Method and system for linkage self-regulation of electric dust collector load

Abstract

The embodiment of the application provides a kind of electric dust collector load linkage self-regulation method and system, belong to thermal power generating unit technical field.The method includes: the operation data of real-time boiler system is collected;Based on the linkage relationship between each operation data, the operation data is audited, and the operation data that passes the audit is retained;Determine whether there is missing data in the operation data that passes the audit, and in the presence of missing data, execute data completion scheme;The operation data after completion is used as training data, and a preset control optimization model training is executed to obtain an optimization control path;Based on the optimization control path, the electric dust collector control parameter is adjusted until the optimization target is reached.The control optimization model trained by historical data in the present application determines the control scheme, which is more accurate and feasible than manual judgment.

Description

Technical Field

[0001] This invention relates to the field of thermal power unit technology, specifically to a load-linked self-regulation method and a load-linked self-regulation system for an electrostatic precipitator. Background Technology

[0002] With the deepening of environmental protection policies, traditional emission-emitting enterprises are undergoing environmental transformation. To meet emission standards, the control and management of environmental protection equipment are becoming increasingly automated. Coal-fired power plants, in particular, have made significant technological improvements in this area. Because coal-fired power plants rely on coal combustion for boiler heating and power generation, they generate substantial emissions, producing numerous pollutants during combustion. Therefore, to achieve clean emissions, numerous environmental protection devices are installed at the end of boiler systems to treat the flue gas after combustion, ensuring that the final exhaust gas meets clean emission standards. Among all environmental protection equipment, dust removal equipment is crucial. Coal combustion produces not only harmful gases but also large amounts of dust, which, when directly released into the air, causes serious ecological pollution. Dust removal systems collect dust from the exhaust gas, ensuring that the dust content meets standards. Currently, the most widely used dust removal equipment is the electrostatic precipitator (ESP). This device has multiple electrodes along the flue gas flow direction. By applying an opposite charge to the dust on the electrodes, the dust is adsorbed onto the electrode plates as it passes through. Then, at predetermined intervals, the electrode plates are vibrated to knock the dust off the plates into the dust hopper for centralized treatment.

[0003] Because the boiler system load is dynamically adjusted according to the power grid load, the dust content in the flue gas also varies. Therefore, to achieve the optimal dust removal effect, it is necessary to adjust the operating parameters of the electrostatic precipitator (ESP) based on changes in parameters such as the boiler system load. In existing control methods, managers mainly rely on experience to judge real-time operating parameters and then manually adjust the ESP. This method is highly dependent on human experience, making it difficult to guarantee precise control. Furthermore, manual adjustments are often relatively slow, easily leading to excessive energy consumption. To address the problems of poor precision and high energy consumption in existing ESP control schemes, a new automatic control scheme for ESPs is needed. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for load linkage self-regulation of electrostatic precipitators, so as to at least solve the problems of poor accuracy and high energy consumption in existing electrostatic precipitator control schemes.

[0005] To achieve the above objectives, the first aspect of the present invention provides a load-linked self-regulation method for an electrostatic precipitator, the method comprising: collecting real-time operating data of a boiler system; reviewing the operating data based on the linkage relationship between various operating data, and retaining the approved operating data; determining whether there is missing data in the approved operating data, and executing a data completion scheme if there is missing data; using the completed operating data as training data to execute a preset regulation optimization model training to obtain an optimization regulation path; and adjusting the control parameters of the electrostatic precipitator based on the optimization regulation path until the optimization target is achieved.

[0006] Optionally, the operating data includes: flue gas outlet dust content, boiler load, coal type, flue gas volume, and flue gas temperature.

[0007] Optionally, the method further includes constructing a linkage relationship between various operating data, including: collecting historical operating data of the current boiler system, determining the data splitting period, and obtaining the boiler system operating data at the same time in each period; using control variable rules to determine the functional relationship between other operating data under each set value operating data; and using this functional relationship as the linkage relationship between the current operating data and other operating data.

[0008] Optionally, the step of reviewing the operational data based on the linkage relationship between each operational data and retaining the operational data that has passed the review includes: traversing each operational data and determining whether it meets the corresponding linkage relationship with other operational data; if it does, the current operational data is determined to have passed the review; if it does not, the current operational data is sequentially determined to meet the linkage relationship with other operational data; if it does not meet the linkage relationship with more than a preset number of other operational data, the current operational data is determined to have failed the review.

[0009] Optionally, the step of determining whether there is missing data in the approved running data and executing a data completion scheme in the case of missing data includes: identifying the type of each running parameter; determining whether there is at least one set of data under each preset parameter type; if each preset parameter type includes at least one set of data, then it is determined that there is no missing data; if there is 0 sets of data under at least one preset parameter type, then it is determined that there is missing data and a data completion scheme is executed.

[0010] Optionally, the data completion scheme includes: initializing all operating parameter acquisition nodes and presetting a future time as the acquisition time; at the corresponding acquisition time, synchronously acquiring various operating data and reviewing the acquired operating data; if the review of operating data under all preset types passes, then data completion stops; if the review of operating data under a certain preset type fails continuously, then based on the linkage relationship between the approved operating data and the missing operating data, fitting the missing operating data, and using the fitted operating parameters as the current missing operating parameters for data completion; if the review of operating data under multiple preset types fails continuously, then outputting corresponding alarm information.

[0011] Optionally, the method further includes constructing a preset control optimization model, including: collecting historical operating data of the current boiler system and pushing the historical operating data to the evaluation terminal for users to determine optimization data; collecting the user's screening results based on the evaluation terminal and using the remaining data as training samples; using the electrostatic precipitator operating parameters as training targets and training the training samples in a preset neural network to obtain the control optimization model.

[0012] Optionally, the operating parameters of the electrostatic precipitator include: the power supply method of the high-frequency power supply of the electrostatic precipitator, the power of each electric field, the rapping sequence, and the heating temperature.

[0013] A second aspect of the present invention provides a load-linked self-regulating system for an electrostatic precipitator, the system comprising: a data acquisition unit for acquiring real-time operating data of a boiler system; a processing unit for: reviewing the operating data based on the linkage relationship between various operating data, and retaining the approved operating data; determining whether there is missing data in the approved operating data, and executing a data completion scheme if there is missing data; a training unit for using the completed operating data as training data to execute a preset regulation optimization model training to obtain an optimization regulation path; and a regulation unit for adjusting the control parameters of the electrostatic precipitator based on the optimization regulation path until the optimization target is achieved.

[0014] 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 electrostatic precipitator load linkage self-regulation method.

[0015] Through the above technical solution, this invention collects the operating parameters of the boiler system in real time. By self-checking and supplementing the execution parameters, the reliability of the reference parameters for subsequent optimization and control is ensured. Then, based on a preset optimization control model, the corresponding electrostatic precipitator control path is trained to determine the optimal operating parameters of the electrostatic precipitator under the current boiler system operating conditions. This invention adjusts the corresponding electrostatic precipitator operating conditions based on real-time operating parameters, resulting in a fast response speed. Furthermore, the control scheme is determined by a control optimization model trained on historical data, offering higher accuracy and feasibility compared to manual judgment.

[0016] 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

[0017] 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:

[0018] Figure 1 This is a flowchart of the steps of an electrostatic precipitator load linkage self-regulation method provided in one embodiment of the present invention;

[0019] Figure 2 This is a system structure diagram of an electrostatic precipitator load linkage self-regulation system provided in one embodiment of the present invention. Detailed Implementation

[0020] 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.

[0021] With the deepening of environmental protection policies, traditional emission-emitting enterprises are undergoing environmental transformation. To meet emission standards, the control and management of environmental protection equipment are becoming increasingly automated. Coal-fired power plants, in particular, have made significant technological improvements in this area. Because coal-fired power plants rely on coal combustion for boiler heating and power generation, they generate substantial emissions, producing numerous pollutants during combustion. Therefore, to achieve clean emissions, numerous environmental protection devices are currently installed at the end of boiler systems to treat the flue gas after combustion, ensuring that the final exhaust gas meets clean emission standards.

[0022] Among all environmental protection equipment, dust removal equipment is crucial because after coal combustion, in addition to generating harmful gases, a large amount of soot will also be produced. If these soot are directly discharged into the air, it will also cause serious ecological pollution. The dust removal system is to collect the dust in the exhaust gas and ensure that the dust content in the exhaust gas meets the standard. The most widely used dust removal equipment at present is the electrostatic precipitator, which has multiple electrodes along the direction of flue gas flow. By setting charges opposite to the dust on the electrodes, the dust is adsorbed onto the electrode plates when passing through the electrodes. Then, at a predetermined interval, the electrode plates are vibrated to bounce the dust on the electrode plates into the ash hopper for centralized treatment.

[0023] Because the load of the boiler system is dynamically adjusted according to the grid load, the dust content in the flue gas also varies. Therefore, if you want to achieve the most ideal dust removal effect, it is necessary to adjust the operating parameters of the electrostatic precipitator accordingly based on changes in parameters such as the boiler system load. In the existing control methods, mainly managers make empirical judgments based on real-time operating parameters and then manually adjust the electrostatic precipitator. This method relies heavily on human experience, and its regulation accuracy is difficult to guarantee. Moreover, manual adjustment is often relatively lagging, which easily causes problems such as excessive energy consumption. Aiming at the problems of poor accuracy and high energy consumption in the existing electrostatic precipitator regulation schemes, the present invention creates a new method for load-linked self-regulation of electrostatic precipitators. The present invention scheme collects the operating parameters of the boiler system in real time, performs parameter self-audit and supplementation through various operating parameters, and ensures the credibility of the reference parameters for subsequent optimization regulation. Then, based on a preset optimization control model, corresponding optimization training of the electrostatic precipitator regulation path is carried out to determine the optimal operating parameters of the electrostatic precipitator under the current operating conditions of the boiler system. The present invention scheme adjusts the operating conditions of the electrostatic precipitator corresponding to the real-time operating parameters, with a fast response speed. Moreover, the regulation scheme is determined through an optimization model trained with historical data, and its accuracy is higher than manual judgment and its feasibility is also higher.

[0024] Figure 1 It is the flowchart of the method for load-linked self-regulation of electrostatic precipitators provided by an embodiment of the present invention. As Figure 1 shown, the embodiment of the present invention provides a method for load-linked self-regulation of electrostatic precipitators, and the method includes:

[0025] Step S10: Collect the operating data of the real-time boiler system.

[0026] Specifically, for the automatic control of the electrostatic precipitator, the present invention hopes that it can perform timely linkage control with changes in the load and other operating parameters of the boiler system. To achieve this effect, it is necessary to collect the real-time operating parameters of the boiler system in detail so as to adjust the operating parameters of the electrostatic precipitator based on the current operating parameters in the subsequent process.

[0027] Preferably, the operating data includes: flue gas outlet dust content, boiler load, coal type, flue gas volume, and flue gas temperature.

[0028] In this embodiment of the invention, the core objective of the linkage control of the electrostatic precipitator (ESP) is to ensure that the real-time operating status of the ESP meets the dust content requirements of the current boiler flue gas, and then verify the adjustment based on the dust content in the final exhaust sample. The boiler load and coal type directly affect the dust content in the combustion flue gas, requiring real-time adjustment of the ESP's operating power based on the dust content. This ensures that the ESP meets clean emission requirements while avoiding excessive energy consumption. Similarly, other parameters directly or indirectly affect the required operating status of the ESP; accurate collection of this data facilitates precise subsequent control of the ESP.

[0029] Step S20: Based on the linkage relationship between various operational data, review the operational data and retain the operational data that has passed the review.

[0030] Specifically, the method further includes constructing the linkage relationship between various operating data, including: collecting historical operating data of the current boiler system, determining the data splitting period, and obtaining the boiler system operating data at the same time in each period; using control variable rules to determine the functional relationship between other operating data under each set value operating data; and using this functional relationship as the linkage relationship between the current operating data and other operating data.

[0031] In this embodiment of the invention, the operating environment of the sensor equipment in the boiler system is relatively harsh, requiring it to operate under high temperature and high corrosion conditions for extended periods. Therefore, the stability of the sensor operation in the boiler system is subject to higher requirements. However, equipment failures often lead to large data acquisition errors, resulting in the inability to acquire data. If complete and effective data acquisition is not possible, the precise control of the subsequent electrostatic precipitator cannot be achieved. Therefore, it is necessary to verify the validity of this data.

[0032] Specifically, given a fixed coal quality and load, the dust content in the post-combustion flue gas is predictable. Similarly, given fixed operating parameters for the electrostatic precipitator and other equipment, the dust content in the final emitted flue gas is also predictable. Leveraging this predictability, this invention determines the interrelationships between collected historical data for subsequent review of various operating parameters. If a data point malfunctions, the difference between its collected value and the preset values ​​for the interrelationship with other data will be significant. By setting a threshold for this difference, the data can be reviewed.

[0033] Specifically, the step of reviewing the operational data based on the linkage relationship between each operational data and retaining the operational data that has passed the review includes: traversing each operational data and determining whether it meets the corresponding linkage relationship with other operational data; if it does, the current operational data is determined to have passed the review; if it does not, the current operational data is sequentially determined to meet the linkage relationship with other operational data; if it does not meet the linkage relationship with more than a preset number of other operational data, the current operational data is determined to have failed the review.

[0034] Step S30: Determine whether there is any missing data in the running data after the review is approved, and if there is missing data, execute the data completion plan.

[0035] Specifically, the step of determining whether there is missing data in the approved running data and executing a data completion scheme in the case of missing data includes: identifying the type of each running parameter; determining whether there is at least one set of data under each preset parameter type; if there is at least one set of data under each preset parameter type, then it is determined that there is no missing data; if there is 0 sets of data under at least one preset parameter type, then it is determined that there is missing data and the data completion scheme is executed.

[0036] In this embodiment of the invention, to achieve precise control of the electrostatic precipitator, the corresponding operating parameters must be comprehensive. Specifically, at any given time, the dust content at the flue gas outlet, boiler load, coal type, flue gas volume, and flue gas temperature must all be present. If any data point is missing due to failure to pass review, subsequent control rules cannot be executed. Therefore, this invention provides a corresponding data completion scheme.

[0037] Specifically, the data completion scheme includes: initializing all operating parameter acquisition nodes and presetting a future time as the acquisition time; at the corresponding acquisition time, synchronously acquiring various operating data and reviewing the acquired operating data; if the review of operating data under all preset types passes, data completion stops; if the review of operating data under a certain preset type fails continuously, the missing operating data is fitted based on the linkage relationship between the approved operating data and the missing operating data, and the fitted operating parameters are used as the current missing operating parameters for data completion; if the review of operating data under multiple preset types fails continuously, the corresponding alarm information is output.

[0038] Step S40: Use the completed running data as training data to train the preset control optimization model and obtain the optimization control path.

[0039] Specifically, the method further includes constructing a preset control optimization model, including: collecting historical operating data of the current boiler system and pushing the historical operating data to the evaluation terminal for users to determine optimization data; collecting the user's screening results based on the evaluation terminal and using the remaining data as training samples; using the electrostatic precipitator operating parameters as training targets and training the training samples in a preset neural network to obtain the control optimization model.

[0040] In this embodiment of the invention, historical data is reviewed by expert experience to select optimized operating conditions. Then, based on the correspondence between data points within these conditions, corresponding data training is performed. The training samples are divided into training data, validation data, and review data to ensure that the final optimization model is the optimal model that meets actual needs.

[0041] Step S50: Adjust the control parameters of the electrostatic precipitator based on the optimization control path until the optimization target is achieved.

[0042] Specifically, after model training, the corresponding training optimization target and the optimization process to achieve the target are obtained. Subsequently, based on the process, corresponding control commands are generated and distributed to each actuator in the electrostatic precipitator, so that these actuators can execute the commands synchronously and realize the automatic linkage control of the electrostatic precipitator.

[0043] In one possible implementation, based on factors such as flue gas inlet and outlet dust levels, boiler load, coal type, flue gas volume, and flue gas temperature, the system predicts and analyzes changes to high-voltage parameter settings to optimize operating parameters. It automatically adjusts different power supply methods and optimizes control of the high-frequency power supply until closed-loop control of line operation is achieved, ensuring timely and reliable energy-saving management. The system also performs graded efficiency predictions for each electric field of the electrostatic precipitator based on coal type and boiler combustion and operating conditions. Key functions include automatic adjustment of power supply operating power, optimized rapping sequence, heating temperature settings, and one-button intelligent voltage boosting. Through continuous debugging, data models are accumulated to find the optimal target value.

[0044] Preferably, the high and low voltage system parameters of the electrostatic precipitator can be called and modified in real time through a certain interface and charts, the current volt-ampere curve can be displayed in real time, and volt-ampere characteristic curve analysis can be performed. It can monitor and judge the back corona in the electric field in real time and automatically control the operation at the back corona critical point (self-regulation).

[0045] Preferably, the electrostatic precipitator (ESP) features self-regulation: The power supply (high-frequency, mains frequency, pulse) of the ESP system operates relatively independently, and its operating data does not automatically change with load variations, resulting in wasted power. Intelligent dust removal, through load signal setpoints (changes) and precise control using the outlet dust meter, establishes an ESP self-regulation operation database and predictive model to optimize high-voltage power supply operation in real time. This involves implementing a high- and low-voltage control system that automatically optimizes high- and low-voltage operation modes for energy saving under different load changes (especially when the operating conditions of major coal types change), automatically adjusting secondary current and voltage, intelligently selecting the rapping mode time, and operating in energy-saving control mode. This achieves load-linked self-regulation and energy-saving operation of the ESP under deep peak shaving.

[0046] Preferably, the rapping program is optimized: the rapping time can be manually adjusted during equipment operation. However, this method is not conducive to automatic time adjustment under different operating conditions. For example, under full load or poor coal quality, the running time can be appropriately increased and the stop time shortened; when the load is low or the coal quality is good, the running time can be reduced and the stop time increased. This can save energy and increase efficiency, and also effectively protect the electrode plates and wires.

[0047] Preferably, the low-pressure system is optimized and the temperature setting is optimized: the conventional heating settings of the insulation box and the ash hopper are within a certain range and do not automatically adapt to seasonal climate changes. The electrostatic precipitator intelligent management platform, in conjunction with the intelligent constant temperature heater, automatically optimizes and adjusts the operation for energy saving according to different seasons, summer and winter.

[0048] Figure 2 This is a system structure diagram of an electrostatic precipitator load-linked self-regulating system provided in one embodiment of the present invention. Figure 2 As shown, this invention provides a load-linked self-regulating system for an electrostatic precipitator. The system includes: a data acquisition unit for acquiring real-time operating data of a boiler system; a processing unit for: reviewing the operating data based on the linkage relationship between various operating data, and retaining the approved operating data; determining whether there is missing data in the approved operating data, and executing a data completion scheme if there is missing data; a training unit for using the completed operating data as training data to execute a preset regulation optimization model training to obtain an optimization regulation path; and a regulation unit for adjusting the control parameters of the electrostatic precipitator based on the optimization regulation path until the optimization target is achieved.

[0049] 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 electrostatic precipitator load linkage self-regulation method.

[0050] 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.

[0051] 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.

[0052] 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 for load linkage self-regulation of an electrostatic precipitator, characterized in that, The method includes: Collect real-time operating data of the boiler system; Based on the interrelationships between various operational data, the operational data is reviewed, and only the reviewed and approved operational data is retained; wherein, The rules for establishing the linkage relationships between various operational data are as follows: Collect historical operating data of the current boiler system, determine the data splitting period, and obtain the boiler system operating data at the same time in each period; use control variable rules to determine the functional relationship between other operating data under each setpoint operating data; use this functional relationship as the linkage relationship between the current operating data and other operating data; The step of reviewing the operational data based on the linkage relationship between each operational data and retaining the operational data that has passed the review includes: traversing each operational data and determining whether it meets the corresponding linkage relationship with other operational data; if it does, the current operational data is determined to have passed the review; if it does not, the current operational data is sequentially determined to meet the linkage relationship with other operational data; if it does not meet the linkage relationship with more than a preset number of other operational data, the current operational data is determined to have failed the review. Determine if there is any missing data in the approved runtime data, and if so, execute a data completion plan, including: Identify the type of each running parameter; determine whether there is at least one set of data under each preset parameter type; if each preset parameter type includes at least one set of data, then it is determined that there is no missing data; if there is at least one preset parameter type with 0 sets of data, then it is determined that there is missing data, and the data completion scheme is executed. The data completion scheme includes: initializing all operating parameter acquisition nodes and presetting a future time as the acquisition time; at the corresponding acquisition time, synchronously acquiring various operating data and reviewing the acquired operating data; if the review of operating data under all preset types passes, data completion stops; if the review of operating data under a certain preset type fails continuously, the missing data is fitted based on the linkage relationship between the approved operating data and the missing data, and the fitted operating parameters are used as the current missing parameters for data completion; if the review of operating data under multiple preset types fails continuously, the corresponding alarm information is output. The completed running data is used as training data to train a preset regulation and optimization model, thereby obtaining the optimization and regulation path; the preset regulation and optimization model is constructed, including: Collect historical operating data of the current boiler system and push the historical operating data to the evaluation terminal for users to determine optimization data; Based on the screening results of the users collected by the evaluation end, the remaining data will be used as training samples; The operating parameters of the electrostatic precipitator are used as training targets, and the training samples are trained in a preset neural network to obtain a regulation and optimization model. The control parameters of the electrostatic precipitator are adjusted based on the optimization control path until the optimization target is achieved.

2. The method according to claim 1, characterized in that, The operational data includes: Dust content at flue gas outlet, boiler load, coal type, flue gas volume, and flue gas temperature.

3. The method according to claim 1, characterized in that, The operating parameters of the electrostatic precipitator include: The power supply method, power of each electric field, rapping sequence, and heating temperature of the high-frequency power supply for the electrostatic precipitator.

4. A load-linked self-regulating system for an electrostatic precipitator, characterized in that, The system is used to execute the electrostatic precipitator load linkage self-regulation method according to any one of claims 1-3, and the system comprises: The data acquisition unit is used to collect real-time operating data of the boiler system. Processing unit, used for: Based on the linkage relationship between various operational data, the operational data is reviewed, and the operational data that has passed the review is retained; Determine if there is any missing data in the approved running data, and if there is missing data, execute a data completion plan; The training unit is used to use the completed running data as training data to execute the training of the preset control optimization model and obtain the optimization control path. The control unit is used to adjust the control parameters of the electrostatic precipitator based on the optimization control path until the optimization target is achieved.

5. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the electrostatic precipitator load linkage self-regulation method according to any one of claims 1-3.

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