scholarly journals A Hybrid Approach for Multi-Step Wind Speed Forecasting Based on Multi-Scale Dominant Ingredient Chaotic Analysis, KELM and Synchronous Optimization Strategy

2019 ◽  
Vol 11 (6) ◽  
pp. 1804 ◽  
Author(s):  
Wenlong Fu ◽  
Kai Wang ◽  
Jianzhong Zhou ◽  
Yanhe Xu ◽  
Jiawen Tan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Singular Spectrum Analysis ◽  
Hybrid Approach ◽  
Predictive Performance ◽  
Hybrid Structure ◽  
Forecasting Model ◽  
Grey Wolf Optimizer ◽  
Optimization Strategy ◽  
Multi Scale

Accurate wind speed prediction plays a significant role in reasonable scheduling and the safe operation of the power system. However, due to the non-linear and non-stationary traits of the wind speed time series, the construction of an accuracy forecasting model is difficult to achieve. To this end, a novel synchronous optimization strategy-based hybrid model combining multi-scale dominant ingredient chaotic analysis and a kernel extreme learning machine (KELM) is proposed, for which the multi-scale dominant ingredient chaotic analysis integrates variational mode decomposition (VMD), singular spectrum analysis (SSA) and phase-space reconstruction (PSR). For such a hybrid structure, the parameters in VMD, SSA, PSR and KELM that would affect the predictive performance are optimized by the proposed improved hybrid grey wolf optimizer-sine cosine algorithm (IHGWOSCA) synchronously. To begin with, VMD is employed to decompose the raw wind speed data into a set of sub-series with various frequency scales. Later, the extraction of dominant and residuary ingredients for each sub-series is implemented by SSA, after which, all of the residuary ingredients are accumulated with the residual of VMD, to generate an additional forecasting component. Subsequently, the inputs and outputs of KELM for each component are deduced by PSR, with which the forecasting model could be constructed. Finally, the ultimate forecasting values of the raw wind speed are calculated by accumulating the predicted results of all the components. Additionally, four datasets from Sotavento Galicia (SG) wind farm have been selected, to achieve the performance assessment of the proposed model. Furthermore, six relevant models are carried out for comparative analysis. The results illustrate that the proposed hybrid framework, VMD-SSA-PSR-KELM could achieve a better performance compared with other combined models, while the proposed synchronous parameter optimization strategy-based model could achieve an average improvement of 25% compared to the separated optimized VMD-SSA-PSR-KELM model.

scholarly journals Vibration Tendency Prediction Approach for Hydropower Generator Fused with Multiscale Dominant Ingredient Chaotic Analysis, Adaptive Mutation Grey Wolf Optimizer, and KELM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-20 ◽  
Author(s):  
Wenlong Fu ◽  
Kai Wang ◽  
Jiawen Tan ◽  
Kaixuan Shao
Keyword(s):  
Singular Spectrum Analysis ◽  
Hybrid Approach ◽  
Vibration Signal ◽  
Adaptive Mutation ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Positive Role ◽  
Vibrational Signal ◽  
Time Frequency ◽  
Prediction Approach

Accurate vibrational tendency forecasting of hydropower generator unit (HGU) is of great significance to guarantee the safe and economic operation of hydropower station. For this purpose, a novel hybrid approach combined with multiscale dominant ingredient chaotic analysis, kernel extreme learning machine (KELM), and adaptive mutation grey wolf optimizer (AMGWO) is proposed. Among the methods, variational mode decomposition (VMD), phase space reconstruction (PSR), and singular spectrum analysis (SSA) are suitably integrated into the proposed analysis strategy. First of all, VMD is employed to decompose the monitored vibrational signal into several subseries with various frequency scales. Then, SSA is applied to divide each decomposed subseries into dominant and residuary ingredients, after which an additional forecasting component is calculated by integrating the residual of VMD with all the residuary ingredients orderly. Subsequently, the proposed AMGWO is implemented to simultaneously adapt the intrinsic parameters in PSR and KELM for all the forecasting components. Ultimately, the prediction results of the raw vibration signal are obtained by assembling the results of all the predicted prediction components. Furthermore, six relevant contrastive models are adopted to verify the feasibility and availability of the modified strategies employed in the proposed model. The experimental results illustrate that (1) VMD plays a positive role for the prediction accuracy promotion; (2) the proposed dominant ingredient chaotic analysis based on the realization of time-frequency decomposition can further enhance the capability of the forecasting model; and (3) the appropriate parameters for each forecasting component can be optimized by the proposed AMGWO effectively, which can contribute to elevating the forecasting performance distinctly.

Sensitivity of Array-Like and Optimized Wind Farm Output to Key Factors and Choice of Wake Models

2013 ◽  
Author(s):  
Weiyang Tong ◽  
Souma Chowdhury ◽  
Ali Mehmani ◽  
Jie Zhang ◽  
Achille Messac
Keyword(s):  
Power Generation ◽  
Sensitivity Analysis ◽  
Wind Speed ◽  
Power Output ◽  
Wind Farm ◽  
Generation Model ◽  
Optimization Strategy ◽  
Key Factors ◽  
Wind Farm Design ◽  
Farm Design

The creation of wakes, with unique turbulence characteristics, downstream of turbines significantly increases the complexity of the boundary layer flow within a wind farm. In conventional wind farm design, analytical wake models are generally used to compute the wake-induced power losses, with different wake models yielding significantly different estimates. In this context, the wake behavior, and subsequently the farm power generation, can be expressed as functions of a series of key factors. A quantitative understanding of the relative impact of each of these factors is paramount to the development of more reliable power generation models; such an understanding is however missing in the current state of the art in wind farm design. In this paper, we quantitatively explore how the farm power generation, estimated using four different analytical wake models, is influenced by the following key factors: (i) incoming wind speed, (ii) land configuration, and (iii) ambient turbulence. The sensitivity of the maximum farm output potential to the input factors, when using different wake models, is also analyzed. The extended Fourier Amplitude Sensitivity Test (eFAST) method is used to perform the sensitivity analysis. The power generation model and the optimization strategy is adopted from the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. In the case of an array-like turbine arrangement, both the first-order and the total-order sensitivity analysis indices of the power output with respect to the incoming wind speed were found to reach a value of 99%, irrespective of the choice of wake models. However, in the case of maximum power output, significant variation (around 30%) in the indices was observed across different wake models, especially when the incoming wind speed is close to the rated speed of the turbines.

scholarly journals Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

2018 ◽  
Vol 10 (12) ◽  
pp. 4601 ◽  
Author(s):  
Yuewei Liu ◽  
Shenghui Zhang ◽  
Xuejun Chen ◽  
Jianzhou Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Wind Farm ◽  
Linear Models ◽  
Search Algorithm ◽  
Forecasting Model ◽  
Neural Network Models ◽  
Wind Speed Forecasting ◽  
Model Based

The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, it is found that the time series of wind speed demonstrate not only linear features but also nonlinear features. Hence, a combined forecasting model based on an improved cuckoo search algorithm optimizes weight, and several single models—linear model, hybrid nonlinear neural network, and fuzzy forecasting model—are developed in this paper to provide more trend change for time series of wind speed forecasting besides improving the forecasting accuracy. Furthermore, the effectiveness of the proposed model is proved by wind speed data from four wind farm sites and the results are more reliable and accurate than comparison models.

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