Control system of photovoltaic energy storage inverter

Abstract

The invention discloses a control system for a photovoltaic energy storage inverter, and the system comprises a maximum power point tracking module which is used for adjusting the output of a photovoltaic panel after the photovoltaic energy storage inverter receives a power input signal, optimizing the energy collection, a dynamic adjustment module which is used for adjusting the output of the photovoltaic panel based on the power grid fluctuation and load change parameters which are monitored in real time, the control system of the photovoltaic energy storage inverter comprises a control module used for dynamically adjusting the output frequency and voltage of the inverter and achieving efficient conversion and matching of electric energy, and a closed-loop feedback module used for continuously adjusting the output parameters of the inverter until the output parameters reach a preset energy conversion efficiency threshold value. The system is ensured to be always in an optimal operation state, and unpredictable factor changes are accurately and intelligently adapted, so that the overall conversion efficiency of the photovoltaic energy storage system is improved.

Description

Technical Field

[0001] The present invention relates to the field of power electronics technology, and in particular to a control system of a photovoltaic energy storage inverter. Background Art

[0002] In modern energy management systems, the control system of photovoltaic energy storage inverters is a key technical field, which is mainly used to manage and optimize the energy flow between solar photovoltaic systems and battery energy storage systems to ensure the efficient operation of energy systems. However, existing technologies face many challenges. Existing photovoltaic energy storage inverter control methods can usually work effectively under stable conditions, but when encountering unpredictable external factors, such as fluctuations in grid voltage or frequency, sudden changes in local load demand, etc., the stability and efficiency of the system are often difficult to guarantee. These problems not only affect the normal operation of the inverter, but also have an adverse effect on the energy efficiency ratio and energy conversion efficiency of the entire system.

[0003] To solve these problems, researchers are developing technical solutions for dynamic compensation and optimized scheduling to maximize the performance and economic benefits of photovoltaic energy storage systems while ensuring stable power supply. In addition, in terms of control strategies, advanced prediction algorithms and technologies have begun to be used in existing technologies to reduce the overall operating performance loss caused by changes in external factors. However, although these improvements have helped, existing technologies still find it difficult to ensure that the system is always in the best operating state when faced with complex and changing environments. Therefore, how to accurately and intelligently adapt to these unforeseen changes in factors to improve the overall conversion efficiency of photovoltaic energy storage systems remains an important and challenging task. Summary of the invention

[0004] The purpose of the present invention is to provide a control system for a photovoltaic energy storage inverter to solve the problem that the prior art is difficult to ensure that the system is always in the best operating state when facing a complex and changeable environment.

[0005] To achieve the above object, the present invention provides the following technical solution: a control system for a photovoltaic energy storage inverter, the system comprising:

[0006] Maximum power point tracking module, used to adjust the output of the photovoltaic panel after the photovoltaic energy storage inverter receives the power input signal to optimize energy collection;

[0007] The dynamic adjustment module connected to the maximum power point tracking module is used to dynamically adjust the output frequency and voltage of the inverter based on the real-time monitoring of grid fluctuations and load change parameters to achieve efficient conversion and matching of electric energy;

[0008] A closed-loop feedback module connected to the dynamic regulation module is used to continuously adjust the output parameters of the inverter until a preset energy conversion efficiency threshold is reached;

[0009] The dynamic adjustment module dynamically adjusts the output frequency and voltage of the inverter based on the real-time monitored grid fluctuation and load change parameters, including obtaining grid fluctuation data based on the current grid frequency, obtaining load change parameters according to the current load power consumption, adjusting the inverter output to ensure that the grid matches the load, performing power conversion efficiency evaluation and updating the inverter output parameters as needed;

[0010] The closed-loop feedback module continuously adjusts the output parameters of the inverter until a preset energy conversion efficiency threshold is reached, including regularly collecting output frequency and voltage change data and transmitting them to the control center to evaluate the effectiveness of the conversion process. If the evaluation finds that the actual energy conversion efficiency of the output is lower than the pre-defined target level, the feedback control mechanism is activated for correction. The control program calculates a corresponding correction signal based on the size of the deviation value, and applies the signal to the output parameter adjustment. The actual operating status after adjustment is continuously measured until the conversion efficiency is detected to be equal to or exceeds the set benchmark. After the established conversion efficiency threshold is reached, it is monitored whether there is room for further improvement and prepared for readjustment operations in the next cycle.

[0011] Preferably, the maximum power point tracking module adjusts the output of the photovoltaic panel after the photovoltaic energy storage inverter receives the power input signal, and optimizes energy collection, including:

[0012] Monitor the output current and voltage of the photovoltaic panel to obtain the actual power value;

[0013] Comparing the actual power value with a preset maximum power point value;

[0014] Based on the comparison result, the working point of the photovoltaic panel is adjusted to approach or reach the maximum power point, wherein the working point of the photovoltaic panel is gradually approached or reaches the maximum power point by adjusting the control parameters ΔV and ΔI of the inverter, specifically:

[0015] ΔV=V mppt -V pv ;

[0016] ΔI=I mppt -I pv ;

[0017] The inverter will increase or decrease the operating voltage V pv and current I pv To approach V mppt and I mppt value until the maximum power output is achieved;

[0018] Continuously scan changes in environmental conditions and adjust the operating status of photovoltaic panels accordingly;

[0019] The operating parameters are adjusted again according to the new maximum power point under the conditions after optimization to maintain the photovoltaic system operating at the best performance level.

[0020] Preferably, the continuously scanning environmental condition changes and making corresponding adjustments to the operating state of the photovoltaic panel, and adjusting the operating parameters again according to the new maximum power point under the conditions after the adjustment include:

[0021] Continuously monitor changes in environmental conditions and optimize the operating status of the photovoltaic panels in real time through the following functions:

[0022] P opt =η sys ×G×A×cos(θ);

[0023] Among them, P opt refers to the optimized power output, η sys is the system efficiency, G is the solar irradiance, A is the area of ​​the photovoltaic panel, and θ is the incident angle of the photovoltaic panel. As the light intensity G and temperature change, the inverter will automatically adjust the working point of the photovoltaic panel to maintain a state close to the maximum power output;

[0024] According to the above optimized operating conditions, the new maximum power point is re-determined, and the inverter operating parameters V are adjusted again. pv and I pv In order to maintain the optimal performance level of the photovoltaic system, this process is achieved through a closed-loop control feedback mechanism, ensuring that the photovoltaic energy storage system always operates at the highest efficiency in a dynamic environment and maximizes energy collection and conversion rate.

[0025] Preferably, the dynamic adjustment module adjusts the inverter output to ensure that the power grid matches the load, including:

[0026] A proportional-integral-derivative controller is introduced to adjust the output voltage V of the inverter. inv and frequency f inv , to match the grid frequency and load demand, the specific formula is:

[0027]

[0028] e(t)=P load( t)-P inv (t),

[0029] Among them, P load (t) is the load power at the current moment, t is the moment, P inv (t) represents the output power of the inverter at the current time t, e f(t) = Δfgrid is the grid frequency deviation, K p , K i , K d They are the proportional, integral and differential coefficients of the PID controller respectively.

[0030] Preferably, the dynamic adjustment module performs power conversion efficiency evaluation and updates inverter output parameters as needed, including:

[0031] Using the real-time efficiency calculation module, based on the input power P in And the output power P out Calculate the current inverter power conversion efficiency η inv When the efficiency is lower than the preset threshold, the inverter control parameter V is automatically updated. inv and f inv , the specific formula is:

[0032]

[0033] Among them, η in v(t) is the current inverter power conversion efficiency, t is the time, P in (t) is the input power of the inverter, P out (t) is the output power, when η inv (t) is lower than the set threshold η threshold When adjusting the inverter output parameters, the energy conversion efficiency can be improved.

[0034] Preferably, the closed-loop feedback module regularly collects the output frequency and voltage change data and transmits them to the control center to evaluate the effectiveness of the conversion process, including:

[0035] Introducing a data analysis method based on wavelet transform, the output frequency f out (t) and output voltage V out (t) The time-frequency analysis of the changing data can accurately capture the transient disturbance and harmonic characteristics in the power conversion process. The specific formula is:

[0036]

[0037] Among them, W f (τ, s) is the continuous wavelet transform result of the output frequency data, t is the time, ψ is the mother wavelet function, τ and s represent the time displacement and translation scale parameters respectively.

[0038] Preferably, if the closed-loop feedback module finds that the actual energy conversion efficiency of the output is lower than a predefined target level, the feedback control mechanism is activated to correct the following:

[0039] By establishing an energy conversion efficiency model, the Kalman filter is used to calculate the actual energy conversion efficiency η real For prediction and correction, the specific formula is:

[0040] η real (t|t-1)=η real (t-1)+K(t)[η measured (t)-η real (t-1)]

[0041]

[0042] Among them, η measured (t) is the efficiency value measured at the current moment, t is the time, η real (t|t-1) is the actual efficiency value after correction by the Kalman filter, K(t) is the Kalman gain, P(t|t-1) is the estimation error covariance, and R is the measurement noise covariance.

[0043] Preferably, the closed-loop feedback module control program calculates a corresponding correction signal based on the size of the deviation value, and applies the signal to the output parameter adjustment, including:

[0044] Fuzzy logic control is used to process the deviation value Δη and generate the corresponding correction signal u(t). The specific formula is:

[0045] u(t)=Fuzzy(Δη,Δη′),

[0046] Among them, u(t) is the correction signal generated by the control program, t is the time, Fuzzy is the function of the fuzzy logic controller, Δη is the efficiency deviation, and Δη′ is the rate of change of the deviation;

[0047] The fuzzy logic controller takes the efficiency deviation Δη and the deviation change rate Δη′ as input and outputs a correction signal u(t) to adjust the output parameter V of the inverter. out (t) and f out (t).

[0048] Preferably, the closed-loop feedback module continuously measures the adjusted actual operating state until it detects that the conversion efficiency is equal to or exceeds a set benchmark, including:

[0049] A continuous supervised learning mechanism is introduced to automatically adjust the control strategy by real-time monitoring of the adjustment results to ensure that the energy conversion efficiency is stable above the target level. The specific formula is:

[0050]

[0051] Among them, η real (t) is the actual energy conversion efficiency at the current moment t, t is the moment, ηthreshold is the preset benchmark efficiency, Adjust Strategy(η real ,u(t)) is the adjustment strategy function. If the actual energy conversion efficiency η real (t) greater than or equal to the preset reference efficiency η threshold , the control strategy is adjusted according to the current actual efficiency and control signal u(t) to ensure that the system performance remains at or exceeds the expected target. If the target is not reached, the control strategy is automatically adjusted to optimize the output.

[0052] Preferably, the closed-loop feedback module monitors whether there is room for further improvement after reaching a predetermined conversion efficiency threshold and prepares for a readjustment operation in the next cycle, including:

[0053] Genetic algorithms are used for optimization to find possible better parameter configurations in the next cycle. Genetic algorithms simulate the natural selection process to explore the global optimal solution in the parameter space. The specific formula is:

[0054]

[0055] Among them, η opt For the optimized energy conversion efficiency, V out Indicates the output voltage f out Indicates the output frequency, indicating the actual energy conversion efficiency η real (t) represents the actual energy conversion efficiency, η threshold is the benchmark efficiency, f is the objective function;

[0056] The genetic algorithm searches for the output voltage V through evolutionary selection, crossover and mutation operations. out and frequency f out The optimal combination of energy conversion efficiency η opt maximize.

[0057] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0058] The control system of the photovoltaic energy storage inverter adjusts the output of the photovoltaic panel after the photovoltaic energy storage inverter receives the power input signal through the maximum power point tracking module to optimize energy collection. The dynamic adjustment module dynamically adjusts the output frequency and voltage of the inverter based on the real-time monitored grid fluctuations and load change parameters to achieve efficient conversion and matching of electric energy. The closed-loop feedback module continuously adjusts the output parameters of the inverter until the preset energy conversion efficiency threshold is reached. It can significantly improve the stability of the photovoltaic energy storage inverter control system under unforeseen factors such as grid voltage or frequency fluctuations and local load demand changes, greatly improve the overall energy conversion efficiency of the photovoltaic energy storage system, and dynamically adjust the operating state of the system according to real-time monitoring data to ensure that the system can flexibly respond to complex and changeable environmental changes and always maintain the best operating state, thereby improving the economic benefits of the photovoltaic energy storage system and reducing operating costs. It solves the problem of difficulty in ensuring that the system is always in the best operating state in the face of complex and changeable environments, and achieves accurate and intelligent adaptation to these unforeseen factors to improve the overall conversion efficiency of the photovoltaic energy storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] like Figure 1 As shown, a control system of a photovoltaic energy storage inverter, the system comprising:

[0062] The maximum power point tracking module is used to adjust the output of the photovoltaic panel after the photovoltaic energy storage inverter receives the power input signal and optimize energy collection. After the photovoltaic energy storage inverter receives the power input signal, it will start. The system will immediately monitor and adjust the power size and direction provided by the photovoltaic power generation components through the integrated maximum power point tracking technology to ensure that the power output of the photovoltaic panel is maximized at any time. This process involves continuous evaluation of the light intensity of the working environment to dynamically change the working state of the circuit to make the system at or close to the maximum power point. For example, a distributed photovoltaic system installed on a residential roof receives strong sunlight at noon and generates a high current output. The system can identify this situation in real time and use appropriate control strategies to make the current flow through the circuit in the best way, thereby reducing losses caused by environmental changes.

[0063] The dynamic adjustment module connected to the maximum power point tracking module is used to dynamically adjust the output frequency and voltage of the inverter based on the real-time monitored grid fluctuations and load change parameters to achieve efficient conversion and matching of electric energy. Based on this, the changes in real-time operating conditions are further considered. This control strategy further responds flexibly according to the actual conditions of the grid and the specific changes in the actual load on site. It uses real-time monitoring equipment to continuously collect feedback information about the quality of the grid and the load level, such as detecting current fluctuations on the common coupling node and the increase or decrease in load caused by the opening or closing of electrical equipment in the home or factory. It adjusts its own operation mode by obtaining and using such dynamically adjusted feedback information. The controller can adjust its frequency and output amplitude based on the information received to maintain good matching between the current and voltage phase after inversion and the grid, maintain a high efficiency of energy conversion capability under actual working conditions, and ensure that the energy generated inside the power system is injected into the public grid in the best form or directly supplied to the load. For example, when it is detected that the grid frequency rises or falls slightly (that is, the grid fluctuates) and the number of electrical appliances in the user's home increases or decreases (causing a load change), the system will make timely and appropriate corrections to the output parameters.

[0064] The closed-loop feedback module connected to the dynamic adjustment module is used to continuously adjust the output parameters of the inverter until the preset energy conversion efficiency threshold is reached. A closed control loop is used to realize the functions of automation and self-correction, which is the so-called feedback correction process. This control loop allows the device to continuously compare the error between the desired state and its actual operation and continuously make fine adjustments to the output characteristics until the two are consistent. Therefore, it can automatically compensate for the adverse changes caused by all external factors, overcome the influence of transient disturbances, and ensure that the inverter output power can reach the ideal range in practical applications. For example, if there is still a small range of incoordination after the initial adjustment stage (that is, the output frequency is not synchronized with the power grid, etc.), the gap will be gradually eliminated through repeated fine-tuning. In this way, it not only helps to improve the working quality of the equipment itself, but also enhances compatibility with the power grid, so that photovoltaic energy can be converted and applied more efficiently.

[0065] It is with the help of the combined effect of the above-mentioned processes that the utilization rate and inversion effect of photovoltaic power generation can be improved under conditions of large grid fluctuations or large load changes, a stable and reliable energy conversion process can be achieved, the application level of equipment for renewable energy management can be improved, and the potential and advantages of renewable energy can be better utilized.

[0066] The dynamic adjustment module dynamically adjusts the output frequency and voltage of the inverter based on real-time monitoring of grid fluctuations and load change parameters, including obtaining grid fluctuation data based on the current grid frequency, obtaining load change parameters based on the current load power consumption, adjusting the inverter output to ensure that the grid matches the load, performing power conversion efficiency evaluation and updating the inverter output parameters as needed.

[0067] By real-time monitoring of grid fluctuations and load change parameters, the output frequency and voltage of the inverter are dynamically adjusted to achieve efficient conversion of electric energy. This process is mainly based on the real-time collection and analysis of grid frequency and load power consumption. The monitoring module obtains grid frequency fluctuation data, and adjusts the output of the inverter according to the power consumption change parameters of the current load, so that the inverter can match the grid and load, thereby optimizing the power conversion efficiency. Further, the power conversion efficiency is continuously monitored through the power conversion efficiency evaluation module, and the output parameters of the inverter are updated as needed to ensure the efficient operation of the system. The control of the photovoltaic energy storage inverter dynamically adjusts the output frequency and voltage of the inverter by real-time monitoring of grid fluctuations and load change parameters to achieve efficient conversion and matching of electric energy.

[0068] By real-time monitoring of grid fluctuations and load change parameters, the output frequency and voltage of the inverter are dynamically adjusted to achieve efficient conversion and matching of electric energy. This dynamic adjustment method can ensure that the inverter always maintains the best energy conversion efficiency under different grid states and load conditions, significantly improving the overall performance of the system. Compared with the traditional fixed parameter control method, the present invention can respond to external changes more quickly, thereby reducing energy loss and improving the stability and reliability of the system.

[0069] The closed-loop feedback module continuously adjusts the output parameters of the inverter until the preset energy conversion efficiency threshold is reached, including regularly collecting output frequency and voltage change data and transmitting them to the control center to evaluate the effectiveness of the conversion process. If the evaluation finds that the actual energy conversion efficiency of the output is lower than the pre-defined target level, the feedback control mechanism is activated for correction. The control program calculates the corresponding correction signal based on the size of the deviation value, and applies the signal to the output parameter adjustment. The actual operating status after adjustment is continuously measured until the conversion efficiency is detected to be equal to or exceeds the set benchmark. After reaching the established conversion efficiency threshold, it monitors whether there is room for further improvement and prepares for readjustment operations in the next cycle.

[0070] The output frequency and voltage of the inverter are dynamically adjusted through a closed-loop feedback control mechanism. First, the system regularly collects data on changes in the output frequency and voltage and transmits this data to the control center. The control center evaluates this data to determine whether the current energy conversion efficiency has reached the pre-defined target level. If the evaluation results show that the actual conversion efficiency is lower than the target value, the system will immediately activate the feedback control mechanism, generate a correction signal by calculating the deviation value, and apply it to the output parameter adjustment of the inverter. The adjusted system operation status will continue to be monitored and evaluated until the conversion efficiency reaches or exceeds the set benchmark.

[0071] The maximum power point tracking module adjusts the output of the photovoltaic panel after the photovoltaic energy storage inverter receives the power input signal, optimizing energy collection including:

[0072] Monitor the output current and voltage of the photovoltaic panel to obtain the actual power value;

[0073] Comparing the actual power value with a preset maximum power point value;

[0074] Based on the comparison result, the working point of the photovoltaic panel is adjusted to approach or reach the maximum power point, wherein the working point of the photovoltaic panel is gradually approached or reaches the maximum power point by adjusting the control parameters ΔV and ΔI of the inverter, specifically:

[0075] ΔV=V mppt -V pv ;

[0076] ΔI=I mppt -I pv ;

[0077] The inverter will increase or decrease the operating voltage V pv and current I pv To approach V mppt and I mppt value until the maximum power output is achieved;

[0078] Continuously scan changes in environmental conditions and make corresponding adjustments to the operating status of photovoltaic panels;

[0079] The operating parameters are adjusted again according to the new maximum power point under the conditions after optimization to maintain the photovoltaic system operating at the best performance level.

[0080] In the above scheme, the control system of the photovoltaic energy storage inverter optimizes the energy output of the photovoltaic panel through the maximum power point tracking module. The module obtains the actual power value by real-time monitoring the output current and voltage of the photovoltaic panel and compares it with the preset maximum power point. Based on this comparison result, the system dynamically adjusts the working point of the photovoltaic panel to gradually approach or reach the maximum power point. The core of this adjustment process lies in the adjustment of the control parameters ΔV and ΔI of the inverter. ΔV represents the current output voltage V of the photovoltaic panel. pv With the maximum power point voltage V mppt The difference between the two, ΔI represents the current photovoltaic panel output current I pv and the maximum power point current I mppt By continuously adjusting these parameters, the inverter can adjust the operating voltage and current of the photovoltaic panel to gradually approach the maximum power point, thereby achieving maximum power output. In addition, the system has a continuous environmental condition scanning function. By monitoring environmental changes, the system can optimize the working state of the photovoltaic panel in real time. When environmental factors such as light intensity and temperature change, the system will recalculate the new maximum power point and adjust the inverter's operating parameters again to ensure that the photovoltaic system always operates at the best performance level under dynamic conditions. Through this closed-loop feedback control mechanism, the photovoltaic energy storage system can effectively respond to various environmental changes and maximize energy collection and conversion efficiency.

[0081] By real-time monitoring and adjusting the output voltage and current of the photovoltaic panels, the system can make the photovoltaic panels always operate near the maximum power point, thereby greatly improving the overall energy conversion efficiency of the photovoltaic system. Even when the lighting conditions or temperature change, the system can respond quickly and re-optimize the operating point to ensure maximum output power. The closed-loop feedback control mechanism and continuous environmental monitoring enable the working state of the photovoltaic panels to be optimized in real time in a dynamic environment. In this way, the system effectively reduces the energy loss caused by environmental changes and significantly improves the energy collection efficiency. In addition, the precise adjustment of ΔV and ΔI enables the photovoltaic panels to quickly reach the optimal working state, further improving the operating efficiency of the photovoltaic system, and continuously monitoring environmental conditions, such as solar irradiance G and the incident angle θ of the photovoltaic panel, and calculating the optimized power output P based on these parameters. opt , and adjust the inverter operating parameters V pv and I pv , in order to keep the photovoltaic system running close to the maximum power point. Through real-time optimization, the system can quickly adjust the working state of the photovoltaic panels according to changes in environmental conditions to maximize energy collection and conversion efficiency.

[0082] The control parameters of the inverter, such as the adjustment range of ΔV and ΔI, can be expanded or reduced according to the actual application requirements. For example, in areas with large light intensity fluctuations, the adjustment range of these parameters can be appropriately expanded to ensure that the system can respond quickly to changing environmental conditions. At the same time, the scanning frequency of the system can also be adjusted according to the type of photovoltaic panel and environmental changes, and the connection method between the photovoltaic panel and the inverter can be optimized according to different application environments. For example, in scenarios that require frequent replacement or maintenance, plug-in connectors can be used to improve the convenience and speed of operation. In the case of long-term fixed installation, a more solid welding or bolting method can be selected to ensure the stability and safety of the system. In addition to the existing control algorithms, artificial intelligence (AI) technology or machine learning algorithms can be introduced to further optimize the energy output of photovoltaic panels. By learning historical data and environmental change laws, the system can predict future light and temperature changes, so as to adjust the working parameters in advance and maximize energy collection efficiency.

[0083] Continuously scan the environmental conditions and adjust the operating status of the photovoltaic panels accordingly. Adjust the operating parameters again according to the new maximum power point under the conditions after adjustment, including:

[0084] Continuously monitor changes in environmental conditions and optimize the operating status of the photovoltaic panels in real time through the following functions:

[0085] P opt =η sys ×G×A×cos(θ);

[0086] Among them, P opt refers to the optimized power output, η sys is the system efficiency, G is the solar irradiance, A is the area of ​​the photovoltaic panel, and θ is the incident angle of the photovoltaic panel. As the light intensity G and temperature change, the inverter will automatically adjust the working point of the photovoltaic panel to maintain a state close to the maximum power output;

[0087] According to the above optimized operating conditions, the new maximum power point is re-determined, and the inverter operating parameters V are adjusted again. pv and I pv In order to maintain the optimal performance level of the photovoltaic system, this process is achieved through a closed-loop control feedback mechanism, ensuring that the photovoltaic energy storage system always operates at the highest efficiency in a dynamic environment and maximizes energy collection and conversion rate.

[0088] The control system of the photovoltaic energy storage inverter continuously monitors changes in environmental conditions to adjust the operating status of the photovoltaic panel in real time to ensure that the system can always maintain the optimal power output state. The energy output of the photovoltaic panel is affected by factors such as solar irradiance, temperature and incident angle, which will fluctuate with time and weather conditions. In order to adapt to these changes, the system optimizes its energy conversion efficiency by dynamically adjusting the working point of the photovoltaic panel. Specifically, it monitors the current solar irradiance G, photovoltaic panel area A and photovoltaic panel incident angle θ in real time, and uses the formula P opt =η sys ×G×A×cos(θ) to calculate the optimized power output P opt Among them, η sys Indicates the overall efficiency of the system. As environmental conditions change, such as light intensity increases or decreases, the system will recalculate the optimal power output point of the photovoltaic panel based on these new conditions and adjust the operating voltage V pv and current I pv To ensure that the photovoltaic panels are always close to or at the maximum power output state, in addition, a closed-loop control feedback mechanism is adopted to automatically adjust the control parameters of the inverter by acquiring and analyzing the output status and environmental data of the photovoltaic panels in real time. When environmental conditions change, such as fluctuations in light intensity and temperature, the system will respond quickly and readjust the operating parameters of the inverter to maintain the optimal performance of the photovoltaic system. Through this closed-loop feedback control method, the system can continuously optimize the energy output of the photovoltaic panels under different environmental conditions, thereby maximizing the energy collection and conversion efficiency and ensuring that the system always operates efficiently in a dynamic environment.

[0089] It can dynamically adjust the working point of the photovoltaic panel according to the real-time changing environmental conditions, so that the photovoltaic system always operates at a position close to the maximum power point. This function ensures that the photovoltaic panel can maximize the conversion of solar energy into electrical energy under all weather conditions, thereby significantly improving the overall energy output performance of the system. Through the closed-loop control feedback mechanism, the system can continuously monitor and optimize the working status of the photovoltaic panel to reduce energy losses caused by environmental changes. The system's real-time tuning function for photovoltaic panels enables the photovoltaic energy storage system to respond quickly to environmental changes, reducing efficiency losses caused by delayed adjustments, thereby effectively improving the system's energy conversion efficiency.

[0090] Control parameters, such as the adjustment range of voltage Vpv and current Ipv, can be expanded or reduced according to different PV panel types or environmental conditions. For example, in areas where light conditions fluctuate greatly, the system can expand its operating voltage range to better adapt to changing light intensities. At the same time, the system's scanning frequency can be adjusted to respond to environmental changes more quickly and improve the overall efficiency of the system. In terms of the connection between the PV panels and the inverter, plug-in connectors can be used to facilitate installation and maintenance. This design is particularly suitable for application scenarios that require frequent disassembly and assembly, which can significantly reduce installation time and improve operational safety. In fixed installations that require higher stability, the use of more robust welding or bolted connections can ensure the long-term reliability of the system.

[0091] The dynamic regulation module adjusts the inverter output to ensure that the grid matches the load, including:

[0092] A proportional-integral-derivative controller is introduced to adjust the output voltage V of the inverter. inv and frequency f inv , to match the grid frequency and load demand, the specific formula is:

[0093]

[0094] e(t)=P load (t)-P inv (t),

[0095] Among them, P load (t) is the load power at the current moment, t is the moment, P inv (t) represents the output power of the inverter at the current time t, e f (t) = Δfgrid is the grid frequency deviation, K p , K i , K d They are the proportional, integral and differential coefficients of the PID controller respectively.

[0096] In the above scheme, the dynamic adjustment module of the photovoltaic energy storage inverter accurately adjusts the output voltage and frequency of the inverter by introducing a proportional-integral-differential controller (PID) to ensure that the grid matches the load demand. The role of the PID controller in this system is to adjust the output parameters in real time so that the inverter can respond quickly to changes in the load and ensure system stability and energy transmission efficiency. The PID controller can accurately adjust the output parameters of the inverter to keep it highly consistent with the frequency and load demand of the grid. The controller calculates the deviation of the grid frequency, e f (t) = Δfgrid, combining the changing trend of load power and inverter output power, automatically adjust the output voltage Vinv and frequency finv to ensure the stable operation of the system.

[0097] The PID controller can accurately adjust the output parameters of the inverter so that the voltage and frequency can quickly respond to changes in the power grid and load and maintain the stability of the system. This real-time adjustment capability ensures that the system can maintain the best working state under various load conditions, thereby significantly improving the overall performance of the inverter. Through the proportional, integral and differential adjustment of the PID controller, the system can minimize the impact of voltage and frequency fluctuations on energy conversion efficiency. The PID controller can quickly eliminate errors and prevent the system from producing excessive overshoot or oscillation, ensuring efficient power transmission, thereby improving the overall energy conversion efficiency of the photovoltaic energy storage system.

[0098] In addition to the traditional PID control algorithm, the system can also introduce other intelligent control algorithms, such as fuzzy logic control or adaptive control, to improve the system's response speed and stability to complex load changes. Through these advanced control strategies, the system can more intelligently adjust the output parameters to adapt to more complex grid conditions and changing load requirements, thereby improving the overall performance. The parameters of the PID controller, such as the proportional coefficient Kp, integral coefficient Ki and differential coefficient Kd, can be adjusted and optimized according to different grid and load characteristics. In order to cope with specific application scenarios, the system can provide a variety of preset control modes, and users can choose the most suitable parameter configuration according to actual needs. In addition, the system can integrate a self-adjusting function to automatically adjust the control parameters according to the real-time operating conditions to ensure that the best operating state can be maintained under various conditions.

[0099] The dynamic regulation module performs power conversion efficiency evaluation and updates inverter output parameters as needed, including:

[0100] Using the real-time efficiency calculation module, based on the input power P in And the output power P out Calculate the current inverter power conversion efficiency η inv When the efficiency is lower than the preset threshold, the inverter control parameter V is automatically updated. inv and f inv , the specific formula is:

[0101]

[0102] Among them, η in v(t) is the current inverter power conversion efficiency, t is the time, P in (t) is the input power of the inverter, P out (t) is the output power, when η inv (t) is lower than the set threshold η threshold When adjusting the inverter output parameters, the energy conversion efficiency can be improved.

[0103] In the above implementation, the dynamic adjustment module of the photovoltaic energy storage inverter evaluates the power conversion efficiency in real time and automatically adjusts the output parameters of the inverter when necessary to ensure the efficient operation of the system. The system monitors the input power P of the inverter through the real-time efficiency calculation module. in And the output power P out , calculate the current energy conversion efficiency η inv (t). This efficiency reflects the effectiveness of the system in converting input energy into output electrical energy. When the real-time calculated electrical energy conversion efficiency η inv (t) is lower than the set threshold η threshold When the system automatically triggers the adjustment mechanism, the output parameter V inv (output voltage) and f inv By adjusting these parameters, the system can optimize the working state of the inverter, improve the efficiency of power conversion, and ensure that the system continues to operate in an efficient manner.

[0104] It can monitor the power conversion efficiency of the inverter in real time and automatically adjust the output parameters when the efficiency drops, ensuring that the inverter always operates in the best state. This real-time adjustment mechanism greatly improves the overall performance of the system and ensures efficient energy conversion under various load and environmental conditions. By automatically adjusting the output voltage and frequency of the inverter, the system can quickly respond to efficiency changes, optimize the working state of the inverter, and reduce energy loss. This dynamic adjustment enables the system to quickly restore efficient operation when the efficiency drops, thereby significantly improving the overall energy conversion efficiency and maximizing the output of the photovoltaic energy storage system.

[0105] The adjustment range of control parameters, such as voltage Vinv and frequency finv, can be expanded or reduced according to application requirements. For example, in a high load fluctuation environment, the adjustment range of these parameters can be expanded to ensure that the system can quickly adapt to frequent load changes while maintaining efficient operation.

[0106] The closed-loop feedback module regularly collects the output frequency and voltage change data and transmits it to the control center to evaluate the effectiveness of the conversion process, including:

[0107] Introducing a data analysis method based on wavelet transform, the output frequency f out (t) and output voltage V out (t) The time-frequency analysis of the changing data can accurately capture the transient disturbance and harmonic characteristics in the power conversion process. The specific formula is:

[0108]

[0109] Among them, W f(τ, s) is the continuous wavelet transform result of the output frequency data, t is the time, ψ is the mother wavelet function, τ and s represent the time displacement and translation scale parameters respectively.

[0110] In the above scheme, the closed-loop feedback module of the photovoltaic energy storage inverter accurately analyzes the changes in output frequency and voltage by introducing a data analysis method based on wavelet transform. Wavelet transform is a powerful tool for non-stationary signal analysis, which can provide detailed information in both the time domain and the frequency domain, making it particularly suitable for analyzing transient disturbances and harmonic characteristics in the inverter output signal. The closed-loop feedback module regularly collects data on the inverter's output frequency fout(t) and output voltage Vout(t), and performs continuous wavelet transform on these data. Through this transformation, the system can decompose the signal into different scales and time positions, thereby identifying and analyzing transient changes in different frequency components. This process enables the system to identify short-term disturbances caused by load changes, grid fluctuations or other external interference. The results of the wavelet transform can accurately capture these transient disturbances and harmonic distortions, thereby providing high-resolution data to the control center and helping the system evaluate the effectiveness of the power conversion process.

[0111] Wavelet transform can analyze in both time domain and frequency domain, accurately capturing transient disturbances and harmonic distortion in the inverter output signal. This high-resolution analysis method can help the system identify transient phenomena caused by load changes, grid fluctuations or other external interferences in real time, so as to adjust the working state of the inverter in time and ensure the stability and efficiency of the power conversion process. Through accurate analysis of output frequency and voltage, the system can better identify and handle factors that affect energy conversion efficiency. The application of wavelet transform enables the system to quickly detect and respond to abnormal fluctuations in frequency or voltage, thereby reducing energy losses and maximizing the output efficiency of photovoltaic energy storage systems.

[0112] Although wavelet transform is a powerful time-frequency analysis tool, other data analysis methods may be more applicable in certain specific cases. For example, short-time Fourier transform (STFT) can be used to analyze stable harmonic components in signals, while Hilbert-Huang transform (HHT) is suitable for processing nonlinear and non-stationary signals. These alternative methods can provide different time-frequency resolutions and analysis effects according to different application requirements, ensuring that the system can accurately capture and analyze the key features of the power conversion process under various working conditions. The technical parameters of the system, such as data acquisition frequency and analysis window size, can be adjusted according to different application scenarios. For example, in a high-dynamic environment, the time interval for data acquisition can be shortened and the frequency of analysis can be increased to ensure that the system can capture and respond to frequently changing signals in a timely manner. In terms of data processing and storage, more advanced processing algorithms and storage technologies can be used. For example, using cloud computing or edge computing technology, the system can perform complex data analysis and storage on the cloud or edge devices, thereby reducing the computing burden of local devices and improving the efficiency and flexibility of data processing.

[0113] If the evaluation finds that the actual energy conversion efficiency of the output is lower than the pre-defined target level, the closed-loop feedback module activates the feedback control mechanism to correct the following:

[0114] By establishing an energy conversion efficiency model, the Kalman filter is used to calculate the actual energy conversion efficiency η real For prediction and correction, the specific formula is:

[0115] η real (t|t-1)=η real (t-1)+K(t)[η measured (t)-η real (t-1)]

[0116]

[0117] Among them, η measured (t) is the efficiency value measured at the current moment, t is the time, η real (t|t-1) is the actual efficiency value after correction by the Kalman filter, K(t) is the Kalman gain, P(t|t-1) is the estimation error covariance, and R is the measurement noise covariance.

[0118] In the above embodiment, the closed-loop feedback module of the photovoltaic energy storage inverter predicts and corrects the actual energy conversion efficiency by establishing an energy conversion efficiency model and using a Kalman filter. When the system evaluation finds that the actual energy conversion efficiency of the output is lower than the predefined target level, the feedback control mechanism will be activated to adjust and optimize the operating state of the system in real time. The Kalman filter is a recursive estimation algorithm widely used in dynamic systems, and is particularly suitable for processing noisy measurement data. In this system, the Kalman filter is used to continuously predict and correct the energy conversion efficiency of the photovoltaic energy storage inverter to ensure that the system can respond quickly and correct efficiency deviations. The basic principle of the Kalman filter includes two main steps: prediction and update. Prediction stage: In this stage, the Kalman filter predicts the actual energy conversion efficiency at the current moment based on the estimated value at the previous moment. This predicted value is based on the previous system state and control input, which represents the best estimate of the current state of the system in the absence of new observation data. Update stage: When the new observation data (that is, the currently measured energy conversion efficiency η measured When K(t) is available, the Kalman filter compares the predicted value with the measured value and corrects the predicted value by the Kalman gain K(t), thereby obtaining a more accurate efficiency estimate.

[0119] The Kalman filter can correct the estimated value of energy conversion efficiency in real time to ensure that the system always maintains high efficiency during operation. Through dynamic prediction and adjustment, the system can quickly respond to changes in efficiency and avoid performance loss due to decreased efficiency. This real-time adjustment mechanism significantly improves the overall performance of the photovoltaic energy storage system, enabling it to maintain optimal operating conditions under various environmental conditions. The application of the Kalman filter effectively reduces the impact of measurement noise on efficiency estimation, allowing the system to obtain more accurate energy conversion efficiency data. This high-precision efficiency estimate provides a reliable basis for the automatic adjustment of the system, ensuring that the system can make accurate adjustments based on actual conditions, thereby improving the stability and operating efficiency of the system.

[0120] The key parameters of the Kalman filter, such as the initial covariance matrix and the noise covariance matrix, can be adjusted according to different application scenarios. By optimizing these parameters, the system can maintain high estimation accuracy and stability in different noise environments. In addition, these parameters can be dynamically adjusted according to real-time operation data to cope with sudden external interference.

[0121] The closed-loop feedback module control program calculates the corresponding correction signal based on the size of the deviation value and applies the signal to the output parameter adjustment including:

[0122] Fuzzy logic control is used to process the deviation value Δη and generate the corresponding correction signal u(t). The specific formula is:

[0123] u(t)=Fuzzy(Δη,Δη′),

[0124] Among them, u(t) is the correction signal generated by the control program, t is the time, Fuzzy is the function of the fuzzy logic controller, Δη is the efficiency deviation, and Δη′ is the rate of change of the deviation;

[0125] The fuzzy logic controller takes the efficiency deviation Δη and the deviation change rate Δη′ as input and outputs a correction signal u(t) to adjust the output parameter V of the inverter. out (t) and f out (t).

[0126] Fuzzy logic control, first, the efficiency deviation Δη and the deviation change rate Δη′ of the system are taken as input variables. The fuzzy logic controller will fuzzify these input variables, that is, convert the precise input values ​​into membership in the fuzzy set. This step assigns the input variables to different fuzzy subsets, such as "high", "medium", and "low". In the fuzzy logic controller, the rule base contains several fuzzy control rules in the form of "if-then". These rules are pre-set based on experience or expert knowledge. Through these rules, the controller can determine the control strategy of the system according to the membership of the input variables. For example, the rule base may contain the following rule: "If the efficiency deviation Δη is high and the deviation change rate Δη′ is positive, then the correction signal u(t) should be large". The fuzzy logic controller infers the fuzzy output signal u(t) based on the membership of the input variables and the rules in the rule base. This signal indicates how the system should adjust the output parameters to correct the deviation. In the last step, the fuzzy logic controller defuzzifies the fuzzy output signal u(t), that is, converts the output signal in the fuzzy set into a precise control signal to adjust the output parameters of the inverter, such as the output voltage V out (t) and output frequency f out (t), the core of fuzzy logic control lies in its adaptability to complex systems and its ability to handle system uncertainty and nonlinearity without an accurate mathematical model. By taking the efficiency deviation Δη and the deviation change rate Δη′ as input, the fuzzy logic controller can generate appropriate correction signals under different operating conditions to ensure that the inverter always operates in the best state. The fuzzy logic controller converts these input signals into appropriate correction signals and applies them to the output parameter adjustment of the inverter, thereby optimizing the operating state of the system. This method is particularly suitable for dynamic environments and complex systems, and provides good robustness and response speed in the control of photovoltaic energy storage inverters.

[0127] The fuzzy logic controller can handle complex nonlinear systems. When faced with dynamically changing environments and variable load conditions, it can quickly generate adaptive correction signals u(t) and effectively adjust the output parameters of the inverter. This fast response capability significantly improves the overall performance of the system, allowing the inverter to always maintain the optimal operating state and ensure the high efficiency of energy conversion. By taking the efficiency deviation Δη and the deviation change rate Δη′ as input, the fuzzy logic controller can accurately calculate and generate appropriate correction signals. Since the fuzzy logic controller makes inferences based on actual measurement data and preset rules, it can more accurately reflect the actual operating conditions of the system, thereby providing more precise regulation control and reducing the errors that may be introduced by traditional control methods.

[0128] Multi-level fuzzy control or hierarchical fuzzy control can be introduced to improve the control accuracy and response speed by performing fuzzy control at different levels. For example, hierarchical fuzzy control can control voltage at the first level and frequency at the second level. By handling complex problems in a hierarchical manner, the system can maintain efficient and stable operation in a larger range. Adaptive fuzzy control strategies can be adopted to adjust the parameters and rule base of the fuzzy logic controller in real time so that it can maintain optimal performance in a dynamically changing environment. For example, the system can dynamically adjust the weight of the fuzzy rule or the range of the membership function according to the real-time monitoring data, thereby improving the flexibility and adaptability of the control.

[0129] The closed-loop feedback module continuously measures the actual operating status after adjustment until it detects that the conversion efficiency is equal to or exceeds the set benchmark, including:

[0130] A continuous supervised learning mechanism is introduced to automatically adjust the control strategy by real-time monitoring of the adjustment results to ensure that the energy conversion efficiency is stable above the target level. The specific formula is:

[0131]

[0132] Among them, η real (t) is the actual energy conversion efficiency at the current moment t, t is the moment, η threshold is the preset benchmark efficiency, Adjust Strategy(η real ,u(t)) is the adjustment strategy function. If the actual energy conversion efficiency η real (t) greater than or equal to the preset reference efficiency η threshold , the control strategy is adjusted according to the current actual efficiency and control signal u(t) to ensure that the system performance remains at or exceeds the expected target. If the target is not reached, the control strategy is automatically adjusted to optimize the output.

[0133] In the above implementation, the closed-loop feedback module of the photovoltaic energy storage inverter introduces a continuous supervised learning mechanism to ensure that the energy conversion efficiency remains stable above the preset target level. The module monitors the actual operating status after adjustment in real time and dynamically adjusts the control strategy according to the measured efficiency value to achieve the purpose of optimizing system performance. The working principle of the continuous supervised learning mechanism is: Real-time monitoring: The system continuously monitors the energy conversion efficiency η of the inverter real (t) and the related control signal u(t). These real-time data reflect the current operating status of the system and the energy conversion effect. Efficiency comparison: The system measures the efficiency η in real time. real (t) and the preset benchmark efficiency η threshold For comparison. If η real (t) equal to or higher than the reference efficiency η threshold , the system will continue to execute the current control strategy, because the existing strategy has achieved the expected performance level, strategy adjustment: if η real (t) lower than the reference efficiency ηt hreshold , the system will automatically update the control strategy. By adjusting the control parameters or changing the control algorithm, the system will try to correct the cause of the efficiency decline and improve the energy conversion efficiency. The specific way to adjust the strategy depends on the current operating data and the historical experience of the system. Strategy optimization: With the support of the supervised learning mechanism, the system can continuously optimize the control strategy by analyzing and summarizing the past adjustment results. This learning mechanism allows the system to continuously improve itself according to its actual performance in operation, thereby improving long-term performance and stability.

[0134] The continuous supervised learning mechanism can monitor and evaluate the actual operating status of the inverter in real time, and dynamically adjust the control strategy as needed. This real-time feedback and adjustment capability significantly improves the system's response speed and control accuracy, ensuring that the energy conversion efficiency is always at the optimal level. Even under complex load and environmental changes, the system can quickly adapt and maintain efficient operation. Through continuous supervised learning, the system can continuously optimize the control strategy, make adjustments based on real-time data and historical operating results, and gradually reduce errors. The system can accurately detect the cause of the decline in efficiency and take effective adjustment measures to ensure that all parameters in the energy conversion process are always kept within the optimal range, thereby improving the accuracy of overall energy management.

[0135] In addition to the continuous supervised learning mechanism, the system can also consider introducing other types of machine learning methods, such as reinforcement learning or deep learning. Reinforcement learning can optimize the control strategy through continuous trial and feedback in a complex dynamic environment, thereby improving the system's adaptability and decision-making ability. Deep learning can process larger-scale data and automatically extract features, which is suitable for more complex control tasks. A hybrid learning strategy can be adopted to combine different types of learning methods. For example, supervised learning and unsupervised learning can be combined, and parameter correction can be performed through supervised learning, while unsupervised learning can be used to extract data features and recognize patterns. This hybrid strategy can better adapt to complex and changing environments and improve the system's intelligence level and control accuracy.

[0136] After reaching a given conversion efficiency threshold, the closed-loop feedback module monitors whether there is room for further improvement and prepares for readjustment in the next cycle, including:

[0137] Genetic algorithms are used for optimization to find possible better parameter configurations in the next cycle. Genetic algorithms simulate the natural selection process to explore the global optimal solution in the parameter space. The specific formula is:

[0138]

[0139] Among them, η opt For the optimized energy conversion efficiency, V out Indicates the output voltage f out Indicates the output frequency, indicating the actual energy conversion efficiency η real (t) represents the actual energy conversion efficiency, η threshold is the benchmark efficiency, f is the objective function;

[0140] The genetic algorithm searches for the output voltage V through evolutionary selection, crossover and mutation operations. out and frequency f out The optimal combination of energy conversion efficiency η opt maximize.

[0141] In the above implementation, after reaching the set conversion efficiency threshold, the closed-loop feedback module of the photovoltaic energy storage inverter will further monitor the operation of the system to find out whether there is room for efficiency improvement and prepare for the next cycle of readjustment. To this end, the system introduces a genetic algorithm to optimize the output parameter combination by simulating the process of natural selection to further improve the energy conversion efficiency. The working principle of the genetic algorithm is as follows: The genetic algorithm first generates a set of initial solutions, called a population. These solutions represent different output parameter combinations, such as the output voltage V out and output frequency f outFor each individual in the population, the algorithm calculates its fitness, i.e., the corresponding energy conversion efficiency η. The fitness function is usually defined as the objective function to be optimized. In this case, the goal is to maximize the energy conversion efficiency. Based on the fitness value, the genetic algorithm selects individuals with higher fitness for reproduction. Individuals with higher fitness have a greater probability of being selected to pass their parameter configuration to the next generation. After the selection operation, the genetic algorithm exchanges the parameter combinations of the two individuals through the crossover operation to generate new individuals. These new individuals inherit the parameter characteristics of the parent individuals and may combine better configurations. In order to maintain the diversity of the population and explore new parameter combinations, the genetic algorithm mutates some parameters of the individuals with a certain probability. This operation helps to jump out of the local optimum and find the global optimal solution. After selection, crossover and mutation operations, the algorithm generates a new population. The new population will replace the old population and enter the next round of iteration. This process will continue until the preset number of iterations is reached or the optimal solution is found. After multiple iterations, the genetic algorithm will converge to a set of optimal parameter combinations, i.e., the output voltage V out and the output frequency f ou t configuration, thereby maximizing the energy conversion efficiency η opt .

[0142] Genetic algorithms can effectively find the global optimal solution in a complex parameter space, which means that the system can automatically explore the best combination of output voltage and frequency to maximize energy conversion efficiency. Compared with traditional step-by-step optimization methods, genetic algorithms are more efficient in dealing with multivariable and nonlinear problems, and can significantly improve the overall performance of the system. By simulating the process of natural selection, crossover and mutation, genetic algorithms can continuously optimize parameter configuration and reduce losses in the energy conversion process. This precise optimization capability enables the system to better adapt to different load conditions and environmental changes, thereby achieving more stable output and higher conversion efficiency.

[0143] In addition to genetic algorithms, the system can also introduce other global optimization algorithms, such as particle swarm optimization and simulated annealing. The particle swarm optimization algorithm imitates the foraging behavior of bird flocks and can quickly converge to the global optimal solution, which is suitable for searching large-scale parameter spaces. The simulated annealing algorithm gradually finds the optimal solution to the problem by simulating the slow cooling process of the material from high temperature to low temperature. These alternative algorithms can be selected according to specific application requirements to achieve more efficient parameter optimization. When processing large-scale data, edge computing or cloud computing architecture can be used for optimization. For example, edge computing can perform preliminary processing close to the data source, reduce data transmission delays, and improve real-time performance. Cloud computing provides powerful computing resources, which are suitable for the operation of complex optimization algorithms and large-scale data processing.

[0144] The main feature of this system is that it can solve the problems caused by grid fluctuations and load changes in traditional control systems through the cooperation of various modules. After the photovoltaic energy storage inverter receives the power input signal, it will start. The system will immediately monitor and adjust the size and direction of the power provided by the photovoltaic power generation components through the integrated maximum power point tracking technology to ensure that the power output of the photovoltaic panels is maximized at any time. This process involves continuous evaluation of the light intensity of the working environment to dynamically change the working state of the circuit to make the system at or close to the maximum power point. For example, a distributed photovoltaic system installed on a residential roof receives strong sunlight at noon and generates a high current output. The system can identify this situation in real time and use appropriate control strategies to make the current flow through the circuit in the best way, thereby reducing losses caused by environmental changes.

[0145] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A control system for a photovoltaic energy storage inverter, characterized in that: The system comprises: Maximum power point tracking module, used to adjust the output of the photovoltaic panel after the photovoltaic energy storage inverter receives the power input signal to optimize energy collection; The dynamic adjustment module connected to the maximum power point tracking module is used to dynamically adjust the output frequency and voltage of the inverter based on the real-time monitoring of grid fluctuations and load change parameters to achieve efficient conversion and matching of electric energy; A closed-loop feedback module connected to the dynamic regulation module is used to continuously adjust the output parameters of the inverter until a preset energy conversion efficiency threshold is reached; The dynamic adjustment module dynamically adjusts the output frequency and voltage of the inverter based on the real-time monitored grid fluctuation and load change parameters, including obtaining grid fluctuation data based on the current grid frequency, obtaining load change parameters according to the current load power consumption, adjusting the inverter output to ensure that the grid matches the load, performing power conversion efficiency evaluation and updating the inverter output parameters as needed; The closed-loop feedback module continuously adjusts the output parameters of the inverter until a preset energy conversion efficiency threshold is reached, including regularly collecting output frequency and voltage change data and transmitting them to the control center to evaluate the effectiveness of the conversion process. If the evaluation finds that the actual energy conversion efficiency of the output is lower than the pre-defined target level, the feedback control mechanism is activated for correction. The control program calculates a corresponding correction signal based on the size of the deviation value, and applies the signal to the output parameter adjustment. The actual operating status after adjustment is continuously measured until the conversion efficiency is detected to be equal to or exceeds the set benchmark. After the established conversion efficiency threshold is reached, it is monitored whether there is room for further improvement and prepared for readjustment operations in the next cycle.

2. A control system for a photovoltaic energy storage inverter according to claim 1, characterized in that: The maximum power point tracking module adjusts the output of the photovoltaic panel after the photovoltaic energy storage inverter receives the power input signal, and optimizes energy collection, including: Monitor the output current and voltage of the photovoltaic panel to obtain the actual power value; Comparing the actual power value with a preset maximum power point value; Based on the comparison result, the working point of the photovoltaic panel is adjusted to approach or reach the maximum power point, wherein the working point of the photovoltaic panel is gradually approached or reaches the maximum power point by adjusting the control parameters ΔV and ΔI of the inverter, specifically: ΔV=V mppt -V pv ; ΔI=I mppt -I pv ; The inverter will increase or decrease the operating voltage V pv and current I pv To approach V mppt and I mppt value until the maximum power output is achieved; Continuously scan changes in environmental conditions and make corresponding adjustments to the operating status of photovoltaic panels; The operating parameters are adjusted again according to the new maximum power point under the conditions after optimization to maintain the photovoltaic system operating at the best performance level.

3. A control system for a photovoltaic energy storage inverter according to claim 2, characterized in that: The continuously scanning environmental condition changes and making corresponding adjustments to the operating state of the photovoltaic panel, and adjusting the operating parameters again according to the new maximum power point under the conditions after the adjustment include: Continuously monitor changes in environmental conditions and optimize the operating status of the photovoltaic panels in real time through the following functions: P opt =the sys ×G×A×cos(θ); Among them, P opt refers to the optimized power output, η sys is the system efficiency, G is the solar irradiance, A is the area of ​​the photovoltaic panel, and θ is the incident angle of the photovoltaic panel. As the light intensity G and temperature change, the inverter will automatically adjust the working point of the photovoltaic panel to maintain a state close to the maximum power output; According to the above optimized operating conditions, the new maximum power point is re-determined, and the inverter operating parameters V are adjusted again. pv and I pv In order to maintain the optimal performance level of the photovoltaic system, this process is achieved through a closed-loop control feedback mechanism, ensuring that the photovoltaic energy storage system always operates at the highest efficiency in a dynamic environment and maximizes energy collection and conversion rate.

4. The control system of a photovoltaic energy storage inverter according to claim 1, characterized in that: The dynamic adjustment module adjusts the inverter output to ensure that the power grid matches the load, including: A proportional-integral-derivative controller is introduced to adjust the output voltage V of the inverter. inv and frequency f inv , to match the grid frequency and load demand, the specific formula is: e(t)=P load (t)-P inv (t), Among them, P load (t) is the load power at the current moment, t is the moment, P inv (t) represents the output power of the inverter at the current time t, e f (t) = Δfgrid is the grid frequency deviation, K p , K i , K d They are the proportional, integral and differential coefficients of the PID controller respectively.

5. A control system for a photovoltaic energy storage inverter according to claim 4, characterized in that: The dynamic adjustment module performs power conversion efficiency evaluation and updates inverter output parameters as needed, including: Using the real-time efficiency calculation module, based on the input power P in And the output power P out Calculate the current inverter power conversion efficiency η inv When the efficiency is lower than the preset threshold, the inverter control parameter V is automatically updated. inv and f inv , the specific formula is: Among them, η in v(t) is the current inverter power conversion efficiency, t is the time, P in (t) is the input power of the inverter, P out (t) is the output power, when η inv (t) is lower than the set threshold η threshold When adjusting the inverter output parameters, the energy conversion efficiency can be improved.

6. The control system of a photovoltaic energy storage inverter according to claim 1, characterized in that: The closed-loop feedback module regularly collects the output frequency and voltage change data and transmits it to the control center to evaluate the effectiveness of the conversion process, including: Introducing a data analysis method based on wavelet transform, the output frequency f out (t) and output voltage V out (t) The time-frequency analysis of the changing data can accurately capture the transient disturbance and harmonic characteristics in the power conversion process. The specific formula is: Among them, W f (τ, s) is the continuous wavelet transform result of the output frequency data, t is the time, ψ is the mother wavelet function, τ and s represent the time displacement and translation scale parameters respectively.

7. A control system for a photovoltaic energy storage inverter according to claim 6, characterized in that: If the closed-loop feedback module finds that the actual energy conversion efficiency of the output is lower than the predefined target level, the feedback control mechanism is activated to correct the following: By establishing an energy conversion efficiency model, the Kalman filter is used to calculate the actual energy conversion efficiency η real For prediction and correction, the specific formula is: or real (t|t-1)=η real (t-1)+K(t)[η measured (t)-η real (t-1)] Among them, η measured (t) is the efficiency value measured at the current moment, t is the time, η real (t|t-1) is the actual efficiency value after correction by the Kalman filter, K(t) is the Kalman gain, P(t|t-1) is the estimation error covariance, and R is the measurement noise covariance.

8. A control system for a photovoltaic energy storage inverter according to claim 7, characterized in that: The closed-loop feedback module control program calculates a corresponding correction signal based on the size of the deviation value, and applies the signal to the output parameter adjustment, including: Fuzzy logic control is used to process the deviation value Δη and generate the corresponding correction signal u(t). The specific formula is: u(t)=Fuzzy(Δη,Δη′), Among them, u(t) is the correction signal generated by the control program, t is the time, Fuzzy is the function of the fuzzy logic controller, Δη is the efficiency deviation, and Δη′ is the rate of change of the deviation; The fuzzy logic controller takes the efficiency deviation Δη and the deviation change rate Δη′ as input and outputs a correction signal u(t) to adjust the output parameter V of the inverter. out (t) and f out (t).

9. A control system for a photovoltaic energy storage inverter according to claim 8, characterized in that: The closed-loop feedback module continuously measures the adjusted actual operating state until the conversion efficiency is detected to be equal to or exceeds the set benchmark, including: A continuous supervised learning mechanism is introduced to automatically adjust the control strategy by real-time monitoring of the adjustment results to ensure that the energy conversion efficiency is stable above the target level. The specific formula is: Among them, η real (t) is the actual energy conversion efficiency at the current moment t, t is the moment, η threshold is the preset benchmark efficiency, Adjust Strategy(η real ,u(t)) is the adjustment strategy function. If the actual energy conversion efficiency η real (t) greater than or equal to the preset reference efficiency η threshold , the control strategy is adjusted according to the current actual efficiency and control signal u(t) to ensure that the system performance remains at or exceeds the expected target. If the target is not reached, the control strategy is automatically adjusted to optimize the output.

10. A control system for a photovoltaic energy storage inverter according to claim 9, characterized in that: After reaching a predetermined conversion efficiency threshold, the closed-loop feedback module monitors whether there is room for further improvement and prepares for re-adjustment operations in the next cycle, including: Genetic algorithms are used for optimization to find possible better parameter configurations in the next cycle. Genetic algorithms simulate the natural selection process to explore the global optimal solution in the parameter space. The specific formula is: Among them, η opt For the optimized energy conversion efficiency, V out Indicates the output voltage f out Indicates the output frequency, indicating the actual energy conversion efficiency η real (t) represents the actual energy conversion efficiency, η threshold is the benchmark efficiency, f is the objective function; The genetic algorithm searches for the output voltage V through evolutionary selection, crossover and mutation operations. out and frequency f out The optimal combination of energy conversion efficiency η opt maximize.

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