Method and system for controlling three-phase isolation type low-voltage multi-port safe power supply equipment

Abstract

The invention provides a three-phase isolation type low-voltage multi-port safety power supply equipment control method and system, and relates to the technical field of power distribution network management, and the method comprises the following steps: collecting state parameters of each port in the three-phase isolation type low-voltage multi-port safety power supply equipment, and carrying out the multi-dimensional state evaluation of each port through an improved splitting method; performing distributed scheduling control on each port in the three-phase isolation type low-voltage multi-port safe power supply equipment through a chaos Levy firefly algorithm according to an equipment evaluation result; and monitoring the operation state and the scheduling control effect of each port in real time, and dynamically adjusting the distributed scheduling control of each port according to the monitoring result. According to the method, the improved slice method is adopted, the state parameters of multiple dimensions can be comprehensively considered, more comprehensive and more accurate multi-dimensional state evaluation can be carried out on the equipment, and meanwhile, the equipment evaluation result obtained through the multi-dimensional state evaluation can provide powerful guidance for subsequent distributed scheduling control.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network management, and in particular to a control method and system for three-phase isolated low-voltage multi-port safe power supply equipment. Background Art

[0002] With the accelerating national transition to cleaner energy, the use of renewable energy sources such as wind power and photovoltaic power generation has been widely promoted, especially with the increasing number of distributed photovoltaic systems connected to low-voltage distribution networks. While this transition has promoted the use of green energy, it has also brought new challenges. In particular, power quality issues are becoming increasingly prominent in low-voltage distribution networks, especially low-voltage single-phase distribution networks. Issues such as high / low voltage, three-phase imbalance, voltage fluctuations and flicker, and harmonics have seriously impacted the safe and stable power supply of low-voltage distribution networks.

[0003] As a crucial link in energy transmission, low-voltage distribution networks are tasked with providing stable power to numerous terminal load devices. However, with the large-scale integration of renewable energy, especially the volatility and intermittent nature of photovoltaic power generation, low-voltage distribution networks are facing increasingly complex power scheduling issues. In particular, in multi-port devices, the mutual coupling and dynamic associations between input and output ports complicate system scheduling and control. Traditional experience-based or rule-based scheduling methods often fail to fully and accurately describe these coupling relationships, resulting in inconsistencies between local scheduling optimization and overall operational objectives during system operation, thus impacting the stability and efficiency of the entire distribution network.

[0004] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0005] In view of this, the present invention provides a control method and system for a three-phase isolated low-voltage multi-port safe power supply device to solve the above-mentioned problems.

[0006] In order to solve the above problems, the specific technical solutions adopted by the present invention are as follows:

[0007] According to one aspect of the present invention, a method for controlling a three-phase isolated low-voltage multi-port safety power supply device is provided, comprising the following steps:

[0008] S1. Collecting status parameters of each port in a three-phase isolated low-voltage multi-port safe power supply device, and performing a multi-dimensional status evaluation on each port using an improved striping method to obtain a device evaluation result, wherein the ports include input ports and output ports;

[0009] S2. Based on the equipment evaluation results, the chaotic Levy firefly algorithm is used to perform distributed scheduling control on each port of the three-phase isolated low-voltage multi-port safe power supply equipment;

[0010] S3. Monitor the operating status and scheduling control effect of each port in real time, and dynamically adjust the distributed scheduling control of each port based on the monitoring results.

[0011] Preferably, the collecting of status parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device and performing multi-dimensional status evaluation on each port by an improved striping method to obtain the device evaluation result comprises the following steps:

[0012] S11. Constructing a constitutive model of the port state parameters according to the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device, wherein the state parameters include voltage, current, temperature and harmonic distortion rate;

[0013] S12. Based on the topological structure of the three-phase isolated low-voltage multi-port safe power supply equipment, divide the ports into several functional blocks, and calculate the stability coefficient of each functional block based on the division results and the constitutive model of the port state parameters;

[0014] S13. Based on the stability coefficient of each functional block, a multi-dimensional status evaluation of the three-phase isolated low-voltage multi-port safety power supply equipment is performed using an approximate ideal solution and a fuzzy comprehensive evaluation algorithm, and the multi-dimensional status evaluation results are weighted and summed to obtain the equipment evaluation result.

[0015] Preferably, constructing a constitutive model of port state parameters according to the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device comprises the following steps:

[0016] S111, using a moving average method to perform a time series analysis on the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device, and extracting process parameters of the three-phase isolated low-voltage multi-port safety power supply device, the process parameters including load change frequency and temperature change;

[0017] S112, obtaining parameter information of a three-phase isolated low-voltage multi-port safety power supply device, and establishing an initial constitutive model of port state parameters in combination with process parameters of the three-phase isolated low-voltage multi-port safety power supply device;

[0018] S113. Optimize the prediction error of the initial constitutive model of the port state parameters based on an artificial neural network algorithm to obtain a final constitutive model of the port state parameters.

[0019] Preferably, the step of optimizing the prediction error of the initial constitutive model of the port state parameters based on the artificial neural network algorithm to obtain the final constitutive model of the port state parameters comprises the following steps:

[0020] S1131. Collect sample data on port status parameters and corresponding process parameters, preprocess the sample data on the port status parameters and corresponding process parameters, and divide the preprocessed port status parameters and corresponding process parameters into a training set and a test set;

[0021] S1132, using the prediction results of the initial constitutive model as input and the actual port state parameters as output, constructing a neural network error correction model, and training the neural network error correction model using the training set;

[0022] S1133. Using the test set, test and evaluate the trained neural network error correction model, and adjust the parameters of the neural network error correction model according to the evaluation results until the preset performance requirements are met;

[0023] S1134. Combining the neural network error correction model with the initial constitutive model to obtain the final port state parameter constitutive model.

[0024] Preferably, the topology structure of the three-phase isolated low-voltage multi-port safety power supply device is divided into several functional blocks, and the stability coefficient of each functional block is calculated based on the division result and the constitutive model of the port state parameter, including the following steps:

[0025] S121. Based on the topological structure of a three-phase isolated low-voltage multi-port safe power supply device, the ports are divided into several functional blocks using a graph theory algorithm;

[0026] S122: For each functional block, extract the state parameters and process parameters of all ports in the functional block and perform average processing;

[0027] S123 , taking the port state parameters and process parameters after the average value processing as input, inputting them into the constitutive model of the port state parameters, and outputting the stability coefficient of each functional block through model calculation.

[0028] Preferably, the multi-dimensional status evaluation of the three-phase isolated low-voltage multi-port safety power supply equipment is performed based on the stability coefficient of each functional block using an approximate ideal solution and a fuzzy comprehensive evaluation algorithm, and a weighted summation process is performed on the multi-dimensional status evaluation results to obtain the equipment evaluation result, which includes the following steps:

[0029] S131. Construct an evaluation index set for a three-phase isolated low-voltage multi-port safety power supply device based on the stability coefficient of each functional block, and establish an evaluation matrix based on the evaluation index set;

[0030] S132. Determine a positive ideal solution and a negative ideal solution for each evaluation indicator based on the evaluation indicator set;

[0031] S133. Calculate the Euclidean distance between each functional block and the positive ideal solution and the negative ideal solution according to the approximate ideal solution method, and calculate the health index of each functional block according to the Euclidean distance;

[0032] S134. Perform weighted summation on the health indexes of all functional blocks to obtain a comprehensive health index;

[0033] S135. Based on the comprehensive health index of the equipment, the health status of the three-phase isolated low-voltage multi-port safety power supply equipment is graded using a fuzzy comprehensive evaluation method to obtain an equipment evaluation result.

[0034] Preferably, the method of performing distributed scheduling control on each port of the three-phase isolated low-voltage multi-port safety power supply device by using the chaotic Levy firefly algorithm according to the device evaluation result includes the following steps:

[0035] S21, initializing the parameters of the chaotic Levy firefly algorithm and randomly generating an initialized firefly population; the fireflies represent the scheduling control strategy of the port;

[0036] S22. Based on the equipment evaluation results, health index, and load energy consumption, a multi-objective fitness function is constructed, and the fitness value of each firefly individual is calculated;

[0037] S23. Update the position of the firefly population according to the update rule of the chaotic Levy firefly algorithm to generate a new scheduling control strategy;

[0038] S24: Based on the fitness values ​​of the firefly individuals, determine whether the iteration termination condition is met. If not, return to step S22. If so, select the scheduling strategy corresponding to the individual with the highest fitness value as the optimal scheduling control strategy.

[0039] S25. Apply the optimal dispatching control strategy to the three-phase isolated low-voltage multi-port safety power supply equipment to achieve distributed dispatching control of each port.

[0040] Preferably, the updating of the positions of the firefly population according to the updating rules of the chaotic Levy firefly algorithm to generate a new scheduling control strategy comprises the following steps:

[0041] S231. Compare the fitness values ​​of the current firefly with those of other fireflies to obtain a fitness difference, and calculate an attraction term based on the fitness difference and the distance between the fireflies.

[0042] S232, generating random items based on the chaos factor and the LevyFlight random search path;

[0043] S233. Combine the attraction term and the random term, update the position of the firefly, generate a new scheduling control strategy, and evaluate its fitness value.

[0044] Preferably, the calculation formula for updating the position of fireflies by combining the attraction term and the random term is:

[0045]

[0046] Where x i (t+1) represents the updated position of firefly i, x i (t) represents the current position of firefly i, θ ij (g ij ) represents the attraction term, x j (t) represents the current position of firefly j, α represents the disturbance intensity, U1 represents the random factor, Levy(x M ) represents the random disturbance term.

[0047] According to another aspect of the present invention, there is also provided a three-phase isolated low-voltage multi-port safety power supply equipment control system, comprising:

[0048] The status assessment module is used to collect the status parameters of each port in the three-phase isolated low-voltage multi-port safe power supply equipment, and perform multi-dimensional status assessment on each port using an improved striping method to obtain the equipment assessment results;

[0049] The dispatching control module is used to perform distributed dispatching control on each port of the three-phase isolated low-voltage multi-port safe power supply equipment through the chaotic Levy firefly algorithm based on the equipment evaluation results;

[0050] The dynamic monitoring module is used to monitor the operating status and scheduling control effect of each port in real time, and dynamically adjust the distributed scheduling control of each port according to the monitoring results.

[0051] The beneficial effects of the present invention are:

[0052] 1. The present invention adopts an improved striping method, which can not only comprehensively consider the status parameters of multiple dimensions, but also perform a more comprehensive and accurate multi-dimensional status evaluation of the equipment. At the same time, the equipment evaluation results obtained by multi-dimensional status evaluation can provide powerful guidance for subsequent distributed scheduling and control. In addition, through status evaluation, intelligent distributed scheduling and control, and dynamic monitoring and adjustment, it can provide comprehensive power safety protection for three-phase isolated low-voltage multi-port safety power supply equipment, effectively prevent the occurrence of electrical fires and electric shock accidents, and ensure the safety of people's lives and property.

[0053] 2. The present invention uses the moving average method to extract process parameters such as load change frequency and temperature change, and establishes an initial constitutive model in combination with equipment parameter information. The initial constitutive model is error-modified through an artificial neural network, which can improve the accuracy and reliability of the port state parameter constitutive model. At the same time, the port is divided into several functional blocks using a graph theory algorithm, and the fuzzy comprehensive evaluation method is used to classify the health status of the equipment, thereby more comprehensively reflecting the health status of the equipment.

[0054] 3. The present invention can perform distributed scheduling control on each port through the chaotic Levy firefly algorithm, and can achieve power balance and load optimization between ports, thereby improving the stability and reliability of the power supply system and ensuring the power demand of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0056] Figure 1 This is a flow chart of a control method for a three-phase isolated low-voltage multi-port safety power supply device according to an embodiment of the present invention;

[0057] Figure 2 The present invention is a block diagram of a three-phase isolated low-voltage multi-port safety power supply equipment control system.

[0058] In the picture:

[0059] 1. Status assessment module; 2. Scheduling control module; 3. Dynamic monitoring module. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the technical solutions in this application, the following will clearly and completely describe the technical solutions in the embodiments of this application in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0061] According to an embodiment of the present invention, a control method and system for a three-phase isolated low-voltage multi-port safety power supply device are provided.

[0062] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 According to one embodiment of the present invention, a method for controlling a three-phase isolated low-voltage multi-port safety power supply device is provided, comprising the following steps:

[0063] S1. Collecting status parameters of each port in a three-phase isolated low-voltage multi-port safe power supply device, and performing a multi-dimensional status evaluation on each port using an improved striping method to obtain a device evaluation result, wherein the ports include input ports and output ports;

[0064] As a preferred embodiment, the collecting of status parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device and performing a multi-dimensional status evaluation on each port by an improved striping method to obtain the device evaluation result includes the following steps:

[0065] S11. Constructing a constitutive model of the port state parameters according to the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device, wherein the state parameters include voltage, current, temperature and harmonic distortion rate;

[0066] As a preferred embodiment, the constitutive model of the port state parameters is constructed according to the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device, including the following steps:

[0067] S111, using a moving average method to perform a time series analysis on the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device, and extracting process parameters of the three-phase isolated low-voltage multi-port safety power supply device, the process parameters including load change frequency and temperature change;

[0068] It's important to note that the moving average method is a time-series analysis method. It uses this method to analyze the time series of port state parameters (such as voltage, current, and temperature) and extract patterns in how these parameters change over time. Furthermore, combined with device process parameters (such as load change frequency and temperature variations), the device's operating environment and operating status can be identified.

[0069] The load change frequency is determined by analyzing the fluctuation period of the load current or power. The temperature change is obtained by monitoring the temperature sensor data of key parts of the equipment.

[0070] S112, obtaining parameter information of a three-phase isolated low-voltage multi-port safety power supply device, and establishing an initial constitutive model of port state parameters in combination with process parameters of the three-phase isolated low-voltage multi-port safety power supply device;

[0071] Specifically, device parameter information includes the physical characteristics and electrical characteristics of the device. Physical characteristics include port size, material type, conductor cross-sectional area, etc., and electrical characteristics include rated voltage, rated current, power factor, electrical topology and other electrical parameters of the device.

[0072] The expression of the initial constitutive model of the port state parameters is:

[0073]

[0074] Where σ represents the stability coefficient, A represents the material constant, and Q e represents electrical activation energy, R represents the ideal gas constant, T represents temperature, f represents comprehensive function, I represents current, U represents voltage, THD represents harmonic distortion rate, f load Indicates the load change rate, f temp Indicates the rate of temperature change;

[0075] S113. Optimize the prediction error of the initial constitutive model of the port state parameters based on an artificial neural network algorithm to obtain a final constitutive model of the port state parameters.

[0076] As a preferred embodiment, the method of optimizing the prediction error of the initial constitutive model of the port state parameters based on the artificial neural network algorithm to obtain the final constitutive model of the port state parameters includes the following steps:

[0077] S1131. Collect sample data on port status parameters and corresponding process parameters, preprocess the sample data on port status parameters and corresponding process parameters, and divide the preprocessed port status parameters and corresponding process parameters into a training set and a test set; the training set and the test set are usually divided according to a certain ratio (such as 70% training set, 30% test set).

[0078] Among them, preprocessing includes data cleaning, feature normalization and time series feature reconstruction.

[0079] S1132, using the prediction results of the initial constitutive model as input and the actual port state parameters as output, constructing a neural network error correction model, and training the neural network error correction model using the training set;

[0080] Specifically, the neural network architecture has an input layer, a hidden layer, and an output layer. The input layer contains the initial model prediction value σ initial And the process parameters after moving average processing (f load ,f temp ,THD);

[0081] The hidden layer adopts a double hidden layer structure (8-12 neurons per layer), the activation function selects ReLU, and the output layer is the output error correction item. The correction range is limited by the Sigmoid function, and the final model is obtained after superposition.

[0082] S1133. Using the test set, test and evaluate the trained neural network error correction model, and adjust the parameters of the neural network error correction model according to the evaluation results until the preset performance requirements are met;

[0083] Specifically, adjusting the neural network error correction model parameters includes learning rate, batch size, number of hidden layer nodes, etc.

[0084] S1134. Combining the neural network error correction model with the initial constitutive model to obtain the final port state parameter constitutive model.

[0085] Specifically, the specific process of combining the neural network error correction model with the initial constitutive model is: using the initial constitutive model for prediction, and then inputting the prediction results into the neural network error correction model for correction.

[0086] S12. Based on the topological structure of the three-phase isolated low-voltage multi-port safe power supply equipment, divide the ports into several functional blocks, and calculate the stability coefficient of each functional block based on the division results and the constitutive model of the port state parameters;

[0087] As a preferred embodiment, the topology structure of the three-phase isolated low-voltage multi-port safety power supply device is divided into several functional blocks, and the stability coefficient of each functional block is calculated based on the division result and the constitutive model of the port state parameter, including the following steps:

[0088] S121. Based on the topology of a three-phase isolated low-voltage multi-port safety power supply device, it is divided into several functional blocks using a graph theory algorithm;

[0089] Specifically, based on the topology of a three-phase isolated low-voltage multi-port safe power supply device, the ports are divided into several functional blocks using a graph theory algorithm, including the following steps:

[0090] Step 1: Represent the topology of the three-phase isolated low-voltage multi-port safety power supply device as a graph, wherein the ports are nodes and the connection relationships between the ports are edges;

[0091] Step 2: Using depth-first search to divide the topology graph, ports with close connections and similar functions can be divided into the same functional block.

[0092] S122: For each functional block, extract the state parameters and process parameters of all ports in the functional block and perform average processing;

[0093] S123 , taking the port state parameters and process parameters after the average value processing as input, inputting them into the constitutive model of the port state parameters, and outputting the stability coefficient of each functional block through model calculation.

[0094] S13. Based on the stability coefficient of each functional block, a multi-dimensional status evaluation of the three-phase isolated low-voltage multi-port safety power supply equipment is performed using an approximate ideal solution and a fuzzy comprehensive evaluation algorithm, and the multi-dimensional status evaluation results are weighted and summed to obtain the equipment evaluation result.

[0095] As a preferred embodiment, the method of performing a multi-dimensional status evaluation on the three-phase isolated low-voltage multi-port safety power supply equipment based on the stability coefficient of each functional block using an approximate ideal solution and a fuzzy comprehensive evaluation algorithm, and performing a weighted summation process on the multi-dimensional status evaluation results to obtain the equipment evaluation result includes the following steps:

[0096] S131. Construct an evaluation index set for a three-phase isolated low-voltage multi-port safety power supply device based on the stability coefficient of each functional block, and establish an evaluation matrix based on the evaluation index set;

[0097] Specifically, the stability coefficient of each functional block includes voltage stability, current stability, temperature stability, etc.

[0098] S132. Determine a positive ideal solution and a negative ideal solution for each evaluation indicator based on the evaluation indicator set;

[0099] It should be noted that the positive ideal solution for each evaluation metric represents the optimal value for that metric. For example, for the voltage stability metric, the positive ideal solution represents the minimum value within the voltage fluctuation range. The negative ideal solution for each evaluation metric represents the worst value for that metric. For example, for the voltage stability metric, the negative ideal solution represents the maximum value within the voltage fluctuation range.

[0100] S133. Calculate the Euclidean distance between each functional block and the positive ideal solution and the negative ideal solution according to the approximate ideal solution method, and calculate the health index of each functional block according to the Euclidean distance;

[0101] Specifically, the calculation formula for the health index of each functional block is:

[0102]

[0103] Where HI i represents the health index of the i-th functional block, represents the Euclidean distance between the i-th functional block and the negative ideal solution, represents the Euclidean distance between the i-th functional block and the positive ideal solution.

[0104] S134. Perform weighted summation on the health indexes of all functional blocks to obtain a comprehensive health index;

[0105] S135. Based on the comprehensive health index of the equipment, the health status of the three-phase isolated low-voltage multi-port safety power supply equipment is graded using a fuzzy comprehensive evaluation method to obtain an equipment evaluation result.

[0106] Fuzzy comprehensive evaluation method is a comprehensive evaluation method based on fuzzy mathematics, which can handle multi-level and multi-factor problems.

[0107] S2. Based on the equipment evaluation results, the chaotic Levy firefly algorithm is used to perform distributed scheduling control on each port of the three-phase isolated low-voltage multi-port safe power supply equipment;

[0108] As a preferred embodiment, the distributed scheduling control of each port in the three-phase isolated low-voltage multi-port safe power supply device by using the chaotic Levy firefly algorithm according to the device evaluation result includes the following steps:

[0109] S21, initializing the parameters of the chaotic Levy firefly algorithm and randomly generating an initialized firefly population; the fireflies represent the scheduling control strategy of the port;

[0110] It should be noted that the parameters for initializing the chaotic Levy firefly algorithm include: firefly population size, maximum number of iterations, attraction coefficient, light absorption coefficient, chaos factor parameter, Levy Flight parameter, etc.

[0111] The port scheduling control strategy includes a combination of port switch status, power allocation, load balancing and other parameters.

[0112] S22. Based on the equipment evaluation results, health index, and load energy consumption, a multi-objective fitness function is constructed, and the fitness value of each firefly individual is calculated;

[0113] S23. Update the position of the firefly population according to the update rule of the chaotic Levy firefly algorithm to generate a new scheduling control strategy;

[0114] As a preferred embodiment, the updating of the positions of the firefly population according to the updating rules of the chaotic Levy firefly algorithm to generate a new scheduling control strategy includes the following steps:

[0115] S231. Compare the fitness values ​​of the current firefly with those of other fireflies to obtain a fitness difference, and calculate an attraction term based on the fitness difference and the distance between the fireflies.

[0116] It should be noted that the attraction term represents the degree of attraction of other fireflies to the current firefly. The greater the attraction, the more the current firefly tends to move towards other fireflies. The calculation formula of the attraction term is:

[0117]

[0118] Where θ ij (g ij ) represents the attraction term, θ0 represents the attraction of fireflies at the maximum fluorescence, γ represents a parameter, the change of attraction is subject to the constraint of this parameter, 0.01≤γ≤100, g ij represents the Euclidean distance between firefly i and firefly j.

[0119] S232, generating random items based on the chaos factor and the LevyFlight random search path;

[0120] It should be noted that LevyFlight is a random walk model with long-range jump characteristics, which helps the algorithm to search globally.

[0121] S233. Combine the attraction term and the random term, update the position of the firefly, generate a new scheduling control strategy, and evaluate its fitness value.

[0122] As a preferred embodiment, the calculation formula for updating the position of fireflies by combining the attraction term and the random term is:

[0123]

[0124] Where x i (t+1) represents the updated position of firefly i, x i (t) represents the current position of firefly i, θ ij (g ij ) represents the attraction term, x j (t) represents the current position of firefly j, α represents the disturbance intensity, U1 represents the random factor, Levy(x M ) represents the random disturbance term.

[0125] S24: Based on the fitness values ​​of the firefly individuals, determine whether the iteration termination condition is met. If not, return to step S22. If so, select the scheduling strategy corresponding to the individual with the highest fitness value as the optimal scheduling control strategy.

[0126] It should be noted that the iteration termination conditions may be: reaching the maximum number of iterations, the fitness value converges, finding a solution that meets the requirements, etc.

[0127] S25. Apply the optimal dispatching control strategy to the three-phase isolated low-voltage multi-port safety power supply equipment to achieve distributed dispatching control of each port.

[0128] Specifically, by rationally allocating power resources to each port based on its actual operating status and load requirements, it is possible to automatically reduce the port's power supply load or switch the power path, effectively preventing electrical fires caused by abnormal temperature rise.

[0129] S3. Monitor the operating status and scheduling control effect of each port in real time, and dynamically adjust the distributed scheduling control of each port based on the monitoring results.

[0130] Specifically, real-time monitoring of the operating status and scheduling control effect of each port, and dynamic adjustment of the distributed scheduling control of each port based on the monitoring results include the following steps:

[0131] First, key parameters such as voltage, current, power, and temperature of each port are monitored in real time, and the health status of each port of the equipment is evaluated. When the evaluation results of the health status of each port of the equipment exceed the normal range, a fault warning is issued. Then, the monitored parameters are analyzed in real time to identify ports with poor operating status or unsatisfactory scheduling and control effects. Finally, the chaotic Levy firefly algorithm is used to optimize the scheduling and control strategy, and the optimized scheduling and control strategy is applied to each port in real time to achieve dynamic adjustment.

[0132] In addition, for each port, the specific adjustment strategy includes:

[0133] 1. If the port voltage or current is abnormal, adjust its power distribution or switch status.

[0134] 2. If the port health status deteriorates, increase its maintenance frequency or adjust its load.

[0135] 3. If the port load energy consumption is too high, optimize its scheduling strategy to reduce energy consumption.

[0136] 4. If the power balance of a port is poor, adjust the power distribution between it and other ports to achieve power balance.

[0137] like Figure 2 According to another embodiment of the present invention, a three-phase isolated low-voltage multi-port safety power supply equipment control system is provided, comprising:

[0138] Status assessment module 1 is used to collect status parameters of each port in the three-phase isolated low-voltage multi-port safe power supply equipment, and perform multi-dimensional status assessment on each port using an improved striping method to obtain equipment assessment results;

[0139] The dispatching control module 2 is used to perform distributed dispatching control on each port of the three-phase isolated low-voltage multi-port safe power supply equipment through the chaotic Levy firefly algorithm according to the equipment evaluation results;

[0140] The dynamic monitoring module 3 is used to monitor the operating status and scheduling control effect of each port in real time, and dynamically adjust the distributed scheduling control of each port according to the monitoring results.

[0141] In summary, with the help of the above technical solution of the present invention, the present invention adopts an improved striping method, which can not only comprehensively consider the state parameters of multiple dimensions, but also perform a more comprehensive and accurate multi-dimensional state evaluation of the equipment. At the same time, the equipment evaluation results obtained by the multi-dimensional state evaluation can provide powerful guidance for the subsequent distributed scheduling control. In addition, through state evaluation, intelligent distributed scheduling control and dynamic monitoring and adjustment, it can provide a full range of power safety protection for three-phase isolated low-voltage multi-port safety power supply equipment, effectively prevent the occurrence of electrical fires and electric shock accidents, and ensure the safety of life and property. The present invention uses the moving average method to extract process parameters such as load change frequency and temperature change, and establishes an initial constitutive model in combination with equipment parameter information, and uses an artificial neural network to perform error correction processing on the initial constitutive model, which can improve the accuracy and reliability of the port state parameter constitutive model. At the same time, the port is divided into several functional blocks using a graph theory algorithm, and the health status of the equipment is graded using a fuzzy comprehensive evaluation method, which can more comprehensively reflect the health status of the equipment. The present invention uses the chaotic Levy firefly algorithm to perform distributed scheduling control on each port, and can achieve power balance and load optimization between ports, thereby improving the stability and reliability of the power supply system and ensuring the power demand of users.

[0142] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, optical storage, etc.) containing computer-usable program code.

[0143] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A control method for a three-phase isolated low-voltage multi-port safe power supply device, characterized in that: The following steps are involved: S1. Collecting status parameters of each port in a three-phase isolated low-voltage multi-port safe power supply device, and performing a multi-dimensional status evaluation on each port using an improved striping method to obtain a device evaluation result, wherein the ports include input ports and output ports; S2. Based on the equipment evaluation results, the chaotic Levy firefly algorithm is used to perform distributed scheduling control on each port of the three-phase isolated low-voltage multi-port safe power supply equipment; S3. Monitor the operating status and scheduling control effect of each port in real time, and dynamically adjust the distributed scheduling control of each port based on the monitoring results.

2. A three-phase isolated low-voltage multi-port safety power supply equipment control method according to claim 1, characterized in that: The collecting of status parameters of each port in the three-phase isolated low-voltage multi-port safe power supply device and performing multi-dimensional status evaluation on each port by an improved striping method to obtain the device evaluation result includes the following steps: S11. Constructing a constitutive model of the port state parameters according to the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device, wherein the state parameters include voltage, current, temperature and harmonic distortion rate; S12. Based on the topological structure of the three-phase isolated low-voltage multi-port safe power supply equipment, divide the ports into several functional blocks, and calculate the stability coefficient of each functional block based on the division results and the constitutive model of the port state parameters; S13. Based on the stability coefficient of each functional block, a multi-dimensional status evaluation of the three-phase isolated low-voltage multi-port safety power supply equipment is performed using an approximate ideal solution and a fuzzy comprehensive evaluation algorithm, and the multi-dimensional status evaluation results are weighted and summed to obtain the equipment evaluation result.

3. A three-phase isolated low-voltage multi-port safety power supply equipment control method according to claim 2, characterized in that: The method of constructing a constitutive model of the port state parameters according to the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device comprises the following steps: S111, using a moving average method to perform a time series analysis on the state parameters of each port in the three-phase isolated low-voltage multi-port safety power supply device, and extracting process parameters of the three-phase isolated low-voltage multi-port safety power supply device, the process parameters including load change frequency and temperature change; S112, obtaining parameter information of a three-phase isolated low-voltage multi-port safety power supply device, and establishing an initial constitutive model of port state parameters in combination with process parameters of the three-phase isolated low-voltage multi-port safety power supply device; S113. Optimize the prediction error of the initial constitutive model of the port state parameters based on an artificial neural network algorithm to obtain a final constitutive model of the port state parameters.

4. A three-phase isolated low-voltage multi-port safety power supply equipment control method according to claim 3, characterized in that: The method of optimizing the prediction error of the initial constitutive model of the port state parameters based on the artificial neural network algorithm to obtain the final constitutive model of the port state parameters includes the following steps: S1131. Collect sample data on port status parameters and corresponding process parameters, preprocess the sample data on the port status parameters and corresponding process parameters, and divide the preprocessed port status parameters and corresponding process parameters into a training set and a test set; S1132, using the prediction results of the initial constitutive model as input and the actual port state parameters as output, constructing a neural network error correction model, and training the neural network error correction model using the training set; S1133. Using the test set, test and evaluate the trained neural network error correction model, and adjust the parameters of the neural network error correction model according to the evaluation results until the preset performance requirements are met; S1134. Combining the neural network error correction model with the initial constitutive model to obtain the final port state parameter constitutive model.

5. A three-phase isolated low-voltage multi-port safety power supply equipment control method according to claim 3, characterized in that: The topology structure of the three-phase isolated low-voltage multi-port safe power supply device is divided into several functional blocks, and the stability coefficient of each functional block is calculated based on the division result and the constitutive model of the port state parameter, including the following steps: S121. Based on the topological structure of a three-phase isolated low-voltage multi-port safe power supply device, the ports are divided into several functional blocks using a graph theory algorithm; S122: For each functional block, extract the state parameters and process parameters of all ports in the functional block and perform average processing; S123 , taking the port state parameters and process parameters after the average value processing as input, inputting them into the constitutive model of the port state parameters, and outputting the stability coefficient of each functional block through model calculation.

6. A three-phase isolated low-voltage multi-port safety power supply equipment control method according to claim 2, characterized in that: The method of performing a multi-dimensional status evaluation on the three-phase isolated low-voltage multi-port safety power supply equipment based on the stability coefficient of each functional block by using an approximate ideal solution and a fuzzy comprehensive evaluation algorithm, and performing a weighted summation process on the multi-dimensional status evaluation results to obtain the equipment evaluation result includes the following steps: S131. Construct an evaluation index set for a three-phase isolated low-voltage multi-port safety power supply device based on the stability coefficient of each functional block, and establish an evaluation matrix based on the evaluation index set; S132. Determine a positive ideal solution and a negative ideal solution for each evaluation indicator based on the evaluation indicator set; S133. Calculate the Euclidean distance between each functional block and the positive ideal solution and the negative ideal solution according to the approximate ideal solution method, and calculate the health index of each functional block according to the Euclidean distance; S134. Perform weighted summation on the health indexes of all functional blocks to obtain a comprehensive health index; S135. Based on the comprehensive health index of the equipment, the health status of the three-phase isolated low-voltage multi-port safety power supply equipment is graded using a fuzzy comprehensive evaluation method to obtain an equipment evaluation result.

7. A control method for three-phase isolated low-voltage multi-port safety power supply equipment according to claim 1, characterized in that: The distributed scheduling control of each port in the three-phase isolated low-voltage multi-port safety power supply device by using the chaotic Levy firefly algorithm according to the device evaluation result includes the following steps: S21, initializing the parameters of the chaotic Levy firefly algorithm and randomly generating an initialized firefly population; the fireflies represent the scheduling control strategy of the port; S22. Based on the equipment evaluation results, health index, and load energy consumption, a multi-objective fitness function is constructed, and the fitness value of each firefly individual is calculated; S23. Update the position of the firefly population according to the update rule of the chaotic Levy firefly algorithm to generate a new scheduling control strategy; S24: Based on the fitness values ​​of the firefly individuals, determine whether the iteration termination condition is met. If not, return to step S22. If so, select the scheduling strategy corresponding to the individual with the highest fitness value as the optimal scheduling control strategy. S25. Apply the optimal dispatching control strategy to the three-phase isolated low-voltage multi-port safety power supply equipment to achieve distributed dispatching control of each port.

8. A control method for three-phase isolated low-voltage multi-port safety power supply equipment according to claim 7, characterized in that: The method of updating the position of the firefly population according to the update rule of the chaotic Levy firefly algorithm and generating a new scheduling control strategy includes the following steps: S231. Compare the fitness values ​​of the current firefly with those of other fireflies to obtain a fitness difference, and calculate an attraction term based on the fitness difference and the distance between the fireflies. S232, generating random items based on the chaos factor and the LevyFlight random search path; S233. Combine the attraction term and the random term, update the position of the firefly, generate a new scheduling control strategy, and evaluate its fitness value.

9. A control method for three-phase isolated low-voltage multi-port safety power supply equipment according to claim 8, characterized in that: The calculation formula for updating the position of fireflies by combining the attraction term and the random term is: Where x i (t+1) represents the updated position of firefly i, x i (t) represents the current position of firefly i, θ ij (g ij ) represents the attraction term, x j (t) represents the current position of firefly j, α represents the disturbance intensity, U1 represents the random factor, Levy(x M ) represents the random disturbance term.

10. A three-phase isolated low-voltage multi-port safety power supply equipment control system, used to implement the three-phase isolated low-voltage multi-port safety power supply equipment control method according to any one of claims 1 to 9, characterized in that: include: The status assessment module is used to collect the status parameters of each port in the three-phase isolated low-voltage multi-port safe power supply equipment, and perform multi-dimensional status assessment on each port using an improved striping method to obtain the equipment assessment results; The dispatching control module is used to perform distributed dispatching control on each port of the three-phase isolated low-voltage multi-port safe power supply equipment through the chaotic Levy firefly algorithm based on the equipment evaluation results; The dynamic monitoring module is used to monitor the operating status and scheduling control effect of each port in real time, and dynamically adjust the distributed scheduling control of each port according to the monitoring results.

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