DCS intelligent control system and method for cogeneration unit

Abstract

The invention relates to the technical field of cogeneration units, in particular to a DCS intelligent control system and method for a cogeneration unit, and the method comprises the steps: obtaining the steam flow of a boiler of the cogeneration unit, the rotating speeds of different pressure cylinders of a steam turbine, and the load end power of the cogeneration unit; obtaining a load power change coefficient of the cogeneration unit, calculating a load power deviation degree of the cogeneration unit, and further obtaining a load fluctuation coefficient of the cogeneration unit; according to the correlation difference between the boiler steam flow and the rotating speeds of different pressure cylinders and the change degree of the boiler steam flow, an inertia delay coefficient of coordination between the boiler steam flow and the rotating speed of a steam turbine is obtained; and obtaining the influence index of the coordination state of the boiler and the steam turbine in the cogeneration unit by combining the correlation between the load fluctuation coefficient and the inertia delay coefficient, and adjusting the proportionality coefficient of the DCS intelligent control system. The stability of electric energy and heat energy supply of the cogeneration unit is improved.

Description

Technical Field

[0001] The present application relates to the technical field of cogeneration units, and in particular to a DCS intelligent control system and method for cogeneration units. Background Art

[0002] The coordinated operation of the boiler and steam turbine in a cogeneration unit is crucial. Their well-coordinated operation can improve energy efficiency while ensuring a stable supply of electricity and heat. In actual applications, the operating conditions of cogeneration units are complex and changeable. For example, rapid changes in grid load and large fluctuations in heat network load will pose challenges to the coordinated operation of the boiler and steam turbine. In addition, due to the inertia of boiler combustion adjustment, it is usually difficult to match the corresponding steam volume. Existing methods fail to fully consider the impact of the above-mentioned complex operating conditions and the inertia of combustion adjustment, resulting in fluctuations in the main steam pressure and turbine operation, and the defect of unstable electricity and heat supply.

[0003] Publication No. CN113250768B describes a method for optimizing the thermal load of a combined heat and power (CHP) unit. This method analyzes the unit's operating status by establishing a turbine characteristic equation model. The control scheme is then integrated into the distributed control system (DCS) for closed-loop optimization. In actual operation, due to load fluctuations and combustion adjustment inertia, the method may not fully adapt to rapidly changing unit operating requirements, thereby impacting unit operational stability. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a DCS intelligent control system and method for a cogeneration unit. The technical solutions adopted are as follows:

[0005] The present invention provides a DCS intelligent control method for a cogeneration unit, comprising the following steps:

[0006] Obtain the steam flow of the cogeneration unit boiler, the speed of different pressure cylinders of the turbine, and the load end power of the cogeneration unit;

[0007] By analyzing the difference between the peak and trough power of the load end of the cogeneration unit in each time period and the random change degree of the power corresponding to the peak and trough, the load power variation coefficient of the cogeneration unit in each time period is obtained. According to the deviation between the load end power of the cogeneration unit in each time period and the power prediction value, the load power deviation of the cogeneration unit in each time period is obtained. Combined with the load power variation coefficient, the load fluctuation coefficient of the cogeneration unit in each time period is obtained.

[0008] According to the difference in the correlation between the boiler steam flow rate data and the speed data of different pressure cylinders in each time period, as well as the degree of change of the boiler steam flow rate in each time period, the inertia delay coefficient of the coordination between the boiler steam flow rate and the turbine speed in each time period is obtained;

[0009] According to the average level of the load fluctuation coefficient and the average level of the inertia delay coefficient in each time period and the previous multiple time periods, combined with the correlation between the load fluctuation coefficient and the inertia delay coefficient, the influence index of the coordination status of the boiler and turbine in the cogeneration unit in each time period is obtained, and the proportional coefficient of the DCS intelligent control system is adjusted.

[0010] Preferably, the method for obtaining the load power variation coefficient of the cogeneration unit in each time period is: extracting all peaks and troughs in the load end power data of the cogeneration unit in each time period, calculating the range of the wave width corresponding to each peak and trough in each time period, and the Shannon entropy of the power data corresponding to all peaks and troughs, and taking the product of the mean of all the ranges calculated in each time period and the Shannon entropy as the load power variation coefficient of the cogeneration unit in each time period.

[0011] Preferably, the method for obtaining the load power deviation of the cogeneration unit in each time period is: predicting the load end power of the cogeneration unit in each time period, obtaining multiple prediction values in each time period, calculating the cumulative sum of the differences between each prediction value in each time period and the average of all powers in each time period, and using it as the power load deviation of the cogeneration unit in each time period.

[0012] Preferably, the load fluctuation coefficient of the cogeneration unit in each time period is the product of the load power variation coefficient and the load power deviation of the cogeneration unit in each time period.

[0013] Preferably, the calculation method of the inertia delay coefficient for the coordination of the boiler steam flow and the turbine speed in each time period is:

[0014] Where H i is the inertia delay coefficient of the coordination between boiler steam flow and turbine speed in the i-th time period, E i Indicates the range of boiler steam flow in the i-th time period, F i is the average correlation between the change in boiler steam flow rate and the change in speed of different pressure cylinders in the i-th time period, and θ is a constant to avoid the denominator being zero.

[0015] Preferably, the calculation method of the average correlation between the change in boiler steam flow rate and the change in speed of different pressure cylinders in the i-th time period is further as follows:

[0016] D i,1 、Di,2 、D i,3 are the SBD distances between the boiler steam flow data and the high-pressure cylinder speed data, the medium-pressure cylinder speed data, and the low-pressure cylinder speed data in the i-th time period.

[0017] Preferably, the calculation method of the influence index of the coordination state of the boiler and the steam turbine in the cogeneration unit in each time period is:

[0018] Y i =L i ×R i Among them, Y i is the influence index of the coordination status of the boiler and steam turbine in the cogeneration unit in the i-th time period, R i is the correlation coefficient between the load fluctuation coefficient sequence and the inertia delay coefficient sequence, L i It is the product of the means corresponding to the load fluctuation coefficient sequence and the inertia delay coefficient sequence.

[0019] Preferably, the load fluctuation coefficients and inertia delay coefficients corresponding to each time period and the multiple time periods before it are arranged in time sequence as the load fluctuation coefficient sequence and inertia delay coefficient sequence of each time period.

[0020] Preferably, adjusting the proportional coefficient of the DCS intelligent control system further includes: normalizing the impact index corresponding to the current time period, taking the product of the obtained normalization result and the preset maximum adjustment range as the proportional coefficient adjustment amount, and taking the sum of the proportional coefficient adjustment amount and the preset initial value of the proportional coefficient as the proportional coefficient of the next time period.

[0021] An embodiment of the present application also provides a DCS intelligent control system for a cogeneration unit, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned DCS intelligent control methods for a cogeneration unit are implemented.

[0022] As can be seen from the above, the DCS intelligent control system and method for a cogeneration unit provided by this application has at least the following beneficial effects:

[0023] This application optimizes the corresponding control algorithm in the control module. By deeply analyzing the random changes in load demand trends and the unstable characteristics of trend deviation states, as well as the differential characteristics of the degree of matching between steam flow changes and changes in the speed of different pressure cylinders in the turbine, and further combining the above-mentioned characteristic size states and their consistent relationship, the influence index of the coordination state between the boiler and the turbine is calculated. Its advantage lies in the ability to accurately assess the impact characteristics of load demand changes and boiler combustion adjustment inertia on the coordination state, and based on this, optimize the parameters of the control algorithm. This helps to improve the stability of the power and heat supply of the cogeneration unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0025] Figure 1 This is a flow chart of the steps of a DCS intelligent control method for a cogeneration unit provided in this application. DETAILED DESCRIPTION

[0026] To further illustrate the technical means and effectiveness of this application's implementation of the intended invention, the following, in conjunction with the accompanying drawings and preferred embodiments, provides a detailed description of the specific implementation, structure, features, and effectiveness of a DCS intelligent control system and method for a cogeneration unit proposed in this application. In the following description, references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0027] Unless otherwise specified and limited, terms such as "comprises", "includes" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs.

[0028] The specific scheme of a DCS intelligent control system and method for a cogeneration unit provided by the present application is described in detail below with reference to the accompanying drawings.

[0029] See also Figure 1 , which shows a flowchart of a DCS intelligent control method for a cogeneration unit provided by one embodiment of the present application, including the following steps:

[0030] Step 1: Obtain the steam flow rate of the cogeneration unit boiler, the speed of different pressure cylinders of the turbine, and the load end power of the cogeneration unit.

[0031] DCS is a control system used for industrial automation. It uses multiple computers to decentralizedly control various subsystems in the production process, achieving centralized monitoring and decentralized control. In this embodiment, the DCS includes a data acquisition module, a control module, an operation station module, a pass module, and a program module. The data acquisition module is used to collect analog signals or switch signals from various devices in the unit, including but not limited to the start and stop of temperature, pressure, and flow operating status, and convert them into digital signals for processing and analysis. Boilers and steam turbines are the core equipment of cogeneration units, and their operating status is crucial to the stable operation of the unit. In this embodiment, the steam flow data of the boiler of the cogeneration unit and the speed data of different pressure cylinders of the steam turbine are collected through the DCS system. The data collection time interval is set to 0.1 seconds. At the same time, the power data of the load end at each moment is obtained through the smart meter, and the time interval of the power data is 1 second.

[0032] At this point, the boiler steam flow data, turbine speed data, and load-end power of the cogeneration unit can be obtained.

[0033] Step 2: By analyzing the difference between the peak and trough power of the load end of the cogeneration unit in each time period and the random change degree of the power corresponding to the peak and trough, the load power variation coefficient of the cogeneration unit in each time period is obtained. According to the deviation between the load end power of the cogeneration unit in each time period and the power prediction value, the load power deviation of the cogeneration unit in each time period is obtained. Combined with the load power variation coefficient, the load fluctuation coefficient of the cogeneration unit in each time period is obtained.

[0034] The control module of a DCS system contains analog and digital outputs, converting control signals into analog signals for controlling various actuators, such as adjusting valve opening and inverter frequency settings. Furthermore, the control algorithm within the module performs control operations on collected process parameters and outputs control signals to adjust actuators to maintain the controlled parameters near their set values.

[0035] The boiler is the core heat source in a cogeneration system. By burning fuel, it converts water into high-temperature, high-pressure steam, providing the energy for subsequent power generation and heat supply. To adapt to changes in external power demand, the boiler can adjust the amount of steam generated by adjusting the fuel input and the amount of water stored. The high-temperature, high-pressure steam generated by the boiler is fed into the steam turbine, rotating its blades, which in turn drive the generator, converting mechanical energy into electrical energy, thereby generating electricity. During the work process, the steam in the turbine reaches a certain pressure and temperature. Some of this steam can be extracted for heat supply. By adjusting the pressure and flow of the extracted steam, varying heating load requirements can be flexibly met. As load demand fluctuates, the boiler steam flow rate or turbine speed is adjusted accordingly. For example, when grid load suddenly increases, the steam turbine can increase steam intake to boost power generation, while the boiler adjusts combustion parameters and other parameters to promptly increase steam supply. Conversely, when heat demand decreases, the steam extraction rate can be reduced, allowing more steam to be used for power generation. This allows for flexible operation and optimized scheduling of the unit under varying operating conditions. During actual operation, the unit is affected by the uncertainty of load demand, and there are varying degrees of delay in the steam generated by the boiler to drive the turbine blades to rotate, which in turn affects the stability of the unit supply. Therefore, the following analysis is conducted based on this feature.

[0036] Some cogeneration plants primarily provide heat, supplemented by electricity, while others primarily provide electricity, supplemented by heat. In this embodiment, taking a cogeneration unit primarily providing electricity as an example, the greater the degree of uncertainty and fluctuation in load-side electricity demand, the greater the impact on unit regulation. Therefore, we first analyze the characteristics of load-side electricity demand fluctuations. Due to the high amplitude and randomness of power data fluctuations, in this embodiment, we set detection time periods, each lasting 5 minutes. The power data in each time period usually has peak and valley characteristics to varying degrees. Taking the i-th time period as an example, in order to obtain the degree of fluctuation of the corresponding power data, this embodiment adopts a multi-scale adaptive peak search algorithm for processing, and outputs all the peak and valley positions in all the load power data in the time period. The distribution between the obtained peaks and valleys is relatively random, and then the range of each peak and valley value point position within its wave width is calculated respectively, as well as the Shannon entropy of the power data corresponding to all peaks and valley points. The product of the mean of all the ranges and the Shannon entropy is used as the load power variation coefficient of the cogeneration unit in the time period. The load power variation coefficient of the cogeneration unit in the i-th time period is recorded as A i . A iThe larger the value, the more obvious the short-term fluctuation amplitude and random characteristics of the load power. In addition, the overall change state of the load power will directly affect the steam generation of the boiler. Then, the exponential smoothing algorithm is used to predict and analyze the power data in the i-th time period. In this embodiment, the output is the last 10 predicted values of the power data. Then, the difference between each predicted value and the mean of all power data in the time period is calculated respectively, and the cumulative sum of all differences is used as the load power deviation of the cogeneration unit in the i-th time period, which is recorded as B i , the B i Indicates the degree of deviation between the power status and the historical average power during the time period.

[0037] Therefore, according to the load power variation coefficient and load power deviation of each time period, the load fluctuation coefficient of the cogeneration unit in each time period is calculated. The load fluctuation coefficient is used to characterize the irregular change characteristics and trend deviation of the load of the cogeneration unit. In this embodiment, the specific calculation formula of the load fluctuation coefficient is: C i =A i ×B i , where C i 、A i 、B i are the load fluctuation coefficient, load power variation coefficient, and load power deviation of the cogeneration unit in the i-th time period. i It reflects the random changes in the load demand trend of the cogeneration unit and the non-stable characteristics of the trend deviation state.

[0038] Step 3: Based on the difference in the correlation between the boiler steam flow data and the speed data of different pressure cylinders in each time period, as well as the degree of change in the boiler steam flow in each time period, the inertia delay coefficient for the coordination of the boiler steam flow and the turbine speed in each time period is obtained.

[0039] To adapt to the real-time fluctuations in grid load, cogeneration units must respond quickly. For example, under high load conditions, as boiler steam flow increases, the turbine impeller speed must rapidly increase. However, due to the inertia of boiler combustion adjustments and the constraints of the boiler's thermal storage capacity, the impeller speed may not be matched in a timely manner. This can lead to significant delays between boiler and turbine regulation, disrupting the steam supply and demand balance and affecting coordinated control. Therefore, in this embodiment, further analysis of steam flow data and turbine speed status is performed.

[0040] The steam turbine contains high-pressure cylinder, medium-pressure cylinder and low-pressure cylinder, which respectively play different roles, and their speed has certain changing characteristics during operation. In the high-pressure cylinder, due to the high steam pressure and fast flow rate, the force impacting the blades is large, resulting in a large torque on the high-pressure cylinder rotor, and the speed change caused by load adjustment is more obvious. The low-pressure cylinder receives exhaust steam from the medium-pressure cylinder or steam directly from the boiler, with lower pressure and temperature, and the force impacting the blades is small. The change in the speed data corresponding to the low-pressure cylinder due to load adjustment is relatively small. Therefore, the SBD (ShapeBased Distance) distance between the flow data of the boiler steam and the corresponding speed data of the high-pressure cylinder, the corresponding speed data of the medium-pressure cylinder, and the corresponding speed data of the low-pressure cylinder in the i-th time period is calculated respectively, and recorded as D i,1 、D i,2 、D i,3 The resulting SBD distance is used to reflect the correlation between the steam flow rate change state and the speed change state. The smaller the SBD distance, the greater the correlation between the steam flow rate change state and the speed change state of different pressure cylinders during this time period. If the impact of boiler combustion adjustment inertia and heat storage capacity constraints is more severe, and the steam flow rate fluctuates greatly, the difference in the degree of matching between the speed of different pressure cylinders and the boiler steam flow rate change will be smaller. In view of this, the formula for calculating the inertia delay coefficient for the coordination of boiler steam flow and turbine speed in the i-th time period is:

[0041] in,

[0042] In the above formula, E i Indicates the range of boiler steam flow in the i-th time period, F i is the average correlation between the change in boiler steam flow rate and the change in speed of different pressure cylinders in the i-th time period, θ is a constant to avoid the denominator being 0, and the value in the embodiment is 0.1, D i,1 、D i,2 、D i,3 are the SBD distances between the boiler steam flow data and the high-pressure cylinder speed data, the medium-pressure cylinder speed data, and the low-pressure cylinder speed data in the i-th time period. It can be understood that the obtained H i It reflects the degree of difference in the correlation between the changes in boiler steam flow and the changes in the speed of different pressure cylinders in the turbine during this period.

[0043] Step 4: Based on the average level of the load fluctuation coefficient and the average level of the inertia delay coefficient in each time period and the previous multiple time periods, combined with the correlation between the load fluctuation coefficient and the inertia delay coefficient, the influence index of the coordination status of the boiler and turbine in the cogeneration unit in each time period is obtained, and the proportional coefficient of the DCS intelligent control system is adjusted.

[0044] Furthermore, this embodiment takes into account that when the load demand fluctuates significantly, due to the influence of combustion adjustment inertia and boiler heat storage capacity constraints, it may take longer to restore the operating state balance between the boiler and the turbine, which may easily exacerbate the dynamic differences in steam flow and speed. For example, the more unstable the load demand state is, the more significant the corresponding inertia delay characteristics are. Therefore, the following analysis is conducted based on the changing characteristics between load demand changes and inertia delay. The specific process is as follows:

[0045] First, the load fluctuation coefficients and inertia delay coefficients corresponding to each time period and the previous multiple time periods are arranged in time sequence as the load fluctuation coefficient sequence and inertia delay coefficient sequence of each time period. Preferably, in this embodiment, the load fluctuation coefficients and inertia delay coefficients corresponding to the i-th time period and the previous 10 time periods are arranged in time sequence as the load fluctuation coefficient sequence and inertia delay coefficient sequence of the i-th time period. Further, the means corresponding to the load fluctuation coefficient sequence and inertia delay coefficient sequence are calculated respectively, and the product of the two means is recorded as L i , the obtained L i The larger the value is, the more significant the unstable state or inertia delay characteristics of the load demand are.

[0046] Furthermore, the influence index formula for calculating the coordination status of the boiler and steam turbine in the cogeneration unit in each time period is as follows:

[0047] Y i =L i ×R i Among them, Y i is the influence index of the coordination status of the boiler and steam turbine in the cogeneration unit in the i-th time period, R i is the correlation coefficient between the load fluctuation coefficient sequence and the inertia delay coefficient sequence. In this embodiment, the Hoeffding correlation coefficient is used for measurement. The obtained R i The larger the value, the more consistent the instability of the load demand state is with the inertia delay characteristics. i is the product of the mean values corresponding to the load fluctuation coefficient sequence and the inertia delay coefficient sequence. i It reflects the coordination characteristics between the load demand change at the load end of the cogeneration unit and the inertia delay coefficient.

[0048] Furthermore, the control algorithm in the control module is optimized based on the above-mentioned coordination degree characteristics. The DCS control system of this embodiment adopts PID control technology to adjust the equipment, wherein the preset initial values of the proportional coefficient, integral coefficient, and differential coefficient are divided into 1, 10, and 10. If the influence index of the coordination state of the boiler and the steam turbine is larger, it means that the cogeneration unit is more difficult to meet the load fluctuation demand in time. At this time, the proportional coefficient in the PID needs to be increased accordingly to speed up the response speed of the system. On the contrary, a smaller proportional coefficient can be set to avoid overshooting the system. Among them, the proportional coefficient is set near a value greater than 1. In this embodiment, its range is set to [1, 1.5] to avoid overshooting due to excessive proportional coefficient in the actual adjustment process, which affects the control accuracy. In this embodiment, the maximum adjustment range is set to ω = 0.5. When calculating the adjustment amount of the proportional coefficient, the sigmoid function is first used to adjust the influence index Y i Normalization is performed, and the product of the normalized result and the preset maximum adjustment amplitude ω is used as the proportional coefficient adjustment amount. The sum of the proportional coefficient adjustment amount calculated in the current time period and the preset initial value of the proportional coefficient is used as the proportional coefficient for the next time period, thereby optimizing the DCS intelligent control system, which helps to improve the stability of the power and heat energy supply of the cogeneration unit.

[0049] In addition, in this embodiment, the operation station is equipped with a high-performance computer and dedicated software for system configuration, configuration, maintenance, and management. Engineers can use the engineering station to write, modify, and download control strategies, as well as set system parameters, diagnose, and debug the system. It also includes an operator interface that monitors various production process parameters, equipment status, and other information in real time, allowing for operational control such as starting and stopping equipment, adjusting setpoints, and viewing alarm messages. The communication module is responsible for network communication between modules within the DCS system and with other external systems. The communication network in this application utilizes fieldbus communication, with data transmission carried out via the TCP / IP communication protocol. The program module, including an operating system, a database management system, and communication middleware, provides the foundational support platform for the operation of the DCS system, responsible for system resource management, data storage and retrieval, and communication coordination. Various predefined functional modules are also provided, such as a control loop module, a sequential control module, an alarm processing module, a data logging module, and a report generation module. In actual application scenarios, implementers can call and configure these modules according to actual needs, quickly implementing various complex control functions and production management functions.

[0050] Based on the same inventive concept as the above method, an embodiment of the present application also provides a DCS intelligent control system for a cogeneration unit, comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned DCS intelligent control methods for a cogeneration unit are implemented.

[0051] It should be understood that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0053] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the scope of protection of the present application.

Claims

1. A DCS intelligent control method for a cogeneration unit, characterized in that: The following steps are involved: Obtain the steam flow of the cogeneration unit boiler, the speed of different pressure cylinders of the turbine, and the load end power of the cogeneration unit; By analyzing the difference between the peak and trough power of the load end of the cogeneration unit in each time period and the random change degree of the power corresponding to the peak and trough, the load power variation coefficient of the cogeneration unit in each time period is obtained. According to the deviation between the load end power of the cogeneration unit in each time period and the power prediction value, the load power deviation of the cogeneration unit in each time period is obtained. Combined with the load power variation coefficient, the load fluctuation coefficient of the cogeneration unit in each time period is obtained. According to the difference in the correlation between the boiler steam flow rate data and the speed data of different pressure cylinders in each time period, as well as the degree of change of the boiler steam flow rate in each time period, the inertia delay coefficient of the coordination between the boiler steam flow rate and the turbine speed in each time period is obtained; According to the average level of the load fluctuation coefficient and the average level of the inertia delay coefficient in each time period and the previous multiple time periods, combined with the correlation between the load fluctuation coefficient and the inertia delay coefficient, the influence index of the coordination status of the boiler and turbine in the cogeneration unit in each time period is obtained, and the proportional coefficient of the DCS intelligent control system is adjusted.

2. A DCS intelligent control method for a cogeneration unit according to claim 1, characterized in that: The method for obtaining the load power variation coefficient of the cogeneration unit in each time period is as follows: extracting all peaks and troughs in the load-end power data of the cogeneration unit in each time period, respectively calculating the range of the wave width corresponding to each peak and trough in each time period, and the Shannon entropy of the power data corresponding to all peaks and troughs, and taking the product of the mean of all the ranges calculated in each time period and the Shannon entropy as the load power variation coefficient of the cogeneration unit in each time period.

3. The DCS intelligent control method for a cogeneration unit according to claim 1, characterized in that: The method for obtaining the load power deviation of the cogeneration unit in each time period is as follows: predicting the load end power of the cogeneration unit in each time period, obtaining multiple prediction values in each time period, calculating the cumulative sum of the differences between each prediction value in each time period and the average of all powers in each time period, and calculating the sum as the power load deviation of the cogeneration unit in each time period.

4. The DCS intelligent control method for a cogeneration unit according to claim 1, characterized in that: The load fluctuation coefficient of the cogeneration unit in each time period is the product of the load power variation coefficient and the load power deviation of the cogeneration unit in each time period.

5. The DCS intelligent control method for a cogeneration unit according to claim 1, characterized in that: The calculation method of the inertia delay coefficient for the coordination of boiler steam flow and turbine speed in each time period is: Where H i is the inertia delay coefficient of the coordination between boiler steam flow and turbine speed in the i-th time period, E i Indicates the range of boiler steam flow in the i-th time period, F i is the average correlation between the change in boiler steam flow rate and the change in speed of different pressure cylinders in the i-th time period, and θ is a constant to avoid the denominator being zero.

6. A DCS intelligent control method for a cogeneration unit according to claim 5, characterized in that: The calculation method of the average correlation between the change of boiler steam flow rate and the change of speed of different pressure cylinders in the i-th time period is further as follows: D i,1 、D i,2 、D i,3 are the SBD distances between the boiler steam flow data and the high-pressure cylinder speed data, the medium-pressure cylinder speed data, and the low-pressure cylinder speed data in the i-th time period.

7. The DCS intelligent control method for a cogeneration unit according to claim 1, characterized in that: The calculation method of the influence index of the coordination state of the boiler and the steam turbine in the cogeneration unit in each time period is as follows: Y i =L i ×R i Among them, Y i is the influence index of the coordination status of the boiler and steam turbine in the cogeneration unit in the i-th time period, R i is the correlation coefficient between the load fluctuation coefficient sequence and the inertia delay coefficient sequence, L i It is the product of the means corresponding to the load fluctuation coefficient sequence and the inertia delay coefficient sequence.

8. A DCS intelligent control method for a cogeneration unit according to claim 7, characterized in that: The load fluctuation coefficients and inertia delay coefficients corresponding to each time period and multiple time periods before it are arranged in time sequence as the load fluctuation coefficient sequence and inertia delay coefficient sequence of each time period.

9. The DCS intelligent control method for a cogeneration unit according to claim 1, characterized in that: The adjusting the proportional coefficient of the DCS intelligent control system further includes: normalizing the impact index corresponding to the current time period, multiplying the obtained normalization result by a preset maximum adjustment range as the proportional coefficient adjustment amount, and taking the sum of the proportional coefficient adjustment amount and a preset initial value of the proportional coefficient as the proportional coefficient of the next time period.

10. A DCS intelligent control system for a combined heat and power unit, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the DCS intelligent control method for a cogeneration unit as described in any one of claims 1 to 9 are implemented.

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