Short-term wind power forecasting method based on cloud evolutionary particle swarm optimization

Abstract

The invention provides a short-term wind power prediction method based on a cloud evolutionary particle swarm algorithm, including the following steps: S1, building a feed-forward neural network prediction model; S2, measuring the wind speed and direction at a prediction position; S3, getting an initial prediction solution set of the output power of a wind turbine; S4, forming an initialized particle swarm; S5, building a cloud evolution model; S6, generating a first updated particle swarm through the cloud evolution model; S7, updating the first updated particle swarm to get a second updated particle swarm; S8, judging whether the second updated particle swarm satisfies an expected value; if the second updated particle swarm satisfies the expected value, taking the second updated particle swarm as an output result; and if the second updated particle swarm does not satisfy the expected value, continuing to perform the subsequent step; and S9, judging whether the current number of iterations reaches a preset maximum value of iterations; if the current number of iterations reaches the preset maximum value of iterations, taking the second updated particle swarm as an output result; and if the current number of iterations does not reach the preset maximum value of iterations, returning to S6. The short-term wind power prediction method based on a cloud evolutionary particle swarm algorithm has the advantages of high accuracy, good stability and high efficiency.

Description

technical field [0001] The invention relates to the field of grid dispatching and forecasting systems, in particular to a short-term wind power forecasting method based on cloud evolution particle swarm algorithm. Background technique [0002] With the rapid growth of my country's wind power installed capacity, wind power accounts for an increasing proportion of the power grid. Due to the random fluctuation of wind power output power, large-scale wind power grid integration has a serious impact on the safe operation of the power system. The traditional power prediction of large wind farms has problems of low prediction accuracy and instability. Therefore, improving the wind power prediction accuracy of wind farms has become particularly important in power grid dispatching and stable operation. [0003] The main methods used in traditional wind power forecasting are neural network, support vector machine, ARMA-ARCH model (autoregressive moving average model-autoregressive con...

AI Technical Summary

Problems solved by technology

However, due to the randomness and instability of wind speed when wind power is connected, the particle fitness value changes greatly, and the inferior particles account for the majority, which makes it difficult for the particles to quickly converge to the optimal value.

Moreover, the conventional PSO algorithm (particle swarm optimization algorithm) is difficult to jump out of its own limitations, and it is difficult to improve the accuracy of wind power prediction. Therefore, it is urgent to propose an effective optimization method for how to improve the accuracy of wind power prediction.

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