Probabilistic transmission line fault diagnosis using autonomous neural models

Ferreira, Vitor Hugo; Zanghi, Rainer; Fortes, Márcio Zamboti; Gomes, Sérgio Bittencourt Varella; Silva, Alexandre P. Alves da

doi:10.1016/j.epsr.2020.106360

Cited by 21 publications

(7 citation statements)

References 33 publications

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“…) (2) Self-adjusting weather trust factor First, calculate the risk of failure of each line [10]. Specifically: (a) According to the external environment, the risk of line failure is divided into four levels, namely high risk, higher risk, lower risk, and low risk; Because this paper mainly studies power grid fault diagnosis under disaster-causing weather, only when the risk of line fault is greater than or equal to a higher level, the line fault probability is introduced into the objective function [11].…”

Section: The Analytic Model For Fault Diagnosismentioning

confidence: 99%

“…Currently, numerous power grid fault diagnosis methods utilizing smart technology have been developed worldwide, including expert systems [8], artificial neural network [9][10][11], Petri net [12,13], Bayesian network [14,15], spiking neural P system [16,17], optimization technology [18,19 ]Wait. Among them, the power system fault diagnosis method based on optimization technology has strict mathematical logic and strong fault tolerance.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis for Power Grids Based on Stochastic Self-Regulating Algorithm Under Disaster-Causing Weather

Wang,

Liu,

Ying

et al. 2024

Preprint

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In recent years, due to shifts in the global climate and environment, a variety of extreme weather events have become more frequent, resulting in numerous accidents. The increased occurrence of extreme weather not only poses risks to the functioning of the power grid but also impacts the accuracy of power grid fault diagnosis outcomes. By integrating research on power grid fault risks during extreme weather events, this study introduces weather data and self-check information to develop a fault diagnosis approach for the power grid based on a random self-regulating algorithm. The fault diagnosis method comprises two main components: Firstly, an analytical model for fault diagnosis is constructed using alarm data, weather information, and self-check data from the affected power outage area. Three types of self-adjusting trust factors are incorporated into the model to enhance its fault diagnosis capabilities in the context of extreme weather events. Subsequently, a random self-regulating algorithm is applied to optimize the model and generate a hypothesis regarding the fault. Through a simulation test on a sample calculation, the results demonstrate that the fault diagnosis method exhibits robust fault tolerance towards abnormal operation of protective devices and inaccuracies in alarm data.

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Section: The Analytic Model For Fault Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Power Grids Based on Stochastic Self-Regulating Algorithm Under Disaster-Causing Weather

Wang,

Liu,

Ying

et al. 2024

Preprint

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“…Solutions to AI-based fault diagnosis systems in electrical power systems [8]- [11] are known to be high in time complexity coupled with chronic low accuracy. Also, region-based solutions [12] obtain much better efficiency as compared to single-pass solutions [6].…”

Section: A Motivationsmentioning

confidence: 99%

Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

Djenouri

Belhadi

Srivastava

et al. 2023

IEEE Internet Things J.

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This paper introduced a new deep learning frame-1 work for fault diagnosis in electrical power systems. The frame-2 work integrates the convolution neural network and different re-3 gression models to visually identify which faults have occurred in 4 electric power systems. The approach includes three main steps, 5 data preparation, object detection, and hyper-parameter opti-6 mization. Inspired by deep learning, evolutionary computation 7 techniques, different strategies have been proposed in each step 8 of the process. In addition, we propose a new hyper-parameters 9 optimization model based on evolutionary computation that can 10 be used to tune parameters of our deep learning framework. 11In the validation of the framework's usefulness, experimental 12 evaluation is executed using the well known and challenging VOC 13 2012, the COCO datasets, and the large NESTA 162-bus system. 14The results are very promising against many current state-of-the-15 art solutions in terms of runtime and accuracy performances.

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“…Therefore, this approach has been highly valued. Many manufacturers such Boeing, Airbus, GE, Honeywell, RR have got lots of technical achievements in this direction [7], [8], and many aircraft operators have also developed some helpful fault diagnose system [9].…”

Section: Development Of Aircraft Fault Diagnostic Technologymentioning

confidence: 99%

Research on the Fault Diagnostic of the Aircraft Cross-Linking Systems

2022

J. Phys.: Conf. Ser.

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High complexity of modern civil aircraft system leads to a huge difficulty to its trouble-shooting. This paper firstly discussed the two main directions of improving the aircraft fault diagnosis capability, and then analysed the pain points encountered by the model-based troubleshooting method. For the pain points, the Colour Fuzzy Fault Petri Net (CFFPN) model was proposed. The basic principles of the model and the basic process of conducting forward and reverse reasoning diagnosis based on the model were illustrated. Then the application of reasoning process in system design integration and airline maintenance was expounded. Finally, a practical software prototype of the model was developed, and effectiveness of the method and model was demonstrated through applications in engineering practice.

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Probabilistic transmission line fault diagnosis using autonomous neural models

Cited by 21 publications

References 33 publications

Fault Diagnosis for Power Grids Based on Stochastic Self-Regulating Algorithm Under Disaster-Causing Weather

Fault Diagnosis for Power Grids Based on Stochastic Self-Regulating Algorithm Under Disaster-Causing Weather

Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

Research on the Fault Diagnostic of the Aircraft Cross-Linking Systems

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