Fault diagnosis of electric power system transformer on CMAC neural network approach

2012 ◽  
Author(s):  
Neng-Sheng Pai ◽  
Yi-Chung Lai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power System ◽  
Electric Power ◽  
Electric Power System ◽  
Network Approach ◽  
Neural Network Approach ◽  
Cmac Neural Network

Internet/Intranet Based Remote Condition Monitoring and Fault Diagnosis Scheme and System for Steam Turboset

2005 ◽  
Vol 293-294 ◽  
pp. 365-372 ◽  
Author(s):  
Yong Yong He ◽  
Wen Xiu Lu ◽  
Fu Lei Chu
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Electric Power ◽  
Condition Monitoring ◽  
Economic Benefits ◽  
Electric Power System ◽  
The Internet ◽  
Time Data ◽  
On Line ◽  
Line Condition

The steam turboset is the key equipment of the electric power system. Thus, it is very important and necessary to monitor and diagnose the running condition and the faults of the steam turboset for the safe and normal running of the electric power system. In this paper, the Internet/Intranet based remote condition monitoring and fault diagnosis scheme is proposed. The corresponding technique and methods are discussed in detail. And a real application system is developed for the 300MW steam turboset. In this scheme, the system is built on the Internet/Intranet and the Client/Server construction and Web/Server model are adopted. The proposed scheme can guarantee real-time data acquisition and on-line condition analysis simultaneously. And especially, the remote condition monitoring and fault diagnosis can be implemented effectively. The developed system has been installed in a power plant of China. And the plant has obtained great economic benefits from it.

scholarly journals Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

2009 ◽  
Vol 19 (2) ◽  
pp. 303-315 ◽  
Author(s):  
Krzysztof Siwek ◽  
Stanisław Osowski ◽  
Ryszard Szupiluk
Keyword(s):  
Neural Network ◽  
Power System ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Weighted Averaging ◽  
Percentage Error ◽  
Network Approach ◽  
Three Solutions ◽  
Neural Network Approach ◽  
Ensemble Neural Network

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power SystemThe paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.

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