A power load adaptation and output regulation control method, device and storage medium

Abstract

The application provides a power load adaptation and output regulation control method, equipment and storage medium, and belongs to the technical field of power load regulation control, and the method comprises the following steps: calculating dynamic parameters based on load electrical parameters; generating an initial solution population of control parameters based on a chaotic mapping mechanism and a dynamic parameter value boundary; iteratively optimizing the initial solution population by using an improved artificial bee colony algorithm to obtain an optimal control parameter set matched with the current load characteristics; fusing the optimal control parameter set as a feedforward control amount and a feedback correction amount to generate a comprehensive control instruction; adjusting a power output unit according to the instruction; determining whether the power output reaches a target set value within a preset adjustment time window; if not, updating an iterative optimization input condition and repeatedly executing the iterative optimization; and if yes, maintaining the comprehensive control instruction. Based on the method, corresponding equipment and storage medium are also provided. The application realizes accurate and stable output of a power system within the first sine wave cycle after load mutation.

Description

A power load adaptation and output regulation control method, device and storage medium Technical Field

[0001] This invention belongs to the field of power load regulation and control technology, and specifically relates to a power load adaptation and output regulation control method, device and storage medium. Background Technology

[0002] Power load adaptation and output regulation control technology is a core supporting technology in fields such as electronic information, industrial control, new energy power generation, and precision instruments. Its performance directly determines the working stability, operating efficiency, and service life of terminal equipment, and it is widely used in key scenarios such as power supply for precision electronic equipment, power systems for new energy vehicles, photovoltaic energy storage grid-connected devices, pulse load drive systems, and industrial automation control power supplies. With the rapid expansion of the global new energy industry, the iterative upgrading of precision manufacturing technology, and the popularization of intelligent terminal devices, the market has placed higher demands on the load adaptation capability, fast response speed, and output accuracy of power systems: the motor drive system of new energy vehicles needs to cope with dynamic load changes under different driving conditions, photovoltaic energy storage systems need to adapt to power fluctuations in the grid and distributed loads, and precision electronic instruments require power supplies to respond to load changes in the microsecond range and maintain stable output.

[0003] Traditional power supply regulation techniques, such as PID-based feedback control, have inherent flaws. Their "detect deviation - correct deviation" operating mode results in response lag, leading to slow adjustment when faced with sudden load changes, typically requiring tens of milliseconds or even longer to stabilize. In practical applications, load types exhibit significant diversity, including resistive, inductive, capacitive, and mixed loads. Different types of loads have vastly different requirements for the voltage amplitude, phase, frequency, and internal resistance characteristics of the power supply output: resistive loads require stable output voltage, inductive loads are sensitive to current change rates, and capacitive loads are prone to voltage surges. Fixed or manually switched control parameters are difficult to adapt to the diversity of load characteristics, easily leading to output oscillations, overshoot, or steady-state errors. These bottlenecks severely restrict the development of high-end power supply systems. In recent years, intelligent optimization algorithms have provided new approaches to solving complex system control problems. The artificial bee colony algorithm, as a highly efficient swarm intelligence optimization algorithm, has been attempted for parameter tuning. However, when standard algorithms address the specific problem of rapid power load adaptation, they suffer from poor initial solution quality, slow convergence speed, and susceptibility to local optima, making it difficult to meet the stringent real-time requirement of "accurately achieving the target within the first sine wave cycle (5ms)".

[0004] Therefore, there is an urgent need for a control method that deeply integrates intelligent optimization and power regulation technologies to achieve real-time perception, dynamic optimization, and lag-free regulation of load characteristics. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a power supply load adaptation and output regulation control method. This method achieves accurate and stable output from the power supply system within the first sinusoidal cycle after a sudden load change, significantly improving response speed, load adaptation range, and steady-state accuracy.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention proposes a power load adaptation and output regulation control method, comprising the following steps:

[0008] Load electrical parameters are collected in real time, and dynamic parameters characterizing the load characteristics are calculated based on the load electrical parameters; an initial solution population for the control parameters is generated based on the chaotic mapping mechanism and the value boundaries of the dynamic parameters.

[0009] An improved artificial bee colony algorithm is used to iteratively optimize the initial solution population to obtain the optimal set of control parameters that matches the current load characteristics. The iterative optimization process includes: dynamically modifying the optimization objective function according to the load power change trend, and adaptively adjusting the search step size and neighborhood range according to the distribution density of individuals in the solution space.

[0010] The optimal control parameter set is used as the feedforward control quantity and dynamically weighted and fused with the feedback correction quantity calculated based on the power output feedback signal to generate a comprehensive control command.

[0011] The power output unit of the power supply is adjusted according to the integrated control command; it is determined whether the power output reaches the target set value within the preset adjustment time window; if not, the input conditions for iterative optimization are updated based on the current output state, and the iterative optimization and subsequent steps are repeated; if the target is reached, the generated integrated control command is maintained.

[0012] Secondly, the present invention proposes a power load adaptation and output regulation control device, including at least one processor and a memory, wherein the memory stores a computer program, and the computer program, when executed by the at least one processor, implements the power load adaptation and output regulation control method.

[0013] Thirdly, the present invention proposes a computer-readable storage medium having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the aforementioned power load adaptation and output regulation control method.

[0014] The effects described in the invention are merely those of the embodiments, and not all the effects of the invention. One of the above technical solutions has the following advantages or beneficial effects:

[0015] This invention proposes a power supply load adaptation and output regulation control method, device, and storage medium. The method includes the following steps: real-time acquisition of load electrical parameters; calculation of dynamic parameters characterizing load characteristics based on the load electrical parameters; generation of an initial solution population of control parameters based on a chaotic mapping mechanism and the value boundaries of the dynamic parameters; iterative optimization of the initial solution population using an improved artificial bee colony algorithm to obtain the optimal control parameter set matching the current load characteristics; the iterative optimization process includes: dynamically modifying the optimization objective function according to the load power change trend, and adaptively adjusting the search step size and neighborhood range according to the distribution density of individuals in the solution space; using the optimal control parameter set as a feedforward control quantity, and dynamically weighting and fusing it with the feedback correction quantity calculated based on the power supply output feedback signal to generate a comprehensive control command; adjusting the power output unit of the power supply according to the comprehensive control command; determining whether the power supply output reaches the target set value within a preset adjustment time window; if not, updating the input conditions for iterative optimization based on the current output state, and repeating the iterative optimization and subsequent steps; if it reaches the target, maintaining the generated comprehensive control command. This invention organically combines chaotic mapping initialization with load feedforward prediction to predict the trend of load changes instantaneously and generate optimized initial control parameters. Combined with a dynamic weighted fusion control mechanism, this enables the power system to reach the target setpoint within the first sine wave cycle after a sudden load change. This response speed is an order of magnitude faster than traditional technologies, meeting the stringent real-time requirements of precision electronic equipment and pulse load drives.

[0016] This invention employs a neighborhood search mechanism constrained by Euclidean distance and a dynamic objective function correction strategy, enabling the algorithm to adaptively adjust the optimization direction and search step size based on load characteristics (characterized by parameters such as dynamic impedance and power factor). When a load type switch or characteristic change is detected, the system can quickly converge to a new optimal solution, ensuring that the output voltage remains highly accurate and stable under various load conditions.

[0017] The feedforward-feedback dynamic fusion mechanism employed in this invention offers dual advantages: feedforward control, based on algorithm optimization, achieves proactive adjustment, quickly suppressing disturbances caused by sudden load changes; feedback control corrects minor deviations between feedforward predictions and actual outputs in real time. This fusion mechanism dynamically allocates weights according to the load state, prioritizing feedforward to maintain speed during load changes and prioritizing feedback to maintain accuracy during load stabilization. This ensures system robustness even under complex operating conditions and significantly reduces the risk of abnormal output due to external disturbances.

[0018] This invention significantly improves the quality of initial solutions through chaotic mapping-guided population initialization, avoiding the ineffective search caused by traditional random initialization. Adaptive step size adjustment based on population distribution density balances global exploration and local exploitation, preventing the algorithm from getting trapped in local optima and avoiding excessive oscillations around high-quality solutions. These improvements enable the improved artificial bee colony algorithm to complete multiple iterations and output reliable optimal control parameters within milliseconds, meeting the computational power and timeliness requirements of real-time power supply control.

[0019] The technical solution of this invention has wide applicability. It is not only applicable to conventional industrial control power supplies, but also meets the needs of new energy grid-connected power generation devices for rapid compensation of grid fluctuations, the high standards of power supply quality required by precision electronic instruments, and the challenges of pulse load systems for instantaneous power response. Attached Figure Description

[0020] Figure 1 is a flowchart of a power load adaptation and output regulation control method proposed in Embodiment 1 of the present invention;

[0021] Figure 2 is a schematic diagram of the traditional artificial bee colony algorithm proposed in Embodiment 1 of the present invention;

[0022] Figure 3 is a flowchart of the improved artificial bee colony algorithm proposed in Embodiment 1 of the present invention;

[0023] Figure 4 is a diagram of the power load adaptation and output regulation architecture proposed in Embodiment 1 of the present invention;

[0024] Figure 5 is a schematic diagram of a power load adaptation and output regulation control device according to Embodiment 2 of the present invention. Detailed Implementation

[0025] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific embodiments and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure of the invention, components and arrangements of specific examples are described below. Furthermore, reference numerals and / or letters may be repeated in different examples. This repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that the components illustrated in the drawings are not necessarily drawn to scale. Descriptions of well-known components, processing techniques, and processes are omitted in this invention to avoid unnecessarily limiting the invention.

[0026] Example 1

[0027] Embodiment 1 of this invention proposes a power load adaptation and output regulation control method based on an improved artificial bee colony algorithm to solve the technical problems of response lag, poor adaptability and insufficient output accuracy in traditional power control technology when the load changes dynamically.

[0028] First, let's introduce the differences between the traditional artificial bee colony algorithm and the improved artificial bee colony algorithm used in this invention.

[0029] Figure 2 is a schematic diagram of the traditional artificial bee colony algorithm proposed in Embodiment 1 of the present invention. The basic working mode of the traditional artificial bee colony algorithm is illustrated through the interaction between bees (hired bees, follower bees, scout bees) and nectar sources (candidate solutions) (search S, return R, honey collection / unloading UF, recruitment EF1, information feedback EF2).

[0030] The core process of traditional artificial bee colony algorithms includes: searching for nectar sources → evaluating fitness (nectar quantity) → information sharing (waggle dance) → selection of follower bees → updating nectar sources.

[0031] Figure 3 is a flowchart of the improved artificial bee colony algorithm proposed in Embodiment 1 of the present invention;

[0032] After the algorithm starts, it first generates an initial population with high ergodicity through chaotic mapping, and calculates the initial fitness value of each candidate solution (honey source).

[0033] The hired bees identify initial marked nectar sources and search for new nectar sources in their neighborhood according to preset rules. Then, they calculate the fitness value of the new nectar sources and decide whether to replace the original nectar sources with the new ones through a greedy selection mechanism (replacing only if the new nectar sources are better).

[0034] Hired bees recruit follower bees through a roulette-like mechanism, sharing nectar source information (including location and fitness) with the colony. Based on this information, the follower bees are assigned to different nectar sources to complete their assigned tasks.

[0035] The algorithm further explores the nectar sources assigned to it by the bees, searching for new nectar sources. During this process, the algorithm employs a strategy of expanding the search neighborhood using Euclidean distance, dynamically adjusting the search range based on population distribution to balance local exploration with global discovery.

[0036] After the follower bee searches, the location of the optimal nectar source in the current iteration is recorded. For nectar sources that are not continuously improved, a new nectar source is generated by the scout bee to replace it, and the new nectar source is marked, thereby maintaining population diversity and avoiding premature convergence.

[0037] The algorithm determines whether the preset constraints have been met (such as reaching the maximum number of iterations, fitness meeting accuracy requirements, or running time limits). If the conditions are met, the algorithm terminates and outputs the current optimal solution; otherwise, it returns to the hired bee exploration phase and begins a new round of iterations until the convergence condition is met.

[0038] Based on Figure 2, this invention adds steps such as chaotic initialization and Euclidean distance constraints to Figure 3, and organizes them into a complete iterative process suitable for optimizing power supply control parameters.

[0039] Figure 1 is a flowchart of a power load adaptation and output regulation control method proposed in Embodiment 1 of the present invention;

[0040] In step S1, load electrical parameters are collected in real time, and dynamic parameters characterizing the load characteristics are calculated based on the load electrical parameters; based on the chaotic mapping mechanism and the value boundaries of the dynamic parameters, an initial solution population for the control parameters is generated.

[0041] The load electrical parameters include voltage signals, current signals, and the phase difference signal between voltage and current; the dynamic parameters characterizing the load characteristics include at least dynamic impedance and the rate of change of active power.

[0042] This invention acquires the voltage at the load terminal using a voltage sensor. The voltage sensor model is: CSNK-03M;

[0043] Current is collected using a current sensor. The current sensor model is CSLA2CD;

[0044] Phase difference is acquired using a phase detection chip. The phase detection chip is model AD8302.

[0045] The collected load electrical parameters are then converted from analog signals to digital signals using an ADC, specifically an ADS8688.

[0046] The scope of protection of this invention is not limited to the specific models of the devices listed in Example 1. Those skilled in the art can make reasonable selections based on the actual situation.

[0047] The process of calculating dynamic parameters characterizing load properties based on load electrical parameters is as follows:

[0048] The dynamic impedance is: ;

[0049] The power factor is: ;

[0050] The rate of change of active power .

[0051] To reduce the blindness of the algorithm and enhance its ability to escape local optima, chaotic concepts are used to initialize the population. This method leverages the ergodicity, randomness, and sensitivity to initial conditions of chaotic variables to introduce chaotic operators for generating initial positions, thereby increasing the diversity of initial positions and improving the algorithm's quality.

[0052] Based on the chaotic mapping mechanism and the boundary values ​​of dynamic parameters, the process of generating the initial solution population for the control parameters is as follows:

[0053] Determine dynamic impedance The boundary values ​​of are and the rate of change of active power Predicted range of change ;

[0054] Generating chaotic sequences using cosine chaotic mapping functions Its iterative formula is:

[0055] ;

[0056] in, For the first random generation within the interval (0,1) One chaotic iteration value; The preset sequence length, and ;

[0057] Will Mapping to the lower bound of the first dimension of the solution space With the upper realm ;Will Mapping to the lower bound of the second dimension of the solution space With the upper realm ;

[0058] Based on the search boundary, two sets of candidate solutions are generated: the first set of candidate solution vectors. Second set of candidate solution vectors ;

[0059]

[0060] Based on the preset fitness evaluation function, calculate all candidate solution vectors. The fitness value is selected, and the one with the highest fitness is chosen. The initial solution population consists of several vectors.

[0061] The fitness function is designed to adapt to the characteristics of the power supply load, serving as the core criterion for selecting the initial solution. Considering that the core requirements of power supply load adaptation are output voltage stability and load impedance matching, the fitness evaluation function for the initial position is defined as follows:

[0062] ;

[0063] in, For from or any candidate solution vector; The target output voltage; To be The predicted output voltage is used as a control parameter; To be The predicted load matching impedance is used as a control parameter; The actual dynamic impedance of the load is calculated based on the real-time acquired signal. A positive weighting factor for balancing voltage accuracy; To weigh the positive weighting coefficients for impedance matching priority, and ; This is the first minimal positive number used to prevent the denominator from being zero; This is used to prevent the second smallest positive number from having a denominator of zero.

[0064] for example , Prioritize ensuring stable output voltage.

[0065] The physical meaning of this fitness evaluation function is: the closer the predicted output of the candidate solution is to the set value, the better the impedance matches the load, the closer the fitness value is to 1, and the higher the quality of the solution. Candidate solution vector All of them For each candidate solution, substitute the control parameters corresponding to that candidate solution into the fitness function described above, and calculate the fitness function for each candidate solution. According to fitness values ​​from largest to smallest Sort the candidate solutions and select the top ones. The candidate solutions with the highest fitness are used to form the initial population of the improved artificial bee colony algorithm.

[0066] The fitness value of each nectar source after exploration by the hired bees is represented. A nectar source with higher fitness has a greater probability of being selected, thus tilting algorithm resources towards high-quality solutions. After the follower bees select a nectar source, they use the same Euclidean distance constraint neighborhood rule as the hired bees to search for new nectar sources. Specifically, they adjust the perturbation step size and neighborhood range based on the average Euclidean distance of the target nectar source, generating new nectar sources and performing a greedy selection. This division of labor—"hired bee exploration + follower bee reinforcement"—ensures the diversity of the search while avoiding repeated exploration of invalid regions, significantly improving the algorithm's optimization efficiency.

[0067] After completing the initial population selection, the core parameters of the improved artificial bee colony algorithm are initialized simultaneously:

[0068] Population size is , usually take This value balances the diversity of algorithm optimization with computational efficiency; if the load is a mixed load, i.e., a combination of resistors, capacitors, and inductors, it is adjusted to... This enhances the adaptability and coverage of the population.

[0069] The iteration threshold, which is the maximum number of iterations that a scout bee can use to replace an invalid nectar source, is set to 20 based on the real-time requirements of power output adjustment (stable output within 5ms). If the fitness of a nectar source does not improve after 20 consecutive iterations, it is determined to be an invalid solution, triggering the scout bee to search for a new nectar source.

[0070] Neighborhood radius coefficient This value has been verified through experiments in multiple load scenarios, and it can ensure local search accuracy while avoiding the algorithm getting trapped in local optima.

[0071] The scope of protection of this invention is not limited to the specific values ​​listed in Example 1, and those skilled in the art can make reasonable adjustments according to the actual situation.

[0072] In step S2, an improved artificial bee colony algorithm is used to iteratively optimize the initial solution population to obtain the optimal set of control parameters that matches the current load characteristics. The iterative optimization process includes: dynamically modifying the optimization objective function according to the load power change trend, and adaptively adjusting the search step size and neighborhood range according to the distribution density of individuals in the solution space.

[0073] The artificial bee colony algorithm is used for optimization. Based on their respective roles, artificial bee colonies can be divided into three types: hired bees, follower bees, and scout bees, to find the optimal parameters. Step S1 initializes the improved artificial bee colony algorithm parameters, including population size and iteration threshold, reducing invalid searches and improving the quality of the initial solution.

[0074] Hired bees and follower bees search for new nectar sources based on optimized neighborhood rules, while scout bees replace invalid nectar sources, improving the efficiency and stability of the search.

[0075] First, the initial position generation rule based on chaotic mapping is used to generate... One position. Secondly, hired bees for The bee performs a neighborhood search at each location. The hired bee recruits follower bees based on the roulette wheel. Based on the distribution of honey sources, the follower bees obtain the location information of the honey sources from the hired bees to record the optimal location of the honey source.

[0076] Finally, after the follower bee selects the nectar source location, it updates the selected location according to the same search rules as the hired bee. It then checks if the number of iterations has reached the prediction threshold; if so, it outputs the optimal value; otherwise, it returns to the initial search step and iterates again.

[0077] In the improved neighborhood optimization framework of the artificial bee colony algorithm with Euclidean distance constraints, hired bees and follower bees achieve accurate and efficient new nectar source search through dynamically adapted neighborhood rules, while scout bees ensure the global exploration capability of the population by replacing invalid nectar sources. The three work together to form an optimization closed loop of accurate local search and efficient global exploration, which greatly improves the optimization efficiency and stability of the algorithm.

[0078] Hired bees, acting as "vanguard explorers" of the population, strictly adhere to neighborhood rules optimized by Euclidean distance in their search for new nectar sources. This is based on the average Euclidean distance between nectar sources calculated earlier. First, determine the current honey source. The effective neighborhood range is determined to ensure that the search range focuses on the area surrounding high-quality solutions. Then, the optimized neighborhood search formula is used to generate new honey sources.

[0079] As a enhancer of high-quality solutions, the follower bee's selection of nectar sources is closely linked to the exploration results of the hired bees, further improving the local discovery efficiency of high-quality solutions. The follower bee selects the nectar source to be searched using a roulette wheel method, with the selection probability determined by the fitness of the nectar source.

[0080] Based on the steps in Figure 3, the improved artificial bee colony algorithm for load feedforward fusion calculates the fitness of the load change trend and transforms it into a weight term of the fitness evaluation function; the artificial bee colony algorithm for neighborhood optimization with Euclidean distance constraint calculates the average Euclidean distance between nectar sources and dynamically adjusts the neighborhood range and search step size, reducing the step size when the neighborhood is concentrated and increasing the step size when the neighborhood is dispersed; hired bees and follower bees search for new nectar sources based on the optimized neighborhood rules, and scout bees replace invalid nectar sources, improving optimization efficiency and stability.

[0081] In the iterative optimization process of this invention, the load active power change rate is introduced as a dynamic adjustment factor into the fitness evaluation function to modify the optimization objective. The modified dynamic objective function is then used. for:

[0082] ;

[0083] in, For any candidate solution vector The baseline fit value; This is a preset sensitivity coefficient used to adjust the algorithm's response strength to changes in load power; To be based on the real-time collected load voltage With current The calculated rate of change of active power of the load;

[0084] To address the blind neighborhood search problem in traditional artificial bee colony algorithms, a dynamic search strategy using Euclidean distance is introduced. ;

[0085] in, For the current nectar source (individual) ) in the Position on the dimension; To start from the current individual Effective search neighborhood Another honey source randomly selected from the middle exist Position on the dimension; The random perturbation factor is within the interval [-1, 1], and its perturbation amplitude depends on the individual. Average Euclidean distance Perform dynamic scaling.

[0086] Calculate the current honey source The average Euclidean distance between the individual and all other individuals in the population , used to quantify the distribution density of the population in the solution space: where the current nectar source is a population individual;

[0087] ;

[0088] in, For individuals in a population Other populations exist Euclidean distance in the solution space; The total dimension of the solution space; based on the average Euclidean distance. Dynamically determine population individuals Effective search neighborhood ;

[0089] ;

[0090] in, The neighborhood radius coefficient is preset; the quality of the honey source search depends on the neighborhood radius; when the neighborhood radius is 0, neighborhood search is no longer performed; The larger the value, the larger the search neighborhood, and vice versa. (This applies to the radius.) Different convergence results were obtained by taking different values.

[0091] When hired bees or follower bees perform neighborhood searches, based on the neighborhood... Dynamically adjust the search step size: when When the density is less than a preset threshold, reduce the step size; when If the density is not less than the preset threshold, the step size is increased. Hired bees and follower bees search for new nectar sources based on optimized neighborhood rules, while scout bees replace invalid nectar sources, improving the efficiency and stability of the search.

[0092] In step S3, the optimal control parameter set is used as the feedforward control quantity and dynamically weighted and fused with the feedback correction quantity calculated based on the power supply output feedback signal to generate a comprehensive control command.

[0093] The optimal control parameter set is weighted and fused with the power output signal collected by feedback, and a load feedforward calculation module is designed; PWM duty cycle and phase adjustment commands are generated to drive the power output module to adjust the output characteristics and achieve rapid response to load changes;

[0094] Let the feedforward control quantity be The feedback correction amount is Then the integrated control command for:

[0095] ;

[0096] in, For feedforward weights; For feedback weights; and .

[0097] When a load mutation is detected ( In order to achieve a fast response, set... , When the load is stable, to achieve high accuracy, set... , ; This is the power surge threshold (e.g., 10% of the rated power).

[0098] The feedforward control quantity is generated based on the real-time predicted load change trend, and is used to proactively address disturbances caused by sudden load changes. The feedback correction quantity is acquired by collecting the power supply output deviation signal, which is obtained by collecting the difference between the actual power supply output voltage and the preset target voltage, and is used to correct minor deviations in the feedforward compensation and algorithm reference parameters.

[0099] Subsequently, adaptive weight allocation is performed based on the dynamic characteristics of the load. The core principle of weight allocation is that the load state determines the adjustment priority. When the load is in a stable state, that is, the rate of change of the load active power is within a preset small threshold, the output accuracy is prioritized. Therefore, the feedback weight is set to a higher proportion, and the feedforward weight is set to a lower proportion, relying on the fineness of feedback adjustment to correct minor deviations. When a sudden load change is detected, that is, the rate of change of the load active power exceeds the preset threshold, the response speed is prioritized. Therefore, the feedforward weight is adjusted to a higher proportion, and the feedback weight is reduced accordingly. The output fluctuation caused by the load change is quickly suppressed by the predictive nature of feedforward compensation, and the sum of the feedforward weight and the feedback weight is always ensured to be 1 to ensure the rationality of the fusion logic.

[0100] Finally, a weighted fusion operation is performed to generate a comprehensive control command. During the fusion process, the optimal control parameter set output by the improved artificial bee colony algorithm is used as the basic framework, and feedforward control and feedback correction quantities, adjusted with dynamic weights, are superimposed. The comprehensive control command determines the core direction of the adjustment, preventing deviation from the basic requirements of load adaptation; the feedforward control quantity enables early intervention in load changes, reducing adjustment lag; and the feedback correction quantity corrects potential errors in the feedforward calculation and algorithm optimization process, ensuring output accuracy. Through this multi-signal collaborative fusion approach, the final generated control quantity possesses both the ability to quickly respond to load changes and the ability to maintain the stability and accuracy of the power supply output, providing a reliable basis for subsequent PWM command generation.

[0101] In step S4, the power output unit of the power supply is adjusted according to the integrated control command.

[0102] Figure 4 is a diagram of the power load adaptation and output regulation architecture proposed in Embodiment 1 of the present invention; the key execution link that transforms the comprehensive control command into the actual power output is the power output module in the architecture, which consists of a PWM control unit, an IGBT full-bridge inverter circuit, a drive circuit and an LC filter circuit. The core objective is to achieve fast and accurate output of load adaptation.

[0103] PWM instruction generation:

[0104] The PWM control unit integrated into the FPGA quickly generates signal parameters that match the load requirements according to the SPWM principle based on the comprehensive control instructions, and dynamically adjusts the duty cycle and phase trigger timing of the PWM to ensure that the instructions accurately match the current load characteristics and output target.

[0105] After the weak PWM signal is amplified and electrically isolated by the driver chip, it drives the switching devices of the IGBT full-bridge topology to turn on and off alternately, inverting the DC bus voltage into a pulse voltage, thus initially realizing the adjustment of the output voltage amplitude and phase.

[0106] The pulse voltage is filtered by an LC low-pass filter circuit to remove high-frequency harmonics and is converted into a smooth sine wave voltage to supply the load. The output terminal samples the voltage and current signals in real time and sends them back to the fusion regulation module to form a closed-loop control, corrects the output deviation in real time, and ensures that the first waveform of the voltage reaches the set value, so as to achieve rapid response and stable adaptation to load changes.

[0107] In step S5, it is determined whether the power output reaches the target set value within a preset adjustment time window; if not, step S6 is executed; if it does, step S7 is executed. That is, it is determined whether the output reaches the set value within the first sine wave cycle, which can be 5ms.

[0108] The specific process is as follows: Set the adjustment time window as... The target output voltage is , The preset error tolerance value is used; the allowable output voltage error band is... ;

[0109] Real-time acquisition of the output voltage signal at the power supply output terminal; starting from the moment a change in load characteristics is detected. Start the timer; adjust the time window. The system continuously determines whether the output voltage signal enters and stabilizes within the allowable output voltage error band.

[0110] In step S6, the input conditions for iterative optimization are updated based on the current output state, and steps S1 to S5 are repeated until the power supply outputs stably under load.

[0111] That is, if in If the standard is not met by the deadline, the currently collected load voltage will be used. Load current Recalculate load dynamic impedance and power factor ;Will and The updated load dynamic impedance and power factor are used as updated load parameters and input into the chaotic mapping initialization step, and the iterative optimization is re-executed until a certain value is found. The internal assessment was deemed satisfactory.

[0112] In step S7, the generated integrated control command is maintained.

[0113] That is, if in If the internal determination is satisfactory, the generated integrated control command will be maintained.

[0114] The power supply load adaptation and output regulation control method proposed in Embodiment 1 of this invention organically combines chaotic mapping initialization and load feedforward prediction. It predicts the trend of load changes instantaneously and generates optimized initial control parameters. Combined with a dynamic weighted fusion control mechanism, this enables the power system to reach the target setpoint within the first sine wave cycle after a sudden load change. This response speed is an order of magnitude faster than traditional technologies, meeting the stringent real-time requirements of precision electronic equipment and pulse load drives.

[0115] Example 2

[0116] Embodiment 2 of the present invention also proposes a power load adaptation and output regulation control device, comprising:

[0117] Memory, used to store computer programs;

[0118] When a processor executes the computer program, the method steps are as follows:

[0119] In step S1, load electrical parameters are collected in real time, and dynamic parameters characterizing the load characteristics are calculated based on the load electrical parameters; based on the chaotic mapping mechanism and the value boundaries of the dynamic parameters, an initial solution population for the control parameters is generated.

[0120] In step S2, an improved artificial bee colony algorithm is used to iteratively optimize the initial solution population to obtain the optimal set of control parameters that matches the current load characteristics. The iterative optimization process includes: dynamically modifying the optimization objective function according to the load power change trend, and adaptively adjusting the search step size and neighborhood range according to the distribution density of individuals in the solution space.

[0121] In step S3, the optimal control parameter set is used as the feedforward control quantity and dynamically weighted and fused with the feedback correction quantity calculated based on the power supply output feedback signal to generate a comprehensive control command.

[0122] In step S4, the power output unit of the power supply is adjusted according to the comprehensive control command;

[0123] In step S5, it is determined whether the power output reaches the target set value within the preset adjustment time window; if it does not reach the target value, step S6 is executed; if it does reach the target value, step S7 is executed.

[0124] In step S6, the input conditions for iterative optimization are updated based on the current output state, and steps S1 to S5 are repeated until the power supply outputs stably under load.

[0125] In step S7, the generated integrated control command is maintained.

[0126] The description of the relevant parts of the power load adaptation and output regulation control storage device provided in Embodiment 2 of this application can be found in the detailed description of the corresponding parts of the power load adaptation and output regulation control method provided in Embodiment 1 of this application, and will not be repeated here.

[0127] It should be noted that the present invention also provides a power load adaptation and output regulation control device, including: a communication interface capable of exchanging information with other devices such as network devices; and a processor connected to the communication interface to enable information exchange with other devices, used to execute a power load adaptation and output regulation control method provided by one or more of the above technical solutions when running a computer program, wherein the computer program is stored in a memory. Of course, in practical applications, the various components in an electronic device are coupled together through a bus system. It is understood that the bus system is used to realize the connection and communication between these components. In addition to a data bus, the bus system also includes a power bus, a control bus, and a status signal bus. The memory in the embodiments of this application is used to store various types of data to support the operation of the electronic device. Examples of this data include any computer program used to operate on the electronic device. It is understood that the memory can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory, flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache.By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM). The memories described in the embodiments of this application are intended to include, but are not limited to, these and any other suitable types of memory. The methods disclosed in the embodiments of this application can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The processor can be a general-purpose processor, a DSP (Digital Signal Processing, i.e., a chip capable of implementing digital signal processing technology), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can be located in a storage medium, which is located in memory. The processor reads the program from the memory and, in conjunction with its hardware, completes the steps of the aforementioned method. When the processor executes the program, it implements the corresponding processes in the various methods of the embodiments of this application; for simplicity, these will not be elaborated further here.

[0128] Example 3

[0129] Embodiment 3 of the present invention provides a computer-readable storage medium storing a computer program thereon. When the program is executed by a processor, it implements the aforementioned power load adaptation and output regulation control method, including:

[0130] In step S1, load electrical parameters are collected in real time, and dynamic parameters characterizing the load characteristics are calculated based on the load electrical parameters; based on the chaotic mapping mechanism and the value boundaries of the dynamic parameters, an initial solution population for the control parameters is generated.

[0131] In step S2, an improved artificial bee colony algorithm is used to iteratively optimize the initial solution population to obtain the optimal set of control parameters that matches the current load characteristics. The iterative optimization process includes: dynamically modifying the optimization objective function according to the load power change trend, and adaptively adjusting the search step size and neighborhood range according to the distribution density of individuals in the solution space.

[0132] In step S3, the optimal control parameter set is used as the feedforward control quantity and dynamically weighted and fused with the feedback correction quantity calculated based on the power supply output feedback signal to generate a comprehensive control command.

[0133] In step S4, the power output unit of the power supply is adjusted according to the comprehensive control command;

[0134] In step S5, it is determined whether the power output reaches the target set value within the preset adjustment time window; if it does not reach the target value, step S6 is executed; if it does reach the target value, step S7 is executed.

[0135] In step S6, the input conditions for iterative optimization are updated based on the current output state, and steps S1 to S5 are repeated until the power supply outputs stably under load.

[0136] In step S7, the generated integrated control command is maintained.

[0137] In the embodiments provided by this invention, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, structures, or units, and may be electrical, mechanical, or other forms.

[0138] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0139] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0140] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

[0141] The description of the relevant part of the power load adaptation and output regulation control storage medium provided in Embodiment 3 of this application can be found in the detailed description of the corresponding part of the power load adaptation and output regulation control method provided in Embodiment 1 of this application, and will not be repeated here.

[0142] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that the elements inherent in a process, method, article, or apparatus that includes a list of elements are included. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. Additionally, portions of the technical solutions provided in the embodiments of this application that are consistent with the implementation principles of corresponding technical solutions in the prior art have not been described in detail to avoid excessive elaboration.

[0143] While specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art can make other modifications or variations based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A power load adaptation and output regulation control method, characterized in that, Includes the following steps: Load electrical parameters are collected in real time, and dynamic parameters characterizing the load characteristics are calculated based on these load electrical parameters. Based on the chaotic mapping mechanism and the value boundaries of the dynamic parameters, an initial solution population for the control parameters is generated. An improved artificial bee colony algorithm is used to iteratively optimize the initial solution population to obtain the optimal set of control parameters that matches the current load characteristics. The iterative optimization process includes: dynamically modifying the objective function based on the load power change trend, and adaptively adjusting the search step size and neighborhood range according to the distribution density of individuals in the solution space; using the optimal set of control parameters as a feedforward control quantity, and dynamically weighting and fusing it with the feedback correction quantity calculated based on the power supply output feedback signal to generate a comprehensive control command; adjusting the power output unit of the power supply according to the comprehensive control command; determining whether the power supply output reaches the target set value within a preset adjustment time window; if not, updating the input conditions for iterative optimization based on the current output state, and repeating the iterative optimization and subsequent steps; if it reaches the target, maintaining the generated comprehensive control command.

2. The power load adaptation and output regulation control method according to claim 1, characterized in that, The load electrical parameters include voltage signals, current signals, and phase difference signals between voltage and current; the dynamic parameters characterizing the load characteristics include at least dynamic impedance and active power change rate.

3. The power load adaptation and output regulation control method according to claim 2, characterized in that, Based on the chaotic mapping mechanism and the value boundaries of the dynamic parameters, an initial solution population for the control parameters is generated, specifically by determining the dynamic impedance. The boundary values ​​of are and the rate of change of active power Predicted range of change ； Generating chaotic sequences using cosine chaotic mapping functions Its iterative formula is: ;in, For the first random generation within the interval (0,1) One chaotic iteration value; The preset sequence length, and ;Will Mapping to the lower bound of the first dimension of the solution space With the upper realm ;Will Mapping to the lower bound of the second dimension of the solution space With the upper realm Based on the search boundary, two sets of candidate solutions are generated: the first set of candidate solution vectors. Second set of candidate solution vectors ； Based on the preset fitness evaluation function, calculate all candidate solution vectors. The fitness value is selected, and the one with the highest fitness is chosen. The initial solution population consists of several vectors.

4. The power load adaptation and output regulation control method according to claim 3, characterized in that, The fitness evaluation function is: ;in, For from or any candidate solution vector; The target output voltage; To be The predicted output voltage is used as a control parameter; To be The predicted load matching impedance is used as a control parameter; The actual dynamic impedance of the load is calculated based on the real-time acquired signal. A positive weighting factor for balancing voltage accuracy; To weigh the positive weighting coefficients for impedance matching priority, and ； This is the first minimal positive number used to prevent the denominator from being zero; This is used to prevent the second smallest positive number from having a denominator of zero.

5. The power load adaptation and output regulation control method according to claim 4, characterized in that, The optimization objective function is dynamically modified based on the load power change trend, and the search step size and neighborhood range are adaptively adjusted according to the distribution density of individuals in the solution space. Specifically, the load active power change rate is introduced as a dynamic adjustment factor into the fitness evaluation function to modify the optimization objective. The modified dynamic objective function is then used. for: ;in, For any candidate solution vector The baseline fit value; This is a preset sensitivity coefficient used to adjust the algorithm's response strength to changes in load power; To be based on the real-time collected load voltage With current Calculated load active power change rate; Calculation of current honey source The average Euclidean distance between the individual and all other individuals in the population , used to quantify the distribution density of the population in the solution space: where the current nectar source is a population individual; ;in, For individuals in a population Other populations exist Euclidean distance in the solution space; The total dimension of the solution space; based on the average Euclidean distance. Dynamically determine population individuals Effective search neighborhood ； ;in, The neighborhood radius coefficient is preset; when hired bees or follower bees perform neighborhood searches, it is based on the neighborhood. Dynamically adjust the search step size: when When the density is less than a preset threshold, reduce the step size; when If the density is not less than the preset density threshold, increase the step size.

6. The power load adaptation and output regulation control method according to claim 1, characterized in that, The optimal control parameter set is used as the feedforward control quantity, and dynamically weighted and fused with the feedback correction quantity calculated based on the power supply output feedback signal to generate a comprehensive control command; specifically: let the feedforward control quantity be... The feedback correction amount is Then the integrated control command for: ;in, For feedforward weights; For feedback weights; and 。 7. The power load adaptation and output regulation control method according to claim 1, characterized in that, The power output unit of the power supply is adjusted according to the comprehensive control command. Specifically, the comprehensive control command is converted into the duty cycle and phase command of the PWM signal to drive the inverter power circuit to adjust the amplitude and phase of the output voltage.

8. The power load adaptation and output regulation control method according to claim 1, characterized in that, Determine whether the power output reaches the target set value within a preset adjustment time window; if not, update the input conditions for iterative optimization based on the current output state, and repeat the iterative optimization and subsequent steps; if it reaches the target value, maintain the generated integrated control command; specifically: set the adjustment time window as... The target output voltage is ， The preset error tolerance value is used; the allowable output voltage error band is... Real-time acquisition of the output voltage signal at the power supply output terminal; Starting from the moment when a change in load characteristics is detected Start the timer; adjust the time window. The system continuously determines whether the output voltage signal enters and stabilizes within the allowable output voltage error band. If in If the internal assessment determines that the target has been met, then the generated integrated control command will be maintained. If in If the standard is not met by the deadline, the currently collected load voltage will be used. Load current Recalculate load dynamic impedance and power factor The updated load dynamic impedance and power factor are used as updated load parameters and input into the chaotic mapping initialization step. Iterative optimization is then re-executed until a certain value is found. The internal assessment was deemed satisfactory.

9. A power load adaptation and output regulation control device, comprising at least one processor and a memory, wherein the memory stores a computer program, characterized in that, When the computer program is executed by the at least one processor, it implements a power load adaptation and output regulation control method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements a power load adaptation and output regulation control method as described in any one of claims 1-8.

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