A DCS intelligent control system and method for a combined heat and power unit

Abstract

The application relates to the technical field of combined heat and power units, in particular to a DCS intelligent control system and method for a combined heat and power unit, which comprises the following steps: obtaining the steam flow of a boiler of the combined heat and power unit, the rotating speeds of different pressure cylinders of a steam turbine and the load end power of the combined heat and power unit; obtaining the load power change coefficient of the combined heat and power unit, calculating the load power deviation degree of the combined heat and power unit, and then obtaining the load fluctuation coefficient of the combined heat and power unit; according to the difference between the correlation of the boiler steam flow and the rotating speeds of the different pressure cylinders and the change degree of the boiler steam flow, obtaining the inertia delay coefficient of the coordination between the boiler steam flow and the rotating speed of the steam turbine, obtaining the influence index of the coordination state between the boiler and the steam turbine in the combined heat and power unit according to the correlation between the load fluctuation coefficient and the inertia delay coefficient, and adjusting the proportional coefficient of the DCS intelligent control system. The application improves the stability of the electric energy and heat energy supply of the combined heat and power unit.

Description

Technical Field

[0001] This application relates to the field of combined heat and power (CHP) unit technology, specifically to a DCS intelligent control system and method for CHP units. Background Technology

[0002] In combined heat and power (CHP) units, the coordinated operation of the boiler and turbine is crucial. Good coordination between the two improves energy efficiency and ensures a stable supply of electricity and heat. However, in practical applications, CHP units operate under complex and variable conditions, such as rapid changes in grid load and significant fluctuations in heating network load, all of which pose challenges to the coordinated operation of the boiler and turbine. Furthermore, due to the inertia of boiler combustion adjustments, it is often difficult to match the corresponding steam volume. Existing methods fail to fully consider the impact of these complex operating conditions and combustion adjustment inertia, leading to fluctuations in main steam pressure and turbine operation, resulting in unstable electricity and heat supply.

[0003] Publication No. CN113250768B discloses a method for optimizing the heat and power load of a combined heat and power (CHP) unit. This method analyzes the unit's operating state by establishing a turbine characteristic equation model and incorporating the control scheme into a distributed control system (DCS) for closed-loop optimization. However, in actual operation, due to load variations and combustion adjustment inertia, the system may not be able to fully match the rapidly changing operational demands of the unit, thus affecting its operational stability. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a DCS intelligent control system and method for a combined heat and power (CHP) unit. The specific technical solution adopted is as follows:

[0005] This application provides a DCS intelligent control method for a combined heat and power (CHP) unit, including the following steps:

[0006] Obtain the steam flow rate of the boiler in the cogeneration unit, the speed of the turbine cylinders at different pressures, and the load-side power of the cogeneration unit;

[0007] By analyzing the differences in the peak and trough power of the cogeneration unit at the load end and the degree of random variation of the power corresponding to the peak and trough in each time period, the load power variation coefficient of the cogeneration unit in each time period is obtained. Based on the deviation between the load power of the cogeneration unit at the load end and the predicted power value in each time period, 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] Based on the degree of correlation between boiler steam flow data and the rotational speed data of different pressure cylinders in each time period, and the degree of change of boiler steam flow in each time period, the inertial delay coefficient for the coordination between boiler steam flow and turbine rotational speed in each time period is obtained.

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

[0010] Preferably, the method for obtaining the load power variation coefficient of the cogeneration unit in each time period is as follows: extract all peaks and troughs in the load-side power data of the cogeneration unit in each time period, calculate the range of the corresponding width range of each peak and trough in each time period, and the Shannon entropy of the power data corresponding to all peaks and troughs, and use 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 as follows: predict the load power of the cogeneration unit in each time period, obtain multiple predicted values ​​in each time period, calculate the sum of the differences between each predicted value in each time period and the average power in each time period, and use 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 change coefficient and the load power deviation of the cogeneration unit in each time period.

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

[0014] In the formula, H i E is the inertial delay coefficient for coordinating boiler steam flow and turbine speed in the i-th time period. i F represents the range of boiler steam flow rate within the i-th time period. i Let θ be the average correlation between the change in boiler steam flow rate and the change in cylinder speed at different pressures during the i-th time period, and let θ be a constant to avoid the denominator being 0.

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

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

[0017] Preferably, the method for calculating the influence index of the boiler and turbine coordination status in the cogeneration unit at each time period is as follows:

[0018] Y i =L i ×R i ; where Y i R is the influence index of the coordination status of the boiler and turbine in the cogeneration unit during the i-th time period. i L is the correlation coefficient between the load fluctuation coefficient sequence and the inertia delay coefficient sequence. i It is the product of the mean values ​​corresponding to the load fluctuation coefficient sequence and the inertia delay coefficient sequence.

[0019] Preferably, the load fluctuation coefficients and inertial delay coefficients corresponding to each time period and the multiple time periods preceding it are arranged in chronological order to form the load fluctuation coefficient sequence and inertial delay coefficient sequence for each time period.

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

[0021] This application also provides a DCS intelligent control system for a cogeneration unit, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the DCS intelligent control method for a cogeneration unit described above.

[0022] As can be seen from the above, the DCS intelligent control system and method for a cogeneration unit provided in this application have 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 instability of trend deviations, as well as the differences in the matching degree between steam flow changes and the speed changes of different pressure cylinders in the turbine, and further combining the magnitude and consistency of these characteristics, the influence index of the boiler-turbine coordination state is calculated. Its advantage lies in its ability to accurately assess the influence characteristics of load demand changes and boiler combustion adjustment inertia on the coordination state, and to optimize the parameters of the control algorithm based on this. This helps improve the stability of the electrical and thermal energy supply of cogeneration units. Attached Figure Description

[0024] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating the steps of a DCS intelligent control method for a combined heat and power (CHP) unit provided in this application. Detailed Implementation

[0026] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a DCS intelligent control system and method for a combined heat and power (CHP) unit proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0027] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0028] The following description, in conjunction with the accompanying drawings, details the specific scheme of the DCS intelligent control system and method for a combined heat and power unit provided in this application.

[0029] Please see Figure 1 The document illustrates a flowchart of a DCS intelligent control method for a combined heat and power (CHP) unit according to an embodiment of this application, including the following steps:

[0030] Step 1: Obtain the steam flow rate of the boiler in the cogeneration unit, the rotational speed of the turbine cylinders at different pressures, and the load-side power of the cogeneration unit.

[0031] DCS is a control system used for industrial automation. It uses multiple computers to distribute and control various subsystems in a production process, achieving centralized monitoring and decentralized control. In this embodiment, the DCS includes a data acquisition module, a control module, an operator station module, a communication module, and a program module. The data acquisition module collects analog or switching signals from various devices in the unit, including but not limited to temperature, pressure, flow rate, and start / stop status, and converts them into digital signals for processing and analysis. The boiler and turbine are core equipment of the cogeneration unit, and their operating status is crucial to the stable operation of the unit. In this embodiment, the DCS system collects steam flow data from the boiler and speed data from different pressure cylinders of the turbine, setting the data acquisition interval to 0.1 seconds. Simultaneously, smart meters acquire power data from the load at various times, with the power data collected at 1-second intervals.

[0032] At this point, we can obtain boiler steam flow data, turbine speed data, and combined heat and power unit load-side power.

[0033] Step 2: By analyzing the differences in power peaks and troughs at the load end of the cogeneration unit within each time period, and the degree of random variation in the power corresponding to the peaks and troughs, the load power variation coefficient of the cogeneration unit in each time period is obtained. Based on the deviation between the load power and the predicted power value of the cogeneration unit in each time period, 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 output sections, converting control signals into analog signals for controlling various actuators, such as regulating valve opening and setting inverter frequency. Furthermore, the module's control algorithm performs control calculations on the acquired process parameters and outputs control signals to adjust the actuators, keeping the controlled parameters near their set values.

[0035] The boiler is the core heat source of a combined heat and power (CHP) system. It heats water by burning fuel, converting it into high-temperature, high-pressure steam to provide the energy foundation for subsequent power generation and heating. Depending on changes in external power demand, the boiler can adjust the amount of steam produced by regulating the amount of fuel input and water accumulation. The high-temperature, high-pressure steam generated by the boiler is then fed into a steam turbine, which drives the turbine blades to rotate, thereby converting mechanical energy into electrical energy for power generation. During the power generation process, the steam in the turbine has a certain pressure and temperature; some of this steam can be extracted for heating. By adjusting the pressure and flow rate of the extracted steam, different heating load requirements can be flexibly met. As load demand changes, the boiler steam flow rate or turbine speed will be adjusted accordingly. For example, when the grid load suddenly increases, the turbine can appropriately increase the steam intake to increase power generation, while the boiler adjusts combustion parameters to promptly increase steam supply. Conversely, when heat load demand decreases, the extracted steam can be reduced, allowing more steam to be used for power generation, thus achieving flexible operation and optimized scheduling of the unit under different operating conditions. Due to the uncertainty of load demand during actual operation, and the varying degrees of delay in the rotation of turbine blades driven by steam generated by the boiler, the stability of the unit's supply is affected. Based on this characteristic, the following analysis is conducted.

[0036] Some combined heat and power (CHP) plants primarily supply heat with secondary power supply, while others primarily supply power with secondary heat supply. This embodiment takes a CHP unit primarily supplying power as an example. The greater the uncertainty and fluctuation in the load-side electricity demand, the greater the impact on unit regulation. Therefore, the characteristics of load-side electricity demand changes are first analyzed. Due to the significant amplitude and randomness of power data changes, this embodiment sets various detection time periods, each lasting 5 minutes. Power data in different time periods typically exhibit varying degrees of peak and trough characteristics. Taking the i-th time period as an example, to obtain the degree of fluctuation in the corresponding power data, this embodiment employs a multi-scale adaptive peak finding algorithm. This algorithm outputs the positions of all peaks and troughs in all load power data within that time period. The distribution of the peaks and troughs is relatively random. Furthermore, the range of each peak and trough point within its width range is calculated, along with the Shannon entropy of the power data corresponding to all peaks and troughs. The product of the mean of all these ranges and the Shannon entropy is used as the load power variation coefficient of the cogeneration unit in that time period. The load power variation coefficient of the cogeneration unit in the i-th time period is denoted as A. i The result A iThe larger the value, the more pronounced the short-term fluctuations and random characteristics of the load power. Furthermore, the overall change in load power directly affects the boiler's steam production. Therefore, an exponential smoothing algorithm is used to predict and analyze the power data for the i-th time period. In this embodiment, the output is the last 10 predicted power values. Then, the difference between each predicted value and the mean of all power data within the time period is calculated. The sum of all differences is taken as the load power deviation of the cogeneration unit in the i-th time period, denoted as B. i The B i It is used to reflect the degree of deviation between the power status and the historical average power during this period.

[0037] Therefore, based on the load power variation coefficient and load power deviation in 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 variation characteristics and trend deviation of the load of the cogeneration unit. In this embodiment, the specific calculation formula for the load fluctuation coefficient is: C i =A i ×B i In the formula, C i A i B i Let C be 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 load demand trends of combined heat and power units and the unstable characteristics of trend deviation.

[0038] Step 3: Based on the degree of correlation between boiler steam flow data and the rotational speed data of different pressure cylinders in each time period, and the degree of change in boiler steam flow in each time period, obtain the inertial delay coefficient for the coordination between boiler steam flow and turbine rotational speed in each time period.

[0039] To adapt to the real-time fluctuations in grid load, combined heat and power (CHP) units need to respond quickly. For example, under high load conditions, as boiler steam flow increases, the turbine needs to rapidly increase its impeller speed. However, due to the inertia of boiler combustion adjustments and the constraints of boiler heat storage capacity, the turbine may not be able to match the corresponding impeller speed in time, resulting in a significant delay between boiler and turbine adjustments. This disrupts the steam supply-demand balance and affects the effectiveness of coordinated control. Therefore, in this embodiment, the steam flow data and turbine speed status are further analyzed.

[0040] A steam turbine contains high-pressure, intermediate-pressure, and low-pressure cylinders, each performing different functions, and their speeds exhibit certain variation characteristics during operation. In the high-pressure cylinder, due to the high steam pressure and fast flow velocity, the force impacting the blades is significant, resulting in a large torque on the high-pressure cylinder rotor, and thus a more pronounced speed change due to load adjustments. In contrast, the low-pressure cylinder receives exhaust steam from the intermediate-pressure cylinder or steam directly from the boiler, with lower pressure and temperature, resulting in a smaller force impacting the blades, and therefore a relatively smaller change in its speed due to load adjustments. Therefore, the SBD (Shape-Based Distance) distances between the boiler steam flow rate data and the corresponding speed data of the high-pressure, intermediate-pressure, and low-pressure cylinders are calculated for each time period (i), denoted as D. i,1 D i,2 D i,3 The obtained SBD distance is used to reflect the correlation between the steam flow rate change and the turbine rotation speed change. The smaller the SBD distance, the greater the correlation between the steam flow rate change and the rotation speed change of different pressure cylinders within that time period. If the influence of boiler combustion adjustment inertia and heat storage capacity constraints is more severe, and the steam flow rate fluctuation is large, the difference in the matching degree between the rotation speed of different pressure cylinders and the boiler steam flow rate change will be smaller. Therefore, the formula for calculating the inertial delay coefficient for the coordination of boiler steam flow rate and turbine rotation speed in the i-th time period is:

[0041] in,

[0042] In the above formula, E i F represents the range of boiler steam flow rate within the i-th time period. i Let θ represent the average correlation between the boiler steam flow rate change and the cylinder speed change at different pressures during the i-th time period, where θ is a constant to avoid a denominator of 0; in this example, the value is 0.1. i,1 D i,2 D i,3 Let H be the SBD distance between the boiler steam flow rate data and the high-pressure cylinder speed data, intermediate-pressure cylinder speed data, and low-pressure cylinder speed data within the i-th time period. It can be understood that the obtained H... i This reflects the degree of difference in the correlation between changes in boiler steam flow and changes in the rotational speed of different pressure cylinders in the steam turbine during this period.

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

[0044] Furthermore, this embodiment considers that when load demand changes significantly, due to the inertia of combustion adjustment and the constraints of boiler thermal storage capacity, it may take longer to rebuild the operating balance between the boiler and turbine, which can easily exacerbate the dynamic differences in steam flow and speed. For example, the more unstable the load demand, the more significant the corresponding inertial delay characteristics. Therefore, the following analysis is conducted based on the variation characteristics between load demand changes and inertial delay, and the specific process is as follows:

[0045] First, the load fluctuation coefficients and inertia delay coefficients corresponding to each time period and the preceding time periods are arranged chronologically to form the load fluctuation coefficient sequence and inertia delay coefficient sequence for each time period. Preferably, in this embodiment, the load fluctuation coefficients and inertia delay coefficients corresponding to the i-th time period and the preceding 10 time periods are arranged chronologically to form the load fluctuation coefficient sequence and inertia delay coefficient sequence for the i-th time period. Further, the mean values ​​corresponding to the load fluctuation coefficient sequence and the inertia delay coefficient sequence are calculated respectively, and the product of the two means is denoted as L. i The obtained L i The larger the value, the more pronounced the unstable state or inertial delay characteristics of the load demand.

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

[0047] Y i =L i ×R i ; where Y i R is the influence index of the coordination status of the boiler and turbine in the cogeneration unit during the i-th time period. i The correlation coefficient between the load fluctuation coefficient sequence and the inertia delay coefficient sequence is used in this embodiment. The obtained R is the Hoeffding correlation coefficient. i The larger the value of L, the more consistent the instability of the load demand state with the inertial delay characteristics. i Y is the product of the mean values ​​corresponding to the load fluctuation coefficient sequence and the inertia delay coefficient sequence. i This reflects the degree of coordination between load demand changes at the load end of a combined heat and power unit and the inertial delay coefficient.

[0048] Furthermore, the control algorithm in the control module is optimized based on the aforementioned coordination characteristics. In this embodiment, the DCS control system uses PID control technology for equipment adjustment. The initial values ​​of the proportional coefficient, integral coefficient, and derivative coefficient are 1, 10, and 10, respectively. If the influence index of the boiler and turbine coordination state is larger, it indicates that the cogeneration unit is less able to meet load fluctuation demands in a timely manner. In this case, the proportional coefficient in the PID controller needs to be increased accordingly to accelerate the system's response speed. Conversely, a smaller proportional coefficient can be set to avoid system overshoot. The proportional coefficient is set near a value greater than 1. In this embodiment, its range is set to [1, 1.5] to avoid overshoot due to an excessively large proportional coefficient during actual adjustment, which would affect 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 range 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. This optimizes the DCS intelligent control system and helps improve the stability of the power and heat supply of the cogeneration unit.

[0049] Furthermore, in this embodiment, a high-performance computer and dedicated software are equipped in the operator station for system configuration, setup, maintenance, and management. Engineers can use the operator station to write, modify, and download control strategies, as well as set, diagnose, and debug system parameters. It also includes an operator interface that can monitor various parameters and equipment status in the production process in real time, and perform operational controls such as starting and stopping equipment, adjusting setpoints, and viewing alarm information. 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 uses fieldbus communication and transmits data via the TCP / IP communication protocol. The program module includes an operating system, a database management system, and communication middleware, providing a basic support platform for the operation of the DCS system and responsible for system resource management, data storage and retrieval, and communication coordination. It also provides various predefined functional modules, such as control loop modules, sequence control modules, alarm processing modules, data recording modules, and report generation modules. In practical application scenarios, implementers can call and configure these modules according to actual needs to quickly implement various complex control and production management functions.

[0050] Based on the same inventive concept as the above method, this application embodiment also provides a DCS intelligent control system for a cogeneration unit, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described DCS intelligent control methods for a cogeneration unit.

[0051] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

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

[0053] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A DCS intelligent control method for a combined heat and power (CHP) unit, characterized in that, Includes the following steps: Obtain the steam flow rate of the boiler in the cogeneration unit, the speed of the turbine cylinders at different pressures, and the load-side power of the cogeneration unit; By analyzing the differences in the peak and trough power at the load end of the cogeneration unit within each time period and the degree of random variation of the power corresponding to the peak and trough, the load power variation coefficient of the cogeneration unit in each time period is obtained. Based on the deviation between the load power at the load end of the cogeneration unit and the predicted power value within each time period, the load power deviation of the cogeneration unit in each time period is obtained. 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. Based on the degree of correlation between boiler steam flow data and turbine rotational speed data for different pressure cylinders within each time period, and the degree of change in boiler steam flow within each time period, the inertial delay coefficient for the coordination between boiler steam flow and turbine rotational speed in each time period is obtained. The method is as follows: In the formula, Let be the inertial delay coefficient for coordinating boiler steam flow and turbine speed in the i-th time period. This represents the range of boiler steam flow rate within the i-th time period. The average correlation between the boiler steam flow rate change and the change in cylinder speed at different pressures during the i-th time period is given. To avoid constants with a denominator of 0; The method for calculating the average correlation between the boiler steam flow rate change and the change in the rotational speed of different pressure cylinders during the i-th time period is further as follows: ; , These represent the SBD distances between the boiler steam flow rate data and the high-pressure cylinder speed data, intermediate-pressure cylinder speed data, and low-pressure cylinder speed data within the i-th time period; Based on the average level of the load fluctuation coefficient and the average level of the inertial delay coefficient in each time period and several previous time periods, and combined with the correlation between the load fluctuation coefficient and the inertial 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 accordingly. The method for calculating the influence index is as follows: ;in, This represents the influence index of the coordination status between the boiler and the steam turbine in the combined heat and power unit during the i-th time period. The correlation coefficient between the load fluctuation coefficient sequence and the inertia delay coefficient sequence. It is the product of the mean values ​​corresponding to the load fluctuation coefficient sequence and the inertia delay coefficient sequence.

2. The DCS intelligent control method for a combined heat and power unit as described in 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: extract all peaks and troughs in the load-side power data of the cogeneration unit in each time period, calculate the range of the corresponding width range of each peak and trough in each time period, and the Shannon entropy of the power data corresponding to all peaks and troughs, and use 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 combined heat and power unit as described in claim 1, characterized in that, The method for obtaining the load power deviation of the cogeneration unit in each time period is as follows: predict the load power of the cogeneration unit in each time period, obtain multiple predicted values ​​for each time period, calculate the sum of the differences between each predicted value in each time period and the average power value in each time period, and use it as the power load deviation of the cogeneration unit in each time period.

4. The DCS intelligent control method for a combined heat and power unit as described in claim 1, characterized in that, The load fluctuation coefficient and inertial delay coefficient corresponding to each time period and the multiple time periods preceding it are arranged in chronological order to form the load fluctuation coefficient sequence and inertial delay coefficient sequence for each time period.

5. The DCS intelligent control method for a combined heat and power unit as described in claim 1, characterized in that, The adjustment of the proportional coefficient of the DCS intelligent control system further includes: normalizing the influence index corresponding to the current time period, using the product of the normalization result and the preset maximum adjustment range as the proportional coefficient adjustment amount, and using the sum of the proportional coefficient adjustment amount and the preset initial value of the proportional coefficient as the proportional coefficient for the next time period.

6. A DCS intelligent control system for a combined heat and power (CHP) 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, it implements the steps of the DCS intelligent control method for a cogeneration unit as described in any one of claims 1-5.

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