A clcc converter closed-loop control method and system

Abstract

This invention relates to a closed-loop control method and system for CLCC converters, belonging to the field of power system operation control technology. It solves the problems in existing technologies where there is a lack of unified control under multiple risks, and the control strategy does not predict and constrain the coupled changes of multiple risks, making it difficult to achieve closed-loop coordination. The method includes: S1, acquiring the operating status data of the CLCC converter in the current control cycle; S2, constructing risk indicators based on the operating status data and performing hierarchical processing to obtain a set of candidate dominant risks, and then determining the final dominant risk; S3, obtaining the current control strategy and predicting the operating status after its application, obtaining predicted operating status parameters; S4, if the predicted operating status parameters satisfy the multi-risk constraint conditions, then the current control strategy is determined as a feasible control strategy and output to the CLCC converter control system for execution, entering the next control cycle and re-executing S1 to S4. This achieves closed-loop control of the CLCC converter.

Description

Technical Field

[0001] This invention relates to the field of power system operation control technology, and in particular to a closed-loop control method and system for a CLCC converter. Background Technology

[0002] As a core component of high-voltage direct current (HVDC) transmission systems, the operation of a CLCC directly affects the safety and stability of the power grid. During operation, a CLCC involves various operating parameters such as voltage, current, turn-off angle, and temperature. These parameters are significantly coupled and influence each other under different operating conditions, collectively determining the converter's operating status.

[0003] Currently, the monitoring and analysis of the operational status of CLCC converters typically involves assessing operational risks through multi-parameter acquisition and modeling, and outputting early warning information when the risk exceeds a preset threshold. This type of method primarily focuses on identifying operational status and assessing risks, concentrating its function at the monitoring-alarm level. It lacks a mechanism to further translate risk assessment results into specific operational control strategies, making it difficult to achieve proactive adjustment and closed-loop control of the converter's operational status.

[0004] In actual engineering operations, due to the coupling effect between electrical and thermal parameters, CLCC converters often face multiple risks simultaneously under heavy load, load fluctuation, or disturbance conditions. These risks include reduced commutation margin, current approaching rated values, and increased equipment temperature. Different risks differ significantly in their mechanisms of action, development speed, and impact on system safety. Commutation risk can lead to commutation failure in a short period, representing a rapidly evolving system-level risk; current risk primarily reflects the equipment's operating load level; and temperature risk exhibits cumulative and delayed characteristics. Existing technologies typically analyze or control single risks, lacking a mechanism for unified assessment and dynamic adjustment of risk importance under conditions of multiple coexisting risks, making it difficult to prioritize the control of critical risks. Furthermore, control measures corresponding to different risks often have coupled effects or even mutual constraints. For example, adjusting the firing angle to improve the turn-off angle margin and suppress commutation risk may lead to current changes, thereby triggering new current risks or exacerbating temperature rise problems; similarly, reducing the current to alleviate overload risk may affect system operating conditions and further alter commutation conditions. Existing technologies typically formulate control strategies based on empirical rules or single objectives, lacking a mechanism for predicting and constraining the multiple risk changes that may be caused by control actions before control is implemented. This can easily introduce new operational risks while suppressing a certain risk, making it difficult to guarantee the overall safety and coordination of the control strategy.

[0005] In summary, existing technologies are limited to passive monitoring and alarms, lack a unified judgment and closed-loop control mechanism for multiple risks, and the control strategies do not predict and constrain the coupled changes of multiple risks, which can easily lead to new operational hazards when suppressing a single risk. Summary of the Invention

[0006] Based on the above analysis, the embodiments of the present invention aim to provide a closed-loop control method and system for CLCC converters, in order to solve the problems in the prior art where the control lacks unified control under multiple risks, and the control strategy does not predict, constrain, or verify the coupled changes of multiple risks in advance, making it difficult to achieve closed-loop coordination.

[0007] On one hand, embodiments of the present invention provide a closed-loop control method for a CLCC converter, comprising the following steps: S1. Acquire multi-source operating data during the operation of the CLCC converter and preprocess it to obtain the operating status data of the current control cycle; S2. Construct risk indicators based on the operating status data of the current control cycle and perform hierarchical processing to obtain a candidate dominant risk set; if there is only one candidate risk in the candidate dominant risk set, then take the candidate risk as the final dominant risk; otherwise, obtain the risk judgment value of each candidate risk and then determine the final dominant risk. S3. Obtain the current control strategy, predict the operating state after its implementation, and obtain the predicted operating state parameters; wherein, the initial control strategy is generated based on the final dominant risk; S4. Based on the predicted operating state parameters, perform multi-risk constraint verification on the current control strategy. If the multi-risk constraint conditions are met, the current control strategy is determined as a feasible control strategy and output to the CLCC converter control system for execution, and enter the next control cycle to re-execute S1 to S4; otherwise, after modifying the current control strategy, re-execute steps S3 to S4 until the iteration termination condition is reached.

[0008] Furthermore, the operating status data of the current control cycle includes operating status parameters and the corresponding sliding window operating status sequence; wherein, the operating status parameters are multi-dimensional vectors including commutation voltage, operating current, turn-off angle, firing angle and equipment temperature, and the corresponding sliding window operating status sequence is a time series dataset composed of operating status parameters that are traced back and extracted from N consecutive control cycles with the current control cycle as the end of the window.

[0009] Furthermore, the risk indicators include commutation risk indicators, current risk indicators, and temperature risk indicators; wherein, Commutation risk indicators Represented as: ; in, ; In the formula, Indicates the warning cutoff angle. Indicates the minimum safe shut-off angle. This indicates the preset safety margin for the turn-off angle. Indicates the current shut-off angle; Current risk index Represented as: ; In the formula, Indicates the current operating current. Indicates the rated current of the equipment; Temperature risk indicators Represented as: ; In the formula, Indicates the current device temperature. This indicates the maximum allowable temperature for the equipment.

[0010] Furthermore, the hierarchical processing involves dividing the constructed risk indicators into three levels: safe state, early warning state, and risk state. The candidate risks in the candidate dominant risk set are one or more risk indicators that are in the early warning state or risk state and have the highest risk level. The risk levels of the safe state, early warning state, and risk state increase sequentially.

[0011] Furthermore, the classification process is carried out in the following ways: For commutation risk indicators ,like If so, it is a safe state; if If it is, then it is a warning state; if If so, it is a risky state; For current risk indicators ,like If it is, then it is a safe state; if If it is, then it is a warning state; if If so, then it is a risky state; among which, , These are the first and second current risk thresholds, respectively. For temperature risk indicators ,like If so, it is a safe state; if If it is a warning state; if If so, then it is a risky state; among which, , These are the first and second temperature risk thresholds, respectively.

[0012] Furthermore, the risk assessment value for each candidate risk is expressed as follows: ; In the formula, This indicates the risk type, with a value of 1 representing commutation risk, a value of 2 representing current risk, and a value of 3 representing temperature risk. Indicates risk type The risk assessment value; Indicates risk type Risk level value; Indicates risk type Risk bias; Indicates risk type Risk trends; Indicates risk type The preset risk priority coefficient; , , , These represent the weighting coefficients for the first, second, third, and fourth risks, respectively.

[0013] Furthermore, the risk bias for each risk type is obtained as follows: ; In the formula, Indicates risk type Risk indicators Indicates risk type The safety boundary threshold, Indicates risk type The limit boundary threshold, This indicates taking the maximum value; Risk trends for each risk type are obtained as follows: ; In the formula, Indicates the current control cycle Risk type Risk indicators Indicates during the control cycle Risk type Risk indicators Indicates the duration of the control cycle.

[0014] Furthermore, the control strategy uses control quantity It means that, among them, Indicates the firing angle adjustment amount. Indicates the adjustment amount of the current reference value. Indicates the amount of power command adjustment; The predicted operating state parameters are obtained by predicting the operating state after the current control strategy is applied using the following formula: ; In the formula, Indicates the first Predicted running state parameters after the control strategy of the next iteration takes effect Indicates the current control cycle The running status parameters, Indicates the current control cycle Operating parameters - control sensitivity matrix Indicates the first The control input for the next iteration; where the operating parameter-control input sensitivity matrix is... This includes the influence of firing angle adjustment on turn-off angle and current, the influence of current reference value adjustment on operating current and temperature, and the influence of power command adjustment on current, turn-off angle, and temperature.

[0015] Furthermore, the multi-risk constraints include: Ultimately, the main risk was reduced to within the preset target range; Furthermore, risks other than the ultimate dominant risk meet the corresponding constraints, which include: the predicted turn-off angle is greater than or equal to the minimum safe turn-off angle; the predicted current is less than or equal to the rated current of the equipment; and the predicted temperature does not exceed the maximum allowable temperature of the equipment. Furthermore, risks other than the ultimate dominant risk must not exceed the preset allowable deterioration amount relative to the current control period.

[0016] On the other hand, embodiments of the present invention provide a closed-loop control system for a CLCC converter, comprising: The data acquisition and preprocessing module is used to acquire multi-source operating data during the operation of the CLCC converter and preprocess it to obtain the operating status data of the current control cycle. The risk perception and judgment module is used to construct risk indicators based on the operating status data of the current control cycle and perform hierarchical processing to obtain a set of candidate dominant risks. If there is only one candidate risk in the set of candidate dominant risks, the candidate risk is taken as the final dominant risk; otherwise, the risk judgment value of each candidate risk is obtained to determine the final dominant risk. The prediction and evaluation module is used to acquire the current control strategy, predict the operating state after its implementation, and obtain the predicted operating state parameters; wherein, the initial control strategy is generated based on the final dominant risk. The verification iteration module is used to perform multi-risk constraint verification on the current control strategy based on the predicted operating state parameters. If the multi-risk constraint conditions are met, the current control strategy is determined as a feasible control strategy and output to the CLCC converter control system for execution; otherwise, the current control strategy is modified and the prediction evaluation and verification iteration are re-executed until the iteration termination condition is reached. The system executes cyclically according to a preset control cycle. After outputting the control strategy in the previous control cycle, the next control cycle re-executes the functions of the above modules based on the updated operating data.

[0017] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: This invention provides a closed-loop control method and system for CLCC converters. 1. By introducing a prediction mechanism and a multi-risk constraint verification mechanism, the multi-risk coupled control problem is transformed into a constrained closed-loop control process, which alleviates or eliminates control conflicts when multiple risks coexist, and improves the safety, stability and reliability of converter operation. At the same time, the control strategy can be calculated and updated online without relying on complex optimization solutions or large-scale data training, which has good engineering feasibility. 2. Based on risk identification, a control strategy generation and execution mechanism is introduced, and a closed-loop control process is formed through prediction, evaluation and feedback updates to improve the real-time adjustment capability and response speed of the system. 3. By constructing a dominant risk determination mechanism that combines risk classification and risk priority, and by conducting a comprehensive assessment in conjunction with risk change trends, the system can automatically identify dominant risks under multiple risk conditions, avoiding misjudgments or omissions caused by fixed priorities or single threshold determinations, thereby improving the scientificity and accuracy of regulatory decisions. 4. By introducing a prediction and evaluation and multi-risk constraint verification mechanism before control execution, the operating state after the selected control strategy is implemented is predicted, and the non-dominant risks are constrained and iteratively corrected. This ensures that the final output control strategy effectively suppresses the dominant risks without causing the non-dominant risks to deteriorate, achieving coordinated optimization under multiple risk constraints and significantly improving the system's operational security. 5. Through a closed-loop control mechanism based on a sliding time window, this invention can continuously predict, verify, and correct risks during their evolution, enabling early intervention in the early stages of risk development, reducing the probability of commutation failure, effectively suppressing abnormal operating conditions such as overcurrent and overtemperature, and reducing the accumulation of long-term thermal and electrical stress in the equipment, thereby improving the operational stability, reliability, and service life of the CLCC converter.

[0018] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0019] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0020] Figure 1 This is a schematic flowchart of the closed-loop control method for a CLCC converter as described in Embodiment 1 of the present invention. Detailed Implementation

[0021] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0022] Example 1 A specific embodiment of the present invention discloses a closed-loop control method for a CLCC converter, such as... Figure 1 As shown, it includes the following steps: S1. Acquire multi-source operating data during the operation of the CLCC converter and preprocess it to obtain the operating status data of the current control cycle; Specifically, the multi-source operating data includes commutation voltage, operating current, turn-off angle, firing angle, and equipment temperature; among which, each operating data comes from the converter station control and protection system, measuring device, temperature monitoring device, and operation monitoring system.

[0023] Specifically, the operating status data of the current control cycle includes operating status parameters and the corresponding sliding window operating status sequence; wherein, the operating status parameters are multi-dimensional vectors including commutation voltage, operating current, turn-off angle, firing angle and equipment temperature, and the corresponding sliding window operating status sequence is a time series dataset composed of operating status parameters that are traced back and extracted from N consecutive control cycles with the current control cycle as the end of the window.

[0024] More specifically, the current control cycle Operating status parameters Represented as: ;in, , , , , These represent the current control cycle. Commutation voltage, operating current, turn-off angle, firing angle, and equipment temperature. Indicates transpose; The corresponding sliding window running state sequence is represented as follows: ;in, , These represent the control cycles. , The running status parameters.

[0025] More specifically, the length N of the sliding window is preset based on the data storage capacity of the control system and the smoothness requirements of the risk trend assessment, with a setting range of 5. 20.

[0026] During implementation, multi-source operational data undergoes preprocessing, including time scale unification, filtering and noise reduction, outlier handling, and sliding window construction; among these, The time scale is unified as follows: the time stamp of the operating data with different sampling frequencies is unified according to the control cycle, and the data within the same control cycle is converted into effective values ​​under the unified time scale; Filtering and denoising involves filtering or averaging each piece of operational data to reduce the impact of instantaneous noise on risk assessment. Outlier handling involves identifying outliers that exceed equipment physical limits, communication anomalies, or sudden anomalies, and correcting them using the previous valid value, window mean, or interpolation. The sliding window is constructed as follows: a sliding time window with a length of N control cycles is constructed to store the operating status data within multiple consecutive control cycles and to be used for subsequent risk change trend calculation.

[0027] Preferably, the preprocessing of multi-source operational data further includes multi-source data confidence fusion, redundancy verification of the same physical quantity from different data sources, assigning dynamic confidence weights to each data source based on the data source's accuracy level, communication status, and historical accuracy, and using the data obtained by the confidence-weighted average as the corresponding data in the operational status parameters; wherein, the dynamic confidence weights... Represented as: ; In the formula, Represents the first physical quantity The static accuracy level coefficient of each data source is determined by the equipment's factory precision; the higher the accuracy, the better. The larger; Represents the first physical quantity The communication quality factor for each data source is calculated based on the current communication bit error rate. When the bit error rate increases... decline; Represents the first physical quantity The historical accuracy factor for each data source is calculated based on the variance of the deviation between the data source and the fused benchmark over several past control cycles. As the variance increases... decline.

[0028] Understandably, in CLCC converter station projects, key parameters such as voltage, current, and turn-off angle are typically collected simultaneously by multiple independent measuring devices. The confidence-weighted fusion mechanism used in this embodiment differs from traditional hard switching or mean filtering. When the communication error rate of a data source increases or its historical deviation variance increases, its weight in the fusion process automatically decreases. This allows the control system to obtain reliable state estimates close to the true values ​​even under conditions of sensor failure or disturbance, thereby improving the fault tolerance of the closed-loop control system in harsh electromagnetic environments.

[0029] S2. Construct risk indicators based on the operating status data of the current control cycle and perform hierarchical processing to obtain a candidate dominant risk set; if there is only one candidate risk in the candidate dominant risk set, then the candidate risk is taken as the final dominant risk; otherwise, obtain the risk judgment value of each candidate risk and then determine the final dominant risk.

[0030] When implementing this risk indicator, the risk indicators include commutation risk indicator, current risk indicator, and temperature risk indicator.

[0031] In practical implementation, commutation risk indicators Represented as: ; in, ; In the formula, Indicates the warning cutoff angle. Indicates the minimum safe shut-off angle. This indicates the preset safety margin for the turn-off angle. This represents the current shut-off angle. Specifically, when constructing risk indicators based on the operational status data of the current control cycle, the current shut-off angle refers to the shut-off angle of the current control cycle.

[0032] Specifically, minimum safety shutdown Based on the reverse recovery time characteristics, maximum allowable junction temperature, and maximum commutation voltage drop of the thyristor device used in the CLCC converter valve, the range is determined by combining device physical limits and electromagnetic transient simulation, and is set to 10°~18°; preferably, it is set to 15° to ensure that there is sufficient time to recover the forward voltage blocking capability after the thyristor exits conduction under any operating condition, fundamentally avoiding device-level commutation failure.

[0033] Optionally, a preset shut-off angle safety margin is provided. Based on the measurement error of the converter station control and protection system, the accuracy of the trigger pulse control, and the normal fluctuation range of the AC bus voltage, the redundancy reserved to ensure that the control does not reach the physical limit is set to a range of 2° to 5°; preferably, it is set to 3°. By setting an early warning zone earlier than the physical limit, the control system can intervene in advance to adjust and avoid frequent over-limits during normal fluctuations, thereby balancing operational safety and control stability.

[0034] Optionally, a preset shut-off angle safety margin is provided. Dynamically adjust using the following formula: ; In the formula, Indicates the baseline margin value. Indicates the reference short-circuit ratio. Indicates the current control cycle The short-circuit ratio, , These represent the first and second adjustment coefficients, respectively. Indicates the rated power. Indicates the current control cycle The power.

[0035] Specifically, the benchmark margin value Based on the baseline operating conditions of the CLCC converter station engineering design, the basic margin value is set after comprehensively considering the characteristics of thyristor devices, measurement errors of the control and protection system, and the accuracy of trigger pulse control. The setting range is 2°~5°.

[0036] Specifically, the current control cycle short-circuit ratio The converter bus voltage and short-circuit capacity are monitored in real time by the converter station's energy management system (EMS) or phasor measurement unit (PMU), and calculated online using the Thevenin equivalent parameters of the AC system. If this method is used to adjust the turn-off angle safety margin, then in step S1, short-circuit ratio data and power are collected. Operating status parameters also include short-circuit ratio and power. (Reference short-circuit ratio) The baseline short-circuit ratio, determined during the engineering design phase, is typically taken as 3, corresponding to the boundary of a strong AC system. The short-circuit ratio is a core indicator for measuring the strength of an AC system, reflecting the supporting capacity of the AC system at the converter bus.

[0037] Specifically, the current control cycle power The DC power measurement value is read in real time from the converter station pole control system. Rated transmission power designed for converter stations.

[0038] Specifically, the first and second adjustment coefficients are obtained by performing a large number of simulations using the electromagnetic transient simulation model of the CLCC converter under different short-circuit ratios and power levels, and then fitting the quantitative relationship between the turn-off angle margin requirement and the short-circuit ratio and power. The first adjustment coefficient is set to a positive value with a range of 0.5° to 2°. The second adjustment coefficient is also set to a positive value with a range of 0.5° to 2°.

[0039] In practical implementation, current risk indicators Represented as: ; In the formula, Indicates the current operating current. This represents the rated current of the equipment. When constructing risk indicators based on the operating status data of the current control cycle, the current operating current refers to the operating current of the current control cycle.

[0040] Preferably, considering that higher harmonic content has a greater impact on equipment heating and stress under the same effective current value, a harmonic distortion correction term is added to the current risk index; Current Risk Index It can also be obtained through the following formula: ; In the formula, This represents the total harmonic distortion rate of the current. This represents the harmonic influence coefficient.

[0041] Specifically, the total harmonic distortion rate (THD) is the ratio of the root mean square (RMS) value of all harmonics to the RMS value of the fundamental frequency, and is obtained through real-time measurement and calculation by the power quality monitoring device at the converter station. The harmonic influence coefficient is the degree of additional influence of harmonic current on the thermal effect of equipment; that is, under the same effective current value, the higher the harmonic content, the greater the heating and insulation stress of the equipment, and the setting range is 0.5~2.

[0042] In practical implementation, temperature risk indicators Represented as: ; In the formula, Indicates the current device temperature. This indicates the maximum allowable temperature of the equipment. When constructing risk indicators based on the operating status data of the current control cycle, the current equipment temperature refers to the equipment temperature of the current control cycle.

[0043] During implementation, the hierarchical processing involves dividing each constructed risk indicator into three levels: safe state, early warning state, and risk state. The candidate risks in the candidate dominant risk set are one or more risk indicators that are in the early warning state or risk state and have the highest risk level. The risk levels of the safe state, early warning state, and risk state increase sequentially.

[0044] In practice, the following hierarchical processing methods are used: For commutation risk indicators ,like If so, it is a safe state; if If it is, then it is a warning state; if If so, it is a risky state; For current risk indicators ,like If so, it is a safe state; if If it is, then it is a warning state; if If so, then it is a risky state; among which, , These are the first and second current risk thresholds, respectively. For temperature risk indicators ,like If it is, then it is a safe state; if If it is a warning state; if If so, then it is a risky state; among which, , These are the first and second temperature risk thresholds, respectively.

[0045] Specifically, the first and second current risk thresholds are set based on the equipment's rated operating parameters and safety margins. The setting range is 0.90. 0.95, It can be set to 1.

[0046] Specifically, the first and second temperature risk thresholds are set based on equipment temperature limits and safety margins. The setting range is 0.85. 0.9, It can be set to 1.

[0047] Understandably, by classifying risk indicators, an early warning range can be formed before the shut-off angle, current, or temperature reach the equipment's safety boundary, enabling the control strategy to intervene in advance and avoiding the need to initiate adjustment only after the limit is exceeded, thereby improving the advance capability and safety margin of closed-loop control.

[0048] During implementation, the risk assessment value for each candidate risk is expressed as follows: ; In the formula, This indicates the risk type, with a value of 1 representing commutation risk, a value of 2 representing current risk, and a value of 3 representing temperature risk. Indicates risk type The risk assessment value; Indicates risk type Risk level value; Indicates risk type Risk bias; Indicates risk type Risk trends; Indicates risk type The preset risk priority coefficient; , , , These represent the weighting coefficients for the first, second, third, and fourth risks, respectively.

[0049] Specifically, the risk level values ​​for each risk type are preset to 0, 1, and 2 for safe, warning, and risk states, respectively. This aligns the risk level dimension with the dimensional contributions of continuous variables such as risk skewness and risk trend, objectively reflecting the severity of the gradual deterioration from safe to warning and then to risk. This arithmetic progression maintains horizontal comparability between levels while avoiding uncontrolled weighting due to non-linear scaling, ensuring that the determination of the dominant risk depends solely on the actual difference in physical safety margins.

[0050] Specifically, the risk bias for each risk type is obtained in the following manner: ; In the formula, Indicates risk type Risk indicators Indicates risk type The safety boundary threshold, Indicates risk type The limit boundary threshold, This indicates taking the maximum value.

[0051] Specifically, the safety boundary threshold describes the critical value at which a risk indicator transitions from a safe state to a warning state; for commutation risk indicators, the safety boundary threshold is set to 0; for current risk indicators, the safety boundary threshold is set to... Temperature risk index, its safety boundary threshold is set to .

[0052] Specifically, the limit boundary threshold describes the critical value at which a risk indicator transitions from a warning state to a risk state; for the commutation risk indicator, its limit boundary threshold is set to... The current risk index has its extreme boundary threshold set as follows: Temperature risk index, its extreme boundary threshold is set to .

[0053] It is understandable that risk skewness is the normalized degree of proximity or exceedance of a risk indicator relative to its corresponding threshold; where risk skewness less than or equal to 0 indicates a safe zone, the smaller the skewness, the safer it is; risk skewness greater than 0 and less than or equal to 1 indicates a warning zone, the larger the skewness, the more urgent it is; risk skewness greater than 1 indicates a risk zone.

[0054] Specifically, the risk trends for each risk type are obtained in the following way: ; In the formula, Indicates the current control cycle Risk type Risk indicators Indicates during the control cycle Risk type Risk indicators Indicates the duration of the control cycle.

[0055] Specifically, control cycle duration Based on the data refresh cycle and adjustment response time settings of the converter station control and protection system, the setting range is 100ms. 500ms.

[0056] It is understandable that the risk trend is the rate of change or slope of change of the risk indicator over multiple consecutive control periods; where a risk trend greater than 0 indicates that the risk is rising, less than 0 indicates that the risk is falling, and the larger the absolute value, the faster the rate of change.

[0057] Specifically, the preset risk priority coefficients for each risk type are directly set based on the converter station's operating procedures, control and protection strategies, and historical fault statistics. The preset priorities are, in order: commutation risk, current risk, and temperature risk.

[0058] More specifically, the risk priority coefficient for commutation risk is set to 1, the risk priority coefficient for temperature risk is set to 0.7~0.9, and the risk priority coefficient for temperature risk is set to 0.5~0.7. Specific values ​​can be determined within the above range based on the converter station's equipment configuration and operational requirements.

[0059] Specifically, the weighting coefficients for the first, second, third, and fourth risks are all non-negative, satisfying the condition that... .

[0060] Optionally, the weighting coefficients for the first, second, third, and fourth risks are adjusted based on engineering operation experience and the focus of the control strategy; preferably, the weighting coefficients for the first, second, third, and fourth risks are set to 0.35, 0.3, 0.2, and 0.15, respectively.

[0061] Optionally, the weighting coefficients for the first, second, third, and fourth risks can also be determined in the following ways: ; In the formula, , , , These represent the baseline values ​​for the weighting coefficients of the first, second, third, and fourth risk assessments, respectively. , These represent the adaptive sensitivity coefficients of the weighting coefficients for the second and third risk assessments, respectively.

[0062] Specifically, the adaptive sensitivity coefficients of the second and third risk assessment weight coefficients are tuned through simulation. Preferably, the adaptive sensitivity coefficients of the second and third risk assessment weight coefficients are set in the range of 0.1 to 0.5.

[0063] Specifically, the benchmark values ​​of the weighting coefficients for the first, second, third, and fourth risks are adjusted based on engineering operation experience and the focus of the control strategy; preferably, the benchmark values ​​of the weighting coefficients for the first, second, third, and fourth risks are set to 0.35, 0.3, 0.2, and 0.15, respectively.

[0064] During implementation, the candidate risk with the highest risk assessment value is determined as the final dominant risk; if there is not a unique candidate risk with the highest risk assessment value, the final dominant risk is determined according to the preset risk priority.

[0065] Preferably, if the commutation risk index is greater than the preset turn-off angle safety margin and the risk trend of commutation risk is greater than the preset threshold, it is determined to be an emergency transient event, steps S3 to S4 are skipped, the pre-tuned fast phase shifting strategy is executed, and step S1 is re-entered in the next control cycle.

[0066] Furthermore, after the fast phase-shifting strategy is activated, the output control quantity and execution time are recorded and used as the initial value for iterative correction in step S4 in the next control cycle.

[0067] Specifically, the pre-set fast phase shift strategy refers to one or more sets of emergency increase commands for the trigger angle obtained through offline electromagnetic transient simulation calculation based on the parameters of the CLCC converter and AC system. These commands are pre-stored in the converter station control and protection system during normal operation and are directly called and executed when triggered.

[0068] S3. Obtain the current control strategy, predict the operating state after its implementation, and obtain the predicted operating state parameters; wherein, the initial control strategy is generated based on the final dominant risk.

[0069] Specifically, the control strategy uses control quantity It means that, among them, Indicates the firing angle adjustment amount. Indicates the adjustment amount of the current reference value. This indicates the amount of power command adjustment.

[0070] During implementation, the initial control policy is generated in the following manner: When the ultimate dominant risk is commutation risk, the control objective is to improve the turn-off angle margin, and the trigger angle adjustment strategy is generated first. When the ultimate dominant risk is current risk, the current reference value adjustment strategy is prioritized to reduce the operating current as the control objective. When the ultimate dominant risk is temperature risk, the operating power adjustment strategy is generated with the goal of reducing the heat load on the equipment.

[0071] Specifically, the firing angle adjustment strategy is determined based on the direction of the firing angle's influence on the turn-off angle under the current operating conditions. The positive and negative directions; more specifically, the firing angle adjustment strategy adjusts the direction through the operating parameter-control quantity sensitivity matrix. middle The sign of the term is determined when hour, Take a positive value to increase the firing angle; when hour, Taking a negative value reduces the trigger angle. The single trigger angle adjustment step of the trigger angle adjustment strategy does not exceed 1°~5°, and the adjusted trigger angle meets the upper and lower limits of the trigger angle and the rate of change constraints.

[0072] It should be noted that, This indicates that the effect of the firing angle adjustment on the turn-off angle under the current operating condition has reached saturation or failure. At this time, the firing angle is not used as the dominant control quantity for commutation risk. Instead, the current reference value adjustment is used as an auxiliary control quantity. The turn-off angle margin is indirectly improved by reducing the operating current. The specific iterative correction method is shown in step S4.

[0073] Specifically, the current reference value regulation strategy lowers the predicted operating current below the current risk threshold by reducing the current reference value or reducing the power command. The single current reference value regulation step size in the current reference value regulation strategy does not exceed 1% of the rated current. 5%.

[0074] Specifically, the operating power regulation strategy aims to keep the predicted temperature below the temperature risk threshold by reducing the power command, implementing derating operation, or adjusting the cooling operation mode. The adjustment step size of a single power command in the operating power load regulation strategy shall not exceed 1% of the rated power. 5%.

[0075] For example, the ultimate dominant risk is the risk of phase conversion, and the initial control strategy is... The ultimate dominant risk is current risk, and the initial control strategy is... The ultimate dominant risk is temperature risk, and the initial control strategy is... .

[0076] During implementation, the predicted operating state parameters are obtained by predicting the operating state after the current control strategy is applied, using the following formula: ; In the formula, Indicates the first Predicted running state parameters after the control strategy of the next iteration takes effect Indicates the current control cycle The running status parameters, Indicates the current control cycle Operating parameters - control sensitivity matrix Indicates the first The control variable for the next iteration.

[0077] Specifically, no. Predicted operating state parameters after the control strategy of the next iteration Represented as: ;in, , , , , They represent the first The predicted values ​​of commutation voltage, operating current, turn-off angle, firing angle, and equipment temperature after the control strategy of the next iteration are implemented.

[0078] Specifically, the operating parameter-control quantity sensitivity matrix includes the influence of firing angle adjustment on turn-off angle and current, the influence of current reference value adjustment on operating current and temperature, and the influence of power command adjustment on current, turn-off angle, and temperature. More specifically, the influence of firing angle adjustment on turn-off angle and current includes the partial derivative of turn-off angle with respect to firing angle. Partial derivative of operating current with respect to firing angle Among them, the partial derivative of the operating current with respect to the firing angle Typically negative, this indicates that increasing the firing angle will decrease the operating current; the relationship between current reference value adjustment and operating current and temperature includes the partial derivative of the operating current with respect to the current reference value. Partial derivative of equipment temperature with respect to current reference value The effects of power command regulation on current, turn-off angle, and temperature include the partial derivative of the operating current with respect to the power command. Partial derivative of turn-off angle with respect to power command Partial derivative of power command with respect to device temperature Among them, the partial derivative of the operating current with respect to the power command Typically positive; the partial derivative of the turn-off angle with respect to the power command. It is usually negative, indicating that increasing the power command will cause the current to rise and the turn-off angle to fall.

[0079] Specifically, the operating parameter-control quantity sensitivity matrix is ​​obtained through offline simulation calibration, historical operating data identification, or online recursive identification within a sliding time window, and is dynamically updated based on changes in operating data over multiple consecutive control cycles. Specifically, offline simulation calibration involves small-disturbance scanning calibration based on the CLCC converter electromagnetic transient simulation model at different operating points; historical operating data identification uses the least squares method or recursive least squares method; and online recursive identification uses a recursive least squares method with a forgetting factor within a sliding time window for online identification and dynamic updating.

[0080] Preferably, the predicted operating state parameters can also be obtained by predicting the operating state after the current control strategy is applied using the following formula: ; in, ; In the formula, Indicates control cycle For the current control cycle Predicted operating state parameters; Indicates the forgetting factor, .

[0081] Specifically, the forgetting factor is manually set based on the actual control effect, with a setting range of 0.80. 0.95; Considering the nonlinearity and high thermal inertia of the CLCC converter, it is preferred to set it to 0.85.

[0082] S4. Based on the predicted operating state parameters, perform multi-risk constraint verification on the current control strategy. If the multi-risk constraint conditions are met, the current control strategy is determined as a feasible control strategy and output to the CLCC converter control system for execution, and enter the next control cycle to re-execute S1 to S4; otherwise, after modifying the current control strategy, re-execute steps S3 to S4 until the iteration termination condition is reached.

[0083] During implementation, multiple risk constraints include: Ultimately, the main risk was reduced to within the preset target range; Furthermore, risks other than the ultimate dominant risk meet the corresponding constraints, which include: the predicted turn-off angle is greater than or equal to the minimum safe turn-off angle; the predicted current is less than or equal to the rated current of the equipment; and the predicted temperature does not exceed the maximum allowable temperature of the equipment. Furthermore, risks other than the ultimate dominant risk must not exceed the preset allowable deterioration amount relative to the current control period.

[0084] In practice, risks other than the ultimate dominant risk must not exceed a preset allowable deterioration level relative to the current control period, expressed as: ; In the formula, Indicates the first Risk type of the next iteration Predictive risk indicators Indicates the current control cycle Risk type Risk indicators Indicates risk type The allowable deterioration amount; representing risks other than the ultimate dominant risk, such as commutation risk, current risk, or temperature risk.

[0085] Specifically, the allowable deterioration amount is set according to the equipment safety margin and control protection settings, representing the maximum extent to which a non-dominant risk is allowed to deteriorate during the control of a dominant risk; the allowable deterioration amount for commutation risk is set in the range of 0.5°~1.5°; the allowable deterioration amount for current risk is set in the range of 0.02~0.55; and the allowable deterioration amount for temperature risk is set in the range of 0.02~0.55. Preferably, the allowable deterioration amount for commutation risk is set to 1°; the allowable deterioration amount for current risk is set to 0.03; and the allowable deterioration amount for temperature risk is set to 0.03.

[0086] In practice, the ultimate goal is to reduce the primary risk to within the pre-set target range, including: If the ultimate dominant risk is in a risk state, the control objective is to reduce it to a warning state or a safe state, that is, each predicted risk indicator corresponding to the predicted operating state parameter is less than or equal to the corresponding limit boundary threshold. If the ultimate dominant risk is in a warning state, the control objective is to further reduce it to below the current value, meaning that all predicted risk indicators corresponding to the predicted operating status parameters are less than or equal to the operating status data of the current control cycle. However, if the current risk indicator value is already close to the safety boundary (the difference is less than a preset small amount), the control objective can be set to maintain the current state and not reduce it further.

[0087] Specifically, the predicted operating state parameters in step S3 are substituted into the risk index formula in step S2 to obtain the predicted risk index, namely the predicted commutation risk index, the predicted current risk index, and the predicted temperature risk index; wherein, in the risk index formula, the current turn-off angle is the predicted turn-off angle, the current equipment temperature is the predicted equipment temperature, and the current operating current is the predicted operating current.

[0088] If the multiple risk constraints are not met during implementation, the current control strategy shall be modified using the following formula: ; In the formula, Indicates the first The control quantity for the next iteration Indicates the first The control quantity for the next iteration Indicates the first Adjust the step size for each iteration.

[0089] Specifically, the correction method for each iteration is as follows: reduce the current control step size to reduce the disturbance of the control strategy to non-dominant risks; or change the control direction or redistribute the control quantity within the upper and lower limits of the control quantity; or introduce auxiliary control quantity to compensate for non-dominant risks.

[0090] Specifically, the direction of iterative correction is determined comprehensively based on the type of unmet constraint and the direction of risk deviation: if the commutation risk constraint is not met, and If the current risk constraint is not met, the current reference value adjustment is adjusted in the negative direction; if the temperature risk constraint is not met, the power command adjustment is adjusted in the negative direction.

[0091] Furthermore, if the commutation risk constraint is not met and If the firing angle adjustment remains unchanged, the turn-off angle margin is indirectly increased by adjusting the current reference value in the negative direction. That is, the current firing angle command value is not changed, and its current setting remains unchanged.

[0092] Specifically, the adjustment step size for each iteration is determined based on the predicted risk deviation, equipment adjustment capability, upper and lower limits of control quantity, and rate of change constraint. Furthermore, the adjustment step size for each iteration can be determined using the gradient method with feasible region projection, specifically using the gradient descent algorithm known in the art, which is combined with the constraint conditions and applied to the iterative correction.

[0093] Furthermore, after each iteration of the correction, the corrected control quantity is constrained to its physical feasible domain: the firing angle adjustment quantity meets the upper and lower limits; the current reference value adjustment quantity and the power command adjustment quantity are constrained to the adjustment range allowed by the device.

[0094] It should be noted that when multiple constraints are not met at the same time, the principle is to prioritize meeting the constraints of the dominant risk. The constraints of non-dominant risks are adjusted iteratively while meeting the objectives of the dominant risk. If the dominant risk is commutation risk and the commutation constraint is not met, the first priority of correction is to adjust the trigger angle to meet the commutation constraint. After the commutation constraint is met, the correction is gradually made to alleviate the current constraint or temperature constraint of the triggered risk.

[0095] Specifically, if all constraints cannot be satisfied even after reaching the maximum number of iterations, at least one of the following operations shall be performed: Derating Operation Command: Outputs a derating operation command to reduce the DC transmission power to 50% of the rated value. 70%, enabling the converter to operate under low load conditions to avoid multiple risk couplings; Lockout adjustment instruction: The control quantity that currently satisfies the constraints is output as the suboptimal feasible solution and executed, locking out further iterative adjustments and waiting for re-evaluation in the next control cycle; Protection and control commands: Execute protection and control commands (such as forcibly moving the firing angle to the upper limit, locking the converter, etc.) and output risk warning information to the operation monitoring system.

[0096] During implementation, the iteration termination condition is when the number of iterations reaches the preset maximum number of iterations.

[0097] Specifically, the maximum number of iterations is set in the range of 3 to 10, preferably 5.

[0098] Specifically, the control cycle is determined based on the data refresh cycle of the converter station control and protection system and the adjustment response time of the converter valve. The setting range is from milliseconds to seconds, such as 100ms to 500ms, to ensure timely adjustment after changes in system status without exceeding the physical response capability of the equipment.

[0099] It should be noted that the control cycle is the time base for the entire closed-loop control method, and steps S1 to S4 are executed cyclically at preset fixed time intervals. Through the rolling update mechanism of the control cycle, after the control strategy is output in the previous cycle, all steps are re-executed in the next cycle based on the latest operating data, forming a continuously optimized closed-loop control.

[0100] Compared with existing technologies, the closed-loop control method for CLCC converters provided in this embodiment transforms the multi-risk coupled control problem into a constrained closed-loop control process by introducing a prediction mechanism and a multi-risk constraint verification mechanism. This alleviates or eliminates control conflicts when multiple risks coexist, improving the safety, stability, and reliability of converter operation. Simultaneously, it achieves online calculation and updating of the control strategy without relying on complex optimization solutions or large-scale data training, demonstrating good engineering feasibility. Based on risk identification, a control strategy generation and execution mechanism is introduced, and a closed-loop control process is formed through prediction, evaluation, and feedback updates, improving the system's real-time adjustment capability and response speed. By constructing a dominant risk determination mechanism that combines risk classification and risk priority, and conducting a comprehensive evaluation based on risk change trends, the system can automatically identify the dominant risk under multi-risk conditions and avoid [risks]. By eliminating misjudgments or omissions caused by fixed priorities or single thresholds, the scientific nature and accuracy of control decisions are improved. By introducing a predictive evaluation and multi-risk constraint verification mechanism before control execution, the operating state after the selected control strategy is implemented is predicted, and constraints on non-dominant risks are determined and iteratively corrected. This ensures that the final output control strategy effectively suppresses dominant risks without causing the deterioration of non-dominant risks, achieving coordinated optimization under multiple risk constraints and significantly improving system operational safety. Through a closed-loop control mechanism based on a sliding time window, this invention can continuously predict, verify, and correct during the risk evolution process, enabling early intervention in the early stages of risk development, reducing the probability of commutation failure, effectively suppressing abnormal operating states such as overcurrent and overtemperature, and reducing the long-term accumulation of thermal and electrical stress in the equipment, thereby improving the operational stability, reliability, and service life of the CLCC converter.

[0101] Example 2 A specific embodiment of the present invention discloses a closed-loop control system for a CLCC converter, comprising: The data acquisition and preprocessing module is used to acquire multi-source operating data during the operation of the CLCC converter and preprocess it to obtain the operating status data of the current control cycle. The risk perception and judgment module is used to construct risk indicators based on the operating status data of the current control cycle and perform hierarchical processing to obtain a set of candidate dominant risks. If there is only one candidate risk in the set of candidate dominant risks, the candidate risk is taken as the final dominant risk; otherwise, the risk judgment value of each candidate risk is obtained to determine the final dominant risk. The prediction and evaluation module is used to acquire the current control strategy, predict the operating state after its implementation, and obtain the predicted operating state parameters; wherein, the initial control strategy is generated based on the final dominant risk. The verification iteration module is used to perform multi-risk constraint verification on the current control strategy based on the predicted operating state parameters. If the multi-risk constraint conditions are met, the current control strategy is determined as a feasible control strategy and output to the CLCC converter control system for execution; otherwise, the current control strategy is modified and the prediction evaluation and verification iteration are re-executed until the iteration termination condition is reached. The system executes cyclically according to a preset control cycle. After outputting the control strategy in the previous control cycle, the next control cycle re-executes the functions of the above modules based on the updated operating data.

[0102] The specific implementation process of this invention can be found in the above method embodiments, and will not be repeated here.

[0103] Since this embodiment is based on the same principle as the above method embodiments, this system also has the corresponding technical effects of the above method embodiments.

[0104] Example 3 One specific embodiment of the present invention discloses an electronic device, comprising: A processor and a memory; the processor, when executing a program, implements the method of embodiment 1.

[0105] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0106] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A closed-loop control method for a CLCC converter, characterized in that, Includes the following steps: S1. Acquire multi-source operating data during the operation of the CLCC converter and preprocess it to obtain the operating status data of the current control cycle; S2. Construct risk indicators based on the operating status data of the current control cycle and perform hierarchical processing to obtain a set of candidate dominant risks; If there is only one candidate risk in the candidate dominant risk set, then the candidate risk is taken as the final dominant risk; otherwise, the risk judgment value of each candidate risk is obtained, and then the final dominant risk is determined. S3. Obtain the current control strategy, predict the operating state after its implementation, and obtain the predicted operating state parameters; wherein, the initial control strategy is generated based on the final dominant risk; S4. Based on the predicted operating state parameters, perform multi-risk constraint verification on the current control strategy. If the multi-risk constraint conditions are met, the current control strategy is determined as a feasible control strategy and output to the CLCC converter control system for execution, and enter the next control cycle to re-execute S1 to S4; otherwise, after modifying the current control strategy, re-execute steps S3 to S4 until the iteration termination condition is reached.

2. The closed-loop control method for a CLCC converter according to claim 1, characterized in that, The operating status data of the current control cycle includes operating status parameters and the corresponding sliding window operating status sequence. The operating status parameters are multi-dimensional vectors including commutation voltage, operating current, turn-off angle, firing angle and equipment temperature. The corresponding sliding window operating status sequence is a time series dataset consisting of operating status parameters from N consecutive control cycles, with the current control cycle as the end of the window.

3. The closed-loop control method for a CLCC converter according to claim 2, characterized in that, The risk indicators include commutation risk indicators, current risk indicators, and temperature risk indicators; among which... Commutation risk indicators Represented as: ； in, ; In the formula, Indicates the warning cutoff angle. Indicates the minimum safe shut-off angle. This indicates the preset safety margin for the turn-off angle. Indicates the current shut-off angle; Current risk index Represented as: ； In the formula, Indicates the current operating current. Indicates the rated current of the equipment; Temperature risk indicators Represented as: ； In the formula, Indicates the current device temperature. This indicates the maximum allowable temperature for the equipment.

4. The closed-loop control method for a CLCC converter according to claim 3, characterized in that, The hierarchical processing involves dividing each constructed risk indicator into three levels: safe status, early warning status, and risk status. The candidate risks in the candidate dominant risk set are one or more risk indicators that are in a warning state or a risk state and have the highest risk level; among them, the risk level of the safe state, the warning state and the risk state increases in that order.

5. The closed-loop control method for a CLCC converter according to claim 4, characterized in that, The classification process is carried out in the following ways: For commutation risk indicators ,like If it is, then it is a safe state; if If it is a warning state; if If so, it is a risky state; For current risk indicators ,like If it is, then it is a safe state; if If it is a warning state; if If so, then it is a risky state; among which, , These are the first and second current risk thresholds, respectively. For temperature risk indicators ,like If so, it is a safe state; if If it is, then it is a warning state; if If so, then it is a risky state; among which, , These are the first and second temperature risk thresholds, respectively.

6. The closed-loop control method for a CLCC converter according to claim 4, characterized in that, The risk assessment value for each candidate risk is expressed as follows: ； In the formula, This indicates the risk type, with a value of 1 representing commutation risk, a value of 2 representing current risk, and a value of 3 representing temperature risk. Indicates risk type The risk assessment value; Indicates risk type Risk level value; Indicates risk type Risk bias; Indicates risk type Risk trends; Indicates risk type The preset risk priority coefficient; , , , These represent the weighting coefficients for the first, second, third, and fourth risks, respectively.

7. The closed-loop control method for a CLCC converter according to claim 6, characterized in that, The risk skewness of each risk type is obtained in the following way: ； In the formula, Indicates risk type Risk indicators Indicates risk type The safety boundary threshold, Indicates risk type The limit boundary threshold, This indicates taking the maximum value; Risk trends for each risk type are obtained as follows: ； In the formula, Indicates the current control cycle Risk type Risk indicators Indicates during the control cycle Risk type Risk indicators Indicates the duration of the control cycle.

8. The closed-loop control method for a CLCC converter according to claim 1, characterized in that, The control strategy uses control quantity It means that, among them, Indicates the firing angle adjustment amount. Indicates the adjustment amount of the current reference value. Indicates the amount of power command adjustment; The predicted operating state parameters are obtained by predicting the operating state after the current control strategy is applied using the following formula: ； In the formula, Indicates the first Predicted running state parameters after the control strategy of the next iteration takes effect Indicates the current control cycle The running status parameters, Indicates the current control cycle Operating parameters - control sensitivity matrix Indicates the first The control input for the next iteration; where the operating parameter-control input sensitivity matrix is... This includes the influence of firing angle adjustment on turn-off angle and current, the influence of current reference value adjustment on operating current and temperature, and the influence of power command adjustment on current, turn-off angle, and temperature.

9. The closed-loop control method for a CLCC converter according to claim 1, characterized in that, The multi-risk constraints include: Ultimately, the main risk was reduced to within the preset target range; Furthermore, risks other than the ultimate dominant risk meet the corresponding constraints, which include: the predicted turn-off angle is greater than or equal to the minimum safe turn-off angle; the predicted current is less than or equal to the rated current of the equipment; and the predicted temperature does not exceed the maximum allowable temperature of the equipment. Furthermore, risks other than the ultimate dominant risk must not exceed the preset allowable deterioration amount relative to the current control period.

10. A closed-loop control system for a CLCC converter, characterized in that, include: The data acquisition and preprocessing module is used to acquire multi-source operating data during the operation of the CLCC converter and preprocess it to obtain the operating status data of the current control cycle. The risk perception and judgment module is used to construct risk indicators based on the operating status data of the current control cycle and perform hierarchical processing to obtain a set of candidate dominant risks. If there is only one candidate risk in the candidate dominant risk set, then the candidate risk is taken as the final dominant risk; otherwise, the risk judgment value of each candidate risk is obtained, and then the final dominant risk is determined. The prediction and evaluation module is used to acquire the current control strategy, predict the operating state after its implementation, and obtain the predicted operating state parameters; wherein, the initial control strategy is generated based on the final dominant risk. The verification iteration module is used to perform multi-risk constraint verification on the current control strategy based on the predicted operating state parameters. If the multi-risk constraint conditions are met, the current control strategy is determined as a feasible control strategy and output to the CLCC converter control system for execution; otherwise, the current control strategy is modified and the prediction evaluation and verification iteration are re-executed until the iteration termination condition is reached. The system executes cyclically according to a preset control cycle. After outputting the control strategy in the previous control cycle, the next control cycle re-executes the functions of the above modules based on the updated operating data.

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