Intelligent electric precipitation system

Abstract

The embodiment of the invention provides an intelligent electric precipitation system. The intelligent electric precipitation system comprises a power management module, a working condition sensing module, a dynamic optimization module and a cooperative control module, the power supply management module comprises a high-frequency power supply unit and a power-frequency power supply unit, and the high-frequency power supply unit adopts a full-bridge series-parallel hybrid resonance circuit; the working condition sensing module is configured with a distributed sensor network; the dynamic optimization module establishes a multi-objective optimization model; the cooperative control module realizes a four-level control architecture; the system realizes optimal balance of dust removal efficiency and energy consumption by collecting working condition parameters in real time and dynamically adjusting working parameters of a power supply. According to the intelligent electric dust removal system, through a hybrid power supply framework, a multi-sensor network and four-stage cooperative control, the dust removal efficiency is improved, and the equipment reliability and the intelligent level are improved.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of dust removal technology, and in particular to intelligent electrostatic precipitators. Background Technology

[0002] Traditional electrostatic precipitators (ESPs) suffer from several drawbacks, including unstable efficiency due to fluctuating coal-fired loads, excessive energy consumption from high-voltage power supplies, and slow maintenance response. Fixed-parameter operation leads to a sharp drop in efficiency under varying conditions, and the power frequency supply structure results in significant energy waste. Manual maintenance takes an average of over four hours. Existing systems lack intelligent adjustment capabilities, making it difficult to meet new emission standards. Retrofitting requires prolonged downtime, severely impacting the economic operation of power plants.

[0003] Therefore, a better solution is urgently needed. Summary of the Invention

[0004] In view of this, the embodiments of this specification provide an intelligent electrostatic precipitator system to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, an intelligent electrostatic precipitator system is provided, including a power management module, a working condition sensing module, a dynamic optimization module, and a collaborative control module; The power management module includes a high-frequency power supply unit and a power frequency power supply unit. The high-frequency power supply unit adopts a full-bridge series-parallel hybrid resonant circuit. The operating condition sensing module is configured with a distributed sensor network; The dynamic optimization module establishes a multi-objective optimization model; The collaborative control module implements a four-level control architecture; The system collects operating parameters in real time and dynamically adjusts power supply parameters to achieve an optimal balance between dust removal efficiency and energy consumption.

[0006] In one possible implementation, the high-frequency power supply unit operates at a frequency of not less than 20kHz, and the output voltage is continuously adjustable from 0 to 72kV. The output voltage of the power frequency power supply unit is adjustable in stages from 0-80kV. Each power supply unit is equipped with intelligent protection circuits to monitor the three-phase input current and output voltage waveforms in real time.

[0007] In one possible implementation, the operating condition sensing module includes a boiler load sensor, a flue gas composition analyzer, a temperature gradient sensor, and a dust concentration detector. The sampling period is 100ms, and the collected parameters include flue gas temperature field distribution, dust particle size distribution, and sulfide concentration.

[0008] In one possible implementation, the dynamic optimization module calculates the optimal operating point using a fuzzy logic algorithm. The output parameters include the high-frequency power supply resonant frequency setting, the pulse power supply duty cycle adjustment coefficient, and the power frequency power supply voltage compensation amount. It also has the function of self-learning and correcting based on historical operating data.

[0009] In one possible implementation, the four-level control architecture of the collaborative control module includes: the device layer implements local power regulation through the improved FMHC-01 control board; The unit layer coordinates the synchronous operation of multiple power supplies within the same electric field. The system layer manages the coordination between power outage vibration and power supply timing; the dispatch layer interacts with the power plant's DCS through the IPEC system; and a timestamp synchronization mechanism is used between each level.

[0010] In one possible implementation, the dynamic optimization module uses the following calculation formula when performing parameter optimization:

[0011] Where Q is the comprehensive energy efficiency index; α i ΔT is the weighting coefficient for the i-th temperature sensor; i β represents the deviation between the measured value and the set value of the i-th temperature sensor; j R is the correction factor for the j-th pollutant; j γ represents the real-time concentration of the j-th pollutant; k and ω k Let V be the efficiency and load factor of the k-th power supply, respectively; k Let be the output voltage of the k-th power supply; n, m, and p represent the number of temperature sensors, the number of pollutant types, and the total number of power supplies, respectively.

[0012] In one possible implementation, the comprehensive energy efficiency index Q is normalized to generate the final control parameters:

[0013] Where P is the final control parameter, used to directly drive power supply regulation; Q t This represents the overall energy efficiency index for the current sampling period and the previous T-1 sampling periods; For Q t The moving average; η s D is the geometric factor of the s-th electric field structure; s λ represents the amount of dust deposited in the s-th electric field; c F represents the weight of the c-th control strategy; c The applicability score for the c-th control strategy is given; T, S, and C are the sampling window size, the number of electric fields, and the number of strategy types, respectively.

[0014] In one possible implementation, the control board program optimization of the high-frequency power supply unit includes a resonant frequency dynamic tracking function and an automatic switching function for intermittent pulse power supply mode, automatically selecting operating parameters according to boiler load and flue gas temperature.

[0015] In one possible implementation, the power distribution section of the system is equipped with a 1000A / 3P molded case circuit breaker and a 380V three-phase intelligent distribution cabinet, which has electronic tripping function and dual power automatic transfer switch.

[0016] In one possible implementation, the system implementation process includes a hardware modification phase and a software upgrade phase. In the hardware modification phase, the high-frequency power supply control board is replaced and a working condition sensor network is added. In the software upgrade phase, intelligent control algorithms are deployed and a digital twin model of the equipment is established. Finally, system integration and testing are carried out.

[0017] This specification provides an intelligent electrostatic precipitator system, which includes a power management module, a condition sensing module, a dynamic optimization module, and a collaborative control module. The power management module comprises a high-frequency power supply unit and a low-frequency power supply unit, with the high-frequency power supply unit employing a full-bridge series-parallel hybrid resonant circuit. The condition sensing module is configured with a distributed sensor network. The dynamic optimization module establishes a multi-objective optimization model. The collaborative control module implements a four-level control architecture. The system dynamically adjusts power supply parameters by real-time acquisition of operating parameters to achieve an optimal balance between dust removal efficiency and energy consumption. This intelligent electrostatic precipitator system, through its hybrid power architecture, multi-sensor network, and four-level collaborative control, improves dust removal efficiency and enhances equipment reliability and intelligence. Attached Figure Description

[0018] Figure 1 This is a system schematic diagram of an intelligent electrostatic precipitator system provided in one embodiment of this specification. Detailed Implementation

[0019] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0020] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0022] This specification provides an intelligent electrostatic precipitator system, which will be described in detail in the following embodiments.

[0023] See Figure 1 , Figure 1 A schematic diagram of an intelligent electrostatic precipitator system according to an embodiment of this specification is shown. Specifically, it includes a power management module, a condition sensing module, a dynamic optimization module, and a collaborative control module. The power management module comprises a high-frequency power supply unit and a low-frequency power supply unit, the high-frequency power supply unit employing a full-bridge series-parallel hybrid resonant circuit. The condition sensing module is configured with a distributed sensor network. The dynamic optimization module establishes a multi-objective optimization model. The collaborative control module implements a four-level control architecture. The system dynamically adjusts power supply parameters by real-time acquisition of condition parameters to achieve an optimal balance between dust removal efficiency and energy consumption.

[0024] Among them, the intelligent electrostatic precipitator system can refer to a dust removal device that integrates power management, operating condition sensing, dynamic optimization, and collaborative control, achieving a dynamic balance between dust removal efficiency and energy consumption through intelligent adjustment. The power management module can refer to a power supply system composed of a high-frequency power supply unit and a power frequency power supply unit, used to provide adjustable high-voltage power to the electrostatic precipitator. The high-frequency power supply unit can refer to a power conversion device using a full-bridge series-parallel hybrid resonant circuit, achieving efficient energy conversion through high-frequency switching technology. The power frequency power supply unit can refer to a power supply device based on a traditional power frequency transformer, serving as a backup or supplementary power supply unit for the system. The full-bridge series-parallel hybrid resonant circuit can refer to a power conversion circuit combining series and parallel resonant topologies, possessing the electrical characteristics of both resonant modes. The operating condition sensing module can refer to a monitoring system configured with a distributed sensor network, used to collect real-time operating condition data such as flue gas parameters and equipment status. The distributed sensor network can refer to a monitoring system composed of multiple spatially distributed sensor nodes, achieving comprehensive acquisition of operating condition parameters. The dynamic optimization module can refer to an intelligent decision-making unit that establishes a multi-objective optimization model, used to generate the optimal combination of operating parameters. A multi-objective optimization model refers to a mathematical optimization model that simultaneously considers multiple factors such as dust removal efficiency, energy consumption, and equipment lifespan. A collaborative control module refers to an intelligent execution unit that implements a four-level control architecture, used to coordinate the coordinated operation of various modules within the system. A four-level control architecture can refer to a hierarchical control system encompassing device-level, unit-level, system-level, and cloud-level controls.

[0025] This technical solution utilizes a hybrid power supply architecture combining high-frequency and industrial-frequency power supplies, along with the real-time operational monitoring capabilities of a distributed sensor network. Based on a multi-objective optimization model, it dynamically adjusts operating parameters and employs a four-level collaborative control system. This effectively solves the problems of unstable efficiency, excessive energy consumption, and sluggish response in traditional electrostatic precipitators under varying operating conditions. It achieves a real-time optimized balance between dust removal efficiency and energy consumption, significantly improving system adaptability and economy, and meeting increasingly stringent environmental emission standards. The system's full-bridge series-parallel resonant circuit enhances power conversion efficiency, and the intelligent control architecture reduces the need for manual maintenance, providing a reliable dust removal solution for industrial scenarios such as coal-fired power plants.

[0026] In one possible implementation, the high-frequency power supply unit operates at a frequency of not less than 20kHz and has a continuously adjustable output voltage of 0-72kV; the power frequency power supply unit has a graded output voltage adjustment of 0-80kV; and each power supply unit is equipped with an intelligent protection circuit to monitor the three-phase input current and output voltage waveforms in real time.

[0027] The high-frequency power supply unit refers to a power conversion device employing an AC-DC-AC-DC conversion topology, operating at a frequency no lower than 20kHz, and achieving continuously adjustable DC output from 0-72kV through high-frequency inverter technology. The power frequency power supply unit refers to a power supply device based on 50Hz AC power stepped up by a rectifier transformer, outputting 0-80kV DC voltage using a graded adjustment method. The intelligent protection circuit refers to an electronic protection system integrating current and voltage monitoring and fault diagnosis functions, acquiring three-phase input current and output voltage waveform parameters in real time. The three-phase input current refers to the three-phase AC power parameters obtained by the power supply unit from the power grid, reflecting the load status of the power supply system. The output voltage waveform refers to the voltage-time characteristic curve of the high-voltage DC power output by the power supply unit, including characteristic information such as ripple and flashover.

[0028] This technical solution achieves high-frequency pulsed power supply through a high-frequency power supply unit, significantly improving corona voltage and dust charging efficiency. Its continuously adjustable output characteristics allow for dynamic optimization of waveform parameters based on operating conditions. A power frequency power supply unit employs graded regulation as a backup power source, ensuring power supply reliability under extreme conditions. An intelligent protection circuit monitors the three-phase current balance and output voltage waveform in real time, proactively identifying grid anomalies and electric field flashover risks. The collaborative operation of the dual power supply units maintains the energy-saving advantages of the high-frequency power supply while compensating for the insufficient adaptability of high-frequency power supply in harsh environments through the power frequency power supply. The overall system effectively suppresses back corona through fine waveform control, shortens the spark control response time to the microsecond level, significantly improves the average electric field voltage and dust removal stability, while reducing power distribution capacity requirements and grid harmonic pollution, meeting the long-term stable operation requirements under ultra-low emission standards.

[0029] In one possible implementation, the operating condition sensing module includes a boiler load sensor, a flue gas composition analyzer, a temperature gradient sensor, and a dust concentration detector. The sampling period is 100ms, and the collected parameters include flue gas temperature field distribution, dust particle size distribution, and sulfide concentration.

[0030] Among these, boiler load sensors refer to measuring devices installed at key parts of the boiler system, used to monitor parameters characterizing boiler operating load, such as steam output or fuel supply, in real time. Flue gas composition analyzers refer to gas detection equipment based on infrared spectroscopy or electrochemical principles, used for online analysis of the concentration of pollutants such as sulfur dioxide and nitrogen oxides in flue gas. Temperature gradient sensors refer to multi-point temperature measuring devices distributed along the flue gas cross-section, used to construct temperature change curves along the flue gas flow direction. Dust concentration detectors refer to particulate matter monitoring instruments using laser scattering or beta-ray absorption principles, used to determine the mass concentration of suspended particulate matter in flue gas. Sampling period refers to the time interval between data acquisition by the sensors, characterizing the system's response speed to changes in operating conditions. Flue gas temperature field distribution refers to the set of temperature values ​​at different spatial locations along the flue gas cross-section, reflecting the thermal distribution state. Dust particle size distribution refers to the statistical characteristics of the quantity or mass proportion of particulate matter in flue gas according to diameter. Sulfide concentration refers to the total content parameter of sulfur-containing compounds, mainly sulfur dioxide, in flue gas.

[0031] This technical solution utilizes a high-density array of boiler load sensors to accurately capture changes in combustion conditions. Combined with a flue gas composition analyzer, it achieves real-time tracking of pollutant concentrations. A three-dimensional thermal field model constructed by a temperature gradient sensor identifies anomalies in the flue gas flow field. A dust concentration detector simultaneously monitors the mass concentration and particle size distribution characteristics of particulate matter. Millisecond-level sampling cycles ensure the system's rapid response to fluctuations in operating conditions. This multi-parameter collaborative monitoring system accurately identifies changes in coal type, load adjustments, and other operational status changes, providing comprehensive operating data support for subsequent dynamic optimization. It effectively solves the control lag problem caused by incomplete parameter acquisition in traditional dust removal systems, significantly improving the system's adaptability to complex operating conditions. Simultaneously, it provides accurate pollutant emission data for environmental monitoring.

[0032] In one possible implementation, the dynamic optimization module calculates the optimal operating point using a fuzzy logic algorithm. The output parameters include the high-frequency power supply resonant frequency setting, the pulse power supply duty cycle adjustment coefficient, and the power frequency power supply voltage compensation amount. It also has the function of self-learning and correcting based on historical operating data.

[0033] The dynamic optimization module can refer to an intelligent decision-making unit based on fuzzy logic algorithms, which determines the optimal operating state of the system through multi-parameter coordinated calculations. Fuzzy logic algorithms can refer to mathematical methods for handling imprecise input variables, achieving approximate reasoning through membership functions and rule bases. The optimal operating point can refer to the combination of system operating parameters that maximizes the objective function under given constraints. The high-frequency power supply resonant frequency setpoint can refer to the optimal operating frequency parameter determined for the series resonant circuit. The pulse power supply duty cycle adjustment coefficient can refer to the adjustment factor controlling the ratio of the high-voltage pulse width to its period. The power frequency power supply voltage compensation amount can refer to the power frequency output voltage adjustment value added to compensate for line losses. The self-learning correction function can refer to the system's ability to automatically optimize control parameters by analyzing historical operating data.

[0034] This technical solution employs fuzzy logic algorithms to handle nonlinear relationships in the dust removal system. It quantifies the fuzzy mapping relationship between flue gas parameters and power supply parameters through membership functions, achieving coordinated optimization of high-frequency resonant frequency, pulse duty cycle, and power frequency compensation. The system's output resonant frequency setpoint maximizes corona power conversion efficiency, while the dynamically adjusted pulse duty cycle effectively balances dust removal performance and electrode losses. Power frequency voltage compensation ensures power supply stability under extreme conditions. The self-learning function continuously analyzes historical operating data to gradually improve the fuzzy rule base and control parameters, enabling the system to autonomously optimize to adapt to different coal types, load changes, and environmental requirements. This solution significantly improves the control accuracy and stability of the electrostatic precipitator system under complex operating conditions, while reducing the frequency of manual intervention, providing core technical support for the long-term efficient operation of intelligent dust removal systems.

[0035] In one possible implementation, the four-level control architecture of the collaborative control module includes: the equipment layer realizes local power supply regulation through the improved FMHC-01 control board; the unit layer coordinates the synchronous operation of multiple power supplies in the same electric field; the system layer manages the timing coordination between power outage vibration and power supply; the dispatching layer interacts with the power plant DCS through the IPEC system; and a timestamp synchronization mechanism is adopted between each level.

[0036] The collaborative control module refers to an intelligent control system with a hierarchical architecture, used to achieve multi-level collaborative operation of electrostatic precipitators. The four-level control architecture refers to a hierarchical management system consisting of an equipment layer, a unit layer, a system layer, and a scheduling layer, with each layer having a clearly defined function. The equipment layer refers to the lowest-level execution unit that directly controls the power supply equipment, implementing basic regulation functions through a local controller. The improved FMHC-01 control board refers to a dedicated circuit board integrating high-frequency resonant control and pulse modulation functions, supporting PID algorithms and fuzzy logic control. Power supply local regulation refers to closed-loop control of parameters such as voltage and frequency implemented within a single power supply unit. The unit layer refers to the coordination control unit that manages multiple power supply units within the same electric field area. The system layer refers to the decision-making unit responsible for interlocking control across electric field systems. Power-off rapping refers to mechanical dust removal operations triggered during power outages. Power supply timing coordination refers to the power start-up and shutdown time sequence arranged according to process requirements. The scheduling layer refers to the highest-level management unit that interfaces with the power plant's main control system. The IPEC system refers to an intelligent centralized dust removal control system with data acquisition and remote command issuance capabilities. A power plant's DCS (Distributed Control System) can refer to a distributed control system used for centralized monitoring of the power plant's production processes. A timestamp synchronization mechanism can refer to a unified clock alignment scheme based on IRIG-B codes or the PTP protocol.

[0037] This technical solution achieves multi-dimensional collaboration of the dust removal system from the equipment level to the plant level through a four-level control architecture. The improved FMHC-01 control board at the equipment level provides precise local parameter adjustment, ensuring that each power supply operates at its optimal resonance point. The unit level employs a master-slave synchronization algorithm to coordinate the phase relationship of multiple power supplies, eliminating mutual interference within the electric field. The system level uses a timing optimization algorithm to precisely align the power-off rapping cycle with the power supply waveform valley value, reducing secondary dust generation. The scheduling level utilizes the bidirectional data channel between the IPEC system and the power plant's DCS to achieve coordinated adjustment between the dust removal system and the power generation load. A timestamp synchronization mechanism between each level ensures the timing consistency of control commands, avoiding control conflicts caused by communication delays. This architecture significantly improves the response speed and control accuracy of the dust removal system under complex operating conditions, while reducing the communication load between controllers at different levels, providing a reliable foundation for collaborative control of intelligent dust removal systems.

[0038] In one possible implementation, the dynamic optimization module uses the following calculation formula when performing parameter optimization:

[0039] Where Q is the comprehensive energy efficiency index, derived from the output of the multi-objective optimization model; α i The weighting coefficient for the i-th temperature sensor is derived from historical data analysis; ΔT i The deviation between the measured value and the set value of the i-th temperature sensor originates from the operating condition sensing module; β jR is the correction factor for the j-th pollutant, derived from flue gas composition analysis; j γ represents the real-time concentration of the j-th pollutant, derived from a flue gas composition analyzer; k and ω k These are the efficiency and load factor of the k-th power supply, respectively, derived from the power management module; V k The output voltage of the k-th power supply is derived from feedback from the power control board; n, m, and p represent the number of temperature sensors, the number of pollutant types, and the total number of power supplies, respectively, derived from system configuration parameters.

[0040] In one possible implementation, the comprehensive energy efficiency index Q is normalized to generate the final control parameters:

[0041] Where P is the final control parameter, used to directly drive power supply regulation; Q t The comprehensive energy efficiency index for the current and previous T-1 sampling periods is derived from historical data from the dynamic optimization module; For Q t The moving average is derived from statistical calculations; η s D is the geometric factor of the s-th electric field structure, derived from the electric field design parameters; s λ represents the amount of dust deposited in the s-th electric field, derived from the dust concentration detector; c The weights for the c-th control strategy are derived from the expert system; F c The applicability score for the c-th control strategy is derived from the output of the fuzzy logic algorithm; T, S, and C are the sampling window size, the number of electric fields, and the number of strategy types, respectively, derived from the system configuration.

[0042] In one possible implementation, the control board program optimization of the high-frequency power supply unit includes a resonant frequency dynamic tracking function and an automatic switching function for intermittent pulse power supply mode, automatically selecting operating parameters according to boiler load and flue gas temperature.

[0043] Among these, the high-frequency power supply unit can refer to a power electronic device employing an LLC resonant topology, which achieves power conversion and regulation through high-frequency switching. The control board program can refer to the firmware algorithm running in the embedded system, used to execute power parameter control logic. The resonant frequency dynamic tracking function can refer to a control mechanism that detects and adjusts the resonant point of the LLC loop in real time, maintaining zero-voltage switching characteristics through adaptive dead time. The intermittent pulse power supply mode can refer to an energy-saving operation mode with periodic start-stop, reducing switching losses through pulse groups under light loads. The automatic switching function can refer to a control strategy conversion module triggered by operating parameters. The boiler load can refer to an operating indicator reflecting steam production demand, directly affecting power demand. The flue gas temperature can refer to the gas thermodynamic parameters of the boiler tail flue, used to evaluate the resistivity characteristics of dust. Operating parameters can refer to a set of adjustable variables including switching frequency, duty cycle, and output voltage.

[0044] This technical solution uses dynamic resonant frequency tracking to match the inherent resonant point of the LLC circuit in real time, and adaptive dead-time control to ensure soft-switching characteristics across the entire load range, significantly reducing the conduction loss of MOSFET devices and the conduction time of the body diode. The intermittent pulse power supply mode intelligently switches operating states according to boiler load changes. During low-load periods, pulse group modulation technology is used to reduce drive losses under light load conditions by periodically locking the switching transistors, while suppressing output voltage ripple. The system constructs a multi-dimensional decision model based on flue gas temperature sensors and load signals, automatically selecting the optimal combination of resonant frequency and pulse duty cycle to meet both power density requirements during high-load periods and energy efficiency optimization requirements during low-load periods. This solution achieves improved efficiency and reduced heat loss of the power supply unit across the entire operating range, while reducing the frequency of manual parameter adjustments, providing a highly adaptive and reliable power supply solution for industrial boiler systems.

[0045] In one possible implementation, the power distribution section of the system is equipped with a 1000A / 3P molded case circuit breaker and a 380V three-phase intelligent distribution cabinet, which has electronic tripping function and dual power automatic transfer switch.

[0046] Among them, a 1000A / 3P molded case circuit breaker refers to a three-pole molded case circuit breaker with a rated current of 1000 amps, employing a thermal-magnetic or electronic tripping mechanism, and possessing short-circuit and overload protection functions. A 380V three-phase intelligent distribution cabinet refers to a three-phase AC power distribution device with an operating voltage of 380 volts, integrating a monitoring module and an intelligent control unit. Electronic tripping function refers to protection characteristics implemented based on a microprocessor, achieving precise tripping curve control through real-time acquisition of current signals. A dual-power automatic transfer switch refers to a mechanical and electrical device that enables seamless switching between two independent power sources, ensuring power supply continuity.

[0047] This technical solution provides reliable short-circuit current breaking capacity by configuring high-capacity molded case circuit breakers. Their compact design saves installation space in the distribution cabinet, while the three-pole configuration ensures balanced protection for three-phase loads. The intelligent distribution cabinet's electronic trip unit enables digital monitoring of current parameters and dynamically adjusts protection thresholds based on load characteristics, significantly improving fault response accuracy compared to traditional thermal-magnetic tripping. The dual-power automatic transfer switch employs a dual protection mechanism of mechanical interlocking and electrical control. When the main power supply is abnormal, it can complete the switching of the backup power supply within the set voltage drop threshold, without manual intervention. The system collects electrical parameters of each circuit in real time through an integrated intelligent monitoring module, forming a complete power distribution network operation database to provide data support for preventative maintenance. This configuration solution ensures power supply reliability while enabling visualization and remote management of the power distribution system's status, effectively reducing maintenance complexity and improving energy efficiency.

[0048] In one possible implementation, the system implementation process includes a hardware modification phase and a software upgrade phase. In the hardware modification phase, the high-frequency power supply control board is replaced and a working condition sensor network is added. In the software upgrade phase, intelligent control algorithms are deployed and a digital twin model of the equipment is established. Finally, system integration and testing are carried out.

[0049] The hardware upgrade phase refers to the process of updating the physical structure of existing electrical equipment, including component replacement and installation of additional devices. A high-frequency power supply control board refers to a printed circuit board assembly used to adjust the operating parameters of a high-frequency switching power supply, integrating a PWM controller and drive circuit. A working condition sensor network refers to a distributed monitoring system composed of various types of sensors such as temperature, vibration, and current, achieving data aggregation through an industrial bus. The software upgrade phase refers to the iterative optimization process of the control system's program code and functional modules. Intelligent control algorithms refer to control strategy programs with self-learning capabilities, such as fuzzy PID or neural network controllers. A digital twin model of equipment refers to a virtual simulation system that integrates 3D modeling and real-time data to achieve full lifecycle mapping of physical equipment. System integration testing refers to the overall functional verification and parameter tuning process performed after the completion of hardware and software upgrades.

[0050] This technical solution ensures a controllable upgrade process through a phased, systematic transformation strategy. In the hardware upgrade phase, a modularly designed high-frequency power control board is used to increase power density, coupled with a multi-dimensional sensor network to build a holographic perception capability of the equipment's operating status. In the software upgrade phase, intelligent control algorithms are deployed to continuously optimize control effects through online parameter identification, while a digital twin model enables virtual simulation of equipment status and fault prediction. Finally, joint testing verifies the synergy between hardware and software, with bidirectional data interaction between the digital twin model and the physical equipment forming a closed-loop verification system. This implementation method significantly reduces the risk of system downtime during the transformation process. The combination of intelligent algorithms and sensor networks enables the equipment to adapt to changing operating conditions, and digital twin technology provides a simulation platform for subsequent predictive maintenance, thus improving the overall reliability and intelligence level of the system.

[0051] This specification provides an intelligent electrostatic precipitator system, which includes a power management module, a condition sensing module, a dynamic optimization module, and a collaborative control module. The power management module comprises a high-frequency power supply unit and a low-frequency power supply unit, with the high-frequency power supply unit employing a full-bridge series-parallel hybrid resonant circuit. The condition sensing module is configured with a distributed sensor network. The dynamic optimization module establishes a multi-objective optimization model. The collaborative control module implements a four-level control architecture. The system dynamically adjusts power supply parameters by real-time acquisition of operating parameters to achieve an optimal balance between dust removal efficiency and energy consumption. This intelligent electrostatic precipitator system, through its hybrid power architecture, multi-sensor network, and four-level collaborative control, improves dust removal efficiency and enhances equipment reliability and intelligence.

[0052] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0053] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0054] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent electrostatic precipitator system, characterized in that, It includes a power management module, a working condition sensing module, a dynamic optimization module, and a collaborative control module; The power management module includes a high-frequency power supply unit and a power frequency power supply unit. The high-frequency power supply unit adopts a full-bridge series-parallel hybrid resonant circuit. The operating condition sensing module is configured with a distributed sensor network; The dynamic optimization module establishes a multi-objective optimization model; The collaborative control module implements a four-level control architecture; The system achieves an optimal balance between dust removal efficiency and energy consumption by collecting operating parameters in real time and dynamically adjusting power supply parameters.

2. The intelligent electrostatic precipitator system according to claim 1, characterized in that, The high-frequency power supply unit operates at a frequency of not less than 20kHz and has an output voltage that is continuously adjustable from 0 to 72kV. The output voltage of the power frequency power supply unit is adjustable in stages from 0-80kV. Each power supply unit is equipped with intelligent protection circuits to monitor the three-phase input current and output voltage waveforms in real time.

3. The intelligent electrostatic precipitator system according to claim 1, characterized in that, The operating condition sensing module includes a boiler load sensor, a flue gas composition analyzer, a temperature gradient sensor, and a dust concentration detector. The sampling period is 100ms, and the collected parameters include flue gas temperature field distribution, dust particle size distribution, and sulfide concentration.

4. The intelligent electrostatic precipitator system according to claim 1, characterized in that, The dynamic optimization module calculates the optimal operating point using a fuzzy logic algorithm. The output parameters include the high-frequency power supply resonant frequency setting, the pulse power supply duty cycle adjustment coefficient, and the power frequency power supply voltage compensation amount. It also has the function of self-learning and correcting based on historical operating data.

5. The intelligent electrostatic precipitator system according to claim 1, characterized in that, The four-level control architecture of the collaborative control module includes: the device layer realizes local power regulation through the improved FMHC-01 control board; The unit layer coordinates the synchronous operation of multiple power supplies within the same electric field. System-level management of the coordination between power-off vibration and power-on timing; The dispatch layer interacts with the power plant's DCS through the IPEC system; a timestamp synchronization mechanism is used between different levels.

6. The intelligent electrostatic precipitator system according to claim 4, characterized in that, The dynamic optimization module uses the following calculation formula when performing parameter optimization: Where Q is the comprehensive energy efficiency index; α i ΔT is the weighting coefficient for the i-th temperature sensor; i β represents the deviation between the measured value and the set value of the i-th temperature sensor; j R is the correction factor for the j-th pollutant; j γ represents the real-time concentration of the j-th pollutant; k and ω k Let V be the efficiency and load factor of the k-th power supply, respectively; k Let be the output voltage of the k-th power supply; n, m, and p represent the number of temperature sensors, the number of pollutant types, and the total number of power supplies, respectively.

7. The intelligent electrostatic precipitator system according to claim 6, characterized in that, The comprehensive energy efficiency index Q is normalized to generate the final control parameters: Where P is the final control parameter, used to directly drive power supply regulation; Q t This represents the overall energy efficiency index for the current sampling period and the previous T-1 sampling periods; For Q t The moving average; η s D is the geometric factor of the s-th electric field structure; s λ represents the amount of dust deposited in the s-th electric field; c F represents the weight of the c-th control strategy; c The applicability score for the c-th control strategy is given; T, S, and C are the sampling window size, the number of electric fields, and the number of strategy types, respectively.

8. The intelligent electrostatic precipitator system according to claim 1, characterized in that, The optimized control board program of the high-frequency power supply unit includes a resonant frequency dynamic tracking function and an automatic switching function for intermittent pulse power supply mode, which automatically selects operating parameters based on boiler load and flue gas temperature.

9. The intelligent electrostatic precipitator system according to claim 1, characterized in that, The power distribution section of the system is equipped with a 1000A / 3P molded case circuit breaker and a 380V three-phase intelligent distribution cabinet. The intelligent distribution cabinet has an electronic tripping function and a dual power automatic transfer switch.

10. The intelligent electrostatic precipitator system according to claim 1, characterized in that, The system implementation process includes a hardware modification phase and a software upgrade phase. In the hardware modification phase, the high-frequency power supply control board is replaced and a working condition sensor network is added. In the software upgrade phase, intelligent control algorithms are deployed and a digital twin model of the equipment is established. Finally, system integration and testing are carried out.

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