Design And Implementation Of Photovoltaic Power Generation Management System

The extensive use of solar energy has promoted the development of the photovoltaic power generation industry.The traditional electric energy collection methods are susceptible to human factors,and have poor real-time performance and low reliability.In addition,traditional enterprises connect their produced Io T devices to self-developed systems or third-party platforms for function display and management.As the number of types of devices increases,the compatibility,scalability,and stability of the system are poor.At the same time,in the era of the Internet of Things,the importance of data has become increasingly prominent.Therefore,the establishment of a data-centric,highly available photovoltaic power generation management system has practical application value.In response to the above problems,this article uses a new type of photovoltaic equipment access protocol to make up for the shortcomings of the original curing protocol,effectively ensuring system security;using multiple sensors to report data analysis rules to improve equipment incompatibility and ensure system stability;improve Docker Swarm The scheduling algorithm improves the resource utilization of the cluster.At the same time,it predicts the photovoltaic power generation and promptly warns of abnormal data.This article studies the above status quo,the main work includes the following three aspects:Firstly,this paper proposes to apply a hybrid neural network architecture combining convolutional neural network and long short-term memory network to actual industrial scenarios to improve the accuracy of photovoltaic power generation prediction.Preprocess single variable data through one-dimensional convolution,and transform it into multi-dimensional data after two-layer time convolution operation,which enhances the predictive ability of long-and short-term memory networks.At the same time,the single-step prediction is extended to a multi-step prediction strategy.In practical engineering applications,only the historical data of photovoltaic power generation in the system can be used to perform short-term,medium-term and long-term power prediction,which proves that the hybrid model is very robustness.Secondly,this article applies the improved ant colony algorithm to Docker Swarm container scheduling to solve the problem of resource utilization and load imbalance.The proposed improved algorithm considers historical scheduling,thereby enhancing scheduling decisions.Under the same configuration,the algorithm is compared with the basic ant colony algorithm(ACO)and the first-come-first-served algorithm(FCFS).The experimental results show that the proposed algorithm has advantages in response time and throughput,and can improve the system The overall performance.Finally,this paper designs and implements a photovoltaic power generation management system with data that can be monitored,analyzed,and connected.Adopt front-end and back-end separation ideas,optimize and improve the traditional Internet of Things architecture,introduce containerization technology,use message queue asynchronous consumption,etc.,build a collection of terminal equipment modules,access gateway modules,business and application support modules,application management platform modules and logs The complete system with the analysis module in one has verified the practicability of the system through functional tests and non-functional tests,and achieved the expected results when the system was officially put into use.