Wind Power System Reliability Estimation Method Using Neural Network and Cross Entropy Sampling

Abstract

The invention discloses a method for estimating reliability of a wind power system using neural network and cross-entropy sampling. Using historical wind power data, the power system network model is trained to estimate the probability distribution of wind speed in the wind farm; the Gaussian random noise is sampled, and the sampled Gaussian random noise is processed through the generation network of the power system network model to output the wind speed, through the Two reliability parameters for calculating the power shortage time probability and power shortage expectation are solved to realize the efficient reliability estimation of the power system including the wind farm. On the premise of considering the spatial correlation of the wind speed of multiple wind farms, the invention estimates the probability distribution of the wind speed of the wind farm, and realizes efficient system state sampling and reliability estimation.

Description

technical field [0001] The invention belongs to a method for estimating power data in the field of power systems, and in particular relates to a method for estimating reliability of a wind power system using neural networks and cross-entropy sampling. Background technique [0002] Due to the strong randomness and instability of wind power, its access will disturb the supply and demand balance of the power system, thereby affecting the reliability of the power system. Therefore, it is of great significance to estimate the reliability of the power system including wind farms. [0003] At present, for the reliability estimation of the power system including wind farms, the mainstream method is to use the Monte Carlo method to sample the operating state of the power system and calculate the corresponding reliability parameters. However, the problem with this method is that for high-reliability systems, the Monte Carlo method requires a large number of samples to achieve converg...

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Problems solved by technology

However, the problem with this method is that for high-reliability systems, the Monte Carlo method requires a large number of samples to achieve convergence, which requires high computing resources.

[0004] Therefore, in the prior art, there is a lack of effective reliability estimation for power systems containing wind farms, and there is also a lack of stable improvement based on accurate reliability results.

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