Risk Assessment of Cascading Outages in Power Systems Using Fuzzy Neural Network

2006 ◽  
pp. 1422-1427 ◽  
Author(s):  
Wei-Hua Chen ◽  
Quan-Yuan Jiang ◽  
Zhi-Yong Wang ◽  
Yi-Jia Cao
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Power Systems ◽  
Fuzzy Neural Network ◽  
Cascading Outages ◽  
Fuzzy Neural

Wind Speed Forecasting Based on FNN in Wind Farm

2014 ◽  
Vol 651-653 ◽  
pp. 1117-1122
Author(s):  
Zheng Ning Fu ◽  
Hong Wen Xie
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Power Plants ◽  
Fuzzy Neural Network ◽  
Negative Impact ◽  
Wind Speed Forecasting ◽  
Wind Power Plants ◽  
Fuzzy Neural

Wind speed forecasting plays a significant role to the operation of wind power plants and power systems. An accurate forecasting on wind power can effectively relieve or avoid the negative impact of wind power plants on power systems and enhance the competition of wind power plants in electric power market. Based on a fuzzy neural network (FNN), a method of wind speed forecasting is presented in this paper. By mining historical data as the learning stylebook, the fuzzy neural network (FNN) forecasts the wind speed. The simulation results show that this method can improve the accuracy of wind speed forecasting effectively.

A NEW FUZZY NEURAL NETWORK STRATEGY OF INSULATORS CONTAMINATION DETECTION FOR POWER SYSTEM TRANSMISSION LINE

2008 ◽  
Vol 22 (01) ◽  
pp. 55-64 ◽  
Author(s):  
YUPING LU ◽  
MIN YU ◽  
L. L. LAI ◽  
XIA LIN
Keyword(s):  
Neural Network ◽  
Power Systems ◽  
Fuzzy Neural Network ◽  
Online Monitoring ◽  
Condition Based Maintenance ◽  
Mean Square ◽  
Field Samples ◽  
Fuzzy Neural ◽  
Online Monitoring System ◽  
Surface Leakage

The detection of insulators contamination is difficult in power systems because many factors can influence the pollution. The contamination condition of insulators is usually estimated by detecting the root mean square (r.m.s) of surface leakage current via online-monitoring system. It ignores the influence of environmental factors, such as temperature, humidity, etc. As these factors are fuzzy-characterized, a new method based on Fuzzy Neural Network (FNN) is proposed to improve traditional insulation contamination detection. The renewed structure of FNN is put forward. The evaluation of contamination severity of insulators is achieved through FNN, which are trained by the field samples. The results prove the validity of the method proposed in the paper and can be used to eliminate the insulator from flashover fault and improve the condition-based maintenance (CBM).

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