Investigations On Short-term Reliability Prediction Models Of Wind Turbines And Active Power Control Strategies Of Wind Farms

As one of the most important and mature renewable energy,wind energy developedrapidly in the word range in recent years. However, economic operation of wind farmsis affected by the reliabilityof WTs and constraint of power grid. On one hand, theoutage rate of wind turbines is much higher than tranditional electrical components dueto the harsh natural environment. On the other hand, part of energy loss under theconstraint of power grid due to the error of wind speed prediction and WT outages.Although related technologies are developed in recent years, there are still manyproblems to be solved. The distribution of forecast error of wind speed and wind power,anomaly identification method of condition monitoring parameters of wind turbines,short-term reliability assessment method for wind turbines, and the economicdispatching method of wind turbines in wind farms are presented in this thesis. Themain contents are shown as follows:①A Kernel density distribution model of forecasting error was proposed, and therelationship between among forecasting error distribution characteristics,wind speedand forecastingtime interval was studied. A variety of typical methods were utilized toforecast wind speed and the forecasting accuracy of single wind turbinewas analyzed incomparison with that of wind farms. Then a power curve based on actual measurementwas established and wind power was forecasted with the wind speed forecastingmethods. Results show that with three forecasting methods the forecasting error in thesingle wind turbine level is significantly greater than that in the wind farm level; theforecasting error increases when the forecasting time interval increases; the differenceamong forecasting error distributions in different wind speed ranges is quite significant.②A generalized fuzzy abnormal identification model for wind turbines wasproposed. The correlation relationship between wind turbine parameters provided by theSCADA system and the natural environment or the wind turbine operationcharacteristics was studied. Then a forecasting model for parameters was establishedand the main factors influencing the accuracy were analyzed. The forecasting accuracyof the native recent data model of the local wind turbines, the history data model of thelocal wind turbines and the recent data modelof other wind turbines were analyzed andcompared to each other. A selection method for the forecasting model and a quantitativemethod for the degree of abnormality of forecasting residual error were put forward. Finally, the fuzzy comprehensive evaluation was utilized to identify the abnormalparameters. The results show that theaccuracy of identifying abnormal parameters ishigher when the identifying results of abnormal state of multiple forecasting models aretaken into account.③Considering the correlation between wind speed and wind turbine outage rate,a wind turbine outage model with the consideration of wind speed was established. Forthe parameters closely related to the natural environment, an off-limitprotectionactionmodel based on parameters probability forecastwas presented.Accordingly, for the parameters which have setting time of protection action, anoff-limit protectionactionmodelbased on the time of off-limit was present. Finally,considering the limit protection probability and the statistical outage probability of eachstate parameter comprehensively, a wind turbine reliability evaluation modelconsidering the parameters limit was put forward. The results show that the accuracy ofthe short-termoutage model for the wind turbine model is much higher by calculatingthe parameters off-limit probability.④An effective controlling method for active power was presented consideringpower forecasting error andshort-term reliability of wind turbines. Without the gridconstrains, the short-term outage probability can be reduced by implementing thepower-reduced control measures on the wind turbines with low reliability. For thelimited power operating condition, the risk of power loss was proposed to quantifypossible power loss. Finally, based on the concept of monte carlo simulation, thecalculating method for the risk of multiple wind turbines power loss was presented, thusthe relation between the risk of single wind power loss and the wind farm total powerloss was obtained. Besides, the coordinated control strategy of wind farm active powerunder limited power conditionwas presented. The results show that the methodsmentioned in this thesis effectively reduce the power loss of wind farms.The above work is active exploration for reducing the high cost of operation andmaintenance of wind farm. The methods proposed in this thesis not only enrich researchin related fields, but also offer feasible solution to efficient use of large-scale windpower.