scholarly journals Wavelet Packet Decomposition for IEC Compliant Assessment of Harmonics under Stationary and Fluctuating Conditions

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4389 ◽  
Author(s):  
Stefano Lodetti ◽  
Jorge Bruna ◽  
Julio J. Melero ◽  
José F. Sanz
Keyword(s):  
Power Systems ◽  
Wavelet Packet ◽  
High Harmonic ◽  
Harmonic Distortion ◽  
Superior Performance ◽  
Infinite Impulse Response ◽  
Wavelet Packet Decomposition ◽  
Decomposition Structure ◽  
Stationary Conditions ◽  
Impulse Response Filters

This paper presents the validation and characterization of a wavelet based decomposition method for the assessment of harmonic distortion in power systems, under stationary and non-stationary conditions. It uses Wavelet Packet Decomposition with Butterworth Infinite Impulse Response filters and a decomposition structure, which allows the measurement of both odd and even harmonics, up to the 63rd order, fully compliant with the requirements of the IEC 61000-4-7 standard. The method is shown to fulfil the IEC accuracy requirements for stationary harmonics, obtaining the same accuracy even under fluctuating conditions. Then, it is validated using simulated signals with real harmonic content. The proposed method is proven to be fully equivalent to Fourier analysis under stationary conditions, being often more accurate. Under non-stationary conditions, instead, it provides significantly higher accuracy, while the IEC strategy produces large errors. Lastly, the method is tested with real current and voltage signals, measured in conditions of high harmonic distortion. The proposed strategy provides a method with superior performance for fluctuating harmonics, but at the same time IEC compliant under stationary conditions.

scholarly journals Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 1997
Author(s):  
Hua Wang ◽  
Wenchuan Wang ◽  
Yujin Du ◽  
Dongmei Xu
Keyword(s):  
Wavelet Packet ◽  
Moving Average ◽  
Back Propagation ◽  
Processing Technique ◽  
Annual Rainfall ◽  
Wavelet Packet Decomposition ◽  
Rainfall Forecasting ◽  
Promising Alternative ◽  
Forecasting Models ◽  
Precipitation Prediction

Accurate precipitation prediction can help plan for different water resources management demands and provide an extension of lead-time for the tactical and strategic planning of courses of action. This paper examines the applicability of several forecasting models based on wavelet packet decomposition (WPD) in annual rainfall forecasting, and a novel hybrid precipitation prediction framework (WPD-ELM) is proposed coupling extreme learning machine (ELM) and WPD. The works of this paper can be described as follows: (a) WPD is used to decompose the original precipitation data into several sub-layers; (b) ELM model, autoregressive integrated moving average model (ARIMA), and back-propagation neural network (BPNN) are employed to realize the forecasting computation for the decomposed series; (c) the results are integrated to attain the final prediction. Four evaluation indexes (RMSE, MAE, R, and NSEC) are adopted to assess the performance of the models. The results indicate that the WPD-ELM model outperforms other models used in this paper and WPD can significantly enhance the performance of forecasting models. In conclusion, WPD-ELM can be a promising alternative for annual precipitation forecasting and WPD is an effective data pre-processing technique in producing convincing forecasting models.

scholarly journals Adaptive frequency scaled wavelet packet decomposition for frog call classification

2016 ◽  
Vol 32 ◽  
pp. 134-144 ◽  
Author(s):  
Jie Xie ◽  
Michael Towsey ◽  
Jinglan Zhang ◽  
Paul Roe
Keyword(s):  
Wavelet Packet ◽  
Wavelet Packet Decomposition ◽  
Call Classification
Sign in / Sign up

Export Citation Format

Share Document