Zhongneng Power Technology Development Co.

Beijing, China

Zhongneng Power Technology Development Co.

Beijing, China
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Zhu L.,Beijing University of Civil Engineering and Architecture | Yang K.,Beijing University of Civil Engineering and Architecture | Bai Y.,Monash University | Sun H.,China Architecture Design and Research Group | Wang M.,Zhong Neng Power Technology Development Company Ltd
Thin-Walled Structures | Year: 2017

This paper studies steel circular hollow section (CHS) X-joints by conducting experiments on the axial compressive strength of unreinforced and reinforced X-joints with external stiffening rings. Three pairs of unreinforced and reinforced X-joints were tested to compare their compressive load capacity. The diameter ratios (β) between the brace and the chord β were 0.25, 0.51 and 0.73 respectively. The experimental setup, parameters and results are presented. The failure modes and load-displacement curves of the unreinforced and reinforced X-joints were compared. It was shown that external stiffening rings greatly increased the axial compressive load capacity of the X-joints, by 86%, 75%, and 58% respectively. Finite element modelling accurately predicted the structural responses of the X-joints with and without external stiffening rings. © 2017 Elsevier Ltd

Chen N.,Beihang University | Qian Z.,Beihang University | Meng X.,Beihang University | Meng K.,Zhongneng Power Technology Development Co.
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | Year: 2013

Accurate wind speed forecasting is necessary for evaluating the safety and economy of the large scale wind farm integration. In this paper, a new multi-step ahead wind speed forecasting model is presented based on spatial correlation and support vector machine (SVM) method. First, a wind direction oriented spatial correlation model is established, of which the optimized input vectors are determined by correlation coefficient. Then in order to eliminate the influence of variable wind direction, SVM method is applied to combine with the former spatial correlation model based on an accurate analysis of how forecast error depends on wind direction. The calculation results, which are obtained by measured data from a wind farm, indicate that the proposed spatial-SVM model has a better performance in forecasting accuracy comparing to the basic SVM model and other classical forecasting models.

Zhao P.,Xi'an Jiaotong University | Xia J.,Xi'an Jiaotong University | Dai Y.,Xi'an Jiaotong University | He J.,Zhong Neng Power Technology Development Co.
Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010 | Year: 2010

In this paper the wind speed forecasting in a wind farm, applying the algorithm of support vector regression (SVR) to the mean 10-minute time series is presented. By comparing its performance with an back propagation neural network model through simulation results, we could find following facts: firstly, both algorithms are applicable for prediction the wind speed time series in future; secondly, the prediction effect of support vector regression outperforms the back propagation neural network model as indicated by the prediction graph and by the mean square errors and mean absolute errors. Finally, we selected three different stages of the wind speed curve to analyze, the results show that the proposed algorithm fit the original wind speed curve well at the whole process, but the back propagation neural network is inapplicability for the rise stage when the ascent rate suddenly become flatness of the original wind speed curve. © 2010 IEEE.

Sun H.,Zhong Neng Power Technology Development Co. | Li X.-W.,Xi'an Jiaotong University
Gaoya Dianqi/High Voltage Apparatus | Year: 2010

Chargeability is a crucial parameter for the design of ZnO arrester. For a 500 kV porcelain ZnO arrester, its potential distribution is calculated with finite element method, where reasonable boundary is set to simplify the solution of the open boundary field problem, and the strategy of coupled degrees of freedom is adopted to deal with the floating conductors. Then the potential distribution of the arrester is tested with optical fiber-current measurement method under the maximum continuous operating voltage. Compared with the calculation results, the validation of the proposed method is verified. Finally, the influences of the size of calculation boundary, the parameters of grading ring and the mounting height on potential distribution are analyzed, and the results demonstrate that the potential distribution coefficient of the upper and lower sections of MOA can be improved through increasing mounting height, and the lower grading ring has more significant influence on the potential distribution.

Zhao P.,Xi'an Jiaotong University | Wang J.,Xi'an Jiaotong University | Xia J.,Xi'an Jiaotong University | Dai Y.,Xi'an Jiaotong University | And 2 more authors.
Renewable Energy | Year: 2012

Wind power forecasting system is useful to increase the wind energy penetration level. Latest statistics show that China has been the biggest wind energy market throughout the world. However, few studies have been published to introduce the wind energy forecasting technologies in China. This paper presents the performance evaluation and accuracy enhancement of a novel day-ahead wind power forecasting system in China. This system consists of a numerical weather prediction (NWP) model and artificial neural networks (ANNs). The NWP model is established by coupling the Global Forecasting system (GFS) with the Weather Research and Forecasting (WRF) system together to predict meteorological parameters. In addition, Kalman filter has been integrated in this system to reduce the systematic errors in wind speed from WRF and enhance the forecasting accuracy. The numerical results from a real world case are proven the effectiveness of this forecasting system in terms of the raw wind speed correction and wind power forecasting accuracy. The Normalized Root Mean Square Error (NRMSE) has a month average value of 16.47%, which is an acceptable error margin for allowing the use of the forecasted values in electric market operations. This forecasting system is profitable for increasing the wind energy penetration level in China. © 2011 Elsevier Ltd.

Zhao P.,Xi'an Jiaotong University | Dai Y.,Xi'an Jiaotong University | Xia J.,Xi'an Jiaotong University | Sheng Y.,Zhong Neng Power Technology Development Co.
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | Year: 2011

A Kalman filter based correction model for short-term wind power prediction was proposed to solve the problem of wind energy prediction accuracy constraint induced by the systematic errors in meteorological parameters from the numerical weather prediction (NWP) model. The wind speed data from NWP were corrected dynamically by using the Kalman filter algorithm and the improved NWP set used for wind power prediction was formed by combining the corrected wind speed data with other meteorological data. The original neural network prediction model and the corrected neural network prediction model were trained by using the raw NWP set and the improved NWP set, respectively. The analysis on the comparison between the simulation data and the measured data in a same time interval shows that, the corrected wind speed series by the Kalman filter are very close to observed wind speed; the mean error and the mean absolute error are smaller; the root mean square error decreases from 17.73% to 11.32%. It seems that the wind power prediction model proposed has a clearly higher accuracy.

Wang M.,Zhong Neng Power Technology Development Co. | Zeng L.,Hebei Electric Power Research Institute
Shuili Fadian Xuebao/Journal of Hydroelectric Engineering | Year: 2011

This paper discusses the existing models of wind speed frequency distribution such as log-normal distribution model, Weibull distribution model and AR-GARCH model, focusing on their features and limitations. An analysis of actual speed frequency distribution of a wind farm suggests that AR-GARCH model is the best in describing the actual wind. This model can fully reflect the randomness and wave character of wind speed that varies with time, and with a universality of application it effectively reduces the large fitting errors of the other models in low-speed and zero-speed zones, particularly for those distribution curves with multiple peaks. Application of the model to three typical engineering projects verified its prediction ability and accuracy. © Copyright.

Wang D.,Zhong Neng Power Technology Development Co Ltd | Ding X.-J.,Zhong Neng Power Technology Development Co Ltd
Jiliang Xuebao/Acta Metrologica Sinica | Year: 2016

Aiming at the early fault feature extraction problem of mechanical vibration signal under noise background, a novel method based on envelope demodulation stochastic resonance and CEEMD is proposed. With the method, the mechanical fault signal with noise is processed by envelope demodulation, and then through stochastic resonance system the rescaling signals are enhanced. Finally the output result is decomposed by CEEMD, obtaining the fault feature components to realize feature extraction and fault diagnosis. The rolling bearing fault diagnosis example shows that the method can not only improve the signal amplitude and reduce the false component, but also improve CEEMD algorithm precision and effectively extract fault signal submerged in noise. © 2016, Acta Metrologica Sinica Press. All right reserved.

Wang D.,Zhong Neng Power Technology Development Co. | Ding X.-J.,Zhong Neng Power Technology Development Co.
Zhendong yu Chongji/Journal of Vibration and Shock | Year: 2015

A new method of adaptive multi-order tracking (AMOT) in rotating machinery was proposed based on the instantaneous frequency estimation with an adaptive peak search algorithm. The time-frequency distribution was obtained by using a certain time-frequency analysis method. According to the frequency peak coordinate, the starting point of peak search was selected automatically and time-frequency peaks of different orders were searched adaptively. The different frequency components were fitted to achieve the instantaneous frequency estimation by using the least square polynomial, and then the resampling time was calculated according to the reference component to resample the original signal. Finally multi-order tracking spectrums were obtained by FFT transform. By virtue of the method of instantaneous frequency estimation, the proposed method can automatically identify all-order components and choose the best one, avoiding the human error owing to choosing reference component and starting point subjectively in traditional algorithms. The method can identify the components of different orders without sampling synchronously the speed signal, greatly simplify the application conditions and improve the accuracy of analysis. It provides a new method for fault diagnosis of rotating machinery. The method was proved to be effective by simulation examples and practical applications. ©, 2015, Chinese Vibration Engineering Society. All right reserved.

Sheng Y.,Zhong Neng Power Technology Development Co. | Zhou J.,Zhong Neng Power Technology Development Co.
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | Year: 2010

Based on the method of spectrum analysis and envelop spectrum analysis, an online wind turbine vibration monitoring system is developed which can diagnose mechanical faults and damages of the wind turbines (the mainshaft, gearbox and generator faults) effectively. It can provide a report of the equipment performance and reasonable maintenance advice which help to manage the equipment scientifically and shorten the nonscheduled shutdown time and improve the availability of the wind turbines.

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