A control parameter optimization method and system for a mine direct current power supply

Abstract

The application provides a control parameter optimization method and system for a mine-used direct-current power supply, the method comprising: collecting voltage data, current data, temperature data and magnetic field data in the running process of the mine-used direct-current power supply at the current moment, and performing filtering and noise reduction processing to obtain filtered and noise-reduced voltage data, current data, temperature data and magnetic field data; determining a fluctuation state characteristic parameter fusion value of the mine-used direct-current power supply according to the filtered and noise-reduced voltage data, current data, temperature data and magnetic field data and by using a multi-physical quantity comprehensive fluctuation algorithm; obtaining a preset fuzzy rule base, determining a control parameter reference value of the mine-used direct-current power supply according to the fluctuation state characteristic parameter fusion value and the preset fuzzy rule base, and optimizing the control parameter of the mine-used direct-current power supply based on the control parameter reference value. The technical scheme provided by the application effectively improves the accuracy of fluctuation evaluation and ensures that the power supply can still maintain stable output in a complex and changeable environment.

Description

Technical Field

[0001] This application relates to the field of power control technology, and in particular to a method and system for optimizing control parameters of a DC power supply for mining applications. Background Technology

[0002] In modern power electronic systems, power supply stability is a key factor in ensuring the normal operation of various devices. With the rapid development of technology, the requirements for power quality of various electronic devices are increasing, especially in critical fields such as precision manufacturing, medical equipment, and data centers. Even small fluctuations in power supply can seriously affect equipment performance, data accuracy, and even personnel safety. Therefore, how to effectively monitor and compensate for power fluctuations has become a technical problem that urgently needs to be solved in the field of power electronics. Power fluctuations not only affect the normal operation of equipment, but may also lead to serious consequences such as equipment damage and data loss, causing huge losses to production and life. Against this background, the research and application of power fluctuation detection and compensation technology is particularly important.

[0003] Traditional power fluctuation detection technologies often rely on monitoring a single physical quantity, such as using only voltage or current sensors to assess the power supply status. This single-dimensional monitoring method is insufficient to comprehensively reflect the true situation of power fluctuations. In practical applications, power fluctuations may involve changes in multiple physical quantities, including voltage, current, temperature, and magnetic field. Monitoring a single physical quantity can easily overlook the influence of other related factors, leading to inaccurate detection results and affecting the effectiveness of compensation strategies. Furthermore, traditional compensation strategies are often based on fixed rules or thresholds, lacking flexibility and adaptability, and are difficult to dynamically adjust according to the actual situation of power fluctuations. This one-size-fits-all compensation approach often fails to accurately match the specific characteristics of power fluctuations, resulting in poor compensation effects and potentially introducing new fluctuations. Therefore, traditional technologies have significant limitations in power fluctuation detection and compensation, making it difficult to meet the high requirements of modern power electronic systems for power stability. Summary of the Invention

[0004] This application provides a method and system for optimizing control parameters of DC power supplies for mining, in order to at least solve the technical problem of poor compensation effect.

[0005] The first aspect of this application proposes a method for optimizing control parameters of a DC power supply for mining applications, the method comprising: The voltage, current, temperature, and magnetic field data of the mining DC power supply at the current moment are collected, and the voltage, current, temperature, and magnetic field data are filtered and denoised to obtain the filtered and denoised voltage, current, temperature, and magnetic field data. Based on the voltage, current, temperature, and magnetic field data after filtering and noise reduction, and using a multi-physical quantity integrated fluctuation algorithm, the fused value of the fluctuation state characteristic parameters of the mining DC power supply is determined. A preset fuzzy rule base is obtained, and the reference values ​​of the control parameters of the mining DC power supply are determined based on the fusion value of the fluctuation state feature parameters and the preset fuzzy rule base. The control parameters of the mining DC power supply are then optimized based on the reference values ​​of the control parameters.

[0006] Preferably, the formula for calculating the fused value of the fluctuation state characteristic parameters of the mining DC power supply is as follows:

[0007] In the formula, This represents the fused value of the fluctuation state characteristic parameters of the DC power supply used in mining. Let i be the weight of the i-th data. Let i be the feature extraction function for the i-th data. The total number of data; The data includes voltage data, current data, temperature data, and magnetic field data.

[0008] Furthermore, determining the reference value of the control parameters for the mining DC power supply based on the fused value of the fluctuation state characteristic parameters and the preset fuzzy rule base includes: The fluctuation value of the mining DC power supply is determined based on the fusion value of the fluctuation state characteristic parameters; The fused values ​​of the fluctuation state characteristic parameters are transformed into fuzzy linguistic variables of the mining DC power supply by using a Gaussian-trigonometric function hybrid membership function. The reference values ​​for the control parameters of the mining DC power supply are determined based on the fluctuation value of the mining DC power supply, the preset fuzzy rule base, and the fuzzy linguistic variables of the mining DC power supply.

[0009] Furthermore, the formula for calculating the fluctuation value of the mining DC power supply is as follows:

[0010] In the formula, This represents the fluctuation value of the DC power supply used in mining. This is the reference value for the normal operation of the DC power supply for mining. The standard deviation of the fused values ​​of the characteristic parameters of the fluctuation state. This is the difference between the fused values ​​of the fluctuation state characteristic parameters at the current time and the previous time. For time intervals.

[0011] Furthermore, the calculation formula for the fuzzy linguistic variables of the mining DC power supply is as follows:

[0012] In the formula, The membership degree is defined as the fusion value of the fluctuation state characteristic parameters when it is F. The central value of the membership function. This is the function width parameter; The fuzzy linguistic variable for the mining DC power supply is the membership degree.

[0013] Furthermore, the preset fuzzy rule base includes: multiple fuzzy rules; The fuzzy rules mentioned above include: When the fluctuation value of the DC power supply for mining is less than the preset lower limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A1; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B1; If the current data is greater than the preset first current value, the output current value will be reduced by C1; If the current data is less than the preset second current value, the output current value will be increased by D1; When the fluctuation value of the mining DC power supply is greater than or equal to the preset lower limit of the fluctuation threshold and less than the preset upper limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A2; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B2; If the current data is greater than the preset first current value, the current output value will be reduced by C2; If the current data is less than the preset second current value, the output current value will be increased by D2; When the fluctuation value of the DC power supply for mining is greater than or equal to the preset fluctuation threshold upper limit: If the voltage data is greater than the preset first voltage value, the output voltage value will be reduced by A3; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B3; If the current data is greater than the preset first current value, the output current value will be reduced by C3; If the current data is less than the preset second current value, the output current value will be increased by D3.

[0014] Furthermore, determining the reference value of the control parameters of the mining DC power supply based on the fluctuation value of the mining DC power supply, the preset fuzzy rule base, and the fuzzy linguistic variables of the mining DC power supply includes: Based on the fluctuation value of the mining DC power supply, the voltage data, and the current data, the voltage and / or current adjustment amount corresponding to the fluctuation value of the mining DC power supply is found in the preset fuzzy rule base. Based on the voltage and / or current adjustment amount corresponding to the fluctuation value of the mining DC power supply, determine the adjusted voltage and / or current data, and determine the adjusted membership degree based on the adjusted voltage and / or current data; The reference values ​​for the voltage control parameters and / or current control parameters of the mining DC power supply are determined based on the adjusted membership degree and the adjusted voltage and / or current data.

[0015] Furthermore, the calculation formulas for the reference values ​​of the voltage control parameters and / or current control parameters of the mining DC power supply are as follows:

[0016] In the formula, These are reference values ​​for the voltage control parameters and / or current control parameters of the DC power supply used in mining. Let r be the membership degree corresponding to the r-th fuzzy rule. Let be the adjusted voltage and / or current data corresponding to the r-th fuzzy rule, and m be the number of fuzzy rules involved in the inference.

[0017] Furthermore, the method also includes: When the reference value of the control parameter of the mining DC power supply is the reference value of the voltage control parameter, the control circuit drives the voltage regulator to change the output voltage value to the reference value of the voltage control parameter. When the reference value of the control parameter of the mining DC power supply is the reference value of the current control parameter, the conduction angle of the control thyristor is changed to change the output current value to the reference value of the current control parameter.

[0018] A second aspect of this application provides a control parameter optimization system for a mining DC power supply, comprising: The acquisition module is used to acquire voltage data, current data, temperature data and magnetic field data during the operation of the mine DC power supply at the current moment, and to filter and reduce noise in the voltage data, current data, temperature data and magnetic field data to obtain filtered and noise-reduced voltage data, current data, temperature data and magnetic field data. The first determining module is used to determine the fusion value of the fluctuation state characteristic parameters of the mining DC power supply based on the voltage data, current data, temperature data and magnetic field data after filtering and noise reduction, and using a multi-physical quantity comprehensive fluctuation algorithm. The second determining module is used to acquire a preset fuzzy rule base, determine the reference value of the control parameter of the mining DC power supply according to the fusion value of the fluctuation state feature parameter and the preset fuzzy rule base, and optimize the control parameter of the mining DC power supply based on the reference value of the control parameter.

[0019] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects: This application proposes a method and system for optimizing control parameters of a mining DC power supply. The method includes: collecting voltage, current, temperature, and magnetic field data during the operation of the mining DC power supply at the current moment; filtering and denoising the voltage, current, temperature, and magnetic field data to obtain filtered and denoised voltage, current, temperature, and magnetic field data; determining the fusion value of the fluctuation state characteristic parameters of the mining DC power supply based on the filtered and denoised voltage, current, temperature, and magnetic field data using a multi-physical quantity comprehensive fluctuation algorithm; acquiring a preset fuzzy rule library; determining reference values ​​for the control parameters of the mining DC power supply based on the fusion values ​​of the fluctuation state characteristic parameters and the preset fuzzy rule library; and optimizing the control parameters of the mining DC power supply based on the reference values. The technical solution proposed in this application not only improves the stability and reliability of the power supply system but also provides a safer and more stable power guarantee for various electronic devices.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for optimizing control parameters of a DC power supply for mining, according to an embodiment of this application. Figure 2 This is a detailed flowchart of a method for optimizing control parameters of a DC power supply for mining, according to an embodiment of this application. Figure 3 This is a first structural diagram of a control parameter optimization system for a mining DC power supply according to an embodiment of this application; Figure 4 This is a second structural diagram of a control parameter optimization system for a mining DC power supply according to an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] This application proposes a method and system for optimizing control parameters of a mining DC power supply. The method includes: collecting voltage, current, temperature, and magnetic field data during the operation of the mining DC power supply at the current moment; filtering and denoising the voltage, current, temperature, and magnetic field data to obtain filtered and denoised voltage, current, temperature, and magnetic field data; determining the fusion value of the fluctuation state characteristic parameters of the mining DC power supply based on the filtered and denoised voltage, current, temperature, and magnetic field data using a multi-physical quantity comprehensive fluctuation algorithm; acquiring a preset fuzzy rule library; determining reference values ​​for the control parameters of the mining DC power supply based on the fusion values ​​of the fluctuation state characteristic parameters and the preset fuzzy rule library; and optimizing the control parameters of the mining DC power supply based on the reference values. The technical solution proposed in this application not only improves the stability and reliability of the power supply system but also provides a safer and more stable power guarantee for various electronic devices.

[0024] The following description, with reference to the accompanying drawings, illustrates a method and system for optimizing control parameters of a DC power supply for mining applications.

[0025] Example 1 Figure 1 This is a flowchart illustrating a method for optimizing control parameters of a DC power supply for mining applications, according to an embodiment of this application. Figure 1 As shown, the method includes: Step 1: Collect voltage, current, temperature, and magnetic field data during the operation of the mine DC power supply at the current moment, and perform filtering and noise reduction processing on the voltage, current, temperature, and magnetic field data to obtain filtered and noise-reduced voltage, current, temperature, and magnetic field data.

[0026] It should be noted that voltage, current, temperature and magnetic field data are collected by voltage, current and temperature sensors respectively during power supply operation. The signals collected by each sensor are amplified and filtered for noise reduction by signal conditioning circuit.

[0027] The voltage sensor is a Hall voltage sensor, which is installed in parallel at the input or output of the power supply, and the sampling frequency is 1000Hz. The current sensor is a Rogowski coil current sensor, which is connected in series in the circuit and has a sampling frequency of 1000Hz. The temperature sensor is a thermocouple temperature sensor, which is attached to the surface of the key heat-generating component of the power supply equipment, and the sampling frequency is 10Hz. The magnetic field sensor is a fluxgate magnetic field sensor, which is placed 10cm away from the power supply equipment at an axial position and the sampling frequency is 50Hz.

[0028] Step 2: Based on the voltage, current, temperature, and magnetic field data after filtering and noise reduction, determine the fusion value of the fluctuation state characteristic parameters of the mining DC power supply using a multi-physical quantity integrated fluctuation algorithm.

[0029] In this embodiment of the disclosure, the formula for calculating the fused value of the fluctuation state characteristic parameters of the mining DC power supply is as follows:

[0030] In the formula, This represents the fused value of the fluctuation state characteristic parameters of the DC power supply used in mining. Let i be the weight of the i-th data. Let i be the feature extraction function for the i-th data. The total number of data; The data includes voltage data, current data, temperature data, and magnetic field data.

[0031] Step 3: Obtain a preset fuzzy rule base, determine the reference value of the control parameter of the mining DC power supply based on the fusion value of the fluctuation state feature parameter and the preset fuzzy rule base, and optimize the control parameter of the mining DC power supply based on the reference value of the control parameter.

[0032] It should be noted that the preset fuzzy rule base includes: multiple fuzzy rules; The fuzzy rules mentioned above include: When the fluctuation value of the DC power supply for mining is less than the preset lower limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A1; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B1; If the current data is greater than the preset first current value, the output current value will be reduced by C1; If the current data is less than the preset second current value, the output current value will be increased by D1; When the fluctuation value of the mining DC power supply is greater than or equal to the preset lower limit of the fluctuation threshold and less than the preset upper limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A2; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B2; If the current data is greater than the preset first current value, the current output value will be reduced by C2; If the current data is less than the preset second current value, the output current value will be increased by D2; When the fluctuation value of the DC power supply for mining is greater than or equal to the preset fluctuation threshold upper limit: If the voltage data is greater than the preset first voltage value, the output voltage value will be reduced by A3; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B3; If the current data is greater than the preset first current value, the output current value will be reduced by C3; If the current data is less than the preset second current value, the output current value will be increased by D3.

[0033] It should be noted that the preset first voltage value is The preset second voltage value is The preset first current value is The preset second current value is 0.8. ; This refers to the standard voltage value when electrical equipment is operating normally. This is the standard current value of the equipment under rated load.

[0034] It should be noted that A1, B1, C1, D1, A2, B2, C2, D2, A3, B3, C3, and D3 can all be adjusted in real time based on the experience of the staff and on-site data.

[0035] In this embodiment of the disclosure, step 3 specifically includes: 3.1 Determine the fluctuation value of the mining DC power supply based on the fused value of the fluctuation state characteristic parameters; The formula for calculating the fluctuation value of the DC power supply used in mining is as follows:

[0036] In the formula, This represents the fluctuation value of the DC power supply used in mining. This is the reference value for the normal operation of the DC power supply for mining. The standard deviation of the fused values ​​of the characteristic parameters of the fluctuation state. This is the difference between the fused values ​​of the fluctuation state characteristic parameters at the current time and the previous time. For time intervals.

[0037] 3.2 The fused values ​​of the fluctuation state characteristic parameters are transformed into fuzzy linguistic variables of the mining DC power supply by using a Gaussian-trigonometric function hybrid membership function; The calculation formula for the fuzzy linguistic variables of the mining DC power supply is as follows:

[0038] In the formula, The membership degree is defined as the fusion value of the fluctuation state characteristic parameters when it is F. The central value of the membership function. This is the function width parameter; The fuzzy linguistic variable for the mining DC power supply is the membership degree.

[0039] 3.3 Determine the reference value of the control parameter of the mining DC power supply based on the fluctuation value of the mining DC power supply, the preset fuzzy rule base, and the fuzzy linguistic variables of the mining DC power supply.

[0040] It should be noted that 3.3 specifically includes: Based on the fluctuation value of the mining DC power supply, the voltage data, and the current data, the voltage and / or current adjustment amount corresponding to the fluctuation value of the mining DC power supply is found in the preset fuzzy rule base. Based on the voltage and / or current adjustment amount corresponding to the fluctuation value of the mining DC power supply, determine the adjusted voltage and / or current data, and determine the adjusted membership degree based on the adjusted voltage and / or current data; The reference values ​​for the voltage control parameters and / or current control parameters of the mining DC power supply are determined based on the adjusted membership degree and the adjusted voltage and / or current data.

[0041] The calculation formulas for the reference values ​​of the voltage control parameters and / or current control parameters of the mining DC power supply are as follows:

[0042] In the formula, These are reference values ​​for the voltage control parameters and / or current control parameters of the DC power supply used in mining. Let r be the membership degree corresponding to the r-th fuzzy rule. Let be the adjusted voltage and / or current data corresponding to the r-th fuzzy rule, and m be the number of fuzzy rules involved in the inference.

[0043] Furthermore, the method also includes: When the reference value of the control parameter of the mining DC power supply is the reference value of the voltage control parameter, the control circuit drives the voltage regulator to change the output voltage value to the reference value of the voltage control parameter. When the reference value of the control parameter of the mining DC power supply is the reference value of the current control parameter, the conduction angle of the control thyristor is changed to change the output current value to the reference value of the current control parameter.

[0044] Furthermore, the method also includes: By deploying sensors, the compensated voltage, current, temperature, and magnetic field data are collected in real time and analyzed. Based on the analysis results, the control parameters of the mine DC power supply are adjusted and optimized.

[0045] For example, the method provided in this embodiment is used to detect and compensate for power fluctuations in a centralized power supply area for medical equipment.

[0046] In areas of hospitals where medical equipment is centrally powered, such as intensive care units (ICUs) and medical imaging centers, various sophisticated medical devices have extremely high requirements for power stability. Power fluctuations can affect diagnostic results and even endanger patients' lives. The workflow in this scenario is as follows: At critical nodes in the power supply lines of medical equipment, Hall voltage sensors are installed in parallel at the input or output terminals of the power supply, continuously collecting voltage data at a frequency of 1000Hz. Figure 2 The voltage data shown reflects the real-time voltage changes at power input or output. The analog signal is transmitted to the signal conditioning circuit in a timely manner. The Rogowski coil current sensor is connected in series in the circuit and collects current data at a frequency of 1000Hz to obtain dynamic information about the current in the circuit. Thermocouple temperature sensors are closely attached to the surface of the key heat-generating components of the power supply and collect temperature data at a frequency of 10Hz to monitor the heat generation of the power supply during operation. The fluxgate magnetic field sensor is arranged axially at a distance of 10cm from the power supply and collects magnetic field data at a frequency of 50Hz to sense changes in the magnetic field around the power supply. The analog signals collected by each sensor may be mixed with noise interference during transmission, so they are first amplified and filtered by the signal conditioning circuit.

[0047] Signal processing and analysis stage: First, the data is filtered and denoised to remove noise and interference signals, ensuring the accuracy of subsequent analysis. Next, the characteristic parameters of the power supply fluctuation state are calculated. Assume that voltage, current, temperature, and magnetic field are collected. The total number of physical quantities is the total number of data points. For each physical quantity, its fusion weight is the weight of the i-th data point. Different physical quantities have varying degrees of importance in reflecting the fluctuation state of the power supply. Once the various physical quantities are determined... Then, the fused feature parameters are calculated using the formula. The calculation formula is: , here It is aimed at the first The feature extraction function for each physical quantity extracts information from the original measured values ​​that effectively reflects the characteristics of power supply fluctuations based on the characteristics of each physical quantity, and finally calculates the comprehensive result. value.

[0048] get After setting the value, the fluctuation assessment function is used to determine the power supply fluctuation situation, and the power supply fluctuation situation judgment value is calculated. The calculation formula is: ,in, Used to measure characteristic parameters The degree of dispersion within a certain time window is expressed by the formula: , Within the time window Characteristic parameters obtained from the measurement The value, Within this time window The mean, It is the measurement sequence number. It refers to the number of measurements within the time window. Represents the characteristic parameters of the current time and the previous time. The difference is calculated using the following formula: , The feature parameters at the current time. The value, These are the characteristic parameters from the previous time step. The value, It is the time interval between the two measurements, calculated as follows: After setting the value, compare it with the preset lower limit threshold, i.e., the preset fluctuation threshold lower limit. And another threshold, namely the preset upper limit of the fluctuation threshold. By comparing the results, the extent of power fluctuations can be determined.

[0049] Control decision stage: A Gaussian-trigonometric function hybrid membership function is used to integrate the feature parameters. The formula for converting to fuzzy linguistic variables is: In this formula, represent The membership degree of a given fuzzy set. It is the center value of the membership function. This is the function width parameter; through this function, precise feature parameters can be obtained. This is transformed into fuzzy language descriptions, such as "high power fluctuation level," "medium power fluctuation level," and "low power fluctuation level," to facilitate subsequent reasoning based on fuzzy logic. A fuzzy rule base is used to determine the fuzzy compensation strategy, containing rules for handling different levels of power fluctuation and abnormal physical quantities. For example, when a moderate fluctuation is identified, if the real-time voltage value is... Based on preset rules for judging excessively high or low voltage, if the voltage is determined to be too high, a corresponding strategy is formulated according to the rule of "moderate power supply fluctuation and excessively high voltage, appropriately reduce the output voltage". If the real-time collected current value is... When the current is determined to be too small according to the preset rules for judging whether the current is too large or too small (which also does not involve specific parameters), a strategy is formulated based on the rule of "moderate power fluctuation and insufficient current, moderately increase the current". Similarly, there are corresponding fuzzy rules for different abnormal combinations of physical quantities.

[0050] Finally, the fuzzy compensation strategy is converted into control parameters using a weighted defuzzification formula. The formula for the weighted average defuzzification method is as follows: ,in It is the sequence number of the fuzzy rule. These are the final, precise control parameters. It is the first The comprehensive membership degree of the fuzzy rule reasoning results reflects the importance of that rule in the entire reasoning process. It is the first The control parameter values ​​corresponding to the fuzzy rules It represents the number of fuzzy rules involved in the reasoning. This formula is used to synthesize multiple fuzzy control quantities obtained from fuzzy reasoning into a precise control parameter.

[0051] The control circuit performs compensation operations according to parameter requirements. If the control parameter requires voltage adjustment, the control circuit will drive the voltage regulator to change the output voltage. For example, if the control parameter indicates that the voltage needs to be reduced, the control circuit will send a corresponding control signal to the voltage regulator. The voltage regulator will adjust its own working state according to the signal to reduce the output voltage so that the voltage output by the power supply meets the normal operation requirements of the medical equipment. If the control parameter requires current adjustment, the control circuit will drive the relevant equipment to change the current magnitude to ensure that the current is stable within a suitable range and to ensure a stable power supply for the medical equipment.

[0052] It should be noted that this method also includes: By using sensors deployed at various key locations to collect real-time data on voltage, current, temperature, and magnetic field after compensation, and then conducting in-depth analysis of this data—for example, comparing the trends of data changes before and after compensation, and analyzing the stability of the data—if fluctuations are found in the power supply after compensation that affect the normal operation of medical equipment (such as blurred images in imaging diagnostic equipment or abnormal alarms issued by ICU life support equipment), the feedback monitoring system will adjust and optimize the system based on the analysis results. This includes reassessing the importance coefficients of each physical quantity, adjusting the feature extraction function, optimizing the rules in the fuzzy rule base, or adjusting the fuzzy method. Through continuous adjustment and optimization, the system ensures that the power supply remains stable, providing a reliable guarantee for the normal operation of medical equipment and safeguarding the lives of patients.

[0053] As can be seen, the method provided in this embodiment, applied to the centralized medical equipment area of ​​the hospital, enables close cooperation between various links, from physical quantity perception to signal processing and analysis, and then to control decision-making and compensation execution. Through precise calculation and logical reasoning, it can quickly respond to power fluctuations, ensure stable power supply to medical equipment, and continuously optimize the system through feedback monitoring mechanisms to ensure normal equipment operation. This not only avoids the impact of power supply problems on medical diagnosis and treatment, but also protects the safety of patients' lives. It is of great significance to the normal operation of hospital medical services and is an indispensable guarantee for the stable operation of medical equipment.

[0054] For example, the power fluctuation detection and compensation for smart factories is as follows: In a highly automated smart factory, numerous precision production equipment and automated control systems have stringent requirements for power stability. Power fluctuations can lead to decreased equipment precision, production halts, or even equipment damage. The power fluctuation detection and compensation system of this invention operates in this scenario as follows: Physical quantity sensing stage: In the power supply line of the smart factory, Hall voltage sensors are connected in parallel to the input or output of the power supply to collect voltage data at a frequency of 1000Hz and transmit the analog voltage signal to the signal conditioning circuit. Rogowski coil current sensors are connected in series in the circuit and collect current data at 1000Hz. Thermocouple temperature sensors are closely attached to the surface of key heat-generating components of the power supply equipment and collect temperature data at a frequency of 10Hz. Fluxgate magnetic field sensors are arranged axially at a distance of 10cm from the power supply equipment and collect magnetic field data at 50Hz. The analog signals collected by each sensor are amplified and filtered by the signal conditioning circuit.

[0055] Signal processing and analysis stage: First, filtering and noise reduction are performed to remove interference signals. Then, a multi-physical quantity integrated fluctuation algorithm is used to calculate the characteristic parameters of the power supply fluctuation state. Suppose that voltage, current, temperature, and magnetic field are collected. The physical quantity, for the There are several physical quantities, with a fusion weight of 1. fused feature parameters The calculation formula is: By combining this formula with the relevant calculations of various physical quantities, we can obtain... The value is then used to determine the power supply fluctuation situation using a fluctuation assessment function. The power supply fluctuation situation assessment value is... The calculation formula is: ,in , Within the time window Characteristic parameters obtained from the measurement The value, Within this time window The mean, It is the sequence number measured within the time window. It refers to the number of measurements within the time window. Represents the characteristic parameters of the current time and the previous time. The difference is calculated using the following formula: , The feature parameters at the current time. The value, These are the characteristic parameters from the previous time step. The value, It is the time interval between the two measurements, obtained through calculation. After the value is calculated, it is compared with a preset threshold to determine the degree of power fluctuation.

[0056] In the control decision-making stage, a Gaussian-trigonometric function hybrid membership function is used to integrate the feature parameters. Converted to fuzzy linguistic variables, the calculation formula is: ,Sure The membership degree of different fuzzy sets is used to determine the fuzzy compensation strategy through logical reasoning based on the fuzzy rule base. For example, if the fluctuation is judged to be moderate, the real-time collected voltage value is... ,when When the overvoltage condition is met, a strategy is formulated based on the rule "moderate power supply fluctuation and overvoltage, appropriately reduce the output voltage". Finally, the fuzzy compensation strategy is converted into control parameters through a weighted defuzzification formula, which is as follows: ,in It is the sequence number of the fuzzy rule. These are the final, precise control parameters. It is the first The comprehensive membership degree of the fuzzy rule inference results. It is the first The control parameter values ​​corresponding to the fuzzy rules It is the number of fuzzy rules involved in the reasoning.

[0057] Compensation execution phase: Compensation is performed according to the control parameters. If the control parameters require voltage adjustment, the control circuit drives the voltage regulator to change the output voltage to ensure that the power output meets the needs of the smart factory equipment and ensures the stable operation of the production equipment.

[0058] Feedback monitoring phase: Real-time collection of compensated voltage, current, temperature, and magnetic field data through deployed sensors. In-depth analysis of this data is conducted. If fluctuations in the compensated power supply still affect equipment operation, the system is adjusted and optimized based on the analysis results to ensure that the power supply remains stable and maintains efficient production in the smart factory.

[0059] As can be seen, in the smart factory scenario, the power fluctuation detection and compensation system collects physical quantity data through multi-sensor collaboration, calculates characteristic parameters through signal processing and analysis, accurately judges the degree of power fluctuation based on the fluctuation evaluation function, formulates compensation strategies using fuzzy logic and converts them into control parameters, adjusts the power output accordingly, and monitors and optimizes the system in real time. This system effectively ensures the stable operation of smart factory equipment, avoids production interruptions and equipment damage caused by power fluctuations, improves production efficiency and product quality, and plays a key supporting role in the stable and efficient production of smart factories.

[0060] It should be noted that the detailed process of the control parameter optimization method for mining DC power supplies provided in this embodiment is as follows: Figure 2 As shown, it will not be elaborated further here.

[0061] This embodiment provides a method for optimizing control parameters of a DC power supply for mining applications, which has the following advantages: 1. This invention achieves comprehensive perception of the power supply's operating status by integrating voltage, current, temperature, and magnetic field sensors. Compared to traditional single-physical-quantity monitoring, this multi-physical-quantity integrated perception method can more comprehensively capture power supply fluctuation characteristics, providing a solid data foundation for subsequent accurate compensation. By employing a multi-physical-quantity integrated fluctuation algorithm, the collected data is filtered and noise-reduced, and the fused characteristic parameters are calculated, effectively improving the accuracy of fluctuation assessment. Through the fluctuation assessment function, the degree of power supply fluctuation can be accurately determined, thereby ensuring that the power supply can maintain stable output in complex and ever-changing environments, improving the reliability and stability of the power supply system.

[0062] 2. This invention, through fuzzy compensation, can flexibly adjust the compensation according to the actual situation of power fluctuations, realizing the transformation from passive response to active control. It ensures the accuracy and effectiveness of the compensation action, precisely adjusts the voltage or current to cope with power fluctuations, and can collect the power output status data after compensation in real time for in-depth analysis. Based on the analysis results, the system is dynamically adjusted and optimized. This closed-loop control mechanism not only improves the system's response speed but also significantly enhances the system's adaptability and long-term stability, providing strong support for the intelligent management of power systems.

[0063] In summary, the control parameter optimization method for DC power supplies used in mining proposed in this embodiment achieves comprehensive perception of the power supply's operating status by integrating multiple physical quantity sensors, performs precise analysis using a multi-physical quantity comprehensive fluctuation algorithm, and formulates intelligent compensation strategies by combining fuzzy logic reasoning. This effectively improves the stability of the power supply, solves the shortcomings of traditional single-dimensional monitoring and fixed compensation strategies, and provides a safer and more stable power guarantee for various electronic devices.

[0064] Example 2 Figure 3 This is a structural diagram of a control parameter optimization system for a mining DC power supply according to an embodiment of this application, as shown below. Figure 3 As shown, the system includes: The acquisition module 100 is used to acquire voltage data, current data, temperature data and magnetic field data during the operation of the mine DC power supply at the current moment, and to perform filtering and noise reduction processing on the voltage data, current data, temperature data and magnetic field data to obtain filtered and noise-reduced voltage data, current data, temperature data and magnetic field data. The first determining module 200 is used to determine the fusion value of the fluctuation state characteristic parameters of the mining DC power supply based on the voltage data, current data, temperature data and magnetic field data after filtering and noise reduction, and by using a multi-physical quantity comprehensive fluctuation algorithm. The formula for calculating the fused value of the fluctuation state characteristic parameters of the mining DC power supply is as follows:

[0065] In the formula, This represents the fused value of the fluctuation state characteristic parameters of the DC power supply used in mining. Let i be the weight of the i-th data. Let i be the feature extraction function for the i-th data. The total number of data; The data includes voltage data, current data, temperature data, and magnetic field data.

[0066] The second determining module 300 is used to acquire a preset fuzzy rule base, determine the control parameter reference value of the mining DC power supply based on the fusion value of the fluctuation state feature parameters and the preset fuzzy rule base, and optimize the control parameters of the mining DC power supply based on the control parameter reference value. In this embodiment of the disclosure, the second determining module 300 is further configured to: The fluctuation value of the mining DC power supply is determined based on the fusion value of the fluctuation state characteristic parameters; The fused values ​​of the fluctuation state characteristic parameters are transformed into fuzzy linguistic variables of the mining DC power supply by using a Gaussian-trigonometric function hybrid membership function. The reference values ​​for the control parameters of the mining DC power supply are determined based on the fluctuation value of the mining DC power supply, the preset fuzzy rule base, and the fuzzy linguistic variables of the mining DC power supply.

[0067] The formula for calculating the fluctuation value of the DC power supply used in mining is as follows:

[0068] In the formula, This represents the fluctuation value of the DC power supply used in mining. This is the reference value for the normal operation of the DC power supply for mining. The standard deviation of the fused values ​​of the characteristic parameters of the fluctuation state. This is the difference between the fused values ​​of the fluctuation state characteristic parameters at the current time and the previous time. For time intervals.

[0069] The calculation formula for the fuzzy linguistic variables of the mining DC power supply is as follows:

[0070] In the formula, The membership degree is defined as the fusion value of the fluctuation state characteristic parameters when it is F. The central value of the membership function. This is the function width parameter; The fuzzy linguistic variable for the mining DC power supply is the membership degree.

[0071] It should be noted that the preset fuzzy rule base includes: multiple fuzzy rules; The fuzzy rules mentioned above include: When the fluctuation value of the DC power supply for mining is less than the preset lower limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A1; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B1; If the current data is greater than the preset first current value, the output current value will be reduced by C1; If the current data is less than the preset second current value, the output current value will be increased by D1; When the fluctuation value of the mining DC power supply is greater than or equal to the preset lower limit of the fluctuation threshold and less than the preset upper limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A2; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B2; If the current data is greater than the preset first current value, the current output value will be reduced by C2; If the current data is less than the preset second current value, the output current value will be increased by D2; When the fluctuation value of the DC power supply for mining is greater than or equal to the preset fluctuation threshold upper limit: If the voltage data is greater than the preset first voltage value, the output voltage value will be reduced by A3; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B3; If the current data is greater than the preset first current value, the output current value will be reduced by C3; If the current data is less than the preset second current value, the output current value will be increased by D3.

[0072] Furthermore, the second determining module 300 is also used for: Based on the fluctuation value of the mining DC power supply, the voltage data, and the current data, the voltage and / or current adjustment amount corresponding to the fluctuation value of the mining DC power supply is found in the preset fuzzy rule base. Based on the voltage and / or current adjustment amount corresponding to the fluctuation value of the mining DC power supply, determine the adjusted voltage and / or current data, and determine the adjusted membership degree based on the adjusted voltage and / or current data; The reference values ​​for the voltage control parameters and / or current control parameters of the mining DC power supply are determined based on the adjusted membership degree and the adjusted voltage and / or current data.

[0073] The calculation formulas for the reference values ​​of the voltage control parameters and / or current control parameters of the mining DC power supply are as follows:

[0074] In the formula, These are reference values ​​for the voltage control parameters and / or current control parameters of the DC power supply used in mining. Let r be the membership degree corresponding to the r-th fuzzy rule. Let be the adjusted voltage and / or current data corresponding to the r-th fuzzy rule, and m be the number of fuzzy rules involved in the inference.

[0075] In the embodiments disclosed herein, such as Figure 4 As shown, the system also includes: a compensation execution module 400; The compensation execution module 400 is used for: When the reference value of the control parameter of the mining DC power supply is the reference value of the voltage control parameter, the control circuit drives the voltage regulator to change the output voltage value to the reference value of the voltage control parameter. When the reference value of the control parameter of the mining DC power supply is the reference value of the current control parameter, the conduction angle of the control thyristor is changed to change the output current value to the reference value of the current control parameter.

[0076] Furthermore, such as Figure 4 As shown, the system also includes: a feedback monitoring module 500; The feedback monitoring module 500 is used to collect and analyze the compensated voltage, current, temperature and magnetic field data in real time through the arranged sensors, and adjust and optimize the control parameters of the mine DC power supply based on the analysis results.

[0077] In summary, the control parameter optimization system for DC power supplies used in mines proposed in this embodiment achieves comprehensive perception of the power supply's operating status by integrating multiple physical quantity sensors, performs precise analysis using a multi-physical quantity comprehensive fluctuation algorithm, and formulates intelligent compensation strategies by combining fuzzy logic reasoning. This effectively improves the stability of the power supply, solves the shortcomings of traditional single-dimensional monitoring and fixed compensation strategies, and provides a safer and more stable power guarantee for various electronic devices.

[0078] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0079] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0080] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for optimizing control parameters of a DC power supply for mining applications, characterized in that, The method includes: The voltage, current, temperature, and magnetic field data of the mining DC power supply at the current moment are collected, and the voltage, current, temperature, and magnetic field data are filtered and denoised to obtain the filtered and denoised voltage, current, temperature, and magnetic field data. Based on the filtered and denoised voltage, current, temperature, and magnetic field data, and using a multi-physical quantity integrated fluctuation algorithm, the fused value of the fluctuation state characteristic parameters of the mining DC power supply is determined. The calculation formula for the fused value of the fluctuation state characteristic parameters of the mining DC power supply is as follows: In the formula, This represents the fused value of the fluctuation state characteristic parameters of the DC power supply used in mining. Let i be the weight of the i-th data. Let i be the feature extraction function for the i-th data. The total number of data points; the data includes: voltage data, current data, temperature data, and magnetic field data; A preset fuzzy rule base is obtained. Based on the fusion value of the fluctuation state feature parameters and the preset fuzzy rule base, reference values ​​for the control parameters of the mining DC power supply are determined. The control parameters of the mining DC power supply are then optimized based on these reference values. Specifically, the fluctuation value of the mining DC power supply is determined based on the fusion value of the fluctuation state feature parameters. A Gaussian-trigonometric function hybrid membership function is used to convert the fusion value of the fluctuation state feature parameters into fuzzy linguistic variables for the mining DC power supply. Based on the fluctuation value of the mining DC power supply, the voltage data, and the current data, the voltage and / or current adjustment amounts corresponding to the fluctuation value of the mining DC power supply are searched in the preset fuzzy rule base. Based on the voltage and / or current adjustment amounts corresponding to the fluctuation value of the mining DC power supply, the adjusted voltage data and / or current data are determined, and the adjusted membership degrees are determined based on the adjusted voltage data and / or current data. Finally, reference values ​​for the voltage control parameters and / or current control parameters of the mining DC power supply are determined based on the adjusted membership degrees and the adjusted voltage data and / or current data.

2. The method as described in claim 1, characterized in that, The formula for calculating the fluctuation value of the DC power supply for mining is as follows: In the formula, This represents the fluctuation value of the DC power supply used in mining. This is the reference value for the normal operation of the DC power supply for mining. The standard deviation of the fused values ​​of the characteristic parameters of the fluctuation state. This is the difference between the fused values ​​of the fluctuation state characteristic parameters at the current time and the previous time. For time intervals.

3. The method as described in claim 2, characterized in that, The calculation formula for the fuzzy linguistic variables of the mining DC power supply is as follows: In the formula, The membership degree is defined as the fusion value of the fluctuation state characteristic parameters when it is F. The central value of the membership function. This is the function width parameter; The fuzzy linguistic variable for the mining DC power supply is the membership degree.

4. The method as described in claim 3, characterized in that, The preset fuzzy rule base includes: multiple fuzzy rules; The fuzzy rules mentioned above include: When the fluctuation value of the DC power supply for mining is less than the preset lower limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A1; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B1; If the current data is greater than the preset first current value, the output current value will be reduced by C1; If the current data is less than the preset second current value, the output current value will be increased by D1; When the fluctuation value of the mining DC power supply is greater than or equal to the preset lower limit of the fluctuation threshold and less than the preset upper limit of the fluctuation threshold: If the voltage data is greater than the preset first voltage value, then the voltage output value will be reduced by A2; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B2; If the current data is greater than the preset first current value, the output current value will be reduced by C2; If the current data is less than the preset second current value, the output current value will be increased by D2; When the fluctuation value of the DC power supply for mining is greater than or equal to the preset fluctuation threshold upper limit: If the voltage data is greater than the preset first voltage value, the output voltage value will be reduced by A3; If the voltage data is less than the preset second voltage value, the output voltage value will be increased by B3; If the current data is greater than the preset first current value, the output current value will be reduced by C3; If the current data is less than the preset second current value, the output current value will be increased by D3.

5. The method as described in claim 1, characterized in that, The calculation formulas for the reference values ​​of the voltage control parameters and / or current control parameters of the mining DC power supply are as follows: In the formula, These are reference values ​​for the voltage control parameters and / or current control parameters of the DC power supply used in mining. Let r be the membership degree corresponding to the r-th fuzzy rule. Let be the adjusted voltage and / or current data corresponding to the r-th fuzzy rule, and m be the number of fuzzy rules involved in the inference.

6. The method as described in claim 1, characterized in that, The method further includes: When the reference value of the control parameter of the mining DC power supply is the reference value of the voltage control parameter, the control circuit drives the voltage regulator to change the output voltage value to the reference value of the voltage control parameter. When the reference value of the control parameter of the mining DC power supply is the reference value of the current control parameter, the conduction angle of the control thyristor is changed to change the output current value to the reference value of the current control parameter.

7. A control parameter optimization system for a DC power supply used in mining, characterized in that, The system includes: The acquisition module is used to acquire voltage data, current data, temperature data and magnetic field data during the operation of the mine DC power supply at the current moment, and to filter and reduce noise in the voltage data, current data, temperature data and magnetic field data to obtain filtered and noise-reduced voltage data, current data, temperature data and magnetic field data. The first determining module is used to determine the fused value of the fluctuation state characteristic parameters of the mining DC power supply based on the filtered and denoised voltage data, current data, temperature data, and magnetic field data, and using a multi-physical quantity comprehensive fluctuation algorithm. The calculation formula for the fused value of the fluctuation state characteristic parameters of the mining DC power supply is as follows: In the formula, This represents the fused value of the fluctuation state characteristic parameters of the DC power supply used in mining. Let i be the weight of the i-th data. Let i be the feature extraction function for the i-th data. The total number of data points; the data includes: voltage data, current data, temperature data, and magnetic field data; The second determining module is used to acquire a preset fuzzy rule base, determine the reference values ​​of the control parameters of the mining DC power supply based on the fusion value of the fluctuation state feature parameters and the preset fuzzy rule base, and optimize the control parameters of the mining DC power supply based on the reference values ​​of the control parameters. Specifically, the fluctuation value of the mining DC power supply is determined based on the fusion value of the fluctuation state feature parameters; the fusion value of the fluctuation state feature parameters is converted into fuzzy linguistic variables of the mining DC power supply using a Gaussian-trigonometric function hybrid membership function; the voltage and / or current adjustment amounts corresponding to the fluctuation value of the mining DC power supply are searched in the preset fuzzy rule base based on the fluctuation value of the mining DC power supply, the voltage data, and the current data; the adjusted voltage and / or current data are determined based on the voltage and / or current adjustment amounts corresponding to the fluctuation value of the mining DC power supply, and the adjusted membership degrees are determined based on the adjusted voltage and / or current data; and the reference values ​​of the voltage control parameters and / or current control parameters of the mining DC power supply are determined based on the adjusted membership degrees and the adjusted voltage and / or current data.

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