A method for analyzing and setting stability of primary frequency modulation parameters of a thermal power unit

Abstract

The application discloses a primary frequency modulation parameter stability analysis setting method for a thermal power generating unit, and mainly relates to the technical field of frequency modulation of the thermal power generating unit. The method comprises the following steps: collecting a historical abnormal primary frequency modulation log set of a historical window from a DCS system of a power plant; obtaining a frequency modulation performance index safety boundary set and a unit stability safety boundary set; outputting a frequency modulation performance index prediction result and a unit stability index prediction result; obtaining an optimization constraint condition, performing double-target optimization on real-time primary frequency modulation parameters, determining optimal frequency modulation parameters, and constructing a load-parameter setting mapping result. The application has the beneficial effect of solving the technical problem that the prior art lacks adaptive adjustment of the primary frequency modulation parameters, has low compatibility with the unit working condition, ignores the coupling influence of multi-dimensional indexes, and influences the unit operation, and achieves the technical effect of improving the setting reliability of the primary frequency modulation parameters.

Description

Technical Field

[0001] This invention relates to the field of frequency regulation technology for thermal power units, specifically to a method for stability analysis and tuning of primary frequency regulation parameters of thermal power units. Background Technology

[0002] With the deepening implementation of the "dual-carbon" strategy, the proportion of intermittent and highly volatile renewable energy sources such as wind power and photovoltaics in the power grid has been increasing year by year, posing unprecedented challenges to the frequency stability of the power grid. As the "ballast" for power grid frequency stability, the primary frequency regulation function of thermal power units (especially coal-fired units) is crucial. Primary frequency regulation refers to the dynamic process by which the unit automatically and quickly adjusts the steam intake of the turbine and changes the generator output through the speed control system when the grid frequency deviates from the rated value, so as to quickly curb the dynamic change in frequency.

[0003] The performance of primary frequency regulation is mainly determined by key parameters of the speed control system, such as the rate of speed variation, speed dead zone, and PID controller parameters (proportional gain, integral time). Currently, the setting of these parameters largely relies on theoretical calculations, simulations, or limited field tests in the early stages of unit design, lacking deep coupling with the actual operating conditions of the unit.

[0004] Existing technologies suffer from a lack of adaptability to primary frequency regulation parameters, low compatibility with unit operating conditions, and neglect of the coupling effects of multi-dimensional indicators, which negatively impact unit operation. Summary of the Invention

[0005] This application provides a method for stability analysis and tuning of primary frequency regulation parameters of thermal power units, which is used to address the technical problems in the prior art that lack adaptability to primary frequency regulation parameters, have low conformity with unit operating conditions, ignore the coupling effect of multi-dimensional indicators, and affect unit operation.

[0006] In view of the above problems, this application provides a method for stability analysis and tuning of primary frequency regulation parameters of thermal power units, the method comprising: Collect a set of historical abnormal frequency regulation logs from the power plant's DCS system. Obtain preset frequency regulation performance indicators and preset unit stability indicators, and combine the historical abnormal primary frequency regulation log set to identify the safety boundaries of multi-operating-condition indicators, thereby obtaining the safety boundary set of frequency regulation performance indicators and the safety boundary set of unit stability. The real-time operating condition data and real-time primary frequency regulation parameters of the target thermal power unit are obtained. The real-time operating condition data and real-time primary frequency regulation parameters are analyzed using a pre-constructed performance index prediction model, and the prediction results of frequency regulation performance index and unit stability index are output. Based on the real-time operating data, the set of safety boundaries for frequency regulation performance indicators and the set of safety boundaries for unit stability are matched. The operating state of the target thermal power unit after frequency regulation of the optimized parameters must simultaneously meet the safety boundaries of the matched frequency regulation performance indicators and the safety boundaries of the matched unit stability as optimization constraints. Combining the prediction results of frequency regulation performance indicators and the prediction results of unit stability indicators, dual-objective optimization is performed on the real-time primary frequency regulation parameters to determine the optimal frequency regulation parameters and construct the load-parameter tuning mapping result. The dual-objective optimization is to maximize frequency regulation performance and minimize unit disturbance.

[0007] Preferably, the preset frequency regulation performance indicators include response lag time and load contribution; the preset unit stability indicators include the absolute value of the maximum deviation of main steam pressure, the recovery time of main steam pressure, and the standard deviation of furnace pressure.

[0008] Preferably, obtaining preset frequency regulation performance indicators and preset unit stability indicators, and combining the historical abnormal primary frequency regulation log set to perform multi-operating condition indicator safety boundary identification, to obtain a set of frequency regulation performance indicator safety boundaries and a set of unit stability safety boundaries, includes: traversing and extracting the historical operating condition data set of the historical abnormal primary frequency regulation log set; based on the preset frequency regulation performance indicators and preset unit stability indicators, extracting the historical primary frequency regulation mapping indicator group set of the historical abnormal primary frequency regulation log set, wherein each historical primary frequency regulation mapping indicator group includes a historical frequency regulation performance indicator and a historical unit stability indicator; and processing the historical operating condition data set... The system performs a similar partitioning to obtain multiple historical operating condition data sets. It then maps and partitions the historical primary frequency regulation mapping index set to obtain multiple historical primary frequency regulation mapping index set sets. From these multiple historical primary frequency regulation mapping index set sets, a first historical primary frequency regulation mapping index set is extracted. Anomaly boundary value coupling identification is performed on the first historical primary frequency regulation mapping index set within the set to obtain a first frequency regulation performance index safety boundary and a first unit stability safety boundary. Finally, the first frequency regulation performance index safety boundary and the first unit stability safety boundary are added to the frequency regulation performance index safety boundary set and the unit stability safety boundary set, respectively.

[0009] Preferably, the method of performing intra-set abnormal boundary value coupling identification on the first historical primary frequency modulation mapping group set to obtain the first frequency modulation performance indicator safety boundary and the first unit stability safety boundary includes: calculating the mean value of the indicators in the first historical primary frequency modulation mapping group set to obtain the first historical primary frequency modulation mapping mean value group; using the first historical primary frequency modulation mapping mean value group as the coupling identification center, performing boundary coupling identification on the first historical primary frequency modulation mapping group set according to a preset identification bandwidth to obtain the target coupling identification center neighborhood; and performing boundary identification on the first historical primary frequency modulation mapping group set based on the target coupling identification center neighborhood to obtain the first frequency modulation performance indicator safety boundary and the first unit stability safety boundary.

[0010] Preferably, the real-time primary frequency regulation parameters are randomly adjusted multiple times based on a preset adjustment mechanism to obtain multiple adjusted primary frequency regulation parameters; a performance index prediction model is used to predict the multiple adjusted primary frequency regulation parameters and the real-time operating data to obtain multiple adjusted frequency regulation performance index prediction results and multiple unit stability index prediction results; based on the optimization constraints, the adjusted primary frequency regulation parameters corresponding to the results that do not meet the conditions in the multiple adjusted frequency regulation performance index prediction results and multiple unit stability index prediction results are eliminated to obtain multiple retained adjusted primary frequency regulation parameters; according to a preset bi-objective optimization coefficient analysis function, combined with the multiple adjusted frequency regulation performance index prediction results and multiple unit stability index prediction results, parameter reliability analysis is performed on the multiple retained adjusted primary frequency regulation parameters to obtain multiple Retain the reliability coefficients of the parameters, and perform parameter reliability analysis on the real-time primary frequency regulation parameters based on the predicted results of the frequency regulation performance index and the unit stability index to obtain the real-time primary frequency regulation parameter reliability coefficients; determine whether all of the multiple retained parameter reliability coefficients are less than the real-time primary frequency regulation parameter reliability coefficients; if not, add the coefficients of the multiple retained parameter reliability coefficients that are greater than or equal to the real-time primary frequency regulation parameter reliability coefficients to the stage retained parameter reliability coefficient set, and add the corresponding retained adjustment primary frequency regulation parameters to the stage adjustment primary frequency regulation parameter set; perform directional bi-objective optimization based on the stage adjustment primary frequency regulation parameter set, the stage retained parameter reliability coefficient set, and the optimization constraints until the preset optimization stopping condition is met to obtain the optimal frequency regulation parameter.

[0011] Preferably, if the reliability coefficients of multiple reserved parameters are all less than the reliability coefficient of the real-time primary frequency modulation parameter, multiple random adjustments are performed again, and the adjustment methods corresponding to the multiple adjusted primary frequency modulation parameters are added to the disabled list until there is a coefficient among the reserved parameter reliability coefficients obtained by adjustment that is greater than or equal to the reliability coefficient of the real-time primary frequency modulation parameter, and this coefficient is added to the stage reserved parameter reliability coefficient set, and the corresponding reserved adjusted primary frequency modulation parameter is added to the stage adjusted primary frequency modulation parameter set.

[0012] Preferably, the preset bi-objective optimization coefficient analysis function is: ; ; ; in, For the parameter reliability coefficient, The weights for frequency modulation performance indicators when performing a reliability analysis of frequency modulation parameters. The weights for frequency regulation parameters in the reliability analysis of unit stability indicators. The frequency modulation performance reliability coefficient, This is the unit disturbance factor. In response to the lag time, As the load contribution, The absolute value of the maximum deviation of the main steam pressure. Main steam pressure recovery time The standard deviation of furnace pressure. , The weights are divided into response lag time, load contribution, absolute value of the maximum deviation of main steam pressure, main steam pressure recovery time, and furnace pressure standard deviation.

[0013] Preferably, the optimal frequency modulation parameter is obtained by performing directional dual-objective optimization based on the set of stage-adjusted primary frequency modulation parameters, the set of stage-retained parameter reliability coefficients, and optimization constraints until a preset optimization stopping condition is met. This includes: using the stage-adjusted primary frequency modulation parameter corresponding to the maximum value in the set of stage-retained parameter reliability coefficients as the directional primary frequency modulation parameter; adjusting the set of stage-adjusted primary frequency modulation parameters according to a preset adjustment scale towards the directional primary frequency modulation parameter to obtain a set of following primary frequency modulation parameters; removing parameters that do not meet the conditions in the set of following primary frequency modulation parameters based on the optimization constraints to obtain a set of retained following primary frequency modulation parameters and a corresponding set of retained following primary frequency modulation parameter reliability coefficients; updating the directional primary frequency modulation parameter according to the retained following primary frequency modulation parameter corresponding to the maximum value in the set of retained following primary frequency modulation parameter reliability coefficients to obtain the updated directional primary frequency modulation parameter; determining whether the preset optimization stopping condition is met; if so, stopping the optimization and obtaining the optimal frequency modulation parameter.

[0014] Preferably, if the preset optimization stopping condition is not met, the frequency modulation parameters are adjusted based on the update direction to perform directional bi-objective optimization until the preset optimization condition is met and the optimal frequency modulation parameters are obtained.

[0015] Preferably, the preset optimization stopping condition is that the number of optimization attempts meets a preset number threshold and / or the reliability coefficient of the first-order frequency modulation parameter obtained in this optimization is less than the reliability coefficient of the first-order frequency modulation parameter obtained in the previous optimization.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: 1. This application differs from the traditional fixed parameter mode, realizing a strategy of optimizing parameters for different operating conditions and determining different primary frequency regulation parameters. It achieves refined and adaptive parameter management, thereby reducing the workload of operators and improving the intelligence level of the unit.

[0017] 2. By incorporating the stability of key internal parameters of the unit into the optimization objectives, problems such as boiler combustion oscillation and drastic fluctuations in main steam pressure caused by improper primary frequency regulation parameter settings are avoided at the source, ensuring the long-term safe and stable operation of the unit. Attached Figure Description

[0018] Appendix Figure 1 This is a schematic flowchart of a method for analyzing and setting the stability of primary frequency regulation parameters of a thermal power unit, provided in an embodiment of the present invention.

[0019] Appendix Figure 2This is a flowchart illustrating the process of obtaining the first frequency regulation performance index safety boundary and the first unit stability safety boundary in a method for determining the stability of primary frequency regulation parameters of a thermal power unit, as provided in an embodiment of the present invention. Detailed Implementation

[0020] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims. It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices.

[0021] Examples, as shown in the appendix Figure 1 As shown, this application provides a method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit, wherein the method includes: Step S100: Collect the historical abnormal primary frequency regulation log set from the power plant's DCS system; Step S200: Obtain preset frequency regulation performance indicators and preset unit stability indicators, and combine the historical abnormal primary frequency regulation log set to identify the safety boundaries of multiple operating conditions, thereby obtaining the safety boundary set of frequency regulation performance indicators and the safety boundary set of unit stability. Furthermore, the preset frequency modulation performance indicators include response lag time and load contribution. The preset unit stability indicators include the absolute value of the maximum deviation of the main steam pressure, the main steam pressure recovery time, and the standard deviation of the furnace pressure.

[0022] It should be noted that DCS (Distributed Control System) is a system used in power plants to monitor and control various production processes. The historical anomaly frequency regulation log set is log data obtained from the DCS system containing anomalies that occurred during past frequency regulation events, used for subsequent analysis of the unit's performance after frequency regulation under different operating conditions. The preset frequency regulation performance indicators and unit stability indicators are types of indicators used to measure the unit's response performance and stability during frequency regulation. The frequency regulation performance indicator safety boundary set and the unit stability safety boundary set represent the boundary conditions for frequency regulation performance and unit stability under different operating conditions.

[0023] The response lag time is the time from frequency exceeding the limit to the start of load change, and should be less than 2 seconds. The load contribution is the load adjustment amount contributed within 15 seconds. The maximum absolute deviation of the main steam pressure is the maximum absolute deviation of the main steam pressure during a single frequency regulation process. The main steam pressure recovery time is the time required for the main steam pressure to recover from the maximum deviation to the stable zone (e.g., ±0.5 MPa). The furnace pressure standard deviation is the standard deviation of the furnace pressure during a single frequency regulation process.

[0024] Specifically, this involves determining the safety boundaries of frequency regulation performance and unit stability during the coupled frequency regulation process, considering both frequency regulation performance and unit stability. This differs from the traditional approach of solely considering frequency regulation performance when performing primary frequency regulation parameter tuning analysis. This approach more closely reflects the actual operating conditions of the unit and differentiates the primary frequency regulation parameter tuning under different operating loads.

[0025] Furthermore, by acquiring preset frequency regulation performance indicators and preset unit stability indicators, and combining the historical abnormal primary frequency regulation log set to perform multi-condition indicator safety boundary identification, a set of frequency regulation performance indicator safety boundaries and a set of unit stability safety boundaries are obtained. In this embodiment, step S200 further includes: Iterate through and extract the historical operating condition data set of the historical abnormal frequency adjustment log set; Based on the preset frequency regulation performance index and preset unit stability index, extract the set of historical primary frequency regulation mapping index groups from the set of historical abnormal primary frequency regulation logs, wherein each set of historical primary frequency regulation mapping index groups includes historical frequency regulation performance index and historical unit stability index. The historical operating condition data set is divided into similar categories to obtain multiple historical operating condition data sets, and the historical primary frequency regulation mapping index group set is mapped and divided to obtain multiple historical primary frequency regulation mapping index group sets. Extract the first set of historical primary frequency modulation mapping groups from the multiple sets of historical primary frequency modulation mapping groups, and perform in-set index anomaly boundary value coupling identification on the first set of historical primary frequency modulation mapping groups to obtain the first frequency modulation performance index safety boundary and the first unit stability safety boundary. Add the first frequency regulation performance index safety boundary and the first unit stability safety boundary to the frequency regulation performance index safety boundary set and the unit stability safety boundary set.

[0026] Further details are attached. Figure 2 As shown, the first set of historical primary frequency regulation mapping groups is subjected to in-set index anomaly boundary value coupling identification to obtain the first frequency regulation performance index safety boundary and the first unit stability safety boundary. In this embodiment, step S200 further includes: Calculate the mean value of the index of the first partitioned historical primary frequency modulation mapping group set to obtain the mean value group of the first partitioned historical primary frequency modulation mapping; Using the first historical primary frequency modulation mapping mean group as the coupling identification center, the boundary coupling identification of the first historical primary frequency modulation mapping group set is performed according to the preset identification bandwidth to obtain the neighborhood of the target coupling identification center. Based on the target coupling identification center neighborhood, the first partitioned historical primary frequency modulation mapping group set is boundary identified to obtain the first frequency modulation performance index safety boundary and the first unit stability safety boundary.

[0027] In one possible embodiment, the historical operating condition data set of the historical abnormal primary frequency regulation log set is extracted by traversing the log. The historical operating condition data set is a set of core parameters extracted from the historical abnormal primary frequency regulation log set, reflecting the unit's operating status when the abnormality occurred, including grid frequency, unit load, opening degree of all regulating valves, main steam pressure, main steam temperature, furnace pressure, coal feed rate of each coal mill, total air volume, feedwater flow rate, etc.

[0028] A historical operating condition data point is randomly extracted from the historical operating condition data set. The cosine similarity between this data and other data points in the historical operating condition data set is calculated. Historical operating condition data points that meet a pre-defined similarity threshold set by those skilled in the art are added to a new historical operating condition data set. Then, data points in this new set are removed from the original historical operating condition data set. Another historical operating condition data point is randomly extracted from the remaining historical operating condition data set, and its cosine similarity is calculated again, resulting in another set of historical primary frequency modulation (PFMC) mapping index groups. Through multiple partitioning operations, multiple sets of historical PFMC mapping index groups are obtained. This achieves the goal of distinguishing and clustering historical operating condition data sets according to different operating conditions.

[0029] Furthermore, based on the one-to-one correspondence between historical operating condition data and historical primary frequency modulation (PFFM) mapping index groups, the historical PFFM mapping index group set is mapped and divided to obtain multiple partitioned historical PFFM mapping index group sets. A first partitioned historical PFFM mapping index group set is extracted from these multiple partitioned historical PFFM mapping index group sets. Here, "first" in the first partitioned historical PFFM mapping index group set does not represent a specific order, but rather refers to any one of the multiple partitioned sets.

[0030] Then, the mean index of the first set of historical primary frequency modulation (FM) mapping groups is calculated to obtain the mean value group of the first set of historical FM mapping groups. The mean value group of the first set of historical FM mapping groups is used as the coupling identification center, which is the most central data in the overall data set. A preset identification bandwidth, i.e., a preset similarity range (e.g., 5%), is used. During initial identification, when constructing the neighborhood, the similarity between the first set of historical FM mapping groups and the initial coupling center must be within the first interval, i.e., (0.95, 1], to be included in the neighborhood, thus obtaining the initial coupling center neighborhood. The amount of data in the initial coupling center neighborhood is counted to obtain the initial coupling center neighborhood data volume.

[0031] Furthermore, the neighborhood edges of the initial coupling center neighborhood are diffused outwards. The two endpoints of the second interval corresponding to this diffusion have a preset reduction in recognition bandwidth compared to the two endpoints of the first interval, which lowers the requirement for being classified into the neighborhood. At this time, the second interval is (0.9, 0.95). The first partitioning historical first frequency modulation mapping group, whose cosine similarity to the mean group of the first partitioning historical first frequency modulation mapping is within the second interval, is added to the initial coupling center neighborhood to complete the edge diffusion and obtain the diffused coupling center neighborhood.

[0032] The amount of data in the neighborhood of the diffusion coupling center is statistically analyzed. It is determined whether the difference between the amount of data in the neighborhood of the diffusion coupling center and the amount of data in the initial coupling center neighborhood meets the difference requirement preset by those skilled in the art. If so, it indicates that the boundary of data aggregation has not yet been reached. Based on the same principle of edge diffusion, edge diffusion continues in the neighborhood of the diffusion coupling center until the difference between the neighborhood data obtained in this diffusion and the neighborhood data obtained in the previous diffusion does not meet the difference requirement preset by those skilled in the art, and / or all first-division historical first-frequency modulation mappings in the first-division historical first-frequency modulation mapping group set are included in the neighborhood. At this point, diffusion stops, and the target coupling identification center neighborhood is obtained. The target coupling identification center neighborhood is a set of common indicators for frequency modulation anomalies under this operating condition.

[0033] Furthermore, the range of each preset frequency regulation performance index and preset unit stability index is obtained. The minimum boundary value of the corresponding index in the neighborhood of the target coupling identification center is taken as the new index range boundary, thereby obtaining the first frequency regulation performance index safety boundary and the first unit stability safety boundary. For example, the index range of response lag time is less than 2s, but in the neighborhood of the target coupling identification center, when the load is above 90%, the response time is as low as 1.8s, and the furnace fluctuation has increased significantly. In this case, the range should be corrected to less than 1.8s.

[0034] Based on the same principle as obtaining the safety boundary of the first frequency regulation performance index and the safety boundary of the first unit stability, the abnormal boundary value coupling identification of the index within the set is performed on multiple sets of historical primary frequency regulation mapping groups.

[0035] Step S300: Obtain the real-time operating condition data and real-time primary frequency regulation parameters of the target thermal power unit, analyze the real-time operating condition data and real-time primary frequency regulation parameters using a pre-built performance index prediction model, and output the frequency regulation performance index prediction results and the unit stability index prediction results. In one possible embodiment, the real-time primary frequency regulation parameters are the parameters to be regulated. Multiple sample operating condition data and multiple sample primary frequency regulation parameters, along with corresponding sample frequency regulation performance indicators and sample unit stability indicators after frequency regulation, are acquired as training data. Preferably, the sample operating condition data can be high-frequency data for at least 10 minutes before and after the primary frequency regulation event, wherein the sampling frequency is ≥1Hz, including: grid frequency, unit load, all valve openings, main steam pressure, main steam temperature, furnace pressure, coal feed rate of each coal mill, total air volume, feedwater flow rate, etc. The multiple sample primary frequency regulation parameters are the frequency regulation parameters at the time of the primary frequency regulation event, including the rate of change of velocity, proportional coefficient, and integral time. The framework based on a feedforward neural network is supervisedly trained using the training data. During training, the network parameters of the framework are updated and adjusted according to the deviation between the output results and the training data until convergence, obtaining the trained performance indicator prediction model.

[0036] Then, the real-time operating data and the real-time primary frequency regulation parameters are input into the performance index prediction model to obtain the frequency regulation performance index prediction results and the unit stability index prediction results. The frequency regulation performance index prediction results and the unit stability index prediction results represent the predicted frequency regulation performance and unit stability after frequency regulation according to the real-time primary frequency regulation parameters, respectively.

[0037] Step S400: Based on the real-time operating data, match the set of safety boundaries for frequency regulation performance indicators and the set of safety boundaries for unit stability. The operating state of the target thermal power unit after frequency regulation of the optimized parameters must simultaneously meet the safety boundaries of the matched frequency regulation performance indicators and the safety boundaries of the matched unit stability as optimization constraints. Combine the prediction results of frequency regulation performance indicators and the prediction results of unit stability indicators, perform dual-objective optimization on the real-time primary frequency regulation parameters, determine the optimal frequency regulation parameters, and construct the load-parameter tuning mapping result. The dual-objective optimization is to maximize frequency regulation performance and minimize unit disturbance.

[0038] In one possible embodiment, the real-time operating data is mapped to the corresponding historical operating data mean sets for the frequency regulation performance index safety boundary set and the unit stability safety boundary set, respectively. Cosine similarity calculation is then performed on these sets. The frequency regulation performance index safety boundary and the unit stability safety boundary corresponding to the maximum cosine similarity are used as the matching frequency regulation performance index safety boundary and the matching unit stability safety boundary. This yields the optimization constraints. These optimization constraints effectively constrain the occurrence of primary frequency regulation parameters that fail to meet requirements due to randomness during the optimization process. Furthermore, the optimization is not only achieved by adjusting the real-time primary frequency regulation parameters but also by optimizing from two dimensions: maximizing frequency regulation performance and minimizing unit disturbance. This determines the optimal frequency regulation parameters, and the load in the real-time operating data is mapped and associated with the optimal frequency regulation parameters to obtain the load-parameter tuning mapping result.

[0039] Furthermore, step S400 in this embodiment of the application also includes: The real-time primary frequency modulation parameter is randomly adjusted multiple times based on a preset adjustment mechanism to obtain multiple adjusted primary frequency modulation parameters; The performance index prediction model is used to predict the multiple primary frequency regulation parameters and the real-time operating data to obtain multiple frequency regulation performance index prediction results and multiple unit stability index prediction results. Based on the optimization constraints, the primary frequency regulation parameters corresponding to the results that do not meet the conditions in the prediction results of multiple frequency regulation performance indicators and multiple unit stability indicators are eliminated, and multiple primary frequency regulation parameters are retained. According to the preset dual-objective optimization coefficient analysis function, the reliability analysis of the multiple retained primary frequency regulation parameters is performed by combining the prediction results of the multiple frequency regulation performance indicators and the prediction results of the multiple unit stability indicators, so as to obtain the reliability coefficients of the multiple retained parameters. Then, the reliability analysis of the real-time primary frequency regulation parameters is performed by combining the prediction results of the frequency regulation performance indicators and the prediction results of the unit stability indicators, so as to obtain the reliability coefficients of the real-time primary frequency regulation parameters. Determine whether all of the reliability coefficients of the multiple reserved parameters are less than the reliability coefficient of the real-time primary frequency modulation parameter. If not, add the coefficients of the multiple reserved parameter reliability coefficients that are greater than or equal to the reliability coefficient of the real-time primary frequency modulation parameter to the set of stage reserved parameter reliability coefficients, and add the corresponding reserved adjustment primary frequency modulation parameter to the set of stage adjustment primary frequency modulation parameters. Based on the set of frequency modulation parameters adjusted in each stage, the set of reliability coefficients of parameters retained in each stage, and the optimization constraints, a directional dual-objective optimization is performed until the preset optimization stopping condition is met, thereby obtaining the optimal frequency modulation parameters.

[0040] Furthermore, if the reliability coefficients of multiple reserved parameters are all less than the reliability coefficient of the real-time primary frequency modulation parameter, multiple random adjustments are performed again, and the adjustment methods corresponding to the multiple adjusted primary frequency modulation parameters are added to the disabled list until there is a coefficient among the reserved parameter reliability coefficients obtained by adjustment that is greater than or equal to the reliability coefficient of the real-time primary frequency modulation parameter. This coefficient is then added to the stage reserved parameter reliability coefficient set, and the corresponding reserved adjusted primary frequency modulation parameter is added to the stage adjusted primary frequency modulation parameter set.

[0041] Furthermore, the pre-defined bi-objective optimization coefficient analysis function is as follows: ; ; ; in, For the parameter reliability coefficient, Weights for frequency modulation performance indicators when performing a reliability analysis of frequency modulation parameters. The weights for frequency regulation parameters in the reliability analysis of unit stability indicators. The frequency modulation performance reliability coefficient, This is the unit disturbance factor. In response to the lag time, As the load contribution, The absolute value of the maximum deviation of the main steam pressure. Main steam pressure recovery time The standard deviation of furnace pressure. , The weights are divided into response lag time, load contribution, absolute value of maximum deviation of main steam pressure, main steam pressure recovery time, and furnace pressure standard deviation.

[0042] In one possible embodiment, the preset adjustment mechanism is designed to increase the randomness of the real-time primary frequency modulation (FM) parameters. Specifically, it involves randomly increasing or decreasing the amplitude of a random number of parameters within the real-time FM parameters. By performing multiple random adjustments on the real-time FM parameters according to the preset adjustment mechanism, the plurality of adjusted FM parameters are obtained.

[0043] Because the adjustment process is random, a performance index prediction model is needed to predict the frequency regulation results of multiple adjusted primary frequency regulation parameters under real-time operating conditions. The multiple adjusted primary frequency regulation parameters are input into the performance index prediction model along with the real-time operating data to obtain the predicted results of the multiple adjusted frequency regulation performance indices and multiple unit stability indices. Furthermore, optimization constraints are used to remove adjusted primary frequency regulation parameters that do not meet the boundaries, resulting in multiple retained adjusted primary frequency regulation parameters. In other words, the multiple retained adjusted primary frequency regulation parameters are primary frequency regulation parameters that meet the optimization constraints.

[0044] Furthermore, using a bi-objective optimization coefficient analysis function, the reliability of multiple retained adjustment primary frequency regulation parameters and real-time primary frequency regulation parameters is analyzed based on the prediction results; that is, the quality of the frequency regulation effect. The larger the obtained parameter reliability coefficient, the better the corresponding frequency regulation effect. The bi-objective optimization coefficient analysis function analyzes from two dimensions, and since the smaller the values ​​of the three indicators in the unit stability index, the higher the corresponding unit stability, therefore... The formula is used to take the reciprocal.

[0045] After function analysis, reliability coefficients for multiple reserved parameters and real-time primary frequency modulation (PMFM) parameters are obtained, reflecting the frequency modulation performance of the multiple reserved adjustment PMFM parameters and the real-time primary frequency modulation (PMFM) parameters, respectively. Then, it is determined whether all the reliability coefficients of the multiple reserved parameters are less than the reliability coefficient of the real-time primary frequency modulation (PMFM) parameter. If not, it indicates that the current real-time primary frequency modulation (PMFM) parameter is not optimal. The coefficients of the multiple reserved parameters that are greater than or equal to the reliability coefficient of the real-time primary frequency modulation (PMFM) parameter need to be added to the stage reserved parameter reliability coefficient set, and the corresponding reserved adjustment PMFM parameter needs to be added to the stage adjustment PMFM parameter set. Further directional dual-objective optimization is then performed for more advanced, directional optimization.

[0046] If so, it indicates that the current real-time primary frequency modulation (PMFM) parameters are optimal. Multiple random adjustments are then performed, and the adjustment methods corresponding to these multiple adjustments are added to a disabled list. The disabled list contains adjustment methods that are prohibited during the adjustment process to prevent undesirable PMFM parameters from recurring. This continues until a reliability coefficient of the retained parameters obtained from the adjustments is greater than or equal to the reliability coefficient of the real-time PMFM parameters. This coefficient is then added to the stage retained parameter reliability coefficient set, and the corresponding retained PMFM parameters are added to the stage adjusted PMFM parameter set.

[0047] Furthermore, based on the set of frequency modulation parameters adjusted in the stage, the set of reliability coefficients of the retained parameters in the stage, and the optimization constraints, a directional dual-objective optimization is performed until a preset optimization stopping condition is met to obtain the optimal frequency modulation parameters. In this embodiment, step S400 further includes: The stage adjustment frequency modulation parameter corresponding to the maximum value in the set of reliability coefficients of the stage retention parameters is used as the direction adjustment frequency modulation parameter; The set of primary frequency modulation parameters for the stage adjustment is adjusted in the direction according to a preset adjustment scale to obtain the set of primary frequency modulation parameters for follow-up adjustment. Based on the optimization constraints, parameters that do not meet the conditions in the set of primary frequency modulation parameters for follow-up adjustment are removed to obtain the set of primary frequency modulation parameters for follow-up adjustment and the corresponding set of reliability coefficients of the primary frequency modulation parameters for follow-up adjustment. The direction adjustment primary frequency modulation parameter is updated based on the primary frequency modulation parameter corresponding to the maximum value in the set of reliability coefficients of the primary frequency modulation parameter retention and follow adjustment. The updated direction adjustment primary frequency modulation parameter is obtained. It is then determined whether the preset optimization stop condition is met. If so, the optimization is stopped and the optimal frequency modulation parameter is obtained.

[0048] Furthermore, if the preset optimization stopping condition is not met, the frequency modulation parameters are adjusted based on the update direction to perform directional bi-objective optimization until the preset optimization condition is met and the optimal frequency modulation parameters are obtained.

[0049] Furthermore, the preset optimization stopping condition is that the number of optimization attempts meets the preset number threshold and / or the reliability coefficient of the first frequency modulation parameter of the direction adjustment obtained in this optimization is less than the reliability coefficient of the first frequency modulation parameter of the direction adjustment obtained in the previous optimization.

[0050] In one possible embodiment, the stage adjustment primary frequency modulation parameter corresponding to the maximum value in the set of stage-retained parameter reliability coefficients is used as the directional adjustment primary frequency modulation parameter. That is, during the directional dual-objective optimization process, when adjusting the stage-retained parameter reliability coefficients, adjustments are made according to a preset adjustment scale (i.e., the magnitude of parameter adjustment) set pre-defined by those skilled in the art, moving towards the direction of the directional adjustment primary frequency modulation parameter, thereby obtaining a set of following adjustment primary frequency modulation parameters. Because the obtained set of following adjustment primary frequency modulation parameters is adjusted with the directional adjustment primary frequency modulation parameter as the target, it is called a following adjustment primary frequency modulation parameter.

[0051] Using the same elimination principle as above, the performance index prediction model is used to predict the frequency modulation performance results of the set of primary frequency modulation parameters for follow-up adjustment, and the parameter reliability coefficient analysis is performed using a preset dual-objective optimization coefficient analysis function to obtain the set of primary frequency modulation parameters for follow-up adjustment that meet the constraints and the corresponding set of reliability coefficients for the primary frequency modulation parameters for follow-up adjustment.

[0052] Furthermore, by updating the directional adjustment primary frequency regulation parameters according to the maximum value in the set of reliability coefficients of the retained follow-up adjustment primary frequency regulation parameters, the optimal solution in this directional adjustment process is obtained. This solution is then compared with the previous optimal solution, and the preset optimal stopping condition is determined from two dimensions: the number of optimizations and the magnitude of the coefficients. If the condition is met, the optimization process stops, and the optimal frequency regulation parameters are obtained. If the preset optimal stopping condition is not met, it indicates that the updated directional adjustment primary frequency regulation parameters are superior to the original directional adjustment primary frequency regulation parameters. In this case, directional dual-objective optimization is performed based on the updated directional adjustment primary frequency regulation parameters until the preset optimization condition is met, and the optimal frequency regulation parameters are obtained. By optimizing the primary frequency regulation parameters from two dimensions—maximizing frequency regulation performance and minimizing unit disturbance—high-quality optimal frequency regulation parameters that closely match the actual operating conditions of the unit are obtained.

[0053] It should be noted that the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0054] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0055] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit, characterized in that, The method includes: Collect a set of historical abnormal frequency regulation logs from the power plant's DCS system. Obtain preset frequency regulation performance indicators and preset unit stability indicators, and combine the historical abnormal primary frequency regulation log set to identify the safety boundaries of multi-operating-condition indicators, thereby obtaining the safety boundary set of frequency regulation performance indicators and the safety boundary set of unit stability. The real-time operating condition data and real-time primary frequency regulation parameters of the target thermal power unit are obtained. The real-time operating condition data and real-time primary frequency regulation parameters are analyzed using a pre-constructed performance index prediction model, and the prediction results of frequency regulation performance index and unit stability index are output. Based on the real-time operating data, the set of safety boundaries for frequency regulation performance indicators and the set of safety boundaries for unit stability are matched. The operating state of the target thermal power unit after frequency regulation of the optimized parameters must simultaneously meet the safety boundaries of the matched frequency regulation performance indicators and the safety boundaries of the matched unit stability as optimization constraints. Combining the prediction results of frequency regulation performance indicators and the prediction results of unit stability indicators, dual-objective optimization is performed on the real-time primary frequency regulation parameters to determine the optimal frequency regulation parameters and construct the load-parameter tuning mapping result. The dual-objective optimization is to maximize frequency regulation performance and minimize unit disturbance.

2. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 1, characterized in that, The preset frequency modulation performance indicators include response lag time and load contribution. The preset unit stability indicators include the absolute value of the maximum deviation of the main steam pressure, the main steam pressure recovery time, and the standard deviation of the furnace pressure.

3. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 1, characterized in that, Obtain preset frequency regulation performance indicators and preset unit stability indicators, and combine them with the historical abnormal primary frequency regulation log set to identify multi-condition indicator safety boundaries, thereby obtaining a set of frequency regulation performance indicator safety boundaries and a set of unit stability safety boundaries, including: Iterate through and extract the historical operating condition data set of the historical abnormal frequency adjustment log set; Based on the preset frequency regulation performance index and preset unit stability index, extract the set of historical primary frequency regulation mapping index groups from the set of historical abnormal primary frequency regulation logs, wherein each set of historical primary frequency regulation mapping index groups includes historical frequency regulation performance index and historical unit stability index. The historical operating condition data set is divided into similar categories to obtain multiple historical operating condition data sets, and the historical primary frequency regulation mapping index group set is mapped and divided to obtain multiple historical primary frequency regulation mapping index group sets. Extract the first set of historical primary frequency modulation mapping groups from the multiple sets of historical primary frequency modulation mapping groups, and perform in-set index anomaly boundary value coupling identification on the first set of historical primary frequency modulation mapping groups to obtain the first frequency modulation performance index safety boundary and the first unit stability safety boundary. Add the first frequency regulation performance index safety boundary and the first unit stability safety boundary to the frequency regulation performance index safety boundary set and the unit stability safety boundary set.

4. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 3, characterized in that, The first set of historical primary frequency regulation mapping groups is subjected to in-set index anomaly boundary value coupling identification to obtain the first frequency regulation performance index safety boundary and the first unit stability safety boundary, including: Calculate the mean value of the index of the first partitioned historical primary frequency modulation mapping group set to obtain the mean value group of the first partitioned historical primary frequency modulation mapping; Using the first historical primary frequency modulation mapping mean group as the coupling identification center, the boundary coupling identification of the first historical primary frequency modulation mapping group set is performed according to the preset identification bandwidth to obtain the neighborhood of the target coupling identification center. Based on the target coupling identification center neighborhood, the first partitioned historical primary frequency modulation mapping group set is subjected to boundary identification to obtain the first frequency modulation performance index safety boundary and the first unit stability safety boundary.

5. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 1, characterized in that, include: The real-time primary frequency modulation parameter is randomly adjusted multiple times based on a preset adjustment mechanism to obtain multiple adjusted primary frequency modulation parameters; The performance index prediction model is used to predict the multiple primary frequency regulation parameters and the real-time operating data to obtain multiple frequency regulation performance index prediction results and multiple unit stability index prediction results. Based on the optimization constraints, the primary frequency regulation parameters corresponding to the results that do not meet the conditions in the prediction results of multiple frequency regulation performance indicators and multiple unit stability indicators are eliminated, and multiple primary frequency regulation parameters are retained. According to the preset dual-objective optimization coefficient analysis function, the reliability analysis of the multiple retained primary frequency regulation parameters is performed by combining the prediction results of the multiple frequency regulation performance indicators and the prediction results of the multiple unit stability indicators, so as to obtain the reliability coefficients of the multiple retained parameters. Then, the reliability analysis of the real-time primary frequency regulation parameters is performed by combining the prediction results of the frequency regulation performance indicators and the prediction results of the unit stability indicators, so as to obtain the reliability coefficients of the real-time primary frequency regulation parameters. Determine whether all of the reliability coefficients of the multiple reserved parameters are less than the reliability coefficient of the real-time primary frequency modulation parameter. If not, add the coefficients of the multiple reserved parameter reliability coefficients that are greater than or equal to the reliability coefficient of the real-time primary frequency modulation parameter to the set of stage reserved parameter reliability coefficients, and add the corresponding reserved adjustment primary frequency modulation parameter to the set of stage adjustment primary frequency modulation parameters. Based on the set of frequency modulation parameters adjusted in each stage, the set of reliability coefficients of parameters retained in each stage, and the optimization constraints, a directional dual-objective optimization is performed until the preset optimization stopping condition is met, thereby obtaining the optimal frequency modulation parameters.

6. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 5, characterized in that, If the reliability coefficients of multiple reserved parameters are all less than the reliability coefficient of the real-time primary frequency modulation parameter, multiple random adjustments are performed again, and the adjustment methods corresponding to the multiple adjusted primary frequency modulation parameters are added to the disabled list until there is a coefficient among the reserved parameter reliability coefficients that is greater than or equal to the reliability coefficient of the real-time primary frequency modulation parameter. Then, this coefficient is added to the stage reserved parameter reliability coefficient set, and the corresponding reserved adjusted primary frequency modulation parameter is added to the stage adjusted primary frequency modulation parameter set.

7. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 5, characterized in that, The preset bi-objective optimization coefficient analysis function is: ； ； ； in, For the parameter reliability coefficient, Weights for frequency modulation performance indicators when performing a reliability analysis of frequency modulation parameters. The weights for frequency regulation parameters in the reliability analysis of unit stability indicators. The frequency modulation performance reliability coefficient, This is the unit disturbance factor. In response to the lag time, As the load contribution, The absolute value of the maximum deviation of the main steam pressure. Main steam pressure recovery time The standard deviation of furnace pressure. , The weights are divided into response lag time, load contribution, absolute value of maximum deviation of main steam pressure, main steam pressure recovery time, and furnace pressure standard deviation.

8. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 5, characterized in that, Based on the aforementioned set of frequency modulation parameters adjusted in each stage, the set of reliability coefficients of parameters retained in each stage, and optimization constraints, a directional bi-objective optimization is performed until a preset optimization stopping condition is met to obtain the optimal frequency modulation parameters, including: The stage adjustment frequency modulation parameter corresponding to the maximum value in the set of reliability coefficients of the stage retention parameters is used as the direction adjustment frequency modulation parameter; The set of primary frequency modulation parameters for the stage adjustment is adjusted in the direction according to a preset adjustment scale to obtain the set of primary frequency modulation parameters for follow-up adjustment. Based on the optimization constraints, parameters that do not meet the conditions in the set of primary frequency modulation parameters for follow-up adjustment are removed to obtain the set of primary frequency modulation parameters for follow-up adjustment and the corresponding set of reliability coefficients of the primary frequency modulation parameters for follow-up adjustment. The direction adjustment primary frequency modulation parameter is updated based on the primary frequency modulation parameter corresponding to the maximum value in the set of reliability coefficients of the primary frequency modulation parameter retention and follow adjustment. The updated direction adjustment primary frequency modulation parameter is obtained. It is then determined whether the preset optimization stop condition is met. If so, the optimization is stopped and the optimal frequency modulation parameter is obtained.

9. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 8, characterized in that, If the preset optimization stopping condition is not met, the frequency modulation parameters are adjusted based on the update direction to perform directional bi-objective optimization until the preset optimization condition is met and the optimal frequency modulation parameters are obtained.

10. The method for stability analysis and tuning of primary frequency regulation parameters of a thermal power unit as described in claim 9, characterized in that, The preset optimization stopping condition is that the number of optimization attempts meets the preset number threshold and / or the reliability coefficient of the direction adjustment frequency modulation parameter obtained in this optimization is less than the reliability coefficient of the direction adjustment frequency modulation parameter obtained in the previous optimization.

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