CN110935567A - Thermal power generating unit dry-type electric precipitator optimization control method and system - Google Patents
Thermal power generating unit dry-type electric precipitator optimization control method and system Download PDFInfo
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Abstract
The invention provides an optimal control method and system for a dry-type electric precipitator of a thermal power generating unit, which comprises the following steps: s1, obtaining a dynamic characteristic model of the dry-type electric precipitator through a disturbance test of the dry-type electric precipitator, and identifying model parameters by adopting an immune genetic algorithm; s2, respectively reading the operation parameters of the unit and the operation parameters of the dry-type electric dust collector; and S3, automatically adjusting secondary current and/or secondary voltage by adopting a grading control method according to the smoke dust concentration at the electric precipitation outlet in the unit operation parameters and the dry-type electric precipitator operation parameters. The invention simultaneously takes the unit operation data and the dry-type electric dust removal high-frequency power supply operation data as input parameters, thereby reducing the dust removal power consumption of the dry-type electric dust remover of the thermal power unit.
Description
Technical Field
The invention belongs to the technical field of thermal power unit dust removal environmental protection control, and particularly relates to a thermal power unit dry-type electric precipitator optimization control method and system.
Background
Thermal power generation is a main power generation mode in China, and the annual smoke emission of coal-fired power stations is the first of all industries and is a main source of smoke pollutants in the atmospheric environment in China. The electrostatic dust collection is an important technology for effectively restraining smoke dust pollution of a thermal power plant, the emission concentration of an electric dust collector is lower and lower along with the continuous improvement of the national environmental protection standard, in order to ensure the dust collection effect during operation, the thermal power plant is forced to improve the electric dust collection output, so that the station power of the electric dust collector is improved, the power consumption of the electric dust collector generally occupies 3 per mill to 5 per mill of the station power of the electric power plant, and therefore how to realize low-power-consumption dust collection has led to the high attention of the thermal power plant.
In order to improve the dust removal effect and save energy, the domestic electric dust removal power supply is generally replaced by a high-frequency power supply, so that the flexibility of power supply control is improved. In order to further reduce the power consumption of the electric dust collector, a great deal of research work is done in scientific research institutions and power plants, such as: step-down rapping, ash bucket/porcelain bushing steam heating, optimized control and the like. Although the idea of energy-saving optimization and control of the outlet concentration of the electric dust collector is already proposed, the concentration feedback closed-loop control of the electric dust collector of the thermal power plant is not put into use at present. The dust removal efficiency of electric dust removal is influenced by many factors, such as flue gas property, flue gas quantity, flue gas concentration, power supply voltage and current, cathode and anode rapping dust removal effect, air flow distribution, air leakage state and the like, mechanism modeling is difficult to accurately establish a mathematical model of electric dust removal, an inlet of the electric dust remover is not provided with a dust concentration measuring point, concentration measuring points are not arranged among electric fields, and an advanced control method is difficult to apply. At present, an open-loop control method is generally adopted for energy conservation optimization of an electric dust collector of a thermal power plant, namely, the set value of the operating parameter of a high-frequency power supply is adjusted in a segmented mode according to unit load, and deviation-free control of the set value of outlet flue gas concentration is not achieved.
Disclosure of Invention
The invention aims to solve the problems and provides an optimal control method for a dry-type electric precipitator of a thermal power generating unit;
the invention aims to solve the problems and provides an optimal control system for a dry-type electric precipitator of a thermal power generating unit.
In order to achieve the purpose, the invention adopts the following technical scheme:
a thermal power generating unit dry-type electric dust remover optimization control method comprises the following steps:
s1, obtaining a dynamic characteristic model of the dry-type electric precipitator through a disturbance test of the dry-type electric precipitator, and identifying model parameters by adopting an immune genetic algorithm;
s2, respectively reading the operation parameters of the unit and the operation parameters of the dry-type electric dust collector;
and S3, automatically adjusting secondary current and/or secondary voltage by adopting a grading control method according to the smoke dust concentration at the electric precipitation outlet in the unit operation parameters and the dry-type electric precipitator operation parameters.
In the optimal control method for the dry-type electric precipitator of the thermal power generating unit, in step S2, the operation parameters of the unit include a unit load, and the operation parameters of the dry-type electric precipitator include a secondary current and a secondary voltage of the dry-type electric precipitator and a dust concentration at an outlet of the electric precipitator.
In the above thermal power generating unit dry-type electric precipitator optimization control method, step S3 specifically includes:
A. the pre-stage electric field of the dry electric dust collector automatically adjusts the secondary current and/or the secondary voltage according to the load of the unit;
B. the final electric field of the dry electric dust remover adopts a PID control method, and the secondary current and/or the secondary voltage are/is automatically adjusted according to the concentration of the smoke dust at the outlet of the electric dust remover.
In the optimal control method for the dry-type electric precipitator of the thermal power generating unit, in step S1, the specific method for obtaining the dynamic characteristic model of the dry-type electric precipitator through a disturbance test of the dry-type electric precipitator includes:
a transfer function model of secondary current of each stage of electric field high-frequency power supply of the dry electric dust remover to the concentration of smoke dust at an electric dust removal outlet is established by adopting a test modeling method, and the model has the following formula:
in the formula (1), the reaction mixture is,
Gd,i(s) is the concentration of the smoke dust at the outlet of the electric precipitation;
Ii(s) is the average value of the secondary currents of all the electric chambers of the ith-stage electric field of the dry electric dust collector;
k, T1, T2 and tau are model parameters of the transfer function model, and the model parameters are subjected to parameter identification by an immune genetic algorithm.
In the above thermal power generating unit dry-type electric precipitator optimization control method, in step S1, the method for performing parameter identification on the model parameters by using the immune genetic algorithm includes:
s11, establishing static models and dynamic models of all levels of electric fields of the dry-type electric dust collector under different load points of the unit;
s12, performing parameter identification on K, T1, T2 and tau model parameters in the transfer function model by adopting an immune genetic algorithm.
In the thermal power generating unit dry-type electric precipitator optimization control method, the step of identifying the parameters of the model comprises the following steps:
1) determining structural parameters of the immune genetic algorithm;
2) antigen recognition, namely selecting a transfer function model formula (1) as an antigen, wherein model parameters are optimization variables;
3) generating an initial antibody population, taking the combination of model parameters as an antibody, randomly generating a plurality of combinations according to the actual interval of the transfer function model solution to form the initial antibody population and initial memory cells, and updating the initial antibody population and the initial memory cells;
4) calculating affinity, calculating antigen affinity of each antibody and affinity between each antibody and other antibodies;
5) refreshing the memory cells, and adding the antibody with high antigen affinity to the memory cells;
6) promotion and inhibition of antibody, calculation of antibody concentration CiAnd antibody selection in combination with antigen affinity:
in the formula (3), (A)g)iFor antigen affinity, λ and μ are weighting coefficients, and in addition, the antibody concentration CiObtained by the following formula;
in the formula (4), CiIs the antibody concentration, theta is the affinity constant, and N is the number of antibodies;
(7) generating new antibodies, and performing cross and variation operations on the selected antibodies in the antibody population to obtain a new antibody population;
(8) judging a termination condition, adopting the maximum evolution algebra as the termination condition, and outputting the optimal antibody of the last generation.
In the optimal control method for the dry-type electric precipitator of the thermal power generating unit, the structural parameters of the immune genetic algorithm comprise evolution algebra, antibody population number, memory cell number, cross frequency and variation frequency;
the value interval of each model parameter is as follows:
τ∈[15,40],K∈[-0.5,0.5],T1∈[0,100],T2∈[0,100]
in the optimal control method for the dry-type electric precipitator of the thermal power generating unit, the affinity of the antigen and the antibody is obtained in the following way:
in the formula (2), CdIs the actual smoke concentration, delta C, of the electric precipitation outletdThe difference value of the actual smoke concentration of the electric precipitation outlet and the calculated value of the transfer function model is obtained.
In the optimal control method for the dry-type electric precipitator of the thermal power generating unit, in step S2, the operation parameters of the unit are obtained from the master control DCS system of the unit, and the operation parameters of the dry-type electric precipitator are obtained from the high-frequency power supply system of the dry-type electric precipitator.
An optimal control system of a dry-type electric precipitator of a thermal power generating unit comprises a first data acquisition module, a second data acquisition module, a preceding-stage electric field optimal control module and a final-stage electric field optimal control module, wherein,
the first data acquisition module is used for acquiring a dynamic characteristic model of the dry-type electric dust collector through a disturbance test of the dry-type electric dust collector and identifying model parameters;
the second data acquisition module is used for reading the operation parameters of the unit and the operation parameters of the dry-type electric dust collector;
the pre-stage electric field of the dry electric dust collector automatically adjusts secondary current and/or secondary voltage according to the load of the unit;
and the final electric field of the dry-type electric dust remover adopts a PID control method, and the secondary current and/or the secondary voltage are/is automatically adjusted according to the concentration of the smoke dust at the outlet of the electric dust remover.
The invention has the advantages that: the dust removal power consumption of the dry-type electric dust remover of the thermal power generating unit is reduced; the dynamic characteristic model of the dry-type electric dust collector is obtained based on a disturbance test, and on the basis, the dry-type electric dust collector optimization control method based on hierarchical control is provided, so that the problem of high power consumption of the dry-type electric dust collector under the influence of disturbance factors such as large-range variable load of a unit, coal quality fluctuation and electrode vibration in the prior art is solved; parameters required in modeling and control algorithms can be directly read from a main control DCS system or a power plant SIS system, expensive auxiliary equipment such as analysis or measuring instruments and the like is not required to be additionally arranged on site, and the cost is low.
Drawings
FIG. 1 is a schematic flow chart of an optimization control method for a dry-type electric precipitator of a thermal power generating unit according to the invention;
FIG. 2 is a block diagram of a modular architecture of an optimal control system diagram of a dry-type electric precipitator of a thermal power generating unit according to the present invention;
fig. 3 is a structural block diagram of the optimal control system of the dry-type electric precipitator of the thermal power generating unit.
Reference numerals: an operator station 1; engineer station 2; an application server 3; a programmable logic controller 4; a redundant network 5; a serial port 6; a high-frequency power supply of the dry-type electric dust remover and a control system 7 thereof; a first data acquisition module 11; a second data acquisition module 21; a preceding stage electric field optimization control module 31; and a final electric field optimization control module 41.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The embodiment discloses an optimal control method and system for a dry-type electric precipitator of a thermal power unit, wherein the concentration of smoke dust at an outlet of the dry-type electric precipitator is controlled by adjusting the secondary current of a high-frequency power supply of the dry-type electric precipitator, so that the technical effect of reducing the energy consumption of electric precipitation is achieved.
Specifically, as shown in fig. 1, the method flow includes the following steps:
s1, obtaining a dynamic characteristic model of the dry-type electric precipitator through a disturbance test of the dry-type electric precipitator, and identifying model parameters by adopting an immune genetic algorithm;
s2, respectively reading the operation parameters of the unit and the operation parameters of the dry-type electric dust collector;
the operation parameters of the unit comprise unit load and the like, and the operation parameters of the dry-type electric dust remover comprise secondary current, secondary voltage, electric dust removal outlet smoke concentration, electric dust removal inlet smoke concentration and the like of the dry-type electric dust remover. The acquisition mode can be that the operation parameters of the dry-type electric dust remover are directly read from the main control DCS system, the power plant SIS system and the high-frequency power supply system of the dry-type electric dust remover, expensive auxiliary equipment such as analysis or measuring instruments and the like are not required to be additionally arranged on the site, and the cost is low.
And S3, automatically adjusting secondary current and/or secondary voltage by adopting a grading control method according to the smoke dust concentration at the electric precipitation outlet in the unit operation parameters and the dry-type electric precipitator operation parameters.
Step S3 specifically includes:
A. the pre-stage electric field of the dry electric dust collector automatically adjusts the secondary current or the secondary voltage according to the load of the unit;
B. the final electric field of the dry electric dust remover adopts a PID control method, and the secondary current or the secondary voltage is automatically adjusted according to the concentration of the smoke dust at the outlet of the electric dust remover.
Specifically, in step S1, the specific method for obtaining the dynamic characteristic model of the dry electric precipitator through the perturbation test thereof includes:
a transfer function model of secondary current of each stage of electric field high-frequency power supply of the dry electric dust remover to the concentration of smoke dust at an electric dust removal outlet is established by adopting a test modeling method, and the model has the following formula:
in the formula (1), the reaction mixture is,
Gd,i(s) is the concentration of the smoke dust at the outlet of the electric precipitation;
Ii(s) is the average value of the secondary currents of all the electric chambers of the ith-stage electric field of the dry electric dust collector;
k, T1, T2 and tau are model parameters of the transfer function model, and the model parameters are subjected to parameter identification by an immune genetic algorithm.
Further, identifying the model parameters in the dynamic characteristic model by adopting an immune genetic algorithm specifically comprises the following steps:
(1) determining structural parameters of the algorithm, such as an evolution algebra G being 500, an antibody population N being 100, a memory cell number Nm being 5, a cross frequency Pc being 0.8, a variation frequency being 0.01 and the like;
(2) antigen identification, selecting target function of problem to be solved as antigen, and taking structure of transfer function model to be identified
In the formula (1), Gd,i(s) is the concentration of dust at the outlet of the electric precipitation (mg/Nm3), Ii(s) is the average value (mA) of secondary currents of all electric chambers of the ith-stage electric field of the dry electric dust collector, and the optimization variables are K, T1, T2 and tau;
(3) generating an initial antibody population, taking the combination of model parameters as an antibody, randomly generating a certain number of combinations according to the actual interval of the transfer function model solution to form the initial antibody population and the initial memory cells, updating the initial antibody population and the initial memory cells, and setting the value interval of the model parameters as follows:
τ∈[15,40],K∈[-0.5,0.5],T1∈[0,100],T2∈[0,100]
(4) calculate affinity, calculate antigen affinity of each antibody (A)g)iAnd affinity to other antibodies. The affinity of an antigen to an antibody may be equated with a fitness function. The goal of parameter identification is to find the appropriate parameters K, T1, T2, τ to minimize the error between the model output value and the actual value, so defining the fitness function:
in the formula (2), CdIs the actual smoke concentration, delta C, of the electric precipitation outletdThe difference value of the actual smoke concentration of the electric precipitation outlet and the calculated value of the model is obtained;
(5) renewing the memory cells, the initial population is designated (A)g)iDescending order, adding the antibody with high antigen affinity to the memory cell;
(6) promotion and inhibition of antibody, calculation of antibody concentration CiAnd in combination with (A)g)iAntibody selection was performed. The criteria for selecting antibodies consist of two parts, antibody affinity and concentration inhibitor:
in the formula, CiIs the antibody concentration, and λ and μ are weighting coefficients, where λ is 0.7 and μ is 1.25
Antibody concentration CiIs defined as:
in the formula, theta is an affinity constant, and is 0.9; and N is the number of antibodies.
(7) Generating new antibodies, and carrying out cross and variation operations on the selected antibodies in the antibody population to obtain a new population;
(8) judging a termination condition, adopting a maximum evolution generation 500 as the termination condition, and outputting a final generation optimal antibody.
As shown in fig. 2, the optimization control system for the dry-type electric precipitator of the thermal power generating unit in this embodiment includes a first data acquisition module 11, a second data acquisition module 21, a preceding-stage electric field optimization control module 31, and a final-stage electric field optimization control module 41, wherein,
the first data acquisition module 11 is used for acquiring a dynamic characteristic model of the dry-type electric dust collector through a disturbance test of the dry-type electric dust collector and identifying model parameters;
the second data acquisition module 21 is used for reading the operation parameters of the unit and the operation parameters of the dry-type electric dust collector; specifically, the data is acquired from a DCS or SIS system through communication modes such as OPC, Modbus and the like. The acquired data is subjected to filtering processing and then is used by a preceding-stage electric field optimization control module and a final-stage electric field optimization control module.
The pre-stage electric field optimization control module 31 is used for automatically adjusting the secondary current and/or the secondary voltage of the pre-stage electric field of the dry electric dust collector according to the load of the unit;
and the final electric field optimization control module 41 adopts a PID control method for the final electric field of the dry electric dust remover, and automatically adjusts the secondary current and/or the secondary voltage according to the concentration of the smoke dust at the outlet of the electric dust remover.
Specifically, as shown in fig. 3, the system of the present embodiment mainly adopts a hardware structure including an operator station 1, an engineer station 2, a programmable logic controller 4, an application server 3, and a dry type dust collector high-frequency power supply and a control system 7 thereof. The programmable logic controller 4 comprises the first data acquisition module 11 and the second data acquisition module 21, and the application server 3 comprises the preceding electric field optimization control module 31 and the final electric field optimization control module 41. Specifically, the programmable logic controller 4 is connected to the unit main control DCS system and the application server 3 so that the application server 3 obtains unit operation data from the unit main control DCS system through the programmable logic controller 4, and meanwhile, the programmable logic controller 4 is connected to the dry type dust collector high-frequency power supply and the control system 7 thereof so that the application server 3 obtains dry type electric dust collector high-frequency power supply operation data from the dry type dust collector high-frequency power supply and the control system 7 thereof through the programmable logic controller 4 and sends a control instruction to the dry type dust collector high-frequency power supply and the control system 7 thereof. Therefore, the application server 3 can simultaneously acquire the unit operation data and the dry-type electric precipitation high-frequency power supply operation data so as to improve the automation control effect and improve the economic benefit of the unit.
Specifically, the operator station 1, the engineer station 2, the application server 3, and the programmable controller 4 are all connected to the redundant network 5, and the operator station 1, the engineer station 2, the application server 3, and the programmable controller 4 are all connected to each other through the redundant network 5 to perform information interaction. And the redundant network 5 here is preferably an ethernet network.
Furthermore, the dry dust collector high-frequency power supply and the control system 7 thereof are connected with the programmable logic controller 4 through the serial port 6 and the hard connecting wire of the serial port 6 in a communication way.
Similarly, the programmable logic controller 4 and the unit main control DCS system are connected by a serial port 6.
Specifically, the dry-type dust collector high-frequency power supply and the control system 7 thereof comprise a dry-type electric dust collector high-frequency power supply control system and a dry-type electric dust collector high-frequency power supply which are connected with each other, and the programmable logic controller 4 is connected to the dry-type electric dust collector high-frequency power supply control system. The programmable logic controller 4 respectively obtains the unit operation data and the dry-type electric dust collector high-frequency power supply operation data from the unit main control DCS system and the dry-type electric dust collector high-frequency power supply system, and transmits the data to the application server 3 through the redundant network 5, the application server 3 can automatically control the dry-type electric dust collector high-frequency power supply according to the unit operation data and the dry-type electric dust collector high-frequency power supply operation data simultaneously so as to realize the automatic control of the dry-type electric dust collector under the influences of disturbance factors such as large-range variable load, coal quality fluctuation, electrode vibration, boiler soot blowing and the like, and achieve the effects.
The optimal control system of the dry-type electric precipitator of the thermal power generating unit is simple in structure, strong in expandability and convenient to maintain and upgrade; meanwhile, the unit main control DCS system is connected through the serial port, and the safety of the control system is improved.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made or substituted in a similar manner to the specific embodiments described herein by those skilled in the art without departing from the spirit or exceeding the scope of the invention as defined in the appended claims, the invention being expressed as directly or indirectly connected.
Although the operator station 1 is used more herein; engineer station 2; an application server 3; a programmable logic controller 4; a redundant network 5; a serial port 6; a high-frequency power supply of the dry-type electric dust remover and a control system 7 thereof; a first data acquisition module 11; a second data acquisition module 21; a preceding stage electric field optimization control module 31; final electric field optimization control block 41, etc., but does not exclude the possibility of using other terms. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Claims (10)
1. A dry-type electric precipitator optimization control method of a thermal power generating unit is characterized by comprising the following steps:
s1, obtaining a dynamic characteristic model of the dry-type electric precipitator through a disturbance test of the dry-type electric precipitator, and identifying model parameters by adopting an immune genetic algorithm;
s2, respectively reading the operation parameters of the unit and the operation parameters of the dry-type electric dust collector;
and S3, automatically adjusting secondary current and/or secondary voltage by adopting a grading control method according to the smoke dust concentration at the electric precipitation outlet in the unit operation parameters and the dry-type electric precipitator operation parameters.
2. The thermal power generating unit dry-type electric precipitator optimization control method as claimed in claim 1, wherein in step S2, the operation parameters of the unit include unit load, and the operation parameters of the dry-type electric precipitator include secondary current, secondary voltage and concentration of dust at an outlet of the electric precipitator.
3. The thermal power generating unit dry-type electric precipitator optimization control method according to claim 2, wherein step S3 specifically includes:
A. the pre-stage electric field of the dry electric dust collector automatically adjusts the secondary current and/or the secondary voltage according to the load of the unit;
B. the final electric field of the dry electric dust remover adopts a PID control method, and the secondary current and/or the secondary voltage are/is automatically adjusted according to the concentration of the smoke dust at the outlet of the electric dust remover.
4. The thermal power generating unit dry-type electric precipitator optimization control method as claimed in claim 3, wherein in step S1, the specific method for obtaining the dynamic characteristic model of the dry-type electric precipitator through the disturbance test of the dry-type electric precipitator comprises the following steps:
a transfer function model of secondary current of each stage of electric field high-frequency power supply of the dry electric dust remover to the concentration of smoke dust at an electric dust removal outlet is established by adopting a test modeling method, and the model has the following formula:
in the formula (1), the reaction mixture is,
Gd,i(s) is the concentration of the smoke dust at the outlet of the electric precipitation;
Ii(s) is the average value of the secondary currents of all the electric chambers of the ith-stage electric field of the dry electric dust collector;
k, T1, T2 and tau are model parameters of the transfer function model, and the model parameters are subjected to parameter identification by an immune genetic algorithm.
5. The thermal power generating unit dry-type electric precipitator optimization control method as claimed in claim 4, wherein in step S1, the method for performing parameter identification on the model parameters by the immune genetic algorithm comprises:
s11, establishing static models and dynamic models of all levels of electric fields of the dry-type electric dust collector under different load points of the unit;
s12, performing parameter identification on K, T1, T2 and tau model parameters in the transfer function model by adopting an immune genetic algorithm.
6. The thermal power generating unit dry-type electric precipitator optimization control method as claimed in claim 5, wherein the step of performing parameter identification on the model parameters comprises:
1) determining structural parameters of the immune genetic algorithm;
2) antigen recognition, namely selecting a transfer function model formula (1) as an antigen, wherein model parameters are optimization variables;
3) generating an initial antibody population, taking the combination of model parameters as an antibody, randomly generating a plurality of combinations according to the actual interval of the transfer function model solution to form the initial antibody population and initial memory cells, and updating the initial antibody population and the initial memory cells;
4) calculating affinity, calculating antigen affinity of each antibody and affinity between each antibody and other antibodies;
5) refreshing the memory cells, and adding the antibody with high antigen affinity to the memory cells;
6) promotion and inhibition of antibody, calculation of antibody concentration CiAnd antibody selection in combination with antigen affinity:
in the formula (3), (A)g)iFor antigen affinity, λ and μ are weighting coefficients;
in the formula (3), CiIs the antibody concentration, theta is the affinity constant, and N is the number of antibodies;
(7) generating new antibodies, and performing cross and variation operations on the selected antibodies in the antibody population to obtain a new antibody population;
(8) judging a termination condition, adopting the maximum evolution algebra as the termination condition, and outputting the optimal antibody of the last generation.
7. The optimal control method for the dry-type electric precipitator of the thermal power generating unit as claimed in claim 6, wherein the structural parameters of the immune genetic algorithm comprise evolution algebra, antibody population number, memory cell number, cross frequency and variation frequency;
the value interval of each model parameter is as follows:
τ∈[15,40],K∈[-0.5,0.5],T1∈[0,100],T2∈[0,100]
8. the optimal control method for the dry-type electric precipitator of the thermal power generating unit as claimed in claim 7, wherein the affinity between the antigen and the antibody is obtained by the following method:
in the formula (2), CdIs the actual smoke concentration, delta C, of the electric precipitation outletdThe difference value of the actual smoke concentration of the electric precipitation outlet and the calculated value of the transfer function model is obtained.
9. The thermal power generating unit dry-type electric precipitator optimization control method as claimed in claim 7, wherein in step S2, the unit operation parameters are obtained from a unit main control DCS system, and the operation parameters of the dry-type electric precipitator are obtained from a dry-type electric precipitator high-frequency power supply system.
10. An optimal control system of a dry-type electric precipitator of a thermal power generating unit is characterized by comprising a first data acquisition module, a second data acquisition module, a preceding-stage electric field optimal control module and a final-stage electric field optimal control module, wherein,
the first data acquisition module is used for acquiring a dynamic characteristic model of the dry-type electric dust collector through a disturbance test of the dry-type electric dust collector and identifying model parameters;
the second data acquisition module is used for reading the operation parameters of the unit and the operation parameters of the dry-type electric dust collector;
the pre-stage electric field of the dry electric dust collector automatically adjusts secondary current and/or secondary voltage according to the load of the unit;
and the final electric field of the dry-type electric dust remover adopts a PID control method, and the secondary current and/or the secondary voltage are/is automatically adjusted according to the concentration of the smoke dust at the outlet of the electric dust remover.
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| CN112742603A (en) * | 2020-12-09 | 2021-05-04 | 东南大学 | Automatic control method for wet-type electric precipitator of thermal power generating unit |
| CN112934467A (en) * | 2021-01-27 | 2021-06-11 | 华能国际电力股份有限公司营口电厂 | Production load-based intelligent control method for output power of electric precipitation rectifier transformer |
| CN112973965A (en) * | 2021-02-07 | 2021-06-18 | 宁夏枣泉发电有限责任公司 | Electric precipitation outlet smoke dust concentration closed-loop control method realized in DCS |
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2019
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| CN112742603A (en) * | 2020-12-09 | 2021-05-04 | 东南大学 | Automatic control method for wet-type electric precipitator of thermal power generating unit |
| CN112742603B (en) * | 2020-12-09 | 2022-11-29 | 东南大学 | Automatic control method for wet-type electric precipitator of thermal power generating unit |
| CN112934467A (en) * | 2021-01-27 | 2021-06-11 | 华能国际电力股份有限公司营口电厂 | Production load-based intelligent control method for output power of electric precipitation rectifier transformer |
| CN112973965A (en) * | 2021-02-07 | 2021-06-18 | 宁夏枣泉发电有限责任公司 | Electric precipitation outlet smoke dust concentration closed-loop control method realized in DCS |
| CN114453136A (en) * | 2022-02-10 | 2022-05-10 | 南方电网电力科技股份有限公司 | Adaptive control system of electrostatic dust collector and control method thereof |
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