Wind Power Bus Groove State Evaluation And Prediction System Based On Neural Network

Wind power generation technology as one of the renewable energy generation,the installed capacity increases year by year.In China,the development of wind power generation technology has been highly valued,and as the main equipment of wind power generation system,the working state of wind power busbar trough directly affects the safety of the whole unit,so real-time monitoring is an important measure to ensure its safety.This paper has carried on the research of wireless sensor monitoring equipment and the state evaluation and prediction algorithm of wind power busbar slot.On the basis of the completion of the theoretical research,the design and implementation of the busbar groove state monitoring and evaluation prediction system are carried out.In this paper,a wireless sensor network monitoring system based on Lora is constructed,which realizes real-time monitoring of busway status parameters.In order to reduce the energy consumption of data transmission,a wireless sensor node approximation sampling strategy ASM suitable for busway signals is studied,which can reduce energy consumption while maintaining the reliability and timeliness of data transmission.Secondly,the running state evaluation algorithm of wind power busway based on grey cloud model is studied.The evaluation index system of wind power bus grooves is established.Use the analytic hierarchy process to obtain the subjective weight,use the entropy weight method to calculate the objective weight,and get the comprehensive weight of the index after combination.Combined with the comprehensive evaluation model of the grey cloud model,carry out simulation analysis to achieve the determination of the operating status of the wind power bus duct,and the evaluation results are reasonable and accurate.Then,the running state prediction algorithm of wind power busbar groove based on neural network is studied.According to the operation characteristics of wind power bus slot,the BP neural network is selected as the prediction algorithm,and the sparrow search algorithm is used to optimize the BP neural network algorithm.Through simulation analysis,the prediction effect is compared with other algorithms,and the prediction results are more accurate and in line with the reality.Finally,the design of the system server and client software is carried out,which displays the data and evaluation results in real time,as well as the data query function,and transplants the busbar slot state evaluation and prediction algorithm to the server to realize the evaluation and prediction function,and the system runs stably.