WO2026007604A1 - 一种火电厂智慧协同运行方法及相关装置 - Google Patents

一种火电厂智慧协同运行方法及相关装置

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Publication number
WO2026007604A1
WO2026007604A1 PCT/CN2025/099318 CN2025099318W WO2026007604A1 WO 2026007604 A1 WO2026007604 A1 WO 2026007604A1 CN 2025099318 W CN2025099318 W CN 2025099318W WO 2026007604 A1 WO2026007604 A1 WO 2026007604A1
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Prior art keywords
thermal power
intelligent
fault
optimal operating
power unit
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PCT/CN2025/099318
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English (en)
French (fr)
Inventor
李军
高林
王林
高海东
金国强
高耀岿
赵章明
周俊波
王明坤
张振伟
雷杨祥
王辰昱
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Xian Thermal Power Research Institute Co Ltd
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Xian Thermal Power Research Institute Co Ltd
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Publication of WO2026007604A1 publication Critical patent/WO2026007604A1/zh
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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/001—Arrangements for handling faults or abnormalities, e.g. emergencies or contingencies
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network
    • H02J13/10—Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network characterised by displaying of information or by user interaction, e.g. supervisory control and data acquisition [SCADA] systems
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network
    • H02J13/12—Monitoring network conditions, e.g. electrical magnitudes or operational status
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network
    • H02J13/14—Circuit arrangements for providing remote monitoring or remote control of equipment in a power distribution network the power network being locally controlled, e.g. home energy management systems [HEMS]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38—Arrangements for feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
    • H02J3/46—Controlling the sharing of generated power between the generators, sources or networks
    • H02J3/466—Scheduling or selectively controlling the operation of the generators or sources, e.g. connecting or disconnecting generators to meet a demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2103/00—Details of circuit arrangements for mains or AC distribution networks
    • H02J2103/30—Simulating, planning, modelling, reliability check or computer assisted design [CAD] of electric power networks

Definitions

  • This application belongs to the field of energy digitalization and intelligence, specifically involving a method and related devices for intelligent collaborative operation of thermal power plants.
  • This application provides a method and related apparatus for intelligent collaborative operation of thermal power plants to solve the problem of low integration and interactivity of various operating modules when thermal power plants operate using existing technologies.
  • a method for intelligent collaborative operation of a thermal power plant includes the following steps:
  • Real-time operating data of thermal power units is collected.
  • the real-time operating data is used to obtain monitoring indicators through a fault monitoring model. It is determined whether the monitoring indicators exceed a preset threshold. If they exceed the threshold, an early warning is issued. If they do not exceed the threshold, the thermal power unit continues to operate normally.
  • the features of the real-time operating data are extracted and compared with the fault feature database to obtain the fault self-healing instruction.
  • the thermal power unit completes closed-loop self-healing of faults according to the fault self-healing command, and at the same time adjusts the operating parameters and control scheme of the thermal power unit according to the optimal operating mode and optimal operating parameters to complete the intelligent collaborative operation of the thermal power plant.
  • the fault monitoring model is obtained through the following steps:
  • Historical operating data of thermal power units are collected, and a fault monitoring model is established based on the historical operating data and a neural network fitting algorithm.
  • the fault feature library is obtained through the following steps:
  • common fault information of thermal power units is obtained through self-encoded fault feature extraction, and a fault feature library is established based on the common faults of thermal power units.
  • the intelligent cruise model is established through the following steps:
  • Collect the unmanageable and manageable parameters of the thermal power unit process the unmanageable and manageable parameters using historical data mining, and establish an intelligent cruise model.
  • the step of obtaining the optimal operating mode and optimal operating parameters of the thermal power unit based on the intelligent cruise model specifically includes:
  • the intelligent cruise model After the intelligent cruise model outputs safety and stability data, economic and environmental data, and flexible maneuverability data, it performs weighted evaluation or fitness calculation to obtain the optimal operating mode and optimal operating parameters.
  • the optimal operating mode and optimal operating parameters are obtained; if the preset number of iterations is not met, the operable parameters are optimized and traversed to update the intelligent cruise model.
  • the optimal operating mode and optimal operating parameters are obtained; if the calculated fitness does not meet the preset fitness, the operable parameters are optimized and traversed to update the intelligent cruise model.
  • the optimization traversal employs a particle swarm optimization algorithm or a genetic algorithm.
  • the step of extracting features from the real-time operational data during early warning and comparing them with a fault feature database to obtain a fault self-healing instruction further includes the following steps:
  • the features of the real-time operating data extracted during the warning are not found in the fault feature library after comparison, the features of the real-time operating data will be updated to the fault feature library.
  • the PID control of the thermal power unit is replaced with predictive control, fuzzy control, decoupling control or variable structure control, and the operating parameters and control scheme of the thermal power unit are adjusted in combination with the optimal operating mode and optimal operating parameters.
  • a smart collaborative operation system for thermal power plants comprising:
  • the intelligent monitoring module is used to collect real-time operating data of thermal power units.
  • the real-time operating data is used to obtain monitoring indicators through a fault monitoring model.
  • the module determines whether the monitoring indicators exceed a preset threshold. If they exceed the threshold, an early warning is issued. If they do not exceed the threshold, the thermal power unit continues to operate normally.
  • the module is used to extract the features of the real-time operating data when issuing an early warning and compare them with a fault feature database to obtain a fault self-healing instruction.
  • the intelligent cruise module is used to establish an intelligent cruise model and obtain the optimal operating mode and optimal operating parameters of the thermal power unit based on the intelligent cruise model.
  • the intelligent control module is used to complete the closed-loop self-healing of the thermal power unit fault according to the fault self-healing command, and at the same time adjust the operating parameters and control scheme of the thermal power unit according to the optimal operating mode and optimal operating parameters to complete the intelligent collaborative operation of the thermal power plant.
  • An electronic device includes a memory, a processor, and a computer program stored in the memory and executable in the processor, wherein the processor executes the computer program to implement the steps of the intelligent collaborative operation method for a thermal power plant.
  • a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the intelligent collaborative operation method for a thermal power plant.
  • This application provides a method for intelligent collaborative operation of thermal power plants.
  • Real-time operating data is used to obtain monitoring indicators through a fault monitoring model. The method determines whether these indicators exceed preset thresholds. If they do, an early warning is issued; otherwise, the thermal power unit continues normal operation.
  • features of the real-time operating data are extracted and compared with a fault feature database to obtain fault self-healing instructions.
  • an intelligent cruise model is established, and the optimal operating mode and parameters of the thermal power unit are obtained based on this model.
  • the thermal power unit completes closed-loop fault self-healing according to the fault self-healing instructions.
  • the operating parameters and control scheme of the thermal power unit are adjusted according to the optimal operating mode and parameters, thus completing the intelligent collaborative operation of the thermal power plant.
  • This application can detect potential equipment anomalies and faults in advance and complete online early warning and closed-loop fault self-healing. While improving human-machine interaction efficiency, it avoids the expansion of the fault scope, reduces the risk of unplanned downtime, and ensures the long-term safe and stable operation of thermal power units. Furthermore, it can output the optimal operating mode and parameters through the intelligent cruise model, enabling adaptive parameter adjustment and intelligent heterogeneous strategy of the thermal power unit, thereby effectively improving the adaptability of the control system to complex operating conditions and disturbances, and ensuring the stability and convergence of the control system. Therefore, through the above, this application can solve the problem of low integration and interactivity of various operating modules in thermal power plants, and organically coordinate and support the goal of unmanned and minimally staffed operation in the production process of thermal power plants.
  • this application uses an intelligent cruise model to output safety and stability data, economic and environmental protection data, and flexible maneuverability data, and then performs weighted evaluation or fitness calculation to obtain the optimal operating mode and optimal operating parameters.
  • This enables the optimization of the best start-up, shutdown, parallel operation, and rotation times for thermal power units, and completes automatic start-up and shutdown of equipment and automatic closed-loop optimization of parameter settings. While improving the overall performance of the unit, it effectively reduces the intensity of manual intervention.
  • This application provides a smart collaborative operation system for thermal power plants, including a smart monitoring module, a smart cruise module, and a smart control module.
  • the system clearly divides the smart functional modules from three dimensions: monitoring, optimization, and control of the thermal power plant production process.
  • Each module has a clear division of labor, and the expected goals and connections are clear. It can organically coordinate and support the goal of unmanned and minimally staffed operation in the thermal power plant production process.
  • Figure 1 is a functional diagram of the intelligent monitoring module in Embodiment 1;
  • Figure 2 is a functional diagram of the intelligent cruise module in Embodiment 1;
  • FIG. 3 is a functional diagram of the intelligent control module in Embodiment 1;
  • Figure 4 is a schematic diagram of the organic integration of the intelligent monitoring module, intelligent cruise module, and intelligent control in Example 1.
  • Figure 5 is a flowchart of a smart collaborative operation method for a thermal power plant provided in Embodiment 1;
  • Figure 6 is a structural schematic diagram of a smart collaborative operation system for a thermal power plant provided in Embodiment 1;
  • Figure 7 is a structural diagram of the electronic device used in this application.
  • this embodiment provides a method for intelligent collaborative operation of a thermal power plant, including the following steps:
  • Step 1 Establish the necessary functional modules for intelligent operation of a smart thermal power plant (1+6+N), forming three major functional systems: intelligent monitoring, intelligent cruise, and intelligent control.
  • Intelligent monitoring is used to detect potential equipment anomalies or faults in advance and report them through online warnings, improving human-machine interaction efficiency. For diagnosed potential faults, it simulates manual operation for proactive intervention, achieving closed-loop fault self-healing.
  • Intelligent cruise optimizes the optimal operating mode and best operating setpoints for the system, equipment, and parameters, generating optimal start-up, shutdown, parallel shutdown, and rotation times for equipment, and completing equipment self-start-up and shutdown. It also generates optimal operating parameter setpoints and completes real-time closed-loop optimization.
  • Intelligent control improves the control system's adaptability to complex operating conditions through online model identification and adaptive parameter updates. Simultaneously, it integrates the coupling and correlation of production process parameters to intelligently heterogeneously modify the control strategy, improving the control system's adaptability to complex disturbances.
  • the self-encoded fault feature extraction method extracts data features from historical operating data, compares them with fault data features in the fault diagnosis center and the fault feature library, locates the fault, and generates a fault self-healing command. Furthermore, for typical fault types not included in the fault feature library, the fault feature library is updated.
  • the optimal operating mode and optimal operating parameters of the thermal power unit are output, including the optimal mill configuration, optimal circulating water pump configuration, optimal sliding pressure setpoint, and optimal oxygen setpoint.
  • the optimal operating mode and optimal operating parameters of the thermal power unit are output, including the optimal mill configuration, optimal circulating water pump configuration, optimal sliding pressure setpoint, and optimal oxygen setpoint.
  • sequential control and analog control optimization are performed to achieve automatic start-up and shutdown of equipment during cruise operation within a wide load range.
  • a genetic algorithm is used to further optimize and traverse the operable parameters of the thermal power unit.
  • Step 4 As shown in Figure 3, the intelligent control parameters of the 1+6+N smart thermal power plant are adaptively adjusted, and the strategy is intelligently heterogeneous. Under intelligent coordination, an energy balance strategy for the unit is constructed based on the main steam pressure setting, actual main steam pressure, steam temperature before the first reducer, separator outlet pressure, and load command after speed limiting, forming energy commands and heat signals suitable for the once-through boiler. Predictive control is used instead of PID control as the boiler feedback controller, and the load command after speed limiting is used as feedforward to form the boiler main control command.
  • a static feedforward for fuel master control is constructed.
  • a dynamic feedforward for fuel master control is constructed based on the target load, post-speed-limited load command, energy command, heat signal, and variable load rate.
  • a dynamic compensation feedforward for energy storage is constructed by combining the main steam pressure setting, intermediate point temperature setting, and post-speed-limited load command.
  • a unit temperature balance loop is constructed based on the intermediate point temperature setting, actual intermediate point temperature, and post-speed-limited load command, generating temperature commands and signals.
  • a distribution coefficient is used to determine the intensity of superheat regulation using coal or water.
  • Predictive control is used instead of PID control as the water-fuel ratio feedback controller, and combined with the three feedforwards, the fuel master control commands are formed.
  • a static feedforward for feedwater master control is constructed.
  • a dynamic feedforward for feedwater master control is constructed. Predictive control is used instead of PID control as the superheat feedback controller. Combining the two feedforwards, the feedwater master control commands are formed.
  • Step 5 Complete the closed-loop self-healing of the thermal power unit fault according to the fault self-healing command. Simultaneously, adjust the operating parameters and control scheme of the thermal power unit according to the optimal operating mode and optimal operating parameters to achieve intelligent collaborative operation of the thermal power plant.
  • Figure 4 illustrates this using the coal mill's self-start-stop process as an example. Intelligent cruise can determine the optimal timing for starting and stopping the mill based on the unit's load conditions, coal calorific value, and coal mill power consumption. Through sequential control and analog control design, it triggers one-button start-stop programmable control of the coal mill.
  • this embodiment also provides a smart collaborative operation system for thermal power plants, including a smart monitoring module, a smart cruise module, and a smart control module.
  • the smart monitoring module collects real-time operating data of the thermal power units.
  • the real-time operating data is used to obtain monitoring indicators through a fault monitoring model. It determines whether the monitoring indicators exceed preset thresholds. If they exceed the thresholds, an early warning is issued; otherwise, the thermal power units continue to operate normally. Simultaneously, when issuing an early warning, the module extracts features from the real-time operating data and compares them with a fault feature database to obtain fault self-healing instructions.
  • the smart cruise module establishes a smart cruise model and obtains the optimal operating mode and optimal operating parameters of the thermal power units based on the smart cruise model.
  • the smart control module completes the closed-loop self-healing of the thermal power units' faults according to the fault self-healing instructions. At the same time, it adjusts the operating parameters and control scheme of the thermal power units according to the optimal operating mode and optimal operating parameters to complete the smart collaborative operation of the thermal power plant.
  • this embodiment adopts a 1+6+N intelligent monitoring system for thermal power plants, which provides online early warning and closed-loop fault self-healing.
  • a 1+6+N intelligent monitoring system for thermal power plants which provides online early warning and closed-loop fault self-healing.
  • it analyzes and processes data on the production process of thermal power plants, making the quality indicators of systems, equipment, and parameters transparent. It reports these indicators through online early warning, improving the efficiency of human-machine interaction.
  • it actively intervenes in the diagnosis of potential faults by simulating manual operation, achieving closed-loop fault self-healing.
  • the intelligent cruise module sends the optimal operating mode and parameters to the intelligent control module for execution, and feeds back the execution results for further iteration and optimization.
  • the intelligent monitoring module proactively detects system, equipment, and parameter anomalies or faults, and actively adjusts the intelligent cruise mode, intervening in the control loop to achieve fault closed-loop self-healing.
  • the intelligent control module prioritizes executing intelligent monitoring commands, followed by intelligent cruise commands. This effectively solves the problem of insufficient integration and interaction between modules and fully supports the goal of unmanned and minimally staffed operation in thermal power plant production processes.
  • the module division in this embodiment is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used.
  • the functional modules in each embodiment of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module.
  • the integrated modules described above can be implemented in hardware or as software functional modules.
  • this embodiment also provides a computer device, which includes a processor and a memory.
  • the memory is used to store a computer program (in this embodiment, the computer program includes a computing component and an iterative component, capable of model calculation and model updating).
  • the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium.
  • the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • CPU Central Processing Unit
  • DSPs digital signal processors
  • ASICs application-specific integrated circuits
  • FPGAs field-programmable gate arrays
  • the processor described in this embodiment can be used for the operation of a smart collaborative operation method for thermal power plants.
  • This embodiment also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data.
  • the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device.
  • the computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code).
  • the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.
  • the processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the intelligent collaborative operation method for thermal power plants in the above embodiment.
  • step 3 of the intelligent collaborative operation method for thermal power plants fitness calculation is replaced by weighted evaluation.
  • weighted evaluation if the preset number of iterations is met, the optimal operation mode and optimal operation parameters are obtained. If the preset number of iterations is not met, the particle swarm optimization algorithm is used to optimize and traverse the operable parameters to update the intelligent cruise model.
  • step 4 the predictive control is replaced with any one of fuzzy control, decoupling control, or variable structure control.
  • this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and/or one or more block diagrams.
  • These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and/or one or more block diagrams.

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Abstract

本申请公开了一种火电厂智慧协同运行方法及相关装置，属于能源数字化、智能化领域，包括以下步骤：实时运行数据通过故障监测模型得到监测指标，判断所述监测指标是否超过预设阈值，若超过则进行预警；进行预警时提取实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令；根据智能巡航模型得到火电机组的最优运行方式和最优运行参数；根据故障自愈指令完成火电机组故障闭环自愈，同时根据最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。本申请能够解决火电厂采用现有技术运行时各运行模块融合交互性低的问题。

Description

一种火电厂智慧协同运行方法及相关装置
本申请要求在2024年7月1日提交中国专利局、申请号为202410872643.9、发明名称为“一种火电厂智慧协同运行方法及相关装置”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于能源数字化、智能化领域，具体涉及一种火电厂智慧协同运行方法及相关装置。
背景技术
在新型电力系统环境下，火电调峰调频已成为常态，面对节能降耗、环保监测和网调考核等多重压力，存在关键系统、设备和参数人工干预频繁的情况，从而导致劳动强度大。目前尽管国内外科研院所、高等院校及控制装备制造商在检测、控制、诊断、优化等方面开展了大量的研发和应用，并形成了相应的功能模块，但各功能模块相对独立，融合交互性不足，难以支撑火电厂生产过程无人干预、少人值守的目标。
因此深入分析火电厂生产过程各功能模块的预期目标和连接关系，研究一种火电厂智慧运行模块的协同方法具有重要意义。
发明内容
本申请提供一种火电厂智慧协同运行方法及相关装置，以解决火电厂采用现有技术运行时各运行模块融合交互性低的问题。
为达到上述目的，本申请采用以下技术方案：
一种火电厂智慧协同运行方法，包括以下步骤：
采集火电机组的实时运行数据，所述实时运行数据通过故障监测模型得到监测指标，判断所述监测指标是否超过预设阈值，若超过则进行预警，若未超过则火电机组保持正常运行；
进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令；
建立智能巡航模型，根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数；
根据所述故障自愈指令完成火电机组故障闭环自愈，同时根据所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。
在一些实施方式中，所述故障监测模型通过以下步骤得到：
采集火电机组的历史运行数据，根据所述历史运行数据并采用神经网络拟合算法，建立故障监测模型。
所述故障特征库通过以下步骤得到：
根据所述历史运行数据并通过自编码故障特征提取得到火电机组常见故障信息，根据所述火电机组常见故障建立故障特征库。
在一些实施方式中，所述智能巡航模型通过以下步骤建立：
采集火电机组的不可操参数和可操参数，采用历史数据挖掘处理所述不可操参数和可操参数，建立智能巡航模型。
在一些实施方式中，所述根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数的步骤，具体包括：
所述智能巡航模型输出安全稳定性数据、经济环保性数据和灵活机动性数据后，进行加权评估或适应度计算，得到最优运行方式和最优运行参数；
采用加权评估时，若满足预设迭代次数，则得到最优运行方式和最优运行参数，若不满足预设迭代次数，则寻优遍历所述可操参数以更新所述智能巡航模型；
采用适应度计算时，若计算得到的适应度满足预设适应度时，则得到最优运行方式和最优运行参数，若计算得到的适应度不满足预设适应度时，则寻优遍历所述可操参数以更新所述智能巡航模型。
在一些实施方式中，所述寻优遍历采用粒子群算法或遗传算法。
在一些实施方式中，所述进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令的步骤，还包括以下步骤：
当预警时提取的实时运行数据的特征对比后未在所述故障特征库内，将所述实时运行数据的特征更新至所述故障特征库内。
在一些实施方式中，还包括以下步骤：
将所述火电机组的PID控制替换为预测控制、模糊控制、解耦控制或变结构控制，并结合所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案。
一种火电厂智慧协同运行系统，包括：
智能监盘模块，用于采集火电机组的实时运行数据，所述实时运行数据通过故障监测模型得到监测指标，判断所述监测指标是否超过预设阈值，若超过则进行预警，若未超过火电机组保持正常运行；同时用于进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令；
智能巡航模块，用于建立智能巡航模型，根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数；
智能控制模块，用于根据所述故障自愈指令完成火电机组故障闭环自愈，同时根据所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。
一种电子设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器中运行的计算机程序，所述处理器执行所述计算机程序时实现所述一种火电厂智慧协同运行方法的步骤。
一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现所述一种火电厂智慧协同运行方法的步骤。
与现有技术相比，本申请具有以下有益效果：
本申请提供一种火电厂智慧协同运行方法，实时运行数据通过故障监测模型得到监测指标，判断所述监测指标是否超过预设阈值，若超过则进行预警，若未超过则火电机组保持正常运行；进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令，然后建立智能巡航模型，根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数；根据所述故障自愈指令完成火电机组故障闭环自愈，同时根据所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。本申请能够提前发现设备潜在异常和故障，并完成在线预警和闭环故障自愈，在提高人机交互效率的同时，避免故障范围扩大化，降低非计划停机风险，保障火电机组长周期安全、稳定运行，同时能够通过智能巡航模型输出最优运行方式和最优运行参数，进行火电机组的参数自适应调整和策略智能异构，进而有效提升控制系统对复杂工况和复杂扰动的适应能力，保障控制系统的稳定性和收敛性。因此，通过上述内容，本申请能够解决火电厂各运行模块融合交互性低的问题，有机协同并支撑火电厂生产过程无人干预、少人值守的目标。
进一步地，本申请采用智能巡航模型输出安全稳定性数据、经济环保型数据和灵活机动性数据后，进行加权评估或适应度计算，得到最优运行方式和最优运行参数，能够寻优获得火电机组最佳启停、并退和轮换时间，并完成设备自动启停和参数定值自动闭环优化，在提升机组综合性能的同时，有效降低人工干预强度。
进一步地，本申请采用预测控制替换PID控制，能够对控制系统进行优化，提高控制系统对复杂工况的适应能力。
本申请提供一种火电厂智慧协同运行系统，包括智能监盘模块、智能巡航模块和智能控制模块，从火电厂生产过程监视、优化和控制三个维度，对智慧化功能模块进行清晰划分，各模块分工明确，且预期目标和连接关系明确，能够有机协同并支撑火电厂生产过程无人干预、少人值守的目标。
附图说明
图1为实施例一中智能监盘模块的功能示意图；
图2为实施例一中智能巡航模块的功能示意图；
图3为实施例一中智能控制模块的功能示意图；
图4为实施例一中智能监盘模块、智能巡航模块、智能控制“三位一体”有机融合示意图；
图5为实施例一提供的一种火电厂智慧协同运行方法的流程图；
图6为实施例一提供的一种火电厂智慧协同运行系统的结构示意图；
图7为本申请采用的电子设备结构图。
具体实施方式
为了使本领域的技术人员更好地理解本申请方案，下面将结合附图，对本申请的技术方案进行进一步地详细描述，所述内容是对本申请的解释而不是限定。
需要说明的是，本申请的说明书和权利要求书中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包括，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、系统、产品或设备固有的其他步骤或单元。
实施例一
如图5所示，本实施例提供一种火电厂智慧协同运行方法，包括以下步骤：
步骤1：搭建1+6+N的智慧火电厂智慧运行必要功能模块，形成智能监盘、智能巡航和智能控制三大功能体系。智能监盘用于提前发现设备潜在异常或故障，并通过在线预警的方式报出，提高人机交互效率，同时对于诊断出的潜在故障，模拟人工操作进行主动干预，实现闭环故障自愈。智能巡航对系统、设备、参数的最优运行方式和最佳运行定值进行寻优，生成设备最佳启停、并退、轮换时机，并完成设备自启停，同时生成最佳运行参数定值，并完成实时闭环优化。智能控制通过模型在线辨识与参数自适应更新，提高控制系统对复杂工况的适应能力；同时综合生产过程参数的耦合关联性，对控制策略进行智能异构，提高控制系统对复杂扰动的适应能力。
步骤2：如图1所示，1+6+N的智慧火电厂智能监盘在线预警与闭环故障自愈，从火电机组采集海量的历史运行数据，通过自编码故障特征提取方法，提取机组系统、设备、参数的常见故障，形成故障特征库。同时基于海量历史运行数据，采用神经网络拟合算法，建立故障监测模型，通过故障监测模型与实时运行数据的比对，产生监测指标。若监测指标未超过一定阈值，则火电机组运行正常；若监测指标超过一定阈值，则产生预警，同时采用自编码故障特征提取方法，提取历史运行数据的数据特征，在故障诊断中心与故障特征库中的故障数据特征进行比对，定位故障并产生故障自愈指令。同时，对于故障特征库中未包含的典型故障类型，更新故障特征库。
步骤3：如图2所示，1+6+N的智慧火电厂智能巡航设备自启停与定值自动优化，将火电机组的运行参数分为不可操参数和可操参数，不可操参数包括机组负荷、煤质热值、环境温度等，可操参数包括爬坡速率、汽压定值、磨组方式、锅炉氧量等。将不可操参数和可操参数作为输入，基于历史数据挖掘的方式建立智能巡航模型，模型的输出包括机组运行的安全稳定性数据、经济环保性数据和灵活机动性数据。采用适应度计算的方式，评估机组运行的综合性能，若适应度指标满足预设适应度要求，则输出火电机组的最优运行方式和最优运行参数，包括最优磨组合方式、最优循环水泵组合方式、最优滑压定值、最优氧量定值等。对于需要启停、并退、轮换的设备，进行顺控和模拟量控制优化，实现机组在宽负荷范围内巡航运行时的设备自启停。若适应度指标无法满足适应度要求，则采用遗传算法对火电机组的可操参数进一步寻优遍历。
步骤4：如图3所示，1+6+N的智慧火电厂智能控制参数自适应调整与策略智能异构，在智能协调方式下，根据主蒸汽压力设定、实际主蒸汽压力、一减前蒸汽温度、分离器出口压力、限速后负荷指令等构建机组能量平衡策略，形成适应于直流炉的能量指令和热量信号。采用预测控制代替PID控制作为锅炉反馈控制器，同时采用限速后负荷指令作为前馈，形成锅炉主控指令。
在锅炉主控指令的基础上，结合主蒸汽压力设定和实际主蒸汽压力修正，构建燃料主控静态前馈；根据目标负荷、限速后负荷指令、能量指令、热量信号、变负荷速率，构建燃料主控动态前馈；并结合主蒸汽压力设定、中间点温度设定、限速后负荷指令，构建蓄能动态补偿前馈；根据中间点温度设定、实际中间点温度、限速后负荷指令，构建机组温度平衡回路，形成温度指令和温度信号，并采用分配系数的方式决定分别采用煤、水调节过热度的强度。采用预测控制代替PID控制作为水燃比反馈控制器，结合三个前馈，形成燃料主控指令。
在锅炉主控指令的基础上，结合中间点温度设定、实际中间点温度、限速后负荷指令的修正，构建给水主控静态前馈。根据一减前蒸汽温度、限速后负荷指令，构建给水主控动态前馈，采用预测控制代替PID控制作为过热度反馈控制器，结合两个前馈，形成给水主控指令。
步骤5：根据所述故障自愈指令完成火电机组故障闭环自愈，同时根据所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。如图4所示，以磨煤机自启停过程为例进行阐述。智能巡航能够根据机组负荷工况、煤质热值以及磨煤机电耗，决策最佳启停磨的时机，通过顺控和模拟量控制设计，触发一键启停磨煤机程控。同时，综合考虑主蒸汽压力、中间点温度的偏差，寻优给出最佳加、减煤速率，并在适当时机完成并退磨。智能控制根据磨煤机的出力需求、暖磨速率、加煤速率以及磨出口温度控制要求，采用预测控制构建冷热风门、给煤量、旋转分离转速的智能控制系统，提高磨煤机启停过程控制对复杂工况和复杂扰动的适应能力。智能监盘则实时监测磨煤机是否存在断煤、堵磨、自燃、风门卡涩等异常工况，模拟运行人员操作，如启动振打、提高一次风压、降低磨入口风温、快速开关冷热风门等，主动干预磨煤机智能控制系统，实现故障闭环自愈。从而完成火电厂智慧协同运行。
如图6所示，本实施例还提供一种火电厂智慧协同运行系统，包括智能监盘模块、智能巡航模块和智能控制模块，智能监盘模块采集火电机组的实时运行数据，所述实时运行数据通过故障监测模型得到监测指标，判断所述监测指标是否超过预设阈值，若超过则进行预警，若未超过火电机组保持正常运行；同时用于进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令；智能巡航模块建立智能巡航模型，根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数；智能控制模块根据所述故障自愈指令完成火电机组故障闭环自愈，同时根据所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。
因此本实施例采用1+6+N的智慧火电厂智能监盘在线预警与闭环故障自愈，能够通过大数据分析、人工智能的方式，对火电厂生产过程数据分析处理，使得系统、设备、参数的优劣程度指标透明化，通过在线预警的方式报出，提高人机交互效率；同时对于诊断出的潜在故障，模拟人工操作进行主动干预，实现闭环故障自愈。
在1+6+N的智慧火电厂智能巡航设备自启停与定值自动优化中，采用历史数据挖掘的方式，对生产设备的启停、并退、轮换时机进行寻优，并结合顺控和模拟量控制优化，实现机组在宽负荷范围内巡航运行时的设备自启停；同时综合考虑机组运行的安全稳定性、经济环保性、灵活机动性，采用多目标优化的方式，对最佳运行参数定值进行寻优，提高机组在宽负荷范围内巡航运行的综合性能。
在1+6+N的智慧火电厂智能控制参数自适应调整与策略智能异构中，采用预测控制代替传统PID控制算法，对控制系统进行优化，并通过模型在线辨识与参数自适应更新，提高控制系统对复杂工况的适应能力；同时综合生产过程参数的耦合关联性，对控制策略进行智能异构，提高控制系统对复杂扰动的适应能力。
最后采用智能监盘模块、智能巡航模块、智能控制模块“三位一体”有机协同，在智能巡航下发最优运行方式和最优运行参数至智能控制进行执行，并将执行结果反馈回智能巡航进一步迭代优化；智能监盘提前发现系统、设备、参数异常或故障，并主动调整智能巡航模式，干预控制回路，实现故障闭环自愈；智能控制优先执行智能监盘指令，其次执行智能巡航指令。能够充分解决模块间融合交互性不足的问题，并充分支撑火电厂生产过程无人干预、少人值守的目标。
本申请实施例中对模块的划分是示意性的，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，另外，在本申请各个实施例中的各功能模块可以集成在一个处理器中，也可以是单独物理存在，也可以两个或两个以上模块集成在一个模块中。上述集成的模块既可以采用硬件的形式实现，也可以采用软件功能模块的形式实现。
如图7所示，本实施例中还提供了一种计算机设备，该计算机设备包括处理器以及存储器，所述存储器用于存储计算机程序(本实施例中计算机程序包括计算组件和迭代组件，能够进行模型计算和模型更新)，所述计算机程序包括程序指令，所述处理器用于执行所述计算机存储介质存储的程序指令。处理器可能是中央处理单元(CentralProcessing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital SignalProcessor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable GateArray，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等，其是终端的计算核心以及控制核心，其适于实现一条或一条以上指令，具体适于加载并执行计算机存储介质内一条或一条以上指令从而实现相应方法流程或相应功能；本申请实施例所述的处理器可以用于一种火电厂智慧协同运行方法的操作。
本实施例还提供了一种存储介质，具体为计算机可读存储介质(Memory)，所述计算机可读存储介质是计算机设备中的记忆设备，用于存放程序和数据。可以理解的是，此处的计算机可读存储介质既可以包括计算机设备中的内置存储介质，当然也可以包括计算机设备所支持的扩展存储介质。计算机可读存储介质提供存储空间，该存储空间存储了终端的操作系统。并且，在该存储空间中还存放了适于被处理器加载并执行的一条或一条以上的指令，这些指令可以是一个或一个以上的计算机程序(包括程序代码)。需要说明的是，此处的计算机可读存储介质可以是高速RAM存储器，也可以是非不稳定的存储器(non-volatile memory)，例如至少一个磁盘存储器。可由处理器加载并执行计算机可读存储介质中存放的一条或一条以上指令，以实现上述实施例中一种火电厂智慧协同运行方法的相应步骤。
实施例二
与实施例一的不同之处在于，在火电厂智慧协同运行方法的步骤3中，将适应度计算替换为加权评估，采用加权评估时，若满足预设迭代次数，则得到最优运行方式和最优运行参数，若不满足预设迭代次数，则采用粒子群算法寻优遍历所述可操参数以更新所述智能巡航模型。
步骤4中的预测控制替换为模糊控制、解耦控制或变结构控制中的任意一种。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
最后应当说明的是：以上实施例仅用以说明本申请的技术方案而非对其限制，尽管参照上述实施例对本申请进行了详细的说明，所属领域的普通技术人员应当理解：依然可以对本申请的具体实施方式进行修改或者等同替换，而未脱离本申请精神和范围的任何修改或者等同替换，其均应涵盖在本申请的权利要求保护范围之内。

Claims (10)

  1. 一种火电厂智慧协同运行方法，其特征在于，包括以下步骤：
    采集火电机组的实时运行数据，所述实时运行数据通过故障监测模型得到监测指标，判断所述监测指标是否超过预设阈值，若超过则进行预警，若未超过则火电机组保持正常运行；
    进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令；
    建立智能巡航模型，根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数；
    根据所述故障自愈指令完成火电机组故障闭环自愈，同时根据所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。
  2. 根据权利要求1所述的火电厂智慧协同运行方法，其特征在于，所述故障监测模型通过以下步骤得到：
    采集火电机组的历史运行数据，根据所述历史运行数据并采用神经网络拟合算法，建立故障监测模型；
    所述故障特征库通过以下步骤得到：
    根据所述历史运行数据并通过自编码故障特征提取得到火电机组常见故障信息，根据所述火电机组常见故障建立故障特征库。
  3. 根据权利要求1所述的火电厂智慧协同运行方法，其特征在于，所述智能巡航模型通过以下步骤建立：
    采集火电机组的不可操参数和可操参数，采用历史数据挖掘处理所述不可操参数和可操参数，建立智能巡航模型。
  4. 根据权利要求3所述的火电厂智慧协同运行方法，其特征在于，所述根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数的步骤，具体包括：
    所述智能巡航模型输出安全稳定性数据、经济环保性数据和灵活机动性数据后，进行加权评估或适应度计算，得到最优运行方式和最优运行参数；
    采用加权评估时，若满足预设迭代次数，则得到最优运行方式和最优运行参数，若不满足预设迭代次数，则寻优遍历所述可操参数以更新所述智能巡航模型；
    采用适应度计算时，若计算得到的适应度满足预设适应度时，则得到最优运行方式和最优运行参数，若计算得到的适应度不满足预设适应度时，则寻优遍历所述可操参数以更新所述智能巡航模型。
  5. 根据权利要求4所述的火电厂智慧协同运行方法，其特征在于，所述寻优遍历采用粒子群算法或遗传算法。
  6. 根据权利要求1所述的火电厂智慧协同运行方法，其特征在于，所述进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令的步骤，还包括以下步骤：
    当预警时提取的实时运行数据的特征对比后未在所述故障特征库内，将所述实时运行数据的特征更新至所述故障特征库内。
  7. 根据权利要求1所述的火电厂智慧协同运行方法，其特征在于，还包括以下步骤：
    将所述火电机组的PID控制替换为预测控制、模糊控制、解耦控制或变结构控制，并结合所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案。
  8. 一种火电厂智慧协同运行系统，其特征在于，包括：
    智能监盘模块，用于采集火电机组的实时运行数据，所述实时运行数据通过故障监测模型得到监测指标，判断所述监测指标是否超过预设阈值，若超过则进行预警，若未超过火电机组保持正常运行；同时用于进行预警时提取所述实时运行数据的特征，并与故障特征库进行对比，得到故障自愈指令；
    智能巡航模块，用于建立智能巡航模型，根据所述智能巡航模型得到火电机组的最优运行方式和最优运行参数；
    智能控制模块，用于根据所述故障自愈指令完成火电机组故障闭环自愈，同时根据所述最优运行方式和最优运行参数调整火电机组的运行参数和控制方案，完成火电厂智慧协同运行。
  9. 一种电子设备，其特征在于，包括存储器、处理器以及存储在所述存储器中并可在所述处理器中运行的计算机程序，所述处理器执行所述计算机程序时实现权利要求1至7中任意一项所述的火电厂智慧协同运行方法的步骤。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至7中任意一项所述的火电厂智慧协同运行方法的步骤。
PCT/CN2025/099318 2024-07-01 2025-06-05 一种火电厂智慧协同运行方法及相关装置 Pending WO2026007604A1 (zh)

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