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Wang F.,North China Electrical Power University | Mi Z.,North China Electrical Power University | Su S.,Yunnan Electric Power Research Institute | Zhao H.,North China Electrical Power University
Energies | Year: 2012

Short-term solar irradiance forecasting (STSIF) is of great significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants. However, STSIF is very complex to handle due to the random and nonlinear characteristics of solar irradiance under changeable weather conditions. Artificial Neural Network (ANN) is suitable for STSIF modeling and many research works on this topic are presented, but the conciseness and robustness of the existing models still need to be improved. After discussing the relation between weather variations and irradiance, the characteristics of the statistical feature parameters of irradiance under different weather conditions are figured out. A novel ANN model using statistical feature parameters (ANN-SFP) for STSIF is proposed in this paper. The input vector is reconstructed with several statistical feature parameters of irradiance and ambient temperature. Thus sufficient information can be effectively extracted from relatively few inputs and the model complexity is reduced. The model structure is determined by cross-validation (CV), and the Levenberg-Marquardt algorithm (LMA) is used for the network training. Simulations are carried out to validate and compare the proposed model with the conventional ANN model using historical data series (ANN-HDS), and the results indicated that the forecast accuracy is obviously improved under variable weather conditions. © 2012 by the authors.

Wang K.,Yunnan Electric Power Research Institute
Gaoya Dianqi/High Voltage Apparatus | Year: 2013

According to the analysis of application features for different detection techniques, the comprehensive diagnostic strategy of three step sequences is summarized focusing on rapid detection of exception, and confirming exception and position using electrical detection technology, and confirming exception again using assisted detection technology. An insulation defect of 110 kV GIS is tested and analyzed following the comprehensive diagnostic strategy. The results verify the effectiveness of the comprehensive diagnostic strategy which provides a new comprehensive diagnostic strategy for insulation defects diagnosis to GIS in operation.

Li Y.,Yunnan Electric Power Research Institute
Proceedings of 2010 World Non-Grid-Connected Wind Power and Energy Conference, WNWEC 2010 | Year: 2010

Vibration signals from the gearbox of a wind turbine are essentially non-stationary and nonlinear in both time and frequency. Empirical Mode Decomposition (EMD) is an ideal method for dealing with this type of signal. Yet the signal containing the fault information was contaminated by the noise, which contains two different types of white noise and impact noise. This makes it so the vibration signal cannot be processed with EMD directly, since it will produce the spurious IMF (Intrinsic Mode Function). The signal has to be pre-processed before implementing EMD. In fact, a wavelet filter is perfect for white noise de-noising and the morphological filter is suitable for impulse interference. In this paper, a confederative filter, which is combined with the wavelet and morphological filter, is designed for signal preprocessing, and a standard processing program is proposed too. An experimental case shows the accuracy and efficiency of the confederative filter and the process program. © 2010 IEEE.

Wang Z.,North China Electrical Power University | Wang F.,North China Electrical Power University | Su S.,Yunnan Electric Power Research Institute
Energy Procedia | Year: 2011

A short-term solar irradiance prediction model is established based on BP neural network and time series. Firstly, several different network structures of the solar irradiance prediction model are established based on BP neural network. Secondly, the most suitable neural network is gained by the comparison of different network structures and cross-validation. Lastly, the chosen model is trained through setting the suitable network parameters. This model can avoid over-fitting and help to gain a more accurate solar irradiance prediction model. The result of simulation indicates that the model can be effectively used for short-term solar irradiance predicting © 2011 Published by Elsevier Ltd.

Li Y.,Yunnan Electric Power Research Institute
ICMREE2011 - Proceedings 2011 International Conference on Materials for Renewable Energy and Environment | Year: 2011

There is a constant need for the reduction of operational and maintenance costs of the onshore or offshore wind turbine (WT) as it suffers higher reliability risk being exposed to extreme running environment and subject to constantly variable loadings, an efficient condition monitoring system (CMS) is indispensable. Unfortunately, the majority CMS for WT are borrowed from other industry fields (such as the thermal power industry) where they achieve success, however, to date these CMS have not proved entirely satisfactory in wind industry. This article makes the analysis on the possible reasons on it, and discussion on the principles of CMS which apply to the wind energy industry. © 2011 IEEE.

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