Parallel power supply efficiency optimization method and system

Abstract

The invention relates to the field, and discloses a parallel power supply efficiency optimization method and system, and the method comprises the steps: collecting the output current of each power supply module in real time through a current sensor of each power supply module, and collecting the total output current of a parallel power supply system, and the input voltage and output voltage of each power supply module; calculating a current sharing error of each power supply module, and adjusting a proportionality coefficient, an integral coefficient and a differential coefficient of a fuzzy PID controller through a fuzzy inference rule; the voltage regulator is used for regulating the power supply module through the control signal; calculating the efficiency of each power supply module according to the input voltage, the output voltage and the output current of each power supply module, and determining the optimal working point of each power supply module and adjusting the input power of each power supply module according to the efficiency and the load condition of each power supply module so as to complete the efficiency optimization of the whole parallel power supply; according to the invention, the working state of the power supply module can be adjusted in real time under different working conditions, and the method has high adaptability and stability.

Description

Technical Field

[0001] The present invention relates to the technical field of power supplies, and in particular to a method and system for optimizing the efficiency of parallel power supplies. Background Art

[0002] In modern power systems, parallel power supply systems are widely used because they can provide high power output, redundant backup and flexible expansion capabilities. However, with the continuous improvement of the efficiency and stability requirements of power electronic equipment, parallel power supply systems face some urgent problems in operation. When distributing load current, traditional parallel power supply systems often have the problem of low current sharing accuracy, resulting in some power modules being overloaded while other modules are lightly loaded. This not only affects the service life of the power modules, but also reduces the efficiency of the entire parallel power supply system. In addition, under different working conditions, such as load changes, input voltage fluctuations, etc., traditional control methods find it difficult to adjust the working status of the power modules in real time to achieve optimal efficiency optimization. Summary of the Invention

[0003] The purpose of the present invention is to solve the above problems and to design a method and system for optimizing the efficiency of parallel power supplies.

[0004] A first aspect of the present 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 through the current sensor of each power module, and the total output current of the parallel power supply system and the input voltage and output voltage of each power module are collected at the same time;

[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 of the fuzzy PID controller. The proportional coefficient, integral coefficient, and differential coefficient of the fuzzy PID controller are adjusted using fuzzy inference rules.

[0007] Calculating the control signal of the voltage regulator of each power module according to the adjusted PID parameters, and adjusting the voltage regulator of the power module by the control signal to adjust the output voltage of the power module;

[0008] 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 module to optimize the efficiency of the entire parallel power supply.

[0009] Optionally, in a first implementation of the first aspect of the present invention, the current sensor of each power module is used to collect the output current of each power module in real time, and the total output current of the parallel power supply system and the input voltage and output voltage of each power module are collected simultaneously, including:

[0010] The built-in current sensor of each power module is used to obtain the output current data of each module in real time, and the total output current of the parallel power supply system and the input voltage and output voltage of each module are collected at the same time;

[0011] The collected output current, input voltage, and output voltage are used as observation values ​​of the Kalman filter. Combined with the state estimation value at 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.

[0012] A probabilistic graphical model is constructed between each power module, and an information exchange channel between adjacent modules is established through a communication network. Based on the multi-source information received, each power module fuses local estimation with neighborhood information through the message passing algorithm of the probabilistic graphical model, updates the posterior distribution of its own state, and forms a global collaborative state estimation result, thereby suppressing the influence of sensor noise of a single module on the overall state judgment.

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

[0014] Optionally, in a third implementation of the first aspect of the present invention, the current sharing error of each power module is calculated, the current sharing error and the rate of change of the current sharing error are used as inputs of the fuzzy PID controller, and the proportional coefficient, integral coefficient, and differential coefficient of the fuzzy PID controller are adjusted by fuzzy inference rules, including:

[0015] Compare the output current of each power module with the average current to obtain the current sharing error of each power module;

[0016] The current sharing error calculated at the current moment is differentiated from the current sharing error of the previous sampling period, and the current sharing error change rate is obtained after normalization by the time interval.

[0017] The calculated current sharing error and its rate of change 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.

[0018] According to the fuzzy linguistic variable combination of the current current sharing error and the error change rate, the proportional coefficient, integral coefficient and differential coefficient of the fuzzy PID controller are obtained through fuzzy logic operation;

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

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

[0021] The three control components of the PID controller, namely the proportional coefficient, integral coefficient and differential coefficient, are linearly superimposed and added to the preset reference control signal value to obtain the final voltage regulator control signal value;

[0022] The voltage regulator control signal value is limited, and the digital control signal after the limit processing is converted into an analog voltage signal through a digital-to-analog converter, and pre-processed through 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 the internal adjustment element according to the signal value to adjust the output voltage of the power module.

[0024] Optionally, in the fifth implementation of the first aspect of the present invention, 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.

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

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

[0027] Taking the maximization of the total efficiency of the parallel power system as the optimization goal, 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, each particle represents 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 historical optimal position and the global optimal position, and update the speed 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 at least include meeting the total load demand, the upper and lower limits of module power, and the voltage and current safety range.

[0031] When the maximum number of iterations is reached, the 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 operating point and achieve overall efficiency optimization of the parallel power supply 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 collect the output current of each power module in real time through the current sensor of each power module, and simultaneously collect the total output current of the parallel power supply system and the input voltage and output voltage of each power module;

[0034] An adjustment module is used to calculate the current sharing error of each power module, use the current sharing error and the rate of change of the current sharing error as inputs of the fuzzy PID controller, and adjust the proportional coefficient, integral coefficient, and differential coefficient of the fuzzy PID controller through fuzzy inference rules;

[0035] a regulating module, configured to calculate a control signal for a voltage regulator of each power module according to the adjusted PID parameters, and regulate the voltage regulator of the power module by 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. It also determines the optimal operating point of each power module based on its efficiency and load conditions, and adjusts the input power of the power module to optimize the efficiency of the entire parallel power supply.

[0037] The third aspect of the present invention provides a parallel power supply efficiency optimization device, which includes a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the parallel power supply efficiency optimization device performs each step of the parallel power supply efficiency optimization method as described in any one of the above items.

[0038] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored thereon, which, when executed by a processor, implement the various steps of the parallel power supply efficiency optimization method as described in any one of the above items.

[0039] In the technical solution provided by the present invention, 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 supply system and the input voltage and output voltage of each power module are collected at the same time; 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 the input of the fuzzy PID controller, and the proportional coefficient, integral coefficient and differential coefficient of the fuzzy PID controller are adjusted by the fuzzy inference rule; according to the adjusted PID parameters, the control signal of 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; according to the input voltage, output voltage and output current of each power module, the efficiency of each power module is calculated, and the efficiency of each power module is calculated based on the input voltage, output voltage and output current of the power module. According to the efficiency and load conditions of each power module, the optimal working point of each power module is determined, and the input power of the power module is adjusted to complete the efficiency optimization of the entire parallel power supply; the present invention collects data such as the output current of each power module in real time, and uses a fuzzy PID controller to control the voltage regulator, thereby achieving output current balance of each power module and improving the current sharing accuracy; at the same time, through efficiency optimization control, the optimal working point is determined according to the efficiency and load conditions of each power module, and the input power is adjusted, thereby improving the efficiency of the entire parallel power supply system, and can adjust the working state of the power module in real time under different working conditions, has strong adaptability and stability, can effectively solve the problems existing in traditional parallel power supply systems, and has significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Various other advantages and benefits will become apparent to those skilled in the art by reading the following detailed description of the preferred embodiment.The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0041] Figure 1 A flow chart of a method for optimizing the efficiency of parallel power supplies provided by an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of the structure of a parallel power supply efficiency optimization system provided by an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of the structure of a parallel power supply efficiency optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The terms "first," "second," "third," "fourth," and the like (if any) in the description and claims of the present invention and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, apparatus, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0045] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 A flow chart of a method for optimizing the efficiency of parallel power supplies provided by an embodiment of the present invention, the method specifically comprising the following steps:

[0046] Step 101: 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 supply system and the input voltage and output voltage of each power module are collected simultaneously;

[0047] In this embodiment, the output current data of each module is obtained in real time through the built-in current sensor of each power module, and the total output current of the parallel power supply system and the input voltage and output voltage of each module are collected at the same time; the collected output current, input voltage, and output voltage are used as the observation values ​​of the Kalman filter, combined with the state estimation value of the previous moment, and the state prediction and update are performed through the adaptively adjusted process noise covariance matrix and the observation noise covariance matrix to complete the filtering processing and suppress real-time noise interference; a probabilistic graphical model is constructed between each power module, and an information interaction channel between adjacent modules is established through the communication network. Each power module, based on the received multi-source information, fuses the local estimation and the neighborhood information through the message passing algorithm of the probabilistic graphical model, updates the posterior distribution of its own state, forms a global collaborative state estimation result, and suppresses the influence of the sensor noise of a single module on the overall state judgment.

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

[0049] In this embodiment, the power module refers to a basic unit with independent power conversion function in the parallel power supply system, which usually includes input filtering, power conversion, output regulation and other circuits, and can supply power to the load independently or in combination. The number of the power module is n (n≥2); the current sensor is a detection device (such as a Hall sensor, a shunt, etc.) installed at the output end of each power module, which is used to monitor the module output current in real time and convert 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 condition of the module, and is the core parameter for judging the load distribution balance; the parallel power supply system consists of multiple The power supply system consists of power modules connected in parallel electrically. The output power can be increased by expanding the number of modules, and it also has redundant backup capabilities to improve power supply reliability. The total output current is the sum of the output currents of all parallel power modules, which is used to measure the overall load level of the system and the target benchmark for current sharing control. The input voltage is the real-time voltage value at the input end of each power module, that is, the voltage provided to the module by the external power supply network, which affects the input power and efficiency calculation of the module. The output voltage is the real-time voltage value at the output end of each power module, that is, the power supply voltage provided by the module to the load, which needs to be maintained stable by a voltage regulator or dynamically adjusted according to the control signal.

[0050] Step 102: Calculate the current sharing error of each power module, use the current sharing error and the rate of change of the current sharing error as inputs of the fuzzy PID controller, and adjust the proportional coefficient, integral coefficient, and differential coefficient of the fuzzy PID controller using 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 normalization by the time interval; 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 through the membership function; based on the fuzzy linguistic variable combination of the current current sharing error and the error change rate, the proportional coefficient, integral coefficient and differential 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 reasoning is converted into parameter adjustment amount through the maximum membership method, and the optimal values ​​of the three control components of the fuzzy PID controller under the current working conditions are determined and updated in real time.

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

[0053] The current sharing error rate of change is the amount of change in the current sharing error per unit time, indicating the changing trend and speed of the current sharing error. By analyzing the positive and negative signs and absolute value of the current sharing error rate of change, it can be determined whether the system is currently in the error convergence, divergence, or steady-state stage, providing dynamic trend information for the control strategy.

[0054] The fuzzy PID controller is a hybrid controller that integrates fuzzy logic and traditional PID control. It uses fuzzy reasoning to adjust PID parameters online to adapt to system dynamics. The fuzzy reasoning rules are based on a pre-defined logical rule base based on expert experience and system operating data. The proportional coefficient is a parameter in the PID controller used to amplify or reduce the current error signal and directly affects the system's response speed. When the current sharing error is large or fluctuates dramatically, increasing the proportional coefficient enhances the controller's response to the current error and speeds up the current sharing adjustment. When the error is small, the proportional coefficient is appropriately reduced to avoid system overshoot. The integral coefficient is a parameter used to accumulate historical error signals and its main function is to eliminate steady-state errors. If the current sharing error persists for a long time, increasing the integral coefficient strengthens the integral effect and gradually eliminates the accumulated deviation. If the system is in a dynamic phase, reducing the integral coefficient avoids regulation delays caused by integral saturation. The differential coefficient is a parameter used to predict the error change trend. The control variable is adjusted based on the error change rate to suppress system oscillations. When the error change rate is positive, increasing the differential coefficient provides a damping effect to slow the error growth. When the error change rate is negative, adjusting the differential coefficient appropriately balances system response speed and stability.

[0055] Step 103: Calculate a control signal for the voltage regulator of each power module based on the adjusted PID parameters, and adjust the voltage regulator of the power module using the control signal to adjust the output voltage of the power module;

[0056] In this embodiment, the three control components of the proportional coefficient, integral coefficient and differential coefficient obtained by the PID controller are linearly superimposed and added to the preset reference control signal value to obtain the final voltage regulator control signal value; the voltage regulator control signal value is limited, and the digital control signal after the limiting process is converted into an analog voltage signal through a digital-to-analog converter and pre-processed through 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 adjustment element according to the signal value to adjust 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 for regulating the output voltage. It is usually composed of a feedback control circuit and a power regulation element. It adjusts the output voltage by receiving a control signal, thereby changing the module 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, and a linear regulator achieves voltage stability by adjusting the power tube voltage drop.

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

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

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

[0062] Differential component calculation: Calculate the difference between the current average current 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 error change trend. This component provides prediction and damping effects.

[0063] In this embodiment, the limiting process is to compare the control signal value with preset upper and lower thresholds. 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. Adjust the input power of the power module to optimize the efficiency of the entire parallel power supply.

[0065] In this embodiment, the efficiency of each power module is calculated based on the input voltage, output voltage, and output current of each power module; with maximizing the total efficiency of the parallel power system as the optimization goal, 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, each particle represents a combination of operating point parameters, and an initial position and velocity vector are assigned to each particle; the fitness value of each particle is calculated, the individual historical optimal position and the global optimal position are updated, and the velocity and position of each particle are updated according to the iterative process of the particle swarm optimization algorithm; during the particle update process, each particle is checked to see whether it meets pre-set constraints. For particles that violate the constraints, boundary processing is used to correct them, where the constraints include at least meeting the total load demand, the upper and lower limits of the module power, and the voltage and current safety range; when the maximum number of iterations is reached, the iteration is stopped, and the currently found global optimal solution is output. The global optimal solution obtained by the particle swarm optimization algorithm is converted into actual control parameters for each power module and sent to each power module to adjust the module operating point to achieve overall efficiency optimization of the parallel power system.

[0066] See also Figure 2 , a schematic diagram of the structure of a parallel power supply efficiency optimization system provided by an embodiment of the present invention, the system includes:

[0067] The acquisition module is used to collect the output current of each power module in real time through the current sensor of each power module, and simultaneously collect the total output current of the parallel power supply system and the input voltage and output voltage of each power module;

[0068] An adjustment module is used to calculate the current sharing error of each power module, use the current sharing error and the rate of change of the current sharing error as inputs of the fuzzy PID controller, and adjust the proportional coefficient, integral coefficient, and differential coefficient of the fuzzy PID controller through fuzzy inference rules;

[0069] a regulating module, configured to calculate a control signal for a voltage regulator of each power module according to the adjusted PID parameters, and regulate the voltage regulator of the power module by 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. It also determines the optimal operating point of each power module based on its efficiency and load conditions, and adjusts the input power of the power module to optimize the efficiency of the entire parallel power supply.

[0071] Figure 3 : is a structural diagram of a parallel power efficiency optimization device provided by an embodiment of the present invention. The parallel power efficiency optimization device 600 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. Among them, the memory 620 and the storage medium 630 can be temporary storage or permanent storage. The program stored in the storage medium 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the parallel power efficiency optimization device 600. Furthermore, the processor 610 can be configured to communicate with the storage medium 630 to execute a series of instruction operations in the storage medium 630 on the parallel power efficiency optimization device 600 to implement the method provided by the above embodiment.

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

[0073] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the various steps of the parallel power supply efficiency optimization method provided in the above embodiments.

[0074] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0075] If the integrated unit is implemented in the form of 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, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., various media that can store program code.

[0076] The above shows and describes 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 above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in 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 comprises the following steps: The output current of each power module is collected in real time through the current sensor of each power module, and the total output current of the parallel power supply system and the input voltage and output voltage of each power module are collected at the same time; 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 of the fuzzy PID controller. The proportional coefficient, integral coefficient, and differential coefficient of the fuzzy PID controller are adjusted using fuzzy inference rules. Calculating the control signal of the voltage regulator of each power module according to the adjusted PID parameters, and adjusting the voltage regulator of the power module by the control signal to adjust the output voltage of the power module; 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 module to optimize the efficiency of the entire parallel power supply.

2. The method for optimizing the efficiency of parallel power supplies according to claim 1, wherein: The current sensor of each power module collects the output current of each power module in real time, and simultaneously collects the total output current of the parallel power supply system and the input voltage and output voltage of each power module, including: The built-in current sensor of each power module is used to obtain the output current data of each module in real time, and the total output current of the parallel power supply system and the input voltage and output voltage of each module are collected at the same time; The collected output current, input voltage, and output voltage are used as observation values ​​of the Kalman filter. Combined with the state estimation value at 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 between each power module, and an information exchange channel between adjacent modules is established through a communication network. Based on the multi-source information received, each power module fuses local estimation with neighborhood information through the message passing algorithm of the probabilistic graphical model, updates the posterior distribution of its own state, and forms a global collaborative state estimation result, thereby suppressing the influence of sensor noise of a single module on the overall state judgment.

3. The method for optimizing the efficiency of parallel power supplies according to claim 2, wherein: Each power module calculates the posterior probability distribution of its own state based on the collected current and voltage data using the Bayesian estimation method, transmits the estimation results to the adjacent modules through the communication link, and receives collaborative information from the adjacent modules.

4. The method for optimizing the efficiency of parallel power supplies according to claim 1, wherein: 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 of the fuzzy PID controller. The proportional coefficient, integral coefficient and differential coefficient of the fuzzy PID controller are adjusted by fuzzy inference rules, including: Compare the output current of each power module with the average current to obtain the current sharing error of each power module; The current sharing error calculated at the current moment is differentiated from the current sharing error of the previous sampling period, and the current sharing error change rate is obtained after normalization by the time interval. The calculated current sharing error and its rate of change 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. According to the fuzzy linguistic variable combination of the current current sharing error and the error change rate, the proportional coefficient, integral coefficient and differential coefficient of the fuzzy PID controller are obtained through fuzzy logic operation; The fuzzy output results of fuzzy PID parameter adjustment obtained by fuzzy reasoning are converted into parameter adjustment quantities through the maximum membership method, and the optimal values ​​of the three control components of the fuzzy PID controller under the current working conditions are determined and updated in real time.

5. The method for optimizing the efficiency of parallel power supplies according to claim 1, wherein: The method of calculating a control signal of a voltage regulator of each power module according to the adjusted PID parameters and adjusting the voltage regulator of the power module by the control signal to adjust the output voltage of the power module includes: The three control components of the PID controller, namely the proportional coefficient, integral coefficient and differential coefficient, are linearly superimposed and added to the preset reference control signal value to obtain the final voltage regulator control signal value; The voltage regulator control signal value is limited, and the digital control signal after the limit processing is converted into an analog voltage signal through a digital-to-analog converter, and pre-processed through 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 adjustment element according to the signal value to adjust the output voltage of the power module.

6. The method for optimizing the efficiency of parallel power supplies according to claim 5, wherein: 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.

7. The method for optimizing the efficiency of parallel power supplies according to claim 1, wherein: The efficiency of each power module is calculated based on the input voltage, output voltage, and output current of each power module, the optimal operating point of each power module is determined based on the efficiency and load of each power module, and the input power of the power module is adjusted to complete the efficiency optimization of the entire parallel power supply, including: Calculate the efficiency of each power module based on its input voltage, output voltage, and output current. Taking the maximization of the total efficiency of the parallel power system as the optimization goal, 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, each particle represents 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 historical optimal position and the global optimal position, and update the speed 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 at least include meeting the total load demand, the upper and lower limits of module power, and the voltage and current safety range. When the maximum number of iterations is reached, the 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 operating point and achieve overall efficiency optimization of the parallel power supply system.

8. A parallel power supply efficiency optimization system, characterized in that: The system includes: The acquisition module is used to collect the output current of each power module in real time through the current sensor of each power module, and simultaneously collect the total output current of the parallel power supply system and the input voltage and output voltage of each power module; An adjustment module is used to calculate the current sharing error of each power module, use the current sharing error and the rate of change of the current sharing error as inputs of the fuzzy PID controller, and adjust the proportional coefficient, integral coefficient, and differential coefficient of the fuzzy PID controller through fuzzy inference rules; a regulating module, configured to calculate a control signal for a voltage regulator of each power module according to the adjusted PID parameters, and regulate the voltage regulator of the power module by 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. It also determines the optimal operating point of each power module based on its efficiency and load conditions, and adjusts the input power of the power module to optimize the efficiency of the entire parallel power supply.

9. 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 instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the parallel power supply efficiency optimization device performs the various steps of the parallel power supply efficiency optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by a processor, the steps of the parallel power supply efficiency optimization method according to any one of claims 1 to 7 are implemented.

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