Research On Photovoltaic Power Prediction Based On WSN And SVM

In the face of increasingly severe global environmental pollution and energy shortage,photovoltaic power generation has become one of the fastest growing industries in the 21st century due to its clean,pollution-free,safe,reliable and abundant resources.However,the randomness and intermittency of photovoltaic power generation will inevitably bring challenges to the management of large-scale grid-connected photovoltaic power stations and the stable operation of the power system.Improving the data monitoring efficiency of photovoltaic power stations and establishing photovoltaic power generation prediction model are the effective ways to solve this problem.Therefore,this paper studies a photovoltaic power prediction based on support vector machines.The combination of wavelet decomposition and ensemble empirical mode decomposition is used to improve the prediction model,and wireless sensor network technology is used to collect the data from photovoltaic panels.Specific research work is shown below.(1)This paper establishes a mathematical model and a simulation model of a photovoltaic cell,and obtains the photovoltaic characteristic curve chart.Then,the main factors affecting the output power of the photovoltaic cell and the correlation between the various factors are analyzed.And put forward the evaluation index of photovoltaic forecast.(2)A photovoltaic prediction model based on support vector machine is proposed.Firstly,a structure of photovoltaic power prediction model based on a single SVM algorithm is proposed.Euclidean distance method is used to select the closest similar day to the measured day to avoid the interference of the weather type on the experimental results.Three parameter optimization methods of SVM are listed,the prediction accuracy of the three are compared,and the prediction experimental results of sunny,cloudy and rainy are analyzed.Then this paper further explores the photovoltaic prediction model based on combinatorial decomposition,using the EEMD method to decompose the photovoltaic sequence,and then using the permutation entropy to calculate the complexity of each IMFs,and using wavelet to decompose the high complexity sequence.The experimental results show that the combined photovoltaic power model proposed in this paper has better prediction accuracy.(3)In practical engineering applications,there are many distributed photovoltaic power stations and their distribution is complex,while the traditional photovoltaic data monitoring system is expensive and difficult to maintain.To solve those problems of the appeal,and collect the photovoltaic panel data more effective,this paper uses the wireless sensor network(WSN)node deployment to the photovoltaic panels,designs the hardware circuit and software function,relevant data of solar pv panels will be collected by the Zig Bee protocol.Then the collected data is transferred to a graphical interface on the PC.And PC can also show some predicted results of photovoltaic power.The wireless photovoltaic monitoring system can bring convenience to the managers of photovoltaic power stations and effectively improve the management level of photovoltaic power stations.