A method and system for optimizing control of dry electrostatic precipitator of thermal power unit

Abstract

The present invention provides a method and system for optimizing control of dry electrostatic precipitator of thermal power generation unit, comprising the following steps: S1. Obtaining its dynamic characteristic model through disturbance test of dry electrostatic precipitator, and identifying the model parameters by using immune genetic algorithm; S2. Reading the operation parameters of the unit and the operation parameters of dry electrostatic precipitator respectively; S3. Using hierarchical control method to automatically adjust the secondary current and / or secondary voltage according to the dust concentration at the outlet of electrostatic precipitator in the operation parameters of the unit and the operation parameters of dry electrostatic precipitator respectively. The present invention uses the operation data of the unit and the operation data of high-frequency power supply of dry electrostatic precipitator as input parameters at the same time, so as to reduce the dust removal power consumption of dry electrostatic precipitator of thermal power generation unit.

Description

Technical Field

[0001] The invention belongs to the technical field of dust removal and environmental protection control of thermal power units, and in particular relates to an optimization control method and system for a dry-type electrostatic precipitator of a thermal power unit. Background Art

[0002] Thermal power generation is the main power generation method in my country. The annual smoke emission of coal-fired power plants ranks first among all industries and is the main source of smoke pollutants in my country's atmospheric environment. Electrostatic dust removal is an important technology that effectively curbs smoke pollution in thermal power plants. With the continuous improvement of the country's environmental protection standards, the emission concentration of electrostatic precipitators is getting lower and lower. In order to ensure the dust removal effect during operation, thermal power plants are forced to increase the output of electrostatic precipitators, which leads to an increase in the power consumption of electrostatic precipitators. Under normal circumstances, the power consumption of electrostatic precipitators accounts for 3‰ to 5‰ of the power consumption of power plants. Therefore, how to achieve low-power dust removal has attracted great attention from thermal power plants.

[0003] In order to improve the dust removal effect and energy-saving operation, the domestic electrostatic precipitator power supply is generally replaced with a high-frequency power supply, which improves the flexibility of power supply control. In order to further reduce the power consumption of the electrostatic precipitator, research institutes and power plants have done a lot of research work, such as: voltage reduction vibration, ash hopper / ceramic sleeve steam heating, optimization control, etc. Although the idea of ​​energy-saving optimization and control of the outlet concentration of the electrostatic precipitator has been proposed for a long time, the electrostatic precipitator in the current thermal power plant has not yet been put into concentration feedback closed-loop control. The dust removal efficiency of the electrostatic precipitator is affected by many factors, such as flue gas properties, flue gas volume, flue gas concentration, power supply voltage and current, anode and cathode vibration cleaning effect, air flow distribution, air leakage state, etc. It is difficult to accurately establish the mathematical model of the electrostatic precipitator by mechanism modeling, and there is no dust concentration measurement point at the entrance of the electrostatic precipitator, and there is no concentration measurement point between the electric fields, so advanced control methods are difficult to apply. At present, the energy-saving optimization of the electrostatic precipitator in thermal power plants generally adopts an open-loop control method, that is, the operating parameter setting value of the high-frequency power supply is adjusted in stages according to the unit load, and the unbiased control of the outlet flue gas concentration setting value is not achieved. Summary of the invention

[0004] The purpose of the present invention is to provide a method for optimizing control of a dry electrostatic precipitator of a thermal power unit in view of the above problems;

[0005] The purpose of the present invention is to provide an optimized control system for a dry electrostatic precipitator of a thermal power unit in view of the above problems.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A method for optimizing and controlling a dry electrostatic precipitator of a thermal power unit comprises the following steps:

[0008] S1. Through the disturbance test of dry electrostatic precipitator, its dynamic characteristic model is obtained, and the model parameters are identified by immune genetic algorithm;

[0009] S2. Read the operating parameters of the unit and the operating parameters of the dry electrostatic precipitator respectively;

[0010] S3. A hierarchical control method is adopted to automatically adjust the secondary current and / or secondary voltage according to the dust concentration at the ESP outlet in the unit operating parameters and the dry ESP operating parameters.

[0011] In the above-mentioned optimization control method for dry electrostatic precipitator of thermal power unit, in step S2, the operating parameters of the unit include the unit load, and the operating parameters of the dry electrostatic precipitator include the secondary current, secondary voltage and dust concentration at the outlet of the dry electrostatic precipitator.

[0012] In the above-mentioned optimization control method for dry electrostatic precipitator of thermal power unit, step S3 specifically includes:

[0013] A. The front electric field of the dry electrostatic precipitator automatically adjusts the secondary current and / or secondary voltage according to the unit load;

[0014] B. The final electric field of the dry electrostatic precipitator adopts PID control method to automatically adjust the secondary current and / or secondary voltage according to the smoke concentration at the electrostatic precipitator outlet.

[0015] In the above-mentioned optimization control method for a dry electrostatic precipitator of a thermal power unit, in step S1, a specific method for obtaining a dynamic characteristic model of the dry electrostatic precipitator through a disturbance test includes:

[0016] The experimental modeling method is used to establish the transfer function model of the secondary current of the high-frequency power supply of each level of the electric field of the dry electrostatic precipitator to the dust concentration at the electrostatic precipitator outlet. The model is as follows:

[0017]

[0018] In formula (1),

[0019] G d,i (s) is the dust concentration at the outlet of the electrostatic precipitator;

[0020] I i (s) is the average value of the secondary current of all the electric chambers in the i-th electric field of the dry electrostatic precipitator;

[0021] K, T1, T2, τ are the model parameters of this transfer function model, and the immune genetic algorithm is used to identify the model parameters.

[0022] In the above-mentioned optimization control method for dry electrostatic precipitator of thermal power unit, in step S1, the method of using immune genetic algorithm to identify model parameters includes:

[0023] S11. Establish static and dynamic models of electric fields at various levels of dry electrostatic precipitator at different load points of the unit;

[0024] S12. Use immune genetic algorithm to identify the K, T1, T2, and τ model parameters in the transfer function model.

[0025] In the above-mentioned optimization control method for dry electrostatic precipitator of thermal power unit, the step of identifying the model parameters includes:

[0026] 1) Determine the structural parameters of the immune genetic algorithm;

[0027] 2) Antigen recognition, select the transfer function model (1) as the antigen, and the model parameters as the optimization variables;

[0028] 3) Generate an initial antibody population, use a combination of model parameters as an antibody, and randomly generate multiple combinations according to the actual interval of the transfer function model solution to form an initial antibody population and initial memory cells, and update each other;

[0029] 4) Calculate affinity, calculate the antigen affinity of each antibody and the affinity with other antibodies;

[0030] 5) Renew memory cells by adding antibodies with high antigen affinity to memory cells;

[0031] 6) Antibody promotion and inhibition, calculation of antibody concentration C i , and select antibodies based on antigen affinity:

[0032]

[0033] In formula (3), (A g ) i is the antigen affinity, λ and μ are weighting coefficients, and the antibody concentration C i Obtained by the following formula;

[0034]

[0035] In formula (4), C i is the antibody concentration, θ is the affinity constant, and N is the number of antibodies;

[0036] (7) generating new antibodies by performing crossover and mutation operations on the selected antibodies in the antibody population to obtain a new antibody population;

[0037] (8) Determine the termination condition, use the maximum evolutionary number as the termination condition, and output the final optimal antibody.

[0038] In the above-mentioned optimization control method for dry electrostatic precipitator of thermal power unit, the structural parameters of immune genetic algorithm include evolutionary generation, antibody population number, memory cell number, crossover frequency and mutation frequency;

[0039] The value range of each model parameter is:

[0040] τ∈[15,40],K∈[-0.5,0.5],T1∈[0,100],T2∈[0,100]

[0041] In the above-mentioned optimization control method for dry electrostatic precipitator of thermal power unit, the affinity between antigen and antibody is obtained by the following method:

[0042]

[0043] In formula (2), C d is the actual dust concentration at the outlet of the electrostatic precipitator, ΔC d It is the difference between the actual smoke concentration at the electrostatic precipitator outlet and the value calculated by the transfer function model.

[0044] In the above-mentioned optimization control method for dry electrostatic precipitator of thermal power unit, in step S2, the unit operation parameters are obtained from the unit main control DCS system, and the operation parameters of the dry electrostatic precipitator are obtained from the high-frequency power supply system of the dry electrostatic precipitator.

[0045] A dry electrostatic precipitator optimization control system for a thermal power unit includes a first data acquisition module, a second data acquisition module, a front-stage electric field optimization control module and a final-stage electric field optimization control module, wherein:

[0046] The first data acquisition module is used to obtain the dynamic characteristic model of the dry electrostatic precipitator through a disturbance test and identify the model parameters;

[0047] The second data acquisition module is used to read the operating parameters of the unit and the operating parameters of the dry electrostatic precipitator;

[0048] A front-stage electric field optimization control module, in which the front-stage electric field of the dry electrostatic precipitator automatically adjusts the secondary current and / or secondary voltage according to the unit load;

[0049] The final electric field optimization control module adopts the PID control method for the final electric field of the dry electrostatic precipitator, and automatically adjusts the secondary current and / or secondary voltage according to the smoke concentration at the electrostatic precipitator outlet.

[0050] The advantages of the present invention are: reducing the dust removal power consumption of the dry electrostatic precipitator of the thermal power unit; proposing a dynamic characteristic model of the dry electrostatic precipitator based on a disturbance test, and on this basis, proposing an optimization control method of the dry electrostatic precipitator based on hierarchical control, which solves the problem of high power consumption of the dry electrostatic precipitator in the prior art under the influence of disturbance factors such as large-scale variable load of the unit, coal quality fluctuation, electrode vibration, etc.; the parameters required in the modeling and control algorithms can be directly read from the main control DCS system or the power plant SIS system, and there is no need to add expensive auxiliary equipment such as analysis or measuring instruments on site, so the cost is low. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic flow chart of the optimization control method of the dry electrostatic precipitator of a thermal power unit of the present invention;

[0052] Figure 2 It is a module structure block diagram of the optimization control system diagram of the dry type electrostatic precipitator of the thermal power unit of the present invention;

[0053] Figure 3 It is a structural block diagram of the optimization control system of the dry electrostatic precipitator of the thermal power unit of the present invention.

[0054] Figure numerals: operator station 1; engineer station 2; application server 3; programmable logic controller 4; redundant network 5; serial port 6; dry electrostatic precipitator high-frequency power supply and control system 7; first data acquisition module 11; second data acquisition module 21; front-stage electric field optimization control module 31; final-stage electric field optimization control module 41. DETAILED DESCRIPTION

[0055] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] This embodiment discloses a method and system for optimizing and controlling a dry electrostatic precipitator of a thermal power unit, which controls the smoke concentration at the dry electrostatic precipitator outlet by adjusting the secondary current of the high-frequency power supply of the dry electrostatic precipitator, thereby achieving the technical effect of reducing the energy consumption of the electrostatic precipitator.

[0057] Specifically, Figure 1 As shown, the method flow includes the following steps:

[0058] S1. Through the disturbance test of dry electrostatic precipitator, its dynamic characteristic model is obtained, and the model parameters are identified by immune genetic algorithm;

[0059] S2. Read the operating parameters of the unit and the operating parameters of the dry electrostatic precipitator respectively;

[0060] The operating parameters of the unit include the unit load, etc., and the operating parameters of the dry electrostatic precipitator include the secondary current, secondary voltage, dust concentration at the outlet of the dry electrostatic precipitator, and dust concentration at the inlet of the dry electrostatic precipitator, etc. The acquisition method can be to directly read the operating parameters of the dry electrostatic precipitator from the main control DCS system, the power plant SIS system, and the high-frequency power supply system of the dry electrostatic precipitator. There is no need to add expensive auxiliary equipment such as analysis or measuring instruments on site, and the cost is low.

[0061] S3. A hierarchical control method is adopted to automatically adjust the secondary current and / or secondary voltage according to the dust concentration at the ESP outlet in the unit operating parameters and the dry ESP operating parameters.

[0062] Step S3 is specifically as follows:

[0063] A. The front electric field of the dry electrostatic precipitator automatically adjusts the secondary current or secondary voltage according to the load of the unit;

[0064] B. The final electric field of the dry electrostatic precipitator adopts PID control method, and automatically adjusts the secondary current or secondary voltage according to the smoke concentration at the electrostatic precipitator outlet.

[0065] Specifically, in step S1, the specific method of obtaining the dynamic characteristic model of the dry electrostatic precipitator through the disturbance test includes:

[0066] The experimental modeling method is used to establish the transfer function model of the secondary current of the high-frequency power supply of each level of the electric field of the dry electrostatic precipitator to the dust concentration at the electrostatic precipitator outlet. The model is as follows:

[0067]

[0068] In formula (1),

[0069] G d,i (s) is the dust concentration at the outlet of the electrostatic precipitator;

[0070] I i (s) is the average value of the secondary current of all the electric chambers in the i-th electric field of the dry electrostatic precipitator;

[0071] K, T1, T2, τ are the model parameters of this transfer function model, and the immune genetic algorithm is used to identify the model parameters.

[0072] Furthermore, the identification of model parameters in the dynamic characteristic model using the immune genetic algorithm specifically refers to the following steps:

[0073] (1) Determine the structural parameters of the algorithm, such as the number of evolutionary generations G = 500, the number of antibody populations N = 100, the number of memory cells Nm = 5, the crossover frequency Pc = 0.8, the mutation frequency = 0.01, etc.;

[0074] (2) Antigen identification: The objective function of the problem to be solved is selected as the antigen, and the transfer function model structure to be identified is

[0075]

[0076] In formula (1), G d,i (s) is the dust concentration at the outlet of the electrostatic precipitator (mg / Nm3), I i (s) is the average value of the secondary current of all electric chambers in the i-th electric field of the dry electrostatic precipitator (mA), and the optimization variables are K, T1, T2, τ;

[0077] (3) Generation of the initial antibody population: The combination of model parameters is used as the antibody. A certain number of combinations are randomly generated according to the actual interval of the transfer function model solution to form the initial antibody population and initial memory cells, which are updated mutually. The value interval of the model parameters is set to:

[0078] τ∈[15,40],K∈[-0.5,0.5],T1∈[0,100],T2∈[0,100]

[0079] (4) Calculate affinity and calculate the antigen affinity (A) of each antibody g ) i And the affinity between antigen and antibody can be equivalent to the 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 the fitness function is defined as:

[0080]

[0081] In formula (2), C d is the actual dust concentration at the outlet of the electrostatic precipitator, ΔC d It is the difference between the actual smoke concentration at the outlet of the electrostatic precipitator and the value calculated by the model;

[0082] (5) Update memory cells and set the initial population to (A g ) i In descending order, antibodies with high antigen affinity are added to memory cells;

[0083] (6) Antibody promotion and inhibition, calculation of antibody concentration C i , and combined with (A g ) i Perform antibody selection. The criteria for selecting antibodies consist of two parts: antibody affinity and concentration inhibition factor:

[0084]

[0085] In the formula, C iis the antibody concentration, λ and μ are weighting coefficients, here λ = 0.7, μ = 1.25

[0086] Antibody concentration C i Defined as:

[0087]

[0088] Wherein, θ is the affinity constant, θ=0.9; N is the number of antibodies.

[0089] (7) generating new antibodies by performing crossover and mutation operations on selected antibodies in the antibody population to obtain a new population;

[0090] (8) Determine the termination condition, use the maximum evolutionary number of 500 as the termination condition, and output the final optimal antibody.

[0091] like Figure 2 As shown, the dry electrostatic precipitator optimization control system of the thermal power unit in this embodiment includes a first data acquisition module 11, a second data acquisition module 21, a front-stage electric field optimization control module 31 and a final-stage electric field optimization control module 41, wherein:

[0092] The first data acquisition module 11 is used to obtain the dynamic characteristic model of the dry electrostatic precipitator through a disturbance test and identify the model parameters;

[0093] The second data acquisition module 21 is used to read the operating parameters of the unit and the operating parameters of the dry electrostatic precipitator; specifically, the data is obtained from the DCS or SIS system through communication methods such as OPC and Modbus. The acquired data is filtered and used by the front-stage electric field optimization control module and the final-stage electric field optimization control module.

[0094] A front-stage electric field optimization control module 31, the front-stage electric field of the dry electrostatic precipitator automatically adjusts the secondary current and / or secondary voltage according to the unit load;

[0095] The final electric field optimization control module 41, the final electric field of the dry electrostatic precipitator adopts a PID control method, and automatically adjusts the secondary current and / or secondary voltage according to the smoke concentration at the electrostatic precipitator outlet.

[0096] Specifically, Figure 3As shown, the hardware structure adopted by the system of this embodiment mainly includes an operator station 1, an engineer station 2, a programmable logic controller 4, an application server 3 and a dry dust collector high frequency power supply and its control system 7. The programmable logic controller 4 includes the aforementioned first data acquisition module 11 and the second data acquisition module 21, and the application server 3 includes the aforementioned front-stage electric field optimization control module 31 and the final-stage electric field optimization control module 41. Specifically, the programmable logic controller 4 is connected to the unit master control DCS system and the application server 3 so that the application server 3 obtains the unit operation data from the unit master control DCS system through the programmable logic controller 4. At the same time, the programmable logic controller 4 is connected to the dry dust collector high frequency power supply and its control system 7 so that the application server 3 obtains the dry electric dust collector high frequency power supply operation data from the dry dust collector high frequency power supply and its control system 7 through the programmable logic controller 4 and sends control instructions to the dry dust collector high frequency power supply and its control system 7. In this way, the application server 3 can simultaneously obtain the unit operation data and the dry electric dust collector high frequency power supply operation data to improve the automation control effect and improve the economic benefits of the unit.

[0097] 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 for information exchange. The redundant network 5 here is preferably Ethernet.

[0098] Furthermore, the high-frequency power supply of the dry dust collector and its control system 7 are connected to the programmable logic controller 4 via the serial port 6 and the hard-wired wire of the serial port 6.

[0099] Similarly, the programmable logic controller 4 and the unit master control DCS system are also connected via the serial port 6.

[0100] Specifically, the dry-type precipitator high-frequency power supply and its control system 7 include a dry-type ESP high-frequency power supply control system and a dry-type ESP high-frequency power supply which are connected to each other, and the programmable logic controller 4 is connected to the dry-type ESP high-frequency power supply control system. The programmable logic controller 4 obtains the unit operation data and the dry-type ESP high-frequency power supply operation data from the unit master control DCS system and the dry-type ESP high-frequency power supply system respectively, and transmits them to the application server 3 through the redundant network 5. The application server 3 can automatically control the dry-type ESP high-frequency power supply according to the unit operation data and the dry-type ESP high-frequency power supply operation data at the same time to realize the automatic control of the dry-type ESP under the influence of large-scale variable load and disturbance factors such as coal quality fluctuation, electrode vibration, and boiler soot blowing, and achieve the effect of energy saving and consumption reduction.

[0101] The dry electrostatic precipitator optimization control system of the thermal power unit of this embodiment has a simple structure, strong scalability, and is easy to maintain and upgrade; at the same time, it is connected to the unit's main control DCS system through a serial port to improve the safety of the control system.

[0102] The specific embodiments described herein are merely examples of the spirit of the present invention. A person skilled in the art of the present invention may make various modifications or additions to the specific embodiments described or replace them in a similar manner, but this will not deviate from the spirit of the present invention or exceed the scope defined in the appended claims. The connection of the present invention refers to direct connection or indirect connection.

[0103] Although this article uses more terms such as operator station 1; engineer station 2; application server 3; programmable logic controller 4; redundant network 5; serial port 6; dry electrostatic precipitator high-frequency power supply and control system 7; first data acquisition module 11; second data acquisition module 21; front-stage electric field optimization control module 31; final-stage electric field optimization control module 41, it does not exclude the possibility of using other terms. These terms are used only to more conveniently describe and explain the essence of the present invention; interpreting them as any additional restrictions is contrary to the spirit of the present invention.

Claims

1. A method for optimizing control of a dry electrostatic precipitator of a thermal power unit, characterized in that: The following steps are involved: S1. Through the disturbance test of dry electrostatic precipitator, its dynamic characteristic model is obtained, and the model parameters are identified by immune genetic algorithm; S2. Read the operating parameters of the unit and the operating parameters of the dry electrostatic precipitator respectively. The operating parameters of the unit include the unit load, and the operating parameters of the dry electrostatic precipitator include the secondary current, secondary voltage and dust concentration of the dry electrostatic precipitator outlet; S3. Use a hierarchical control method to automatically adjust the secondary current and / or secondary voltage according to the unit operating parameters and the dust concentration at the ESP outlet in the dry ESP operating parameters, including: A. The front electric field of the dry electrostatic precipitator automatically adjusts the secondary current and / or secondary voltage according to the unit load; B. The final electric field of the dry electrostatic precipitator adopts PID control method, and automatically adjusts the secondary current and / or secondary voltage according to the dust concentration at the electrostatic precipitator outlet; In step S1, the specific method of obtaining the dynamic characteristic model of the dry electrostatic precipitator through the disturbance test includes: The experimental modeling method is used to establish the transfer function model of the secondary current of the high-frequency power supply of each level of the electric field of the dry electrostatic precipitator to the dust concentration at the electrostatic precipitator outlet. The model is as follows: （1） In formula (1), is the dust concentration at the outlet of the electrostatic precipitator; is the average value of the secondary current of all the electric chambers in the i-th electric field of the dry electrostatic precipitator; K, T1, T2, The model parameters of the transfer function model are identified by immune genetic algorithm; In step S1, the method of using immune genetic algorithm to identify model parameters includes: S11. Establish static and dynamic models of electric fields at various levels of dry electrostatic precipitator at different load points of the unit; S12. Use immune genetic algorithm to transform K, T1, T2, Parameter identification of model parameters; The steps of parameter identification of model parameters include: 1) Determine the structural parameters of the immune genetic algorithm; 2) Antigen recognition, select the transfer function model (1) as the antigen, and the model parameters as the optimization variables; 3) Generate an initial antibody population, use the combination of model parameters as the antibody, and randomly generate multiple combinations according to the actual interval of the transfer function model solution to form the initial antibody population and initial memory cells, and update each other; 4) Calculate affinity, calculate the antigen affinity of each antibody and its affinity with other antibodies; 5) Renew memory cells by adding antibodies with high antigen affinity to memory cells; 6) Antibody promotion and inhibition, calculation of antibody concentration, and selection of antibodies based on antigen affinity: （3） In formula (3), is the antigen affinity, and is the weighting coefficient; （4） In formula (4), is the antibody concentration, is the affinity constant, N is the number of antibodies; 7) Generate new antibodies by performing crossover and mutation operations on the selected antibodies in the antibody population to obtain a new antibody population; 8) Determine the termination condition, use the maximum evolutionary number as the termination condition, and output the last optimal antibody; The affinity of antigen and antibody is obtained in the following way. The affinity of antigen and antibody is equivalent to the fitness function. The fitness function is: （2） In formula (2), is the actual dust concentration at the outlet of the electrostatic precipitator, It is the difference between the actual smoke concentration at the electrostatic precipitator outlet and the value calculated by the transfer function model.

2. The optimization control method for dry electrostatic precipitator of thermal power unit according to claim 1, characterized in that: The structural parameters of immune genetic algorithm include evolutionary generations, antibody population number, memory cell number, crossover frequency and mutation frequency; The value range of each model parameter is: 。 3. The optimization control method for dry electrostatic precipitator of thermal power unit according to claim 2, characterized in that: In step S2, the unit operating parameters are obtained from the unit master control DCS system, and the operating parameters of the dry electrostatic precipitator are obtained from the dry electrostatic precipitator high-frequency power supply system.

4. An optimization control system for a dry electrostatic precipitator of a thermal power unit, characterized in that: The method for executing any one of claims 1 to 3 comprises a first data acquisition module, a second data acquisition module, a front-stage electric field optimization control module and a final-stage electric field optimization control module, wherein: The first data acquisition module is used to obtain the dynamic characteristic model of the dry electrostatic precipitator through a disturbance test and identify the model parameters; The second data acquisition module is used to read the operating parameters of the unit and the operating parameters of the dry electrostatic precipitator; A front-stage electric field optimization control module, in which the front-stage electric field of the dry electrostatic precipitator automatically adjusts the secondary current and / or secondary voltage according to the unit load; The final electric field optimization control module adopts the PID control method for the final electric field of the dry electrostatic precipitator, and automatically adjusts the secondary current and / or secondary voltage according to the smoke concentration at the electrostatic precipitator outlet.

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