Study On Short-term Probability Interval Prediction Of Photovoltaic Power Based On Wavelet Packet Theory

With the increasingly prominent environmental problems and the growing demand for energy,the development of new energy typified by wind power and photovoltaics has become the consensus of countries around the world.Among them,as an important method of solar energy development and utilization,photovoltaic power generation has the advantages of diverse application forms,flexible capacity scale,convenient maintenance,and has a broad application prospect.However,because photovoltaic power generation is closely related to various meteorological factors,it is restricted by them and has obvious periodicity and non-stationary characteristics,large-scale access to the power grid makes it difficult to balance power generation and power consumption,and the uncertainty of power system operation has increased significantly.Therefore,in order to meet this challenge,countries around the world have carried out research on photovoltaic power prediction technology.Based on the consideration of the characteristics of photovoltaic power generation,this paper realizes the effective identification of abnormal photovoltaic power generation data and constructed a short-term prediction model of photovoltaic power,finally,through an error analysis of the short-term forecast results,a prediction model of photovoltaic power generation probability interval based on parameter estimation is proposed.Firstly,by analyzing the periodicity and non-stationary characteristics of photovoltaic power,the periodic graph method is used to extract the periodic components and random components of the photovoltaic power sequence,using factor analysis and Pearson correlation coefficients to screen various meteorological factors that affect photovoltaic power,based on the obtained meteorological factors,a similar day option is proposed,according to the similar day data obtained,forms a training sample set of subsequent models as the basis for research.Secondly,accurate and reliable photovoltaic data is the basis of most photovoltaic research,but due to factors such as human or communication failures,a certain percentage of abnormal data appears in the photovoltaic collection system.Therefore,after analyzing various meteorological factors that affect the power of photovoltaic power generation,it is found that the environmental temperature,environmental humidity and the distribution of the irradiance-power scatter plot are closely related.After classifying the irradiance-power scatter under different distribution characteristics,and Copula theory is used to describe the probability power curve of the correlation between solar irradiance and photovoltaic power,combining the characteristics and identification principles of four typical abnormal data,the effective identification of photovoltaic power abnormal data is realized.Thirdly,for the non-stationary,short-duration,time-domain and frequency-domain localization characteristics of photovoltaic data,the ability to process data based on the characteristics of the data using wavelet packets,multi-scale decomposition of photovoltaic data,combined with LSSVM for frequency-by-frequency for prediction,the prediction results of each frequency band are superimposed to form a final prediction result.And compare and analyze three general weather types,using the combination of wavelet packet and LSSVM to predict the photovoltaic power generation,the short-term output power prediction results of photovoltaic power generation are obtained,finally,an example is given to verify the correctness and effectiveness of the method in this paper.Finally,through error analysis of the real-time prediction results of photovoltaic power generation,the hybrid t Location-scale distribution is used as the optimal probability density distribution to describe the point prediction error,based on the established error distribution,the probability prediction can be performed,and the probability interval prediction results are compared with the traditional probability interval prediction results based on normal distribution,the method in this paper is superior in various evaluation indicators,which verifies the effectiveness of the method in this paper and provides a practical method for the safe and stable operation of photovoltaic grid-connected.