Lian J.-J.,Tianjin University |
Zhang Y.,Tianjin University |
Liu F.,Tianjin University |
Yu X.-H.,Hydrochina XIBEI Engineering Corporation
Zhendong yu Chongji/Journal of Vibration and Shock | Year: 2013
Vibration source compositions and their effects on powerhouse were investigated for a roof overflow hydropower station with a bulb tubular unit. Based on the theoretical analysis and prototype observation data, powerhouse structure vibration frequencies excited by the unit operation and flow discharge were identified, and dynamic load sources were determined. Furthermore, the contributions (%) of different frequencies were calculated when the vibrations of the main measured points reached the maximum values. The contributions of the measured frequency bands energy to whole vibration response were also examined. The change laws of different frequency vibrations versus the load were presented. The results showed that the slot jet impingement, the vibration of the runner blade and the vibration due to the incorrect connection relationship are the main vibration sources; the powerhouse vibration induced by flow discharge is small, and it has no effect on the powerhouse structure's operation and safety. The results provided a basis for evaluating this model hydropower station's powerhouse vibration with a certain level.
Yang G.,Hohai University |
Gu C.,Hohai University |
Huang Y.,Hydrochina XIBEI Engineering Corporation |
Yang K.,Hohai University
International Journal of Computational Intelligence Systems | Year: 2014
Abstract: Several potential network structures are chosen to do a large number of experimental analysis, historical data is divided into training sample and testing sample, and the corresponding neural network model is established with BP learning algorithm. After checking the testing sample, a superior network integration model which can be applied for hydraulic metal structure health grade diagnosing is determined. By plenty of experimental tests and verification analysis, it is concluded that the two-hidden-layer neural network model suits hydraulic metal structure health diagnosing better. As for the gate health diagnosing, based on Bagging technology, the BP neural network integration model for hydraulic metal structure health diagnosing is researched and constructed. The analysis of the sample showed that its accuracy rate (78%) is obviously better than the single neural network model(67%). The BP neural network integration model will work together with the FAHP model the author studied, that can make the diagnosis results more reasonable and reliable. © 2014, Copyright: the authors.
Jia R.,Xian University of Technology |
Zhang Y.,Hydrochina XIBEI Engineering Corporation |
Hong G.,Guangxi Power Grid Beihai Power Supply Bureau
Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | Year: 2010
An intelligent method for fault diagnosis of transformer based on least squares support vector machine(LSSVM) is proposed. In order to improve the accuracy of fault diagnosis, the improved particle swarm optimization(IPSO) algorithm is adopted to optimize the parameters of LSSVM algorithm. The improved IPSO algorithm can adjust the balance between global and local search capabilities suitably. Experiment results prove that the method not only gets good classification results, but also achieves higher diagnostic accuracy than normal LSSVM and BP neural network. Consequently, the IPSO-LSSVM model is proper in fault diagnosis of transformer.
Li X.,Xian Jiaotong University |
Li Z.,China Electric Power Research Institute |
Zhang Q.,Xian Jiaotong University |
Li J.,Hydrochina XIBEI Engineering Corporation
Gaodianya Jishu/High Voltage Engineering | Year: 2013
Spark resistance is a key parameter influencing the life and efficiency of switch delivering power. In order to measure spark resistance accurately, we proposed a method that obtained spark resistance of gas gap through optical analysis. Firstly, we used a spectrograph to measure the emission spectrum of spark discharge under a high voltage pulse. Based on the emission spectrum, the time-dependent electron temperature and density in different locations of the discharge channel were calculated; meanwhile, the conductivity of the spark channel in different stages of discharge was calculated in accordance with an improved formula raised by Spitzer. Then, through radial images of the spark channel taken by the spectrograph, we measured the length of spectrum to calculate the diameter of the spark channel. Using the conductivity and the diameter, we obtained the resistance of spark channel. The results showed that the conductivity increased rapidly at first and then decreased slowly till it kept at about 104 S/m; meanwhile, the spark resistance decreased sharply from insulation state to about 0.1 Ω and then kept steady. The steady values fitted well with those calculated by former empirical formulae. It is concluded that, as the current increases, the conductivity of spark channel keeps unchanged. The increase of spark radius will result in the decrease of spark resistance; meanwhile, the increasing pressure and decreasing electron temperature of spark channel will lead to the decrease of conductivity and the increase of spark resistance.
Cong L.,Changan University |
Song Y.,Changan University |
Meng X.,Hydrochina XIBEI Engineering Corporation
Advances in Science and Technology of Water Resources | Year: 2014
Taking the Zhen'an Pumped-storage Hydropower Station as an example, the mechanical parameters of a rock mass were calculated with the connectivity rate method and Hoek-Brown criterion. The results show that the calculated results of the two methods are close, the calculated values of the Hoek-Brown criterion are slightly smaller than those of the connectivity rate method, and the cohesion and internal friction coefficient calculated with the Hoek-Brown criterion are 13% and 3% smaller than those with the connectivity rate method, respectively. This indicates that the connectivity rate method is a simple and effective way to determine the mechanical parameters of rock mass. © 2014, Editorial Board of Advances in Science and Technology of Water Resources, Hohai University. All right reserved.