Abstract: The invention discloses a battery pack control method in a mixed storage state based on light intensity, which includes S1: detecting the status of multiple energy storage battery packs in real time. When it is in the standby state, it goes to S2. When it is in the charging state, it goes to S3, when it is in the discharge state, go to S4, when it is in the simultaneous charge-discharge state, go to S5; S2: When it is in the standby state: detect the lighting situation, and compare the structure with the current light intensity coefficient value and the set threshold. Adjust the circuit parameters; S3: Work in Buck mode when charging; S4: Work in Boost mode when discharging; S5: When in charge-discharge state, proceed according to the output-input power difference. Adjustment; S6: Return to S1 and cycle through multi-energy storage battery control.

Abstract: The present invention provides a DC power conversion device for bridging new energy generation, energy storage and microgrid. The DC power conversion device includes a direct current power output port (Vout+) connected in series to a denoising circuit to provide a stable direct current output, wherein the denoising circuit includes a differential amplifier, a standard voltage output module, a current sampling circuit, a transient response enhancement circuit, a PMOS power transistor, and four resistance negative feedback networks, wherein the differential amplifier, the standard voltage output module, the current sampling circuit, the transient response enhancement circuit, the PMOS power transistor, and the four resistance negative feedback networks form a closed loop; the standard voltage output module is connected to the differential amplifier; an input power supply is connected to a resistor R3, the standard voltage output module and the PMOS power transistor.

Abstract: The present invention is a hybrid energy storage battery status monitoring method and system based on big data processing. The method aims at the non-linear and difficult online evaluation problems of the hybrid energy storage battery status. It is set up to collect charging sample information and discharge sample information in sequence. Steps include data sorting and fusion steps, hybrid energy storage battery SOH estimation steps, and hybrid energy storage battery health level evaluation steps to implement business hybrid energy storage battery status monitoring. Among them, the sample data information elements include voltage and current in the charge and discharge state, power, temperature, internal resistance, through data collection and fusion processing, and data prediction through preset models, it can fully cover the hybrid energy storage battery status, conduct a comprehensive assessment, and improve the accuracy of the assessment.

Abstract: The present invention designs a fault diagnosis method and system for energy storage power stations based on distributed neural networks. The method includes: obtaining the operating data of various batteries in each energy storage station, and performing preprocessing operations on the data, including data cleaning. and standardization; obtain the diagnostic data of the corresponding energy storage station based on the data preprocessing results; input the preprocessed diagnostic data into the pretrained distributed neural network to diagnose the fault of the energy storage power station. The technical solution proposed in this application can accurately diagnose energy storage power station faults and improve the maintenance efficiency of energy storage power stations.