Research On Fault Diagnosis Model Based On Internal Parameters Identification Of Photovoltaic Modules

In the current form of energy shortage and energy crisis,photovoltaic power generation is receiving more and more attention from the outside world as a clean and pollution-free renewable power generation method.In recent years,due to the strong support and development of photovoltaic industry,photovoltaic power generation has become the third most important form of renewable energy after water power generation and wind power generation.Therefore,the fault detection of photovoltaic modules becomes more and more important..At present,most researchers are in-depth analysis of the fault diagnosis type of photovoltaic modules from the external characteristics of photovoltaic modules as an entry point,and diagnosed by the external characteristics of photovoltaic modules,although this diagnostic system can easily obtain the fault results and have Certain engineering values,but these basic studies have not gone deep into the battery model level,and cannot study and analyze the failure principle from the internal mechanism of the battery model.In this paper,under the support of the Jiangxi Provincial Science and Technology Support Plan Project?Industrial Sector?:“PV modules intelligence and fault fast positioning”?20142BBE50002?,the failure types of PV modules are studied and diagnosed by internal parameter identification mechanisms.The details are as follows:This article starts from the internal parameters of the PV modules,and deeply studies the mechanism of the internal parameters of the PV modules in the event of failure,and studies and diagnoses the fault types of the PV modules through internal mechanisms.Based on this,we must first understand the basic principles of photovoltaic cell power generation,through the detailed analysis of the internal parameters of the photovoltaic cell to get the corresponding mathematical model and simulation,through the simulation model to analyze the internal parameters of the external output characteristics and electrical parameters,so as to When the internal parameters change,the external output characteristics will change.It is feasible to reflect the principle of the failure type by the nonlinear law of internal parameter changes.Secondly,the most critical issue that needs to be solved in this paper is how to accurately and efficiently identify the internal parameters(I_ph,I_o,A,R_s,R_sh)of PV modules.By studying the improved strategy of quantum particle swarm optimization algorithm,the algorithm can identify the internal parameters of PV modules.Accurately identify.Then,through the improved quantum particle swarm algorithm?HPSO?to identify the PV modules under different working conditions,the PV module internal parameter values under different working conditions were obtained,and the corresponding three-dimensional graphs and the corresponding fitting analytical formulas were drawn.Photovoltaic modules under normal operation provide a quick identification method and provide a theoretical basis for fault diagnosis.Finally,by analyzing the fault characteristics of PV modules,the fault types are divided into short circuit,open circuit,and aging,and an analysis model between fault characteristics and internal parameters is established.The internal parameters of the PV modules under different fault conditions are identified by an algorithm and the corresponding identification results are recorded.The BP neural network is used to train the identified samples to establish a photovoltaic module fault diagnosis model.First,the internal parameters of(I_ph,I_o,A,R_s,R_sh)are used as input variables of the model,and then the normal,short circuit,open circuit,and aging statuses of the components are used as the diagnosis.As a result,training data is invoked to train the algorithm model,and a photovoltaic module fault diagnosis model is established.Finally,the accuracy of the diagnosis model is verified by different test samples.