Comprehensive Review of Intelligent Operation and Maintenance of Power System in China

Zhang, Jun; Fu, Yichun; Hu, Yang; Wang, Dongliang; Zhao, Yuji

doi:10.1007/978-3-030-78615-1_55

Cited by 1 publication

(1 citation statement)

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“…The implementation models, fundamental assumptions, and potential uses of artificial intelligence technology in the power system will serve as the starting points for the subsequent discussion. Zhang et al [ 14 ] illustrate the concept of an artificially intelligent power system that is used. It initially provides a thorough analysis of the concept before outlining how operation and maintenance have evolved inside the Chinese power system.…”

Section: Related Workmentioning

confidence: 99%

Application Based on Artificial Intelligence in Substation Operation and Maintenance Management

Xin

Zhang

Shi

2022

Computational Intelligence and Neuroscience

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To fulfill state grid Industry’s demands for smart and digitized business growth, traditional technological approaches have fallen short. Artificial intelligence (AI) technology enables coming up with solutions because electricity business types and volumes are constantly expanding and developing. Intelligent automation was a part of China’s smart grid development from the outset, and it continues to grow in the country’s electricity system. Smart substation operations and maintenance could benefit from the use of this system. There are new technological tools and theoretical concepts for the repair and control of power equipment owing to AI’s advancements in performance, accuracy, and self-learning capacity in the detection, forecasting, improvement, and judgment jobs. Substation operations and maintenance management are examined in this research using a new hybridized convolutional neural network and tweaked long short-term memory (HCNN-TLSTM) technique. Normalization is used to gather and preprocess the data immediately. Kernel-based linear discriminant analysis (K-LDA) is used to extract the features. A substation’s functioning and maintenance can then be investigated using the new approach. The genetic algorithm (GA) is used to improve the effectiveness of the proposed method. Finally, the presented technique’s performance is analyzed and compared with specific current models to achieve the largest performance in the proposed method for the management of substation operation and upkeep.

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Section: Related Workmentioning

confidence: 99%