Photovoltaic Data Acquisition And Power Prediction Based On Wireless Sensor Networks

With the country's strong support for the photovoltaic industry,distributed photovoltaic power generation has entered thousands of households,bringing great convenience to people's daily life.In order to make photovoltaics better serve people's lives,there are still many problems.Need to solve,the most important problem is the impact of photovoltaic power generation system on the grid after grid connection.Since photovoltaic power generation is related to the weather,the power generation is very unstable.Therefore,after grid connection,the dispatching of the power grid becomes very difficult.If the photovoltaic power generation can be predicted in advance,the grid dispatching can be provided to guide the grid dispatching.More scientific and efficient.For power generation power prediction,accurate data must be used for support.However,many of the photovoltaic power generation prediction systems are derived from weather forecasts,and the data has large errors.Based on the above problems,after in-depth study of direct prediction based on BP neural network,it is improved,and the similarity algorithm is added to make up for the shortcomings of the direct prediction method.Secondly,in order to obtain more accurate data,this paper designs a photovoltaic power station data acquisition system based on wireless sensor network.It can not only collect data,but also monitor the power generation system in real time to ensure the safe and stable operation of the photovoltaic system.The data acquisition system of this paper is developed based on wireless sensor network,which is a wireless transmission network composed of a coordinator and several collection nodes.After collecting data,the collection node transmits the data to the coordinator through the established ZigBee network.The coordinator node transmits the data to the upper computer through RS232,and finally completes the data storage through the upper computer software to provide data support for power prediction.In addition,in order to make the power generation system more stable and good operation,this paper displays the collected data through the host computer software,which is convenient for users or staff to monitor the status of the power generation system in real time and eliminate potential safety hazards in time.In the design of the power supply circuit of the acquisition system,this paper designs a ZigBee chip power supply circuit,which directly supplies power from the photovoltaic cell to the acquisition circuit through voltage division,voltage reduction and voltage regulation,which reduces the development cost.The main research content of this paper is power generation power prediction.The traditional neural network prediction method is improved.The similarity theory is introduced into the PV output prediction.The learning model is selected by the similar day theory to train the prediction model,which improves the training efficiency and improves the training efficiency.The accuracy of the forecast.At the end of the paper,Matlab is used for simulation,and the prediction results are compared.The experimental results show that the accuracy of the power prediction method is significantly improved compared with the traditional method.The prediction results can provide reliable guidance for power system scheduling.