Abductive reasoning network based diagnosis system for fault section estimation in power systems

Huang, Yann-Chang

doi:10.1109/61.997901

Cited by 14 publications

(3 citation statements)

References 23 publications

(16 reference statements)

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“…1 is the circuit breaker energy storage motor current data acquisition system, in which 1 is the auxiliary switch, 2 is the opening spring, 3 is the closing spring, 4 is the closing electromagnet, 5 is the opening electromagnet, and 6 is the transmission gear. 7 is an energy storage motor. We set the fault by adjusting the voltage regulator, closing the spring, limit switch, and transmission gear.…”

Section: Methodology 31 Energy Storage Motor Signal Collectionmentioning

confidence: 99%

“…The development of IoT-embedded technology and artificial intelligence algorithms has provided new ideas for non-invasive diagnosis of equipment. Various methods based on artificial intelligence algorithms have emerged in the field of fault diagnosis: methods based on expert systems [5,6], artificial neural Networks [7,8], fuzzy theory [9], support vector machine [10], probabilistic neural networks, Petri net and other methods. Among them, artificial neural networks and support vector machines show great potential in feature parameter identification and can obtain high diagnostic accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

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Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

Li,

Zhang,

et al. 2024

ENERGY

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Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into local optimal solutions. The Sparrow Search Algorithm (SSA) with self-adjusting weight factors combined with bidirectional mutations is added to optimize the selection of BPNN hyperparameters. The results show that the ISSA-BPNN can accurately and quickly distinguish six conditions of motor voltage reduction: motor voltage increase, motor voltage decrease, energy storage spring stuck, transmission gear stuck, regular state and energy storage spring not locked. It is suitable for fault diagnosis and detection of the energy storage part of LVCB.

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Section: Methodology 31 Energy Storage Motor Signal Collectionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

Li,

Zhang,

et al. 2024

ENERGY

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show abstract

“…Fault diagnosis, based on the protective devices, is used to address this challenge by employing the alarming signals and other relevant information. A lot of efforts have already been engaged in this field, and many fault diagnosis methods have been proposed, such as the expert system (ES) [1,2], analytic model [3][4][5][6][7], artificial neural network (ANN) [8], fuzzy set (FS) [9], Petri net [10], multi-agent system (MAS) [11], abductive reasoning network [12], waveform matching [13], Bayesian network [14], logic cause-effect model [15], and flexible model-based method [16]. In [17], RS, ANN, and ES are incorporated to overcome respective deficiency and 2 of 13 exert respective excellence.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information

Hui-zhong¹,

Dong

Han³

et al. 2018

Energies

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When a fault occurs in a section or a component of a given power system, the malfunctioning of protective relays (PRs) and circuit breakers (CBs), and the false and missing alarms, may manifestly complicate the fault diagnosis procedure. It is necessary to develop a methodologically appropriate framework for this application. As a branch of stochastic programming, the well-developed chance-constrained programming approach provides an efficient way to solve programming problems fraught with uncertainties. In this work, a novel fault diagnosis analytic model is developed with the ability of accommodating the malfunctioning of PRs and CBs, as well as the false and/or missing alarms. The genetic algorithm combined with Monte Carlo simulations are then employed to solve the optimization model. The feasibility and efficiency of the developed model and method are verified by a real fault scenario in an actual power system. In addition, it is demonstrated by simulation results that the computation speed of the developed method meets the requirements for the on-line fault diagnosis of actual power systems.

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Fault diagnosis system for large power generation station and its transmission lines based on fuzzy relations

Mansour

Wahab

Soliman³

2014

Int. Trans. Electr. Energ. Syst.

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Summary This paper proposes a fault diagnosis system based on the fuzzy relation theory to estimate the faulty item/section(s) of a large power generation station and its transmission lines. Therefore, it diagnoses the faulted item/section(s) in terms of the degree of membership. Besides that, it also examines the mis‐operation of corresponding circuit breakers. The proposed method utilizes the final information of protective relays and corresponding circuit breakers to build the sagittal diagrams which represent the fuzzy relations for each item/section(s). In order to testify the validity and feasibility of that method, a computer simulation of High Dam power generation station and its 500 kV double‐circuit transmission lines is used. It is shown from two study cases that proposed fault diagnosis system is useful in diagnosing multiple faults as well as simple faults. Moreover, it has rapid reasoning, correct efficient and using flexible in a power generation station and its transmission lines for online fault diagnosis. Finally, it enables, assists and supports the operator of the power generation station control room to make the right decision, especially in case of loss of the communication with the dispatch center. Copyright © 2014 John Wiley & Sons, Ltd.

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Abductive reasoning network based diagnosis system for fault section estimation in power systems

Cited by 14 publications

References 23 publications

Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

Fault Diagnosis Method of Energy Storage Unit of Circuit Breakers Based on EWT-ISSA-BP

Stochastic Programming-Based Fault Diagnosis in Power Systems Under Imperfect and Incomplete Information

Fault diagnosis system for large power generation station and its transmission lines based on fuzzy relations

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