Application of Wavelet Denoising Algorithm in Monthly Runoff Series of Fuchun River Hydropower Station

With the continuous development of human society and economy, human demand for electricity is increasing. Hydropower is one of the main sources of power supply. However, the amount of hydropower generation is affected by the upstream runoff. However, the prediction accuracy of runoff has always been a difficult problem in the scientific community. Due to the existence of many tributaries and the influence of human activities, there is more or less noise in the runoff of natural river. The existence of these noises masks the real characteristics of runoff. In order to get the real characteristics of runoff, this paper applies the Wavelet Denoising Algorithm to the denoising of runoff series. The runoff series is denoised by selecting appropriate wavelet function, multi-resolution decomposition layers and threshold quantization method. The denoising effect is determined by calculating the amount of information contained in the runoff sequence before and after denoising. In order to verify the calculation results, this paper introduces the representative station of the main stream of Qiantang River as an example. There are Fuchun River hydropower station and other power stations on the Qiantang River. The example calculation shows that the Wavelet Denoising Algorithm can well remove the noise of the runoff series of the main stream of Qiantang River.Accurate runoff prediction can provide basic support for power generation dispatching of downstream power stations.