Research On Power Fluctuation Suppression Strategy Of Wind-PV System Based On Energy Storage And On-line Battery Detection

Wind power generation and photovoltaic power generation are clean energy sources with rapid development,which play a very important role in saving energy and reducing pollutant emissions in various countries.However,wind power generation and photovoltaic power generation are vulnerable to external influence,with strong intermittent and volatility,which makes the output of wind farm and photovoltaic power station fluctuate.This will have a negative impact on the integration of wind and solar power generation,and even lead to the collapse of the power grid.With the development of energy storage technology,a certain capacity of energy storage equipment can be used to solve the problem of fluctuation of new energy output.Considering that the energy storage system has the advantages of high power density and high energy density,different energy storage devices are often used to form hybrid energy storage.In this thesis,supercapacitors and batteries are used to compose hybrid energy storage,which can give full play to the advantages of the two energy storage,which is ideal for the suppression of grid-connected power fluctuations.Firstly,the mathematical model,control mode and working characteristics of wind power generation,photovoltaic cells,lead-acid batteries and supercapacitor hybrid energy storage system are analyzed.Then,the improved empirical mode decomposition method is used to analyze the spectrum of the power signal of the wind and solar system.Empirical Mode Decomposition(EMD)method is often used to decompose and process non-stationary signals.However,due to the impulse interference,modal aliasing and false components are easy to occur.Therefore,this thesis adopts Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)method to decompose and process power signals.Spectrum decomposition is used to filter the power signal and get its three components: high frequency,sub-high frequency and low frequency.The low-frequency power component is directly connected to the grid,the high-frequency power component is suppressed by supercapacitor,and the sub-high-frequency power component is suppressed by battery.Secondly,the order of suppressed power and intrinsic mode function(IMF)of energy storage equipment is adaptively adjusted by using the fuzzy control strategy with the charging state of battery and supercapacitor as the power distribution constraint.Through the fuzzy adaptive power redistribution control,the wind power fluctuation can be smoothed better.Finally,considering that the state of Charge(SOC)is the key parameter in the strategy of suppressing the output fluctuation of wind and solar system,but it is not easy to measure directly,this thesis proposes an on-line detection method for SOC of storage battery based on Internet of Things platform.On the one hand,the SOC parameters can be predicted by neural network,on the other hand,in order to ensure the safe operation of storage battery,its voltage and voltage are measured.Real-time on-line measurement of charge/discharge current and temperature.Firstly,the working parameters of the battery are obtained by the hardware detection system of the lower computer,and then the data are uploaded to the Internet of Things platform by WIFI technology.Secondly,C# PC client and Internet of Things platform carry out information transmission,obtain relevant data,and use neural network algorithm for data processing.Combining the Internet of Things technology,C# and MATLAB neural network algorithm,the on-line SOC prediction and parameter detection of storage battery are realized.