Fault Analysis in Wind Power using Discrete Wavelet Transform

2012 ◽  
Author(s):  
Atthapol Ngaopitakkul ◽  
Apichart Yodkhuang
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wind Power ◽  
Fault Analysis ◽  
Discrete Wavelet

scholarly journals Line to Ground and Line to Line Fault Analysis in IEEE Standard 9 Bus System

2020 ◽  
Vol 93 (1-4) ◽  
pp. 10-18
Author(s):  
Sagnik Datta ◽  
Aveek Chattopadhyaya ◽  
Surajit Chattopadhyay ◽  
Arabinda Das
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fault Analysis ◽  
Discrete Wavelet ◽  
Statistical Parameters ◽  
Fault Type ◽  
Protection Systems ◽  
Rule Set ◽  
Ieee Standard ◽  
Bus System

Line to Ground (LG) and Line to Line (LL) faults are the two most frequently encountered faults in any power system network. For the purpose of designing advanced protection systems, detection of the location as well as the identification of the type of fault, from a remote location is of paramount importance. In this paper a Discrete Wavelet Transform based statistical analysis has been carried out to detect the fault type and location of LG and LL faults. IEEE standard 9 bus system has been considered for this purpose. Faults are made to occur in the load buses and outgoing currents from the generator buses are analyzed by Discrete Wavelet Transform (DWT) as these current waveforms are non-stationary in nature. Statistical parameters are calculated from the approximate and detail coefficients which have been derived from the DWT. Based upon these parameters, a rule set has also been made. Simulation work is performed with the help of MATLAB. Methods proposed here can be helpful for designing better protection schemes.

scholarly journals Wind Power Short-Term Prediction Based on LSTM and Discrete Wavelet Transform

2019 ◽  
Vol 9 (6) ◽  
pp. 1108 ◽  
Author(s):  
Yao Liu ◽  
Lin Guan ◽  
Chen Hou ◽  
Hua Han ◽  
Zhangjie Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Wind Power ◽  
Short Term Memory ◽  
Wind Farms ◽  
Absolute Error ◽  
Percentage Error ◽  
Discrete Wavelet ◽  
Short Term

A wind power short-term forecasting method based on discrete wavelet transform and long short-term memory networks (DWT_LSTM) is proposed. The LSTM network is designed to effectively exhibit the dynamic behavior of the wind power time series. The discrete wavelet transform is introduced to decompose the non-stationary wind power time series into several components which have more stationarity and are easier to predict. Each component is dug by an independent LSTM. The forecasting results of the wind power are obtained by synthesizing the prediction values of all components. The prediction accuracy has been improved by the proposed method, which is validated by the MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) of experimental results of three wind farms as the benchmarks. Wind power forecasting based on the proposed method provides an alternative way to improve the security and stability of the electric power network with the high penetration of wind power.

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