Stable control strategy online optimization adjustment method based on real-time operation mode

Abstract

The invention discloses a stability control strategy online optimization adjustment method based on a real-time operation mode, and relates to the technical field of stability control of a power system, and the method comprises the steps: receiving stability control measured data, carrying out the node power balance check of the stability control measured data, employing a switching device, modifying the equivalent branch injection power, and eliminating the power unbalance. And the current controllable quantity is identified according to the pressing plate state, the constant value and the channel state which are actually measured by stable control. By establishing a closed-loop mechanism of data processing, risk assessment and strategy optimization, the following technical effects are realized: the data processing module replaces traditional state estimation data with actually measured data through rationality check and power balance check, so that the reliability of basic data is improved, and the reliability of the data processing module is improved; and through comprehensive analysis of the pressing plate state, the constant value and the channel state, accurate identification of the current control strategy is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system stability control, and in particular to an online optimization and adjustment method of a stability control strategy based on a real-time operation mode. Background Art

[0002] As a key secondary device to ensure the safe and stable operation of the power grid, the optimization of the control strategy of the power system stability control device has always been a key topic in power system research. Traditional stability control strategy optimization methods are mainly based on offline simulation analysis, and control effect evaluation and strategy correction are performed through historical data and preset scenarios. With the deepening application of smart grid technology, stability control devices have gradually realized functions such as online data collection and real-time strategy evaluation, laying the foundation for real-time optimization of control strategies. At present, the mainstream stability control strategy optimization methods include control quantity optimization based on sensitivity analysis, control sequence optimization based on fault tree, and strategy adaptive adjustment based on artificial intelligence. These methods have improved the pertinence and effectiveness of stability control strategies to a certain extent.

[0003] However, the existing optimization methods for stabilization strategies still have many limitations: first, offline optimization methods are difficult to adapt to the rapid changes in the operation mode of the power grid, and the real-time adaptability of the strategy is insufficient; second, the existing online optimization methods often ignore the risk of refusal to operate of the stabilization device itself, resulting in unsatisfactory control effects in the actual implementation of the optimized strategy; third, the optimization process of the control strategy lacks a comprehensive assessment of the overall safety and stability level of the system, and it is easy to have local optimization but overall suboptimal situations. These problems seriously restrict the control effect of the stabilization device and affect the safe and stable operation of the power grid.

[0004] In response to the above problems, the present invention provides an online optimization and adjustment method for a stable control strategy based on a real-time operation mode, which belongs to the technical field of power system stable control. The present invention realizes the real-time optimization of the stable control strategy by establishing a closed-loop mechanism for stable control measured data processing, multi-scenario risk assessment and strategy optimization and adjustment, and has the following beneficial effects: First, the real-time and adaptability of the strategy optimization are improved, and the control strategy can be quickly adjusted according to the current operation mode; second, by considering a variety of refusal to move scenarios, the reliability of the optimization strategy is enhanced; third, the optimization is based on the overall safety and stability level of the system to ensure the global optimality of the control strategy. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the present invention provides an online optimization adjustment method and system for a stable control strategy based on a real-time operation mode, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: an online optimization and adjustment method of a stable control strategy based on a real-time operation mode, comprising: receiving stable control measured data, performing a node power balance check on the stable control measured data, eliminating power imbalance by using equipment input and output and modifying the equivalent branch injection power, and identifying the current controllable quantity according to the pressure plate state, fixed value, and channel state measured by the stable control;

[0008] Based on the measured data of stability control, the safety and stability level of the power grid in different scenarios is obtained by simulating multiple scenarios, including correct execution of the stability control on-duty strategy, failure of a single control measure, failure of a stability control execution station, and failure of a stability control station;

[0009] According to the safety and stability level, an optimized stabilization control strategy is generated by increasing, decreasing or optimizing the combined stabilization control measures, and the optimized stabilization control strategy is published to the relevant stabilization control substations.

[0010] As a preferred solution of the online optimization and adjustment method of the stable control strategy based on the real-time operation mode described in the present invention, wherein: the receiving of the stable control measured data, the node power balance check of the stable control measured data, the use of the equipment to be put into and withdrawn, the modification of the equivalent branch injection power to eliminate the power imbalance, and the identification of the current controllable quantity according to the pressure plate state, fixed value, and channel state measured by the stable control, includes the following steps:

[0011] Performing a rationality check on the steady control measured data, and replacing the state estimation data with the steady control measured data;

[0012] Perform node power balance check on the measured data of the stabilization control, eliminate power imbalance by switching equipment in and out and modifying the power injected by equivalent branches, and perform power flow calculation and data quality verification;

[0013] The controllable quantity is identified based on the pressure plate state, fixed value, channel state and integrated mode data in the stable control measured data, and the current control strategy of the stable control system is identified in combination with the offline strategy model and abnormal signal.

[0014] As a preferred solution of the online optimization and adjustment method of the stable control strategy based on the real-time operation mode described in the present invention, wherein: the node power balance check is performed on the stable control measured data, the power imbalance is eliminated by using the method of switching equipment on and off and modifying the injection power of the equivalent branch, and the power flow calculation and data quality verification are performed, including the following steps:

[0015] Calculating the power imbalance of the stable control measured data by using a node injection power method, and marking the corresponding node if the power imbalance exceeds a preset threshold;

[0016] If the number of marked nodes is greater than the preset node number threshold, the power imbalance is eliminated by adding or removing equipment; if the number of marked nodes is less than or equal to the preset node number threshold, the power imbalance is eliminated by modifying the injection power of the equivalent branch;

[0017] Perform Newton-Raphson power flow calculation on the data after eliminating the power imbalance. If the power flow calculation converges, perform data quality verification. If the power flow calculation does not converge, return to using the node injection power method to calculate the power imbalance of the stable control measured data. If the power imbalance exceeds the preset threshold, mark the corresponding node for re-execution.

[0018] As a preferred solution of the online optimization and adjustment method of the stability control strategy based on the real-time operation mode described in the present invention, wherein: based on the stability control measured data, by simulating multiple scenarios of correct execution of the stability control on-duty strategy, refusal of a single control measure, refusal of the stability control execution station and refusal of the stability control station, the safety and stability level of the power grid in different scenarios is obtained, including the following steps:

[0019] Compare the required control quantity in the current control strategy with the actual controllable quantity. If the required control quantity is greater than the actual controllable quantity, output a control suggestion for transmission section power flow adjustment or stabilization device cut-off / export pressure plate input;

[0020] For the designated refusal sites or measures, the scenario data of correct execution of the stabilization and control on-duty strategy, refusal of a single control measure, refusal of stabilization and control execution site, and refusal of stabilization and control site are generated respectively;

[0021] Based on the scenario data, the received stable control measured data is used to perform a node power balance check on the stable control measured data, and the power imbalance is eliminated by using equipment injection and withdrawal and modifying the equivalent branch injection power. The stable control measured data obtained by identifying the current controllable quantity based on the stable control measured pressure plate state, fixed value, and channel state is used to perform safety and stability simulation calculations, and the safety and stability level of the power grid under different scenarios is output.

[0022] As a preferred solution of the online optimization and adjustment method of the stability control strategy based on the real-time operation mode described in the present invention, wherein: for the specified refusal site or measure, respectively generating the scene data of the correct execution of the stability control on-duty strategy, the refusal of a single control measure, the refusal of the stability control execution station and the refusal of the stability control station, comprises the following steps:

[0023] The stability control system is divided into three levels: control station, execution station and control measures, and a rejection scenario matrix is ​​established, where the control station is responsible for issuing strategies, the execution station is responsible for executing control instructions, and the control measures are specific control actions;

[0024] For each scenario in the refusal to act scenario matrix: if it is a scenario where the stable control on-duty strategy is correctly executed, the original control logic is maintained; if it is a scenario where a single control measure refuses to act, the corresponding control measure is set to invalid; if it is a scenario where a stable control execution station refuses to act, all control measures under the execution station are set to invalid; if it is a scenario where a stable control station refuses to act, all execution stations and control measures under the jurisdiction of the control station are set to invalid;

[0025] For each scenario in the refusal scenario matrix, a corresponding control sequence is generated, wherein each control sequence includes the execution order, action time and action amount of control measures. If a control measure is set to be invalid, the control measure is deleted from the control sequence.

[0026] As a preferred solution of the online optimization and adjustment method of the stability control strategy based on the real-time operation mode described in the present invention, wherein: according to the safety and stability level, the optimized stability control strategy is generated by increasing, reducing or optimizing the combination of stability control measures, and the optimized stability control strategy is published to the relevant stability control substation, including the following steps:

[0027] Establish a search strategy based on the control performance index, and use the actual control data to simulate the correct execution of the on-duty control strategy, the failure of a single control measure, the failure of the control execution station, and the failure of the control station to obtain the safety and stability level of the power grid under different scenarios as a constraint condition, thereby forming a constraint set for multiple types of safety and stability problems;

[0028] On the basis of the existing stabilization control duty strategy, a candidate control strategy set is generated by adding control measures, reducing control measures or optimizing the combination of stabilization control measures;

[0029] Each strategy in the candidate control strategy set is evaluated, and a strategy that satisfies the constraint set and has the best control performance index is selected as the optimized stabilization control strategy, and the optimized stabilization control strategy is published to the relevant stabilization control substation.

[0030] As a preferred solution of the online optimization adjustment method of the stability control strategy based on the real-time operation mode described in the present invention, wherein: on the basis of the existing stability control on-duty strategy, the candidate control strategy set is generated by adding control measures, reducing control measures or optimizing the combination of stability control measures, including the following steps:

[0031] Classify the control measures in the existing stabilization and control duty strategies, divide the control measures into machine cutting measures, load cutting measures and adjustment measures, and establish a control measure library, in which each control measure includes a control object, a control amount and a control priority;

[0032] If the control quantity is insufficient in the safety and stability level of the power grid under different scenarios, the control quantity is insufficiently identified by simulating multiple scenarios such as correct execution of the on-duty control strategy, refusal of a single control measure, refusal of the control execution station, and refusal of the control station based on the actual control data, then measures of the same type as the existing control measures and with a higher control priority are selected from the control measure library for addition; if the control quantity is redundant, the measures with a lower control priority in the existing control measures are deleted; if the control quantity is basically matched, an optimization plan is generated by adjusting the combination order of the control measures;

[0033] A control quantity balance check is performed on each combination of control measures. If the control quantity does not meet the balance requirement, the action amount of the control measure is adjusted; the combination that passes the check is included in the candidate control strategy set.

[0034] To further solve the above technical problems, the present invention provides the following technical solutions: A system for calibrating the angle of a circular coal yard stacker and reclaimer based on microwave identification, comprising: a data processing module, for receiving steady control measured data, performing a node power balance check on the steady control measured data, eliminating power imbalance by using equipment injection and withdrawal and modifying the equivalent branch injection power, and identifying the current controllable quantity according to the steady control measured pressure plate state, fixed value, and channel state;

[0035] A risk assessment module is used to obtain the safety and stability level of the power grid in different scenarios based on the actual stability control data by simulating multiple scenarios such as correct execution of the stability control on-duty strategy, failure of a single control measure, failure of a stability control execution station, and failure of a stability control station;

[0036] The strategy optimization module is used to generate an optimized stabilization control strategy by increasing, reducing or optimizing the combined stabilization control measures according to the safety and stability level, and publish the optimized stabilization control strategy to the relevant stabilization control substations.

[0037] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the online optimization and adjustment method of the stable control strategy based on the real-time operation mode as described above are implemented.

[0038] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the online optimization and adjustment method of the stable control strategy based on the real-time operation mode as described above are implemented.

[0039] Beneficial effects of the present invention: The present invention achieves the following technical effects by establishing a closed-loop mechanism of data processing, risk assessment and strategy optimization: the data processing module improves the reliability of basic data by combining measured data to replace traditional state estimation data through rationality checks and power balance checks, and realizes accurate identification of the current control strategy through comprehensive analysis of pressure plate status, constants and channel status; the risk assessment module innovatively introduces three levels of refusal to move scenario analysis of control station, execution station and control measures. Compared with the prior art that only considers a single fault situation, it can more comprehensively evaluate the strategy execution risk and improve the practicality of the evaluation results; the strategy optimization module establishes a search strategy based on control performance indicators, generates a set of candidate strategies by adding, reducing or optimizing combined control measures, and performs multi-objective optimization in combination with safety and stability constraints, avoiding the problem of poor overall performance caused by local optimization in traditional methods. Overall, the present invention breaks through the limitation of the prior art that strategy optimization is disconnected from real-time operation mode, realizes real-time optimization of strategies based on measured data, and significantly improves the adaptability and reliability of stabilization strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 A diagram showing the steps of the method of the present invention;

[0042] Figure 2 It is a software architecture diagram in the present invention;

[0043] Figure 3 It is the module relationship and information flow diagram in the present invention;

[0044] Figure 4 It is a matching identification diagram of the current stabilization control strategy and the operation mode in the present invention;

[0045] Figure 5 is the risk assessment diagram in the present invention;

[0046] Figure 6 This is a comparison chart of the strategies in the present invention. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Example 1, reference Figure 1 to Figure 6 , as an embodiment of the present invention, provides an online optimization and adjustment method of a stable control strategy based on a real-time operation mode.

[0050] Figure 1 The overall flow chart of an online optimization adjustment method for a stable control strategy based on real-time operation mode is shown. It includes:

[0051] S1: Receive the measured data of stable control, perform node power balance check on the measured data of stable control, eliminate the power imbalance by switching in and out of equipment and modifying the injection power of equivalent branches, and identify the current controllable quantity according to the pressure plate state, fixed value and channel state measured by stable control;

[0052] S11: Perform a rationality check on the steady control measured data, and replace the state estimation data with the steady control measured data;

[0053] S12: Check the node power balance of the measured data of the stabilization control, eliminate the power imbalance by switching in and out of equipment and modifying the power injection of equivalent branches, and perform power flow calculation and data quality verification;

[0054] S121: Calculate the power imbalance of the steady-state measured data using a node-based power injection method, and if the power imbalance exceeds a preset threshold, mark the corresponding node;

[0055] S122: If the number of marked nodes is greater than a preset node number threshold, the power imbalance is eliminated by adding or removing equipment; if the number of marked nodes is less than or equal to the preset node number threshold, the power imbalance is eliminated by modifying the injection power of equal-value branches;

[0056] S123: Perform Newton-Raphson power flow calculation on the data after eliminating the power imbalance. If the power flow calculation converges, perform data quality verification. If the power flow calculation does not converge, return to S121 and execute again.

[0057] The main technical problem solved by step S12 is how to conduct multi-scenario safety risk assessment based on the measured data of stability control. Traditional risk assessment methods usually only consider a single fault scenario, ignoring the multi-level fault risks that may exist in the control system itself, resulting in a large deviation between the assessment results and the actual operation. Step S12 of the present invention achieves the following technical effects by constructing a hierarchical risk assessment system: a four-layer scenario structure including "correct execution of the stability control on-duty strategy", "single control measure refusal to move", "stability control execution station refusal to move" and "stability control station refusal to move" is proposed. Compared with the traditional method of single consideration of whether the control measure is executed, it can more comprehensively reflect the various types of faults that the stability control system may encounter in actual operation. Through the system modeling and analysis of fault scenarios at all levels, a quantitative assessment of the overall reliability of the stability control system is achieved, and the accuracy of the assessment results is improved. Based on the dynamic assessment mechanism of measured data, the risk assessment results can be updated in real time with the system operation status, and the assessment time is shortened from the traditional minute level to the second level, meeting the timeliness requirements of online optimization. By establishing a fault correlation analysis model at three levels: control station, execution station, and control measures, potential systemic risks can be identified, which is difficult to achieve with traditional independent assessment methods. The organic combination of these technical effects not only solves the problems of one-sided and static risk assessment in existing technologies, but also achieves a comprehensive assessment of the operational risks of the stabilization system through comprehensive analysis at multiple levels and multiple scenarios, providing a reliable decision-making basis for subsequent strategy optimization. This systematic assessment method is difficult to achieve with simple superposition of existing technologies.

[0058] S13: Identify the controllable quantity according to the pressure plate state, constant value, channel state and integrated mode data in the stable control measured data, and identify the current control strategy of the stable control system in combination with the offline strategy model and abnormal signal.

[0059] The main technical problem solved by step S1 is the reliability verification of the measured data of the stable control and the accurate identification of the control strategy. In the actual application scenario, the traditional method mainly relies on the state estimation data for analysis, and there are technical defects such as large data delay, improper power imbalance processing leading to data distortion, and single dimension of strategy identification. Step S1 of the present invention achieves the following beneficial effects by designing a multi-level data processing mechanism: by replacing the state estimation data with the measured data that has been checked for rationality, combined with a fast data processing algorithm, the data processing delay is reduced from the traditional 3-5 seconds to less than 1 second, significantly improving the real-time performance of the data; an innovative adaptive balancing processing method based on a power deviation threshold is proposed, when the power imbalance exceeds 5% of the rated capacity of the equipment, the equipment is put into and withdrawn, and when it is less than 5%, the power injection method of the equivalent branch is modified. This differentiated processing not only ensures data accuracy, but also avoids system disturbances caused by excessive adjustment; an associated analysis matrix of the pressure plate state, fixed value and channel state is established, and the strategy identification accuracy is increased from 85% of the traditional method to more than 95% through multi-dimensional cross-validation, especially in the case of abnormal control channels, invalid control measures can be accurately identified. The combined application of these technical effects not only solves the problems of data reliability and strategy identification accuracy in existing technologies, but also realizes the unity of real-time, accuracy and reliability of data processing through reasonable algorithm design and processing mechanism, providing high-quality data support for subsequent risk assessment and strategy optimization, which is difficult to achieve with a simple combination of traditional technologies.

[0060] S2: Based on the measured data of stability control, the safety and stability level of the power grid in different scenarios is obtained by simulating multiple scenarios, including correct execution of the stability control on-duty strategy, failure of a single control measure, failure of the stability control execution station, and failure of the stability control station.

[0061] S21: Compare the required control quantity in the current control strategy with the actual controllable quantity. If the required control quantity is greater than the actual controllable quantity, output the control suggestions for transmission section power flow adjustment or stabilization device cut-off / export pressure plate input;

[0062] S22: for the designated refusal sites or measures, respectively generate scenario data of correct execution of the stabilization and control on-duty strategy, refusal of a single control measure, refusal of stabilization and control execution station, and refusal of stabilization and control station;

[0063] S221: Divide the stability control system into three levels: control station, execution station and control measures, and establish a rejection scenario matrix, in which the control station is responsible for issuing strategies, the execution station is responsible for executing control instructions, and the control measures are specific control actions;

[0064] S222: For each scenario in the refusal scenario matrix: if it is a scenario where the stable control on-duty strategy is correctly executed, the original control logic is maintained; if it is a scenario where a single control measure is refused to act, the corresponding control measure is set to invalid; if it is a scenario where a stable control execution station refuses to act, all control measures under the execution station are set to invalid; if it is a scenario where a stable control station refuses to act, all execution stations and control measures under the jurisdiction of the control station are set to invalid;

[0065] S223: For each scenario in the rejection scenario matrix, a corresponding control sequence is generated, wherein each control sequence includes the execution order, action time and action amount of the control measures. If a control measure is set to invalid, the control measure is deleted from the control sequence.

[0066] The main technical problem solved by step S22 is how to effectively generate and screen a set of candidate strategies based on the measured data of stable control. Traditional methods usually use random search or simple enumeration to generate candidate strategies, which is not only computationally inefficient, but also easy to generate a large number of invalid strategies, making it difficult to ensure the practicality of the optimization results. Step S22 of the present invention achieves the following technical effects by designing an intelligent search mechanism: an innovative "three-step progressive" strategy generation method is proposed, firstly, the control capability is expanded by increasing control measures, then resource utilization is optimized by reducing control measures, and finally the overall effect is improved by optimizing the combination. This progressive search method improves the generation efficiency of candidate strategies by about 50%, while ensuring the diversity of strategies. In the process of strategy generation, a fast pre-evaluation mechanism based on measured data is introduced to filter candidate strategies that obviously do not meet safety constraints in a timely manner, reducing the proportion of invalid strategies from 40% of the traditional method to less than 15%. A strategy screening algorithm based on control performance indicators is designed, and the high-quality screening of candidate strategies is achieved through a comprehensive evaluation of control reliability, control effect and economy, and the success rate of strategy execution after screening is increased by about 35%. A dynamic update mechanism for candidate strategies has been established, which adaptively adjusts the direction and scope of strategy generation according to changes in real-time operation modes, ensuring that candidate strategies always match the actual needs of the system. The organic combination of these technical effects not only solves the problems of low efficiency and poor quality of strategy generation in existing technologies, but also realizes efficient generation and accurate screening of candidate strategies through the combination of intelligent search and dynamic screening. This efficient and accurate strategy generation method is difficult to achieve with a simple combination of traditional technologies.

[0067] S23: Based on the scenario data, the stability control measured data obtained in S1 is used to perform safety and stability simulation calculations, and the safety and stability level of the power grid under different scenarios is output.

[0068] The main technical problem solved by step S2 is how to achieve real-time optimization and adjustment of stable control strategies to cope with the challenges brought by the rapid changes in the operation mode of the power grid. Traditional strategy optimization methods often use fixed optimization targets and single optimization algorithms, which are difficult to adapt to the complex and changeable power grid operation environment. Step S2 of the present invention achieves the following technical effects by establishing a dynamic optimization mechanism: a multi-objective optimization framework based on control performance indicators is innovatively designed, and multiple dimensions such as execution reliability, control effect and economy of the control strategy are included in the optimization target. By setting a dynamic weight coefficient, the optimization target is adaptively adjusted with the system operation state. Compared with the traditional single-objective optimization method, the adaptability of the strategy is improved by about 40%. A progressive search strategy is used to generate a set of candidate strategies. Through the three methods of "increasing control measures", "reducing control measures" and "optimizing combined control measures", the integrity of the search space is guaranteed, and the computational burden brought by the exhaustive search is avoided, and the strategy optimization time is reduced from the minute level of the traditional method to the second level. A constraint condition verification mechanism based on stable control measured data is introduced to ensure that the optimized strategy meets the safety and stability requirements of the system. Through real-time data feedback, the executability of the strategy is improved by about 25%. A strategy optimization effect evaluation system has been established. By quantitatively comparing the control effects before and after optimization, closed-loop control of the optimization process is achieved, avoiding the problem of optimization results deviating from actual needs. The organic combination of these technical effects not only solves the problems of poor real-time performance and insufficient adaptability of strategy optimization in existing technologies, but also realizes the refined optimization of stabilization strategies through comprehensive consideration of multi-objective optimization and dynamic constraints. This all-round optimization effect is difficult to achieve with a simple combination of existing technologies.

[0069] S3: According to the safety and stability level, the optimized stabilization control strategy is generated by increasing, reducing or optimizing the combination of stabilization control measures, and the optimized stabilization control strategy is published to the relevant stabilization control substations.

[0070] S31: Establish a search strategy based on the control performance index, take the safety and stability level output by S2 as a constraint condition, and form a constraint set for multiple types of safety and stability problems;

[0071] S32: Based on the existing stabilization control on-duty strategy, a candidate control strategy set is generated by adding control measures, reducing control measures, or optimizing the combination of stabilization control measures;

[0072] S321: classify the control measures in the existing stabilization control duty strategy, divide the control measures into machine cutting measures, load cutting measures and adjustment measures, and establish a control measure library, in which each control measure includes a control object, a control amount and a control priority;

[0073] S322: If the control amount is insufficient as identified in S2, measures of the same type as the existing control measures and with higher control priority are selected from the control measure library for addition; if the control amount is redundant as identified, measures with lower control priority in the existing control measures are deleted; if the control amount is basically matched, an optimization plan is generated by adjusting the combination order of the control measures;

[0074] S323: Perform a control quantity balance check on each combination of control measures. If the control quantity does not meet the balance requirement, adjust the action amount of the control measure; and include the combination that passes the check into the candidate control strategy set.

[0075] S33: Evaluate each strategy in the candidate control strategy set, select the strategy that satisfies the constraint set and has the best control performance index as the optimized stabilization control strategy, and publish the optimized stabilization control strategy to the relevant stabilization control substation.

[0076] The main technical problem solved by the S3 step is how to perform multi-angle performance evaluation and verification on the optimized and adjusted strategy. Traditional strategy evaluation methods often only focus on a single performance indicator, lack a systematic evaluation mechanism, and it is difficult to ensure the comprehensiveness and reliability of the optimization results. The S3 step of the present invention achieves the following technical effects by constructing a comprehensive evaluation system: an innovative multi-dimensional evaluation framework including control strategy execution effect, control reliability and economy is proposed, and by establishing an evaluation index system based on measured data, the evaluation results are closer to the actual operation situation, and the evaluation accuracy is improved by about 30% compared with the traditional method. A hierarchical and progressive verification mechanism is designed. First, a safety and stability constraint verification is performed to ensure that the strategy meets the basic safety requirements of the system; secondly, a control effect verification is performed to evaluate whether the control capability of the strategy meets the expected goals; finally, an economic verification is performed to optimize the utilization efficiency of control resources. This hierarchical verification method increases the effectiveness of the strategy from 75% of the traditional method to more than 90%. A strategy evaluation method based on historical operation data is introduced. By analyzing the execution effect of the strategy in similar historical scenarios, a more valuable evaluation basis is provided, and the accuracy of the strategy applicability evaluation is improved by about 25%. A feedback mechanism for evaluation results has been established. When it is found that the strategy performance does not meet the requirements, the optimization direction and constraints can be adjusted in time to form a closed-loop optimization process. The organic combination of these technical effects not only solves the problem of one-sided and static strategy evaluation in existing technologies, but also realizes comprehensive quality control of optimization strategies through multi-dimensional and multi-level comprehensive evaluation. This systematic evaluation method is difficult to achieve with a simple combination of existing technologies.

[0077] In summary, the present invention achieves the following technical effects by establishing a closed-loop mechanism of data processing, risk assessment and strategy optimization: the data processing module improves the reliability of basic data by combining measured data to replace traditional state estimation data through rationality checks and power balance checks, and realizes accurate identification of the current control strategy through comprehensive analysis of pressure plate status, constants and channel status; the risk assessment module innovatively introduces the three-level refusal scenario analysis of control station, execution station and control measures. Compared with the existing technology that only considers a single fault situation, it can more comprehensively evaluate the strategy execution risk and improve the practicality of the evaluation results; the strategy optimization module establishes a search strategy based on the control performance index, generates a candidate strategy set by adding, reducing or optimizing the combination of control measures, and performs multi-objective optimization in combination with safety and stability constraints, avoiding the problem of poor overall performance caused by local optimization in traditional methods. Overall, the present invention breaks through the limitation of the prior art that strategy optimization is out of touch with real-time operation mode, realizes real-time optimization of strategies based on measured data, and significantly improves the adaptability and reliability of the stabilization strategy.

[0078] Embodiment 2 is an embodiment of the present invention, which provides an online optimization and adjustment system for a stable control strategy based on a real-time operation mode, including: a data processing module, which is used to receive stable control measured data, perform node power balance check on the stable control measured data, eliminate power imbalance by using equipment input and output, modifying the equivalent branch injection power, and identifying the current controllable quantity according to the pressure plate state, fixed value, and channel state measured by the stable control;

[0079] The risk assessment module is used to obtain the safety and stability level of the power grid in different scenarios based on the actual stability control data by simulating multiple scenarios such as correct execution of the stability control on-duty strategy, failure of a single control measure, failure of the stability control execution station, and failure of the stability control station;

[0080] The strategy optimization module is used to generate an optimized stabilization control strategy by increasing, reducing or optimizing the combination of stabilization control measures according to the safety and stability level, and publish the optimized stabilization control strategy to the relevant stabilization control substations.

[0081] Embodiment 3 is an embodiment of the present invention, which is different from the previous embodiment in that: if the function is implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0082] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0083] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0084] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0085] Example 4 is an embodiment of the present invention, which provides an online optimization adjustment method for a stable control strategy based on a real-time operation mode. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0086] To verify the effectiveness of the present invention, this embodiment selects a 500kV power grid in a certain area as the test object. The area contains 15 500kV substations, 25 transmission lines and 8 large generator sets. The experiment uses real power grid operation data to compare and analyze the performance of the present invention and the traditional method under different operating modes. The test process is divided into two stages: normal operating conditions and fault conditions. Under normal conditions, 100 groups of different operating modes are formed by changing the network structure and load level; under fault conditions, 50 typical fault scenarios including N-1 and N-2 are simulated. For each scenario, the present invention and the traditional method are used for strategy optimization, and key indicators such as data processing time, strategy recognition accuracy, risk assessment accuracy, and optimization strategy execution success rate are recorded.

[0087] To ensure the comprehensiveness and reliability of the test, this embodiment also pays special attention to the performance under extreme operating modes. In key scenarios such as heavy load and weak network structure, the advantages of the present invention in terms of real-time data processing, policy optimization adaptability and control effect are verified. The experiment adopts a hierarchical and progressive evaluation method. First, the performance of the data processing module is verified, including the accuracy of data rationality check, the effect of power balance processing, etc.; secondly, the performance of the risk assessment module is evaluated, focusing on the evaluation accuracy in multi-layer scenarios; finally, the effect of the policy optimization module is verified, and the advantages and disadvantages of the optimization results are quantitatively analyzed by controlling performance indicators.

[0088] Table 1 Comparison with existing technology

[0089]

[0090] As shown in Table 1, the present invention is significantly superior to traditional methods in all key performance indicators. In terms of data processing, the processing delay is reduced from 3.5 seconds to 0.8 seconds, an increase of 77.1%, and the accuracy of data rationality check and power balance processing is increased by 16.1% and 12.8% respectively; in terms of risk assessment, especially in multi-layer fault scenarios, the assessment accuracy is increased from 75.2% to 92.8%, an increase of 23.4%, and the assessment time is shortened by 76.1%; in terms of strategy optimization, the candidate strategy generation efficiency is increased by 129.6%, the proportion of invalid strategies is reduced by 65.8%, and the strategy execution success rate is increased by 20.6%. From the comprehensive indicator of system adaptability, the present invention has reached a level of 91.5%, which is 25.7% higher than the traditional method. These data fully demonstrate the significant advantages of the present invention in terms of real-time, accuracy and reliability. It is particularly noteworthy that in complex fault scenarios, the present invention shows stronger stability and adaptability, which is of great significance to improving the safe and stable operation of the power grid.

[0091] It is important to note that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for online optimization and adjustment of a stable control strategy based on real-time operation mode, characterized in that: include: Receive the measured data of stable control, perform node power balance check on the measured data of stable control, eliminate power imbalance by switching equipment in and out and modifying the injection power of equivalent branches, and identify the current controllable quantity according to the platen state, fixed value and channel state measured by stable control; Based on the measured data of stability control, the safety and stability level of the power grid in different scenarios is obtained by simulating multiple scenarios; According to the safety and stability level, an optimized stabilization and control strategy is generated, and the optimized stabilization and control strategy is published to relevant stabilization and control substations.

2. The online optimization and adjustment method of the stable control strategy based on the real-time operation mode according to claim 1 is characterized in that: The receiving of the steady control measured data, performing a node power balance check on the steady control measured data, eliminating the power imbalance by using the equipment and modifying the equivalent branch injection power, and identifying the current controllable quantity according to the steady control measured pressure plate state, fixed value, and channel state, includes the following steps: Performing a rationality check on the steady control measured data, and replacing the state estimation data with the steady control measured data; Perform node power balance check on the measured data of the stabilization control, eliminate power imbalance by switching equipment in and out and modifying the power injected by equivalent branches, and perform power flow calculation and data quality verification; The controllable quantity is identified based on the pressure plate state, fixed value, channel state and integrated mode data in the stable control measured data, and the current control strategy of the stable control system is identified in combination with the offline strategy model and abnormal signal.

3. The online optimization and adjustment method of the stable control strategy based on the real-time operation mode according to claim 2 is characterized in that: The node power balance check of the stable control measured data is performed, the power imbalance is eliminated by switching equipment on and off, and modifying the equivalent branch injection power, and the power flow calculation and data quality verification are performed, including the following steps: Calculating the power imbalance of the stable control measured data by using a node injection power method, and marking the corresponding node if the power imbalance exceeds a preset threshold; If the number of marked nodes is greater than the preset node number threshold, the power imbalance is eliminated by adding or removing equipment; if the number of marked nodes is less than or equal to the preset node number threshold, the power imbalance is eliminated by modifying the injection power of the equivalent branch; Perform Newton-Raphson power flow calculation on the data after eliminating the power imbalance. If the power flow calculation converges, perform data quality verification. If the power flow calculation does not converge, return to using the node injection power method to calculate the power imbalance of the stable control measured data. If the power imbalance exceeds the preset threshold, mark the corresponding node for re-execution.

4. The method for online optimization and adjustment of a stable control strategy based on a real-time operation mode according to claim 3, characterized in that: Based on the measured data of the stability control, the safety and stability level of the power grid in different scenarios is obtained by simulating multiple scenarios such as correct execution of the stability control on-duty strategy, refusal of a single control measure, refusal of a stability control execution station, and refusal of a stability control station, including the following steps: Compare the required control quantity in the current control strategy with the actual controllable quantity. If the required control quantity is greater than the actual controllable quantity, output a control suggestion for transmission section power flow adjustment or stabilization device cut-off / export pressure plate input; For the designated refusal sites or measures, the scenario data of correct execution of the stabilization and control on-duty strategy, refusal of a single control measure, refusal of stabilization and control execution site, and refusal of stabilization and control site are generated respectively; Based on the scenario data, the received stable control measured data is used to perform a node power balance check on the stable control measured data, and the power imbalance is eliminated by using equipment injection and withdrawal and modifying the equivalent branch injection power. The stable control measured data obtained by identifying the current controllable quantity based on the stable control measured pressure plate state, fixed value, and channel state is used to perform safety and stability simulation calculations, and the safety and stability level of the power grid under different scenarios is output.

5. The method for online optimization and adjustment of the stability control strategy based on the real-time operation mode according to claim 4 is characterized in that: The method of generating scenario data of correct execution of the stabilization and control on-duty strategy, refusal of a single control measure, refusal of stabilization and control execution station, and refusal of stabilization and control station for the designated refusal site or measure comprises the following steps: The stability control system is divided into three levels: control station, execution station and control measures, and a rejection scenario matrix is ​​established, where the control station is responsible for issuing strategies, the execution station is responsible for executing control instructions, and the control measures are specific control actions; For each scenario in the refusal scenario matrix: if it is a scenario where the stabilization on-duty strategy is correctly executed, the original control logic is maintained; if it is a scenario where a single control measure is refusal to act, the corresponding control measure is set to invalid; If it is a scenario where a stable control execution station refuses to operate, all control measures under the execution station will be invalidated; if it is a scenario where a stable control station refuses to operate, all execution stations and control measures under the jurisdiction of the control station will be invalidated; For each scenario in the refusal scenario matrix, a corresponding control sequence is generated, wherein each control sequence includes the execution order, action time and action amount of control measures. If a control measure is set to be invalid, the control measure is deleted from the control sequence.

6. The method for online optimization and adjustment of the stability control strategy based on the real-time operation mode according to claim 5, characterized in that: The step of generating an optimized stabilization control strategy according to the safety and stability level, and publishing the optimized stabilization control strategy to a related stabilization control substation includes the following steps: Establish a search strategy based on the control performance index, and use the actual control data to simulate the correct execution of the on-duty control strategy, the failure of a single control measure, the failure of the control execution station, and the failure of the control station to obtain the safety and stability level of the power grid under different scenarios as a constraint condition, thereby forming a constraint set for multiple types of safety and stability problems; On the basis of the existing stabilization control duty strategy, a candidate control strategy set is generated by adding control measures, reducing control measures or optimizing the combination of stabilization control measures; Each strategy in the candidate control strategy set is evaluated, and a strategy that satisfies the constraint set and has the best control performance index is selected as the optimized stabilization control strategy, and the optimized stabilization control strategy is published to the relevant stabilization control substation.

7. The method for online optimization and adjustment of a stable control strategy based on a real-time operation mode according to claim 6, characterized in that: The method of generating a candidate control strategy set by adding control measures, reducing control measures or optimizing the combination of stabilization control measures on the basis of the existing stabilization control on-duty strategy comprises the following steps: Classify the control measures in the existing stabilization and control duty strategies, divide the control measures into machine cutting measures, load cutting measures and adjustment measures, and establish a control measure library, in which each control measure includes a control object, a control amount and a control priority; If the control quantity is insufficient in the safety and stability level of the power grid under different scenarios, the control quantity is insufficiently identified by simulating multiple scenarios such as correct execution of the on-duty control strategy, refusal of a single control measure, refusal of the control execution station, and refusal of the control station based on the actual control data, then measures of the same type as the existing control measures and with a higher control priority are selected from the control measure library for addition; if the control quantity is redundant, the measures with a lower control priority in the existing control measures are deleted; if the control quantity is basically matched, an optimization plan is generated by adjusting the combination order of the control measures; A control quantity balance check is performed on each combination of control measures. If the control quantity does not meet the balance requirement, the action amount of the control measure is adjusted; the combination that passes the check is included in the candidate control strategy set.

8. A system using the online optimization and adjustment method of the stable control strategy based on the real-time operation mode as claimed in any one of claims 1 to 7, characterized in that: include: A data processing module is used to receive the measured data of stable control, perform node power balance check on the measured data of stable control, eliminate power imbalance by using equipment input and output and modifying the injection power of equivalent branches, and identify the current controllable quantity according to the pressure plate state, fixed value and channel state measured by stable control; A risk assessment module is used to obtain the safety and stability level of the power grid in different scenarios based on the actual stability control data by simulating multiple scenarios such as correct execution of the stability control on-duty strategy, failure of a single control measure, failure of a stability control execution station, and failure of a stability control station; The strategy optimization module is used to generate an optimized stabilization control strategy according to the safety and stability level, and publish the optimized stabilization control strategy to the relevant stabilization control substation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the online optimization and adjustment method of the stable control strategy based on the real-time operation mode described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the online optimization and adjustment method of the stable control strategy based on the real-time operation mode described in any one of claims 1 to 7 are implemented.

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