Methods and systems for optimizing operation of a wind farm are disclosed. The method includes providing a farm-level wake model for the wind farm based on historical wake parameters corresponding to reference sets of interacting wind turbines in the wind farm. Another step includes monitoring one or more real-time wake parameters for wind turbines in the wind farm. A further step includes identifying at least two interacting wind turbines from the reference sets based on the wake parameters. Another step includes determining a wake offset angle between the interacting wind turbines as a function of at least one of a wind direction, a geometry between the interacting wind turbines, or a wake meandering component. The method also includes continuously updating the wake model online based at least partially on the wake parameters and the wake offset angle and controlling the interacting wind turbines based on the updated wake model.

Systems and methods for optimizing operation of a wind farm

A method and apparatus for forecasting output power of wind turbine in a wind farm. The present invention provides a method for forecasting output power of a wind turbine in a wind farm, including: generating a corrected data set based on environmental data collected from at least one sensor in the wind farm; correcting a weather forecasting model by using the corrected data set; obtaining a forecast value of wind information at the wind turbine based on the corrected weather forecasting model; and forecasting the output power of the wind turbine based on the forecast value and a power forecasting model.

Forecasting output power of wind turbine in wind farm

The invention relates to a wind electric power prediction method and a device thereof. The method comprises the following steps of: step one: extracting data from SCADA (Supervisory Control and Data Acquisition) relative to a numerical weather prediciton system or a power system, and carrying out smoothing processing on the extracted data; step two: determining input and output of training samples of a least squares support vector machine according to the processed data; step three: initializing relevant parameters of a smallest squares support vector machine and an improved self-adaptive particle swarm algorithm; step four: optimizing model parameters according to an optimization process; step five: acquiring a model of the smallest squares support vector machine according to the optimized parameters; and step six: carrying out prediction according to the model of the smallest squares support vector machine. According to the wind electric power prediction method disclosed by the invention, a modelling process is simple and practical, wind electric power can be rapidly and effectively predicted, and the wind electric power prediction method has an important significance on safety and stability, and scheduling and running of the electric power system, and therefore, the wind electric power prediction method has wide popularization and application values.

Wind electric power prediction method and device thereof

The invention relates to the field of wind power generation, and provides a sharing maintenance system ensuring normal operation of wind turbines and a realization method thereof The sharing maintenance system can rapidly diagnose the failure state of the wind turbines and effectively solve the problems of difficult building of the spare parts store of each wind power plant and difficult technical training for maintenance personnel The sharing maintenance system is characterized in that the system comprises a wind turbine state monitoring device and a central data server, wherein the central data server is mainly composed of a wind turbine state analyzing unit, a user management unit, an inventory management unit, a transaction management unit, a technical training unit and a comprehensive analysis inquiring unit The central data server is provided with a client-side interactive interface, and a user can use the client-side interactive interface to operate on a client-side computer through a network By adopting the system, the sharing and the regulation of spare part inventory among different wind power plants can be realized The system provides a professional assurance for wind power plant hosts to rapidly diagnose the failure state of the system and produces a great promotion role for increasing the operation reliability, safety and economic benefit of the wind power plants

Sharing maintenance system ensuring normal operation of wind turbines and realization method thereof

The invention provides a super short-period wind power prediction method. The method mainly comprises the following steps: S10, obtaining a wind power prediction result based on numerical weather prediction (NWP) and a wind power prediction result based on wind power farm historic/real time data, and correcting the error of the wind power prediction result based on the NWP in real time; S20, adopting an empirical distribution model, respectively building a prediction error absolute value probability distribution based on an NWP power prediction method at the time and a prediction error absolute value probability distribution based on a wind power farm historic/real time data prediction method; step 30, calculating the wind power prediction result based on the NWP at the time in a prediction time period and a weighting coefficient of the prediction result based on the wind power farm historic/real time data, and obtaining the prediction result of the time; and repeating step S20 and S30 until completing all the prediction time period, and accordingly, obtaining the prediction result of the prediction time period.

Super short-period wind power prediction method

Ultra-short term wind power station generated power forecasting system

Expert system for designing wind power station

Method for forecasting wind speed in wind power station

The utility model provides a wind farm design expert system, which belongs to the technical field of expert system design and comprises an existing wind farm project anemometry data module, an existing wind farm project weather station data module, a new project original input data module for inputting new constructed wind farm original data, a wind source analyzing module, a wind turbine unit data module and a wind turbine unit model selection module. The wind source analyzing module is used for generating a wind source analyzing graph by using original data provided by the new project original input data module and referring to relevant data of multiple existing wind farms near a new constructed wind farm, and the relevant data are provided by the existing wind farm project anemometry data module and the existing wind farm project weather station data module. The wind turbine unit data module is used for building a wind turbine unit data form. The wind turbine unit model selection module recommends an optimum wind turbine unit model and arrangement according to wind source data obtained by analyzing the wind source analyzing module and by adopting wind turbine unit data provided by the wind turbine unit data module. The system solves the technical problem that the wind farm can be designed reasonably and accurately.

Wang Xiaolin

Papers

Ultra-short term wind power station generated power forecasting system

Expert system for designing wind power station

Method for forecasting wind speed in wind power station

Wind farm design expert system

Wind field planning method for flat region