Wind turbine generator set ultra-short period wind power prediction method based on improved RBF network

Abstract

The invention discloses a wind turbine generator set ultra-short period wind power prediction method based on an improved RBF network. A wind turbine generator set is adopted for the operation of statistic data, parameters closely influencing the output of wind power are reasonably selected, such as the wind speed, wind direction, propeller pitch angle and wind power of the previous period, and a manual neural network-a radial basis function network is utilized to establish a model of corresponding relations between related parameters and the wind power output; and the improved RBF network is utilized to modify the model, whether the node number of a current hidden layer meets a precision requirements is judged, whether the output of nodes of one hidden layer is smaller than one value in a continuous period of learning is judged, the node number of the hidden layer is modified on line in real time, and new learning samples are continuously added along with the development of prediction. The wind power prediction method is high in precision and high in speed.

Description

technical field [0001] The invention belongs to the field of energy power and electrical control, belongs to the technical field of ultra-short-term forecasting of wind farm power, and relates to the establishment of an artificial neural network based on wind farm operating data—radial basis function network (RBF) and its improved model for power output The correction of the model provides a reference for the operation and management of wind farms. Background technique [0002] The installed capacity of wind power in my country is increasing year by year, the scale of wind farms is expanding, and the impact of wind power on the power grid is becoming more and more significant; especially the randomness of wind is large, which is not conducive to power grid dispatching and safe operation. Therefore, the prediction of wind power plays an important role in reducing the adverse impact of wind power on the grid. [0003] The power system requires that the wind power prediction s...

AI Technical Summary

Problems solved by technology

Using the classical neural network can effectively predict the output power of wind power, which has been verified in previous work, but the number of learning samples and the number of hidden layer nodes in the classical network have been determined before the start of learning. None of them will change, that is, they cannot follow the changes of the input conditions, the number of iterations is large, the learning time is long, the requirements for computer software and hardware are high, and it is difficult to cope with the prediction of wind farm power such as time-varying wind farm output performance.

Images