Turbine cooling system operation optimization method and system

Abstract

The invention discloses a steam turbine cooling system operation optimization method and system, and the method comprises the steps: obtaining the real-time load data of a steam turbine, analyzing the real-time load data, and determining the operation state of the steam turbine; setting initial operation conditions of the cooling system according to the operation state of the steam turbine, wherein the initial operation conditions comprise water pump frequency and fan rotating speed; acquiring real-time temperature data of the environment, analyzing the real-time temperature data, and determining a change coefficient of the environment temperature; and determining an optimization coefficient for the change coefficient of the running state of the steam turbine according to the environment temperature, and optimizing the initial running condition of the cooling system according to the optimization coefficient. Through real-time data monitoring, a data intelligent analysis technology and a self-adaptive control decision, operation parameters can be dynamically adjusted according to actual conditions so as to adapt to changes of the operation state of the steam turbine and fluctuations of environmental conditions, intelligent optimization of operation of the cooling system is achieved, and therefore the efficiency and performance of the cooling system are improved to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and in particular to an operation optimization method and system for a steam turbine cooling system. Background Art

[0002] As an essential energy conversion device, steam turbines play a key role in industrial production and energy generation. To ensure efficient and stable operation of steam turbines, effective operation and optimization of the cooling system are particularly important. Steam turbines generate significant heat during operation. Failure to effectively dissipate heat can lead to overheating, performance degradation, and even damage. Therefore, optimizing the cooling system is crucial to ensuring the proper operation of steam turbines.

[0003] However, traditional methods are often based on static designs and cannot be dynamically adjusted according to real-time data and environmental changes. As a result, the cooling system cannot respond flexibly to load fluctuations or environmental changes, and its efficiency and performance are limited. In addition, traditional methods lack intelligent decision-making support and cannot achieve intelligent analysis and optimization adjustments of data. The lack of adaptability and intelligent decision-making limits the system's response speed and efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for optimizing the operation of a steam turbine cooling system, comprising: Obtain the real-time load data of the steam turbine, analyze the real-time load data, and determine the operating status of the steam turbine; Set the initial operating conditions of the cooling system according to the operating status of the steam turbine, including the water pump frequency and fan speed; Obtain real-time temperature data of the environment, analyze the real-time temperature data, and determine the coefficient of change of the ambient temperature; The optimization coefficient is determined according to the variation coefficient of the operating state of the steam turbine due to the ambient temperature, and the initial operating conditions of the cooling system are optimized according to the optimization coefficient.

[0005] Furthermore, the obtaining of real-time load data of the steam turbine and analyzing the real-time load data to determine the operating status of the steam turbine include: Obtain the real-time load data of the steam turbine, perform cluster analysis on the real-time load data, and divide the real-time load data of the steam turbine into several operating stages according to the cluster analysis results; Obtain the real-time load data corresponding to each operation stage, and calculate the average value of the real-time load data corresponding to each operation stage; determining an operation stage in which the average value of the real-time load data is less than or equal to a first preset value as a low-load operation stage, determining an operation stage in which the average value of the real-time load data is greater than or equal to a second preset value as a high-load operation stage, and determining an operation stage in which the average value of the real-time load data is between the first preset value and the second preset value as a fluctuating load operation stage, wherein the first preset value is less than the second preset value; The operating status evaluation value of the steam turbine is determined based on the average value of the real-time load data corresponding to the low-load operating stage, the high-load operating stage, and the fluctuating load operating stage. The calculation formula of the operating status evaluation value of the steam turbine is: P=a*L+b*H+c*M, Among them, P is the operating status evaluation value of the turbine, a is the first preset evaluation coefficient, L is the average value of the real-time load data corresponding to the low-load operation stage, b is the second preset evaluation coefficient, H is the average value of the real-time load data corresponding to the high-load operation stage, c is the third preset evaluation coefficient, and M is the average value of the real-time load data corresponding to the fluctuating load operation stage.

[0006] Furthermore, the real-time load data is clustered and divided into several operation stages according to the cluster analysis results, including: Determine the number of data in the real-time load data and randomly select k initial cluster centers based on the number of data; Calculate the distance between each data point and the initial cluster center in the real-time load data, and divide each data point into clusters according to the distance between each data point and the initial cluster center. Calculate the average value of all data based on the data in each cluster after clustering, and update the cluster center based on the average value of all data; Repeat the iteration and set a new cluster center until the cluster center no longer changes, and obtain several clusters. Then, divide the real-time load data of the steam turbine into several operation stages according to the cluster division results.

[0007] Furthermore, the initial operating conditions of the cooling system are set according to the operating state of the steam turbine. The initial operating conditions include the water pump frequency and the fan speed, including: Obtaining an operating status evaluation value ΔD of the steam turbine and a preset standard evaluation value D0, and determining a first preset difference D1, a second preset difference D2, a third preset difference D3, and a fourth preset difference D4, wherein D1 < D2 < D3 < D4; presetting a first preset operating condition K1 (a1, b1), a second preset operating condition K2 (a2, b2), a third preset operating condition K3 (a3, b3), and a fourth preset operating condition K4 (a4, b4) of the cooling system, wherein the cooling system includes a circulating water pump and a cooling tower fan, a1-a4 are first to fourth preset water pump frequencies, a1 < a2 < a3 < a4, b1-b4 are first to fourth preset fan speeds, and b1 < b2 < b3 < b4; According to the difference between the evaluation value △D and the preset standard evaluation value D0, the preset working condition Ki is selected as the initial operating condition of the cooling system; When △D-D0≤D1, the first preset working condition K1 is selected as the initial operating condition of the cooling system; When D1<△D-D0≤D2, the second preset working condition K2 is selected as the initial operating condition of the cooling system; When D2<△D-D0≤D3, the third preset working condition K3 is selected as the initial operating condition of the cooling system; When D3<△D-D0≤D4, the fourth preset working condition K4 is selected as the initial operating condition of the cooling system; The cooling system is controlled to operate according to the selected preset initial operating condition Ki (ai, bi) as the initial operating condition of the cooling system.

[0008] Furthermore, the step of obtaining real-time temperature data of the environment and analyzing the real-time temperature data to determine a coefficient of variation of the ambient temperature includes: Obtain real-time temperature data of the environment and construct a temperature change curve chart of the time progress based on the real-time temperature data of the environment; Determine the peak points and valley points in the temperature change curve graph, and divide the temperature change curve graph into a plurality of curve segments according to the peak points and valley points; The slope value and the variation amplitude of each curve segment are determined, and the variation coefficient of the ambient temperature is determined based on the slope value and the variation amplitude of each curve segment.

[0009] Furthermore, determining the coefficient of change of the ambient temperature based on the slope value and the change amplitude of each curve segment includes: The coefficient of change of the ambient temperature is calculated based on the slope value and change amplitude of each curve segment. The calculation formula of the coefficient of change of the ambient temperature is: , Where S is the coefficient of change of the ambient temperature, α is the preset conversion coefficient, vi is the slope value of the i-th curve segment, and fi is the amplitude of change of the i-th curve segment.

[0010] Furthermore, determining an optimization coefficient based on a coefficient of change of the operating state of the steam turbine according to the ambient temperature, and optimizing the initial operating conditions of the cooling system according to the optimization coefficient, includes: A correspondence between the optimization coefficient and the variation coefficient interval is pre-set, wherein the correspondence between the optimization coefficient and the variation coefficient interval is associated with a corresponding optimization coefficient for each variation coefficient interval; Obtaining a coefficient of variation of the ambient temperature to the operating state of the steam turbine, and based on a mapping relationship between a variation coefficient interval to which the variation coefficient belongs and an optimization coefficient corresponding to the variation coefficient interval within a corresponding relationship between an optimization coefficient and a variation coefficient interval, selecting an optimization coefficient corresponding to the variation coefficient interval as an optimization coefficient corresponding to a combustion parameter of the thermal power generating set; The initial operating condition Ki (ai, bi) of the cooling system is optimized according to the variation coefficient zi, and the operating condition of the optimized cooling system is Ki (ai*zi, bi*zi).

[0011] The present invention also provides a steam turbine cooling system operation optimization system, comprising: The analysis module is used to obtain the real-time load data of the steam turbine, analyze the real-time load data, and determine the operating status of the steam turbine; A setting module is used to set the initial operating conditions of the cooling system according to the operating status of the steam turbine, and the initial operating conditions include the water pump frequency and the fan speed; A determination module is used to obtain real-time temperature data of the environment, analyze the real-time temperature data, and determine the coefficient of change of the ambient temperature; The optimization module is used to determine the optimization coefficient according to the change coefficient of the turbine operating state of the ambient temperature, and optimize the initial operating conditions of the cooling system according to the optimization coefficient.

[0012] Compared with the prior art, the steam turbine cooling system operation optimization method and system according to the embodiment of the present invention have the following beneficial effects: The present invention can achieve real-time monitoring and analysis of the steam turbine operating status and environmental conditions by acquiring the steam turbine load data and ambient temperature data in real time and combining data analysis technology; Based on the analysis results of ambient temperature data and turbine operating status, the present invention can realize intelligent optimization decision-making and adjust the initial operating conditions of the cooling system to improve system efficiency and performance; The present invention determines the optimization coefficient according to the variation coefficient of the ambient temperature, realizes the adaptive adjustment of the operating conditions of the cooling system, and enables the system to make corresponding optimization adjustments according to changes in the external environment; The present invention can improve system energy efficiency, reduce energy consumption costs, and extend the service life of equipment by optimizing the operating state of the steam turbine and the operating conditions of the cooling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 1 is a schematic diagram of the flow structure of a method for optimizing the operation of a steam turbine cooling system according to an embodiment of the present invention; Figure 2 Schematic diagram of the composition of the steam turbine cooling system operation optimization system in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0015] In the description of this application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on this application.

[0016] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Therefore, a feature specified with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0017] In the description of this application, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they can refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Persons of ordinary skill in the art will understand the specific meanings of the above terms in this application based on specific circumstances.

[0018] like Figure 1As shown, in an embodiment of the present application, a method for optimizing the operation of a steam turbine cooling system is provided, including: S100: obtaining real-time load data of the steam turbine, and analyzing the real-time load data to determine the operating status of the steam turbine; S200: setting the initial operating conditions of the cooling system according to the operating status of the steam turbine, the initial operating conditions including the water pump frequency and the fan speed; S300: obtaining real-time temperature data of the environment, and analyzing the real-time temperature data to determine the variation coefficient of the ambient temperature; S400: determining an optimization coefficient based on the variation coefficient of the operating status of the steam turbine to the ambient temperature, and optimizing the initial operating conditions of the cooling system according to the optimization coefficient.

[0019] Furthermore, the present invention can realize real-time monitoring and analysis of the operating status and environmental conditions of the turbine by acquiring the load data and ambient temperature data of the turbine in real time and combining it with data analysis technology; the present invention can realize intelligent optimization decision-making based on the analysis results of the ambient temperature data and the operating status of the turbine, and adjust the initial operating conditions of the cooling system to improve the efficiency and performance of the system; the present invention determines the optimization coefficient according to the coefficient of change of the ambient temperature, realizes adaptive adjustment of the operating conditions of the cooling system, and enables the system to make corresponding optimization adjustments according to changes in the external environment; the present invention can improve the energy efficiency of the system, reduce energy consumption costs, and extend the service life of the equipment by optimizing the operating status of the turbine and the operating conditions of the cooling system.

[0020] In an embodiment of the present application, a method for optimizing the operation of a steam turbine cooling system is provided, wherein the method acquires real-time load data of the steam turbine, analyzes the real-time load data, and determines the operating state of the steam turbine, including: acquiring real-time load data of the steam turbine, performing cluster analysis on the real-time load data, and dividing the real-time load data of the steam turbine into a plurality of operating stages according to the cluster analysis results; acquiring real-time load data corresponding to each operating stage, and calculating an average value of the real-time load data corresponding to each operating stage; determining an operating stage in which the average value of the real-time load data is less than or equal to a first preset value as a low-load operating stage, determining an operating stage in which the average value of the real-time load data is greater than or equal to a second preset value as a high-load operating stage, and determining an operating stage in which the average value of the real-time load data is between a first preset value and a second preset value as a fluctuating load operating stage, wherein the first preset value is less than the second preset value; and determining an operating state evaluation value of the steam turbine based on the average values of the real-time load data corresponding to the low-load operating stage, the high-load operating stage, and the fluctuating load operating stage, wherein the calculation formula of the operating state evaluation value of the steam turbine is: P=a*L+b*H+c*M, Among them, P is the operating status evaluation value of the turbine, a is the first preset evaluation coefficient, L is the average value of the real-time load data corresponding to the low-load operation stage, b is the second preset evaluation coefficient, H is the average value of the real-time load data corresponding to the high-load operation stage, c is the third preset evaluation coefficient, and M is the average value of the real-time load data corresponding to the fluctuating load operation stage.

[0021] Specifically, the real-time load data of the steam turbine is obtained, and then cluster analysis is performed on these data to divide the data into different operating stages, namely low-load, high-load and fluctuating load operating stages; for each operating stage, the average value of the corresponding real-time load data is calculated to obtain the average value (L) of the low-load stage, the average value (H) of the high-load stage and the average value (M) of the fluctuating load stage; based on the given preset evaluation coefficients (a, b, c) and the average load values of each stage, the operating status evaluation value (P) of the steam turbine is calculated, and the operating status of the steam turbine is determined by the formula. This step can achieve real-time evaluation of the turbine operating status by acquiring and processing the turbine load data in real time, helping operators to understand the turbine operating status in a timely manner; through cluster analysis and calculation of average load values, the turbine operating status can be automatically divided into different stages, reducing human intervention and improving the system's degree of automation; based on the calculation results of the operating status evaluation value, it can provide a basis for subsequent operating status optimization, helping to adjust the turbine operating parameters to improve system efficiency and performance; this method comprehensively considers the three operating states of low load, high load and fluctuating load, and comprehensively reflects the operating status of the turbine under different load states through the calculation of the evaluation value.

[0022] In an embodiment of the present application, a method for optimizing the operation of a steam turbine cooling system is provided, wherein cluster analysis is performed on real-time load data, and the real-time load data of the steam turbine is divided into several operation stages according to the cluster analysis results, including: determining the number of data in the real-time load data, and randomly selecting k initial clustering centers according to the number of data; calculating the distance value between each data in the real-time load data and the initial clustering center, and dividing each data into clusters according to the distance value between each data and the initial clustering center; calculating the average value of all data based on the data in each cluster after clustering, and updating the cluster center based on the average value of all data; repeating the iteration and setting a new cluster center until the cluster center no longer changes, obtaining several clusters, and dividing the real-time load data of the steam turbine into several operation stages according to the cluster division results.

[0023] Specifically, the number of data in the real-time load data is determined, and k initial cluster centers are randomly selected, where k is the number of clusters pre-set by the user; the distance value between each data point and the k initial cluster centers is calculated, and each data point is divided into the cluster closest to it according to the distance value; the position of the cluster center is updated according to the average value of the data in each cluster, that is, each cluster center is updated to the average value of all the data in the cluster; the iterative calculation is repeated and the cluster center is continuously updated until the cluster center no longer changes and reaches a stable state, at which time several clusters are obtained; according to the results of the cluster analysis, the real-time load data of the turbine is divided into several operating stages, such as low load, high load and fluctuating load stages. This step uses the K-means clustering algorithm to automatically perform cluster analysis on the real-time load data of the turbine without manual intervention, thereby improving efficiency and accuracy. This method can quickly process large amounts of real-time load data and perform real-time cluster analysis, helping to monitor the operating status of the turbine in real time. The cluster analysis results can be used to visualize data points into different clusters, intuitively displaying the distribution of the real-time load data of the turbine and helping operators understand the system status. By dividing the real-time load data into different operating stages, the operating status of the turbine under different operating conditions can be more accurately evaluated, providing an important reference for subsequent optimization and adjustment.

[0024] In an embodiment of the present application, a method for optimizing the operation of a steam turbine cooling system is provided, wherein the initial operating conditions of the cooling system are set according to the operating state of the steam turbine, the initial operating conditions including the water pump frequency and the fan speed, and the method includes: obtaining an operating state evaluation value ΔD of the steam turbine and a preset standard evaluation value D0, and determining a preset first preset difference D1, a second preset difference D2, a third preset difference D3, and a fourth preset difference D4, and D1 < D2 < D3 < D4; presetting a first preset working condition K1 (a1, b1), a second preset working condition K2 (a2, b2), a third preset working condition K3 (a3, b3), and a fourth preset working condition K4 (a4, b4) of the cooling system, wherein the cooling system includes a circulating water pump and a cooling tower fan, a1-a4 are the first to fourth preset water pump frequencies, and a1 < a2 < a3<a4, b1-b4 are the first to fourth preset fan speeds respectively, b1<b2<b3<b4; according to the difference between the evaluation value △D and the preset standard evaluation value D0, the preset working condition Ki is selected as the initial operating condition of the cooling system; when △D-D0≤D1, the first preset working condition K1 is selected as the initial operating condition of the cooling system; when D1<△D-D0≤D2, the second preset working condition K2 is selected as the initial operating condition of the cooling system; when D2<△D-D0≤D3, the third preset working condition K3 is selected as the initial operating condition of the cooling system; when D3<△D-D0≤D4, the fourth preset working condition K4 is selected as the initial operating condition of the cooling system; the cooling system is controlled to operate according to which preset initial operating condition Ki (ai, bi) is selected as the initial operating condition of the cooling system.

[0025] Specifically, a given turbine operating status evaluation value △D and a preset standard evaluation value D0 are used to measure the operating status of the turbine; the first to fourth preset difference values D1, D2, D3, and D4, and corresponding cooling system working conditions K1 to K4 are set, where the working conditions include water pump frequency and fan speed; based on the difference between the evaluation value △D and the standard evaluation value D0, determine which preset working condition is selected as the initial operating condition of the cooling system: when △D-D0≤D1, K1 is selected as the initial operating condition; when D1<△D-D0≤D2, K2 is selected as the initial operating condition; when D2<△D-D0≤D3, K3 is selected as the initial operating condition; when D3<△D-D0≤D4, K4 is selected as the initial operating condition; based on the selected preset initial operating condition Ki (ai, bi), the water pump frequency and fan speed of the cooling system are controlled so that the cooling system operates according to the set conditions. This step realizes intelligent control of the cooling system by selecting appropriate initial operating conditions of the cooling system based on real-time evaluation values, thereby improving the stability and efficiency of the system; by comparing preset conditions and evaluation values, it realizes automatic selection of appropriate working conditions, reduces the need for manual intervention, and improves the automation level of system operation; according to the difference range between different evaluation values and preset standards, appropriate working conditions are selected, and the operating efficiency of the cooling system can be preliminarily set to improve the reliability and stability of the cooling system and reduce operational risks.

[0026] In an embodiment of the present application, a method for optimizing the operation of a turbine cooling system is provided, wherein real-time temperature data of the environment is obtained, and the real-time temperature data is analyzed to determine the coefficient of variation of the ambient temperature, including: obtaining real-time temperature data of the environment, and constructing a temperature variation curve graph with a time progress based on the real-time temperature data of the environment; determining peak points and valley points in the temperature variation curve graph, and dividing the temperature variation curve graph into a number of curve segments according to the peak points and valley points; determining the slope value and variation amplitude of each curve segment, and determining the coefficient of variation of the ambient temperature based on the slope value and variation amplitude of each curve segment.

[0027] Specifically, real-time temperature data is obtained from environmental sensors and usually recorded in the form of a time series; a temperature change curve graph is drawn based on the real-time temperature data to show the overall temperature change trend; peak points (maximum temperature points) and valley points (minimum temperature points) are identified in the temperature change curve graph, which represent the extreme values in the temperature change curve; based on the peak points and valley points, the temperature change curve graph is divided into multiple curve segments, each curve segment represents a stage of temperature change; the slope value (temperature change rate) and change amplitude (temperature change amplitude) of each curve segment are calculated to quantify the speed and amplitude of the temperature change; based on the slope value and change amplitude of each curve segment, the ambient temperature change coefficient is comprehensively calculated, which can reflect the speed and amplitude of the ambient temperature change. This step can achieve real-time monitoring and recording of ambient temperature changes by acquiring real-time temperature data of the environment and constructing a temperature change curve graph; by determining the peak points and valley points in the curve graph, the temperature change trend can be analyzed and the period and amplitude of temperature fluctuation can be identified; by segmenting the temperature change curve and calculating the slope value and change amplitude, the characteristics of temperature change can be understood in more detail; by calculating the coefficient of change of the ambient temperature, the frequency and amplitude of the ambient temperature change can be quantitatively evaluated, providing a basis for subsequent environmental control and adjustment; by comprehensively utilizing the information of the temperature change curve, intelligent environmental monitoring and control can be achieved, and energy utilization efficiency and environmental comfort can be improved.

[0028] In an embodiment of the present application, a method for optimizing the operation of a steam turbine cooling system is provided, wherein determining the variation coefficient of the ambient temperature based on the slope value and variation amplitude of each curve segment includes: calculating the variation coefficient of the ambient temperature based on the slope value and variation amplitude of each curve segment, and the calculation formula of the variation coefficient of the ambient temperature is: , Where S is the coefficient of change of the ambient temperature, α is the preset conversion coefficient, vi is the slope value of the i-th curve segment, and fi is the amplitude of change of the i-th curve segment.

[0029] In an embodiment of the present application, a method for optimizing the operation of a steam turbine cooling system is provided, wherein an optimization coefficient is determined based on a coefficient of variation of the operating state of the steam turbine to ambient temperature, and the initial operating conditions of the cooling system are optimized based on the optimization coefficient, including: presetting an optimization coefficient-variation coefficient interval correspondence, wherein the optimization coefficient-variation coefficient interval correspondence is associated with a corresponding optimization coefficient for each variation coefficient interval; obtaining the coefficient of variation of the operating state of the steam turbine to ambient temperature, and based on a mapping relationship between the variation coefficient interval to which the variation coefficient belongs within the optimization coefficient-variation coefficient interval correspondence, selecting the optimization coefficient corresponding to the variation coefficient interval as the optimization coefficient corresponding to the combustion parameter of the thermal power generator set; optimizing the initial operating condition Ki (ai, bi) of the cooling system based on the variation coefficient zi, and the operating condition of the optimized cooling system is Ki (ai*zi, bi*zi).

[0030] Specifically, in a pre-set optimization scheme, a mapping relationship between the variation coefficient interval and the corresponding optimization coefficient is established to ensure that each variation coefficient interval has a corresponding optimization coefficient; the variation coefficient of the ambient temperature on the operating state of the turbine is obtained, and according to the variation coefficient interval to which the variation coefficient of the ambient temperature belongs, the corresponding optimization coefficient is found within the optimization coefficient-variation coefficient interval correspondence relationship as the optimization coefficient of the combustion parameters of the thermal power generator set; according to the variation coefficient zi, the initial operating conditions Ki (ai, bi) of the cooling system are optimized and adjusted, and finally the optimized cooling system operating conditions Ki (ai*zi, bi*zi) are obtained, so that the cooling system can better adapt to changes in ambient temperature. This step maps the ambient temperature variation coefficient to the optimization coefficient, which can realize intelligent adjustment of the combustion parameters of the thermal power generator set, making it operate more efficiently under different environmental conditions; optimizing the initial operating conditions of the cooling system according to the ambient temperature variation coefficient can improve the performance and efficiency of the system and reduce energy consumption and costs; by dynamically adjusting the operating conditions of the cooling system, it can be adaptively adjusted according to changes in the ambient temperature, ensuring that the system can operate stably under various working conditions; optimizing the operating conditions of the cooling system can improve the reliability and stability of the system, reduce failures and damage caused by environmental changes, and thus extend the service life of the equipment.

[0031] like Figure 2As shown, in an embodiment of the present application, a turbine cooling system operation optimization system is provided, including: an analysis module for obtaining real-time load data of the turbine, and analyzing the real-time load data to determine the operating status of the turbine; a setting module for setting the initial operating conditions of the cooling system according to the operating status of the turbine, the initial operating conditions including the water pump frequency and the fan speed; a determination module for obtaining real-time temperature data of the environment, and analyzing the real-time temperature data to determine the variation coefficient of the ambient temperature; an optimization module for determining an optimization coefficient according to the variation coefficient of the turbine operating status to the ambient temperature, and optimizing the initial operating conditions of the cooling system according to the optimization coefficient.

[0032] In summary, an embodiment of the present invention provides a method and system for optimizing the operation of a steam turbine cooling system, which includes: obtaining real-time load data of the steam turbine, analyzing the real-time load data, and determining the operating status of the steam turbine; setting the initial operating conditions of the cooling system according to the operating status of the steam turbine, the initial operating conditions including the water pump frequency and the fan speed; obtaining real-time temperature data of the environment, analyzing the real-time temperature data, and determining the coefficient of variation of the ambient temperature; determining an optimization coefficient based on the coefficient of variation of the operating status of the steam turbine due to the ambient temperature, and optimizing the initial operating conditions of the cooling system according to the optimization coefficient. The present invention can dynamically adjust the operating parameters according to actual conditions through real-time monitoring data, data intelligent analysis technology, and adaptive control decisions to adapt to changes in the operating status of the steam turbine and fluctuations in environmental conditions, thereby realizing intelligent optimization of the operation of the cooling system and maximizing the efficiency and performance of the cooling system.

[0033] Finally, it should be noted that it is apparent that various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the present invention. Thus, the present invention is intended to include such modifications and variations as long as they fall within the scope of the present invention and its equivalents.

[0034] The above description is only an example of an embodiment of the present invention, but it does not limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be considered to fall within the scope of protection of the present invention and be subject to restrictions. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0035] The term "comprise," "comprises," or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0036] Thus far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to closely related technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A method for optimizing the operation of a steam turbine cooling system, characterized in that: include: Obtain the real-time load data of the steam turbine, analyze the real-time load data, and determine the operating status of the steam turbine; Set the initial operating conditions of the cooling system according to the operating status of the steam turbine, including the water pump frequency and fan speed; Obtain real-time temperature data of the environment, analyze the real-time temperature data, and determine the coefficient of change of the ambient temperature; The optimization coefficient is determined according to the variation coefficient of the operating state of the steam turbine due to the ambient temperature, and the initial operating conditions of the cooling system are optimized according to the optimization coefficient.

2. A method for optimizing the operation of a steam turbine cooling system according to claim 1, characterized in that: The step of obtaining the real-time load data of the steam turbine and analyzing the real-time load data to determine the operating status of the steam turbine includes: Obtain the real-time load data of the steam turbine, perform cluster analysis on the real-time load data, and divide the real-time load data of the steam turbine into several operating stages according to the cluster analysis results; Obtain the real-time load data corresponding to each operation stage, and calculate the average value of the real-time load data corresponding to each operation stage; determining an operation stage in which the average value of the real-time load data is less than or equal to a first preset value as a low-load operation stage, determining an operation stage in which the average value of the real-time load data is greater than or equal to a second preset value as a high-load operation stage, and determining an operation stage in which the average value of the real-time load data is between the first preset value and the second preset value as a fluctuating load operation stage, wherein the first preset value is less than the second preset value; The operating status evaluation value of the steam turbine is determined based on the average value of the real-time load data corresponding to the low-load operating stage, the high-load operating stage, and the fluctuating load operating stage. The calculation formula of the operating status evaluation value of the steam turbine is: P=a*L+b*H+c*M, Among them, P is the operating status evaluation value of the turbine, a is the first preset evaluation coefficient, L is the average value of the real-time load data corresponding to the low-load operation stage, b is the second preset evaluation coefficient, H is the average value of the real-time load data corresponding to the high-load operation stage, c is the third preset evaluation coefficient, and M is the average value of the real-time load data corresponding to the fluctuating load operation stage.

3. A method for optimizing the operation of a steam turbine cooling system according to claim 2, characterized in that: The real-time load data is clustered and divided into several operation stages according to the cluster analysis results, including: Determine the number of data in the real-time load data and randomly select k initial cluster centers based on the number of data; Calculate the distance between each data point and the initial cluster center in the real-time load data, and divide each data point into clusters according to the distance between each data point and the initial cluster center. Calculate the average value of all data based on the data in each cluster after clustering, and update the cluster center based on the average value of all data; Repeat the iteration and set a new cluster center until the cluster center no longer changes, and obtain several clusters. Then, divide the real-time load data of the steam turbine into several operation stages according to the cluster division results.

4. A method for optimizing the operation of a steam turbine cooling system according to claim 2, characterized in that: The initial operating conditions of the cooling system are set according to the operating state of the steam turbine. The initial operating conditions include the water pump frequency and the fan speed, including: Obtaining an operating status evaluation value ΔD of the steam turbine and a preset standard evaluation value D0, and determining a first preset difference D1, a second preset difference D2, a third preset difference D3, and a fourth preset difference D4, wherein D1 < D2 < D3 < D4; presetting a first preset operating condition K1 (a1, b1), a second preset operating condition K2 (a2, b2), a third preset operating condition K3 (a3, b3), and a fourth preset operating condition K4 (a4, b4) of the cooling system, wherein the cooling system includes a circulating water pump and a cooling tower fan, a1-a4 are first to fourth preset water pump frequencies, a1 < a2 < a3 < a4, b1-b4 are first to fourth preset fan speeds, and b1 < b2 < b3 < b4; According to the difference between the evaluation value △D and the preset standard evaluation value D0, the preset working condition Ki is selected as the initial operating condition of the cooling system; When △D-D0≤D1, the first preset working condition K1 is selected as the initial operating condition of the cooling system; When D1<△D-D0≤D2, the second preset working condition K2 is selected as the initial operating condition of the cooling system; When D2<△D-D0≤D3, the third preset working condition K3 is selected as the initial operating condition of the cooling system; When D3<△D-D0≤D4, the fourth preset working condition K4 is selected as the initial operating condition of the cooling system; The cooling system is controlled to operate according to the selected preset initial operating condition Ki (ai, bi) as the initial operating condition of the cooling system.

5. A method for optimizing the operation of a steam turbine cooling system according to claim 4, characterized in that: The step of obtaining real-time temperature data of the environment and analyzing the real-time temperature data to determine a coefficient of change of the ambient temperature includes: Obtain real-time temperature data of the environment and construct a temperature change curve chart of the time progress based on the real-time temperature data of the environment; Determine the peak points and valley points in the temperature change curve graph, and divide the temperature change curve graph into a plurality of curve segments according to the peak points and valley points; The slope value and the variation amplitude of each curve segment are determined, and the variation coefficient of the ambient temperature is determined based on the slope value and the variation amplitude of each curve segment.

6. A method for optimizing the operation of a steam turbine cooling system according to claim 5, characterized in that: Determining the coefficient of change of the ambient temperature based on the slope value and the change amplitude of each curve segment includes: The coefficient of change of the ambient temperature is calculated based on the slope value and change amplitude of each curve segment. The calculation formula of the coefficient of change of the ambient temperature is: ， Where S is the coefficient of change of the ambient temperature, α is the preset conversion coefficient, vi is the slope value of the i-th curve segment, and fi is the amplitude of change of the i-th curve segment.

7. A method for optimizing the operation of a steam turbine cooling system according to claim 5, characterized in that: The step of determining an optimization coefficient based on a coefficient of change of the operating state of the steam turbine according to the ambient temperature, and optimizing the initial operating conditions of the cooling system according to the optimization coefficient, includes: A correspondence between the optimization coefficient and the variation coefficient interval is pre-set, wherein the correspondence between the optimization coefficient and the variation coefficient interval is associated with a corresponding optimization coefficient for each variation coefficient interval; Obtaining a coefficient of variation of the ambient temperature to the operating state of the steam turbine, and based on a mapping relationship between a variation coefficient interval to which the variation coefficient belongs and an optimization coefficient corresponding to the variation coefficient interval within a corresponding relationship between an optimization coefficient and a variation coefficient interval, selecting an optimization coefficient corresponding to the variation coefficient interval as an optimization coefficient corresponding to a combustion parameter of the thermal power generating set; The initial operating condition Ki (ai, bi) of the cooling system is optimized according to the variation coefficient zi, and the operating condition of the optimized cooling system is Ki (ai*zi, bi*zi).

8. A steam turbine cooling system operation optimization system, characterized in that: include: The analysis module is used to obtain the real-time load data of the steam turbine, analyze the real-time load data, and determine the operating status of the steam turbine; A setting module is used to set the initial operating conditions of the cooling system according to the operating status of the steam turbine, and the initial operating conditions include the water pump frequency and the fan speed; A determination module is used to obtain real-time temperature data of the environment, analyze the real-time temperature data, and determine the coefficient of change of the ambient temperature; The optimization module is used to determine the optimization coefficient according to the change coefficient of the turbine operating state of the ambient temperature, and optimize the initial operating conditions of the cooling system according to the optimization coefficient.

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