Differential evolution-based optimized hierarchical extreme learning machines for fault section diagnosis of large-scale power systems

Xiong, Guojiang; Xie, Xuan; Yuan, Zixia; Fu, Xiaofan

doi:10.1016/j.eswa.2023.120937

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The work presents a method of data processing and then, using machine learning, their appropriate division in order to classify damage. The issue of appropriate diagnosis of faults in the system was also dealt with in [168][169][170][171][172][173]. In [173], fault diagnosis was proposed based on noise-assisted multi-variate empirical mode decomposition (NA-MEMD) and multi-level iterative-LightGBM (MI-LightGBM).…”

Section: Identification and Analysis Related To Power System Disturba...mentioning

confidence: 99%

“…Appropriate diagnostics and quick action in the event of fault section diagnosis (FSD) are essential for the proper operation of the power system. The authors of [172] proposed a diagnostic model designed for extreme learning. In the authors' work, hierarchical extreme learning machines (HELM) are responsible not only for performing diagnostics of the internal sections of the subsystem themselves, but also for performing diagnostics of adjacent connections.…”

Section: Identification and Analysis Related To Power System Disturba...mentioning

confidence: 99%

“…Deep learning [167,180] Neural networks [168][169][170][171][172][174][175][176][177][178][179] Classification [173,[184][185][186] Expert system [187][188][189] Fuzzy logic [170,[181][182][183]…”

Section: Supervised Learningmentioning

confidence: 99%

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Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

Belowski

2024

Energies

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The challenges currently faced by network operators are difficult and complex. Presently, various types of energy sources with random generation, energy storage units operating in charging or discharging mode and consumers with different operating characteristics are connected to the power grid. The network is being expanded and modernised. This contributes to the occurrence of various types of network operating states in practice. The appearance of a significant number of objects with random generation in the power system complicates the process of planning and controlling the operation of the power system. It is therefore necessary to constantly search for new methods and algorithms that allow operators to adapt to the changing operating conditions of the power grid. There are many different types of method in the literature, with varying effectiveness, that have been or are used in practice. So far, however, no one ideal, universal method or methodology has been invented that would enable (with equal effectiveness) all problems faced by the power system to be solved. This article presents an overview and a short description of research works available in the literature in which the authors have used modern methods to solve various problems in the field of power engineering. The article is an introduction to the special issue entitled Advances in the Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering. It is an overview of various current problems and the various methods used to solve them, which are used to cope with difficult situations. The authors also pointed out potential research gaps that can be treated as areas for further research.

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Section: Identification and Analysis Related To Power System Disturba...mentioning

confidence: 99%

Section: Identification and Analysis Related To Power System Disturba...mentioning

confidence: 99%

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Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Pijarski,

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2024

Energies

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Exponential hybrid mutation differential evolution for economic dispatch of large-scale power systems considering valve-point effects

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et al. 2023

Appl Intell

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Many-objective coevolutionary learning algorithm with extreme learning machine auto-encoder for ensemble classifier of feedforward neural networks

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Differential evolution-based optimized hierarchical extreme learning machines for fault section diagnosis of large-scale power systems

Cited by 6 publications

References 29 publications

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue

Exponential hybrid mutation differential evolution for economic dispatch of large-scale power systems considering valve-point effects

Many-objective coevolutionary learning algorithm with extreme learning machine auto-encoder for ensemble classifier of feedforward neural networks

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