CN202075597U - Intelligent optimization control system used in thermal power station - Google Patents

Abstract

The utility model discloses an intelligent optimization control system for a thermal power plant. The system includes: an intelligent control prediction module, which is used to provide the predicted value of the controlled variable in the control subsystem under different working conditions. The control subsystem includes a boiler steam temperature control subsystem, a coordinated control subsystem and a combustion control subsystem; the DCS system is used to provide operating data under different working conditions, and to perform prediction on the predicted value in the intelligent control prediction module Optimal control; a communication module, used to provide two-way data communication between the intelligent control prediction module and the DCS system.

Description

Intelligent optimization control system for thermal power plant

technical field

The utility model relates to the field of thermal power generation, in particular to an intelligent optimization control system for large thermal power plants. the

Background technique

Realizing the optimal control of boilers and steam turbines in large-scale thermal power plants is of great significance to the energy saving and consumption reduction of units and the reduction of pollutant emissions. At present, with the unit unit as the control object, the overall automatic control system of the thermal power plant includes many subsystems such as the steam temperature control system of the boiler, the combustion process control system, and the coordination control system. the

The steam temperature control system of the boiler, referred to as the air temperature control system, is a typical large-inertia, multi-capacity link system, and its control object has a large delay and parameter perturbation characteristics. Therefore, steam temperature control is a recognized problem in the power industry. Traditional PID control algorithm Because the PID controller does not require an accurate mathematical model, and the physical relationship of each control parameter of the PID is clear, it is easy to tune online. At present, the PID control strategy is still dominant. However, when there are external disturbances such as large and frequent changes in the power load command, soot blowing in the furnace, and abnormalities in the unit itself, the traditional control PID strategy is difficult to achieve satisfactory control results, and it is often easy to cause the steam temperature to lose control or even Occurrence of over-temperature, for example, when the power load of the unit changes at a rate of 2% MCR/min, the steam temperature will deviate from the set value by more than 10°C. At this time, the steam temperature can only be controlled by manual operation, and the operation is performed by lowering the set value of the steam temperature, which reduces the economy of the unit and increases the labor intensity of the operators. the

In the operation of large-scale thermal power plants, due to the influence of actual factors such as power load changes and fuel composition fluctuations, such as coal quality, the actual state of the boiler is constantly changing. The current DCS (Distributed Control System, Distributed Control System) combustion control The system often cannot fully control the operating conditions in real time according to the characteristics of boiler combustion. Moreover, the change of the load of the random group and the change of the operating efficiency are also very large, which cannot guarantee that the unit will remain on the best operating curve. As time goes by, the original operating control standard will also change, and the experience of the operating personnel may not be able to adapt to the change of the unit in time. the

At present, many power plant boilers have applied the combustion optimization closed-loop control system, but it is difficult to obtain a long-term stable effect of the system, mainly because the boiler and the unit need to maintain the stability of the main steam temperature and the reheat steam temperature, but the traditional optimization closed-loop control On the contrary, the system makes these control objects more difficult to control; secondly, the traditional combustion optimization closed-loop control technology cannot handle common operations such as coal mill start and stop, soot blowing, load lifting and other processes. Therefore, the system cannot be put into full operation, cannot run stably for a long time, and the economy and safety of the boiler and unit cannot be guaranteed. the

Contents of the invention

Aiming at the deficiencies of the existing control systems, the utility model provides an intelligent optimization control system for thermal power plants, which is used to realize steam temperature optimization control, combustion optimization control, and coordination control optimization. The system itself has good robustness Performance and stability, strong compatibility, easy to implement. the

The utility model provides an intelligent optimization control system for a thermal power plant, and the intelligent optimization control system is realized through the following technical solutions:

An intelligent optimization control system for thermal power plants, including:

The intelligent control prediction module is used to provide the predicted value of the controlled variable in the automatic control system under different working conditions, and the control system includes the boiler steam temperature control subsystem, the coordination control subsystem and the combustion control subsystem;

The DCS system is used to provide operating data under different working conditions, and optimize the control of the predicted value in the intelligent control prediction module;

The communication module is used to provide two-way data communication between the intelligent control prediction module and the DCS system. the

The utility model adopts an intelligent control prediction module and an intelligent optimization control system, uses IHMPC (Intelligent Hybrid Model Predictive Control, Intelligent Hybrid Model Predictive Control) technology to replace the original traditional PID control technology, and divides the entire control object into two categories of work One is a relatively accurate steady-state model under stable conditions, and the other is a variable-condition model for relatively large disturbance conditions, such as the soot blowing process, lifting load, and the start-stop process of the coal mill. This kind of hybrid model predictive control embodies the flexibility of predictive control, and shows certain advantages when dealing with large disturbance conditions, can effectively realize the control requirements of all working conditions, has strong compatibility, and is easy to implement. the

Description of drawings

Below in conjunction with accompanying drawing and embodiment the utility model is further described:

Fig. 1 is the schematic diagram of the intelligent control prediction module of embodiment 1 of the present utility model;

Fig. 2 is a schematic diagram of an intelligent optimization control system in Embodiment 2 of the present utility model. the

Detailed ways

Fig. 1 is a schematic diagram of the intelligent control prediction module of Embodiment 1 of the present utility model. the

An intelligent control prediction module for thermal power plants, including:

The collection sub-module is used to collect operation data and real-time working condition data from the DCS control system;

The steady-state working condition prediction processing sub-module is used to obtain the steady-state predicted value of the controlled variable under the current working state according to the operating data;

Disturbance condition prediction processing sub-module, when the working condition changes, it is used to obtain the dynamic forecast value of the controlled variable under the current working condition;

An optimization submodule, used to optimize the predicted value of the controlled variable to obtain the optimized target value of the controlled variable;

The state switching trigger submodule is used to switch to the disturbance working condition prediction submodule when the working state changes. the

Preferably, the change in working condition includes a change in electrical load, start or stop of a coal mill, or a soot blowing process. the

In Example 1 of the utility model, although the main steam flow rate in the boiler and the desuperheating water volume at each stage do not directly affect the boiler efficiency, they have a great impact on the cycle efficiency, because the increase of the main steam flow rate makes the steam entering the condenser The increase in the amount will increase the loss of the cold source. The increase in the amount of desuperheating water also increases the heat required to heat the water to the rated parameters in the boiler, thereby increasing the heat loss of the unit. the

The dynamic characteristics of the controlled object change significantly with the boiler load (the boiler load refers to the ability to generate steam per unit time, and the boiler load changes with the change of the electric load), and the traditional control system cannot guarantee uniformity within the entire boiler load range can be effectively controlled. the

The steady-state working condition prediction processing sub-module in the intelligent control prediction module observes the temperature before and after each section of steam, the combustion status of the boiler, predicts and controls the water injection valve and the flue gas baffle, realizes precise temperature control, and always maintains the optimal steam temperature control. Through IHMPC technology, using the disturbance working condition prediction and processing sub-module, it can deal with the non-linearity, hysteresis and time-varying nature of the boiler steam temperature object, and at the same time solve the problems such as large and frequent changes in power load instructions, soot blowing in the furnace or abnormalities in the unit itself, etc. The problem of steam temperature control under the condition of large external disturbance. the

In addition, in the heat loss of boilers, first, exhaust smoke loss is the largest item, generally accounting for 5-7%; second, incomplete combustion loss accounts for 1-2%; and chemical incomplete combustion loss, Heat loss and ash heat loss account for only a small percentage. Therefore, the heat loss of the boiler is usually reduced mainly by optimizing the control of the exhaust gas volume (the oxygen content of the exhaust gas is used to mark the size) or the exhaust gas temperature, and the content of fly ash combustibles. the

At this time, it is necessary to control the blower circuit. The task of the blower circuit is to control the opening of the moving blade of the blower to change the size of the secondary air volume, so as to ensure the total air volume and oxygen volume required by the boiler. The overburned air baffle OFA and auxiliary air baffle are mainly to meet the requirements of low NOX combustion and optimized air distribution. Currently, the control of OFA baffles mainly considers the power load, but does not take into account changes in coal quality and operating conditions during boiler operation. As these operating conditions change, combustion emissions, steam temperature, and other related variables will also vary. characteristics. Therefore, the dynamic control of OFA baffles will bring additional benefits to the boiler. the

The steady-state working condition prediction processing sub-module in the intelligent control prediction module monitors the main heat loss of the boiler in real time, such as exhaust heat loss, mechanical incomplete combustion loss, etc., and pays attention to the emission of nitrogen oxides. When the machine is started to grind coal, etc., the disturbance working condition prediction and processing sub-module controls the air volume bias at the outlet of the blower and the boiler coal blending and air blending to make the boiler operate in an economical hypoxic state and the lowest nitrogen oxides according to the relevant parameter changes. emission status. the

In the coordinated control mode, the boiler controls the main steam pressure, and the unit controls the electrical load. The feed-forward loop of the power load command is used as the coarse adjustment part of the main control of the boiler, and the coal quantity is adjusted through the function of the sub-module of the prediction and processing of the disturbance working condition, and the command of the main steam pressure deviation after the action of the sub-module of the prediction and processing of the steady-state working condition play a fine-tuning role. In this way, the intelligent control prediction module can carry out model predictive control according to the relevant disturbance quantity and feedforward quantity, so as to achieve the purpose of stable control. the

The intelligent optimization control system for a thermal power plant provided by Embodiment 2 of the utility model includes:

The intelligent control prediction module is used to provide the predicted value of the controlled variable in the automatic control system under different working conditions, and the control system includes the boiler steam temperature control subsystem, the coordination control subsystem and the combustion control subsystem;

The DCS system is used to provide operating data under different working conditions, and optimize the control of the predicted value in the intelligent control prediction module;

The communication module is used to provide two-way data communication between the intelligent control prediction module and the DCS system. the

Wherein, the intelligent control prediction module includes:

The collection sub-module is used to collect operation data from the DCS control system;

The steady-state working condition prediction processing sub-module is used to obtain the steady-state predicted value of the controlled variable under the current working state according to the operating data;

Disturbance condition prediction processing sub-module, when the working condition changes, it is used to obtain the dynamic forecast value of the controlled variable under the current working condition;

An optimization submodule, used to optimize the predicted value of the controlled variable to obtain the optimized target value of the controlled variable;

The state switching trigger submodule is used to switch to the disturbance working condition prediction submodule when the working state changes. the

The internal calculation process of the intelligent optimization control system: the operation data is collected from the real-time database of the DCS system, and the data first enters the intelligent control prediction module, and after multi-model prediction processing in advance, a dynamic prediction value and a steady state prediction value are formed. Among them, the dynamic prediction value is transferred to the disturbance working condition prediction processing sub-module, and the steady-state prediction value is optimized by the DCS system to obtain the steady-state target value of the controlled variable, and then the value of the controlled variable is output through the disturbance working condition prediction processing sub-module To the DCS system for optimal control. the

In terms of specific implementation, we can apply the multi-model hybrid predictive control method adopted by the above-mentioned intelligent control prediction module to the following functional control subsystems. For example, the combustion control subsystem is used to dynamically monitor the real-time operating parameters of the boiler, and control the total air volume and oxygen volume of the blower circuit. The real-time operating parameters include coal quality parameters, exhaust gas volume, exhaust gas temperature, and fly ash combustibles content and nitrogen content and steam temperature; the steam temperature control subsystem is used to monitor the steam temperature data and working condition change data of the boiler in real time, and obtain the feedforward of the electric load according to the working condition change data, such as the electric load change The feedforward quantity of the loop, and the nonlinear change quantity of the steam temperature, the said nonlinear change quantity is a disturbance quantity; the coordinated control subsystem is used to perform predictive control according to the feedforward quantity and the disturbance quantity. the

The utility model adopts the intelligent optimization control system of the intelligent control prediction module, uses IHMPC (Intelligent Hybrid Model Predictive Control, Intelligent Hybrid Model Predictive Control) technology to replace the original traditional PID control technology, and divides the entire control object into two categories of work Space, one is a relatively accurate steady-state model under stable conditions, and the other is a variable-condition model for relatively large disturbance conditions, such as the soot blowing process, lifting and lowering loads, and the start-stop process of coal mills. This kind of hybrid model predictive control embodies the flexibility of predictive control, and shows certain advantages when dealing with large disturbance conditions, can effectively realize the control requirements of all working conditions, has strong compatibility, and is easy to implement. the

Those skilled in the art should realize that the above-mentioned specific embodiments are only exemplary, and are intended to enable those skilled in the art to better understand the content of this patent, and should not be construed as limiting the scope of protection of this patent. Any equivalent changes or modifications made to the spirit disclosed in the patent fall within the protection scope of this patent. the

Claims (1)

1. an intelligent optimizing control system that is used for the fuel-burning power plant is characterized in that, described system comprises:

The Based Intelligent Control prediction module is used to provide the predicted value of the controlled variable in the control subsystem under the different work condition states, and described control subsystem comprises the boiler steam temperature control subsystem, coordinates control subsystem and burning control subsystem;

The DCS system is used to provide the service data under the different work condition states, and the predicted value in the described Based Intelligent Control prediction module is optimized control;

Communication module is used to provide the two-way data communication between described Based Intelligent Control prediction module and the described DCS system.

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