Intelligent optimization control system for peak regulation of thermal power generating unit

Abstract

The invention relates to the technical field of thermal power generating unit control, and discloses an intelligent optimization control system for peak regulation of a thermal power generating unit. According to the system, related data of a thermal power generating unit is acquired through an acquisition unit in a data acquisition module, and a state monitoring and diagnosis module calculates an equipment health state index, an equipment performance attenuation coefficient and an equipment fault expected value based on the data acquired by the data acquisition module; the operation state of the thermal power generating unit is monitored according to the calculated numerical value, fault diagnosis of the thermal power generating unit is conducted, a diagnosis signal is generated, and when the dispatching optimization decision module receives the diagnosis signal, the optimal power generation cost, the maximum power generation load, the unit response time, the waste gas emission amount and the energy storage equipment state are calculated; the control module calculates the operation data of the thermal power generating unit and formulates a peak regulation optimization strategy according to the calculated values and the historical operation data, the control execution module executes a peak regulation optimization control command and generates a thermal power generating unit peak regulation optimization report according to the peak regulation optimization strategy, and the user visualization module displays the thermal power generating unit peak regulation optimization report through a data panel.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power unit control, in particular to an intelligent optimization control system for peak load regulation of thermal power units. Background Art

[0002] Thermal power peak regulation refers to a method of regulating the power generation capacity of thermal power units in the power system to cope with load fluctuations and changes in power demand. As the proportion of renewable energy such as wind power and solar power gradually increases, the volatility and instability of power load are becoming increasingly severe, especially during the peak period in summer and the low temperature period in winter. Thermal power peak regulation is particularly important. Its main purpose is to balance the supply and demand of electricity, avoid power shortages during peak demand, and improve the reliability and economy of the power grid. At the same time, thermal power peak regulation also helps to ensure the smooth operation of the power market and the effective allocation of resources, thereby providing a solid power guarantee for economic development.

[0003] At present, thermal power units need to be started and stopped frequently during the peak load regulation process, which causes great wear and fatigue to the equipment and shortens the service life of the units. Frequent start and stop will also increase maintenance costs and downtime, affecting the overall operating efficiency. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent optimization control system for peak load regulation of thermal power units, which is equipped with a collection unit in a data collection module to collect relevant data of the thermal power units, a state monitoring and diagnosis module to calculate the equipment health status index Ztzs, the equipment performance attenuation coefficient Sjxs and the equipment failure expectation value Gzyq based on the data collected by the data collection module, monitor the operating status of the thermal power units according to the calculated values, perform fault diagnosis of the thermal power units, and generate a diagnosis signal ZD to send to the scheduling optimization decision module. When the scheduling optimization decision module receives the diagnosis signal ZD, it calculates the optimal power generation cost Zycb, the maximum power generation load Fdfh, the unit response time Jzxy, and the exhaust gas emission Fqp f and the state of the energy storage device Cnzt, and formulate a peak-shaving optimization strategy based on the above calculated values ​​and historical operation data. The control execution module executes the peak-shaving optimization control command according to the peak-shaving optimization strategy, adjusts the unit output, changes the fuel supply, and optimizes the cooling water flow, and generates a peak-shaving optimization report for the thermal power unit. The user visualization module displays the peak-shaving optimization report for the thermal power unit through the data panel, analyzes and calculates the relevant data of the thermal power unit, formulates a peak-shaving optimization strategy after receiving the diagnostic signal, and then performs thermal power peak-shaving, which avoids the need for frequent start and stop of the thermal power unit during the peak-shaving process, reduces maintenance costs and shortens downtime, and improves the service life and overall operation efficiency of the unit, thereby solving the above problems.

[0006] (II) Technical solution

[0007] To achieve the above-mentioned object, the present invention provides the following technical solutions: an intelligent optimization control system for peak load regulation of thermal power units, comprising a data acquisition module, a state monitoring and diagnosis module, a scheduling optimization decision module, a control execution module and a user visualization module;

[0008] The data acquisition module includes a power data acquisition unit, a temperature data acquisition unit, a humidity data acquisition unit, a pressure acquisition unit, a wind speed acquisition unit and a power grid status acquisition unit;

[0009] The power collection unit collects current and voltage data of the thermal power unit through a power sensor;

[0010] The temperature acquisition unit acquires the operating temperature data of the thermal power unit through a temperature sensor;

[0011] The humidity collection unit collects humidity data of the operating environment of the thermal power unit through a humidity sensor;

[0012] The pressure collection unit collects the operating pressure data of the thermal power unit through a pressure sensor;

[0013] The wind speed collection unit collects wind speed data around the thermal power unit through a wind speed sensor;

[0014] The power grid status acquisition unit is connected to the remote terminal system through the PLC device to obtain the power grid load, power generation and power generation frequency data;

[0015] The above collected data is sent to the condition monitoring and diagnosis module;

[0016] The state monitoring and diagnosis module calculates the equipment health status index Ztzs, the equipment performance attenuation coefficient Sjxs and the equipment failure expectation value Gzyq based on the data collected by the data acquisition module, monitors the operating status of the thermal power unit according to the calculated values, performs fault diagnosis of the thermal power unit, and generates a diagnosis signal ZD to send to the scheduling optimization decision module;

[0017] When the dispatch optimization decision module receives the diagnostic signal ZD, it calculates the optimal power generation cost Zycb, the maximum power generation load Fdfh, the unit response time Jzxy, the exhaust gas emission Fqpf and the energy storage device status Cnzt, and formulates the peak load optimization strategy according to the above calculated values ​​and historical operation data;

[0018] The control execution module executes the peak-shaving optimization control command according to the peak-shaving optimization strategy, adjusts the unit output, changes the fuel supply, and optimizes the cooling water flow, and generates a peak-shaving optimization report for the thermal power unit and sends it to the user visualization module;

[0019] The user visualization module displays the peak load optimization report of the thermal power unit through the data panel.

[0020] Preferably, the state monitoring and diagnosis module calculates the equipment health status index Ztzs based on the data transmitted by the data acquisition module, and the calculation formula is as follows:

[0021]

[0022] In the formula, Ztzs represents the equipment health status index, w i Represents the weight coefficient of the i-th performance indicator, which is dynamically assigned by the system based on historical data. i represents the i-th performance indicator, and n represents the total number of performance indicators.

[0023] Preferably, the state monitoring and diagnosis module calculates the equipment performance attenuation coefficient Sjxs based on the data transmitted by the data acquisition module, and the calculation formula is as follows:

[0024]

[0025] In the formula, Sjxs represents the equipment performance attenuation coefficient, P0 represents the initial performance value, and P t represents the current performance value, e represents the base of the natural logarithm, which is 2.718, λ represents the decay rate, and t represents the running time.

[0026] Preferably, the state monitoring and diagnosis module calculates the expected value of equipment failure Gzyq based on the data transmitted by the data acquisition module, and the calculation formula is as follows:

[0027]

[0028] In the formula, Gzyq represents the expected value of equipment failure, α and β represent adjustment coefficients, which are set by the system to reflect the relationship between health status and failure expectation, Ztzs represents the equipment health status index, Sjxs represents the equipment performance attenuation coefficient, m represents the number of failure modes, and F j represents the impact factor of the jth failure mode, c j The weight coefficient of the j-th fault mode is assigned by the system based on historical fault data.

[0029] Preferably, the state monitoring and diagnosis module monitors the operating state of the thermal power unit according to the calculated equipment failure expected value, and performs fault diagnosis of the thermal power unit, and the diagnosis method is as follows:

[0030] When the expected value of equipment failure Gzyq is less than or equal to 80, it means that the equipment is operating normally;

[0031] When the expected value of equipment failure Gzyq is greater than 80 and less than or equal to 90, it means that the equipment is in a warning state and needs to be continuously monitored;

[0032] When the expected value of equipment failure Gzyq is greater than 90, it indicates that the risk of equipment failure is high and immediate measures need to be taken to generate a diagnostic signal ZD.

[0033] Preferably, when the scheduling optimization decision module receives the diagnostic signal ZD, it calculates the optimal power generation cost Zycb, and the calculation formula is as follows:

[0034]

[0035] In the formula, Zycb represents the optimal power generation cost, p represents the number of units, and F k represents the power generation of the kth unit, C fuel，k represents the fuel price of the kth unit, O k represents the operation and maintenance cost of the kth unit, C fixed，k represents the fixed cost of the kth unit.

[0036] Preferably, when the scheduling optimization decision module receives the diagnostic signal ZD, it calculates the maximum power generation load Fdfh, and the calculation formula is as follows:

[0037]

[0038] In the formula, Fdfh represents the maximum power generation load, p represents the number of units, and Edgl x Indicates the rated power of the xth unit, Xl x Represents the efficiency of the xth unit.

[0039] Preferably, when the scheduling optimization decision module receives the diagnostic signal ZD, the computer group response time Jzxy is calculated as follows:

[0040]

[0041] In the formula, Jzxy represents the unit response time, Qdsj represents the unit start-up time, AP represents the load change, and Rt represents the unit response rate.

[0042] Preferably, when the scheduling optimization decision module receives the diagnostic signal ZD, it calculates the exhaust gas emission Fqpf, and the calculation formula is as follows:

[0043]

[0044] In the formula, Fqpf represents the exhaust gas emission, F q represents the power generation of the qth unit, Pfyz qrepresents the emission factor of the qth unit.

[0045] Preferably, when the scheduling optimization decision module receives the diagnostic signal ZD, it calculates the energy storage device state Cnzt, and the calculation formula is as follows:

[0046]

[0047] In the formula, Cnzt represents the state of the energy storage device, S0 represents the initial energy, Cdxl represents the energy storage efficiency, Cdgl represents the charging power, T represents the energy storage time, Fhxq represents the load demand, and Fdxl represents the discharge efficiency.

[0048] Compared with the prior art, the present invention provides an intelligent optimization control system for peak load regulation of thermal power units, which has the following beneficial effects:

[0049] The present invention collects relevant data of the thermal power unit through the collection unit in the data collection module. The state monitoring and diagnosis module calculates the equipment health status index Ztzs, the equipment performance attenuation coefficient Sjxs and the equipment failure expectation value Gzyq based on the data collected by the data collection module, monitors the operating state of the thermal power unit according to the calculated values, performs fault diagnosis of the thermal power unit, and generates a diagnosis signal ZD to send to the scheduling optimization decision module. When the scheduling optimization decision module receives the diagnosis signal ZD, it calculates the optimal power generation cost Zycb, the maximum power generation load Fdfh, the unit response time Jzxy, the exhaust gas emission Fqpf and the energy storage device state Cn zt, and formulate a peak-shaving optimization strategy based on the above-mentioned calculated values ​​and historical operating data. The control execution module executes the peak-shaving optimization control command according to the peak-shaving optimization strategy, adjusts the unit output, changes the fuel supply, and optimizes the cooling water flow, and generates a peak-shaving optimization report for the thermal power unit. The user visualization module displays the peak-shaving optimization report for the thermal power unit through the data panel, analyzes and calculates the relevant data of the thermal power unit, formulates a peak-shaving optimization strategy after receiving the diagnostic signal, and then performs thermal power peak-shaving, which avoids the need for frequent start and stop of the thermal power unit during the peak-shaving process, reduces maintenance costs and shortens downtime, and improves the service life and overall operating efficiency of the unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0051] 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.

[0052] In view of the fact that thermal power units need to be frequently started and stopped during peak load regulation, which causes great wear and fatigue to the equipment, shortens the service life of the units, increases maintenance costs and downtime, and affects the overall operating efficiency of the units, an intelligent optimization control system for peak load regulation of thermal power units is proposed. Figure 1 ,The system includes a data acquisition module, a condition monitoring and diagnosis module, a ,scheduling optimization decision module, a control execution module and a user ,visualization module;

[0053] The data acquisition module is a comprehensive information collection system, which includes:

[0054] The power data acquisition unit integrates high-precision power sensors, which can monitor the current and voltage data of thermal power units in real time. By adopting digital signal processing technology, it can effectively filter out noise and improve measurement accuracy. At the same time, the unit supports multi-channel simultaneous acquisition, ensuring more comprehensive power parameter monitoring;

[0055] The temperature data acquisition unit uses a high-precision platinum resistance thermometer, which can not only monitor the operating temperature inside the thermal power unit, but also monitor the temperature distribution of key components such as boilers and steam turbines. The data fusion technology of passive and active temperature sensors ensures the accuracy and stability of temperature data.

[0056] The humidity data acquisition unit collects humidity data of the operating environment of the thermal power unit through a highly sensitive humidity sensor. The sensor uses digital output to improve measurement accuracy and response speed, and has a self-checking function to ensure data reliability.

[0057] The pressure acquisition unit uses a precise pressure sensor to monitor the operating pressure data of various links of the thermal power unit, such as boilers, pumps, pipelines, etc. in real time. The sensor uses an advanced analog converter to convert the pressure signal into a digital signal to facilitate subsequent analysis and processing of the data;

[0058] The wind speed collection unit uses an ultrasonic anemometer, which can accurately measure the wind speed and wind direction data around the thermal power unit. The non-contact measurement principle of the anemometer ensures the real-time and accuracy of data collection. By combining the wind speed data with the unit operating parameters, the unit's operating strategy can be optimized, especially in terms of auxiliary regulation.

[0059] The power grid status acquisition unit is connected to the remote terminal system through the PLC programmable logic controller device to obtain key data such as power grid load, power generation and power generation frequency. The PLC device has powerful data processing and communication capabilities, can be compatible with multiple communication protocols, and supports remote real-time monitoring. This unit can not only monitor the current power grid status, but also analyze the change rules of power grid load based on historical data, and provide data support for power dispatching;

[0060] The condition monitoring and diagnosis module calculates the equipment health status index Ztzs, the equipment performance attenuation coefficient Sjxs and the equipment failure expectation value Gzyq based on the data collected by the data acquisition module, where:

[0061] The equipment health index calculation formula is as follows:

[0062]

[0063] The equipment health index provides a benchmark indicator for equipment performance, which can be used to evaluate equipment performance under different operating conditions and promote more efficient peak load response. In the formula, Ztzs represents the equipment health index, w i Represents the weight coefficient of the i-th performance indicator, which is dynamically assigned by the system based on historical data. i represents the i-th performance indicator, and n represents the total number of performance indicators. By calculating the equipment health status index, potential health problems can be identified in advance, and early warning information can be provided to help operation and maintenance personnel take measures for maintenance or adjustment to reduce the failure rate.

[0064] The calculation formula of equipment performance attenuation coefficient is as follows:

[0065]

[0066] The equipment performance attenuation coefficient reflects the degree of performance degradation of the equipment due to changes in operating time and usage conditions. In the formula, Sjxs represents the equipment performance attenuation coefficient, P0 represents the initial performance value, and P t represents the current performance value, e represents the base of the natural logarithm, which is 2.718, λ represents the attenuation rate, and t represents the operating time. By tracking the equipment performance attenuation coefficient over a long period of time, data-driven improvements can be made to equipment design, procurement, and replacement strategies, thereby improving the economy and reliability of the overall power generation system.

[0067] The calculation formula for the expected value of equipment failure is as follows:

[0068]

[0069] The expected value of equipment failure reflects the probability and potential consequences of equipment failure. In the formula, Gzyq represents the expected value of equipment failure, α and β represent adjustment coefficients, which are set by the system to reflect the relationship between health status and failure expectation, Ztzs represents the equipment health status index, Sjxs represents the equipment performance attenuation coefficient, m represents the number of failure modes, and F j represents the impact factor of the jth failure mode, c j The weight coefficient representing the jth fault mode is assigned by the system in combination with historical fault data. Based on the expected value of equipment failure, the dispatching system can respond to emergencies more flexibly, optimize the operation layout of the generator sets, and ensure the stability and economy of power supply during peak load regulation.

[0070] The status monitoring and diagnosis module monitors the operating status of the thermal power unit according to the calculated expected value of equipment failure and performs fault diagnosis of the thermal power unit. The diagnosis method is as follows:

[0071] When the expected value of equipment failure Gzyq is less than or equal to 80, it means that the equipment is operating normally;

[0072] When the expected value of equipment failure Gzyq is greater than 80 and less than or equal to 90, it means that the equipment is in a warning state and needs to be continuously monitored;

[0073] When the expected value of equipment failure Gzyq is greater than 90, it means that the risk of equipment failure is high and immediate measures need to be taken. At this time, a diagnostic signal ZD is generated;

[0074] When the dispatch optimization decision module receives the diagnostic signal ZD, it calculates the optimal power generation cost Zycb, the maximum power generation load Fdfh, the unit response time Jzxy, the exhaust gas emission Fqpf and the energy storage device status Cnzt, and formulates the peak load optimization strategy based on the above calculated values ​​and historical operation data, where:

[0075] The optimal power generation cost calculation formula is as follows:

[0076]

[0077] By calculating the optimal power generation cost, we can effectively identify and control various costs in the power generation process, including fuel, maintenance and operating costs, thereby improving economic benefits. In the formula, Zycb represents the optimal power generation cost, p represents the number of units, and F k represents the power generation of the kth unit, C fuel,k represents the fuel price of the kth unit, O k represents the operation and maintenance cost of the kth unit, C fixed，k represents the fixed cost of the kth unit. The calculation of the optimal power generation cost helps to allocate resources reasonably and ensure that unnecessary expenses are minimized while meeting the power demand;

[0078] The maximum power generation load calculation formula is as follows:

[0079]

[0080] By calculating the maximum power generation load, we can more accurately predict power demand and ensure the stability and reliability of the power grid. In the formula, Fdfh represents the maximum power generation load, p represents the number of units, and Edgl x Indicates the rated power of the xth unit, Xl x Indicates the efficiency of the xth unit. Knowing the maximum power generation load of the unit can help dispatchers optimize the dispatching strategy of the generator unit, reasonably arrange power generation tasks, and improve the overall operating efficiency;

[0081] The unit response time calculation formula is as follows:

[0082]

[0083] By calculating the response time of the computer group, its ability to quickly adjust when the load changes can be evaluated, thereby improving the flexibility of the power system. In the formula, Jzxy represents the response time of the unit, Qdsj represents the start-up time of the unit, AP represents the load change, and Rt represents the response rate of the unit. The unit with a short response time can participate in peak regulation more effectively, quickly adapt to the fluctuation of the power grid load, and improve the stability of the power grid;

[0084] The formula for calculating exhaust emissions is as follows:

[0085]

[0086] The calculation of exhaust gas emissions can help identify the efficiency of fuel use, promote the rational use of fuel, and reduce operating costs. In the formula, Fqpf represents exhaust gas emissions, F q represents the power generation of the qth unit, Pfyz q It represents the emission factor of the qth unit. Real-time monitoring of exhaust emissions helps to detect abnormalities in time, take measures to reduce emissions and protect the environment;

[0087] The energy storage device status calculation formula is as follows:

[0088]

[0089] By calculating the state of the energy storage device, the use of the energy storage system can be optimized to ensure that it can provide necessary support during peak power demand. In the formula, Cnzt represents the state of the energy storage device, S0 represents the initial energy, Cdxl represents the energy storage efficiency, Cdgl represents the charging power, T represents the energy storage time, Fhxq represents the load demand, and Fdxl represents the discharge efficiency. The state calculation of the energy storage device helps to balance the load fluctuations of the power grid and improve the stability of the power system.

[0090] The above calculation results are comprehensively analyzed to form a comprehensive peak-shaving optimization strategy, which will take into account multiple factors such as power generation cost, load demand, response time, exhaust emissions and energy storage status, and use the particle swarm optimization algorithm to iteratively optimize the peak-shaving strategy to ensure the optimal solution under various constraints;

[0091] After receiving the peak-shaving optimization strategy, the control execution module will execute the peak-shaving optimization control command according to this strategy to achieve efficient operation of the unit and optimal allocation of resources. Through real-time communication with the unit control system interface, new instructions will be sent to each generator unit. According to the load distribution results in the optimization strategy, the priority scheduling algorithm is adopted to promote each unit to make dynamic output adjustments, and the proportional-integral-differential control algorithm is implemented to fine-tune the output of the unit to ensure rapid response to load changes under minimum impact, and dynamically adjust the air and fuel ratio to reduce emissions. The cooling water flow is dynamically adjusted according to the unit output power and ambient temperature. The real-time data, control instructions and status monitoring data in the peak-shaving process are gathered, and the peak-shaving optimization report is generated using big data analysis technology. The report includes the actual operation of the unit, output power changes, fuel consumption, cooling water usage, exhaust emissions, economic analysis and peak-shaving effect evaluation. The user visualization module displays the key information and real-time status of the optimization report through the data panel, allowing users to interact and query in real time.

[0092] By analyzing and calculating the relevant data of the thermal power units, a peak-shaving optimization strategy is formulated after receiving the diagnostic signal, and then thermal power peak-shaving is carried out, which avoids the need for frequent start and stop of thermal power units during the peak-shaving process, reduces maintenance costs and shortens downtime, and improves the service life and overall operating efficiency of the units.

[0093] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent optimization control system for peak load regulation of thermal power units, characterized in that: It includes data acquisition module, status monitoring and diagnosis module, scheduling optimization decision module, control execution module and user visualization module; The data acquisition module includes a power data acquisition unit, a temperature data acquisition unit, a humidity data acquisition unit, a pressure acquisition unit, a wind speed acquisition unit and a power grid status acquisition unit; The power collection unit collects current and voltage data of the thermal power unit through a power sensor; The temperature acquisition unit acquires the operating temperature data of the thermal power unit through a temperature sensor; The humidity collection unit collects humidity data of the operating environment of the thermal power unit through a humidity sensor; The pressure collection unit collects the operating pressure data of the thermal power unit through a pressure sensor; The wind speed collection unit collects wind speed data around the thermal power unit through a wind speed sensor; The power grid status acquisition unit is connected to the remote terminal system through the PLC device to obtain the power grid load, power generation and power generation frequency data; The above collected data is sent to the condition monitoring and diagnosis module; The state monitoring and diagnosis module calculates the equipment health status index Ztzs, the equipment performance attenuation coefficient Sjxs and the equipment failure expectation value Gzyq based on the data collected by the data acquisition module, monitors the operating status of the thermal power unit according to the calculated values, performs fault diagnosis of the thermal power unit, and generates a diagnosis signal ZD to send to the scheduling optimization decision module; When the dispatch optimization decision module receives the diagnostic signal ZD, it calculates the optimal power generation cost Zycb, the maximum power generation load Fdfh, the unit response time Jzxy, the exhaust gas emission Fqpf and the energy storage device status Cnzt, and formulates the peak load optimization strategy according to the above calculated values ​​and historical operation data; The control execution module executes the peak-shaving optimization control command according to the peak-shaving optimization strategy, adjusts the unit output, changes the fuel supply, and optimizes the cooling water flow, and generates a peak-shaving optimization report for the thermal power unit and sends it to the user visualization module; The user visualization module displays the peak load optimization report of the thermal power unit through the data panel.

2. According to claim 1, the intelligent optimization control system for peak load regulation of thermal power units is characterized by: The status monitoring and diagnosis module calculates the equipment health status index Ztzs based on the data transmitted by the data acquisition module. The calculation formula is as follows: In the formula, Ztzs represents the equipment health status index, w i Represents the weight coefficient of the i-th performance indicator, which is dynamically assigned by the system based on historical data. i represents the i-th performance indicator, and n represents the total number of performance indicators.

3. The intelligent optimization control system for peak load regulation of thermal power units according to claim 2, characterized in that: The state monitoring and diagnosis module calculates the equipment performance attenuation coefficient Sjxs based on the data transmitted by the data acquisition module. The calculation formula is as follows: In the formula, Sjxs represents the equipment performance attenuation coefficient, P0 represents the initial performance value, and P t represents the current performance value, e represents the base of the natural logarithm, which is 2.718, λ represents the decay rate, and t represents the running time.

4. The intelligent optimization control system for peak load regulation of thermal power generation units according to claim 1, characterized in that: The state monitoring and diagnosis module calculates the expected value of equipment failure Gzyq based on the data transmitted by the data acquisition module. The calculation formula is as follows: In the formula, Gzyq represents the expected value of equipment failure, α and β represent adjustment coefficients, which are set by the system to reflect the relationship between health status and failure expectation, Ztzs represents the equipment health status index, Sjxs represents the equipment performance attenuation coefficient, m represents the number of failure modes, and F j represents the impact factor of the jth failure mode, c j The weight coefficient of the j-th fault mode is assigned by the system based on historical fault data.

5. The intelligent optimization control system for peak load regulation of thermal power generation units according to claim 4, characterized in that: The state monitoring and diagnosis module monitors the operating state of the thermal power unit according to the calculated equipment failure expected value and performs fault diagnosis of the thermal power unit. The diagnosis method is as follows: When the expected value of equipment failure Gzyq is less than or equal to 80, it means that the equipment is operating normally; When the expected value of equipment failure Gzyq is greater than 80 and less than or equal to 90, it means that the equipment is in a warning state and needs to be continuously monitored; When the expected value of equipment failure Gzyq is greater than 90, it indicates that the risk of equipment failure is high and immediate measures need to be taken to generate a diagnostic signal ZD.

6. The intelligent optimization control system for peak load regulation of thermal power generation units according to claim 5, characterized in that: When the scheduling optimization decision module receives the diagnostic signal ZD, it calculates the optimal power generation cost Zycb, and the calculation formula is as follows: In the formula, Zycb represents the optimal power generation cost, p represents the number of units, and F k represents the power generation of the kth unit, C fuel,k represents the fuel price of the kth unit, O k represents the operation and maintenance cost of the kth unit, C fixed,k represents the fixed cost of the kth unit.

7. The intelligent optimization control system for peak load regulation of thermal power generation units according to claim 6, characterized in that: When the dispatch optimization decision module receives the diagnostic signal ZD, it calculates the maximum power generation load Fdfh, and the calculation formula is as follows: In the formula, Fdfh represents the maximum power generation load, p represents the number of units, and Edgl x Indicates the rated power of the xth unit, Xl x Represents the efficiency of the xth unit.

8. The intelligent optimization control system for peak load regulation of thermal power generation units according to claim 7, characterized in that: When the scheduling optimization decision module receives the diagnostic signal ZD, the computer group response time Jzxy is calculated as follows: In the formula, Jzxy represents the unit response time, Qdsj represents the unit start-up time, ΔP represents the load change, and Rt represents the unit response rate.

9. The intelligent optimization control system for peak load regulation of thermal power generation units according to claim 8, characterized in that: When the scheduling optimization decision module receives the diagnostic signal ZD, it calculates the exhaust gas emission Fqpf, and the calculation formula is as follows: In the formula, Fqpf represents the exhaust gas emission, F q represents the power generation of the qth unit, Pfyz q represents the emission factor of the qth unit.

10. The intelligent optimization control system for peak load regulation of thermal power generation units according to claim 9, characterized in that: When the scheduling optimization decision module receives the diagnostic signal ZD, it calculates the energy storage device state Cnzt, and the calculation formula is as follows: In the formula, Cnzt represents the state of the energy storage device, S0 represents the initial energy, Cdxl represents the energy storage efficiency, Cdgl represents the charging power, T represents the energy storage time, Fhxq represents the load demand, and Fdxl represents the discharge efficiency.

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