Metal Quality Control and Reliability of Aging Equipment in Thermal Power Plants

Grin, E. A.; Sarkisyan, V. A.; Bochkarev, V. I.

doi:10.1007/s10749-021-01401-4

Cited by 1 publication

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because the minimum load loss percentage not only conforms to the safety limit of the model, but also takes into account the state of the model after model failure and the maximum value of the model load loss, the minimum load loss percentage is the most accurate embodiment of the model in the safe operation of the model after an accident, and can more truly reflect the severity of the model in terms of the model load loss percentage, the maximum power range of the model, the maximum transmission capacity, etc. [17]. There is a certain difference between the impact of the existing power equipment operation capacity evaluation index on the cascading fault of power equipment and the actual operation state.…”

Section: Statementioning

confidence: 99%

Construction of power equipment fault feature model based on unified semantic expression

Pei¹,

Peng²,

Chen³

et al. 2023

International Conference on Mechatronics Engineering and Artificial Intelligence (MEAI 2022)

View full text Add to dashboard Cite

In view of the poor recognition effect of power equipment fault features in China, a method for building power equipment fault feature model based on unified semantic expression is proposed. The power equipment fault information is identified by combining the unified semantic expression principle. And the phase space reconstruction algorithm is constructed according to the feature semantics of the identified fault information. The power equipment fault feature model is optimized based on the reconstruction results. Finally, it is verified by experiments, the power equipment fault feature model based on unified semantic expression can quickly identify the semantic features of fault information in the process of practical application, and effectively improve the recognition effect.

show abstract

Section: Statementioning

confidence: 99%