Teng Yun

Notice of Retraction A Novel Power Predicting Model of Wind Farm Based on Double ANNs

To solve the problem of the variancy of the wind power when wind farm connect with the power grid, a wind power predicting model of wind farm based on double ANNs is proposed in the paper. Wind velocity and wind direction on wind farm are the key of wind power predicting, and other circumstance conditions such as temperature, humidity, atmospheric pressure, are also great influence on it. The observed values of these five circumstance conditions can be treated as a nonlinear time series and be analyzed by the nonlinear time series ANNs model. The wind power predicting model consists of double artificial neural networks. The first is consisted of five artificial neural networks which is used to prediction the circumstance conditions time series, the second is employed to prediction the power of wind farm use predicting value of the five conditions. A series of simulation show that the results of the predicting model is acceptable in engineering application.

Design and implementation of the forecasting system for wind farm power

With the large-scale wind farm in the Grid, the stability of power system is becoming the hot issue. It makes higher requirements to the accuracy of power put forward in the forecasting system. This paper is raised the forecasting model based on multi-Agent technology, given prediction algorithm module flow and used BP neural network to predict. According to the characteristics of self-learning Agent, the paper makes use of all interactivities of Agent and modifies the model of BP neural network constantly to make predictions more accurately. The result of actual system application show that the forecasting model based on multi-Agent technology is feasibility and effectiveness.

Study of Polluted Insulator Flashover Forecasting Based on Nonlinear Time Series Analysis

To solve the problem of the flashover forecasting of contaminated or polluted insulator, a flashover forecasting model of contaminated insulators based on nonlinear time series analysis is proposed in the paper. The equivalent salt deposit density (ESDD) is the key of flashover on polluted insulator. The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper. The forecasting model consists of two artificial neural networks that reflect relationship of environment, ESDD and flashover probability. The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover. A series of artificial pollution tests show that the results of the forecasting model is acceptable.

Study on application of the forecasting system for wind farm power

With the large-scale wind farm in the Grid, the stability of power system is becoming the hot issue. But the forecasting methods and development of the system is not enough perfect. So the paper is proposed the architecture of the Forecasting System for Wind Farm Power, and expounded the function of every module in the system. It is designed the class diagram of the Forecasting System for Wind Farm Power and the the forecasting methods based on BP. In the end, the application of the system is based on JAVA. The result is showed that the forecasting model based on JAVA technology is feasibility and effectiveness.

Study of wind farm power output predicting model based on nonlinear time series

To solve the problem of the wind power variancy when wind farm connect with the power grid, a wind power output predicting model based on nonlinear time series is proposed in the paper. Wind velocity and wind direction on wind farm are the key of wind power predicting, and other circumstance conditions such as temperature, humidity, atmospheric pressure are also influence greatly on it. Values of these five circumstance conditions can be treated as a nonlinear time series and be analyzed by ANNs model. The wind power predicting model consists of double artificial neural networks. The first is consisted of five artificial neural networks which is used to prediction the circumstance conditions time series, the second is employed to prediction the power of wind farm use predicting value of the five conditions. A series of simulation show that the results of the predicting model is acceptable in engineering application.

Power system transformer substation voltage stability of line assessment and arrangement scheme of pmu

Considered the optimum arrangement scheme of PMU in a region of electric network, this paper primary introduced the definition of voltage stabilitys weak nodes and their region through a tidal current emulation and calculation method. The optimum arrangement scheme of PMU in electric network was also given. The Least-square procedure method was used in solving the equivalent resistance, which completed the weak nodes dynamic voltage stability evaluation. The estimative accuracy of the scheme has been indicated according to the result of simulating test, which presents a theory method lying the foundation for the dynamic voltage stability evaluation of wide region electric power network.

A novel forecasting model of contaminated insulator flashover voltage

To solve the problem of the selecting of the external insulation under complex circumstance conditions, a flashover voltage forecasting model of contaminated insulators based on double ANNs is proposed in the paper. The equivalent salt deposit density (ESDD) is the key of flashover voltage on contaminated insulator, and circumstance conditions are also great influence on it. The equivalent salt deposit density (ESDD) value of insulator can be treated as a nonlinear time series and be forecasted by the nonlinear time series ANNs model. The flashover voltage forecasting model consists of two artificial neural networks. The first is used to forecast the equivalent salt deposit density (ESDD) time series and the second is employed to calculate the withstand voltage of insulator. A series of artificial pollution tests show that the results of the forecasting model is acceptable in engineering application.

Forecasting of polluted insulator flashover based on multivariate nonlinear time series analysis

To solve the problem of the flashover forecasting of contaminated or polluted insulator, a flashover forecasting model of contaminated insulators based on multivariate nonlinear time series analysis is proposed in the paper. The equivalent salt deposit density (ESDD) is the key of flashover on polluted insulator. The ESDD value of insulator can be forecasted by the method of nonlinear time series analysis of the ESDD time series and a forecasting model of polluted insulator flashover is proposed in the paper. The forecasting model consists of two artificial neural networks that reflect relationship of environment, ESDD and flashover probability. The first is used to estimate the ESDD time series of insulator and the second is employed to calculate the probability of the flashover. A series of artificial pollution tests show that the results of the forecasting model is acceptable.