Method, system, device and medium for adjusting sliding pressure curve based on performance coefficient

Abstract

The present invention discloses a method, system, device and medium for adjusting a sliding pressure curve based on a coefficient of performance, and relates to the technical field of control and optimization of generator sets, including establishing a relationship model between electric load and heat dissipation, and dynamically generating an initial sliding pressure curve using the relationship model; collecting electric power, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature under the running state of the unit, and calculating the actual heat load; using the relationship model to calculate the design heat load under real-time electric power, and comparing the actual heat load with the design heat load to obtain the coefficient of performance; correcting the initial sliding pressure curve according to the coefficient of performance to obtain an optimized sliding pressure curve; and monitoring the load stability of the unit. The present invention is simple to implement, easy to thermal control and edit, on the one hand, reduces the heat consumption of the power plant operation, so that the thermal economy of the whole plant is always in the best state, and on the other hand, it is easy to edit logical control, and improves the automation level of the power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of generator set control and optimization, and in particular to a method, system, equipment and medium for adjusting a sliding pressure curve based on a coefficient of performance. Background Art

[0002] In the field of generator set control and optimization, sliding pressure operation is a key technology to improve unit efficiency and operational flexibility. Traditional sliding pressure curve adjustment methods mainly rely on preset parameters and static models, which cannot effectively cope with complex and changing operating environments and load demands. This static adjustment strategy makes it difficult for the unit to always maintain the best operating state, affecting the overall power generation efficiency and economy.

[0003] At present, the real-time optimization and dynamic adjustment of the sliding pressure curve still face many challenges. First, there is a lack of effective tracking and compensation mechanism for the performance degradation of the unit, which makes it difficult to accurately reflect the changes in equipment characteristics caused by long-term operation. Secondly, the existing methods are difficult to respond quickly to load changes and fluctuations in environmental conditions, and cannot achieve real-time optimization and adjustment of the sliding pressure curve. In addition, traditional technologies fail to make full use of advanced data analysis and artificial intelligence algorithms, and cannot achieve intelligent optimization of the sliding pressure curve based on massive operating data.

[0004] On the other hand, the lack of coordination between the sliding pressure curve adjustment and other control systems restricts the improvement of the overall control level of the unit. At the same time, it is difficult to take into account the safe and stable operation of the unit while pursuing high efficiency, which poses potential risks. These problems seriously limit the operating efficiency, flexibility and safety of the generator set under different working conditions. Summary of the invention

[0005] In view of the problems existing in the existing sliding pressure curve adjustment method, the present invention is proposed.

[0006] The problem to be solved by the present invention is how to dynamically adjust the sliding pressure curve based on the performance coefficient in real time in a dynamic environment of unit load changes, so that it can better match the actual operating requirements of the unit, thereby improving the operating efficiency and safety of the unit.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides a method for adjusting a sliding pressure curve based on a performance coefficient, which includes calibrating the heat dissipation in a design state, establishing a relationship model between electric load and heat dissipation, and dynamically generating an initial sliding pressure curve using the relationship model; executing a sliding pressure adjustment process, the sliding pressure adjustment process includes: under the operation state of the unit, collecting electric power, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature, and calculating the actual heat load; using the relationship model to calculate the design heat load under real-time electric power, comparing the actual heat load with the design heat load, and obtaining the performance coefficient; correcting the initial sliding pressure curve according to the performance coefficient to obtain an optimized sliding pressure curve; monitoring the load stability of the unit, and when it is determined that the load is stable, applying the optimized sliding pressure curve to the unit operation control; when it is determined that the load is in dynamic change, selecting the main steam pressure according to the preset thermal logic; continuously monitoring the unit operation parameters, and when a new stable load point is detected, re-executing the sliding pressure adjustment process.

[0009] As a preferred solution of the method for adjusting the sliding pressure curve based on the performance coefficient described in the present invention, wherein: dynamically generating the initial sliding pressure curve using the relationship model includes the following steps: under multiple preset load conditions, measuring the electric power output and the corresponding heat dissipation of the unit, and recording multiple groups of data points; establishing a nonlinear relationship model between electric power and heat dissipation based on the multiple groups of data points; calculating the thermal efficiency correction coefficient, and correcting the nonlinear relationship model using the thermal efficiency correction coefficient; constructing an adaptive sliding pressure curve generation algorithm based on the corrected nonlinear relationship model to generate an initial sliding pressure curve; setting an update time interval for the initial sliding pressure curve; comparing the initial sliding pressure curve with the historical operation data of the unit to verify the rationality, and saving the verified initial sliding pressure curve; the specific formula of the thermal efficiency correction coefficient is as follows:

[0010] ,

[0011] in, is the thermal efficiency correction factor, is the current load, is the rated load, is the ambient temperature, is the reference temperature, t is the number of hours in a year, is the fitting coefficient.

[0012] As a preferred solution of the method for adjusting the sliding pressure curve based on the performance coefficient of the present invention, the specific formula of the actual heat load is as follows:

[0013] ,

[0014] Among them, Q is the circulating water heat change value, in which the circulating water heat change value is taken as the actual heat load of the unit, m is the circulating water mass flow rate, is the specific heat capacity of water, is the circulating water outlet temperature, is the circulating water inlet temperature, is the correction factor, is the current load, is the rated load, is the ambient temperature, is the reference temperature.

[0015] As a preferred solution of the method for adjusting the sliding pressure curve based on the performance coefficient of the present invention, the specific formula of the performance coefficient is as follows:

[0016] ,

[0017] in, is the performance coefficient, is the actual heat load, For the design heat load, is the load correction factor, is the time attenuation coefficient, g is the unit operation time, is the time constant, is the current load, For rated load.

[0018] As a preferred solution of the method for adjusting the sliding pressure curve based on the performance coefficient described in the present invention, wherein: correcting the initial sliding pressure curve according to the performance coefficient includes the following steps: calculating the sliding pressure curve correction coefficient according to the performance coefficient, that is, taking the inverse of the performance coefficient as the correction coefficient; applying the sliding pressure curve correction coefficient to the pressure value of the initial sliding pressure curve to obtain the corrected pressure value; generating a sliding pressure curve point set based on the corrected pressure value using an interpolation algorithm; smoothing the sliding pressure curve point set; dynamically adjusting the sliding pressure curve based on the performance coefficient according to the maximum pressure change rate allowed by the unit, so that the pressure change rate between adjacent points does not exceed a preset threshold; using the historical operation data of the unit, establishing a correlation model between the sliding pressure curve and the unit efficiency; based on the correlation model, locally optimizing the adjusted sliding pressure curve; comparing the locally optimized sliding pressure curve with the safety limit, and adjusting the points beyond the safe operation range; using an adaptive weight method, fusing the initial sliding pressure curve and the optimized sliding pressure curve to generate a final optimized sliding pressure curve; discretizing the final optimized sliding pressure curve to generate a pressure-load comparison table that can be used by the control system.

[0019] As a preferred solution of the method for adjusting the sliding pressure curve based on the performance coefficient described in the present invention, wherein: monitoring the load stability of the unit includes the following steps: real-time acquisition of the current load data of the unit; calculation of the load change rate, the load change rate is the load change amount per unit time; setting a load stability judgment threshold, the load stability judgment threshold is determined based on the unit characteristics and historical operation data; comparing the calculated load change rate with the load stability judgment threshold to determine the load stability; when the load change rate is less than the load stability judgment threshold, the load is determined to be stable, and the following steps are performed: obtaining the current load value, according to the pressure-load comparison table, finding the optimal main steam pressure value corresponding to the current load value, and sending the found optimal main steam pressure value to the control system for unit operation control; when the load change rate is greater than or equal to the load stability judgment threshold, it is determined that the load is in dynamic change, and the following steps are performed: activating a preset thermal logic module, based on the current load value, load change trend and unit operation parameters, using thermal logic to calculate the main steam pressure, and sending the calculated main steam pressure to the control system for unit operation control; continuously monitoring load changes, regularly repeating the unit load stability monitoring process, real-time judgment of load stability and corresponding pressure control strategy switching.

[0020] As a preferred solution of the method for adjusting the sliding pressure curve based on the performance coefficient described in the present invention, when a new stable load point is detected, re-executing the sliding pressure adjustment process includes the following steps: setting a unit operation parameter monitoring cycle, the monitoring cycle is determined based on the unit response characteristics; in each monitoring cycle, collecting unit operation parameters, the unit operation parameters include electric power output, main steam pressure, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature; calculating the load difference between the current load point and the previous stable load point; setting a new load point determination threshold, the new load point determination threshold is based on the unit characteristics and historical operation The row data is determined; when the load difference is greater than the new load point judgment threshold, and the load change rate is less than the load stability judgment threshold, the current load point is marked as a new stable load point; for the new stable load point, the sliding pressure adjustment process is re-executed to generate a smoothed sliding pressure curve; the smoothed sliding pressure curve is discretized to generate a new pressure-load comparison table; the comparison table in the control system is updated with the newly generated pressure-load comparison table, and the new stable load point and its corresponding optimized sliding pressure curve are recorded; the current stable load point is set as the reference point for load difference calculation, and the parameter collection and analysis of the next monitoring cycle are continued.

[0021] In the second aspect, an embodiment of the present invention provides a system for adjusting a sliding pressure curve based on a performance coefficient, which includes a relationship model construction module for calibrating the heat dissipation in the design state, establishing a relationship model between the electric load and the heat dissipation, and dynamically generating an initial sliding pressure curve using the relationship model; an actual heat load calculation construction module for collecting electric power, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature under the operation state of the unit to calculate the actual heat load; a comparison module for calculating the design heat load under real-time electric power using the relationship model, comparing the actual heat load with the design heat load, and obtaining the performance coefficient; a correction module for correcting the initial sliding pressure curve according to the performance coefficient to obtain an optimized sliding pressure curve; an application module for monitoring the load stability of the unit, and when the load is determined to be stable, applying the optimized sliding pressure curve to the unit operation control; when the load is determined to be in dynamic change, selecting the main steam pressure according to the preset thermal logic; continuously monitoring the unit operation parameters, and when a new stable load point is detected, re-executing the sliding pressure adjustment process.

[0022] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the method for adjusting the sliding pressure curve based on the performance coefficient as described in the first aspect of the present invention are implemented.

[0023] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the method for adjusting the sliding pressure curve based on the performance coefficient as described in the first aspect of the present invention are implemented.

[0024] The beneficial effects of the present invention are as follows: the present invention can dynamically adjust the sliding pressure curve based on the performance coefficient according to the real-time load changes, thereby improving the adaptability and flexibility of the unit operation and ensuring that the unit can operate efficiently under different load conditions. By accurately calculating the difference between the actual heat load and the designed heat load, and dynamically adjusting the sliding pressure curve based on the performance coefficient, the present invention can effectively improve the thermal efficiency of the unit and reduce energy consumption. The present invention ensures that the unit is within the safe operating range by real-time monitoring and dynamically adjusting the sliding pressure curve based on the performance coefficient, thereby reducing the operating risks caused by excessive pressure changes and improving the safety of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1The framework flow chart of the method for adjusting the sliding pressure curve based on the performance coefficient is shown in FIG.

[0027] Figure 2 The flowchart of the method for adjusting the sliding pressure curve based on the performance coefficient is shown in FIG.

[0028] Figure 3 A computer equipment diagram for the method of adjusting the sliding pressure curve based on the performance coefficient. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, 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 accompanying drawings.

[0030] 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.

[0031] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0032] Example 1, reference Figure 1~Figure 3 , which is the first embodiment of the present invention, and provides a method for adjusting a sliding pressure curve based on a performance coefficient, comprising:

[0033] S1: Calibrate the heat dissipation in the design state, establish a relationship model between electrical load and heat dissipation, and use the relationship model to dynamically generate the initial sliding pressure curve.

[0034] Specifically, the following steps are included:

[0035] S1.1: Under multiple preset load conditions, measure the unit's electrical power output and corresponding heat dissipation, and record multiple sets of data points.

[0036] S1.2: Based on multiple sets of data points, a nonlinear relationship model between electric power and heat dissipation is established.

[0037] Specifically, the collected electric power and heat dissipation data are preprocessed to remove abnormal values ​​and outliers; the preprocessed electric power and heat dissipation data are normalized to obtain normalized electric power and normalized heat dissipation; based on the distribution characteristics of the normalized data (normalized electric power and normalized heat dissipation), candidate nonlinear model types are selected, including polynomial regression, exponential regression, logarithmic regression and compound function model; for each candidate nonlinear model type, the least squares method is used to calculate the model parameters and establish a preliminary nonlinear relationship model; the determination coefficient and root mean square error of each preliminary nonlinear relationship model are calculated to evaluate the degree of model fitting; based on the evaluation results, the preliminary nonlinear relationship model with the best fitting effect is selected; the selected preliminary nonlinear relationship model is subjected to k-fold cross validation to verify the generalization ability of the model; the residual distribution of the preliminary nonlinear relationship model is analyzed to ensure that the residuals are randomly distributed and have no obvious pattern; based on the cross validation and residual analysis results, the final nonlinear relationship model is determined.

[0038] S1.3: Calculate the thermal efficiency correction coefficient and use the thermal efficiency correction coefficient to correct the nonlinear relationship model.

[0039] Specifically, obtain the parameter values ​​under the current operating conditions, including the current load P, ambient temperature T amb and the number of hours in the year t; according to the historical operation data of the unit, determine the fitting coefficient Calculate the thermal efficiency correction factor ; The final nonlinear relationship model and thermal efficiency correction coefficient Multiply them to obtain a modified nonlinear relationship model; verify the modified nonlinear relationship model to ensure that it can accurately reflect the actual heat dissipation of the unit under different operating conditions.

[0040] It should be noted that the specific formula of the thermal efficiency correction coefficient is as follows:

[0041] ,

[0042] in, is the thermal efficiency correction factor, is the current load, is the rated load, is the ambient temperature, is the reference temperature, t is the number of hours in a year, is the fitting coefficient.

[0043] S1.4: Based on the modified nonlinear relationship model, an adaptive sliding-pressure curve generation algorithm is constructed to generate an initial sliding-pressure curve.

[0044] Preferably, the rated power and minimum stable load of the unit are obtained as the power range of the sliding pressure curve; the pressure range of the sliding pressure curve is determined, including the maximum allowable main steam pressure and the minimum allowable main steam pressure; based on the unit characteristics, the sliding pressure curve function type is selected , such as polynomial function or piecewise function; use the modified nonlinear relationship model to calculate the heat dissipation corresponding to different power points; establish a relationship model between heat dissipation and main steam pressure, and construct the initial mapping relationship between power and main steam pressure; select N power points within the pressure range of the sliding pressure curve , use the initial mapping relationship to calculate the main steam pressure corresponding to each power point ;based on Data points, using the least squares method to fit the sliding pressure curve function Parameters; introduce adaptive factors , so that the sliding pressure curve can be dynamically adjusted according to the actual operating conditions; define the adaptive sliding pressure curve function , dynamically adjust the adaptive factor according to the real-time operation data of the unit The value of The initial value is 1; within the power range of the sliding pressure curve, select M power points, calculate the corresponding main steam pressure value, and generate the initial sliding pressure curve discrete data set ,in ; Smooth the discrete data set of the initial sliding pressure curve to ensure that the pressure change rate between adjacent points does not exceed the preset threshold; connect the smoothed discrete data points to generate a continuous initial sliding pressure curve; verify whether the generated initial sliding pressure curve meets the unit operation requirements and safety restrictions, and make local adjustments if not.

[0045] S1.5: Set the update time interval of the initial sliding pressure curve to ensure that the update frequency matches the unit response time.

[0046] S1.6: Compare the initial sliding pressure curve with the historical operating data of the unit to verify its rationality, and save the verified initial sliding pressure curve.

[0047] S2: When the unit is in operation, collect the electric power, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature to calculate the actual heat load.

[0048] Specifically, the current electric power data of the unit is collected; the circulating water mass flow rate, circulating water inlet temperature and circulating water outlet temperature are measured; the circulating water heat change value is calculated based on the circulating water mass flow rate, circulating water inlet temperature and circulating water outlet temperature; the circulating water heat change value is used as the actual heat load of the unit.

[0049] Preferably, the calculation formula for the circulating water heat change value is as follows:

[0050] ,

[0051] Among them, Q is the circulating water heat change value, m is the circulating water mass flow rate, is the specific heat capacity of water, is the circulating water outlet temperature, is the circulating water inlet temperature, is the correction factor, is the current load, is the rated load, is the ambient temperature, is the reference temperature.

[0052] S3: Calculate the design heat load under the real-time electric power using the relational model, compare the actual heat load with the design heat load, and obtain the performance coefficient.

[0053] Specifically, the real-time electric power data of the current unit is obtained; the real-time electric power data is input into the modified nonlinear relationship model; the corresponding design heat load is calculated using the modified nonlinear relationship model; the actual heat load calculated in S2 is obtained; the performance coefficient is calculated, and the specific formula is as follows:

[0054] ,

[0055] in, is the performance coefficient, is the actual heat load, For the design heat load, is the load correction factor, is the time attenuation coefficient, g is the unit operation time, is the time constant, is the current load, For rated load.

[0056] S4: Correct the initial sliding pressure curve according to the performance coefficient to obtain an optimized sliding pressure curve.

[0057] Specifically, the following steps are included:

[0058] S4.1: Calculate the sliding pressure curve correction factor based on the performance coefficient.

[0059] It should be noted that the correction factor is the reciprocal of the performance coefficient.

[0060] S4.2: Apply the sliding pressure curve correction coefficient to the pressure value of the initial sliding pressure curve to obtain a corrected pressure value.

[0061] It should be noted that the corrected pressure value is the product of the correction coefficient of the sliding pressure curve and the pressure value of the initial sliding pressure curve.

[0062] S4.3: Based on the corrected pressure value, generate a set of sliding pressure curve points using an interpolation algorithm.

[0063] S4.4: Smooth the sliding pressure curve point set and use the moving average method or low-pass filter to reduce the curve fluctuation.

[0064] S4.5: Based on the maximum pressure change rate allowed by the unit, the sliding pressure curve is dynamically adjusted based on the performance coefficient so that the pressure change rate between adjacent points does not exceed the preset threshold.

[0065] S4.6: Use the historical operating data of the unit to establish a correlation model between the sliding pressure curve and the unit efficiency.

[0066] S4.7: Based on the correlation model, locally optimize the adjusted sliding pressure curve to improve the overall efficiency of the unit.

[0067] S4.8: Compare the locally optimized sliding pressure curve with the safety limit and adjust the points that are beyond the safe operating range.

[0068] S4.9: Adopting the adaptive weight method, the initial sliding-pressure curve and the optimized sliding-pressure curve are integrated to generate the final optimized sliding-pressure curve.

[0069] Preferably, an adaptive weight method is used to fuse the initial sliding pressure curve and the optimized sliding pressure curve, and a pressure-load comparison table is generated to ensure that the control system can quickly respond to load changes and optimize the operation control strategy of the unit.

[0070] S4.10: Discretize the final optimized sliding pressure curve to generate a pressure-load comparison table that can be used by the control system.

[0071] S5: Monitor unit load stability.

[0072] Specifically, the current load data of the unit is collected in real time, including the electric power output; the load change rate is calculated, and the load change rate is the change in load per unit time; the load stability judgment threshold is set, and the load stability judgment threshold is determined based on the unit characteristics and historical operation data; the calculated load change rate is compared with the load stability judgment threshold to determine the load stability; when the load change rate is less than the load stability judgment threshold, the load is determined to be stable, and the following steps are performed: the current load value is obtained, and the optimal main steam pressure value corresponding to the current load value is found according to the pressure-load comparison table, and the found optimal main steam pressure value is sent to the control system for unit operation control; when the load change rate is greater than or equal to the load stability judgment threshold, the load is determined to be in dynamic change, and the following steps are performed: the preset thermal logic module is activated, and the main steam pressure is calculated using thermal logic based on the current load value, load change trend and unit operation parameters, and the calculated main steam pressure is sent to the control system for unit operation control; the load changes are continuously monitored, and the unit load stability monitoring process is regularly repeated to make real-time judgments on the load stability and switch the corresponding pressure control strategy.

[0073] It should be noted that the calculation of the main steam pressure includes the following steps: calculating according to the current load value and the load value at the previous moment to obtain the load change trend; calculating according to the initial sliding pressure curve pressure value corresponding to the current load value, the load change trend and the unit operating parameters to obtain the initial main steam pressure, wherein the initial main steam pressure is the product of the load change trend and the unit operating parameters (i.e., the proportional coefficient) plus one, and the product of the initial sliding pressure curve pressure value; judging the initial main steam pressure based on thermal logic, if the initial main steam pressure is greater than the maximum main steam pressure, the main steam pressure is the maximum main steam pressure; if the initial main steam pressure is less than the minimum main steam pressure, the main steam pressure is the minimum main steam pressure; if the initial main steam pressure is less than or equal to the maximum main steam pressure and greater than or equal to the minimum main steam pressure, the main steam pressure is the initial main steam pressure.

[0074] S6: Continuously monitor the unit operating parameters and re-execute the sliding pressure adjustment process when a new stable load point is detected.

[0075] Specifically, a monitoring cycle of the unit operation parameters is set, and the monitoring cycle is determined based on the unit response characteristics; in each monitoring cycle, the unit operation parameters are collected, including electric power output, main steam pressure, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature; the load difference between the current load point and the previous stable load point is calculated; a new load point determination threshold is set, and the new load point determination threshold is determined based on the unit characteristics and historical operation data; when the load difference is greater than the new load point determination threshold, and the load change rate is less than the load stability determination threshold (i.e., S5 determines that the load is stable), the current load point is marked as a new stable load point; for the new stable load point, the sliding pressure adjustment process is re-executed to generate a smoothed sliding pressure curve; the smoothed sliding pressure curve is discretized to generate a new pressure-load comparison table; the comparison table in the control system is updated with the newly generated pressure-load comparison table, and the new stable load point and its corresponding optimized sliding pressure curve are recorded; the current stable load point is set as the reference point for load difference calculation, and parameter collection and analysis of the next monitoring cycle are continued.

[0076] Furthermore, the present embodiment also provides a system for adjusting the sliding pressure curve based on the performance coefficient, including a relationship model construction module, which is used to calibrate the heat dissipation in the design state, establish a relationship model between the electric load and the heat dissipation, and dynamically generate an initial sliding pressure curve using the relationship model; an actual heat load calculation construction module, which is used to collect the electric power, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature under the operation state of the unit, and calculate the actual heat load; a comparison module, which is used to calculate the design heat load under the real-time electric power using the relationship model, compare the actual heat load with the design heat load, and obtain the performance coefficient; a correction module, which is used to correct the initial sliding pressure curve according to the performance coefficient to obtain an optimized sliding pressure curve; an application module, which is used to monitor the load stability of the unit, and when the load is determined to be stable, the optimized sliding pressure curve is applied to the unit operation control; when the load is determined to be in dynamic change, the main steam pressure is selected according to the preset thermal logic; the unit operation parameters are continuously monitored, and when a new stable load point is detected, the sliding pressure adjustment process is re-executed.

[0077] This embodiment also provides a computer device, which is suitable for the method of adjusting the sliding pressure curve based on the performance coefficient, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the method of adjusting the sliding pressure curve based on the performance coefficient as proposed in the above embodiment.

[0078] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0079] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for adjusting a sliding pressure curve based on a performance coefficient as proposed in the above embodiment is implemented.

[0080] In summary, the present invention can dynamically adjust the sliding pressure curve based on the performance coefficient according to the real-time load changes, thereby improving the adaptability and flexibility of the unit operation and ensuring that the unit can operate efficiently under different load conditions. By accurately calculating the difference between the actual heat load and the designed heat load, and dynamically adjusting the sliding pressure curve based on the performance coefficient, the present invention can effectively improve the thermal efficiency of the unit and reduce energy consumption. The present invention ensures that the unit is within the safe operating range by real-time monitoring and dynamically adjusting the sliding pressure curve based on the performance coefficient, thereby reducing the operating risks caused by excessive pressure changes and improving the safety of the unit.

[0081] Example 2, reference Figure 1~Figure 3 , which is the second embodiment of the present invention, and this embodiment provides a method for adjusting the sliding pressure curve based on the performance coefficient. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0082] Specifically, in order to verify the effectiveness of the present invention, a simulation experiment was conducted on a 300MW thermal power unit. First, a nonlinear relationship model between electric power and heat dissipation was established according to step S1. By collecting multiple groups of data points, the final corrected nonlinear relationship model was obtained. Based on this model, an initial sliding pressure curve was constructed, and the update time interval was set to 30 minutes.

[0083] Next, real-time data was collected while the unit was in operation. In one acquisition, the power was 250MW, the circulating water mass flow was 12,000 t / h, the circulating water inlet temperature was 25°C, and the outlet temperature was 35°C. According to step S2, the actual heat load was calculated to be 139.5MW. At the same time, the design heat load was calculated to be 145.2MW using the relational model. By comparison, the performance coefficient was obtained to be 0.96. Based on this performance coefficient, the initial sliding pressure curve was corrected and optimized according to step S4, and a new pressure-load comparison table was generated.

[0084] During continuous operation, the load stability of the unit is continuously monitored. When the load gradually increases from 250MW to 270MW, the load change rate exceeds the preset 1MW / min threshold, and the system automatically switches to the thermal logic module to calculate the main steam pressure. When the load finally stabilizes at 270MW, the system re-executes the sliding pressure adjustment process and generates a new optimized sliding pressure curve. After multiple iterations of optimization, it is found that the method of the present invention can effectively improve the operating efficiency of the unit.

[0085] Preferably, the comparison indicators of the present invention and the traditional method are shown in Table 1.

[0086] Table 1 Comparison indexes of the present invention and the traditional method

[0087] ,

[0088] The experimental results show that the method of the present invention has significant improvements in multiple key indicators compared with the traditional method, especially in improving thermal efficiency, reducing coal consumption and reducing main steam pressure fluctuations. This not only brings considerable economic benefits, but also makes a positive contribution to environmental protection.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. 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 adjusting a sliding pressure curve based on a performance coefficient, characterized in that: include, Calibrate the heat dissipation in the design state, establish the relationship model between the electrical load and the heat dissipation, and use the relationship model to dynamically generate the initial sliding pressure curve; Execute a sliding pressure adjustment process, the sliding pressure adjustment process comprising: When the unit is in operation, collect the electric power, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature to calculate the actual heat load; Calculating the design heat load under the real-time electric power by using the relational model, comparing the actual heat load with the design heat load, and obtaining a performance coefficient; Correcting the initial sliding pressure curve according to the performance coefficient to obtain an optimized sliding pressure curve; Monitor the load stability of the unit, and when it is determined that the load is stable, apply the optimized sliding pressure curve to the unit operation control; when it is determined that the load is in dynamic change, select the main steam pressure according to the preset thermal logic; Continuously monitor the unit operating parameters, and when a new stable load point is detected, re-execute the sliding pressure adjustment process; The method of dynamically generating an initial sliding pressure curve using a relational model comprises the following steps: Under multiple preset load conditions, measure the unit's electrical power output and corresponding heat dissipation, and record multiple sets of data points; Based on the multiple groups of data points, a nonlinear relationship model between electric power and heat dissipation is established; Calculating a thermal efficiency correction coefficient, and using the thermal efficiency correction coefficient to correct the nonlinear relationship model; Based on the modified nonlinear relationship model, an adaptive sliding-pressure curve generation algorithm is constructed to generate the initial sliding-pressure curve; Set the update time interval of the initial sliding pressure curve; Comparing the initial sliding pressure curve with historical operation data of the unit to verify rationality, and saving the verified initial sliding pressure curve; The specific formula of the thermal efficiency correction coefficient is as follows: Among them, η corr is the thermal efficiency correction coefficient, P is the current load, P rated is the rated load, T amb is the ambient temperature, T ref is the reference temperature, t is the number of hours in a year, α, β, γ are the fitting coefficients; The step of correcting the initial sliding pressure curve according to the performance coefficient comprises the following steps: According to the performance coefficient, the correction coefficient of the sliding pressure curve is calculated, that is, the reciprocal of the performance coefficient is used as the correction coefficient; Applying the sliding pressure curve correction coefficient to the pressure value of the initial sliding pressure curve to obtain a corrected pressure value; Based on the corrected pressure value, an interpolation algorithm is used to generate a sliding pressure curve point set; Smoothing the sliding pressure curve point set; According to the maximum pressure change rate allowed by the unit, the sliding pressure curve is dynamically adjusted based on the performance coefficient so that the pressure change rate between adjacent points does not exceed the preset threshold; Using historical operation data of the unit, a correlation model between sliding pressure curve and unit efficiency is established; Based on the correlation model, the adjusted sliding pressure curve is locally optimized; Compare the locally optimized sliding pressure curve with the safety limit and adjust the points that are beyond the safe operating range; Adopting adaptive weight method, the initial sliding pressure curve and the optimized sliding pressure curve are integrated to generate the final optimized sliding pressure curve; The final optimized sliding pressure curve is discretized to generate a pressure-load comparison table that can be used by the control system.

2. The method for adjusting a sliding pressure curve based on a performance coefficient according to claim 1, characterized in that: The specific formula of the actual heat load is as follows: Among them, Q is the circulating water heat change value, in which the circulating water heat change value is taken as the actual heat load of the unit, m is the circulating water mass flow rate, c p is the specific heat capacity of water, T out is the circulating water outlet temperature, T in is the circulating water inlet temperature, ε and δ are correction coefficients, P is the current load, P rated is the rated load, T amb is the ambient temperature, T ref is the reference temperature.

3. The method for adjusting a sliding pressure curve based on a performance coefficient according to claim 1, characterized in that: The specific formula of the performance coefficient is as follows: Among them, K pref is the performance coefficient, Q actual is the actual heat load, Q design is the design heat load, λ is the load correction factor, μ is the time attenuation factor, g is the unit operation time, τ is the time constant, P is the current load, P rated For rated load.

4. The method for adjusting a sliding pressure curve based on a performance coefficient according to claim 1, characterized in that: The monitoring of unit load stability comprises the following steps: Real-time collection of current load data of the unit; Calculate the load change rate, where the load change rate is the amount of change in load per unit time; Setting a load stability judgment threshold, wherein the load stability judgment threshold is determined based on unit characteristics and historical operation data; Compare the calculated load change rate with the load stability judgment threshold to determine the load stability; When the load change rate is less than the load stability judgment threshold, the load is determined to be stable, and the following steps are performed: obtaining the current load value, searching for the optimal main steam pressure value corresponding to the current load value according to the pressure-load comparison table, and sending the found optimal main steam pressure value to the control system for unit operation control; When the load change rate is greater than or equal to the load stability judgment threshold, it is determined that the load is in dynamic change, and the following steps are performed: activating a preset thermal logic module, using thermal logic to calculate the main steam pressure based on the current load value, load change trend and unit operation parameters, and sending the calculated main steam pressure to the control system for unit operation control; Continuously monitor load changes, regularly repeat the unit load stability monitoring process, make real-time judgments on load stability and switch the corresponding pressure control strategy.

5. The method for adjusting a sliding pressure curve based on a performance coefficient according to claim 4, characterized in that: When a new stable load point is detected, re-executing the sliding pressure adjustment process includes the following steps: Setting a monitoring period for unit operation parameters, wherein the monitoring period is determined based on the unit response characteristics; In each monitoring cycle, the unit operating parameters are collected, and the unit operating parameters include electric power output, main steam pressure, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature; Calculate the load difference between the current load point and the last stable load point; Setting a new load point determination threshold, wherein the new load point determination threshold is determined based on unit characteristics and historical operation data; When the load difference is greater than the new load point determination threshold, and the load change rate is less than the load stability determination threshold, the current load point is marked as a new stable load point; For the new stable load point, the sliding pressure adjustment process is re-executed to generate a smoothed sliding pressure curve; Discretize the smoothed sliding pressure curve to generate a new pressure-load comparison table; Update the comparison table in the control system with the newly generated pressure-load comparison table, and record the new stable load point and its corresponding optimized sliding pressure curve; The current stable load point is set as the reference point for load difference calculation, and parameter collection and analysis for the next monitoring cycle are continued.

6. A system for adjusting a sliding pressure curve based on a performance coefficient, based on the method for adjusting a sliding pressure curve based on a performance coefficient according to any one of claims 1 to 5, characterized in that: Also includes, The relational model building module is used to calibrate the heat dissipation in the design state, establish the relational model between the electric load and the heat dissipation, and dynamically generate the initial sliding pressure curve using the relational model; The actual heat load calculation building module is used to collect the electric power, circulating water mass flow, circulating water inlet temperature and circulating water outlet temperature under the operation state of the unit to calculate the actual heat load; A comparison module, used to calculate the design heat load under the real-time electric power by using the relational model, compare the actual heat load with the design heat load, and obtain a performance coefficient; A correction module, used for correcting the initial sliding pressure curve according to the performance coefficient to obtain an optimized sliding pressure curve; An application module is used to monitor the load stability of the unit. When the load is determined to be stable, the optimized sliding pressure curve is applied to the unit operation control; when the load is determined to be in dynamic change, the main steam pressure is selected according to the preset thermal logic; The unit operating parameters are continuously monitored, and when a new stable load point is detected, the sliding pressure adjustment process is re-executed.

7. 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 method for adjusting the sliding pressure curve based on the performance coefficient according to any one of claims 1 to 5 are implemented.

8. 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 method for adjusting a sliding pressure curve based on a performance coefficient according to any one of claims 1 to 5 are implemented.

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