| Distributed photovoltaic power generation systems have developed rapidly with the promotion of various countries,and more and more distributed photovoltaic power plants have been put into use.Improving the return on investment of photovoltaic power generation systems and promoting the sustainable development of the photovoltaic industry have become hot issues in the field of new energy power system research today.Accurate photovoltaic output forecasting can not only provide data support for grid power generation plan formulation,power flow optimization,peak and frequency regulation,equipment maintenance and other scheduling actions,and play a key role in grid acceptance of large-scale intermittent power supplies.A large amount of natural dust on photovoltaic modules will cause the energy efficiency of photovoltaic modules to decline.Studying the impact of dust accumulation on the efficiency of photovoltaic modules will help photovoltaic power plant design and operating personnel to more accurately predict the power output of photovoltaic modules and arrange cleaning and maintenance work more effectively.National policy subsidies for photovoltaic power generation systems are based on grid-connected transmission of electricity.However,the internal energy consumption of the microgrid is uncertain,resulting in low energy efficiency in power generation,which is a loss to both the state and owners of photovoltaic power generation systems.The comprehensive energy efficiency assessment method applied to photovoltaic power generation systems is still a blank,so it is imperative to propose a reasonable microgrid energy efficiency assessment method following the guidelines of energy interconnection,energy conservation and emission reduction,and refined management of the power supply demand side.The main research results were as follows:1)In order to comprehensively grasp the operating status of photovoltaic power generation systems,a monitoring platform for operating parameters of photovoltaic power generation systems had been established in this paper.The monitoring platform included electrical parameter acquisition terminals and meteorological parameter acquisition terminals;the sensor network transmission layer was mainly the RTU remote data transmission terminal,which was connected in series through the CAN networking.The RTU terminal uploads data to the cloud server via Ethernet.The electrical data acquisition board of this system uses single-chip microcomputer,power quality measurement chip and GPS.RTU data transmission board adopts single-chip microcomputer,data transmission chip and operating system.Meteorological data acquisition terminal using microcontroller,high-precision weather sensor and GPS.Cloud platform is mainly based on cloud database,cloud server and web.2)This paper studied the attenuation law of photovoltaic power generation in Hangzhou area under the influence of dust accumulation.The morphology of the dust particles on the photovoltaic module was observed with a scanning electron microscope,and the composition of the dust components was determined by X-ray fluorescence method.The study found that under the influence of rainfall,the surface area of photovoltaic modules was easy to form clusters,which was one of the main reasons for the rapid decline in photovoltaic output.Dust deposited in Hangzhou area were mainly composed of silica and calcium carbonate.Further analysis showed that the average weekly dust concentration of photovoltaic modules in eastern China was 0.644 g/m~2,which caused a decrease of 7.4%in the output power of photovoltaic modules in a week.This study analyzed the dust accumulation characteristics of photovoltaic panels based on measured data,which was great significance to reveal the relationship between power output attenuation and ash accumulation,which will help improve the accuracy of photovoltaic power output prediction and formulate effective cleaning strategies for photovoltaic modules.3)The meteorological factors which affecting the natural dust accumulation on photovoltaic modules were analyzed,and on this basis,the relevant meteorological factors were used as multi-input variables,we built a dust accumulation predicted model based on particle swarm optimization(PSO)and least square support vector machine(LSSVM).By improving the attenuation method of the inertia factor in the particle swarm optimization algorithm,the algorithm's optimization robustness was improved.Consider the local spatiotemporal factors in the dust accumulation prediction model,and establish the dust accumulation prediction model under natural rainfall cleaning.In Hangzhou,50 sets of image data and daily dust data were continuously collected,and the example showed that this model could predict the dust accumulation on PV module and the power attenuation rate caused by dust accumulation in the statistical period.Aiming at the application of research on dust accumulation,a prediction method by dust accumulation correction acceptance irradiance is proposed,and the short-term power prediction model of photovoltaic power generation system is built using Elman neural network.4)The power quality of the system in steady state will adversely affect the rate of the system energy,which is reflected in the reduction of the energy efficiency of the system.A power quality evaluation method based on incentive punishment factor and the ordered weighted aggregation operators was proposed.By using incentive and penalty factors to calculate the single index evaluation value without considering the time factor.By introducing the time weights and adopting the linear target programming model and ordered the weighted averaging operator to get the primary evaluation values of the indices in the inspection period.Then,the minimum variance method was used to get index weights,and the ordered weighted geometric averaging operator was applied to obtain the comprehensive evaluation value of the power quality.Finally,the power quality of a distribution system with photovoltaic generation under different load conditions was analysed as an example.The results showed that the method proposed in this paper distinguishes the power quality level under various load conditions significantly,which verify the feasibility and effectiveness of the model.5)The energy efficiency evaluation method of photovoltaic power generation system based on power generation efficiency(PR)and capacity factor(CF)is analyzed,and the loss mechanism during energy transfer of the system is studied.On this basis,the index system of energy efficiency of the photovoltaic power generation system was established.The index system reflects the level of hardware construction of the photovoltaic system,the quality of the operating power quality and the loss in energy transfer.Finally,the system comprehensive energy efficiency evaluation model is established based on the fuzzy analytic hierarchy process,and the feasibility of the algorithm is demonstrated through calculation.This paper studied the power impact of natural dust accumulation on photovoltaic modules under the influence of rainfall,and for the first time to reveals the impact of dust accumulation on module losses in East China(represented by Hangzhou),providing a support for power prediction and cleaning strategies.Put forward a dust accumulation prediction model based on intelligent algorithm,and modify the photovoltaic power prediction model.The influence and evaluation method of the steady-state power quality of the photovoltaic power generation system are analyzed,and the dynamic change process of the power quality of the system in time series is revealed,which provides a reference for energy-saving renovation and energy efficiency evaluation.Finally,an energy efficiency assessment method of photovoltaic power system based on modules output efficiency,power quality,design technology of system,and the loss of the system is proposed to improve system energy efficiency and get a new supervision methods. |
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