Steam turbine steam circulation system and operation control method thereof

Abstract

The invention relates to the field of steam turbines, and particularly discloses a steam turbine steam circulation system and an operation control method thereof.The steam turbine steam circulation system comprises a steam generator, a steam turbine, a condenser, at least one reheater and an integrated intelligent control unit; the intelligent control unit monitors steam flow, pressure, temperature and steam turbine rotating speed in real time through a high-precision sensor network, and optimal operation of steam circulation under different working conditions is achieved by applying a self-adaptive learning algorithm and a predictive control algorithm; according to the invention, key parameters are monitored in real time by using a high-precision sensor, and optimal operation under different working conditions is realized in combination with an adaptive learning algorithm and a predictive control algorithm; multiple modules in the intelligent control unit work cooperatively, accurate control over steam circulation is ensured, and meanwhile key indexes such as the power generation amount, fuel consumption and pollutant emission are monitored and optimized in real time.

Description

Technical Field

[0001] The invention belongs to the field of steam turbines, in particular to a steam turbine steam cycle system and an operation control method thereof. Background Art

[0002] A steam turbine, also known as a steam turbine engine, is a rotary steam power device whose main function is to convert the thermal energy of steam into mechanical energy. A steam turbine uses high-temperature and high-pressure steam to pass through a fixed nozzle to become an accelerated airflow, which is sprayed onto the blades, causing the rotor with blade rows to rotate and perform work externally.

[0003] However, the existing traditional steam turbine steam cycle system lacks real-time and accurate parameter monitoring and control mechanisms, resulting in the inability to always maintain the optimal operating state under different operating conditions. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a steam turbine steam cycle system to solve the problem that the steam turbine steam cycle system in the prior art lacks a real-time and accurate parameter monitoring and control mechanism, resulting in the inability to always maintain the optimal operating state under different operating conditions.

[0005] A steam turbine steam cycle system, comprising:

[0006] comprising a steam generator, a steam turbine, a condenser and at least one reheater, and an integrated intelligent control unit;

[0007] The intelligent control unit monitors the steam flow, pressure, temperature and turbine speed in real time through a high-precision sensor network, and uses adaptive learning algorithms and predictive control algorithms to achieve optimal operation of the steam cycle under different operating conditions;

[0008] The intelligent control unit includes a data acquisition module, a data processing module, a decision execution module, an adaptive learning module, a multi-objective optimization module and a performance evaluation and optimization module, and each module works together to achieve precise control of the steam cycle.

[0009] Preferably, the data acquisition module is used to collect the system's power generation, fuel consumption cost, emissions of various pollutants, mean time between failures, mean time to repair, and safety-related data in real time.

[0010] Preferably, the data processing module uses advanced intelligent algorithms to process and analyze the collected data, including using adaptive learning algorithms to continuously optimize the control strategy, and finding a balance point between multiple optimization objectives through a multi-objective optimization algorithm.

[0011] Preferably, the decision execution module generates accurate control instructions according to the output results of the data processing module, and directly adjusts the state of each actuator in the steam cycle system through the control signal to achieve accurate control of the steam cycle.

[0012] Preferably, the adaptive learning module automatically adjusts control parameters and algorithm models according to real-time feedback and long-term data accumulation during system operation to adapt to changes in operating conditions and environmental conditions, thereby improving the adaptability and stability of the system.

[0013] Preferably, the multi-objective optimization module is able to balance multiple optimization objectives such as economy, environmental protection, reliability and safety, and determine a near-optimal control scheme to improve the overall performance of the system.

[0014] Preferably, the performance evaluation and optimization module is connected to the data acquisition module, and comprises at least one processor and a memory, wherein the memory stores a computer program for executing the following steps:

[0015] i. Receive real-time data provided by the data acquisition module;

[0016] ii. Calculate the Comprehensive Performance Factor (CPF) using the following formula:

[0017]

[0018] Among them, CPF (comprehensive performance factor) is a quantitative indicator of the system's comprehensive performance; t0 and t f Respectively represent the start and end time of the evaluation period; E gen (t) is the power generation at time t; E max is the theoretical maximum power generation of the system; C fuel (t) is the fuel consumption cost at time t; C avg is the average fuel consumption cost; is the emission of the ith pollutant at time t; is the emission limit of the i-th pollutant; α is the environmental protection weight factor; MTBF(t) is the mean time between failures at time t; MTTR(t) is the mean repair time at time t; β is the reliability weight factor; SafetyIndex(t) is the safety index at time t, calculated from a series of safety-related parameters; Complexity(t) is the measure of the complexity of the j-th system at time t; m is the number of complexity measures considered;

[0019] iii. Adjust the operating parameters of the steam turbine steam cycle system based on the calculated comprehensive performance factor (CPF) to optimize the system performance.

[0020] Preferably, the performance evaluation and optimization module further includes a user interface for adjusting weight factors α and β, so as to allow the user to customize the emphasis of the comprehensive performance evaluation according to actual needs.

[0021] An operation control method includes the steam turbine steam cycle system as described above, and further includes:

[0022] a. Initialize the system: start the steam turbine steam cycle system and activate the integrated intelligent control unit, including the data acquisition module, data processing module, decision execution module, adaptive learning module, multi-objective optimization module and performance evaluation and optimization module;

[0023] b. Data collection and preprocessing: The high-precision sensor network collects steam flow, pressure, temperature, turbine speed, power generation, fuel consumption cost, emissions of various pollutants, mean time between failures, mean time to repair, and safety-related data in real time, and the data collection module performs preliminary sorting and storage;

[0024] c. Data processing and analysis: The data processing module uses advanced intelligent algorithms to process and analyze the collected data, including the use of adaptive learning algorithms to continuously optimize the control strategy, and the use of multi-objective optimization algorithms to find a balance between multiple optimization goals such as economy, environmental protection, reliability and safety;

[0025] d. Decision-making and execution: The decision-making execution module generates precise control instructions based on the output results of the data processing module, and directly adjusts the state of each actuator in the steam cycle system through control signals to achieve precise control of the steam cycle;

[0026] e. Adaptive learning and optimization: The adaptive learning module automatically adjusts the control parameters and algorithm models according to the real-time feedback and long-term data accumulation during the system operation to adapt to the changes in working conditions and environmental conditions, and improve the adaptability and stability of the system. At the same time, the multi-objective optimization module continuously searches for the optimal control solution among multiple optimization objectives;

[0027] f. Performance evaluation and adjustment: The performance evaluation and optimization module receives real-time data, evaluates system performance using predefined calculation formulas, and adjusts system operating parameters based on the evaluation results to continuously optimize system performance. It also provides a user interface that allows users to adjust the economic, environmental, reliability and safety weight factors according to actual needs to customize the focus of the comprehensive performance evaluation;

[0028] g. Feedback and iteration: The system achieves long-term efficient, stable and safe operation of the steam cycle system through continuous feedback loops and iterative optimization.

[0029] Compared with the prior art, the present invention has the following beneficial effects: by using high-precision sensors to monitor key parameters in real time, combined with adaptive learning algorithms and predictive control algorithms, the optimal operation under different working conditions is achieved; the multi-modules in the intelligent control unit work together to ensure the precise control of the steam cycle, while real-time monitoring and optimization of key indicators such as power generation, fuel consumption and pollutant emissions; the system can adapt to changes in working conditions through an adaptive learning mechanism, enhancing adaptability and stability. Under the multi-objective optimization framework, the system balances economy, environmental protection, reliability and safety, and achieves a significant improvement in overall performance. This innovation not only improves energy efficiency and reduces operating costs, but also promotes environmental protection, showing significant ecological and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the system of the present invention;

[0031] Figure 2 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] like Figure 1 to Figure 2 As shown:

[0034] Embodiment 1: The present invention provides a steam turbine steam cycle system, comprising:

[0035] comprising a steam generator, a steam turbine, a condenser and at least one reheater, and an integrated intelligent control unit;

[0036] The intelligent control unit monitors the steam flow, pressure, temperature and turbine speed in real time through a high-precision sensor network, and uses adaptive learning algorithms and predictive control algorithms to achieve optimal operation of the steam cycle under different operating conditions;

[0037] The intelligent control unit includes a data acquisition module, a data processing module, a decision execution module, an adaptive learning module, a multi-objective optimization module and a performance evaluation and optimization module. The modules work together to achieve precise control of the steam cycle.

[0038] Specifically, the data acquisition module is used to collect the system's power generation, fuel consumption cost, emissions of various pollutants, mean time between failures, mean time to repair, and safety-related data in real time.

[0039] Specifically, the data processing module uses advanced intelligent algorithms to process and analyze the collected data, including the use of adaptive learning algorithms to continuously optimize the control strategy, and the use of multi-objective optimization algorithms to find a balance between multiple optimization objectives.

[0040] Specifically, the decision execution module generates accurate control instructions according to the output results of the data processing module, and directly adjusts the state of each actuator in the steam cycle system through the control signal to achieve accurate control of the steam cycle.

[0041] Specifically, the adaptive learning module automatically adjusts the control parameters and algorithm models according to the real-time feedback and long-term data accumulation during the operation of the system to adapt to the changes in working conditions and environmental conditions, thereby improving the adaptability and stability of the system.

[0042] Specifically, the multi-objective optimization module can balance multiple optimization objectives such as economy, environmental protection, reliability and safety, and determine a near-optimal control scheme to improve the overall performance of the system.

[0043] From the above, it can be seen that this system integrates an intelligent control unit, monitors the key parameters of the steam cycle in real time through high-precision sensors, and uses adaptive learning algorithms and predictive control algorithms to achieve the optimal operation of the system under different operating conditions; the intelligent control unit includes multiple modules such as data acquisition, processing, decision execution, adaptive learning, multi-objective optimization, and performance evaluation and optimization, which work together to ensure precise control of the steam cycle; this system can not only monitor and optimize key indicators such as power generation, fuel consumption, and pollutant emissions in real time, but also improve the system's adaptability to changes in operating conditions through adaptive learning, while balancing economy, environmental protection, reliability and safety under the multi-objective optimization framework, significantly improving the overall performance of the system.

[0044] Embodiment 2: This embodiment is basically the same as the previous embodiment, except that it further includes: a performance evaluation and optimization module is connected to a data acquisition module, and includes at least one processor and a memory, wherein the memory stores a computer program for executing the following steps:

[0045] i. Receive real-time data provided by the data acquisition module;

[0046] ii. Calculate the Comprehensive Performance Factor (CPF) using the following formula:

[0047]

[0048] Among them, CPF (comprehensive performance factor) is a quantitative indicator of the system's comprehensive performance; t0 and t f Respectively represent the start and end time of the evaluation period; Egen (t) is the power generation at time t; E max is the theoretical maximum power generation of the system; C fuel (t) is the fuel consumption cost at time t; C avg is the average fuel consumption cost; is the emission of the ith pollutant at time t; is the emission limit of the i-th pollutant; α is the environmental protection weight factor; MTBF(t) is the mean time between failures at time t; MTTR(t) is the mean repair time at time t; β is the reliability weight factor; SafetyIndex(t) is the safety index at time t, calculated from a series of safety-related parameters; Complexity(t) is the measure of the complexity of the j-th system at time t; m is the number of complexity measures considered;

[0049] iii. Adjust the operating parameters of the steam turbine steam cycle system based on the calculated comprehensive performance factor (CPF) to optimize the system performance.

[0050] Specifically, the performance evaluation and optimization module also includes a user interface for adjusting the weight factors α and β, so as to allow the user to customize the emphasis of the comprehensive performance evaluation according to actual needs.

[0051] From the above, it can be seen that an enhanced performance evaluation and optimization module is introduced in this system, which can receive real-time data and calculate the comprehensive performance factor (CPF) through complex formulas. This factor comprehensively considers multiple dimensions such as power generation, fuel efficiency, environmental protection, reliability, safety and system complexity; in addition, users can flexibly adjust the weight factor through the user interface of the module to adapt to different evaluation needs and customize the optimization strategy; through real-time monitoring and feedback adjustment, the module can further optimize the operating parameters of the steam turbine steam cycle system, thereby comprehensively improving the overall performance of the system.

[0052] Embodiment 3: This embodiment is basically the same as the previous embodiment, except that it further comprises: an operation control method, comprising the steam turbine steam cycle system as described above, and further comprising:

[0053] a. Initialize the system: start the steam turbine steam cycle system and activate the integrated intelligent control unit, including the data acquisition module, data processing module, decision execution module, adaptive learning module, multi-objective optimization module and performance evaluation and optimization module;

[0054] b. Data collection and preprocessing: The high-precision sensor network collects steam flow, pressure, temperature, turbine speed, power generation, fuel consumption cost, emissions of various pollutants, mean time between failures, mean time to repair, and safety-related data in real time, and the data collection module performs preliminary sorting and storage;

[0055] c. Data processing and analysis: The data processing module uses advanced intelligent algorithms to process and analyze the collected data, including the use of adaptive learning algorithms to continuously optimize the control strategy, and the use of multi-objective optimization algorithms to find a balance between multiple optimization goals such as economy, environmental protection, reliability and safety;

[0056] d. Decision-making and execution: The decision-making execution module generates precise control instructions based on the output results of the data processing module, and directly adjusts the state of each actuator in the steam cycle system through control signals to achieve precise control of the steam cycle;

[0057] e. Adaptive learning and optimization: The adaptive learning module automatically adjusts the control parameters and algorithm models according to the real-time feedback and long-term data accumulation during the system operation to adapt to the changes in working conditions and environmental conditions, and improve the adaptability and stability of the system. At the same time, the multi-objective optimization module continuously searches for the optimal control solution among multiple optimization objectives;

[0058] f. Performance evaluation and adjustment: The performance evaluation and optimization module receives real-time data, evaluates system performance using predefined calculation formulas, and adjusts system operating parameters based on the evaluation results to continuously optimize system performance. It also provides a user interface that allows users to adjust the economic, environmental, reliability and safety weight factors according to actual needs to customize the focus of the comprehensive performance evaluation;

[0059] g. Feedback and iteration: The system achieves long-term efficient, stable and safe operation of the steam cycle system through continuous feedback loops and iterative optimization.

[0060] From the above, it can be seen that this method covers comprehensive steps from initialization to performance evaluation and adjustment, including data collection and preprocessing, intelligent processing and analysis, precise decision-making and execution, adaptive learning and optimization, and user-customizable performance evaluation. Through continuous feedback and iteration, the system can automatically adapt to changes in working conditions, optimize operating parameters, and ensure long-term efficient, stable and safe operation. This method not only improves the intelligence level of the system, but also enhances the flexibility and satisfaction of users.

[0061] Application Process:

[0062] First, the steam turbine steam cycle system is started and the integrated intelligent control unit is activated to ensure that all modules are ready. Then, a high-precision sensor network is used to collect key parameters in the steam cycle in real time, such as steam flow, pressure, temperature, turbine speed, etc., as well as power generation, fuel consumption, pollutant emissions, failure time, repair time and safety-related data;

[0063] After the collected data is initially sorted and stored by the data acquisition module, it is sent to the data processing module for in-depth processing and analysis. Using advanced intelligent algorithms, including adaptive learning algorithms and multi-objective optimization algorithms, the data processing module continuously optimizes the control strategy to find the best balance between economy, environmental protection, reliability and safety.

[0064] Based on the processing results, the decision-making execution module generates precise control instructions and directly adjusts the state of each actuator in the steam cycle system through control signals to achieve precise control of the steam cycle. At the same time, the adaptive learning module automatically adjusts the control parameters and algorithm models based on real-time feedback and long-term data accumulation to improve the adaptability and stability of the system.

[0065] The performance evaluation and optimization module uses predefined calculation formulas to comprehensively evaluate system performance and adjust operating parameters accordingly to continuously optimize system performance. Users can also use the user interface of this module to adjust the weight factors of economy, environmental protection, reliability and safety according to actual needs and customize the focus of comprehensive performance evaluation;

[0066] Finally, through continuous feedback loops and iterative optimization, the system automatically adapts to changes in operating conditions, optimizes operating parameters, and ensures the long-term efficient, stable, and safe operation of the steam cycle system. This process not only improves the intelligence level and comprehensive performance of the system, but also enhances the flexibility and satisfaction of users, providing reliable technical support for power production.

[0067] The standard parts used in the present invention can all be purchased from the market, and the special-shaped parts can be customized according to the description and the drawings. The specific connection methods of each part adopt conventional means such as mature bolts, rivets, welding, etc. in the prior art. The machinery, parts and equipment all adopt conventional models in the prior art, and the circuit connection adopts the conventional connection method in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0068] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0069] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0070] In the present invention, unless otherwise clearly specified and limited, a first feature being "above" or "below" a second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, a first feature being "above", "above" or "above" a second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is higher in level than the second feature. A first feature being "below", "below" or "below" a second feature may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is lower in level than the second feature.

[0071] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0072] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0073] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A steam turbine steam cycle system, characterized in that: include: comprising a steam generator, a steam turbine, a condenser and at least one reheater, and an integrated intelligent control unit; The intelligent control unit includes a data acquisition module, a data processing module, a decision execution module, an adaptive learning module, a multi-objective optimization module and a performance evaluation and optimization module, and each module works together to achieve precise control of the steam cycle; The intelligent control unit monitors the steam flow, pressure, temperature and turbine speed in real time through a high-precision sensor network, and uses adaptive learning algorithms and predictive control algorithms to achieve optimal operation of the steam cycle under different operating conditions.

2. A steam turbine steam cycle system according to claim 1, characterized in that: The data acquisition module is used to collect the system's power generation, fuel consumption cost, emissions of various pollutants, mean time between failures, mean time to repair, and safety-related data in real time.

3. A steam turbine steam cycle system as claimed in claim 2, characterized in that: The data processing module uses advanced intelligent algorithms to process and analyze the collected data, including using adaptive learning algorithms to continuously optimize control strategies, and finding a balance point between multiple optimization goals through a multi-objective optimization algorithm.

4. A steam turbine steam cycle system as claimed in claim 3, characterized in that: The decision-making execution module generates accurate control instructions according to the output results of the data processing module, and directly adjusts the states of various actuators in the steam cycle system through control signals to achieve accurate control of the steam cycle.

5. A steam turbine steam cycle system as claimed in claim 4, characterized in that: The adaptive learning module automatically adjusts control parameters and algorithm models according to real-time feedback and long-term data accumulation during system operation to adapt to changes in operating conditions and environmental conditions, thereby improving the adaptability and stability of the system.

6. A steam turbine steam cycle system as claimed in claim 5, characterized in that: The multi-objective optimization module can balance multiple optimization objectives such as economy, environmental protection, reliability and safety, and determine a near-optimal control scheme to improve the overall performance of the system.

7. A steam turbine steam cycle system as claimed in claim 5, characterized in that: The performance evaluation and optimization module is connected to the data acquisition module and includes at least one processor and a memory, wherein the memory stores a computer program for executing the following steps: i. Receive real-time data provided by the data acquisition module; ii. Calculate the Comprehensive Performance Factor (CPF) using the following formula: Among them, CPF (comprehensive performance factor) is a quantitative indicator of the system's comprehensive performance; t0 and t f Respectively represent the start and end time of the evaluation period; E gen (t) is the power generation at time t; E max is the theoretical maximum power generation of the system; C fuel (t) is the fuel consumption cost at time t; C avg is the average fuel consumption cost; is the emission of the ith pollutant at time t; is the emission limit of the i-th pollutant; α is the environmental protection weight factor; MTBF(t) is the mean time between failures at time t; MTTR(t) is the mean repair time at time t; β is the reliability weight factor; SafetyIndex(t) is the safety index at time t, calculated from a series of safety-related parameters; Complexity(t) is the measure of the complexity of the j-th system at time t; m is the number of complexity measures considered; iii. Adjust the operating parameters of the steam turbine steam cycle system based on the calculated comprehensive performance factor (CPF) to optimize the system performance.

8. A steam turbine steam cycle system as claimed in claim 7, characterized in that: The performance evaluation and optimization module also includes a user interface for adjusting weight factors α and β, so as to allow the user to customize the emphasis of the comprehensive performance evaluation according to actual needs.

9. An operation control method, characterized in that: The steam turbine steam cycle system according to any one of claims 1 to 8 further comprises: a. Initialize the system: start the steam turbine steam cycle system and activate the integrated intelligent control unit, including the data acquisition module, data processing module, decision execution module, adaptive learning module, multi-objective optimization module and performance evaluation and optimization module; b. Data collection and preprocessing: The high-precision sensor network collects steam flow, pressure, temperature, turbine speed, power generation, fuel consumption cost, emissions of various pollutants, mean time between failures, mean time to repair, and safety-related data in real time, and the data collection module performs preliminary sorting and storage; c. Data processing and analysis: The data processing module uses advanced intelligent algorithms to process and analyze the collected data, including the use of adaptive learning algorithms to continuously optimize the control strategy, and the use of multi-objective optimization algorithms to find a balance between multiple optimization goals such as economy, environmental protection, reliability and safety; d. Decision-making and execution: The decision-making execution module generates precise control instructions based on the output results of the data processing module, and directly adjusts the state of each actuator in the steam cycle system through control signals to achieve precise control of the steam cycle; e. Adaptive learning and optimization: The adaptive learning module automatically adjusts the control parameters and algorithm models according to the real-time feedback and long-term data accumulation during the system operation to adapt to the changes in working conditions and environmental conditions, and improve the adaptability and stability of the system. At the same time, the multi-objective optimization module continuously searches for the optimal control solution among multiple optimization objectives; f. Performance evaluation and adjustment: The performance evaluation and optimization module receives real-time data, evaluates system performance using predefined calculation formulas, and adjusts system operating parameters based on the evaluation results to continuously optimize system performance. It also provides a user interface that allows users to adjust the economic, environmental, reliability and safety weight factors according to actual needs to customize the focus of the comprehensive performance evaluation; g. Feedback and iteration: The system achieves long-term efficient, stable and safe operation of the steam cycle system through continuous feedback loops and iterative optimization.

Images