Access

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Power grid fault diagnosis methods based on analytical models possess advantages such as logical rigor, strong interpretability, and high applicability. However, the existing design of their objective functions is presented as a 0-1 integer nonlinear programming model, making it difficult to approximate the optimal solution. To address this issue, this paper proposes an improvement to the expression of the second backup protection expectation involved in the diagnostic model. Thus, the order of the objective function is successfully reduced, transforming the original 0-1 integer nonlinear programming model into a constrained 0-1 integer linear programming model, which can be efficiently solved by typical commercial solvers based on a linear integer programming framework. Numerical results demonstrate that the solution time of the proposed linearized model is lower than that of existing nonlinear programming models, and the diagnostic accuracy of the proposed model is higher than that of other state-of-the-art methods.

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“…In the 1960s, the SCADA system was adopted in the power system. In order to realize the automatic processing of power grid faults, methods like expert systems [1,2] , fuzzy sets, Petri nets, analytical models, and machine learning were gradually introduced into power grid fault diagnosis. The algorithm adopted in Wang's research [3] could fully use the topology and protection information of the power system and adopt Petri nets to perform logical reasoning based on knowledge representation.…”

Section: Introductionmentioning

confidence: 99%

A Grid Fault Diagnosis Method Based on Stacking Algorithm

Wang

Zhang

et al. 2023

J. Phys.: Conf. Ser.

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Power grid dispatching is developing towards digitalization and intellectualization. Using the data generated in power grid operations to diagnose faults through artificial intelligence technology directly has become the development trend of the power grid. However, traditional machine learning algorithms often need help to achieve good diagnostic results in practical applications. Therefore, a fusion model based on the Stacking integration algorithm is proposed, and four commonly used machine learning algorithms are selected for fusion. Furthermore, the model is trained using the fault alarm information features extracted from the SCADA system. Then, by comparing and analyzing the diagnostic effect of four single algorithms and the Stacking model for various fault types, the conclusion that the Stacking model has a better overall effect than other algorithms in the diagnosis of various fault types is obtained, and the advantage of the stacking fusion algorithm is proved.

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A logic based expert system (LBES) for fault diagnosis of power system

Cited by 92 publications

References 2 publications

Research on Power System Fault Diagnosis Technology

Research on Power System Fault Diagnosis Technology

A Novel Reduced-Order Analytical Fault Diagnosis Model for Power Grid

A Grid Fault Diagnosis Method Based on Stacking Algorithm

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