A frequency modulation control method and system for a thermal power unit

Abstract

The present invention relates to the technical field of frequency modulation control, and particularly to a frequency modulation control method and system for thermal power units. The method includes the following steps: collecting load fluctuation data of the transmission power grid through sensors, and performing time-series load trend analysis to obtain load fluctuation time-series trend data. Subsequently, based on these data, the thermal power and steam pressure states are collected in real time, and the non-linear demand correlation identification of thermal energy conversion and steam pressure is performed to obtain thermal energy conversion-steam pressure correlation data. According to this data, the frequency modulation demand of grid power generation is quantified, and an energy input control strategy is formulated. Finally, a global frequency modulation control strategy is designed based on these control data and fed back to the cloud platform to execute the frequency modulation control method. The present invention makes the frequency modulation control technology more perfect through the optimization of the frequency modulation control technology.

Description

Technical Field

[0001] The present invention relates to the technical field of frequency modulation control, and particularly to a frequency modulation control method and system for thermal power units. Background Art

[0002] As an important part of the power system, thermal power units are responsible for providing stable and reliable power. Especially in the case of large load fluctuations, their frequency modulation control ability is particularly important. Frequency modulation control refers to the process of maintaining the system frequency stable by adjusting the output power of the generating unit when the power system is disturbed by load fluctuations or changes in the output of the generating unit. In the power system, frequency stability is directly related to the safe operation of the power grid and the reliability of power supply. Especially in a system with a large proportion of thermal power units, frequency modulation control technology is particularly critical. In actual operation, the load fluctuations of thermal power units are affected by various factors, such as weather changes, seasonal load differences, production activities, etc. These fluctuations usually show complex time-series changes, resulting in fluctuations and instability of the power grid frequency. In order to effectively respond to these load fluctuations, thermal power units need to have a fast frequency modulation response ability on the basis of ensuring safe and stable operation. However, there are problems in a traditional frequency modulation control method for thermal power units, such as inaccurate analysis of losses and delays in the energy conversion process and large errors in frequency modulation control accuracy. Summary of the Invention

[0003] Based on this, it is necessary to provide a frequency modulation control method and system for thermal power units to solve at least one of the above technical problems.

[0004] To achieve the above object, a frequency modulation control method for a thermal power unit, the method includes the following steps:

[0005] Step S1: Collect the load fluctuation data of the transmission power grid of the thermal power unit through sensors to obtain the load fluctuation data of the transmission power grid; perform a time-series load trend analysis on the load fluctuation data of the transmission power grid to obtain the load fluctuation time-series trend data;

[0006] Step S2: Through built-in sensors, and based on the load fluctuation time-series trend data, collect the real-time states of thermal power and steam pressure respectively to obtain the real-time state data of thermal power and the real-time state data of steam pressure; perform an energy conversion - steam pressure demand correlation identification on the real-time state data of steam pressure according to the real-time state data of thermal power to obtain the non-linear demand correlation data of energy conversion - steam pressure;

[0007] Step S3: Quantify the power generation frequency modulation demand for the load fluctuation time-series trend data according to the non-linear demand correlation data of energy conversion - steam pressure to obtain the quantified data of the power generation frequency modulation demand of the power grid; perform energy input demand control according to the quantified data of the power generation frequency modulation demand of the power grid to obtain the thermal power energy input control data;

[0008] Step S4: Design a global frequency modulation control strategy based on the thermal power energy input control data to obtain a global frequency modulation control strategy for energy input; feedback the global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method.

[0009] In the present invention, a sensor is used to collect the load fluctuation data of the power transmission grid of the thermal power unit, which can accurately capture the load changes of the grid. After the time-series load trend analysis of these data, the time-series trend of the grid load fluctuation can be extracted, and then basic data support can be provided for subsequent power dispatching and control. The load fluctuation time-series trend data reveals the change law of the grid load, which can provide a reliable basis for the adjustment and optimization of the generator set, and helps to improve the stability and dispatching efficiency of the power system. The built-in sensor collects the data of the thermal power and steam pressure in real time. Through the analysis of these real-time status data, the operation status of the thermal power unit can be accurately monitored. By correlating and identifying the non-linear demand relationship between the real-time status data of the thermal power and the real-time status data of the steam pressure, the dynamic interaction relationship between the heat energy conversion and the steam pressure can be revealed. This non-linear demand correlation data helps to deeply understand the energy efficiency characteristics of the thermal power unit, optimize the unit operation strategy, improve the energy conversion efficiency, and reduce resource waste. Based on the non-linear demand correlation data of heat energy conversion - steam pressure, the power generation frequency modulation demand of the load fluctuation time-series trend data is quantified, and the frequency modulation demand of the grid can be accurately calculated. This process clarifies the power adjustment requirements of the grid for the thermal power unit by quantifying the frequency modulation demand. According to the quantification result, further control the energy input demand, which can reasonably allocate the energy use of the thermal power unit to ensure stable and efficient energy supply during the load fluctuation. This control strategy effectively improves the load response ability of the thermal power unit and reduces energy waste. According to the thermal power energy input control data, a global frequency modulation control strategy is designed to achieve the optimal regulation of the thermal power unit in the entire power grid dispatching. This control strategy will ensure that the grid can efficiently achieve frequency regulation during load fluctuations, and ensure the stability and reliability of power supply. Feedback the global frequency modulation control strategy to the cloud platform, which means that the frequency modulation scheme can be monitored and adjusted in real time to ensure the continuous optimization of the regulation effect. The real-time feedback and regulation ability of this cloud platform realizes the intelligent dispatching of the whole system, enables the thermal power unit to respond more flexibly and efficiently to the power grid dispatching requirements, and thus improves the overall operation efficiency of the power system. Therefore, the present invention makes an optimized treatment for a traditional frequency modulation control method of a thermal power unit, solves the problems of inaccurate analysis of the loss and delay in the energy conversion process and large error in the frequency modulation control accuracy existing in the traditional frequency modulation control method of a thermal power unit, improves the accuracy of the analysis of the loss and delay in the energy conversion process, and reduces the error of the frequency modulation control accuracy.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Collect the transmission grid load fluctuation data of the thermal power unit through sensors to obtain the transmission grid load fluctuation data;

[0012] Step S12: Fill in the missing values of the transmission grid load fluctuation data to obtain the filled grid load fluctuation data;

[0013] Step S13: Conduct a time-series load trend analysis on the filled grid load fluctuation data to obtain the load fluctuation time-series trend data.

[0014] In the present invention, the transmission grid load fluctuation data of the thermal power unit is collected through sensors, which can monitor the load fluctuation of the grid in real time. These data provide a basis for subsequent analysis and can reflect the load demand changes of the grid at different time periods. The beneficial effect of this data collection link is to provide accurate load fluctuation information, which helps to timely discover the laws and abnormalities of the grid load fluctuation, and then provides data support for subsequent load trend analysis, energy dispatch, and power generation frequency modulation strategies, ensuring the stability and efficient operation of the power system. During the data collection process, there may be missing data, which affects the accuracy of subsequent analysis. Step S12 ensures the integrity of the data by filling in the missing values of the transmission grid load fluctuation data. Missing value filling is usually processed by interpolation or other statistical methods to avoid analysis deviation caused by data missing. The beneficial effect of this step is to ensure the continuity and stability of the analysis process, enabling the subsequent time-series load trend analysis to be carried out based on the complete load fluctuation data, thereby improving the accuracy and reliability of the analysis results. After completing the data filling, step S13 conducts a time-series load trend analysis on the filled grid load fluctuation data to extract the trend characteristics of the load fluctuation. This process can reveal the long-term trend and short-term fluctuations of the grid load change, helping to identify the periodic laws of load growth and abnormal sudden fluctuations. Through time-series analysis, the future trend of load fluctuation can be effectively predicted, providing a scientific basis for the adjustment strategy of the thermal power unit. The beneficial effect of this step is to provide data support for the load prediction, energy dispatch of the grid, and the optimal dispatch of the generator set, optimizing the energy distribution and use efficiency, and improving the flexibility and response speed of the power system.

[0015] Preferably, step S2 includes the following steps:

[0016] Step S21: Through the built-in sensors, and based on the load fluctuation time-series trend data, collect the real-time states of the thermal power and steam pressure respectively to obtain the real-time state data of the thermal power and the real-time state data of the steam pressure;

[0017] Step S22: Evaluate the energy conversion efficiency loss of the steam pressure real-time status data based on the heat power real-time status data to obtain the heat energy conversion efficiency loss data;

[0018] Step S23: Identify the correlation between heat energy conversion and steam pressure demand for the steam pressure real-time status data based on the heat energy conversion efficiency loss data to obtain the correlation data between heat energy conversion and steam pressure demand;

[0019] Step S24: Analyze the non-linear structure of the correlation data between heat energy conversion and steam pressure demand to obtain the non-linear demand correlation data between heat energy conversion and steam pressure.

[0020] The present invention collects the state data of thermal power and steam pressure in real time through built-in sensors based on the time-series trend data of load fluctuations. This process helps to accurately capture the fluctuations in thermal power output and steam pressure during the operation of thermal power units, thereby reflecting the real-time working state of thermal power units. The real-time data of thermal power and steam pressure can provide accurate basis for subsequent energy efficiency analysis and dispatching decisions, ensuring that the unit can respond in a timely manner and adjust its operating state under load fluctuations to optimize the power generation process and improve the overall energy utilization efficiency of the system. The energy conversion efficiency loss is evaluated for the real-time state data of steam pressure according to the real-time state data of thermal power. The thermal energy conversion efficiency of thermal power units directly affects the economy and environmental protection of the units. By evaluating the relationship between thermal power and steam pressure, the efficiency losses existing in the energy conversion process can be identified, thereby providing a basis for optimizing the operation of the unit. Through this step, potential problems during the unit operation (such as the decrease in thermal efficiency caused by too low steam pressure) can be revealed, and corresponding measures can be taken to improve, enhance the energy utilization efficiency of the unit, and reduce the operating cost. Based on the data of thermal energy conversion efficiency loss, step S23 identifies the correlation between thermal energy conversion and steam pressure demand for the real-time state data of steam pressure. Through this step, the dynamic demand relationship between steam pressure and thermal power during the thermal energy conversion process can be analyzed in depth. This demand correlation identification helps to reveal the impact of steam pressure fluctuations on the thermal energy conversion efficiency and provides a basis for predicting the future operating demands of the unit. The beneficial effect of this step is that the potential relationship between load fluctuations and system efficiency can be identified in advance, helping to adjust the steam pressure and optimize the unit load regulation, thereby improving the response speed and stability of the unit. By performing a non-linear structure analysis on the thermal energy conversion-steam pressure demand correlation data, the complex interaction relationship between thermal power and steam pressure is further explored. Since there is usually a non-linear relationship between the thermal energy conversion efficiency and steam pressure of thermal power units, traditional linear analysis methods are difficult to accurately capture this complex dependence relationship. Through non-linear structure analysis, the interaction mechanism between thermal energy conversion and steam pressure can be more accurately modeled, and its deep-level dynamic correlation can be identified. This process provides more refined data support for the optimization of dispatching strategies, helps to improve the overall operating efficiency of the unit, reduce energy losses, and provides a scientific basis for intelligent dispatching.

[0021] Preferably, step S23 includes the following steps:

[0022] Step S231: Analyze the time-varying characteristics of the thermal energy conversion efficiency loss data to obtain the time-varying data of conversion efficiency loss;

[0023] Step S232: Based on the time-varying data of conversion efficiency loss, conduct a steam pressure response evaluation for the real-time state data of steam pressure among different time periods to obtain steam pressure responsiveness evaluation data;

[0024] Step S233: Perform a classification of the response rate gradient levels on the steam pressure responsiveness evaluation data to obtain the pressure response rate gradient levels;

[0025] Step S235: Based on the pressure response rate gradient levels, perform an induction of the time-varying correlation subsets of response rate - energy conversion on the time-varying data of conversion efficiency loss to obtain the time-varying correlation subsets of response rate - energy conversion;

[0026] Step S236: Through Takagi-Sugeno inference, perform an identification of the correlation between heat energy conversion - steam pressure demand on the time-varying correlation subsets of response rate - energy conversion to obtain the correlation data of heat energy conversion - steam pressure demand.

[0027] The present invention conducts a time-varying characteristic analysis on the data of heat energy conversion efficiency loss, which can identify the dynamic change trends between heat power and steam pressure in different time periods. Through the time-varying characteristic analysis, the time dependence of the efficiency loss can be revealed, that is, how the conversion efficiency loss changes with time under different load fluctuations. The beneficial effect of this step lies in helping to understand the law of energy efficiency fluctuations, providing more accurate time series data for subsequent scheduling strategies and unit optimization, contributing to better predicting the changes in efficiency loss, and taking effective measures for adjustment to improve the overall operating efficiency of the unit. Based on the time-varying data of the conversion efficiency loss, the real-time state data of the steam pressure are evaluated for responses in different time periods. This process can reveal the time dependence of the steam pressure on the unit operating efficiency by evaluating the response of the steam pressure to the heat energy conversion efficiency loss in different time periods. The response evaluation data of the steam pressure helps to understand how the steam pressure affects the heat energy conversion process during load fluctuations and changes in energy demand. The beneficial effect of this step is to provide a more comprehensive perspective on the steam pressure management for the scheduling system, ensuring that the dynamic regulation of the steam pressure during the energy conversion process is more reasonable and accurate. Based on the steam pressure response evaluation data, a classification of the response rate gradient levels is carried out. This step can more clearly characterize the influence intensity and response rate of the steam pressure change on the energy conversion process by defining different pressure response rate gradient levels. For example, a level with a faster response rate indicates that the unit can quickly adjust to avoid efficiency loss, while a level with a slower response rate indicates a potential problem of adjustment lag. Through this classification, more detailed operation guidance can be provided for subsequent unit regulation and optimization. The beneficial effect of this step is to support the refined management of the regulation strategy by accurately identifying the rate of change of the steam pressure, ensuring that the unit response is more timely and effective. According to the pressure response rate gradient levels, the time-varying data of the conversion efficiency loss are summarized into a time-varying correlation subset of the response rate - energy conversion. By summarizing the time-varying correlation subsets of the energy conversion efficiency at different rates, a more refined correlation model between energy conversion and pressure response can be formed. This step helps to further refine the relationship between heat energy conversion and steam pressure, providing quantitative data support for scheduling optimization. The summarized subset data can help identify the specific impact of different response rates on energy conversion, optimize energy management, and improve the unit's ability to respond to load fluctuations in different time periods. The beneficial effect of this step is to better achieve dynamic adjustment, ensure the maximization of energy utilization efficiency, and reduce efficiency loss. Using the Takagi-Sugeno inference method, a correlation identification of heat energy conversion - steam pressure demand is carried out for the time-varying correlation subset of the response rate - energy conversion. The Takagi-Sugeno inference method is a fuzzy inference method that can model non-linear relationships through inference rules and identify the complex demand relationship between heat energy conversion and steam pressure.Through this reasoning method, the deep connection between the thermal energy conversion efficiency and the steam pressure change can be revealed from the perspectives of multiple dimensions and multiple time scales. The beneficial effect of this step is that it can accurately capture the non-linear correlation between thermal energy conversion and steam pressure, thus providing more scientific and intelligent decision-making support for the scheduling, energy efficiency optimization and load management of the generator set, and further improving the stability of the power system and the energy utilization efficiency.

[0028] Preferably, step S3 includes the following steps:

[0029] Step S31: Normalize the non-linear demand correlation data of thermal energy conversion - steam pressure to obtain the normalized non-linear demand correlation data;

[0030] Step S32: Perform delayed coupling on the load fluctuation time series trend data according to the normalized non-linear demand correlation data to obtain the grid load delay data;

[0031] Step S33: Quantify the power generation frequency modulation demand for the load fluctuation time series trend data based on the grid load delay data to obtain the quantified grid power generation frequency modulation demand data;

[0032] Step S34: Control the energy input demand for the non-linear demand correlation data of thermal energy conversion - steam pressure according to the quantified grid power generation frequency modulation demand data to obtain the thermal power energy input control data.

[0033] The present invention normalizes the non - linear demand - related data of heat energy conversion - steam pressure, aiming to eliminate the dimensional differences of the data, so that data of different dimensions can be compared and analyzed under the same standard. Through normalization, the fluctuation and variation range of the non - linear demand - related data can be unified to a standard scale, facilitating subsequent analysis, model construction, and optimization decisions. This process helps improve the stability and consistency of the data, ensuring the accuracy and reliability of data processing in subsequent steps, and thus providing unified basic data for further optimization algorithms and scheduling strategies. Based on the non - linear demand - related normalized data, the load fluctuation time - series trend data is subjected to delayed coupling, aiming to reveal the relationship between the dynamic characteristics of the power grid load and heat energy conversion and steam pressure. There is usually a certain delay effect in load fluctuations, that is, the change of the power grid load often has a significant impact on energy demand after a period of time. Through delayed coupling, the influence law of load fluctuations on power grid operation and energy scheduling can be more accurately identified, and timely and reasonable adjustment strategies can be provided for power grid load scheduling. The beneficial effect of this step is that through the delayed analysis of load fluctuations, power grid scheduling and energy management become more forward - looking and responsive, thus optimizing the operation efficiency of the power system. Based on the power grid load delayed data, the power generation frequency modulation demand of the load fluctuation time - series trend data is quantified. This step quantifies the power adjustment amount required for power generation frequency modulation by analyzing the delayed response of the power grid load. The load fluctuation of the power grid often leads to frequency instability, so the generator sets need to perform timely frequency modulation according to the load fluctuation. This process quantifies the power generation frequency modulation demand of the power grid, clarifies the power range that the dispatching center needs to adjust, and provides clear dispatching instructions for unit adjustment. The beneficial effect of this step is that it can help the power grid accurately predict and respond to load fluctuations, reduce the risk of power system instability caused by frequency fluctuations, and thus improve the response speed and stability of power grid scheduling. Based on the quantified data of power grid power generation frequency modulation demand, the energy input demand of the non - linear demand - related data of heat energy conversion - steam pressure is controlled. This process adjusts the energy input of thermal power units according to the frequency modulation demand of the power grid and the non - linear relationship between heat energy conversion and steam pressure, optimizing the load regulation strategy of the units. By controlling the energy input, it can ensure that the units respond effectively under load fluctuations and frequency modulation demands, while avoiding energy waste and efficiency loss. The beneficial effect of this step is that through precise energy input control, the heat energy conversion efficiency is optimized, and a more stable power supply is provided for the power grid, improving the operation efficiency and stability of the power system.

[0034] Preferably, step S32 includes the following steps:

[0035] Step S321: Analyze the slowness of the heat energy conversion - steam pressure conversion efficiency for the non - linear demand - related normalized data to obtain the slowness data of the energy conversion efficiency;

[0036] Step S322: Calculate the energy conversion multi-peak extreme slowness variance for the non-linear demand correlation normalized data based on the energy conversion efficiency slowness data, and obtain the energy conversion multi-peak extreme slowness variance;

[0037] Step S323: Perform delayed coupling on the load fluctuation time series trend data based on the energy conversion multi-peak extreme slowness variance to obtain the grid load delay data.

[0038] The present invention conducts an analysis on the slowness of heat energy conversion - steam pressure conversion efficiency for non-linear demand correlation normalized data. This analysis aims to reveal the lag effect of steam pressure on heat energy conversion efficiency during the energy conversion process, that is, the response time delay between the change in steam pressure and the conversion efficiency. Through this slowness analysis, the impact of the dynamic change in conversion efficiency on the system response can be accurately identified. Especially when there are load fluctuations or steam pressure adjustments, the change in efficiency occurs gradually. The beneficial effect of this step is that it can provide reliable time series data for subsequent scheduling optimization, enabling the power system to promptly identify and adjust the system operation strategy when facing load fluctuations, thereby reducing the negative impact of the lag effect on system stability and efficiency. Based on the energy conversion efficiency slowness data obtained in Step S321, calculate the energy conversion multi-peak extreme slowness variance. The calculation of the multi-peak extreme slowness variance is mainly to evaluate the response lag and variance of changes in different peaks during the heat energy conversion process. By conducting slowness analysis on the extreme values at different time points, the difference in the response speed of the system when facing different load peaks and fluctuations can be reflected. The beneficial effect of this step is that it helps to quantify the response time and regulation efficiency of the energy conversion system under different load change conditions, providing a basis for optimizing unit scheduling. Through accurate slowness variance data, the strategy of unit response can be adjusted to avoid energy waste and efficiency loss caused by lag, and improve the overall energy utilization rate. Based on the energy conversion multi-peak extreme slowness variance data, perform delayed coupling on the load fluctuation time series trend data. The core objective of this process is to identify and quantify the delayed response of grid load fluctuations, and by combining with the energy conversion efficiency slowness data, establish a more accurate relationship between load changes and energy conversion. Delayed coupling can reveal the specific impact of grid load fluctuations on the energy conversion process at different time periods, especially the lag effect brought by load fluctuations. The beneficial effect of this step lies in that it provides more dynamic and time-series data support for grid scheduling and energy management, enabling the grid system to better respond to load fluctuations and system changes, ensuring the stability of energy supply and the high efficiency of system scheduling, and thus improving the operation stability and energy efficiency of the grid.

[0039] Preferably, Step S34 includes the following steps:

[0040] Step S341: Analyze the power regulation requirements of the power grid's power generation frequency modulation quantization data to obtain the power generation power regulation requirement data;

[0041] Step S342: Approximate the instability mutation amplitude of the power generation power regulation requirement data to obtain the power requirement instability approximation data;

[0042] Step S343: Perform time-series collaborative optimization on the thermal energy conversion-steam pressure nonlinear demand correlation data according to the power requirement instability approximation data to obtain the steam pressure collaborative optimization requirement data;

[0043] Step S344: Control the energy input requirements based on the steam pressure collaborative optimization requirement data to obtain the thermal power energy input control data.

[0044] The present invention first analyzes the power regulation demand of the power grid power generation frequency regulation demand quantification data. The core goal of this step is to quantify the required regulation power by analyzing the power demand fluctuation of the power grid under different load and frequency regulation conditions. The power regulation demand analysis provides a clear regulation range for power grid dispatching and determines the power generation power required to be regulated under different power load fluctuation conditions. This process helps the power system to clarify the real-time power generation dispatching demand, thereby providing a scientific basis for the subsequent power generation unit regulation and control, ensuring the stability of the power grid frequency and sufficient energy supply. The power generation power regulation demand data is approximated by the instability mutation amplitude. This analysis is mainly used to quantify the sudden load fluctuations that occur in power grid dispatching, especially large and rapid load changes. By approximating the instability mutation amplitude, the system instability that occurs when the power grid encounters sudden load fluctuations can be predicted. The beneficial effect of this process is that it can help the power grid identify potential instability risks in advance and take preventive measures in actual dispatching to avoid large-scale power instability and failures, thereby improving the reliability and safety of the system. Based on the power demand instability approximation data, the thermal energy conversion-steam pressure nonlinear demand correlation data is time-series collaboratively optimized. This step aims to analyze the amplitude of the unstable mutation of power demand and combine it with the steam pressure change in the thermal energy conversion process to optimize the timing. By coordinating the timing relationship between steam pressure adjustment and grid load change, the best match between dynamic regulation of generator sets and load demand can be achieved. The beneficial effect of this process is that it can optimize the dynamic response of the energy conversion system, avoid efficiency loss and system fluctuations caused by delayed or uncoordinated steam pressure adjustment, and thus improve the load response capability and energy efficiency of the grid. Based on the steam pressure collaborative optimization demand data, energy input demand control is carried out to obtain thermal power energy input control data. The goal of this process is to accurately control the energy input of thermal power units according to the optimized steam pressure collaborative demand to ensure that it matches the grid frequency regulation and load fluctuation requirements. By optimizing the energy input of thermal power units, energy waste and excessive consumption can be avoided while meeting the frequency regulation requirements, thereby improving the overall energy utilization efficiency. The beneficial effect of this step is that it can achieve collaborative optimization between the grid and the generator sets, ensuring that the generator sets can meet the grid stability requirements during the dispatching process, while maximizing energy utilization efficiency and reducing unnecessary energy consumption and emissions.

[0045] Preferably, step S342 includes the following steps:

[0046] Performing a time-series demand increase analysis on the power generation regulation demand data to obtain time-series demand increase data;

[0047] Discretize the time-series point demand increase data to obtain discrete time-series point demand increase data;

[0048] Perform symmetric instability time series deviation analysis on the time series point demand increase data among different time series points based on the discrete data of the time series point demand increase according to the time series point requirements, to obtain symmetric instability time series deviation data;

[0049] Based on the symmetric instability time series deviation data, approximate the instability mutation amplitude to obtain approximate data of power demand instability.

[0050] The present invention first performs an analysis of the time series point demand increase on the power generation power regulation demand data. The analysis of the time series point demand increase helps to identify the trend and intensity of load demand fluctuations by calculating the change amplitude of the power grid power regulation demand at different time points. The core value of this step lies in revealing the speed and amplitude of the power demand change in the power grid dispatching process, providing clear quantitative data for subsequent load regulation. This enables the power grid to more accurately predict future load changes, facilitating timely adjustment of the power output of the generating units to ensure the stability and reliability of the power grid. Discretize the time series point positions for the time series point demand increase data to obtain discrete data of the time series point demand increase. The main purpose of this step is to convert the continuous power demand increase data into discrete data points for further analysis and calculation. Through the discretization process, the power grid dispatching system can more easily analyze the load increase of each time series point independently, reducing the complexity of data processing. The discretized data can provide more detailed load fluctuation information, enabling the control system to more precisely respond to the dispatching requirements of the power grid and reducing the risk of instability. According to the discrete data of the time series point demand increase, perform symmetric instability time series deviation analysis on the time series point demand increase data among different time series points. By calculating the deviation between different time series points, the instability phenomenon that occurs when the power grid faces load fluctuations at different time points can be revealed, especially the symmetric instability deviation. The analysis of the symmetric instability time series deviation data helps to identify the system deviation or response lag generated by the power grid dispatching under certain specific load change conditions. This analysis can provide a powerful warning signal for power grid dispatchers, ensuring that countermeasures can be taken in a timely manner when the instability risk occurs to prevent system instability. Based on the symmetric instability time series deviation data, approximate the instability mutation amplitude to obtain approximate data of power demand instability. The approximation of the instability mutation amplitude simulates and predicts the sudden load changes that occur in the power grid dispatching and quantifies their impact on the power grid operation. This process helps to evaluate the load imbalance or instability phenomenon that occurs during the response process of the power grid under extreme load fluctuations. By approximately calculating the mutation amplitude, the potential instability risk of the power grid when facing large-scale load changes can be identified and predicted in advance, so as to provide timely adjustment suggestions for power grid dispatching to ensure the safe operation of the power system.

[0051] Preferably, step S4 includes the following steps:

[0052] Step S41: Perform iterative learning on the thermal power energy input control data to obtain energy input control iterative data;

[0053] Step S42: Design a global frequency modulation control strategy based on the energy input control iterative data to obtain a global frequency modulation control strategy for energy input;

[0054] Step S43: Feed back the global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method.

[0055] Through iterative learning of the thermal power energy input control data, the present invention can continuously optimize the energy input strategy of thermal power units. Iterative learning enables the system to gradually improve the accuracy and efficiency of energy input under different operating conditions by repeatedly training and optimizing the model. The beneficial effect of this step is that by continuously adjusting the learning process, the system can identify and respond to complex patterns of grid load changes, improve the energy dispatch of thermal power units, make the energy input more accurate, ensure that the grid load demand is met in a timely and stable manner, and avoid energy waste and excessive consumption. Based on the energy input control iterative data obtained through iterative learning, a global frequency modulation control strategy is designed. This process integrates the dispatch data of different generating units to form a comprehensive frequency modulation control scheme, thereby achieving precise regulation of the grid frequency. The global frequency modulation control strategy not only considers the energy input requirements of thermal power units but also comprehensively takes into account the load changes and dispatch needs of the grid, aiming to optimize the overall grid frequency stability. The beneficial effect of this step is that it can provide a global perspective and solution, improve the grid's response ability to sudden load changes, reduce the risk of frequency fluctuations and system instability, and ensure the safe and efficient operation of the grid. Feed back the designed global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method. This process ensures the real-time execution and dynamic adjustment of the frequency modulation control strategy through the centralized computing and control capabilities of the cloud platform. The cloud platform can quickly respond to the frequency fluctuations of the grid and perform precise adjustments by receiving and processing real-time data from each generating unit. The beneficial effect of this step is that the real-time feedback mechanism of the cloud platform makes the frequency modulation control more flexible and efficient, can dynamically adjust the strategy according to the real-time state of the grid, improve the automation level of the system, and ensure the stability and security of the grid under various complex load changes and emergencies.

[0056] Preferably, the present invention also provides a frequency modulation control system for a thermal power unit, which is used to execute the frequency modulation control method for the thermal power unit as described above. The frequency modulation control system for the thermal power unit includes:

[0057] The time-series load trend analysis module is used to collect the load fluctuation data of the power transmission grid of the thermal power unit through sensors, and obtain the load fluctuation data of the power transmission grid; perform time-series load trend analysis on the load fluctuation data of the power transmission grid to obtain the load fluctuation time-series trend data;

[0058] The demand management identification module is used to collect the real-time states of thermal power and steam pressure based on the time-series trend data of load fluctuations through built-in sensors, and obtain the real-time state data of thermal power and the real-time state data of steam pressure respectively; perform energy conversion-steam pressure demand correlation identification on the real-time state data of steam pressure according to the real-time state data of thermal power to obtain the non-linear demand correlation data of energy conversion-steam pressure;

[0059] The energy input demand control module is used to quantify the power generation frequency modulation demand for the time-series trend data of load fluctuations according to the non-linear demand correlation data of energy conversion-steam pressure, and obtain the quantified data of the power grid power generation frequency modulation demand; perform energy input demand control according to the quantified data of the power grid power generation frequency modulation demand to obtain the thermal power energy input control data;

[0060] The global frequency modulation control strategy design module is used to design the global frequency modulation control strategy according to the thermal power energy input control data, and obtain the global frequency modulation control strategy for energy input; feedback the global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method.

[0061] The beneficial effects of the present invention are as follows. By using sensors to collect data on the load fluctuations of the power grid for thermal power units, the load changes of the power grid can be accurately captured. After analyzing these data through time-series load trend analysis, the time-series trend of the power grid load fluctuations can be extracted, providing basic data support for subsequent power dispatching and control. The time-series trend data of load fluctuations reveals the change law of the power grid load, providing a reliable basis for the adjustment and optimization of the generator set, and helping to improve the stability and dispatching efficiency of the power system. The built-in sensors collect data on thermal power and steam pressure in real time. By analyzing these real-time status data, the operating status of the thermal power unit can be accurately monitored. By correlating and identifying the non-linear demand relationship between the real-time status data of thermal power and the real-time status data of steam pressure, the dynamic interaction relationship between heat energy conversion and steam pressure can be revealed. This non-linear demand correlation data helps to deeply understand the energy efficiency characteristics of the thermal power unit, optimize the unit operation strategy, improve the energy conversion efficiency, and reduce resource waste. Based on the non-linear demand correlation data of heat energy conversion - steam pressure, the power generation frequency modulation demand of the time-series trend data of load fluctuations is quantified, and the frequency modulation demand of the power grid can be accurately calculated. This process clarifies the power adjustment requirements of the power grid for thermal power units by quantifying the frequency modulation demand. According to the quantification result, further control of the energy input demand can rationally allocate the energy use of the thermal power unit to ensure stable and efficient energy supply during load fluctuations. This control strategy effectively improves the load response ability of the thermal power unit and reduces energy waste. According to the thermal power energy input control data, a global frequency modulation control strategy is designed to achieve the optimal adjustment of the thermal power unit in the entire power grid dispatching. This control strategy ensures that the power grid can efficiently achieve frequency regulation during load fluctuations, ensuring the stability and reliability of power supply. Feeding back the global frequency modulation control strategy to the cloud platform means that the frequency modulation scheme can be monitored and adjusted in real time to ensure continuous optimization of the adjustment effect. The real-time feedback and control ability of this cloud platform realizes the intelligent dispatching of the entire system, enabling the thermal power unit to respond more flexibly and efficiently to the power grid dispatching requirements, thereby improving the overall operation efficiency of the power system. Therefore, the present invention is an optimized treatment of a traditional frequency modulation control method for thermal power units, solving the problems of inaccurate analysis of losses and delays in the energy conversion process and large errors in frequency modulation control accuracy in the traditional frequency modulation control method for thermal power units, improving the accuracy of analyzing losses and delays in the energy conversion process, and reducing the error of frequency modulation control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the step flow of a frequency modulation control method for a thermal power unit;

[0063] Figure 2 For Figure 1 It is a schematic diagram of the detailed implementation step flow of step S2 in

[0064] Figure 3 For Figure 1 a detailed implementation step flow diagram of step S3 in

[0065] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific implementation manners

[0066] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0067] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus the repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor means and / or microcontroller means.

[0068] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0069] To achieve the above object, please refer to Figures 1 to 3 , a frequency modulation control method for a thermal power unit, the method comprising the following steps:

[0070] Step S1: Collect the transmission grid load fluctuation data of the thermal power unit through a sensor to obtain the transmission grid load fluctuation data; perform a time-series load trend analysis on the transmission grid load fluctuation data to obtain the load fluctuation time-series trend data;

[0071] Step S2: Through the built-in sensors, and based on the load fluctuation time-series trend data, collect the real-time states of the thermal power and steam pressure respectively to obtain the real-time state data of the thermal power and the real-time state data of the steam pressure; according to the real-time state data of the thermal power, identify the correlation between the thermal energy conversion and the steam pressure demand for the real-time state data of the steam pressure, and obtain the non-linear demand correlation data of the thermal energy conversion - steam pressure;

[0072] Step S3: Quantify the power generation frequency modulation demand for the load fluctuation time-series trend data according to the non-linear demand correlation data of the thermal energy conversion - steam pressure to obtain the quantified data of the power grid power generation frequency modulation demand; control the energy input demand according to the quantified data of the power grid power generation frequency modulation demand to obtain the thermal power energy input control data;

[0073] Step S4: Design a global frequency modulation control strategy according to the thermal power energy input control data to obtain a global frequency modulation control strategy for energy input; feedback the global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method.

[0074] In the embodiment of the present invention, refer to Figure 1 As shown, it is a schematic diagram of the step flow of a frequency modulation control method for a thermal power unit of the present invention. In this example, the frequency modulation control method for the thermal power unit includes the following steps:

[0075] Step S1: Collect the transmission grid load fluctuation data of the thermal power unit through sensors to obtain the transmission grid load fluctuation data; perform a time-series load trend analysis on the transmission grid load fluctuation data to obtain the load fluctuation time-series trend data;

[0076] In the embodiment of the present invention, first, the load fluctuations of the transmission grid are collected in real time through a plurality of sensors installed on the thermal power unit. The sensors should include current sensors, voltage sensors, power meters, etc. When monitoring the load fluctuations, these sensors record the load change data of the power grid in real time. All the collected data will be recorded in a time series manner to obtain the preliminary "transmission grid load fluctuation data". Then, through the time-series load trend analysis method, analyze these load fluctuation data. This process mainly relies on statistical-based trend analysis methods, such as time series analysis and autoregressive integrated moving average (ARIMA) models, to identify the long-term trends and short-term fluctuations of the load fluctuations. The specific steps are as follows: segment the collected load data by hour or minute, remove the short-term noise through smoothing techniques, and use time series analysis to extract the trend component, seasonal component, and residual component. Finally, obtain the "load fluctuation time-series trend data". This data describes the long-term change trend of the power grid load fluctuations, as well as the patterns of seasonal and sudden fluctuations, providing important basic data for subsequent control and prediction.

[0077] Step S2: Through the built-in sensors, and based on the load fluctuation time-series trend data, collect the real-time status of thermal power and steam pressure respectively to obtain the real-time status data of thermal power and the real-time status data of steam pressure; according to the real-time status data of thermal power, conduct an association identification of thermal energy conversion - steam pressure demand, and obtain the non-linear demand association data of thermal energy conversion - steam pressure;

[0078] In the embodiment of the present invention, first, the real-time status of thermal power and steam pressure is collected through the built-in sensors. Thermal power sensors and steam pressure sensors should be set at various key parts of the unit to record the thermal power data output by the boiler and the pressure data in the steam system respectively. These real-time data will form the "real-time status data of thermal power" and the "real-time status data of steam pressure". Based on the "load fluctuation time-series trend data", combined with the real-time collected thermal power and steam pressure data, conduct an association identification of thermal energy conversion - steam pressure demand. The core of this process is to quantitatively analyze the relationship between thermal power and steam pressure through physical principles. The specific method is to use thermodynamic analysis and the thermal energy conversion efficiency formula. According to the real-time thermal power and steam pressure data, calculate the influence of steam pressure on the thermal energy conversion efficiency, and identify the non-linear demand association between the two. This association analysis mainly relies on non-linear regression analysis and least squares method optimization. By inputting the thermal power and steam pressure data into the thermal energy conversion efficiency formula, the "non-linear demand association data of thermal energy conversion - steam pressure" is obtained. This data reveals the non-linear interdependent relationship between thermal power and steam pressure under different load conditions, which is the basis for precise control.

[0079] Step S3: Quantify the power generation frequency modulation demand for the load fluctuation time-series trend data according to the non-linear demand association data of thermal energy conversion - steam pressure to obtain the quantified data of the grid power generation frequency modulation demand; control the energy input demand according to the quantified data of the grid power generation frequency modulation demand to obtain the thermal power energy input control data;

[0080] In the embodiment of the present invention, "thermal energy conversion - steam pressure non - linear demand correlation data" is utilized, and combined with "load fluctuation time - series trend data" to quantify the power generation frequency modulation demand. The specific operation is as follows: First, the non - linear demand correlation data is standardized to facilitate the correlation analysis with the load fluctuation time - series trend data. Then, the weighted average method and the dynamic programming algorithm are used to quantify the power generation frequency modulation demand for the load fluctuation time - series trend data. The dynamic programming algorithm determines the required power adjustment amount in each time period according to the historical load fluctuation and equipment response. Finally, based on the quantification result of the frequency modulation demand, the energy input demand control is carried out. By analyzing the frequency modulation demand in each time period, an appropriate energy input amount is formulated to achieve the purpose of balancing the load fluctuation of the power grid. This process relies on the gradual iterative optimization of the data to ensure the accuracy and adaptability of each control strategy. Finally, the "thermal power energy input control data" is generated and provided as input data for the subsequent design of the frequency modulation control strategy.

[0081] Step S4: Design a global frequency modulation control strategy according to the thermal power energy input control data to obtain a global frequency modulation control strategy for energy input; feedback the global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method.

[0082] In the embodiment of the present invention, according to the previously generated "thermal power energy input control data", a global frequency modulation control strategy is designed. First, a deep analysis of the "thermal power energy input control data" is carried out, and a multi - parameter collaborative optimization algorithm based on system dynamics is used to model and simulate the mutual relationship between various parameters. This simulation process includes the dynamic changes of multiple parameters such as load fluctuation, power generation efficiency, steam pressure, and thermal power. Through the PID control algorithm in control theory, the changes of these parameters are finely adjusted to obtain the optimal "global frequency modulation control strategy for energy input". This strategy can ensure that under different load fluctuation conditions, the thermal power unit can respond precisely and adjust the power generation smoothly. Finally, the obtained control strategy is feedback to the cloud platform through a secure and reliable communication protocol to execute the real - time frequency modulation control method. The cloud platform will adjust the unit in real time according to the control strategy to achieve an accurate match of the power generation load and ensure the stable operation of the power grid.

[0083] Preferably, step S1 includes the following steps:

[0084] Step S11: Collect the load fluctuation data of the transmission power grid of the thermal power unit through sensors to obtain the load fluctuation data of the transmission power grid;

[0085] Step S12: Fill in the missing values of the load fluctuation data of the transmission power grid to obtain the filled load fluctuation data of the power grid;

[0086] Step S13: Conduct a time-series load trend analysis on the filled data of the power grid load fluctuations to obtain the time-series trend data of the load fluctuations.

[0087] In the embodiments of the present invention, first, a plurality of sensors are used to collect the load fluctuation data of the transmission power grid of a thermal power unit. To ensure the comprehensiveness and accuracy of the data, the types of sensors used include current sensors, voltage sensors, power meters, etc. These sensors are respectively installed at the grid entrance and key nodes where the unit is connected to the grid. By detecting the changes in current, voltage, and power, these sensors record the load fluctuations of the grid in real time, and the collected data includes a detailed record of the grid load changing over time. The data collection frequency needs to be adjusted according to the fluctuation frequency of the grid load, usually between milliseconds and seconds, to ensure that all details of the load fluctuations can be captured. The "transmission power grid load fluctuation data" collected forms a set of time series datasets, and each record represents the load value at a time point. This dataset then provides the basis for subsequent load trend analysis. The key in this process lies in how to ensure the real-time and accuracy of the collected data. Especially in the case of high load fluctuations and load jumps, the sensors should be able to quickly capture the instantaneous changes in the grid load to avoid data lag or omission. For the "transmission power grid load fluctuation data" collected in step S11, first, the missing values in it need to be filled to ensure the integrity and validity of the data. Missing values are usually caused by various factors, such as sensor failures, data transmission problems, etc. For these missing data, interpolation methods are used for filling. The specific method is to use a linear interpolation algorithm based on time series and estimate by combining the data at adjacent time points. Suppose the data at a certain time point is missing, the load values of the previous valid data point and the next valid data point can be used to fill it by weighted average method or linear interpolation method. In addition, for long-term missing data, more complex interpolation algorithms, such as cubic spline interpolation or Lagrange interpolation method, are used. These methods can provide higher accuracy, especially in the case of relatively large load fluctuations. During the filling process, attention needs to be paid to the smoothness of the data to ensure that the interpolated data does not introduce too much fluctuation or outliers. After filling the missing values, the obtained "power grid load fluctuation filled data" will become more complete and provide reliable data support for the next step of time series analysis. Use the "power grid load fluctuation filled data" for time series load trend analysis. First, through data preprocessing, the filled power grid load data is sorted in chronological order and input into the load trend analysis system in the form of a time series. The core of the analysis is to identify the trends and periodic changes of the load fluctuations. Specifically, a time series analysis method based on the autoregressive integrated moving average (ARIMA) model is used. This method can accurately predict the load fluctuation trend in the future period by modeling the historical trend of the load data. In actual operation, the ARIMA model will first perform differencing on the load data to remove the trend component in the data, and then calculate the optimal model parameters. These parameters include autoregressive terms, differencing terms, and moving average terms, and a model is fitted by the least squares method to minimize the error.After the model is established, the model will fit the input load data to identify components such as long-term trends, seasonal fluctuations, and sudden load changes in the load fluctuations. Finally, through the analysis of the ARIMA model, the "load fluctuation time series trend data" obtained can reflect the dynamic change law of the power grid load and clarify the rising, falling, or fluctuating trend of the power grid load. This data can not only provide a theoretical basis for the design of frequency modulation control strategies but also help thermal power units to grasp the change trend of the power grid load in real time, so as to make timely adjustments when the load fluctuates.

[0088] Preferably, step S2 includes the following steps:

[0089] Step S21: Through the built-in sensor, and based on the load fluctuation time series trend data, collect the real-time states of thermal power and steam pressure respectively to obtain the real-time state data of thermal power and the real-time state data of steam pressure;

[0090] Step S22: Evaluate the energy conversion efficiency loss of the real-time state data of steam pressure according to the real-time state data of thermal power to obtain the energy conversion efficiency loss data;

[0091] Step S23: Based on the energy conversion efficiency loss data, identify the correlation between energy conversion and steam pressure demand for the real-time state data of steam pressure to obtain the correlation data between energy conversion and steam pressure demand;

[0092] Step S24: Analyze the nonlinear structure of the correlation data between energy conversion and steam pressure demand to obtain the nonlinear demand correlation data between energy conversion and steam pressure.

[0093] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0094] Step S21: Through the built-in sensor, and based on the load fluctuation time series trend data, collect the real-time states of thermal power and steam pressure respectively to obtain the real-time state data of thermal power and the real-time state data of steam pressure;

[0095] In the embodiments of the present invention, real-time data collection is first carried out through a variety of built-in sensors (such as thermal power sensors, pressure sensors, temperature sensors, etc.). The main task of this process is to obtain the real-time status of thermal power and steam pressure during the operation of the thermal power unit. The sensors are respectively installed on the boiler, steam turbine and other key equipment to monitor the output of thermal power and the change of steam pressure in real time. The thermal power sensor measures the thermal energy generated by fuel combustion in the boiler and converts it into the efficiency of electric energy, and records the real-time output value of the boiler's thermal power. The steam pressure sensor obtains the steam pressure data in a timely manner by monitoring the pressure of steam flow in the boiler. These sensors collect data at a millisecond-level frequency to ensure that the impact of instantaneous load changes on the unit status can be reflected. Through the real-time data collection of the built-in sensors, "real-time thermal power status data" and "real-time steam pressure status data" are respectively obtained. These two sets of data will be used as the basic data for subsequent thermal energy conversion efficiency analysis and demand correlation identification. The key to this process is to ensure the accuracy and stability of the sensors. Especially in the high-temperature and high-pressure environment, the sensors must have anti-interference ability to ensure the accuracy of data collection.

[0096] Step S22: Evaluate the energy conversion efficiency loss of the real-time steam pressure status data according to the real-time thermal power status data to obtain the thermal energy conversion efficiency loss data;

[0097] In the embodiments of the present invention, the evaluation of energy conversion efficiency loss is first carried out on the "real-time thermal power status data" and the "real-time steam pressure status data". Through the comparative analysis of these two sets of data, the efficiency loss in the thermal energy conversion process can be identified. In practical applications, an energy conversion efficiency model is usually used to analyze the mutual relationship between thermal power and steam pressure, so as to evaluate the loss in the energy conversion process. Specifically, first, a functional relationship between thermal power and steam pressure needs to be established through a mathematical model. The nonlinear regression analysis method can be used to fit the relationship between thermal power and steam pressure data. After fitting, the theoretically ideal conversion efficiency can be calculated and compared with the actually collected thermal power data to calculate the gap between the actual conversion efficiency and the ideal efficiency. This gap is the thermal energy conversion efficiency loss. On this basis, the "thermal energy conversion efficiency loss data" calculated by using this model can provide an important basis for the next-step demand correlation identification. The core technology of this process lies in accurately modeling the nonlinear relationship between thermal power and steam pressure, and reasonably estimating the magnitude of the efficiency loss. Through accurate evaluation, the efficiency problems existing in the thermal energy conversion process can be effectively found out, and a theoretical basis for subsequent optimization can be provided.

[0098] Step S23: Based on the thermal energy conversion efficiency loss data, perform thermal energy conversion-steam pressure demand correlation identification on the real-time steam pressure status data to obtain the thermal energy conversion-steam pressure demand correlation data;

[0099] In the embodiment of the present invention, based on the "thermal energy conversion efficiency loss data", the "real-time steam pressure state data" is subjected to thermal energy conversion - steam pressure demand correlation identification. First of all, the data set adopted is the relationship between the efficiency loss and the steam pressure that occur during the thermal energy conversion process, and these data can reflect the influence of the steam pressure change on the thermal energy conversion process. Therefore, by analyzing the efficiency loss data, the direct influence of the steam pressure change on the system demand can be identified. Specifically, by correlating the thermal energy conversion efficiency loss data with the real-time steam pressure state data and applying the multivariate regression analysis technique, the non-linear relationship between the two is identified. At this time, regression algorithms such as the generalized linear model (GLM) or support vector regression (SVR) are adopted, and by fitting these data, the specific influence value of the steam pressure change on the thermal energy conversion efficiency is obtained. When conducting the demand correlation identification, it should be particularly noted that the fluctuation of the steam pressure not only affects the thermal energy conversion efficiency but also directly affects the load demand of the unit. Therefore, by identifying these correlation relationships, the "thermal energy conversion - steam pressure demand correlation data" can be obtained. This data will provide basic information for the subsequent quantification of control requirements and frequency modulation requirements.

[0100] Step S24: Perform non-linear structure analysis on the thermal energy conversion - steam pressure demand correlation data to obtain the thermal energy conversion - steam pressure non-linear demand correlation data.

[0101] In the embodiment of the present invention, the "thermal energy conversion - steam pressure demand correlation data" is used for non-linear structure analysis to further study the complex non-linear demand correlation between thermal energy conversion and steam pressure. The goal of non-linear structure analysis is to analyze the complex interaction between thermal energy conversion and steam pressure through mathematical methods and reveal the behavioral characteristics of the system under different loads and different operating conditions. First of all, the non-linear least squares method is used for data fitting to model the "thermal energy conversion - steam pressure demand correlation data". At this time, non-linear models such as the Lennard-Jones potential model or the polynomial regression model are selected to capture the complex relationships that cannot be analyzed by the linear model in the data. During the model fitting process, by continuously adjusting the parameters and minimizing the fitting error, a non-linear function that can accurately describe the complex interaction relationship between thermal energy conversion and steam pressure is finally obtained. After obtaining the non-linear model, the structure analysis of the system is carried out to reveal the non-linear demand correlation characteristics between steam pressure and thermal energy conversion. Through the analysis results, it can be found that the steam pressure change under different operating conditions has different influences on the thermal energy conversion efficiency and the load demand, providing more refined demand data for the design of subsequent frequency modulation control strategies.

[0102] Preferably, step S23 includes the following steps:

[0103] Step S231: Perform a time-varying characteristic analysis on the heat energy conversion efficiency loss data to obtain the time-varying data of the conversion efficiency loss;

[0104] Step S232: Based on the time-varying data of the conversion efficiency loss, evaluate the steam pressure response between different time periods for the real-time state data of the steam pressure to obtain the steam pressure responsiveness evaluation data;

[0105] Step S233: Divide the steam pressure responsiveness evaluation data into response rate gradient levels to obtain the pressure response rate gradient levels;

[0106] Step S235: According to the pressure response rate gradient levels, induce the response rate-energy conversion time-varying correlation subsets for the time-varying data of the conversion efficiency loss to obtain the response rate-energy conversion time-varying correlation subsets;

[0107] Step S236: Through Takagi-Sugeno inference, identify the heat energy conversion-steam pressure demand correlation for the response rate-energy conversion time-varying correlation subsets to obtain the heat energy conversion-steam pressure demand correlation data.

[0108] In the embodiments of the present invention, time-varying characteristics analysis is performed on the "thermal energy conversion efficiency loss data", and the goal is to identify the law of the thermal energy conversion efficiency loss changing with time. First, collect the thermal energy conversion efficiency loss data over a period of time, and ensure that the data collection frequency is high enough to reflect the instantaneous changes during the system operation. Through the time series analysis of these data, the moving average method is used for data smoothing to remove noise and ensure the accuracy of the analysis results. Next, the Fourier transform (FFT) method is used to perform frequency domain analysis on the thermal energy conversion efficiency loss data to identify the periodic fluctuation characteristics therein. If there are periodic changes, the Fourier transform can extract the main frequency components in the data and reveal the behavior characteristics of the system at different time scales. In addition, the autoregressive model (AR) can also be used to model the thermal energy conversion efficiency loss data to evaluate the changing trend and law of its time series. The autoregressive model can predict the future efficiency loss changes based on historical data and reveal whether there are seasonal or periodic fluctuations in the system. Through the above analysis, the "time-varying data of conversion efficiency loss" can be obtained, which reflects the law of the efficiency loss changing with time during the thermal energy conversion process and provides a basis for the evaluation and correlation analysis in the subsequent steps. Based on the "time-varying data of conversion efficiency loss", the steam pressure response evaluation is carried out between different time periods for the "real-time state data of steam pressure". The goal of this evaluation is to analyze the influence of the steam pressure changes in different time periods on the thermal energy conversion efficiency loss. First, group the steam pressure data according to different time periods to ensure that the data within each time period is detailed enough for accurate analysis. Next, adopt the linear regression analysis method. By comparing the steam pressure values within each time period with the corresponding thermal energy conversion efficiency loss data, evaluate the responsiveness of the steam pressure changes to the efficiency loss. The linear regression model can help identify the linear relationship between the steam pressure and the efficiency loss and judge whether there is a significant influence. If the efficiency loss fluctuations caused by the steam pressure changes are large, it indicates that the steam pressure has a strong responsiveness to the thermal energy conversion; otherwise, it indicates that the influence is small. In addition, adopt the piecewise regression analysis method to perform local modeling on the steam pressure and the thermal energy conversion efficiency loss in different time periods to reveal the heterogeneity of the steam pressure response under different time periods. Finally, obtain the "steam pressure responsiveness evaluation data", which can be used for further analysis of the pressure response rate in the subsequent steps. Perform a gradient level division on the "steam pressure responsiveness evaluation data", and the purpose is to divide the response rate of the steam pressure into different levels, so as to more accurately describe the influence of different response levels on the thermal energy conversion process. First, use the gradient analysis method to calculate the change rate in the steam pressure responsiveness evaluation data. The specific approach is to calculate the change rate of the steam pressure at different time points through derivative operations and obtain the response rate at each time point. According to the magnitude of the response rate, the response of the steam pressure can be divided into multiple gradient levels.Adopt the method based on interval division, set different threshold ranges, and specifically divide them into low response, medium response, and high response levels. The low response level corresponds to a relatively small change in steam pressure, and the system has a weak adaptability to load fluctuations; the medium response level corresponds to a moderate pressure change, and the system response is moderate; the high response level corresponds to a relatively large change in steam pressure, and the system has a strong response ability. The key to this process is how to set the threshold of the response rate, which can be determined by the clustering analysis method according to the distribution of historical data. The "pressure response rate gradient level" obtained after division can reflect the response ability of the system under different steam pressure change conditions, providing a basis for subsequent energy conversion correlation analysis. Induce the response rate-energy conversion time-varying correlation subset for the "time-varying data of conversion efficiency loss" according to the "pressure response rate gradient level". The goal of this step is to summarize the relationship between steam pressure at different response rate levels and the loss of thermal energy conversion efficiency into a subset, and further deeply understand the specific impact of steam pressure on the loss of thermal energy conversion efficiency under different response conditions. First, according to the "pressure response rate gradient level" obtained in the previous step, divide the response rate into three levels: high, medium, and low. Then, for each level, select the corresponding "time-varying data of conversion efficiency loss" for analysis. For each response rate level, use the weighted average method to weight the data at this level, and extract the "energy conversion time-varying correlation subset" that can represent the response characteristics of this level. In this process, by grouping the steam pressure response data at different levels, ensure that each subset can accurately reflect the impact of the response rate on energy conversion. During the induction process, use the adaptive weighted average method (AWM) to determine the weight of each data point. This method can dynamically adjust the weight according to the relative importance of the data, ensuring that the finally obtained subset accurately reflects the impact of the response rate on the loss of thermal energy conversion efficiency. The induced "response rate-energy conversion time-varying correlation subset" provides a more accurate understanding of the loss of thermal energy conversion efficiency and lays a data foundation for subsequent demand correlation analysis. Identify the thermal energy conversion-steam pressure demand correlation for the "response rate-energy conversion time-varying correlation subset" through Takagi-Sugeno inference. Takagi-Sugeno inference is a fuzzy logic-based inference method that can handle complex nonlinear relationships in the system. First, input the "response rate-energy conversion time-varying correlation subset" into the Takagi-Sugeno inference model. The model performs inference based on fuzzy rules to identify the impact mode of steam pressure change on thermal energy conversion. Specifically, use the fuzzy rule set in the Takagi-Sugeno model, such as "if the steam pressure response rate is high, then the loss of thermal energy conversion efficiency is large", and derive the specific demand correlation between steam pressure and thermal energy conversion through these rules.During the inference process, a membership function is used to quantify the response rate of the steam pressure, convert it into a numerical value and input it into the model for inference, and finally obtain the "thermal energy conversion - steam pressure demand correlation data". This data can provide a more accurate demand quantification basis for the subsequent frequency modulation control strategy of thermal power units.

[0109] Preferably, step S3 includes the following steps:

[0110] Step S31: Normalize the thermal energy conversion - steam pressure non - linear demand correlation data to obtain non - linear demand correlation normalized data;

[0111] Step S32: Perform delayed coupling on the load fluctuation time - series trend data according to the non - linear demand correlation normalized data to obtain grid load delayed data;

[0112] Step S33: Quantify the power generation frequency modulation demand for the load fluctuation time - series trend data based on the grid load delayed data to obtain grid power generation frequency modulation demand quantification data;

[0113] Step S34: Control the energy input demand for the thermal energy conversion - steam pressure non - linear demand correlation data according to the grid power generation frequency modulation demand quantification data to obtain thermal power energy input control data.

[0114] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0115] Step S31: Normalize the thermal energy conversion - steam pressure non - linear demand correlation data to obtain non - linear demand correlation normalized data;

[0116] In the embodiment of the present invention, the purpose of normalizing the "thermal energy conversion - steam pressure non - linear demand correlation data" is to unify the dimension of the data to make it suitable for subsequent analysis and processing. First, analyze the data range of the "thermal energy conversion - steam pressure non - linear demand correlation data" to identify the maximum and minimum values in this dataset. Adopt the linear normalization method. For the normalized data, outlier detection is also required. The box - plot method is used for detection to screen out abnormal data points. This method calculates the quartiles and inter - quartile range (IQR) of the data, sets the threshold range of outliers, and ensures that the processed data has high quality. Through the above steps, the obtained "non - linear demand correlation normalized data" can provide a data basis with consistency and comparability for subsequent delayed coupling and power generation frequency modulation demand quantification.

[0117] Step S32: Perform delayed coupling on the load fluctuation time - series trend data according to the non - linear demand correlation normalized data to obtain grid load delayed data;

[0118] In the embodiments of the present invention, the "load fluctuation time-series trend data" is delay-coupled based on the "nonlinear demand-correlated normalized data" with the aim of revealing the time-delay effect between load fluctuation and heat energy conversion. First, it is necessary to define the lag period in the load fluctuation data, that is, the time difference between the impact of load fluctuation on the grid load and its change. Using the correlation analysis method, the time-delay correlation between the nonlinear demand-correlated normalized data and the load fluctuation time-series trend data is calculated. Specifically, autocorrelation analysis and cross-correlation analysis are adopted to detect the time-delay relationship between the two. The autocorrelation analysis method helps determine the delay of the impact of load fluctuation on the grid load by calculating the autocorrelation values of the data at different lag periods; while the cross-correlation analysis method identifies the delay effect of load fluctuation on heat energy conversion by calculating the correlation between two time series at different lag periods. In this way, the response delay time of load fluctuation to the grid load can be accurately obtained, and based on this, delay coupling is carried out to obtain the "grid load delay data". This delay-coupling process can help analyze the actual impact of load fluctuations in different time periods on the heat energy conversion process, thus providing accurate time data support for the subsequent quantification of power generation frequency regulation requirements.

[0119] Step S33: Quantify the power generation frequency regulation requirements for the load fluctuation time-series trend data based on the grid load delay data to obtain the quantified data of the grid power generation frequency regulation requirements;

[0120] In the embodiments of the present invention, the power generation frequency regulation requirements for the "load fluctuation time-series trend data" are quantified based on the "grid load delay data" with the aim of accurately calculating the power generation frequency regulation requirements of the grid under specific load fluctuations. First, according to the delay effect of grid load fluctuations and the actual load data, a relationship model between load fluctuation and power generation frequency regulation requirements is established. This model can adopt the method of dynamic system modeling, considering the time-series impact of grid load fluctuations on power generation frequency regulation. Specifically, a discretized difference equation model is used to describe the dynamic relationship between grid load fluctuations and power generation frequency regulation requirements. By introducing a delay term, the grid load delay data is coupled with the power generation frequency regulation requirements. By solving this difference equation, the "quantified data of the grid power generation frequency regulation requirements" is obtained, which will provide the actual power generation frequency regulation requirement values for the frequency regulation control strategy of thermal power units to ensure that the frequency regulation process can effectively respond to grid load fluctuations.

[0121] Step S34: Control the energy input requirements for the heat energy conversion-steam pressure nonlinear demand-correlated data based on the quantified data of the grid power generation frequency regulation requirements to obtain the thermal power energy input control data.

[0122] In the embodiments of the present invention, the energy input demand control is performed on the "nonlinear demand correlation data of heat energy conversion - steam pressure" according to the "quantified data of power grid power generation frequency modulation demand", aiming to adjust the energy input of thermal power units according to the power generation frequency modulation demand of the power grid to achieve the best frequency modulation effect. First, the quantified data of power generation frequency modulation demand is used as the input, and combined with the nonlinear demand relationship between heat energy conversion and steam pressure, an energy input control strategy is formulated. Specifically, a nonlinear optimization method is used to solve the optimal configuration of energy input according to the quantified data of power grid power generation frequency modulation demand and the nonlinear demand correlation data of heat energy conversion - steam pressure. First, by setting the objective function, the matching degree between energy input and grid demand is maximized. Through this control process, the "thermal power energy input control data" can be obtained, which determines the energy input amount of the thermal power unit, ensuring that the unit can adjust the output in a timely manner in the face of grid load fluctuations and achieving an accurate frequency modulation effect.

[0123] Preferably, step S32 includes the following steps:

[0124] Step S321: Perform an analysis on the sluggishness of the heat energy conversion - steam pressure conversion efficiency for the normalized data of the nonlinear demand correlation to obtain the data of the sluggishness of the energy conversion efficiency;

[0125] Step S322: Calculate the multi-peak extreme sluggish variance of energy conversion for the normalized data of the nonlinear demand correlation according to the data of the sluggishness of the energy conversion efficiency to obtain the multi-peak extreme sluggish variance of energy conversion;

[0126] Step S323: Perform a delayed coupling on the time series trend data of load fluctuations based on the multi-peak extreme sluggish variance of energy conversion to obtain the grid load delay data.

[0127] In the embodiments of the present invention, first, a thermal energy conversion - steam pressure conversion efficiency sluggishness analysis is performed on the "non - linear demand - related normalized data" with the aim of revealing the time - delay characteristics between steam pressure and conversion efficiency during the thermal energy conversion process. This analysis is carried out by calculating the hysteresis and sluggishness of the time series of the "non - linear demand - related normalized data". The specific operation is as follows: perform time - series analysis on the "non - linear demand - related normalized data", and obtain the time - delay effect of the thermal energy conversion process by calculating the autocorrelation function of the data at different lags (such as 1 hour, 2 hours, etc.). By analyzing the autocorrelation values at different lags, the sluggishness of the conversion efficiency can be quantified to obtain the "energy conversion efficiency sluggishness data". Calculate the multi - peak extreme value sluggish variance of energy conversion based on the "energy conversion efficiency sluggishness data". The core of this step is to identify the multi - peak characteristics existing in the process of energy conversion efficiency change through statistical analysis and evaluate the impact of the sluggishness of these extreme values on the entire conversion process. First, it is necessary to identify the multi - peak extreme values of the "energy conversion efficiency sluggishness data" using statistical methods such as local extreme value detection (for example, determining local maximum and minimum values through the difference method or filtering method) to mark the positions of the multi - peak values. Subsequently, for these extreme points, calculate their sluggish variances. The calculation of the sluggish variance is completed through the following steps: perform a time - series interval analysis on the identified extreme points and calculate the time difference between adjacent extreme values. Then, by calculating the variance, the sluggish change characteristics between multi - peak extreme values, that is, the "multi - peak extreme value sluggish variance of energy conversion", can be obtained. Through this sluggish variance data, the multiple extreme value changes and their sluggish effects existing in the energy conversion process can be understood, thereby revealing the hysteretic response characteristics of the system to steam pressure changes. Based on the "multi - peak extreme value sluggish variance of energy conversion", perform a delay coupling on the "load fluctuation time - series trend data" with the aim of further analyzing the time - series impact of grid load fluctuations on the frequency modulation demand of the power generation system after considering the hysteretic response characteristics of the conversion efficiency. This step is carried out by coupling the time series of the sluggish variance data and the load fluctuation data to identify the delay relationship between load fluctuations and energy conversion efficiency. The specific operations include the following steps: calculate the cross - correlation between the "multi - peak extreme value sluggish variance of energy conversion" and the load fluctuation time - series trend data, and combine the cross - correlation data to adjust the load fluctuation time - series trend data through a delay coupling method. The coupling method aligns the time periods with larger hysteretic effects with the load fluctuation time - series data through weighted average or interpolation method to obtain the "grid load delay data". This data can reflect the delay effect of load fluctuations and their impact on the grid power generation frequency modulation demand, providing more accurate input data for subsequent power generation frequency modulation control.

[0128] Preferably, step S34 includes the following steps:

[0129] Step S341: Perform a power regulation demand analysis on the grid power generation frequency modulation demand quantification data to obtain the power generation power regulation demand data;

[0130] Step S342: Approximate the instability mutation amplitude of the power generation power regulation demand data to obtain the power demand instability approximation data;

[0131] Step S343: Perform time-series collaborative optimization on the thermal energy conversion-steam pressure nonlinear demand correlation data according to the power demand instability approximation data to obtain the steam pressure collaborative optimization demand data;

[0132] Step S344: Control the energy input demand based on the steam pressure collaborative optimization demand data to obtain the thermal power energy input control data.

[0133] In the embodiment of the present invention, first, a power adjustment demand analysis is performed on the "quantified data of power grid power generation frequency modulation demand". The purpose of this analysis is to extract the real-time demand data required for power generation power adjustment from the time series data of power grid load fluctuations. The specific operations include calculating the instantaneous changes in the load fluctuation data, obtaining its fast fluctuation part, and analyzing the adjustment amount of power demand in combination with historical load data. By performing differential calculation on the load fluctuations (such as the first-order difference method), the load change rate can be obtained, and further, the power adjustment demand can be calculated based on the load change rate. In step S342, an approximation of the instability mutation amplitude of the "power generation power adjustment demand data" is further performed. The core of this step lies in detecting and approximating the mutation points in the power grid power generation frequency modulation demand, and identifying the mutation amplitude of the power demand when the system becomes unstable. This step analyzes the mutation part in the power demand by setting a threshold. First, calculate the change rate of the power demand data. If the change rate is greater than the set threshold, it is considered that the system has become unstable. By gradient analysis or the second-order difference method, calculate the amplitude of the power demand change and obtain an approximate value of the mutation amplitude. In step S343, a time-series collaborative optimization of the "nonlinear demand correlation data of heat energy conversion - steam pressure" is performed based on the "approximate data of power demand instability". The goal of this process is to optimize the response of steam pressure during frequency modulation by coupling the nonlinear relationship between the instability mutation amplitude of power demand and the change in steam pressure. First, analyze the response characteristics of steam pressure at the moment of power demand instability, and use time-series correlation analysis methods such as cross-correlation or cross-correlation to establish a time-series coupling relationship between power demand and steam pressure. Then, use optimization algorithms (such as the gradient descent method, Newton's method, etc.) to perform collaborative optimization on these data to obtain the best strategy for steam pressure adjustment. In step S344, based on the "steam pressure collaborative optimization demand data", energy input demand control is performed, and finally, the "thermal power energy input control data" is obtained. The core goal of this step is to reasonably arrange the energy input amount of thermal power units according to the adjustment demand of steam pressure. By analyzing the steam pressure demand and combining the energy conversion characteristics of the unit, the appropriate energy input amount is determined. This process uses the PID adjustment algorithm in control theory, sets a control target, such as the stability of steam pressure, and calculates the adjustment amount of energy input according to the adjustment demand.

[0134] Preferably, step S342 includes the following steps:

[0135] Perform a time-series point demand increase analysis on the power generation power adjustment demand data to obtain time-series point demand increase data;

[0136] Perform a time-series point discretization process on the time-series point demand increase data to obtain time-series point demand increase discrete data;

[0137] Perform symmetric instability time series deviation between different time series points on the time series point demand increase data according to the discrete data of the time series point demand increase, and obtain symmetric instability time series deviation data;

[0138] Based on the symmetric instability time series deviation data, approximate the instability mutation amplitude to obtain power demand instability approximation data.

[0139] In the embodiment of the present invention, first, perform time series point demand increase analysis on the power generation power adjustment demand data. The goal of this process is to identify the demand change amplitude at each moment in the time series of the power adjustment demand data. The specific operation method is to calculate the difference in power adjustment demand between adjacent time points to obtain the demand increase between time series points. The calculation method of the increase uses the time difference method, that is, in the time series, take the data at each moment and perform a difference with the data at the previous moment to obtain the increase data. Next, perform time series point discretization processing on the time series point demand increase data. The purpose of this step is to convert continuous time series data into discrete points for further analysis of the changes between time series points. The specific operation method is to segment and discretize the time series point demand increase data, and discretize the increase data according to a predetermined interval or threshold. For example, by setting a fixed increase interval, the demand increase data is divided according to this interval to obtain the discretized time series point demand increase data. The discretization method can use equal interval segmentation technology or non-uniform segmentation based on the data distribution characteristics to better capture the important characteristics in the power demand change. Based on the discrete data of the time series point demand increase, perform symmetric instability time series deviation analysis between different time series points. The purpose of this step is to identify the symmetry difference in the demand increase between time series points, that is, to analyze the deviation of power demand in the symmetric time interval. The specific method is to perform a comparative analysis on the increase at symmetric time points and calculate the deviation of the demand increase in the front and back time intervals. This deviation reflects the instability characteristics of the power demand change. Finally, based on the symmetric instability time series deviation data, perform an approximate calculation of the instability mutation amplitude. The purpose of this step is to approximate the instability mutation amplitude in the power adjustment demand through the analysis of the time series deviation. The specific operation method is to perform an approximation process on the instability time series data to identify the mutation amplitude that occurs at a specific moment in the system. The calculation of the mutation amplitude is usually based on the maximum change rate of the data, and uses the high-frequency difference method or local extreme value detection method to identify the change amplitude of the power demand in the unstable state.

[0140] Preferably, it is characterized in that step S4 includes the following steps:

[0141] Step S41: Perform iterative learning on the thermal power energy input control data to obtain energy input control iterative data;

[0142] Step S42: Design a global frequency modulation control strategy according to the energy input control iterative data to obtain an energy input global frequency modulation control strategy;

[0143] Step S43: Feed back the energy input global frequency modulation control strategy to the cloud platform to execute the frequency modulation control method.

[0144] In the embodiment of the present invention, firstly, iterative learning is performed on the thermal power energy input control data to optimize the energy input control strategy. The key to this process is to gradually adjust and improve the control parameters by repeatedly learning historical data and real-time feedback information. In specific implementation, by applying iterative optimization algorithms such as the gradient descent method or the Newton method, the parameters of the control strategy are repeatedly updated. Each iteration is adjusted based on the output of the previous round and the feedback data of the current system, thereby gradually reducing the error and optimizing the frequency modulation control effect. During each iteration process, the control data is used to evaluate the effectiveness of the current strategy, and the iterative direction is guided by calculating the error function. The error function is usually defined based on the deviation between the actual and the desired, and the parameter update is guided by minimizing the error function. Finally, a set of iterative data is obtained, which can describe how the system adjusts the energy input according to the historical frequency modulation effect. Design the global frequency modulation control strategy for energy input according to the iterative data of energy input control. The goal of this process is to design a global frequency modulation control strategy based on the iterative learning results, so that the thermal power unit can operate stably under various load changes. The specific implementation method is to perform global optimization based on the iterative learning data, and adopt an optimization algorithm based on the feedback control theory, such as the linear quadratic regulator (LQR) method, to design a global frequency modulation controller. This controller can adjust the energy input in real time according to the change of the grid load to ensure the best matching between the output power of the thermal power unit and the grid load fluctuation. The design of the control strategy needs to consider the conversion efficiency of energy, the change of steam pressure, and the non-linear relationship of heat energy conversion. Through the comprehensive analysis of these factors, a control scheme that can effectively cope with different load fluctuation scenarios is obtained. Feed back the designed global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method. The core of this step is to transfer the optimized control strategy to the cloud platform for execution in a distributed environment. In specific implementation, the control strategy is transmitted to the cloud platform through a secure network communication protocol. After receiving the control strategy, the cloud platform performs real-time frequency modulation operations according to the real-time grid data and the operating status of the thermal power unit. The cloud platform will instruct the thermal power unit to adjust its energy input according to the load fluctuation situation to ensure that the output of the generating unit matches the demand of the grid load. Through the cloud platform, remote scheduling and optimized control of the thermal power unit can be realized, enhancing the response ability and frequency modulation efficiency of the system. At the same time, the cloud platform can realize coordinated scheduling among multiple units to ensure the stability of the entire power system and the maximization of energy utilization.

[0145] Preferably, the present invention also provides a frequency modulation control system for a thermal power unit, which is used to execute the frequency modulation control method for the thermal power unit as described above. The frequency modulation control system for the thermal power unit includes:

[0146] A time - series load trend analysis module, which is used to collect the load fluctuation data of the transmission power grid of a thermal power unit through a sensor to obtain the load fluctuation data of the transmission power grid; perform a time - series load trend analysis on the load fluctuation data of the transmission power grid to obtain the load fluctuation time - series trend data;

[0147] A demand management identification module, which is used to collect the real - time status of thermal power and steam pressure based on the time - series trend data of load fluctuations through a built - in sensor, and respectively obtain the real - time status data of thermal power and the real - time status data of steam pressure; perform an association identification of heat energy conversion - steam pressure demand based on the real - time status data of thermal power and the real - time status data of steam pressure to obtain the non - linear demand association data of heat energy conversion - steam pressure;

[0148] An energy input demand control module, which is used to quantify the power generation frequency modulation demand for the time - series load trend data according to the non - linear demand association data of heat energy conversion - steam pressure to obtain the quantified data of the power grid power generation frequency modulation demand; perform energy input demand control according to the quantified data of the power grid power generation frequency modulation demand to obtain the thermal power energy input control data;

[0149] A global frequency modulation control strategy design module, which is used to design a global frequency modulation control strategy according to the thermal power energy input control data to obtain a global frequency modulation control strategy for energy input; feedback the global frequency modulation control strategy for energy input to the cloud platform to execute the frequency modulation control method.

[0150] Therefore, from any perspective, the embodiments should be regarded as exemplary and non - restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0151] The above - mentioned are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A frequency modulation control method for a thermal power unit, characterized in that: The following steps are involved: Step S1: collecting transmission grid load fluctuation data of the thermal power unit through sensors to obtain transmission grid load fluctuation data; Conduct time series load trend analysis on load fluctuation data of transmission power grid to obtain load fluctuation time series trend data; Step S2: Using built-in sensors and based on the load fluctuation time series trend data, real-time status data of thermal power and steam pressure are collected to obtain real-time status data of thermal power and real-time status data of steam pressure respectively; according to the real-time status data of thermal power, the real-time status data of steam pressure is subjected to thermal energy conversion-steam pressure demand association identification to obtain thermal energy conversion-steam pressure nonlinear demand association data; Step S3: quantify the power generation frequency regulation demand of the load fluctuation time series trend data according to the heat energy conversion-steam pressure nonlinear demand correlation data to obtain the power generation frequency regulation demand quantification data of the power grid; control the energy input demand according to the power generation frequency regulation demand quantification data of the power grid to obtain the thermal power energy input control data; Step S4: designing a global frequency modulation control strategy according to the thermal power energy input control data to obtain the energy input global frequency modulation control strategy; feeding back the energy input global frequency modulation control strategy to the cloud platform to execute the frequency modulation control method; Step S3 includes the following steps: Step S31: normalizing the heat energy conversion-steam pressure nonlinear demand correlation data to obtain nonlinear demand correlation normalized data; Step S32: Delay coupling is performed on the load fluctuation time series trend data according to the nonlinear demand associated normalized data to obtain grid load delay data; Step S33: quantifying the power generation and frequency regulation demand of the load fluctuation time series trend data based on the power grid load delay data to obtain the power grid power generation and frequency regulation demand quantification data.

2. The frequency modulation control method of a thermal power unit according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: collecting transmission grid load fluctuation data of the thermal power unit through sensors to obtain transmission grid load fluctuation data; Step S12: Filling missing values ​​in the transmission grid load fluctuation data to obtain grid load fluctuation filling data; Step S13: Perform time series load trend analysis on the power grid load fluctuation filling data to obtain load fluctuation time series trend data.

3. The frequency modulation control method of a thermal power unit according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: collecting the real-time status of thermal power and steam pressure through built-in sensors and based on the load fluctuation time series trend data, and obtaining the real-time status data of thermal power and the real-time status data of steam pressure respectively; Step S22: evaluating the energy conversion efficiency loss of the steam pressure real-time status data according to the thermal power real-time status data to obtain thermal energy conversion efficiency loss data; Step S23: Based on the heat energy conversion efficiency loss data, the real-time state data of steam pressure is subjected to heat energy conversion-steam pressure demand correlation identification to obtain heat energy conversion-steam pressure demand correlation data; Step S24: performing nonlinear structural analysis on the thermal energy conversion-steam pressure demand correlation data to obtain thermal energy conversion-steam pressure nonlinear demand correlation data.

4. The frequency modulation control method of a thermal power unit according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing time-varying characteristic analysis on the heat energy conversion efficiency loss data to obtain conversion efficiency loss time-varying data; Step S232: Based on the conversion efficiency loss time-varying data, the real-time state data of steam pressure is evaluated for steam pressure response in different time periods to obtain steam pressure responsiveness evaluation data; Step S233: dividing the steam pressure responsiveness evaluation data into response rate gradient grades to obtain pressure response rate gradient grades; Step S235: performing response rate-energy conversion time-varying correlation subset induction on the conversion efficiency loss time-varying data according to the pressure response rate gradient level to obtain a response rate-energy conversion time-varying correlation subset; Step S236: using Takagi-Sugeno reasoning to identify the thermal energy conversion-steam pressure demand association of the response rate-energy conversion time-varying association subset, and obtain thermal energy conversion-steam pressure demand association data.

5. The frequency modulation control method of a thermal power unit according to claim 1, characterized in that: Step S3 also includes the following steps: Step S34: energy input demand control is performed on the thermal energy conversion-steam pressure nonlinear demand correlation data according to the quantified data of the power grid power generation frequency regulation demand to obtain thermal power energy input control data.

6. The frequency modulation control method of a thermal power unit according to claim 5, characterized in that: Step S32 includes the following steps: Step S321: performing heat energy conversion-steam pressure conversion efficiency retardation analysis on the nonlinear demand-related normalized data to obtain energy conversion efficiency retardation data; Step S322: Calculate the energy conversion multi-peak extreme value retardation variance of the nonlinear demand-related normalized data according to the energy conversion efficiency retardation data to obtain the energy conversion multi-peak extreme value retardation variance; Step S323: Delay coupling is performed on the load fluctuation time series trend data based on the energy conversion multi-peak extreme value delay variance to obtain the grid load delay data.

7. The frequency modulation control method of a thermal power unit according to claim 5, characterized in that: Step S34 includes the following steps: Step S341: Perform power regulation demand analysis on the power grid power generation frequency regulation demand quantified data to obtain power generation power regulation demand data; Step S342: Approximate the instability mutation amplitude of the power generation power regulation demand data to obtain power demand instability approximate data; Step S343: performing time series collaborative optimization on the thermal energy conversion-steam pressure nonlinear demand correlation data according to the power demand instability approximate data to obtain the steam pressure collaborative optimization demand data; Step S344: Perform energy input demand control based on the steam pressure collaborative optimization demand data to obtain thermal power energy input control data.

8. The frequency modulation control method of a thermal power unit according to claim 7, characterized in that: Step S342 includes the following steps: Performing a time-series demand increase analysis on the power generation regulation demand data to obtain time-series demand increase data; Discretize the time-series point demand increase data to obtain discrete time-series point demand increase data; According to the discrete data of the demand increase at the time point, the demand increase data at the time point is subjected to the symmetrical instability time series deviation between different time series points to obtain the symmetrical instability time series deviation data; The instability mutation amplitude is approximated based on the symmetrical instability timing deviation data to obtain the approximate data of power demand instability.

9. The frequency modulation control method of a thermal power unit according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: iteratively learning the thermal power energy input control data to obtain energy input control iterative data; Step S42: designing a global frequency modulation control strategy according to the energy input control iteration data to obtain an energy input global frequency modulation control strategy; Step S43: Feedback the energy input global frequency modulation control strategy to the cloud platform to execute the frequency modulation control method.

10. A frequency modulation control system for a thermal power unit, characterized in that: Used to execute the frequency regulation control method of a thermal power unit according to claim 1, the frequency regulation control system of the thermal power unit comprises: The time series load trend analysis module is used to collect the load fluctuation data of the transmission power grid of the thermal power unit through sensors to obtain the load fluctuation data of the transmission power grid; perform time series load trend analysis on the load fluctuation data of the transmission power grid to obtain the load fluctuation time series trend data; The demand management identification module is used to collect the real-time status of thermal power and steam pressure through built-in sensors and based on the load fluctuation time series trend data, and obtain the real-time status data of thermal power and steam pressure respectively; perform thermal energy conversion-steam pressure demand association identification on the real-time status data of steam pressure according to the real-time status data of thermal power, and obtain the thermal energy conversion-steam pressure nonlinear demand association data; The energy input demand control module is used to quantify the power generation frequency regulation demand of the load fluctuation time series trend data according to the heat energy conversion-steam pressure nonlinear demand correlation data, and obtain the power generation frequency regulation demand quantification data; the energy input demand is controlled according to the power generation frequency regulation demand quantification data of the power grid, and the thermal power energy input control data is obtained; The global frequency regulation control strategy design module is used to design the global frequency regulation control strategy according to the thermal power energy input control data, and obtain the energy input global frequency regulation control strategy; the energy input global frequency regulation control strategy is fed back to the cloud platform to execute the frequency regulation control method.

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