Method for judging critical point of reverse corona of electric dust collector
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 浙江菲达环保科技股份有限公司
- Filing Date
- 2023-10-30
- Publication Date
- 2026-06-23
Smart Images
Figure CN117380397B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electrostatic precipitator technology, specifically to a method for determining the back corona critical point of an electrostatic precipitator and a system for determining the back corona critical point of an electrostatic precipitator. Background Technology
[0002] The power supply is the core equipment of an electrostatic precipitator, and its performance directly affects the dust removal efficiency. Therefore, analyzing the impact of the power supply on the dust removal efficiency is extremely necessary and significant. Different operating parameters, different electric field levels, and different power supply types have different effects on dust emissions. Current technology only allows for manual adjustment based on the experience of technicians, and it is impossible to detect or determine the back corona critical point. Back corona is a localized reverse discharge phenomenon generated in the high resistivity dust layer deposited on the surface of the collecting electrode. It consumes power and seriously affects the performance of the electrostatic precipitator, reducing dust removal efficiency. Therefore, this phenomenon should be prevented in electrostatic precipitators.
[0003] Manually adjusting various power supply operating parameters requires relatively high technical skills from technicians, and the debugging process is very lengthy, consuming significant human and time resources. Furthermore, the debugging results are limited by experience, and the accuracy cannot be guaranteed. To address the problems of high human resource consumption and unreliable adjustment accuracy in existing solutions, a method for accurately determining the back corona critical point is needed. Summary of the Invention
[0004] The purpose of this invention is to provide a method and system for determining the back corona critical point of an electrostatic precipitator, so as to at least solve the problems of high manpower consumption and inability to guarantee adjustment accuracy in existing solutions.
[0005] To achieve the above objectives, the first aspect of the present invention provides a method for determining the back corona critical point of an electrostatic precipitator. The method includes: collecting state parameter information of a target electrostatic precipitator and performing missing value filling processing on the state parameter information to obtain a basic parameter set; constructing a typical structural model of the target electrostatic precipitator based on the basic parameter set; adaptively adjusting power supply parameters in the typical structural model and simulating operation to a stable state; collecting simulation parameters in the stable state to obtain multiple simulation parameter sets; and determining the power supply parameters of the back corona critical point based on each power supply parameter and the corresponding simulation parameter set.
[0006] Optionally, the status parameter information includes: electrostatic precipitator design information and dust collection parameter information; wherein, the electrostatic precipitator design information includes: electric field level, power supply type, and electric field body size information; the dust collection parameter information includes: dust type, dust collection surface aging status, average dust concentration at the inlet within a sampling period, and average dust concentration at the outlet within the same sampling period.
[0007] Optionally, the missing value filling process for the state parameter information includes: predicting the outlet dust concentration based on the electrostatic precipitator design information, the dust type, the average dust concentration at the inlet within a sampling period; obtaining a deviation value based on the predicted outlet dust concentration and the average outlet dust concentration within the same sampling period; fitting the dust collection surface aging condition based on the deviation value, and using the obtained dust collection surface aging condition as the missing value filling value.
[0008] Optionally, constructing a typical structural model of the target electrostatic precipitator based on the basic parameter set includes: performing a redundant data filtering operation on the basic parameter set to obtain a filtered basic parameter set; and in the pre-constructed basic model of the electrostatic precipitator, modifying the corresponding basic model of the electrostatic precipitator based on the filtered basic parameter set to obtain a typical structural model of the target electrostatic precipitator.
[0009] Optionally, the power supply parameters include: the secondary voltage values of each power supply stage and the secondary current values of each power supply stage.
[0010] Optionally, the simulation parameters include: the inlet dust concentration value and the outlet dust concentration value.
[0011] Optionally, determining the power parameters for the anti-corona critical point based on each power supply parameter and the corresponding set of simulation parameters includes: adaptively adjusting the power supply parameters to obtain multiple multivariate arrays; performing a typical structural model simulation run based on each multivariate array until a stable state is reached to obtain simulation parameters; calculating the dust removal effect and energy consumption under each multivariate array and simulation parameters based on preset dust removal effect and energy consumption calculation rules; constructing an optimization problem with energy consumption as the optimization objective; and selecting the power parameters corresponding to the simulation parameters with the lowest energy consumption as the power parameters for the anti-corona critical point based on the dust removal effect and energy consumption under each multivariate array and simulation parameters.
[0012] Optionally, the preset energy consumption calculation rule is as follows:
[0013] W 能耗 =(U 12 *I 12 )+(U 22 *I 22 )+…+(U n2 *I n2 )
[0014] Among them, W 能耗 To calculate energy consumption; U n2 I represents the secondary voltage value of the nth stage power supply. n2 The value is the secondary current of the nth power supply; the preset dust removal effect calculation rule is as follows:
[0015] S除尘效果 = (ab) / a*100%
[0016] Among them, S 除尘效果 The calculated dust removal effect; a is the dust concentration value at the inlet; b is the dust concentration value at the outlet.
[0017] A second aspect of the present invention provides a system for determining the back corona critical point of an electrostatic precipitator. The system includes: a data acquisition unit for acquiring state parameter information of a target electrostatic precipitator and performing missing value filling processing on the state parameter information to obtain a basic parameter set; a model building unit for constructing a typical structural model of the target electrostatic precipitator based on the basic parameter set; a simulation unit for adaptively adjusting power supply parameters in the typical structural model and simulating operation to a stable state, acquiring simulation parameters in the stable state to obtain multiple simulation parameter sets; and a judgment unit for determining the power supply parameters of the back corona critical point based on each power supply parameter and the corresponding simulation parameter set.
[0018] On the other hand, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for determining the back corona critical point of an electrostatic precipitator.
[0019] Through the above technical solution, this invention collects state parameters based on the corresponding electrostatic precipitator. For data that cannot be directly collected, a missing value filling scheme is used to fill in the missing values, ensuring that the full state data of the target electrostatic precipitator is obtained. Based on the determined state data, a model is constructed to obtain the corresponding model of the target electrostatic precipitator. Under this model, various operating conditions are adaptively trained to train the correspondence between energy consumption and dust removal effect, thereby identifying the back corona critical point. This invention solves the problems of high manpower consumption and inability to guarantee adjustment accuracy in existing electrostatic precipitator control schemes. By finding the back corona critical point, this invention can effectively improve the level of intelligent automation and achieve energy-saving operation. It eliminates the need for traditional manual adjustments by technicians, reducing the workload of operators. Furthermore, after forming a back corona membership table based on operating data, parameters can be automatically adjusted, greatly reducing manpower and material resources. After automatic adjustment, optimal operating parameters are obtained, effectively achieving energy saving, cost reduction, and efficiency improvement.
[0020] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description
[0021] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:
[0022] Figure 1 This is a flowchart of the steps of a method for determining the back corona critical point of an electrostatic precipitator according to one embodiment of the present invention;
[0023] Figure 2 This is a system structure diagram of the electrostatic precipitator back corona critical point judgment system provided in one embodiment of the present invention. Detailed Implementation
[0024] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.
[0025] The power supply is the core equipment of an electrostatic precipitator, and its performance directly affects the dust removal efficiency. Therefore, analyzing the impact of the power supply on the dust removal efficiency is extremely necessary and significant. Different operating parameters, different electric field levels, and different power supply types have different effects on dust emissions. Current technology only allows for manual adjustment based on the experience of technicians, and it is impossible to detect or determine the back corona critical point. Back corona is a localized reverse discharge phenomenon generated in the high resistivity dust layer deposited on the surface of the collecting electrode. It consumes power and seriously affects the performance of the electrostatic precipitator, reducing dust removal efficiency. Therefore, this phenomenon should be prevented in electrostatic precipitators.
[0026] Manually adjusting various power supply operating parameters requires relatively high technical skills from technicians, and the debugging process is very lengthy, consuming a lot of human and time resources. Moreover, the debugging results are limited by experience, and the debugging accuracy cannot be guaranteed.
[0027] To address the problems of high manpower consumption and inconsistent adjustment accuracy in existing solutions, this invention proposes a method for determining the back corona critical point of an electrostatic precipitator (ESP). This method involves collecting state parameters of the corresponding ESP. For data that cannot be directly collected, a missing value filling scheme is used to ensure that the full state data of the target ESP is obtained. Based on the determined state data, a model is constructed to obtain the corresponding model of the target ESP. Under this model, various operating conditions are adaptively trained to train the correlation between energy consumption and dust removal effect, thereby identifying the back corona critical point. This invention solves the problems of high manpower consumption and inconsistent adjustment accuracy in existing ESP control schemes. By finding the back corona critical point, this invention can effectively improve the level of intelligent automation and achieve energy-saving operation. It eliminates the need for manual adjustments by traditional technicians, reducing the workload of operators. Furthermore, after forming a back corona membership table based on operating data, parameters can be automatically adjusted, greatly reducing manpower and material resources. Optimal operating parameters are obtained after automatic adjustment, effectively achieving energy saving, cost reduction, and efficiency improvement.
[0028] Figure 1 This is a flowchart of a method for determining the back corona critical point of an electrostatic precipitator according to one embodiment of the present invention. Figure 1 As shown, this invention provides a method for determining the back corona critical point of an electrostatic precipitator, the method comprising:
[0029] Step S10: Collect the status parameter information of the target electrostatic precipitator, and fill in the missing values of the status parameter information to obtain the basic parameter set.
[0030] Specifically, the state parameter information includes: electrostatic precipitator design information and dust collection parameter information; wherein, the electrostatic precipitator design information includes: electric field level, power supply type, and electric field body size information; the dust collection parameter information includes: dust type, dust collection surface aging status, average dust concentration at the inlet within a sampling period, and average dust concentration at the outlet within the same sampling period.
[0031] In this embodiment of the invention, the so-called back corona discharge is a partial discharge phenomenon generated by a high-resistivity dust layer deposited on the surface of the collecting electrode. After being charged, the high-resistivity dust reaches the collecting electrode, and the charge is not easily released. As the dust layer deposited on the electrode plate thickens, releasing the charge becomes even more difficult. At this point, on the one hand, because the dust layer has not released all the charge, its surface still retains the same polarity as the corona electrode, repelling subsequent charged dust; on the other hand, because the charge release from the dust layer is slow, a large potential gradient is formed between the dust particles. When the electric field strength in the dust layer exceeds its critical value, local breakdown occurs in the pores of the dust layer, generating positive ions with the opposite polarity to the corona electrode. These ions then move towards the corona electrode, neutralizing the negatively charged particles in the corona region. As a result, the current increases, the voltage decreases, and secondary dust re-entrainment becomes severe, leading to a significant deterioration in dust collection performance.
[0032] It is evident that the back corona critical point varies among different electrostatic precipitators (ESPs). Different structural parameters, equipment aging conditions, and the charge status of the flue gas all affect the back corona critical point. To find the back corona critical point suitable for each ESP, an adaptive search is needed based on the specific parameters of each ESP. Therefore, the present invention requires the complete collection of state parameter information of the target ESP to facilitate monitoring of the target ESP's operational status.
[0033] Furthermore, the missing value filling process for the state parameter information includes: predicting the outlet dust concentration based on the electrostatic precipitator design information, the dust type, the average dust concentration at the inlet within a sampling period; obtaining a deviation value based on the predicted outlet dust concentration and the average outlet dust concentration within the same sampling period; fitting the dust collection surface aging condition based on the deviation value, and using the obtained dust collection surface aging condition as the missing value filling value.
[0034] In this embodiment of the invention, the aging conditions of different electrostatic precipitators vary, and inferring the degree of aging based solely on usage time is inaccurate. Differences in application environment, single usage time, and usage frequency all lead to variations in aging conditions. Therefore, it is impossible to directly collect data on the aging condition of electrostatic precipitators. Consequently, corresponding parameters will have missing values. This invention uses a fitting method to predict and accurately infer the aging condition, enabling subsequent structural training based on this aging data.
[0035] Step S20: Construct a typical structural model of the target electrostatic precipitator based on the aforementioned basic parameter set.
[0036] Specifically, a redundant data filtering operation is performed on the basic parameter set to obtain a filtered basic parameter set; in the pre-constructed electrostatic precipitator basic model, the corresponding electrostatic precipitator basic model is corrected based on the filtered basic parameter set to obtain a typical structural model of the target electrostatic precipitator.
[0037] In this embodiment of the invention, a large amount of redundant data exists in the collected state parameters. Since the present invention only aims to obtain the relationship between power consumption and dust removal effect, it only needs to train a model that can represent this relationship. As for other functions of the electrostatic precipitator, such as the rapping system and the dust removal system, no model building is required. Therefore, the state parameters corresponding to these functions can be filtered as redundant data. This approach can reduce the workload of model building, requiring only the construction of a typical structural model.
[0038] Furthermore, although there are slight structural differences among various electrostatic precipitator systems, they share many commonalities due to their shared technical principles. To further improve model building efficiency and shorten model building time, this invention first constructs a basic model based on the commonalities of various electrostatic precipitators. Subsequent modifications only need to be made to this basic model, avoiding the need to build a model from scratch and thus improving model building efficiency.
[0039] Step S30: In the typical structural model, the power supply parameters are adaptively adjusted and the simulation is run to a stable state. Simulation parameters are collected in the stable state to obtain multiple sets of simulation parameters.
[0040] Specifically, the power supply parameters include: the secondary voltage values and secondary current values of each stage of the power supply. The simulation parameters include: the inlet dust concentration value and the outlet dust concentration value.
[0041] In this embodiment of the invention, the power supply parameters are adaptively adjusted to obtain multiple multivariate arrays; based on each multivariate array, a typical structural model is simulated and run until a stable state is reached to obtain simulation parameters.
[0042] In one possible implementation, under the conditions of the same electric field level, fixed-level power supply type, and the same electric field body, the power supply parameters of each level are configured with different ratios. After running for 1 hour and reaching a steady state, the inlet and outlet concentrations are detected using a turbidimeter to form a membership table. Specific parameters include (secondary voltage U2, secondary current I2, inlet concentration a mg / Nm³). 3 Export concentration b mg / Nm 3 (Turbidimeter parameters)
[0043] Step S40: Based on each power supply parameter and the corresponding simulation parameter set, determine the power supply parameters at the back corona critical point.
[0044] Specifically, the power supply parameters are adaptively adjusted to obtain multiple multivariate arrays; based on each multivariate array, a typical structural model is simulated and run until a stable state is obtained to obtain simulation parameters; based on preset dust removal effect and energy consumption calculation rules, the dust removal effect and energy consumption under each multivariate array and simulation parameters are calculated; energy consumption is used as the optimization objective to construct an optimization problem, and based on the dust removal effect and energy consumption under each multivariate array and simulation parameters, the power supply parameters corresponding to the simulation parameters with the lowest energy consumption are selected as the power supply parameters at the anti-corona critical point.
[0045] The preset energy consumption calculation rule is as follows:
[0046] W 能耗 =(U 12 *I 12 )+(U 22 *I 22 )+…+(U n2 *I n2 )
[0047] Among them, W 能耗 To calculate energy consumption; U n2 I represents the secondary voltage value of the nth stage power supply. n2 This represents the secondary current value of the nth stage power supply.
[0048] The preset dust removal effect calculation rule is as follows:
[0049] S 除尘效果 = (ab) / a*100%
[0050] Among them, S 除尘效果 The calculated dust removal effect; a is the dust concentration value at the inlet; b is the dust concentration value at the outlet.
[0051] Figure 2 This is a system structure diagram of a back corona critical point determination system for an electrostatic precipitator provided in one embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides a system for determining the back corona critical point of an electrostatic precipitator, the system comprising:
[0052] The acquisition unit is used to acquire the status parameter information of the target electrostatic precipitator and fill in the missing values of the status parameter information to obtain the basic parameter set.
[0053] Specifically, the state parameter information includes: electrostatic precipitator design information and dust collection parameter information; wherein, the electrostatic precipitator design information includes: electric field level, power supply type, and electric field body size information; the dust collection parameter information includes: dust type, dust collection surface aging status, average dust concentration at the inlet within a sampling period, and average dust concentration at the outlet within the same sampling period.
[0054] In this embodiment of the invention, the so-called back corona discharge is a partial discharge phenomenon generated by a high-resistivity dust layer deposited on the surface of the collecting electrode. After being charged, the high-resistivity dust reaches the collecting electrode, and the charge is not easily released. As the dust layer deposited on the electrode plate thickens, releasing the charge becomes even more difficult. At this point, on the one hand, because the dust layer has not released all the charge, its surface still retains the same polarity as the corona electrode, repelling subsequent charged dust; on the other hand, because the charge release from the dust layer is slow, a large potential gradient is formed between the dust particles. When the electric field strength in the dust layer exceeds its critical value, local breakdown occurs in the pores of the dust layer, generating positive ions with the opposite polarity to the corona electrode. These ions then move towards the corona electrode, neutralizing the negatively charged particles in the corona region. As a result, the current increases, the voltage decreases, and secondary dust re-entrainment becomes severe, leading to a significant deterioration in dust collection performance.
[0055] It is evident that the back corona critical point varies among different electrostatic precipitators (ESPs). Different structural parameters, equipment aging conditions, and the charge status of the flue gas all affect the back corona critical point. To find the back corona critical point suitable for each ESP, an adaptive search is needed based on the specific parameters of each ESP. Therefore, the present invention requires the complete collection of state parameter information of the target ESP to facilitate monitoring of the target ESP's operational status.
[0056] Furthermore, the missing value filling process for the state parameter information includes: predicting the outlet dust concentration based on the electrostatic precipitator design information, the dust type, the average dust concentration at the inlet within a sampling period; obtaining a deviation value based on the predicted outlet dust concentration and the average outlet dust concentration within the same sampling period; fitting the dust collection surface aging condition based on the deviation value, and using the obtained dust collection surface aging condition as the missing value filling value.
[0057] In this embodiment of the invention, the aging conditions of different electrostatic precipitators vary, and inferring the degree of aging based solely on usage time is inaccurate. Differences in application environment, single usage time, and usage frequency all lead to variations in aging conditions. Therefore, it is impossible to directly collect data on the aging condition of electrostatic precipitators. Consequently, corresponding parameters will have missing values. This invention uses a fitting method to predict and accurately infer the aging condition, enabling subsequent structural training based on this aging data.
[0058] The model building unit is used to construct a typical structural model of the target electrostatic precipitator based on the basic parameter set.
[0059] Specifically, a redundant data filtering operation is performed on the basic parameter set to obtain a filtered basic parameter set; in the pre-constructed electrostatic precipitator basic model, the corresponding electrostatic precipitator basic model is corrected based on the filtered basic parameter set to obtain a typical structural model of the target electrostatic precipitator.
[0060] In this embodiment of the invention, a large amount of redundant data exists in the collected state parameters. Since the present invention only aims to obtain the relationship between power consumption and dust removal effect, it only needs to train a model that can represent this relationship. As for other functions of the electrostatic precipitator, such as the rapping system and the dust removal system, no model building is required. Therefore, the state parameters corresponding to these functions can be filtered as redundant data. This approach can reduce the workload of model building, requiring only the construction of a typical structural model.
[0061] Furthermore, although there are slight structural differences among various electrostatic precipitator systems, they share many commonalities due to their shared technical principles. To further improve model building efficiency and shorten model building time, this invention first constructs a basic model based on the commonalities of various electrostatic precipitators. Subsequent modifications only need to be made to this basic model, avoiding the need to build a model from scratch and thus improving model building efficiency.
[0062] The simulation unit is used to adaptively adjust the power supply parameters in the typical structural model and simulate operation to a steady state. In the steady state, the simulation parameters are collected to obtain multiple sets of simulation parameters.
[0063] Specifically, the power supply parameters include: the secondary voltage values and secondary current values of each stage of the power supply. The simulation parameters include: the inlet dust concentration value and the outlet dust concentration value.
[0064] In this embodiment of the invention, the power supply parameters are adaptively adjusted to obtain multiple multivariate arrays; based on each multivariate array, a typical structural model is simulated and run until a stable state is reached to obtain simulation parameters.
[0065] In one possible implementation, under the conditions of the same electric field level, fixed-level power supply type, and the same electric field body, the power supply parameters of each level are configured with different ratios. After running for 1 hour and reaching a steady state, the inlet and outlet concentrations are detected using a turbidimeter to form a membership table. Specific parameters include (secondary voltage U2, secondary current I2, inlet concentration a mg / Nm³). 3 Export concentration b mg / Nm 3 (Turbidimeter parameters)
[0066] The judgment unit is used to determine the power supply parameters at the back corona critical point based on each power supply parameter and the corresponding set of analog parameters.
[0067] Specifically, the power supply parameters are adaptively adjusted to obtain multiple multivariate arrays; based on each multivariate array, a typical structural model is simulated and run until a stable state is obtained to obtain simulation parameters; based on preset dust removal effect and energy consumption calculation rules, the dust removal effect and energy consumption under each multivariate array and simulation parameters are calculated; energy consumption is used as the optimization objective to construct an optimization problem, and based on the dust removal effect and energy consumption under each multivariate array and simulation parameters, the power supply parameters corresponding to the simulation parameters with the lowest energy consumption are selected as the power supply parameters at the anti-corona critical point.
[0068] The preset energy consumption calculation rule is as follows:
[0069] W 能耗 =(U 12 *I 12 )+(U 22 *I 22 )+…+(U n2 *I n2 )
[0070] Among them, W 能耗 To calculate energy consumption; U n2 I represents the secondary voltage value of the nth stage power supply. n2 This represents the secondary current value of the nth stage power supply.
[0071] The preset dust removal effect calculation rule is as follows:
[0072] S 除尘效果 = (ab) / a*100%
[0073] Among them, S 除尘效果 The calculated dust removal effect; a is the dust concentration value at the inlet; b is the dust concentration value at the outlet.
[0074] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the above-described method for determining the back corona critical point of an electrostatic precipitator.
[0075] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.
[0076] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.
[0077] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.
Claims
1. A method of determining a critical point of reverse corona of an electro-precipitator, characterized by, The method includes: The status parameter information of the target electrostatic precipitator is collected, and missing values are filled in to obtain a basic parameter set; wherein, The status parameter information includes: electrostatic precipitator design information and dust collection parameter information; the dust collection parameter information includes: dust type, dust collection surface aging status, average dust concentration at the inlet within a sampling period, and average dust concentration at the outlet within the same sampling period; The missing value filling process for the state parameter information includes: predicting the outlet dust concentration based on the electrostatic precipitator design information, the dust type, and the average inlet dust concentration within a sampling period; obtaining a deviation value based on the predicted outlet dust concentration and the average outlet dust concentration within the same sampling period; fitting the dust collection surface aging condition based on the deviation value, and using the obtained dust collection surface aging condition as the missing value filling value; A typical structural model of the target electrostatic precipitator is constructed based on the aforementioned basic parameter set; In the typical structural model, the power supply parameters are adaptively adjusted, and the simulation is run until a steady state is reached. Simulation parameters are then collected in this steady state, resulting in multiple sets of simulation parameters; among which, The power supply parameters include: the secondary voltage values of each power supply stage and the secondary current values of each power supply stage. The simulation parameters include: the dust concentration at the inlet and the dust concentration at the outlet; Based on each power supply parameter and the corresponding simulation parameter set, determine the power supply parameters at the critical point of back corona. The step of determining the power parameters for the anti-corona critical point based on each power parameter and the corresponding set of simulation parameters includes: adaptively adjusting the power parameters to obtain multiple multivariate arrays; performing a typical structural model simulation based on each multivariate array and running it to a stable state to obtain simulation parameters; calculating the dust removal effect and energy consumption under each multivariate array and simulation parameters based on preset dust removal effect and energy consumption calculation rules; constructing an optimization problem with energy consumption as the optimization objective; and selecting the power parameters corresponding to the simulation parameters with the lowest energy consumption as the power parameters for the anti-corona critical point based on the dust removal effect and energy consumption under each multivariate array and simulation parameters. The energy consumption calculation rule is as follows: in, To calculate energy consumption; This represents the secondary voltage value of the nth stage power supply. This represents the secondary current value of the nth stage power supply. The preset dust removal effect calculation rule is as follows: in, Calculated dust removal effect; This represents the dust concentration at the inlet. This refers to the dust concentration value at the export point.
2. The method according to claim 1, characterized in that, The electrostatic precipitator design information includes: Information on electric field order, power source type, and electric field body dimensions.
3. The method according to claim 1, characterized in that, The construction of a typical structural model of the target electrostatic precipitator based on the aforementioned basic parameter set includes: Perform a redundant data filtering operation on the basic parameter set to obtain the filtered basic parameter set; In the pre-constructed basic model of the electrostatic precipitator, the corresponding basic model of the electrostatic precipitator is modified based on the filtered basic parameter set to obtain the typical structural model of the target electrostatic precipitator.
4. A system for determining the critical point of back corona discharge in an electrostatic precipitator, characterized in that, The system is used to execute the method for determining the back corona critical point of an electrostatic precipitator according to any one of claims 1-3, and the system includes: The acquisition unit is used to acquire the status parameter information of the target electrostatic precipitator and fill in the missing values of the status parameter information to obtain a basic parameter set. The model building unit is used to construct a typical structural model of the target electrostatic precipitator based on the basic parameter set. The simulation unit is used to adaptively adjust the power supply parameters in the typical structural model and simulate operation to a steady state, and to collect simulation parameters in the steady state to obtain multiple sets of simulation parameters. The judgment unit is used to determine the power parameters at the back corona critical point based on each power supply parameter and the corresponding set of analog parameters.
5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the method for determining the back corona critical point of an electrostatic precipitator as described in any one of claims 1-3.