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Meng Wang
School of Economics and Management, North China Electric Power University, Beijing 102206, China

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Journal article
Published: 04 August 2018 in Renewable Energy
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The safety operation and economic benefits of wind farms are paid more attention by industry and society. Therefore, it's necessary to evaluate the wind power projects to find the deviation between actual situation, forecast target and first-class level. The commonly used methods of post-evaluation are AHP and fuzzy comprehensive evaluation which have three problems to be solved. The first is AHP method can't represent the correlation among the indexes. The second is the uncertainty of project data and experts' judgment. The third is the rectangle membership function can't realize data classification between adjacent levels. ANP can describe the relationship between indicators to eliminate deviation caused by independent calculation. The trapezoidal membership function is useful for rapid classification data between adjacent levels by maximum membership degree. And the interval can utilize imperfect information to solve the limitation of point estimation. So this paper proposes ANP model and fuzzy comprehensive evaluation model based on trapezoid membership which are all improved by interval numbers to evaluate projects. The paper makes a calculation of Pinglu wind farm, and the result shows new model is more stable with accuracy and applicability for post-evaluation which can solve the problems such as incomplete information, data fluctuation and subjective judgment.

ACS Style

Meng Wang; Dongxiao Niu. Research on project post-evaluation of wind power based on improved ANP and fuzzy comprehensive evaluation model of trapezoid subordinate function improved by interval number. Renewable Energy 2018, 132, 255 -265.

AMA Style

Meng Wang, Dongxiao Niu. Research on project post-evaluation of wind power based on improved ANP and fuzzy comprehensive evaluation model of trapezoid subordinate function improved by interval number. Renewable Energy. 2018; 132 ():255-265.

Chicago/Turabian Style

Meng Wang; Dongxiao Niu. 2018. "Research on project post-evaluation of wind power based on improved ANP and fuzzy comprehensive evaluation model of trapezoid subordinate function improved by interval number." Renewable Energy 132, no. : 255-265.

Journal article
Published: 13 August 2017 in Energies
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Stable and accurate forecasting of icing thickness is of great significance for the safe operation of the power grid. In order to improve the robustness and accuracy of such forecasting, this paper proposes an innovative combination forecasting model using a modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) based on the variance-covariance (VC) weight determination method. Firstly, the initial weights and thresholds of BPNN are optimized by mind evolutionary computation (MEC) to prevent the BPNN from falling into local optima and speed up its convergence. Secondly, a bat algorithm (BA) is utilized to optimize the key parameters of SVM. Thirdly, the kernel function is introduced into an extreme learning machine (ELM) to improve the regression prediction accuracy of the model. Lastly, after adopting the above three modified models to predict, the variance-covariance weight determination method is applied to combine the forecasting results. Through performance verification of the model by real-world examples, the results show that the forecasting accuracy of the three individual modified models proposed in this paper has been improved, but the stability is poor, whereas the combination forecasting method proposed in this paper is not only accurate, but also stable. As a result, it can provide technical reference for the safety management of power grid.

ACS Style

Dongxiao Niu; Yi Liang; Haichao Wang; Meng Wang; Wei-Chiang Hong. Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method. Energies 2017, 10, 1196 .

AMA Style

Dongxiao Niu, Yi Liang, Haichao Wang, Meng Wang, Wei-Chiang Hong. Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method. Energies. 2017; 10 (8):1196.

Chicago/Turabian Style

Dongxiao Niu; Yi Liang; Haichao Wang; Meng Wang; Wei-Chiang Hong. 2017. "Icing Forecasting of Transmission Lines with a Modified Back Propagation Neural Network-Support Vector Machine-Extreme Learning Machine with Kernel (BPNN-SVM-KELM) Based on the Variance-Covariance Weight Determination Method." Energies 10, no. 8: 1196.