Parallel power supply efficiency optimization method and system

Abstract

The present application relates to the field, disclose a kind of parallel power supply efficiency optimization method and system, the output current of each power module is collected in real time by the current sensor of each power module, while collecting the total output current of parallel power supply system and the input voltage, output voltage of each power module;The current sharing error of each power module is calculated, the proportional coefficient, integral coefficient and differential coefficient of fuzzy PID controller are adjusted by fuzzy inference rule;The voltage regulator of power module is adjusted by control signal;According to the input voltage, output voltage and output current of each power module, the efficiency of each power module is calculated, according to the efficiency and load condition of each power module, the best working point of each power module is determined, the input power of power module is adjusted, to complete the efficiency optimization of entire parallel power supply;The present application can adjust the working state of power module in real time under different working conditions, has strong adaptability and stability.

Description

Technical Field

[0001] This invention relates to the field of power supply technology, and specifically to a method and system for optimizing the efficiency of parallel power supplies. Background Technology

[0002] In modern power systems, parallel power supply systems are widely used due to their ability to provide high power output, redundancy, and flexible expansion capabilities. However, with the increasing demands for efficiency and stability from power electronic devices, parallel power supply systems face some pressing problems during operation. Traditional parallel power supply systems often suffer from low current sharing accuracy when distributing load current, leading to overload of some power modules while other modules are lightly loaded. This not only affects the lifespan of the power modules but also reduces the efficiency of the entire parallel power supply system. Furthermore, under different operating conditions, such as load changes and input voltage fluctuations, traditional control methods struggle to adjust the operating status of the power modules in real time to achieve optimal efficiency. Summary of the Invention

[0003] The purpose of this invention is to solve the above-mentioned problems by designing a method and system for optimizing the efficiency of parallel power supplies.

[0004] The first aspect of this invention provides a method for optimizing the efficiency of parallel power supplies, the method comprising the following steps:

[0005] The output current of each power module is collected in real time by the current sensor of each power module, and the total output current of the parallel power system and the input voltage and output voltage of each power module are also collected.

[0006] The current sharing error of each power module is calculated, and the current sharing error and the rate of change of the current sharing error are used as inputs to the fuzzy PID controller. The proportional coefficient, integral coefficient and derivative coefficient of the fuzzy PID controller are adjusted by fuzzy inference rules.

[0007] Based on the adjusted PID parameters, calculate the control signal for the voltage regulator of each power module, and adjust the voltage regulator of the power module by controlling the control signal to adjust the output voltage of the power module.

[0008] Based on the input voltage, output voltage, and output current of each power module, the efficiency of each power module is calculated. Based on the efficiency of each power module and the load conditions, the optimal operating point of each power module is determined, and the input power of the power modules is adjusted to complete the efficiency optimization of the entire parallel power supply.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the step of acquiring the output current of each power module in real time through the current sensor of each power module, and simultaneously acquiring the total output current of the parallel power system and the input voltage and output voltage of each power module, includes:

[0010] The output current data of each module is acquired in real time through the built-in current sensor of each power module, and the total output current of the parallel power system, as well as the input voltage and output voltage of each module, are also collected.

[0011] The collected output current, input voltage, and output voltage are used as the observation values ​​of the Kalman filter. Combined with the state estimate value of the previous moment, the state is predicted and updated through the adaptively adjusted process noise covariance matrix and observation noise covariance matrix, thus completing the filtering process and suppressing real-time noise interference.

[0012] A probabilistic graphical model is constructed among the power supply modules, and an information exchange channel between adjacent modules is established through a communication network. Each power supply module updates its posterior distribution of state by fusing local estimation and neighborhood information through the message passing algorithm of the probabilistic graphical model based on the received multi-source information, forming a globally collaborative state estimation result and suppressing the influence of sensor noise from a single module on the overall state judgment.

[0013] Optionally, in a second implementation of the first aspect of the present invention, each power module calculates its own posterior probability distribution based on the collected current and voltage data using a Bayesian estimation method, and transmits the estimation results to adjacent modules through a communication link to receive cooperative information from adjacent modules.

[0014] Optionally, in a third implementation of the first aspect of the present invention, the step of calculating the current sharing error of each power module, using the current sharing error and the rate of change of the current sharing error as inputs to the fuzzy PID controller, and adjusting the proportional coefficient, integral coefficient, and derivative coefficient of the fuzzy PID controller through fuzzy inference rules, includes:

[0015] The output current of each power module is compared with the average current to obtain the current sharing error of each power module.

[0016] The current average error calculated at the current moment is compared with the current average error of the previous sampling period. The rate of change of the current average error is obtained after normalization by time interval.

[0017] The calculated flow rate error and its rate of change are used as input variables of the fuzzy PID controller. The input variables are converted into corresponding fuzzy linguistic variables and their membership degrees through the membership function.

[0018] Based on the current combination of fuzzy linguistic variables of the current flow sharing error and the error change rate, the proportional coefficient, integral coefficient and derivative coefficient for the fuzzy PID controller are obtained through fuzzy logic operation;

[0019] The fuzzy output results of the fuzzy PID parameter adjustment obtained by fuzzy inference are converted into parameter adjustment quantities by the maximum membership method. The optimal values ​​of the three control components of the fuzzy PID controller under the current operating conditions are determined and updated in real time.

[0020] Optionally, in a fourth implementation of the first aspect of the present invention, the step of calculating the control signal of the voltage regulator of each power module based on the adjusted PID parameters, and adjusting the voltage regulator of the power module through the control signal to adjust the output voltage of the power module, includes:

[0021] The three control components obtained from the PID controller—proportional coefficient, integral coefficient, and derivative coefficient—are linearly superimposed and added to a pre-set reference control signal value to obtain the final voltage regulator control signal value.

[0022] The voltage regulator control signal value is limited, and the limited digital control signal is converted into an analog voltage signal by a digital-to-analog converter and pre-processed by a signal conditioning circuit.

[0023] The processed analog control signal is transmitted to the corresponding power module voltage regulator. After receiving the control signal, the voltage regulator adjusts its internal regulating element according to the signal value to regulate the output voltage of the power module.

[0024] Optionally, in a fifth implementation of the first aspect of the present invention, during the voltage regulation process, the output voltage and current data of the power supply module are continuously collected, the actual output voltage is compared with the target value, the regulation error is calculated, and the error information is fed back to the fuzzy PID controller.

[0025] Optionally, in a sixth implementation of the first aspect of the present invention, the step of calculating the efficiency of each power supply module based on its input voltage, output voltage, and output current, determining the optimal operating point of each power supply module based on its efficiency and load conditions, and adjusting the input power of the power supply modules to complete the overall parallel power supply efficiency optimization includes:

[0026] Calculate the efficiency of each power module based on its input voltage, output voltage, and output current.

[0027] With the goal of maximizing the overall efficiency of the parallel power supply system, a mathematical model is established that includes the efficiency characteristics of each module, load distribution constraints, and input power limits.

[0028] Randomly generate an initial particle swarm, with each particle representing a combination of working point parameters, and assign an initial position and velocity vector to each particle;

[0029] Calculate the fitness value of each particle, update the individual's historical best position and global best position, and update the velocity and position of each particle according to the iterative process of the particle swarm optimization algorithm.

[0030] During the particle update process, each particle is checked to see if it meets the pre-set constraints. For particles that violate the constraints, boundary processing is used to correct them. The constraints include at least meeting the total load requirements, the upper and lower limits of module power, and the safe range of voltage and current.

[0031] When the maximum number of iterations is reached, the iteration stops, the currently found global optimal solution is output, the global optimal solution obtained by the particle swarm optimization algorithm is converted into the actual control parameters of each power module, and sent to each power module to adjust the module operating point and realize the overall efficiency optimization of the parallel power system.

[0032] A second aspect of the present invention provides a parallel power supply efficiency optimization system, the system comprising:

[0033] The acquisition module is used to acquire the output current of each power module in real time through the current sensor of each power module, and at the same time acquire the total output current of the parallel power system and the input voltage and output voltage of each power module.

[0034] The adjustment module is used to calculate the current sharing error of each power module. The current sharing error and the rate of change of the current sharing error are used as inputs to the fuzzy PID controller. The proportional coefficient, integral coefficient and derivative coefficient of the fuzzy PID controller are adjusted by fuzzy inference rules.

[0035] The adjustment module is used to calculate the control signal of the voltage regulator of each power module based on the adjusted PID parameters, and adjust the voltage regulator of the power module through the control signal to adjust the output voltage of the power module.

[0036] The optimization module is used to calculate the efficiency of each power module based on its input voltage, output voltage, and output current. Based on the efficiency of each power module and the load conditions, it determines the optimal operating point of each power module and adjusts the input power of the power modules to complete the efficiency optimization of the entire parallel power supply.

[0037] A third aspect of the present invention provides a parallel power supply efficiency optimization device, the parallel power supply efficiency optimization device comprising a memory and at least one processor, the memory storing instructions; the at least one processor invokes the instructions in the memory to cause the parallel power supply efficiency optimization device to perform the steps of the parallel power supply efficiency optimization method as described in any of the preceding claims.

[0038] A fourth aspect of the present invention provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the steps of the parallel power supply efficiency optimization method as described in any of the preceding claims.

[0039] In the technical solution provided by this invention, the output current of each power module is collected in real time by a current sensor of each power module, and the total output current of the parallel power system and the input and output voltages of each power module are also collected. The current sharing error of each power module is calculated, and the current sharing error and its rate of change are used as inputs to a fuzzy PID controller. The proportional, integral, and derivative coefficients of the fuzzy PID controller are adjusted using fuzzy inference rules. Based on the adjusted PID parameters, the control signal for the voltage regulator of each power module is calculated, and the voltage regulator of the power module is adjusted by the control signal to adjust the output voltage of the power module. The efficiency of each power module is calculated based on its input voltage, output voltage, and output current. Based on the efficiency and load of each power module, the optimal operating point of each power module is determined, and the input power of the power modules is adjusted to optimize the efficiency of the entire parallel power supply. This invention achieves output current balance of each power module and improves current sharing accuracy by collecting output current and other data of each power module in real time and using a fuzzy PID controller to control the voltage regulator. At the same time, through efficiency optimization control, the optimal operating point is determined according to the efficiency and load of each power module, and the input power is adjusted, which improves the efficiency of the entire parallel power supply system. It can adjust the working state of the power modules in real time under different working conditions, has strong adaptability and stability, and can effectively solve the problems existing in traditional parallel power supply systems, with significant economic and social benefits. Attached Figure Description

[0040] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention.

[0041] Figure 1 A flowchart of a parallel power supply efficiency optimization method provided in an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the parallel power supply efficiency optimization system provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the structure of the parallel power supply efficiency optimization device provided in an embodiment of the present invention. Detailed Implementation

[0044] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” or “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0045] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 A flowchart of the parallel power supply efficiency optimization method provided in this embodiment of the invention is shown. The method specifically includes the following steps:

[0046] Step 101: Collect the output current of each power module in real time through the current sensor of each power module, and at the same time collect the total output current of the parallel power system and the input voltage and output voltage of each power module.

[0047] In this embodiment, the output current data of each module is acquired in real time through the current sensor built into each power module. At the same time, the total output current of the parallel power system and the input and output voltages of each module are collected. The collected output current, input voltage, and output voltage are used as the observation values ​​of the Kalman filter. Combined with the state estimate value of the previous moment, the state prediction and update are performed through the adaptively adjusted process noise covariance matrix and observation noise covariance matrix to complete the filtering process and suppress real-time noise interference. A probabilistic graphical model is constructed among the power modules, and an information exchange channel between adjacent modules is established through a communication network. Each power module updates its posterior distribution of state based on the received multi-source information and fuses local estimation and neighborhood information through the message passing algorithm of the probabilistic graphical model, forming a globally collaborative state estimation result and suppressing the influence of sensor noise from a single module on the overall state judgment.

[0048] In this embodiment, each power module calculates its own posterior probability distribution based on the collected current and voltage data using the Bayesian estimation method, and transmits the estimation results to the adjacent modules through the communication link to receive cooperation information from the adjacent modules.

[0049] In this embodiment, a power module refers to a basic unit in a parallel power supply system that has independent power conversion capabilities. It typically includes circuits for input filtering, power conversion, and output regulation, and can supply power to the load individually or collaboratively. The number of power modules is n (n≥2). Current sensors (such as Hall effect sensors, shunts, etc.) are detection devices installed at the output terminals of each power module to monitor the module's output current in real time, converting the current into a voltage or digital signal that can be recognized by the control system. The output current is the current value provided by a single power module to the load, reflecting the real-time load status of that module and is a core parameter for judging the load distribution balance. The parallel power supply system consists of multiple... A power supply system composed of multiple power modules connected in parallel via electrical connections can expand its output power by increasing the number of modules, while also possessing redundancy backup capabilities to improve power supply reliability; the total output current is the sum of the output currents of all parallel power modules, used to measure the overall load level of the system and as a target benchmark for current sharing control; the input voltage is the real-time voltage value at the input terminal of each power module, i.e., the voltage supplied to the module by the external power supply network, which affects the calculation of the module's input power and efficiency; the output voltage is the real-time voltage value at the output terminal of each power module, i.e., the supply voltage provided by the module to the load, which needs to be maintained stable by a voltage regulator or dynamically adjusted according to control signals.

[0050] Step 102: Calculate the current sharing error of each power module, and use the current sharing error and the rate of change of the current sharing error as inputs to the fuzzy PID controller. Adjust the proportional coefficient, integral coefficient and derivative coefficient of the fuzzy PID controller through fuzzy inference rules.

[0051] In this embodiment, the output current of each power module is compared with the average current to obtain the current sharing error of each power module; the current sharing error calculated at the current moment is differentially processed with the current sharing error of the previous sampling period, and the current sharing error change rate is obtained after time interval normalization; the calculated current sharing error and its change rate are used as input variables of the fuzzy PID controller, and the input variables are converted into corresponding fuzzy linguistic variables and their membership degrees through the membership function; based on the combination of fuzzy linguistic variables of the current current sharing error and the error change rate, the proportional coefficient, integral coefficient, and derivative coefficient of the fuzzy PID controller are obtained through fuzzy logic operation; the fuzzy output result of the fuzzy PID parameter adjustment obtained by fuzzy inference is converted into parameter adjustment amount through the maximum membership method, the optimal values ​​of the three control components of the fuzzy PID controller under the current operating condition are determined, and real-time updates are performed.

[0052] In this embodiment, the current sharing error is the difference between the actual output current of a single power module and the average output current of the system, directly reflecting the degree of imbalance in the load distribution among the modules. If the current sharing error > 0, it indicates that the load of the module is higher than the average level; if the current sharing error < 0, the load is lower than the average level. This parameter is the core basis for judging the current sharing control requirements and is used to quantify the deviation of each module from the ideal current sharing state.

[0053] The rate of change of the flow sharing error is the amount of change in the flow sharing error per unit time, characterizing the trend and speed of change of the flow sharing error. By analyzing the sign and absolute value of the rate of change of the flow sharing error, it is possible to determine whether the system is currently in the error convergence, divergence, or steady-state stage, providing dynamic trend information for the control strategy.

[0054] A fuzzy PID controller is a composite controller that integrates fuzzy logic and traditional PID control. It adjusts PID parameters online through fuzzy inference to adapt to dynamic system changes. The fuzzy inference rules are based on a pre-defined logical rule base derived from expert experience and system operating data. The proportional gain is a parameter in the PID controller used to amplify or reduce the current error signal, directly affecting the system's response speed. When the current sharing error is large or changes drastically, increasing the proportional gain enhances the controller's response to the current error and accelerates the current sharing adjustment speed. When the error is small, appropriately decreasing the proportional gain avoids system overshoot. The integral gain is a parameter used to accumulate historical error signals, primarily eliminating steady-state errors. If the current sharing error persists for a long time, increasing the integral gain strengthens the integral action, gradually eliminating the accumulated deviation. If the system is in a dynamic change phase, decreasing the integral gain avoids adjustment delay caused by integral saturation. The derivative gain is a parameter used to predict the error change trend, adjusting the control quantity based on the error change rate to suppress system oscillations. When the error change rate is positive, increasing the derivative gain provides damping and slows error growth. When the error change rate is negative, appropriately adjusting the derivative gain balances system response speed and stability.

[0055] Step 103: Calculate the control signal of the voltage regulator of each power module according to the adjusted PID parameters, and adjust the voltage regulator of the power module by adjusting the control signal to adjust the output voltage of the power module.

[0056] In this embodiment, the three control components—proportional coefficient, integral coefficient, and derivative coefficient—obtained by the PID controller are linearly superimposed and added to a pre-set reference control signal value to obtain the final voltage regulator control signal value. The voltage regulator control signal value is then subjected to amplitude limiting, and the amplitude-limited digital control signal is converted into an analog voltage signal via a digital-to-analog converter and pre-processed by a signal conditioning circuit. The processed analog control signal is then transmitted to the corresponding power module voltage regulator. After receiving the control signal, the voltage regulator adjusts its internal regulating elements according to the signal value to regulate the output voltage of the power module.

[0057] In this embodiment, during the voltage regulation process, the output voltage and current data of the power module are continuously collected, the actual output voltage is compared with the target value, the regulation error is calculated, and the error information is fed back to the fuzzy PID controller.

[0058] In this embodiment, the voltage regulator is the core component inside the power module used to regulate the output voltage. It is usually composed of a feedback control circuit and a power regulation element. It adjusts the output voltage by receiving control signals, thereby changing the module's output current. According to Ohm's law, voltage changes will affect the load current distribution. For example, a switching voltage regulator changes the output voltage by adjusting the PWM duty cycle, while a linear regulator achieves voltage stability by adjusting the voltage drop of the power transistor.

[0059] In this embodiment, the current flow sharing error value is substituted into the PID controller with adjusted parameters to calculate the proportional, integral, and derivative control components respectively:

[0060] Proportional component calculation: Multiply the flow sharing error value by the proportional coefficient to obtain the proportional control component that reflects the current error magnitude. This component provides rapid response capability.

[0061] Integral component calculation: The historical average flow error values ​​are accumulated and summed, and then multiplied by the integral coefficient to obtain the integral control component that reflects the cumulative effect of error. This component is used to eliminate steady-state error.

[0062] Differential component calculation: Calculate the difference between the current flow averaging error value and the error value at the previous moment, and multiply it by the differential coefficient to obtain the differential control component that reflects the trend of error change. This component provides prediction and damping effects.

[0063] In this embodiment, the limiting process involves comparing the control signal value with preset upper and lower threshold values. If the value exceeds the threshold range, it is limited to the threshold boundary to prevent system instability or equipment damage caused by excessive control signal.

[0064] Step 104: Calculate the efficiency of each power module based on its input voltage, output voltage, and output current. Determine the optimal operating point of each power module based on its efficiency and load conditions, and adjust the input power of the power modules to complete the efficiency optimization of the entire parallel power supply.

[0065] In this embodiment, the efficiency of each power module is calculated based on its input voltage, output voltage, and output current. With the goal of maximizing the overall efficiency of the parallel power system, a mathematical model is established that includes the efficiency characteristics of each module, load distribution constraints, and input power limits. An initial particle swarm is randomly generated, with each particle representing a combination of operating point parameters. An initial position and velocity vector are assigned to each particle. The fitness value of each particle is calculated, and its historical best position and global best position are updated. The velocity and position of each particle are updated according to the iterative process of the particle swarm optimization algorithm. During particle updates, each particle is checked to ensure it meets pre-set constraints. For particles that violate constraints, boundary conditions are used for correction. These constraints include at least meeting the total load requirement, module power limits, and voltage and current safety ranges. When the maximum number of iterations is reached, iteration stops, and the currently found global optimal solution is output. The global optimal solution obtained by the particle swarm optimization algorithm is converted into the actual control parameters of each power module and sent to each power module to adjust the module's operating point, thereby achieving overall efficiency optimization of the parallel power system.

[0066] Please see Figure 2 A schematic diagram of the parallel power supply efficiency optimization system provided in this embodiment of the invention. The system includes:

[0067] The acquisition module is used to acquire the output current of each power module in real time through the current sensor of each power module, and at the same time acquire the total output current of the parallel power system and the input voltage and output voltage of each power module.

[0068] The adjustment module is used to calculate the current sharing error of each power module. The current sharing error and the rate of change of the current sharing error are used as inputs to the fuzzy PID controller. The proportional coefficient, integral coefficient and derivative coefficient of the fuzzy PID controller are adjusted by fuzzy inference rules.

[0069] The adjustment module is used to calculate the control signal of the voltage regulator of each power module based on the adjusted PID parameters, and adjust the voltage regulator of the power module through the control signal to adjust the output voltage of the power module.

[0070] The optimization module is used to calculate the efficiency of each power module based on its input voltage, output voltage, and output current. Based on the efficiency of each power module and the load conditions, it determines the optimal operating point of each power module and adjusts the input power of the power modules to complete the efficiency optimization of the entire parallel power supply.

[0071] Figure 3 This is a schematic diagram of a parallel power efficiency optimization device 600 provided in an embodiment of the present invention. The parallel power efficiency optimization device 600 can vary significantly due to different configurations or performance characteristics. It may include one or more central processing units (CPUs) 610 (e.g., one or more processors) and a memory 620, and one or more storage media 630 (e.g., one or more mass storage devices) storing application programs 633 or data 632. The memory 620 and storage media 630 can be temporary or persistent storage. The program stored in the storage media 630 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the parallel power efficiency optimization device 600. Furthermore, the processor 610 may be configured to communicate with the storage media 630 and execute the series of instruction operations in the storage media 630 on the parallel power efficiency optimization device 600 to implement the method provided in the above embodiment.

[0072] The parallel power efficiency optimization device 600 may also include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating devices 631, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will understand that... Figure 3 The parallel power supply efficiency optimization device structure shown does not constitute a limitation on the computer device provided by the present invention. It may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0073] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the various steps of the parallel power supply efficiency optimization method provided in the above embodiments.

[0074] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the above-described equipment, apparatus, or unit can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0075] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the efficiency of parallel power supplies, characterized in that, The method includes the following steps: The output current of each power module is collected in real time by the current sensor of each power module, and the total output current of the parallel power system and the input voltage and output voltage of each power module are also collected. The current sharing error of each power module is calculated, and the current sharing error and the rate of change of the current sharing error are used as inputs to the fuzzy PID controller. The proportional coefficient, integral coefficient and derivative coefficient of the fuzzy PID controller are adjusted by fuzzy inference rules. Based on the adjusted PID parameters, calculate the control signal for the voltage regulator of each power module, and adjust the voltage regulator of the power module by controlling the control signal to adjust the output voltage of the power module. Based on the input voltage, output voltage, and output current of each power module, the efficiency of each power module is calculated. Based on the efficiency of each power module and the load conditions, the optimal operating point of each power module is determined, and the input power of the power modules is adjusted to complete the efficiency optimization of the entire parallel power supply. The step of calculating the control signal for the voltage regulator of each power module based on the adjusted PID parameters, and adjusting the voltage regulator of the power module through the control signal to adjust the output voltage of the power module includes: The three control components obtained from the PID controller—proportional coefficient, integral coefficient, and derivative coefficient—are linearly superimposed and added to a pre-set reference control signal value to obtain the final voltage regulator control signal value. The voltage regulator control signal value is limited, and the limited digital control signal is converted into an analog voltage signal by a digital-to-analog converter and pre-processed by a signal conditioning circuit. The processed analog control signal is transmitted to the corresponding power module voltage regulator. After receiving the control signal, the voltage regulator adjusts the internal regulating element according to the signal value to regulate the output voltage of the power module. During the voltage regulation process, the output voltage and current data of the power module are continuously collected, the actual output voltage is compared with the target value, the regulation error is calculated, and the error information is fed back to the fuzzy PID controller. The process involves calculating the efficiency of each power module based on its input voltage, output voltage, and output current; determining the optimal operating point of each power module based on its efficiency and load conditions; and adjusting the input power of the power modules to optimize the efficiency of the entire parallel power supply. This includes: Calculate the efficiency of each power module based on its input voltage, output voltage, and output current. With the goal of maximizing the overall efficiency of the parallel power supply system, a mathematical model is established that includes the efficiency characteristics of each module, load distribution constraints, and input power limits. Randomly generate an initial particle swarm, with each particle representing a combination of working point parameters, and assign an initial position and velocity vector to each particle; Calculate the fitness value of each particle, update the individual's historical best position and global best position, and update the velocity and position of each particle according to the iterative process of the particle swarm optimization algorithm. During the particle update process, each particle is checked to see if it meets the pre-set constraints. For particles that violate the constraints, boundary processing is used to correct them. The constraints include at least meeting the total load requirements, the upper and lower limits of module power, and the safe range of voltage and current. When the maximum number of iterations is reached, the iteration stops, the currently found global optimal solution is output, the global optimal solution obtained by the particle swarm optimization algorithm is converted into the actual control parameters of each power module, and sent to each power module to adjust the module operating point and realize the overall efficiency optimization of the parallel power system.

2. The method for optimizing the efficiency of a parallel power supply as described in claim 1, characterized in that, The method involves real-time acquisition of the output current of each power module via a current sensor, and simultaneously acquiring the total output current of the parallel power system and the input and output voltages of each power module, including: The output current data of each module is acquired in real time through the built-in current sensor of each power module, and the total output current of the parallel power system, as well as the input voltage and output voltage of each module, are also collected. The collected output current, input voltage, and output voltage are used as the observation values ​​of the Kalman filter. Combined with the state estimate value of the previous moment, the state is predicted and updated through the adaptively adjusted process noise covariance matrix and observation noise covariance matrix, thus completing the filtering process and suppressing real-time noise interference. A probabilistic graphical model is constructed among the power supply modules, and an information exchange channel between adjacent modules is established through a communication network. Each power supply module updates its posterior distribution of state by fusing local estimation and neighborhood information through the message passing algorithm of the probabilistic graphical model based on the received multi-source information, forming a globally collaborative state estimation result and suppressing the influence of sensor noise from a single module on the overall state judgment.

3. The method for optimizing the efficiency of a parallel power supply as described in claim 2, characterized in that, Each power module calculates its own posterior probability distribution based on the collected current and voltage data using Bayesian estimation methods, and transmits the estimation results to neighboring modules through a communication link to receive cooperative information from neighboring modules.

4. The method for optimizing the efficiency of a parallel power supply as described in claim 1, characterized in that, The calculation of the current sharing error for each power module, using the current sharing error and its rate of change as inputs to a fuzzy PID controller, and adjusting the proportional, integral, and derivative coefficients of the fuzzy PID controller using fuzzy inference rules, includes: The output current of each power module is compared with the average current to obtain the current sharing error of each power module. The current average error calculated at the current moment is compared with the current average error of the previous sampling period. The rate of change of the current average error is obtained after normalization by time interval. The calculated flow rate error and its rate of change are used as input variables of the fuzzy PID controller. The input variables are converted into corresponding fuzzy linguistic variables and their membership degrees through the membership function. Based on the current combination of fuzzy linguistic variables of the current flow sharing error and the error change rate, the proportional coefficient, integral coefficient and derivative coefficient for the fuzzy PID controller are obtained through fuzzy logic operation; The fuzzy output results of the fuzzy PID parameter adjustment obtained by fuzzy inference are converted into parameter adjustment quantities by the maximum membership method. The optimal values ​​of the three control components of the fuzzy PID controller under the current operating conditions are determined and updated in real time.

5. A parallel power supply efficiency optimization system, used to execute the parallel power supply efficiency optimization method as described in claim 1, characterized in that, The system includes: The acquisition module is used to acquire the output current of each power module in real time through the current sensor of each power module, and at the same time acquire the total output current of the parallel power system and the input voltage and output voltage of each power module. The adjustment module is used to calculate the current sharing error of each power module. The current sharing error and the rate of change of the current sharing error are used as inputs to the fuzzy PID controller. The proportional coefficient, integral coefficient and derivative coefficient of the fuzzy PID controller are adjusted by fuzzy inference rules. The adjustment module is used to calculate the control signal of the voltage regulator of each power module based on the adjusted PID parameters, and adjust the voltage regulator of the power module through the control signal to adjust the output voltage of the power module. The optimization module is used to calculate the efficiency of each power module based on its input voltage, output voltage, and output current. Based on the efficiency of each power module and the load conditions, it determines the optimal operating point of each power module and adjusts the input power of the power modules to complete the efficiency optimization of the entire parallel power supply.

6. A parallel power supply efficiency optimization device, characterized in that, The parallel power supply efficiency optimization device includes a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the parallel power supply efficiency optimization device to perform the various steps of the parallel power supply efficiency optimization method as described in any one of claims 1-4.

7. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the steps of the parallel power supply efficiency optimization method as described in any one of claims 1-4.

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