A control optimization method and system for intelligent phase-changing switch

Abstract

The present invention discloses a control optimization method and system for an intelligent phase-changing switch, which relates to the field of smart grid technology. The method includes: traversing multiple key nodes of the intelligent phase-changing switch to deploy sensors and determine a sensor device group; periodically collecting data on the intelligent phase-changing switch through the sensor device group to generate an electrical operation parameter set; performing error calculation on the electrical operation parameter set to generate a control error data set; constructing a predictive control channel to generate multiple control signals; introducing control constraints to formulate a control strategy; executing the control strategy to perform error feedback, dynamically adjusting the control strategy, and generating a control optimization strategy. The present invention solves the technical problems in the prior art that the phase-changing switch is insufficient in accuracy and adaptability, resulting in unstable power quality and low system operation efficiency, and achieves the technical effect of improving control accuracy and the operation efficiency of the power system and enhancing adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a control optimization method and system for a smart phase-changing switch. Background Art

[0002] With the continuous development of power systems and the growing demand for intelligence, smart phase-changing switches play an increasingly important role in power distribution and optimization. In traditional power systems, phase-changing operations often rely on fixed rules and preset parameters, which are difficult to adapt to complex and changing power loads and operating conditions. Due to the complexity and uncertainty of power systems, the control of smart phase-changing switches faces many challenges.

[0003] The existing technology has technical problems that the phase-changing switch is insufficient in accuracy and adaptability, resulting in unstable power quality and low system operation efficiency. Summary of the invention

[0004] The present application provides a control optimization method and system for an intelligent phase-changing switch, which is used to solve the technical problems in the prior art that the phase-changing switch has deficiencies in accuracy and adaptability, resulting in unstable power quality and low system operation efficiency.

[0005] In view of the above problems, the present application provides a control optimization method and system for an intelligent phase-changing switch.

[0006] In a first aspect of the present application, a control optimization method for an intelligent phase-changing switch is provided, the method comprising:

[0007] Sensors are deployed through multiple key nodes of the intelligent phase-changing switch to determine a sensor device group; the intelligent phase-changing switch is regularly collected according to the operation cycle through the sensor device group to generate an electrical operation parameter set; the electrical operation parameter set is calculated based on the sensor device group to generate a control error data set; a predictive control channel is constructed to process the control error data set to generate multiple control signals; control constraints are introduced, and a control strategy is formulated based on the control constraints and the multiple control signals; the intelligent phase-changing switch is simulated to execute the control strategy for error feedback, the control strategy is dynamically adjusted according to the error feedback result, and a control optimization strategy is generated to optimize the control of the intelligent phase-changing switch.

[0008] A second aspect of the present application provides a control optimization system for an intelligent phase-changing switch, the system comprising:

[0009] A sensor device group determination module, the sensor device group determination module is used to traverse multiple key nodes of the intelligent phase-changing switch to deploy sensors and determine the sensor device group; an electrical operation parameter set generation module, the electrical operation parameter set generation module is used to periodically collect data according to the operation cycle of the intelligent phase-changing switch through the sensor device group to generate an electrical operation parameter set; a control error data set generation module, the control error data set generation module performs error calculation on the electrical operation parameter set based on the sensor device group to generate a control error data set; a control signal generation module, the control signal generation module is used to construct a predictive control channel to process the control error data set and generate multiple control signals; a control strategy formulation module, the control strategy formulation module is used to introduce control constraints and formulate a control strategy based on the control constraints and the multiple control signals; a control optimization module, the control optimization module is used to simulate the execution of the control strategy on the intelligent phase-changing switch for error feedback, dynamically adjust the control strategy according to the error feedback result, and generate a control optimization strategy to optimize the control of the intelligent phase-changing switch.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] Sensors are deployed at multiple key nodes of the intelligent phase-changing switch to determine the sensor device group; the intelligent phase-changing switch is regularly collected according to the operation cycle through the sensor device group to generate an electrical operation parameter set; the error of the electrical operation parameter set is calculated based on the sensor device group to generate a control error data set; a predictive control channel is constructed to generate multiple control signals; control constraints are introduced to formulate a control strategy; the intelligent phase-changing switch simulates the execution of the control strategy for error feedback, the control strategy is dynamically adjusted according to the error feedback result, and a control optimization strategy is generated to optimize the control of the intelligent phase-changing switch. The technical effect of improving the control accuracy and the operation efficiency of the power system and enhancing the adaptability is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0013] Figure 1 A schematic diagram of a flow chart of a control optimization method for an intelligent phase-changing switch provided in an embodiment of the present application;

[0014] Figure 2 A schematic diagram of the structure of a control optimization system for an intelligent phase-changing switch provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: sensing device group determination module 10 , electrical operation parameter set generation module 20 , control error data set generation module 30 , control signal generation module 40 , control strategy formulation module 50 , control optimization module 60 . DETAILED DESCRIPTION

[0016] The present application provides a control optimization method and system for an intelligent phase-changing switch, which is used to solve the technical problems in the prior art that the phase-changing switch has deficiencies in accuracy and adaptability, resulting in unstable power quality and low system operation efficiency.

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] Embodiment 1

[0019] like Figure 1 As shown, the present application provides a control optimization method for an intelligent phase-changing switch, the method comprising:

[0020] Step S100: traverse multiple key nodes of the intelligent phase-changing switch to deploy sensors and determine a sensor device group.

[0021] Specifically, the structure and working principle of the intelligent phase-changing switch are studied in depth to identify the key parts that may affect its performance and operating status. These key nodes include the power input node, which is used to monitor the stability of the input voltage; the main path node through which the current flows to obtain the magnitude and change of the current; the node where the phase-changing action is performed to understand the accuracy and timeliness of the phase-changing; and the interface node connected to the external device to grasp the quality of signal transmission. Then, according to the characteristics and monitoring requirements of each key node, the appropriate type of sensor is selected. For example, for voltage monitoring, a high-precision voltage sensor is selected; for current measurement, a current sensor that can withstand large currents and has a fast response capability is required. When laying out the sensors, the location and method of installation should be considered to ensure that the sensors can accurately sense the parameters of the key nodes without interfering with the normal operation of the intelligent phase-changing switch. Attention should also be paid to the wiring of the sensors to ensure the stability and reliability of signal transmission. After the sensor layout is completed, all installed sensors are uniformly debugged and calibrated so that they can accurately measure and transmit data. Finally, these sensors installed at different key nodes are combined together and determined as a sensor device group to prepare for subsequent data collection and analysis. It helps to achieve more precise performance optimization, improve control accuracy and operating efficiency of the power system, and enhance adaptability.

[0022] Step S200: The sensor device group periodically collects data from the intelligent commutation switch according to the operation cycle to generate an electrical operation parameter set.

[0023] Specifically, the operation cycle is reasonably set according to the performance requirements, working environment and expected monitoring accuracy of the intelligent phase-changing switch. For example, the operation cycle can be set to once every 15 minutes, every hour or every day. When the operation cycle starts, the voltage sensor in the sensor device group starts quickly and measures the voltage across the intelligent phase-changing switch at a very high sampling frequency. It not only records the instantaneous value of the voltage, but also calculates the key parameters such as the average value, effective value, and peak value of the voltage through the sampling data over a period of time. The current sensor also starts working at the same time, accurately measuring the magnitude and direction of the current passing through the intelligent phase-changing switch. It also obtains the instantaneous value, average value, root mean square value, etc. of the current, while monitoring the harmonic components of the current. The frequency sensor focuses on capturing the frequency changes of the circuit where the intelligent phase-changing switch is located, records the frequency value in real time, and calculates the frequency fluctuation range and average frequency. During the entire operation cycle, these sensors continuously perform measurements and data acquisition according to the pre-set sampling frequency. The collected raw data is preliminarily screened and sorted to remove obvious outliers and noise interference, and the relevant data such as voltage, current and frequency are arranged and combined in chronological order. For each parameter, a corresponding data curve is generated to intuitively show its changing trend during the operation cycle. Finally, these sorted and analyzed electrical parameters such as voltage, current, frequency, etc. form a complete set of electrical operation parameters. This parameter set contains rich information and can fully reflect the electrical operation status of the intelligent phase-changing switch during this specific operation cycle, thereby optimizing the operation strategy and improving the control accuracy and operation efficiency of the power system.

[0024] Step S300: performing error calculation on the electrical operation parameter set based on the sensor device group to generate a control error data set.

[0025] Specifically, a set of electrical operating parameters collected by the sensor device group is obtained. These parameters include voltage, current, frequency, etc. Then, the actual operating parameters collected are compared with the pre-set standard or ideal electrical operating parameters. For voltage parameters, the difference between the actual voltage value and the standard voltage value is calculated, and the absolute value, relative value or root mean square value of the difference is calculated, etc., to characterize the voltage error in different ways. For current parameters, the difference between the actual current value and the standard current value is also calculated, considering the errors in the magnitude and phase of the current. When processing frequency parameters, the deviation between the actual frequency and the standard frequency is calculated. The error of each electrical parameter is comprehensively analyzed and calculated, considering the relationship and weight between different parameters. All calculated error values ​​are sorted and stored in a certain format and structure to form a control error data set. By reducing the operating error, the performance consistency and reliability of the intelligent phase-changing switch are improved, and the reliability of the entire power system is enhanced.

[0026] Step S400: construct a predictive control channel to process the control error data set to generate multiple control signals.

[0027] Specifically, the parameters of the PID algorithm are determined, namely the proportional coefficient (P), integral coefficient (I) and differential coefficient (D). The selection of these parameters will be adjusted and optimized according to the characteristics and control requirements of the system. The proportional link (P) generates a control signal in proportion to the current error size. The larger the error, the stronger the effect of the proportional control signal. The integral link (I) accumulates and sums the error. Even if the current error is small, if the error persists, the integral action will gradually increase the control signal to eliminate the steady-state error. The differential link (D) generates a control signal based on the rate of change of the error. When the error changes rapidly, the differential control signal will play a suppressive role, which helps to reduce overshoot and improve stability. The PID algorithm comprehensively considers the effects of these three links and performs real-time calculation and processing of the input control error data set. By continuously adjusting these three coefficients, the PID controller can generate multiple appropriate control signals according to different error conditions. These control signals are designed to quickly and accurately reduce the control error, so that the operating state of the intelligent commutation switch reaches the desired goal and improves the control accuracy.

[0028] Step S500: introducing control constraints, and formulating a control strategy based on the control constraints and the multiple control signals.

[0029] Specifically, various control constraints are identified and sorted out. These constraints include the maximum current carrying capacity of the intelligent commutation switch, the maximum voltage tolerance value, the allowable frequency range, the thermal limit of the equipment, the stability requirements of the system, etc. Then, the control strategy is formulated by comprehensively considering the multiple control signals generated previously and these control constraints. For example, if a control signal indicates that the current needs to be greatly increased to reduce the error, but the control constraint indicates that the current device cannot withstand such a current increase, then it is necessary to adjust the control strategy to gradually increase the current or find other alternative control methods. When formulating the control strategy, the interaction and priority between different control signals must also be considered. Some control signals are more effective in reducing specific types of errors, but are more restricted by constraints; while some control signals are relatively weaker in effect but easier to implement within the constraints. In addition, the real-time and adaptability of the control strategy must also be considered. If the state changes or new constraints appear, the control strategy can be adjusted and optimized in time. The final control strategy is to integrate and use multiple control signals on the basis of strictly following all control constraints to achieve efficient, stable and reliable operation of the intelligent commutation switch.

[0030] Step S600: simulating the execution of the control strategy on the intelligent phase-changing switch to perform error feedback, dynamically adjusting the control strategy according to the error feedback result, and generating a control optimization strategy to perform control optimization on the intelligent phase-changing switch.

[0031] Specifically, the formulated control strategy is applied to the simulated execution environment of the intelligent phase-changing switch. In this simulated environment, the real working conditions and external interference factors are simulated as much as possible to obtain a control effect close to the actual situation. In the process of simulating the execution of the control strategy, the various operating parameters of the intelligent phase-changing switch are monitored in real time, compared with the expected target parameters, and the generated errors are calculated. These error feedback information includes but is not limited to the specific values ​​and change trends of voltage deviation, current fluctuation, frequency instability, etc. After obtaining the error feedback results, they are analyzed in depth. For example, it is determined whether the error is caused by over-adjustment or under-adjustment of the control strategy, or a temporary abnormality caused by external sudden factors. According to the analysis results of the error feedback, the relevant parameters in the control strategy are dynamically adjusted. The intensity, action time, priority, etc. of the control signal may be adjusted, or different control signals may be recombined. Through multiple simulation executions and error feedback analysis, the control strategy is continuously optimized and improved until a satisfactory control effect is achieved, and the final control optimization strategy is generated. The generated control optimization strategy is actually applied to the intelligent phase-changing switch for real-time control and optimization. In actual operation, error feedback information will continue to be monitored and collected in order to further improve and adjust the control optimization strategy to ensure that the intelligent commutation switch is always in the best working state and achieve efficient, stable and reliable operation.

[0032] In a possible implementation, step S300 further includes:

[0033] Step S310: Retrieving a historical electrical operation record data set based on the sensor device group.

[0034] Step S320: Draw an electrical operation trend using the historical electrical operation record data set, and calculate the electrical operation expected parameters according to the electrical operation trend.

[0035] Step S330: traverse the electrical operation parameter set and compare and determine with the electrical operation expected parameters, perform difference calculation on multiple electrical operation parameters that do not reach the electrical operation expected parameters, and generate the control error data set.

[0036] Specifically, a request instruction is sent to the sensor device group. This instruction contains specific identification information to clarify the sensor device group to which the data to be retrieved belongs. After receiving the request, the control unit of the sensor device group screens and retrieves according to the conditions in the instruction. It will find the electrical operation records related to the current sensor device group from a large amount of historical data based on factors such as time range, device number, parameter type, etc. During the retrieval process, the query language and indexing mechanism of the database are used to improve the retrieval efficiency and quickly and accurately obtain the required data. Once the data that meets the conditions is found, the data storage unit extracts it and organizes and packages it according to the predetermined data format. Finally, the historical electrical operation record data set is obtained for subsequent analysis and processing.

[0037] After obtaining the historical electrical operation record data set, select a suitable data visualization tool or drawing library in the programming language. According to the time series in the data set and the corresponding electrical parameter values, map the data to the coordinate axis respectively. It is common to use time as the horizontal axis and electrical parameters (such as voltage, current, frequency, etc.) as the vertical axis. By drawing line graphs, bar graphs, smooth curves, etc., the changes of electrical parameters over time are clearly shown, thereby forming an electrical operation trend graph. Next, analyze the drawn electrical operation trend. Statistical analysis methods, such as calculating the mean, median, standard deviation, etc., are used to describe the central tendency and dispersion of the data. According to the results of trend analysis, the expected parameters of electrical operation are calculated, which are stable values, optimal values ​​based on historical data, or expected values ​​at a certain point in the future based on trend prediction. For example, if the trend shows that the electrical parameters fluctuate around a certain average value over a period of time, then this average value can be used as the expected parameters of electrical operation; if the trend is gradually rising or falling, the expected parameter value at a certain point in the future may be predicted based on the rate of rise or fall. By calculating the expected parameters of electrical operation, clear goals and standards are provided for the formulation and adjustment of control strategies, making control more accurate and effective, improving system operation efficiency and enhancing adaptability.

[0038] Take out each electrical operation parameter value in the electrical operation parameter set in turn, and compare and judge it with the corresponding electrical operation expected parameter value one by one. For each parameter value, if it is less than or greater than the electrical operation expected parameter value, it is determined that the parameter does not meet the expectation. Then, for these electrical operation parameters that do not meet the expectations, subtract the actual electrical operation parameter value from the electrical operation expected parameter value to calculate the difference. All these differences are sorted and stored in a certain order and format to form a control error data set. Each difference in this data set represents the degree of deviation between the actual operating parameter and the expected parameter. This makes the subsequent adjustment and optimization measures more targeted and improves the control efficiency and effect.

[0039] In a possible implementation, step S400 further includes:

[0040] Step S410: constructing the predictive control channel through a PID control algorithm, wherein the predictive control channel includes a proportional branch, an integral branch, and a differential branch.

[0041] Step S420: performing error change response on the control error data set through the proportional branch to generate a first processed data set.

[0042] Step S430: performing static error elimination on the control error data set through the integral branch to generate a second processed data set.

[0043] Step S440: performing error change correction on the control error data set through the differential branch to generate a third processed data set.

[0044] Step S450: linearly combine the first processed data set, the second processed data set, and the third processed data set to generate the multiple control signals.

[0045] Specifically, the process of building a predictive control channel is started, and the core control logic is set to the PID control algorithm. As a classic and widely used control strategy, the PID control algorithm has strong adaptability and stability. Under this algorithm framework, three key branches are carefully divided and created: Proportional branch (P), the core mechanism of this branch is to establish a direct proportional relationship between the control error and the output. Through a pre-set proportional coefficient, the input control error data is multiplied by the coefficient to obtain the preliminary output of the proportional branch. For example, if the proportional coefficient is 2 and the current control error is 5, then the output of the proportional branch is 10. The advantage of the proportional branch is that it can respond quickly to the current error. Integral branch (I), this branch focuses on handling static errors in the system. It is achieved by continuously integrating the control error. The process of integration is to accumulate the error over a period of time. As time goes by, even if the error at a single moment may be small, the effect of integration will gradually accumulate and produce an increasing output until the error is completely eliminated. Differential branch (D), the uniqueness of this branch is that it is highly sensitive to the rate of change of the control error. It obtains information about the rate of error change by calculating the first-order derivative of the control error. When the error changes rapidly, the differential branch will produce a larger output, which plays a role in suppressing this rapid change. The introduction of the differential branch can significantly enhance the stability of the system and reduce the occurrence of overshoot. By carefully designing and adjusting the parameters of these three branches, including the proportional coefficient, integral time constant and differential time constant, they can work together to form a complete, efficient and accurate predictive control channel to cope with complex and changing control requirements and achieve precise control and optimization of the system.

[0046] In the proportional branch, the control error data set is first obtained. This data set contains a series of error values ​​that reflect the deviation between the actual operation and the desired state. For each error value in the data set, the proportional branch will calculate according to the preset proportional coefficient. The proportional coefficient is a fixed value that determines the degree of response of the proportional branch to the error. The specific calculation method is to multiply the error value by the proportional coefficient. For example, if the error value is 10 and the proportional coefficient is 2, the calculation result is 20. This multiplication operation is performed on each error value in the control error data set to obtain a new set of values. This new set of values ​​constitutes the first processed data set. This data set reflects the immediate response of the proportional branch to the control error data set, and its size is proportional to the error value. The role of the proportional branch is to be able to quickly produce a large response to a large error, so as to make preliminary adjustments to the system at the early stage of the error.

[0047] When the integral branch starts working, it first obtains the control error data set. Then, it will continue to add each error value in the data set for a period of time. For example, set a fixed time interval. In this interval, if the error value at the first moment is E1, the error value at the second moment is E2, and so on. The integral branch adds these error values ​​one by one. Assuming that the integral value at the beginning is 0, then after the first time interval, the integral value becomes E1; at the end of the second time interval, the integral value becomes E1+E2. In this way, the error values ​​in each time interval are accumulated. When all the error values ​​in the control error data set are accumulated in this way, the final accumulated result constitutes the second processing data set. The main function of the integral branch is to slowly eliminate the error that has always existed and unchanged (that is, the static error). Even if the error is very small or even 0 at a certain moment, as long as this error exists, the cumulative effect of the integral will continue to work, causing the output to change continuously until the static error is completely eliminated, allowing the entire system to reach a stable and accurate operating state.

[0048] After the differential branch is started, the control error data set is first obtained. Then, for each error value in the data set, the differential branch calculates its rate of change. That is, the difference between the error value at the current moment and the error value at the previous moment is calculated, and divided by the time interval to obtain the speed of error change. By performing such a rate of change calculation for each error value, a series of rate of change values ​​are obtained. These rate of change values ​​constitute the third processing data set. The role of the differential branch is to be able to keenly perceive the changing trend of the error. When the error changes too quickly, the output generated by the differential branch can suppress this rapid change, thereby correcting the dynamic response of the system, making the system more stable, and avoiding excessive overshoot or oscillation.

[0049] Get the first processing data set, the second processing data set and the third processing data set generated in the previous step. Then, assign a corresponding weight coefficient to each data set. The size of these weight coefficients determines the relative importance of each data set in the final combination. Next, in a linear combination manner, multiply each data in the first processing data set by its corresponding weight coefficient, multiply each data in the second processing data set by its corresponding weight coefficient, and multiply each data in the third processing data set by its corresponding weight coefficient. Then, add the data in the corresponding positions of the three data sets multiplied by the weight coefficients. This operation is performed on all the data in the three data sets, and the final set of results is the generated multiple control signals. Through such a linear combination method, the information processed by the proportional branch, the integral branch and the differential branch is integrated, so that the generated control signal can more comprehensively and accurately reflect the control requirements of the system, thereby achieving effective control and optimization of the system.

[0050] In a possible implementation, step S500 further includes:

[0051] Step S510: determining a control parameter domain, and defining initial control constraints based on the control parameter domain in combination with a plurality of limiting factors.

[0052] Step S520: performing a restriction evaluation on the initial control constraint condition, and determining a restriction value range according to the evaluation result.

[0053] Step S530: performing constrained iterative optimization on the initial control constraint condition according to the restricted numerical range to formulate the control constraint condition.

[0054] Specifically, conduct a comprehensive analysis of the control system to clarify the possible value ranges of the various parameters involved, so as to determine the control parameter domain. For example, determine the adjustable range of voltage, the upper and lower limits of current, the fluctuation range of power, etc. Next, collect a wide range of various limiting factors related to the control system. In terms of physical limitations, understand the maximum workload of the equipment in detail, such as the maximum pressure and maximum speed that a component can withstand; understand the strength limitations of materials, such as the maximum current density that a conductor can carry. For legal and regulatory restrictions, carefully study and follow relevant safety standards to ensure that the operation of the system will not cause casualties or property losses; follow environmental protection requirements and control possible pollutant emissions. Consider budget constraints and clarify the upper limit of resources that can be invested under existing funding conditions, including equipment procurement and maintenance costs. Pay attention to performance requirements and determine the response time that the system needs to achieve, such as the maximum time from receiving instructions to completing actions under specific circumstances; clarify accuracy requirements, such as the accuracy of measurement or control. At the same time, pay attention to other limiting factors that may affect the operation of the system, such as the impact of ambient temperature on equipment performance and electromagnetic interference restrictions. Based on the above determination of the control parameter domain and the collection and analysis of numerous limiting factors, a series of rules and conditions are initially formulated, which constitute the initial control constraints. These initial constraints provide a preliminary framework and foundation for the subsequent formulation of more precise and complete control strategies.

[0055] For each of the initial control constraints, conduct in-depth analysis and research. For example, if there is a constraint on the maximum workload of the equipment, it is necessary to evaluate how this will limit the system's ability to handle high-load tasks, and whether it is necessary to adjust the system configuration or add backup equipment to cope with possible overload situations. Analyze the relationship between the constraints and the control system goals. For example, if the control system goal is to achieve a fast response speed, but there is a constraint on the response time, it is necessary to clarify the extent to which this constraint hinders the achievement of the fast response goal, and whether it is necessary to optimize other aspects to compensate for this limitation. Study the interaction between different constraints. For example, budget constraints may affect the selection of high-performance equipment that can be adopted, and performance requirements may prompt the need to increase budget investment. It is necessary to analyze the comprehensive impact of this conflicting or mutually reinforcing relationship on system design. Through the above comprehensive and detailed evaluation, the specific impact of each constraint on the system design and the possible limitations are obtained. Based on these evaluation results, the specific numerical range corresponding to each constraint is further determined. For example, if it is a constraint on voltage fluctuation, the maximum and minimum values ​​allowed for voltage fluctuation are determined based on the evaluation; if it is a constraint on cost, the acceptable upper and lower limits of the budget are determined. These determined limiting value ranges will provide clear and specific boundaries and references for subsequent optimization, ensuring that the control accuracy of the intelligent commutation switch is significantly improved while meeting various constraints.

[0056] For each initial constraint, compare and analyze it with the corresponding restricted numerical range. If the initial constraint is too loose and cannot fully meet the requirements of the restricted numerical range, gradually tighten the constraint to make it more stringent and precise. For example, if the voltage fluctuation range initially specified is large, and the restricted numerical range requires a narrower fluctuation range, tighten the constraint on voltage fluctuation. On the contrary, if the initial constraint is too strict, it may make the system difficult to implement in actual operation or cause unnecessary cost increase. The constraint is appropriately relaxed, but it must still be ensured to be within the restricted numerical range. In the process of adjusting the constraint, multiple iterations are required. After each iteration, the impact of the adjusted constraint on the system operation and whether it meets other related requirements and goals must be re-evaluated. Through continuous adjustment, evaluation and re-adjustment, the initial control constraint is gradually optimized until an optimal balance is reached, that is, it can meet the requirements of the restricted numerical range and maximize performance and operability. The optimized constraint is finally determined, which is the final control constraint, providing clear, accurate and feasible rules and restrictions for the control of the intelligent commutation switch.

[0057] In a possible implementation, step S600 further includes:

[0058] Step S610: constructing a simulated operating environment based on the operating status trend graph.

[0059] Step S620: Execute the control strategy based on the simulated operating environment to perform continuous monitoring and extract multiple output signals, where the multiple output signals are output signals at multiple time scales.

[0060] Step S630: performing difference calculations on the multiple output signals and the expected output signal based on the multiple time scales, thereby performing iterations to generate multiple error continuous values.

[0061] Step S640: adding the plurality of error continuous values ​​to the error feedback result.

[0062] Specifically, obtain the previously generated operating status trend chart. This trend chart is a comprehensive display of the system's past operating status, including the changes of many key parameters over time. Perform in-depth analysis of the trend chart to extract important features and information such as the range of parameter changes, periodicity, extreme points, mean values, and the relationship between different parameters. When building a simulated operating environment, a circuit model based on Kirchhoff's law is selected. Its current law states that at any node in the circuit, the sum of the current flowing in must be equal to the sum of the current flowing out; the voltage law states that in any closed loop, the sum of the voltages of all components must be zero. For example, for a circuit containing a resistor, an inductor, and a capacitor in series, the operating rules of this circuit can be derived according to Kirchhoff's law. Then, in order to solve the operating status of this circuit, we use the Runge-Kutta method, which can gradually deduce the changes in current, voltage, etc. in the circuit through some clever calculations in each small time interval, just like walking step by step along time to calculate the state of the circuit at each moment, thereby building the entire simulated operating environment, allowing us to understand the working status of this circuit in advance.

[0063] When we have the simulation running environment built above, we start to operate in this environment according to the control strategy determined in advance. During the whole process, we keep observing various situations in this environment. We get a lot of output signals from this simulation environment. These output signals are not generated only in a fixed time period, but come from different time periods. For example, some output signals are generated in a very short time, such as a few milliseconds or seconds; some are generated in a medium-length time period such as a few minutes or hours; and some are even generated in a long time such as many days, weeks or months. By collecting these output signals that appear in various time periods of different lengths, we can understand the operation of this simulation environment from multiple angles and multiple time periods, and get more comprehensive and richer information.

[0064] Considering the numerous output signals covering multiple time scales obtained earlier, for each specific time scale, all output signals at this scale are processed in turn. Each output signal at each time scale is compared with the expected output signal set in advance, and the difference value between the actual output signal and the expected output signal is obtained through simple mathematical operations, that is, difference calculation. Then, this process is repeated in this way, and rounds of calculations and comparisons are performed. In each iteration, the new output signal is subtracted from the expected output signal to generate a series of continuous error values. Through continuous iterations, the error changes between the output and the expectation over time and at different time scales can be captured, providing rich data basis for subsequent analysis and adjustment.

[0065] Through the previous steps, multiple continuous error values ​​at different time scales have been calculated. Then, these continuous error values ​​are added one by one to the error feedback result that is specifically used to store error information. In the process of adding, it is necessary to ensure that each error value can be accurately recorded in the corresponding position to form a complete set of all calculated error values. In this way, the error feedback results are further enriched and improved, providing comprehensive and detailed data support for the subsequent evaluation of system performance, adjustment and optimization of control strategies, and in-depth analysis of the entire simulation operation process.

[0066] In a possible implementation, step S600 further includes:

[0067] Step S650: traverse the control strategy to perform data mining based on the multiple continuous error values ​​to generate N data items, where N is an integer greater than or equal to 1.

[0068] Step S660: Perform an impact analysis on the N data items to generate a plurality of impact coefficients, and determine whether there is a data item greater than or equal to a preset error threshold among the N data items based on the plurality of impact coefficients.

[0069] Step S670: If there are data items greater than or equal to the preset error threshold among the N data items, M data items greater than or equal to the preset error threshold are extracted, where M is an integer greater than 0 and less than N.

[0070] Step S680: using the M data items as indexes to traverse the control strategy and determine M control data to be adjusted.

[0071] Step S690: dynamically adjusting the control strategy based on the M control data to be adjusted to generate the control optimization strategy.

[0072] Specifically, a series of multiple error continuous values ​​are obtained through the previous steps. These error continuous values ​​reflect the deviation between the actual operation and the expected output. Then, a comprehensive and detailed inspection of the control strategy is started. In this traversal process, various data mining methods and techniques are used. For example, association analysis is used to find the potential association between the error continuous value and the specific parameters or operations in the control strategy; or cluster analysis is used to classify similar error conditions and corresponding control strategy parts. Through such in-depth mining and analysis, various valuable information and features are extracted from the control strategy. These extracted contents are sorted and converted into N data items. These N data items may include but are not limited to: the specific values ​​of certain key control parameters and their frequency of occurrence; the execution order of different control steps and their possible impact on the error; the selection mode of the control strategy under specific conditions, etc. The specific value of N depends on the results of data mining and the complexity of the control strategy. It can be 1 or more, as long as it is an integer greater than or equal to 1. Through in-depth mining of multiple error continuous values ​​and control strategies, it is possible to accurately find out the data items that have a significant impact on the control effect of the intelligent commutation switch, providing an accurate direction for subsequent optimization.

[0073] For the N data items generated, various data analysis and statistical methods are used to evaluate their influence on the control effect of the intelligent phase-changing switch. These methods may include but are not limited to regression analysis, sensitivity analysis, variance analysis, etc. During the analysis process, a specific influence coefficient is calculated for each data item. This influence coefficient is a quantitative value used to indicate the influence of the change of the data item on the control result. For example, if the influence coefficient of a data item is 0.8, it means that its change will have a greater impact on the control result; while if the influence coefficient is 0.2, it indicates that its impact is relatively small. After completing the calculation of the influence coefficient, these coefficients are compared with the preset error threshold set in advance. The preset error threshold is a critical value determined according to the performance requirements of the intelligent phase-changing switch, the stability standard of the power system, and other relevant factors. Check the influence coefficient of each data item one by one to determine whether the influence coefficient of any data item reaches or exceeds the preset error threshold. If such data items exist, it means that they have a greater impact on the control effect and need to be given special attention and processing in the subsequent optimization process. For example, assuming that the preset error threshold is 0.5, and among the multiple influence coefficients calculated, the influence coefficient of data item A is 0.7, and the influence coefficient of data item B is 0.4. Then, data item A is determined to be a data item greater than or equal to the preset error threshold, while data item B is not. The priority of data item optimization adjustment is determined according to the size of the influence coefficient, and data items with greater influence on the control effect are processed first to improve the optimization efficiency.

[0074] After the influence coefficients of N data items are compared with the preset error threshold, if there are data items whose influence coefficients reach or exceed the preset error threshold, the next step is to perform screening and extraction. Determine all data items that meet the conditions greater than or equal to the preset error threshold. Then, select M from these data items that meet the conditions, where M is a specific number, which is greater than 0, indicating that at least one data item with significant influence will be extracted; at the same time, M is less than N, which means that not all data items that meet the conditions will be extracted, but some of them will be selected according to certain rules or requirements. The selected rules are sorted based on the size of the influence coefficients, and data items with larger influence coefficients are extracted first; or they are determined based on factors such as the importance and urgency of the control strategy associated with the data items. For example, suppose there are 10 data items (N=10), of which 5 have influence coefficients greater than or equal to the preset error threshold. According to specific requirements and rules, select the most important 3 (M=3) for subsequent processing and analysis. By extracting M key data items in this targeted way, the optimization focus can be focused on the part that has the most significant impact on the control effect of the intelligent commutation switch, thereby improving the efficiency and effect of the optimization work.

[0075] After obtaining M key data items, use these M data items as clues or indexes for searching and positioning. Then, conduct a comprehensive and detailed search and inspection of the entire control strategy. In the process of traversal, the control data directly related to these M data items are accurately found through the correspondence between the data items and the various rules, parameter settings, conditional judgments, etc. in the control strategy. These found control data are the objects that need to be adjusted and optimized later, and a total of M control data to be adjusted are determined. Through such targeted traversal and determination, it is possible to accurately find the key control data that have an important impact on the control effect and need to be adjusted, so as to prepare for further optimization of the control strategy.

[0076] For the M control data to be adjusted related to the intelligent phase-changing switch, such as the phase-changing timing judgment parameters, current or voltage threshold settings, and the priority of different phase switching, an in-depth analysis is conducted. Then, based on the performance optimization goals of the intelligent phase-changing switch, such as improving the accuracy of phase-changing, shortening the phase-changing time, and reducing the impact on the power grid, a dedicated adjustment strategy is formulated for each control data to be adjusted, combined with the actual operation status and needs of the power grid. In the adjustment process, the complex characteristics of the power system in which the intelligent phase-changing switch is located are fully considered. For example, the load balancing requirements of the power grid, the power quality standards, and the coordinated working conditions of other related equipment should be taken into account. For example, if the control data to be adjusted is about the judgment of the phase-changing timing, the sensitivity of the judgment will be appropriately increased or decreased according to the real-time load change law of the power grid to ensure that the phase-changing is carried out at the most appropriate time. For the adjustment of the current or voltage threshold, it is necessary to comprehensively consider the bearing capacity of the power grid, the safe operation range of the equipment, and the energy-saving requirements. After completing the adjustment of the M control data to be adjusted, these adjustments are fully integrated into the original intelligent phase-changing switch control strategy. The newly generated control optimization strategy can adapt to the dynamic changes of the power grid more accurately, enabling the intelligent phase-changing switches to play a better role in ensuring the stable operation of the power grid, improving the quality of power, and reducing losses, thereby improving the operating efficiency and reliability of the entire power system.

[0077] Embodiment 2

[0078] Based on the same inventive concept as the control optimization method of an intelligent phase-changing switch in the aforementioned embodiment, Figure 2 As shown, the present application provides a control optimization system for an intelligent phase-changing switch, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0079] The sensor device group determination module 10 is used to traverse multiple key nodes of the intelligent phase-changing switch to deploy sensors and determine the sensor device group.

[0080] The electrical operation parameter set generation module 20 is used to periodically collect data from the intelligent phase-changing switch according to the operation cycle through the sensor device group to generate an electrical operation parameter set.

[0081] The control error data set generation module 30 performs error calculation on the electrical operation parameter set based on the sensor device group to generate a control error data set.

[0082] The control signal generating module 40 is used to construct a predictive control channel to process the control error data set and generate a plurality of control signals.

[0083] The control strategy formulation module 50 is used to introduce control constraints and formulate a control strategy based on the control constraints and the multiple control signals.

[0084] The control optimization module 60 is used to simulate the execution of the control strategy for the intelligent phase-changing switch to perform error feedback, dynamically adjust the control strategy according to the error feedback result, and generate a control optimization strategy to perform control optimization on the intelligent phase-changing switch.

[0085] Furthermore, the control error data set generation module 30 further includes:

[0086] A historical electrical operation record data set retrieving unit is used to retrieve the historical electrical operation record data set based on the sensor device group.

[0087] The electrical operation expected parameter calculation unit is used to draw the electrical operation trend by using the historical electrical operation record data set, and calculate the electrical operation expected parameter according to the electrical operation trend.

[0088] A control error data set generation unit is used to traverse the electrical operation parameter set and compare and determine it with the electrical operation expected parameters, perform difference calculation on multiple electrical operation parameters that do not reach the electrical operation expected parameters, and generate the control error data set.

[0089] Furthermore, the control signal generating module 40 further includes:

[0090] A predictive control channel construction unit, wherein the predictive control channel construction unit is used to construct the predictive control channel through a PID control algorithm, and the predictive control channel includes a proportional branch, an integral branch, and a differential branch.

[0091] A first processing data set generating unit is configured to generate a first processing data set by performing an error change response on the control error data set through the proportional branch.

[0092] The second processing data set generating unit is used for performing static error elimination on the control error data set through the integral branch to generate a second processing data set.

[0093] A third processing data set generating unit is used to perform error change correction on the control error data set through the differential branch to generate a third processing data set.

[0094] A plurality of control signal generating units are configured to linearly combine the first processed data set, the second processed data set, and the third processed data set to generate the plurality of control signals.

[0095] Furthermore, the control strategy formulation module 50 also includes:

[0096] An initial control constraint condition generating unit is used to determine a control parameter domain, and define initial control constraint conditions based on the control parameter domain in combination with a plurality of limiting factors.

[0097] A restricted numerical range determining unit is used to perform a restricted numerical range evaluation on the initial control constraint condition and determine a restricted numerical range according to the evaluation result.

[0098] A control constraint condition formulation unit is used to perform constrained iterative optimization on the initial control constraint condition according to the restricted numerical range to formulate the control constraint condition.

[0099] Furthermore, the control strategy formulation module 50 also includes:

[0100] An operation status trend diagram constructing unit is configured to continuously monitor the intelligent phase-changing switch based on the electrical operation parameter set to construct an operation status trend diagram.

[0101] An adjustment result generating unit, wherein the adjustment result generating unit dynamically adjusts the first processed data set, the second processed data set, and the third processed data set based on the operation status trend diagram to generate an adjustment result, wherein the adjustment result includes the first adjusted data set, the second adjusted data set, and the third adjusted data set.

[0102] A control strategy generating unit is used to make a comprehensive decision according to the control constraint conditions based on the first adjustment data set, the second adjustment data set, and the third adjustment data set to generate the control strategy.

[0103] Furthermore, the control optimization module 60 also includes:

[0104] A simulated operating environment construction unit is configured to construct a simulated operating environment based on the operating status trend graph.

[0105] An output signal extraction unit, wherein the output signal extraction unit executes the control strategy based on the simulated operating environment to perform continuous monitoring and extract multiple output signals, wherein the multiple output signals are output signals at multiple time scales.

[0106] An error continuous value generating unit is configured to perform difference calculations between the multiple output signals and the expected output signal based on the multiple time scales, thereby iterating to generate multiple error continuous values.

[0107] An error continuous value adding unit, wherein the error continuous value adding unit is used to add the plurality of error continuous values ​​to the error feedback result.

[0108] Furthermore, the control optimization module 60 also includes:

[0109] N data item generating units, wherein the N data item generating units traverse the control strategy to perform data mining based on the multiple continuous error values ​​to generate N data items, where N is an integer greater than or equal to 1.

[0110] An influence coefficient generating unit is used to perform influence analysis on the N data items, generate multiple influence coefficients, and determine whether there is a data item greater than or equal to a preset error threshold value among the N data items based on the multiple influence coefficients.

[0111] A data item extraction unit, wherein the data item extraction unit is used to extract M data items greater than or equal to the preset error threshold if there are data items greater than or equal to the preset error threshold among the N data items, where M is an integer greater than 0 and less than N.

[0112] The control data to be adjusted determining unit is used to use the M data items as indexes to traverse the control strategy and determine M control data to be adjusted.

[0113] A control optimization strategy generating unit is configured to dynamically adjust the control strategy based on the M control data to be adjusted to generate the control optimization strategy.

[0114] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0115] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0116] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A control optimization method for an intelligent phase-changing switch, characterized in that: The method comprises: Traverse multiple key nodes of the intelligent commutation switch to deploy sensors and determine the sensor device group; The intelligent phase-changing switch is regularly collected according to the operation cycle by the sensor device group to generate an electrical operation parameter set; Performing error calculation on the electrical operation parameter set based on the sensor device group to generate a control error data set; Constructing a predictive control channel to process the control error data set to generate a plurality of control signals; Introducing control constraints, and formulating a control strategy based on the control constraints and the multiple control signals; Perform error feedback on the intelligent phase-changing switch by simulating the execution of the control strategy, dynamically adjust the control strategy according to the error feedback result, and generate a control optimization strategy to optimize the control of the intelligent phase-changing switch; The method of performing error feedback on the intelligent commutation switch by simulating the execution of the control strategy includes: Build a simulated operation environment based on the operation status trend chart; Executing the control strategy based on the simulated operating environment to perform continuous monitoring and extracting multiple output signals, wherein the multiple output signals are output signals at multiple time scales; Based on the multiple time scales, the multiple output signals are traversed to perform difference calculations with the expected output signal, thereby iterating to generate multiple error continuous values; adding the plurality of error continuous values ​​to the error feedback result; The control strategy is dynamically adjusted according to the error feedback result, and the method includes: Traversing the control strategy to perform data mining based on the multiple continuous error values ​​to generate N data items, where N is an integer greater than or equal to 1; Performing an impact analysis on the N data items to generate a plurality of impact coefficients, and determining whether there is a data item greater than or equal to a preset error threshold among the N data items according to the plurality of impact coefficients; If there is a data item greater than or equal to the preset error threshold among the N data items, extract M data items greater than or equal to the preset error threshold, where M is an integer greater than 0 and less than N; Using the M data items as indexes to traverse the control strategy, determining M control data to be adjusted; The control strategy is dynamically adjusted based on the M control data to be adjusted to generate the control optimization strategy.

2. A control optimization method for an intelligent phase-changing switch according to claim 1, characterized in that: Based on the sensor device group, error calculation is performed on the electrical operation parameter set to generate a control error data set, and the method includes: Retrieving a historical electrical operation record data set based on the sensor device group; Plotting electrical operation trends using the historical electrical operation record data set, and calculating electrical operation expected parameters based on the electrical operation trends; The electrical operation parameter set is traversed to compare and determine with the electrical operation expected parameters, and a plurality of electrical operation parameters that do not reach the electrical operation expected parameters are subjected to difference calculation to generate the control error data set.

3. The control optimization method of an intelligent phase-changing switch according to claim 1, characterized in that: Constructing a predictive control channel to process the control error data set to generate multiple control signals, the method comprising: The predictive control channel is constructed by a PID control algorithm, wherein the predictive control channel includes a proportional branch, an integral branch, and a differential branch; Performing an error change response on the control error data set through the proportional branch to generate a first processed data set; Performing static error elimination on the control error data set through the integral branch to generate a second processed data set; performing error change correction on the control error data set through the differential branch to generate a third processed data set; The first processed data set, the second processed data set, and the third processed data set are linearly combined to generate the multiple control signals.

4. A control optimization method for an intelligent phase-changing switch according to claim 1, characterized in that: The control constraint condition is introduced, wherein the method for formulating the control constraint condition comprises: Determine a control parameter domain, and define initial control constraints based on the control parameter domain combined with a plurality of limiting factors; Performing a restriction evaluation on the initial control constraint condition, and determining a restriction value range according to the evaluation result; The initial control constraint condition is subjected to constrained iterative optimization according to the restricted numerical range to formulate the control constraint condition.

5. The control optimization method of an intelligent phase-changing switch according to claim 3, characterized in that: Formulating a control strategy based on the control constraint and the multiple control signals, the method includes: Based on the electrical operation parameter set, the intelligent commutation switch is continuously monitored to construct an operation status trend diagram; Dynamically adjusting the first processed data set, the second processed data set, and the third processed data set based on the running status trend graph to generate an adjustment result, wherein the adjustment result includes the first adjusted data set, the second adjusted data set, and the third adjusted data set; The control strategy is generated by making a comprehensive decision according to the first adjustment data set, the second adjustment data set, and the third adjustment data set in accordance with the control constraint conditions.

6. A control optimization system for an intelligent phase-changing switch, characterized in that: The system is used to implement a control optimization method for an intelligent phase-changing switch according to any one of claims 1 to 5, and the system comprises: A sensor device group determination module, which is used to traverse multiple key nodes of the intelligent phase-changing switch to arrange sensors and determine the sensor device group; An electrical operation parameter set generation module, the electrical operation parameter set generation module is used to periodically collect data from the intelligent phase-changing switch according to the operation cycle through the sensor device group to generate an electrical operation parameter set; A control error data set generation module, wherein the control error data set generation module performs error calculation on the electrical operation parameter set based on the sensor device group to generate a control error data set; A control signal generation module, the control signal generation module is used to construct a predictive control channel to process the control error data set to generate multiple control signals; A control strategy formulation module, the control strategy formulation module is used to introduce control constraints and formulate a control strategy based on the control constraints and the multiple control signals; A control optimization module is used to simulate the execution of the control strategy for the intelligent phase-changing switch to perform error feedback, dynamically adjust the control strategy according to the error feedback result, and generate a control optimization strategy to perform control optimization on the intelligent phase-changing switch.

Images