Intelligent access type photovoltaic energy storage cabinet and control method thereof

Abstract

The invention discloses an intelligent access type photovoltaic energy storage cabinet and a control method thereof, and relates to the technical field of photovoltaic energy storage, the photovoltaic energy storage cabinet comprises the following components: a cabinet body structure, a heat dissipation system, a battery mounting and fixing structure, a sensor configuration, an energy storage converter and a flexible power supply network topology; according to the photovoltaic energy storage cabinet, by optimizing the cabinet body structure and the heat dissipation system, the environmental adaptability and operation stability of the photovoltaic energy storage cabinet are remarkably improved, external heat can be effectively isolated through the improvements, the influence of temperature fluctuation on internal equipment is reduced, stable operation of the equipment can be kept in a vibration environment, and therefore the service life of the equipment is prolonged, and the service life of the photovoltaic energy storage cabinet is prolonged. And the overall operation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of photovoltaic energy storage, and in particular to an intelligent access photovoltaic energy storage cabinet and a control method thereof. Background Art

[0002] With the growing global demand for renewable energy, photovoltaic power generation has been widely used as a clean and sustainable energy solution. However, photovoltaic power generation is greatly affected by weather conditions, and its power output is intermittent and unstable, which poses challenges to the stable operation of the power grid and the efficient use of energy.

[0003] Although traditional photovoltaic energy storage systems have alleviated the instability of photovoltaic power generation to a certain extent, they still have many shortcomings. First, the cabinet structure and heat dissipation system design of traditional energy storage cabinets are relatively simple, making it difficult to adapt to complex and changing environmental conditions such as high temperature, high humidity, and vibration, resulting in shortened equipment life and reduced operating efficiency. Second, traditional energy storage management strategies often lack intelligence and cannot be accurately adjusted according to real-time environmental parameters and load demands, resulting in low energy utilization efficiency. In addition, the traditional power supply network topology is fixed and difficult to flexibly adjust according to different load demands, which limits the adaptability and reliability of the system. Finally, the traditional safety protection mechanism is single, making it difficult to comprehensively monitor and prevent potential failures, increasing the risk of system operation.

[0004] In view of the above-mentioned problems existing in traditional photovoltaic energy storage systems, the present invention proposes an intelligent access photovoltaic energy storage cabinet and a control method thereof, which is particularly important. Summary of the Invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an intelligent access photovoltaic energy storage cabinet and a control method thereof. It can significantly improve the environmental adaptability and operational stability of the photovoltaic energy storage cabinet by optimizing the cabinet structure, heat dissipation system, battery installation and fixing structure, sensor configuration, energy storage inverter and flexible power supply network topology. At the same time, it integrates advanced sensor configuration and energy storage inverter, combined with optimized control algorithm, to realize intelligent management and precise charging and discharging control of the photovoltaic energy storage cabinet. In particular, the flexible power supply network topology adopts a reconfigurable circuit design, and through the intelligent switch matrix and dynamic circuit configuration module, it can freely switch between various topologies such as series, parallel and hybrid, thereby improving the flexibility and reliability of the power supply network. In addition, the multi-level safety protection mechanism ensures the safe and stable operation of the system under various working conditions.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: On the one hand, an intelligent access photovoltaic energy storage cabinet includes the following components: a cabinet structure, a heat dissipation system, a battery installation and fixing structure, a sensor configuration, an energy storage converter, and a flexible power supply network topology; The cabinet body is made of high-strength, corrosion-resistant alloy material and is a sealed structure with multiple layers of heat insulation boards inside and a shock-absorbing device installed at the bottom; The heat dissipation system: combines air cooling and liquid cooling, and intelligently adjusts heat dissipation through temperature sensors; The battery installation and fixing structure adopts an adjustable mounting frame and elastic fixing method; The sensor configuration includes conventional sensors and humidity, pressure, and smoke sensors; The energy storage converter is highly efficient, has high power density and is equipped with multiple protection functions; The flexible power supply network topology adopts a reconfigurable circuit design and can freely switch between series, parallel, and hybrid topologies through an intelligent switch matrix and a dynamic circuit configuration module.

[0007] Furthermore, in terms of the cabinet structure, the material and layout of the insulation board are further optimized. The insulation board adopts a new type of nano-aerogel insulation material, which has extremely low thermal conductivity and can effectively prevent external heat from entering the interior of the cabinet. Its layout adopts a layered staggered design, and the gaps between two adjacent layers of insulation boards are staggered to form a maze-like insulation channel, which further enhances the insulation effect. In the shock absorbing device, a magnetorheological fluid shock absorber is used, which is filled with magnetorheological fluid. The damping force of the shock absorber can be adjusted in real time by controlling the magnetic field strength. When the cabinet is vibrated, the acceleration sensor installed on the cabinet monitors the vibration intensity in real time and transmits the signal to the controller. The controller calculates the magnetic field strength according to the preset algorithm model and adjusts the viscosity of the magnetorheological fluid, thereby changing the damping force of the shock absorber to achieve adaptive shock absorption of vibrations of different intensities. After this optimization The cabinet structure can better protect the internal equipment, adapt to more severe environmental conditions, and extend the service life of the equipment. In the heat dissipation system, the fan of the air-cooled part adopts a new axial-flow fan, and its blades adopt a bionic airfoil design, which can provide larger air volume and higher air pressure at the same power. At the same time, the fan motor adopts a brushless DC motor, which has the characteristics of high efficiency, low noise and long life. The coolant of the liquid-cooled part adopts a nanofluid coolant with high specific heat capacity and good thermal conductivity. The nanoparticles are evenly dispersed in the basic coolant, which can significantly improve the thermal conductivity of the coolant. The liquid cooling pipeline adopts a microchannel structure design, which increases the contact area between the coolant and the heat-generating components and improves the heat dissipation efficiency. Through this coordinated optimization of air cooling and liquid cooling, the temperature inside the cabinet can be controlled more accurately to ensure that the equipment operates within the optimal operating temperature range.

[0008] Furthermore, in the battery installation and fixing structure, the adjustable mounting frame adopts an electric screw transmission mechanism to realize the adjustment function. The screw is driven by a stepper motor. By accurately controlling the pulse signal of the stepper motor, the precise displacement adjustment of the mounting frame can be realized to adapt to batteries of different specifications and sizes. The elastic fixing structure adopts a combination of rubber springs and disc springs. The rubber spring has good elasticity and buffering performance, and the disc spring has high load-bearing capacity and stability. The buffer pad arranged around the battery is made of porous polyurethane material, which has good energy absorption and shock absorption performance, can effectively absorb vibration and impact energy, and protect the battery from damage. At the same time, a battery positioning device is also provided on the battery mounting frame. The laser sensor and mechanical positioning pins are used to ensure the position accuracy and stability of the battery installation. In terms of sensor configuration, the humidity sensor adopts a capacitive humidity sensor, which has the characteristics of high precision, fast response and good long-term stability. The pressure sensor adopts a piezoresistive pressure sensor, which can monitor the pressure changes inside the cabinet in real time and accurately. The smoke sensor adopts an ion smoke sensor, which has the characteristics of high sensitivity and fast response, and can issue an alarm in time at the early stage of fire hazards. In addition, a gas sensor is added to monitor whether there is any harmful gas leakage inside the cabinet. The data of these sensors are transmitted to the data processing server in real time through the wireless communication module for real-time monitoring and analysis.

[0009] Furthermore, the energy storage converter adopts a multi-modal power conversion algorithm based on a fuzzy neural network control strategy. The algorithm automatically adjusts the converter's operating mode by real-time monitoring and analysis of input voltage, current, and frequency parameters. The algorithm formula is: ,in is the output power of the converter, is the input voltage, is the input current, is the input frequency, is the battery's state of charge, is the ambient temperature, and are the output functions of the fuzzy neural network, and is the weight coefficient, the weight coefficient and The determination is obtained through a large amount of experimental data and optimization algorithms. First, experiments are carried out under different working conditions to collect data on input and output parameters. Then, the genetic algorithm is used to optimize the weight coefficient to minimize the error in the output power. The training process of the fuzzy neural network adopts the back propagation algorithm. By continuously adjusting the weights and thresholds of the network, the output of the network can accurately reflect the relationship between the input parameters and the output power. This multimodal power conversion algorithm can improve the efficiency and stability of the converter and adapt to different load requirements and grid conditions. At the same time, the converter also adopts a heat dissipation structure and electromagnetic shielding technology to further improve its reliability and safety.

[0010] Furthermore, the intelligent switch matrix of the flexible power supply network topology adopts a switch control strategy optimized by a quantum genetic algorithm. The goal of this strategy is to achieve fast and accurate switching of circuit topologies while meeting different load requirements and reducing switching losses. The algorithm formula is: ,in is the state vector of the switch matrix, is the optimal switching state vector, For the switch matrix in state The power loss under The cost of switching, power loss The calculation takes into account the conduction loss and switching loss of the switch. The conduction loss is related to the on-resistance and current of the switch, and the switching loss is related to the switching frequency and voltage change rate of the switch. Taking into account the life and reliability of the switch, the quantum genetic algorithm is obtained by weighted calculation of the number of switch operations and the operation time. By encoding the switch state vector, it utilizes the superposition and entanglement characteristics of quantum bits to perform a global search in the search space. During the algorithm's iteration process, the quantum bits are updated through quantum rotating gates, and mutation operations are introduced to avoid the algorithm falling into a local optimal solution. The weight is determined by analyzing different load types and working modes and adjusting them in combination with actual experimental data. This optimized switch control strategy can improve the flexibility and reliability of the flexible power supply network topology and reduce the energy consumption of the system.

[0011] On the other hand, a control method for an intelligent access photovoltaic energy storage cabinet is characterized in that the method comprises the following specific steps: S1, data acquisition and processing process collects, processes and analyzes sensor data; S2, energy storage management control strategy adopts optimized MPPT algorithm and Estimation algorithm for charge and discharge management; S3. The security protection control mechanism includes multi-level protection at the hardware and software levels.

[0012] Furthermore, in the data collection and processing process, an improved particle filter algorithm is used to process and analyze the sensor data. The purpose of this algorithm is to improve the accuracy and reliability of the data and to detect and process abnormal data in real time. The algorithm formula is: ,in For the The estimated state value at time t, For the The particle in The state of the moment, For the The particle in The weight at the moment, the particle state update formula is: ,in is the state transition function, is the control input, is process noise, and the particle weight update formula is: ,in is the observation probability density function, For the In order to avoid the problem of particle degradation, the resampling technology is adopted to obtain the observation value at each moment. During the resampling process, particles are copied and deleted according to their weights. An adaptive particle number adjustment strategy is introduced to adjust the number of particles in real time according to the system state changes and observation errors. The weight is determined by evaluating the accuracy and reliability of the sensor and combining it with statistical analysis of historical data. This improved particle filter algorithm can effectively handle noise and outliers in sensor data, and improve the system's state estimation accuracy and fault diagnosis capabilities.

[0013] Furthermore, in the energy storage management control strategy, the optimized MPPT algorithm adopts a maximum power point tracking algorithm with chaotic search and adaptive step size adjustment. The core idea of this algorithm is to use the ergodicity and randomness of the chaotic sequence to perform a global search on the power-voltage curve of the photovoltaic panel, and adaptively adjust the step size according to the search results to improve the tracking speed and accuracy. The algorithm formula is: ,in For the The reference voltage at the moment, For the The voltage step size at the moment, the voltage step size adjustment formula is: ,in is the step size adjustment coefficient, For the The power change at each moment, is the historical maximum power value, and the chaotic sequence is generated using Logistic mapping: ,in is the control parameter, For the In the iterative process of the algorithm, a search point is selected on the power-voltage curve according to the value of the chaotic sequence, the power value of the point is calculated, and compared with the historical maximum power value, and the step size adjustment coefficient is By adaptively adjusting the power change rate and search range during the search process, this optimized MPPT algorithm can quickly and accurately track the maximum power point of the photovoltaic panel under rapid changes in light intensity and temperature, thereby improving photovoltaic power generation efficiency.

[0014] Furthermore, in the energy storage management control strategy, The estimation algorithm adopts an improved Kalman filter and support vector machine fusion algorithm. This algorithm combines the real-time performance of the Kalman filter and the nonlinear mapping capability of the support vector machine, and can more accurately estimate the battery state of charge. The algorithm formula is: in For the Moment Estimated value, For the The predicted value at time, is the Kalman gain, is the measured value, is the measurement function, For the The current at the moment, the state prediction formula of Kalman filter is: ,in The support vector machine is used to model the nonlinear characteristics of the battery by training a large amount of experimental data. The nonlinear relationship between the voltage, current and temperature parameters is firstly analyzed by Kalman filtering in the process of algorithm implementation. A preliminary estimate is made, and then the estimated value and the measured value are input into the support vector machine for correction. The weight is determined by error analysis of Kalman filter and support vector machine, combined with actual battery characteristics and experimental data for adjustment. This fusion algorithm can improve The accuracy and reliability of the estimation provide a more accurate decision-making basis for energy storage management.

[0015] Furthermore, the safety protection control mechanism adopts a fault diagnosis and protection strategy based on multi-sensor fusion and fuzzy decision-making. This strategy fuses the data of multiple sensors, uses a fuzzy decision-making algorithm to judge the fault status of the system, and takes corresponding protection measures. The algorithm formula is: ,in is the comprehensive fault assessment value, For the The weight of each sensor, For the The fault membership function of each sensor is For the The measurement value of each sensor, fault membership function According to the normal working range and fault threshold of the sensor, the sensor's measurement value is converted into fault membership through fuzzy processing, and the weight The determination is made by evaluating the reliability and importance of the sensors and adjusting them in combination with actual fault cases. In the fuzzy decision-making process, the system fault level is judged according to the comprehensive fault evaluation value and preset fuzzy rules, and corresponding protection actions are taken. This fault diagnosis and protection strategy based on multi-sensor fusion and fuzzy decision-making can improve the accuracy of system fault diagnosis and the timeliness of protection, ensuring the safe and stable operation of the energy storage cabinet.

[0016] Compared with the existing technology, this intelligent access photovoltaic energy storage cabinet and its control method have the following beneficial effects: 1. The present invention significantly improves the environmental adaptability and operational stability of photovoltaic energy storage cabinets by optimizing the cabinet structure and heat dissipation system. These improvements not only effectively isolate external heat and reduce the impact of temperature fluctuations on internal equipment, but also maintain stable operation of the equipment in a vibrating environment, thereby extending the service life of the equipment and improving overall operational efficiency.

[0017] Second, the present invention integrates advanced sensor configuration, energy storage converter, and adopts optimized control algorithm to realize intelligent management and precise charge and discharge control of photovoltaic energy storage cabinet. The MPPT algorithm can quickly and accurately track the maximum power point of photovoltaic panels, thereby improving photovoltaic power generation efficiency. The estimation algorithm can accurately estimate the battery's state of charge, providing accurate decision-making basis for energy storage management, thereby optimizing energy utilization and improving the overall efficiency of the system.

[0018] 3. The present invention uses an intelligent switch matrix and a dynamic circuit configuration module to freely switch between multiple topological structures such as series, parallel, and hybrid. This design not only improves the flexibility and adaptability of the power supply network and can quickly adjust the circuit structure according to different load requirements, but also reduces switching losses and improves system reliability and energy efficiency by optimizing the switch control strategy. At the same time, the reconfigurable circuit design also facilitates future system expansion and upgrades, enhancing the long-term investment value of the system.

[0019] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0021] Figure 1 This is a process operation diagram of an intelligent access photovoltaic energy storage cabinet; Figure 2 This is a flow chart of the control method for an intelligent access photovoltaic energy storage cabinet. DETAILED DESCRIPTION

[0022] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.

[0023] Example 1: This example describes a newly built residential community in a certain city. To achieve green and intelligent household electricity consumption, some residents installed smart access photovoltaic energy storage cabinets. The community has sufficient sunlight, and residents have diverse daily electricity needs, covering lighting, household appliance use, etc.

[0024] The energy storage cabinet body is made of a sealed structure made of high-strength, corrosion-resistant alloy material. The new nano-aerogel insulation board inside is arranged in a layered and staggered manner to effectively reduce heat transfer. The magnetorheological fluid shock absorber installed at the bottom can monitor the vibration intensity through the acceleration sensor during earthquakes or external vibrations. The controller adjusts the magnetic field strength according to the preset algorithm, changes the damping force of the shock absorber, and protects the equipment inside the cabinet. The heat dissipation system combines air cooling and liquid cooling. The new axial fan blades of the air cooling part are designed with a bionic airfoil shape, and the motor adopts a brushless DC motor. The liquid cooling part uses nanofluid coolant and microchannel structure liquid cooling pipes to intelligently adjust the cabinet temperature.

[0025] In the battery installation and fixing structure, the adjustable mounting frame uses an electric screw transmission mechanism, which is driven by a stepper motor to precisely adjust the position. The elastic fixing structure composed of rubber springs and disc springs and the buffer pad made of porous polyurethane material ensure the stability of the battery and reduce the impact of vibration. At the same time, the battery positioning device with laser sensors and mechanical positioning pins facilitates battery installation and maintenance. The sensor configuration is rich, and capacitive humidity sensors, piezoresistive pressure sensors, ion smoke sensors and gas sensors monitor the internal environment of the cabinet in real time.

[0026] The energy storage converter adopts a multi-mode power conversion algorithm, the formula is During the day, the photovoltaic panels generate electricity, and the converter converts the power into electricity according to the input voltage, current, frequency parameters and the battery charge state ( ) and ambient temperature ( ), automatically adjusts the working mode, efficiently converts photovoltaic power and stores it in the battery. At night or when there is insufficient light, the inverter converts the battery power into AC power for home use.

[0027] The flexible power supply network topology adopts reconfigurable circuit design, and the intelligent switch matrix uses the switch control strategy optimized by quantum genetic algorithm (formula is ), freely switch between series, parallel and hybrid topologies to optimize power transmission and reduce losses.

[0028] In terms of control methods, the data acquisition and processing process adopts an improved particle filter algorithm (formula is ) Process sensor data, improve data accuracy, and optimize the MPPT algorithm (formula is ) Using chaos search and adaptive step size adjustment, the maximum power point is found on the photovoltaic panel power-voltage curve to improve the efficiency of photovoltaic power generation. Estimation algorithm (formula is ) Combined with the improved Kalman filter and support vector machine, the battery state of charge is accurately estimated, charging and discharging are arranged reasonably, and the safety protection control mechanism adopts the fault diagnosis and protection strategy of multi-sensor fusion and fuzzy decision-making (the formula is ), timely diagnose and handle system failures to ensure household electricity safety.

[0029] Example 2: This example describes a remote mountainous area where communication base stations often face unstable power supply due to poor grid coverage. To ensure the normal operation of the base stations, intelligent access photovoltaic energy storage cabinets are installed. The area has abundant sunlight resources, but the climate is changeable, with large temperature differences between day and night, and is often accompanied by severe weather such as strong winds and heavy rains.

[0030] The energy storage cabinet's sealed structure of high-strength, corrosion-resistant alloy material, combined with the layered and staggered layout of the internal new nano-aerogel insulation panels and the magnetorheological fluid shock absorber at the bottom, can effectively withstand the harsh mountain environment and reduce the impact of external factors on the equipment inside the cabinet. The heat dissipation system's combination of air cooling and liquid cooling can be intelligently adjusted according to the temperature sensor monitoring data to ensure the stable operation of the energy storage cabinet in high or low temperature environments.

[0031] The battery installation and fixing structure's adjustable mounting bracket and elastic fixing method adapt to installation requirements in complex mountainous terrain, ensuring battery stability. The rich sensor configuration can monitor the humidity, pressure, smoke and harmful gases inside the cabinet in real time to ensure equipment safety.

[0032] The energy storage inverter's multi-modal power conversion algorithm efficiently converts electrical energy based on the photovoltaic panel's output under different weather and lighting conditions and the battery status, ensuring stable power supply to base station equipment. The reconfigurable circuit design for flexible power supply network topology, through the optimized control strategy of the intelligent switch matrix, flexibly adjusts the circuit topology to reduce power loss and improve power supply efficiency.

[0033] In terms of control methods, the data acquisition and processing process uses an improved particle filter algorithm to process sensor data, overcoming the problem of high data noise in complex mountainous environments and providing accurate data for subsequent control. In the energy storage management control strategy, the optimized MPPT algorithm is combined with chaos search and adaptive step size adjustment to quickly find the maximum power point of the photovoltaic panel under the condition of large changes in light in mountainous areas, thereby improving photovoltaic power generation. The estimation algorithm integrates improved Kalman filtering and support vector machines to accurately estimate the battery state of charge, rationally plan base station equipment power consumption and battery charging and discharging, extend battery life, and the multi-sensor fusion and fuzzy decision-making fault diagnosis and protection strategy of the safety protection control mechanism can timely detect and handle system faults to ensure the normal operation of base station communication equipment.

[0034] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. An intelligent access photovoltaic energy storage cabinet, characterized in that: The photovoltaic energy storage cabinet includes the following components: cabinet structure, cooling system, battery installation and fixing structure, sensor configuration, energy storage converter and flexible power supply network topology: The cabinet body is made of high-strength, corrosion-resistant alloy material and is a sealed structure with multiple layers of heat insulation boards inside and a shock-absorbing device installed at the bottom; The heat dissipation system: combines air cooling and liquid cooling, and intelligently adjusts heat dissipation through temperature sensors; The battery installation and fixing structure adopts an adjustable mounting frame and elastic fixing method; The sensor configuration includes conventional sensors and humidity, pressure, and smoke sensors; The energy storage converter is highly efficient, has high power density and is equipped with multiple protection functions; The flexible power supply network topology adopts a reconfigurable circuit design and can freely switch between series, parallel, and hybrid topologies through an intelligent switch matrix and a dynamic circuit configuration module.

2. The intelligent access photovoltaic energy storage cabinet according to claim 1, characterized in that: In terms of the cabinet structure, the material and layout of the insulation board are further optimized. The insulation board adopts a new type of nano-aerogel insulation material with extremely low thermal conductivity. Its layout adopts a layered staggered design. The gaps between two adjacent layers of insulation boards are staggered to form a maze-like insulation channel. The shock absorption device adopts a magnetorheological fluid shock absorber, which is filled with magnetorheological fluid. The damping force of the shock absorber can be adjusted in real time by controlling the magnetic field strength. When the cabinet is vibrated, the acceleration sensor installed on the cabinet monitors the vibration intensity in real time and transmits the signal to the controller. The controller calculates the magnetic field strength according to the preset algorithm model and adjusts the viscosity of the magnetorheological fluid, thereby changing the damping force of the shock absorber. In the heat dissipation system, the fan of the air-cooled part adopts a new type of axial flow fan, and its blades adopt a bionic airfoil design. At the same time, the fan motor adopts a brushless DC motor. The coolant of the liquid-cooled part adopts a nanofluid coolant with specific heat capacity and thermal conductivity, wherein the nanoparticles are evenly dispersed in the base coolant, and the liquid cooling pipeline adopts a microchannel structure design.

3. The intelligent access photovoltaic energy storage cabinet according to claim 1, characterized in that: In the battery installation and fixing structure, the adjustable mounting frame adopts an electric screw transmission mechanism, and the screw is driven by a stepper motor. By precisely controlling the pulse signal of the stepper motor, precise displacement adjustment of the mounting frame can be achieved. The elastic fixing structure adopts a combination of rubber springs and disc springs. The rubber springs have elasticity and buffering properties, and the disc springs have load-bearing capacity and stability. The buffer pads arranged around the batteries are made of porous polyurethane material. At the same time, a battery positioning device is also provided on the battery mounting frame, through a laser sensor and a mechanical positioning pin. In terms of sensor configuration, the humidity sensor adopts a capacitive humidity sensor, the pressure sensor adopts a piezoresistive pressure sensor, and the smoke sensor adopts an ion smoke sensor. In addition, a gas sensor is added to monitor whether there is any harmful gas leakage inside the cabinet.

4. The intelligent access photovoltaic energy storage cabinet according to claim 1, characterized in that: The energy storage converter adopts a multi-modal power conversion algorithm based on a fuzzy neural network control strategy. By real-time monitoring and analysis of input voltage, current, and frequency parameters, the converter's operating mode is automatically adjusted. The algorithm formula is: ,in is the output power of the converter, is the input voltage, is the input current, is the input frequency, is the battery's state of charge, is the ambient temperature, and are the output functions of the fuzzy neural network, and For the weight coefficient, first, experiments are carried out under different working conditions to collect data on input and output parameters. Then, the genetic algorithm is used to optimize the weight coefficient to minimize the error of the output power. The training process of the fuzzy neural network adopts the back propagation algorithm. By continuously adjusting the weights and thresholds of the network, the output of the network can accurately reflect the relationship between the input parameters and the output power.

5. The intelligent access photovoltaic energy storage cabinet according to claim 1, characterized in that: The intelligent switch matrix of the flexible power supply network topology adopts a switch control strategy optimized by a quantum genetic algorithm. The algorithm formula is: ,in is the state vector of the switch matrix, is the optimal switching state vector, For the switch matrix in state The power loss under The cost of switching, power loss The calculation takes into account the conduction loss and switching loss of the switch. The conduction loss is related to the on-resistance and current of the switch, and the switching loss is related to the switching frequency and voltage change rate of the switch. Taking into account the lifespan and reliability of the switch, the quantum genetic algorithm encodes the switch state vector and utilizes the superposition and entanglement properties of quantum bits to perform a global search in the search space. During the algorithm's iteration process, the quantum bits are updated through quantum rotation gates, and mutation operations are introduced to prevent the algorithm from falling into a local optimal solution.

6. A control method for an intelligent access photovoltaic energy storage cabinet, applicable to the intelligent access photovoltaic energy storage cabinet according to any one of claims 1 to 5, characterized in that: The specific steps of this method are: S1, data acquisition and processing process collects, processes and analyzes sensor data; S2, energy storage management control strategy uses optimized MPPT algorithm and Estimation algorithm for charge and discharge management; S3. The security protection control mechanism includes multi-level protection at the hardware and software levels.

7. The intelligent access photovoltaic energy storage cabinet according to claim 6, characterized in that: In the data acquisition and processing process, an improved particle filter algorithm is used to process and analyze sensor data. The algorithm formula is: ,in For the The estimated state value at time t, For the The particle in The state of the moment, For the The particle in The weight at the moment, the particle state update formula is: ,in is the state transition function, is the control input, is process noise, and the particle weight update formula is: ,in is the observation probability density function, For the In order to avoid the problem of particle degradation, the resampling technology is used to obtain the observation value at each moment. During the resampling process, particles are copied and deleted according to their weights. An adaptive particle number adjustment strategy is introduced to adjust the number of particles in real time according to the system state changes and observation errors.

8. The intelligent access photovoltaic energy storage cabinet according to claim 6, characterized in that: In the energy storage management control strategy, the optimized MPPT algorithm adopts a maximum power point tracking algorithm with chaotic search and adaptive step size adjustment. The core idea of this algorithm is to use the ergodicity and randomness of the chaotic sequence to perform a global search on the power-voltage curve of the photovoltaic panel, and adaptively adjust the step size according to the search results. The algorithm formula is: ,in For the The reference voltage at the moment, For the The voltage step size at the moment, the voltage step size adjustment formula is: ,in is the step size adjustment coefficient, For the The power change at each moment, is the historical maximum power value, and the chaotic sequence is generated using Logistic mapping: ,in is the control parameter, For the In the iterative process of the algorithm, a search point is selected on the power-voltage curve according to the value of the chaotic sequence, the power value of the point is calculated, and compared with the historical maximum power value, and the step size adjustment coefficient is Adaptive adjustment is performed according to the power change rate and search range during the search process.

9. The intelligent access photovoltaic energy storage cabinet according to claim 6, characterized in that: In the energy storage management control strategy, The estimation algorithm adopts an improved Kalman filter and support vector machine fusion algorithm, which combines the real-time performance of Kalman filter and the nonlinear mapping capability of support vector machine. The algorithm formula is: in For the Moment Estimated value, For the The predicted value at time, is the Kalman gain, is the measured value, is the measurement function, For the The current at the moment, the state prediction formula of Kalman filter is: ,in The support vector machine is used to model the nonlinear characteristics of the battery by training a large amount of experimental data. The nonlinear relationship between the voltage, current and temperature parameters is firstly analyzed by Kalman filtering in the process of algorithm implementation. A preliminary estimate is made, and then the estimate and the measured values are fed into a support vector machine for refinement.

10. The intelligent access photovoltaic energy storage cabinet according to claim 6, characterized in that: The safety protection control mechanism adopts a fault diagnosis and protection strategy based on multi-sensor fusion and fuzzy decision-making. This strategy fuses the data of multiple sensors, uses a fuzzy decision-making algorithm to judge the fault status of the system, and takes corresponding protection measures. The algorithm formula is: ,in is the comprehensive fault assessment value, For the The weight of each sensor, For the The fault membership function of each sensor is For the The measurement value of each sensor, fault membership function According to the normal working range and fault threshold of the sensor, the sensor's measurement value is converted into fault membership through fuzzy processing, and the weight The determination is made by evaluating the reliability and importance of the sensor and adjusting it in combination with actual fault cases. In the fuzzy decision-making process, the fault level of the system is judged according to the comprehensive fault evaluation value and the preset fuzzy rules, and corresponding protection actions are taken.

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