Daily consumption monitoring method of photovoltaic microgrid based on genetic wavelet neural network

Abstract

Abstract In order to comprehensively monitor the daily consumption of photovoltaic power and power generation of photovoltaic microgrid, a daily consumption monitoring method of photovoltaic microgrid based on genetic wavelet neural network is proposed to reduce the relative error of daily consumption monitoring. Considering the power generation forms of various units such as wind power, thermal power, hydropower and photovoltaic power generation, the upper and lower limits of daily consumption of different units and the constraints of consumption calculation are analyzed to obtain the daily consumption of photovoltaic microgrid. On this basis, the daily consumption monitoring model of photovoltaic microgrid including multiple inputs and outputs is constructed by using Morlet wavelet function, and the power generation is calculated by wavelet neural network. The genetic algorithm is used to optimize the individual fitness of wavelet neural network through the training of the number of wavelet bases and related thresholds and weights, and to normalize the optimal individual fitness to realize the daily consumption monitoring of photovoltaic microgrid. The experiment shows that this method can monitor the actual photovoltaic power in sunny weather, and after 12 o’clock, the photovoltaic power gradually drops below 30 kW. In cloudy weather, the actual photovoltaic power reaches its peak at around 12 o’clock, ~45–50 kW, and drops to about 10 kW at 17 o’clock. And the power generation in cloudy days is relatively low, and the power generation in rainy days is the lowest. When the relative humidity is 30%, the power generation increases rapidly and keeps at 8 kWh. When the relative humidity is 50%, the power generation gradually drops to 2 kWh. When the temperature is 20°C, the maximum radiation intensity is about 0.6 kW m2. When the temperature is 30°C, the maximum radiation intensity is greater than 0.8 kW m2. At 11:00 and 12:00, the power generation error is 0.02 kWh. In order to improve the monitoring accuracy of photovoltaic power and daily power generation of photovoltaic microgrid in different environments.