Method for predicting working temperature of meteorology-related photovoltaic module
A photovoltaic module and operating temperature technology, applied in thermometers, meteorology, thermometers, etc. that use electric/magnetic elements that are directly sensitive to heat, can solve the problem of the lack of mature photovoltaic power plant power prediction system methods, affecting power grid peak regulation, and grid stability Questions and other questions
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Embodiment 1
[0068] Embodiment 1: A weather-related method for predicting the operating temperature of a photovoltaic module, which establishes a nonlinear model between the operating temperature of the photovoltaic module and the ambient temperature, radiation intensity, and wind speed.
[0069] According to the law of energy conservation, the equations containing parameters are obtained, and then the values of the parameters are obtained through data analysis.
[0070] According to a large amount of data, linear fitting is carried out through the method of mathematical statistics, and finally a linear model is obtained.
[0071] For linear models:
[0072] The simplest explicit formula for the operating temperature of a PV cell Variation with ambient temperature and incident solar radiation intensity
[0073]
[0074]
[0075] This linear expression is only valid for no electric load and no wind, the dimension parameter k , known as the Ross coefficient, which is equal to the...
Embodiment 2
[0116] Example 2: The meteorological and electrical data of the 18kWp grid-connected photovoltaic power station on the roof of the third west floor in the whole year of 2015 were recorded in the database of the Wuhan experimental measurement platform. All data have been preprocessed, using the Gaussian distribution 3σ principle to eliminate data other than 3σ. From the scatterplot, there are basically no outliers. From historical data, the average wind speed in Wuhan is very small. Therefore, in order to simplify the analysis, we adopt the formula
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[0118] Regression analysis is carried out in units of months, and the data of two months are selected, the results are shown in Figure 6 ,
[0119] According to the above analysis, it can be found that the regression coefficients A, B have an obvious corresponding relationship with the month. statistics see Figure 7 :
[0120] When considering the influence of wind speed, take July as an example to analyze the i...
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