Circuit breaker phase control switch system and circuit breaker control unit

Abstract

The invention relates to the technical field of circuit breaker phase control switches, and provides a circuit breaker phase control switch system and a circuit breaker control unit.The circuit breaker phase control switch system comprises a three-layer protection framework composed of a sensing layer, a control layer and an execution layer, and the sensing layer comprises a high-precision CT / PT, a temperature sensor, a vibration sensor, a partial discharge UHF sensor and a laser ranging sensor; the multi-dimensional fault characteristic parameter acquisition module is used for acquiring multi-dimensional fault characteristic parameters such as current and voltage waveforms, mechanical vibration frequency spectrums and insulation discharge signals in real time and providing basic data support for protection decision making; and the control layer comprises an AI edge calculation box, a digital twin engine, a federated learning module and a 5G communication module, and is used for realizing data processing, phase prediction and decision control. The AI prediction algorithm is introduced to be combined with the high-precision sensor and the time synchronization module, the phase control precision is improved from traditional + / -1ms to + / -0.05 ms, improvement of two orders of magnitude is achieved, and the high-precision phase control can remarkably reduce overvoltage and overcurrent impact during switching operation.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit breaker phase control switches, in particular to a circuit breaker phase control switch system and a circuit breaker control unit. Background Art

[0002] In power systems, circuit breakers are critical switching devices whose phase control accuracy and response speed directly impact the stability and reliability of the power system. Traditional circuit breaker phase control switching systems primarily rely on mechanical contacts and simple zero-crossing detection technology. Faced with the increasingly complex load characteristics and fault scenarios in modern power systems, these systems are gradually exposing numerous technical bottlenecks. For example, the phase control accuracy of traditional systems is generally around ±1ms, making it difficult to meet the extremely high power quality requirements of scenarios such as photovoltaic power plants and data centers.

[0003] In actual applications, traditional circuit breaker phase control switch systems and control units have the following problems: phase control: the traditional system adopts zero-crossing detection, which has an inherent detection delay, and in a harmonic pollution power grid environment, the judgment of the zero-crossing point is prone to deviation, resulting in insufficient phase control accuracy and inability to achieve high-precision synchronous switching operations, which in turn affects the service life of power equipment and system stability; response speed: the action time of traditional mechanical circuit breakers is usually 10-50ms. For some rapidly changing fault scenarios, such as reverse power supply in photovoltaic power stations at night, this response speed makes it difficult to cut off the fault current in a short time, which may cause equipment damage or even expand the scope of the accident; protection function: traditional protection systems are mostly based on a single current or voltage parameter for fault judgment, lacking comprehensive monitoring of multiple physical quantities such as temperature, vibration, partial discharge, etc., and are prone to false operation or refusal to operate, and cannot fully and accurately reflect the operating status and fault type of the circuit breaker.

[0004] To address these issues, several improvements have been proposed in existing technologies. For example, higher-precision sensors are used to improve phase detection accuracy, but due to a lack of effective algorithmic support, detection results in complex power grid environments remain suboptimal. Solid-state circuit breakers are introduced to improve response speed, but using solid-state breakers alone is expensive and has limitations in handling steady-state currents. Auxiliary monitoring sensors are added, but due to the lack of a unified fusion decision-making mechanism, multi-parameter data cannot be effectively utilized. None of these solutions offer comprehensive innovations in system architecture and core technologies, and cannot simultaneously meet the requirements for high-precision phase control, rapid response, multi-dimensional protection, and intelligent operation and maintenance. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a circuit breaker phase control switch system and a circuit breaker control unit, which solves the problem that the traditional system adopts a zero-crossing detection method, has an inherent detection delay, and in a harmonic pollution power grid environment, the judgment of the zero-crossing point is prone to deviation, resulting in insufficient phase control accuracy.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a circuit breaker phase control switching system includes a three-layer protection architecture consisting of a perception layer, a control layer, and an execution layer. The perception layer includes a high-precision CT / PT, a temperature sensor, a vibration sensor, a partial discharge UHF sensor, and a laser ranging sensor, which is used to collect multi-dimensional fault characteristic parameters such as current and voltage waveforms, mechanical vibration spectra, and insulation discharge signals in real time, providing basic data support for protection decision-making; The control layer includes an AI edge computing box, a digital twin engine, a federated learning module, and a 5G communication module, which are used to implement data processing, phase prediction, and decision control; The execution layer includes a hybrid switch mechanism, a magnetostrictive drive and a buffer circuit, and is used to perform opening and closing operations; The system achieves ±0.05ms phase control accuracy through the above architecture to ensure accurate disconnection of the zero-crossing fault point. The breaking time meets the demand for rapid removal of short-circuit faults, and the low-energy breaking effect of arc energy <0.1mJ forms a full-link protection closed loop from fault perception, intelligent decision-making to execution of breaking. It is suitable for scenarios with strict reliability requirements such as data centers and high-speed rail traction.

[0007] Preferably, the AI ​​edge computing box adopts the STM32MP1+NPU architecture, with a built-in LSTM or Transformer model, and implements phase prediction through the following process: obtaining 1000 points of waveform data in the past 10ms as input, predicting the next three zero-crossing timestamps through the pre-trained model, and calculating the optimal switching time. The formula is: in, is the input waveform data, is the optimal time calculation function.

[0008] Preferably, the hybrid switch mechanism includes a main path vacuum interrupter and a parallel path 1200V SiC MOSFET array, and the action sequence is: after a fault occurs The internal AI detects a fault, The internal SiC is turned on, the mechanical contacts separate within 5ms, and then the SiC is turned off.

[0009] Preferably, the multi-parameter protection decision system builds a logic matrix based on the fault type and detection parameters. When di / dt>5kA / ms and a sudden increase in vibration is detected, the solid-state priority disconnection strategy is triggered, and the disconnection time < .

[0010] Preferably, the digital twin operation and maintenance platform is built based on ANSYS Twin Builder, which includes an electromagnetic field model, a multi-body dynamics model and a thermal network model. By comparing sensor data with the digital twin model, health scoring, remaining life prediction and maintenance recommendation generation are achieved.

[0011] The circuit breaker control unit includes an AI-enhanced phase prediction control unit, an ultra-fast solid-state hybrid circuit breaker unit, a multi-physical quantity fusion protection unit, a digital twin and remote operation and maintenance unit, and a self-powered unit. The AI-enhanced phase prediction control unit uses an edge computing module to run a lightweight AI model, combined with Kalman filtering, to achieve phase control with a switching time error of <0.1ms.

[0012] Preferably, the ultra-fast solid-state hybrid circuit breaker unit consists of a mechanical contact main circuit, a parallel SiC MOSFET, an energy absorption module and an FPGA control circuit, and the breaking time is < , the formula is: in, is the fault detection time, is the SiC on-time, is the mechanical contact separation time.

[0013] Preferably, the multi-physical quantity fusion protection unit adds local discharge, contact resistance and vibration signal monitoring dimensions, integrates multi-sensor data through federated learning, and the protection accuracy is >99.9%.

[0014] Preferably, the digital twin and the remote operation and maintenance unit realize data interaction between the physical layer and the digital twin layer through 5G / optical fiber, and perform real-time simulation and fault pre-diagnosis based on ANSYS Twin Builder, reducing operation and maintenance costs by 40%.

[0015] Preferably, the self-powered unit includes a CT power module, a supercapacitor and a photovoltaic auxiliary module to achieve completely maintenance-free power supply. In extreme cases, it can perform more than 3 operations. The present invention provides a circuit breaker phase control switch system and a circuit breaker control unit. The system has the following beneficial effects: 1. This invention improves phase control accuracy from the traditional ±1ms to ±0.05ms by introducing an AI prediction algorithm combined with high-precision sensors and a time synchronization module, achieving a two-order-of-magnitude improvement. This high-precision phase control can significantly reduce overvoltage and overcurrent shocks during switching operations. For example, in nighttime reverse power protection scenarios for photovoltaic power plants, it can precisely control switching timing, effectively protecting equipment such as photovoltaic inverters and improving system reliability and stability. This effect is unpredictable with traditional technologies through simple sensor upgrades.

[0016] 2. The present invention adopts a solid-state hybrid switch actuator, combines a mechanical switch with a SiC MOSFET array, and utilizes the SiC device's The ultra-fast conduction speed and extremely short delay controlled by FPGA shorten the system's minimum breaking time and improve breaking speed. In the event of a sudden short-circuit fault, it can quickly interrupt the fault current, significantly reducing arc energy and minimizing equipment damage. This rapid response capability is particularly effective in scenarios such as high-speed rail traction power supply, which require high vibration and rapid fault handling, and is unattainable with traditional mechanical circuit breakers.

[0017] 3. The present invention achieves high-precision protection by constructing a multi-parameter fusion protection decision-making system, adding monitoring dimensions such as partial discharge, vibration, and temperature, and integrating multi-sensor data through a federated learning algorithm. Compared with traditional single-parameter protection, this solution can more accurately identify the type of fault, such as judging short-circuit faults by combining di / dt>5kA / ms with a sudden increase in vibration, thus avoiding the false operation and refusal of traditional overcurrent protection. At the same time, predictive maintenance is achieved based on multi-parameter monitoring, such as predicting insulation degradation through partial discharge and temperature gradient, detecting mechanical jamming through vibration spectrum analysis, and discovering potential equipment failures in advance. This effect exceeds the functional scope of traditional protection systems and brings unexpected technical advantages.

[0018] 4. This invention integrates digital twins with a remote operation and maintenance platform, building a real-time simulation model based on ANSYS Twin Builder to achieve real-time mapping of circuit breaker status and health scoring. By comparing the digital twin with actual sensor data, it can accurately predict the remaining life of the equipment and generate maintenance recommendations, reducing operation and maintenance costs while achieving "digital-first" testing and "zero downtime" maintenance. This intelligent operation and maintenance model completely transforms the traditional passive maintenance approach of regular inspections. In scenarios with extremely high reliability requirements, such as data centers, it can significantly reduce power outage losses, bringing significant economic benefits and technological innovations that are unattainable with traditional operation and maintenance technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the circuit breaker phase control switching system of the present invention; Figure 2 This is a system diagram of the circuit breaker control unit of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] Example: Please see the attached Figure 1 -Attached Figure 2 An embodiment of the present invention provides a circuit breaker phase control switch system, including a three-layer protection architecture consisting of a perception layer, a control layer, and an execution layer. The perception layer includes a high-precision CT / PT, a temperature sensor, a vibration sensor, a partial discharge UHF sensor, and a laser ranging sensor, which are used to collect multi-dimensional fault characteristic parameters such as current and voltage waveforms, mechanical vibration spectra, and insulation discharge signals in real time, providing basic data support for protection decisions. Taking the photovoltaic power station application scenario as an example, the high-precision CT / PT collects current and voltage waveform data in real time, the vibration sensor monitors the spectrum characteristics of the mechanical mechanism in a high-frequency vibration environment, the partial discharge UHF sensor captures the ultra-high frequency signal generated when the insulation deteriorates, and the laser ranging sensor is used to detect the contact gap distance, providing multi-dimensional fault characteristic parameters for protection decisions; The control layer includes AI edge computing box, digital twin engine, federated learning module and 5G communication module, which are used to realize data processing, phase prediction and decision control. The ADC samples the perception layer data at high speed and, combined with the IEEE 1588 PTPv2 time synchronization module, achieves precise alignment of waveform data timestamps. The execution layer includes a hybrid switch mechanism, magnetostrictive drive, and snubber circuit, which are used to perform opening and closing operations. The main path vacuum interrupter is responsible for conducting the rated current, the parallel path SiC MOSFET array is responsible for transient disconnection, and the snubber circuit is used to absorb the energy impact during the disconnection process, targeting short-circuit fault disconnection scenarios. The system achieves ±0.05ms phase control accuracy through the above architecture to ensure accurate disconnection of the zero-crossing fault point. The breaking time meets the demand for rapid removal of short-circuit faults, and the low-energy breaking effect of arc energy <0.1mJ forms a full-link protection closed loop from fault perception, intelligent decision-making to execution of breaking. It is suitable for scenarios with strict reliability requirements such as data centers and high-speed rail traction.

[0022] The AI ​​edge computing box uses the STM32MP1+NPU architecture and a built-in LSTM or Transformer model to achieve phase prediction through the following process: 1000 points of waveform data in the past 10ms are obtained as input, and the next three zero-crossing timestamps are predicted using a pre-trained model to calculate the optimal switching time. The formula is: in, is the input waveform data, is the optimal time calculation function.

[0023] The hybrid switch mechanism includes a main path vacuum interrupter and a parallel path 1200V SiC MOSFET array. The action sequence is: after a fault occurs The internal AI detects a fault, The internal SiC is turned on, the mechanical contacts separate within 5ms, and then the SiC is turned off; The specific action sequence of the execution layer is as follows: Within 50μs after the fault occurs, the AI ​​edge computing box detects the fault through di / dt>5kA / ms and vibration surge characteristics; Trigger the SiC MOSFET array to turn on within 1μs, bearing the transient breaking current; The mechanical contacts separate within 5ms, at which point the SiC MOSFET turns off, and the vacuum interrupter takes over the subsequent current interruption task. This timing achieves a minimum breaking time of 86μs, and the arc energy is controlled at <0.1mJ (traditional circuit breakers are about 50-100mJ). It solves the high-voltage isolation problem through magnetic coupling energy transmission technology and ensures reliable operation of the drive circuit.

[0024] The multi-parameter protection decision system builds a logic matrix based on the fault type and detection parameters. When di / dt>5kA / ms and a sudden increase in vibration is detected, the solid-state priority disconnection strategy is triggered. The disconnection time is < When di / dt > 5kA / ms and the vibration spectrum is abnormal, the solid-state priority disconnection strategy is triggered, and the SiC MOSFET completes disconnection within 100μs, avoiding the operation delay of traditional mechanical switches that may cause the fault to expand. Through the joint analysis of the partial discharge signal amplitude and the temperature gradient rise rate, when the local discharge exceeds the threshold and the temperature gradient is > 2℃ / min, an early warning signal is generated and a power outage is planned.

[0025] The digital twin operation and maintenance platform, built on ANSYS Twin Builder, includes electromagnetic field models, multibody dynamics models, and thermal network models. By comparing sensor data with the digital twin model, it can achieve health scoring, remaining life prediction, and maintenance recommendation generation. It uses vibration spectrum analysis (such as the energy proportion in the 100-300Hz frequency band) combined with travel time monitoring. When the travel time deviation exceeds 15%, the tripping operation is locked and the backup circuit breaker is activated.

[0026] The circuit breaker control unit includes an AI-enhanced phase prediction control unit, an ultra-fast solid-state hybrid circuit breaker unit, a multi-physical quantity fusion protection unit, a digital twin and remote operation and maintenance unit, and a self-powered unit. The AI-enhanced phase prediction control unit uses an edge computing module to run a lightweight AI model, combined with Kalman filtering, to achieve phase control with a switching time error of <0.1ms.

[0027] The ultra-fast solid-state hybrid circuit breaker unit consists of a mechanical contact main circuit, parallel SiC MOSFET, energy absorption module and FPGA control circuit, with a breaking time of < , the formula is: in, is the fault detection time, is the SiC conduction time, is the mechanical contact separation time.

[0028] The multi-physical quantity fusion protection unit adds local discharge, contact resistance and vibration signal monitoring dimensions, integrates multi-sensor data through federated learning, and has a protection accuracy rate of >99.9%.

[0029] The digital twin and remote operation and maintenance unit use 5G / fiber optics to exchange data between the physical layer and the digital twin layer. Real-time simulation and fault prediction based on ANSYS Twin Builder are performed, reducing operation and maintenance costs by 40%. The digital twin model built with ANSYS Twin Builder includes: Electromagnetic field model: The finite element method is used to simulate the arc plasma distribution when the switch is disconnected, and to predict the arc voltage and energy release process; Multi-body dynamics model: Perform stress analysis on the connecting rod and contact system of the operating mechanism and calculate the peak mechanical stress during the opening and closing process; Thermal network model: Based on the contact resistance-temperature coupling relationship, the contact temperature rise curve is predicted (error <1.5°C); During the operation and maintenance process, sensor data (current / voltage / temperature / vibration) is synchronized to the digital twin layer in real time, and a health score (0-100 points) is generated through model comparison. When the score is lower than 70 points, the remaining life prediction algorithm (based on the Arrhenius model) is automatically started and maintenance recommendations are generated.

[0030] The self-powered unit includes a CT power supply module, a supercapacitor and a photovoltaic auxiliary module, achieving completely maintenance-free power supply and can perform more than three operations in extreme cases.

[0031] The control unit integrates five functional units: AI-enhanced phase prediction control unit: Utilizes the NVIDIA Jetson edge computing module to run a lightweight LSTM model. Training data includes over 2,000 fault waveforms, such as those for motor starting, transformer excitation, and photovoltaic inverter grid connection. This model achieves switching time errors of <0.1ms and is adaptable to harmonic pollution grids with THD >15%. AI-enhanced phase prediction control unit: Utilizes the NVIDIA Jetson edge computing module to run a lightweight LSTM model. Training data includes over 2,000 fault waveforms, such as those for motor starting, transformer excitation, and photovoltaic inverter grid connection. This model achieves switching time errors of <0.1ms and is adaptable to harmonic pollution grids with THD >15%. Ultra-fast solid-state hybrid circuit breaker unit: mechanical contacts and SiC MOSFETs in parallel, with FPGA control circuitry achieving a drive delay of <1μs; Multi-physics fusion protection unit: New contact resistance monitoring (0.1mΩ accuracy) and partial discharge monitoring (1pC resolution) are added. Using a federated learning algorithm to integrate data from five types of sensors, the protection accuracy reaches 99.92%, a four-order-of-magnitude improvement over traditional overcurrent protection. Digital Twin and Remote Operation and Maintenance Unit: This unit uses 5G URLLC mode (communication latency <2ms) to enable data exchange between the physical layer and the digital twin layer, and provides remote maintenance guidance based on AR technology, reducing operation and maintenance costs by 40% (traditional solutions cost $12,000 / year, while this solution costs $3,200 / year). Self-powered unit: The CT power module can output 5W power at 10% of the rated current. The supercapacitor (10F / 2.7V) stores energy and supports three opening and closing operations. In outdoor scenarios, it is equipped with a 10W photovoltaic panel to achieve completely maintenance-free power supply.

[0032] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A circuit breaker phase control switching system, characterized in that: The system comprises a three-layer protection architecture consisting of a perception layer, a control layer, and an execution layer. The perception layer includes high-precision CT / PT, temperature sensors, vibration sensors, partial discharge UHF sensors, and laser ranging sensors. These sensors collect multi-dimensional fault characteristic parameters such as current and voltage waveforms, mechanical vibration spectra, and insulation discharge signals in real time, providing basic data support for protection decision-making. The control layer includes an AI edge computing box, a digital twin engine, a federated learning module, and a 5G communication module, which are used to implement data processing, phase prediction, and decision control; The execution layer includes a hybrid switch mechanism, a magnetostrictive drive and a buffer circuit, and is used to perform opening and closing operations; The system achieves ±0.05ms phase control accuracy through the above architecture to ensure accurate disconnection of the fault zero crossing point, with the shortest The breaking time meets the need for rapid disconnection of short-circuit faults, and the low-energy breaking effect of arc energy < 0.1mJ forms a full-link protection closed loop from fault perception, intelligent decision-making to execution of breaking. It is suitable for scenarios with strict reliability requirements such as data centers and high-speed rail traction.

2. The circuit breaker phase control switching system according to claim 1, characterized in that: The AI ​​edge computing box uses the STM32MP1+NPU architecture and a built-in LSTM or Transformer model to achieve phase prediction through the following process: 1000 points of waveform data in the past 10ms are obtained as input, and the next three zero-crossing timestamps are predicted using a pre-trained model to calculate the optimal switching time. The formula is: in, is the input waveform data, is the optimal time calculation function.

3. The circuit breaker phase control switching system according to claim 1, characterized in that: The hybrid switch mechanism includes a main path vacuum interrupter and a parallel path 1200V SiC MOSFET array. The action sequence is: after a fault occurs The internal AI detects a fault, The internal SiC is turned on, the mechanical contacts separate within 5ms, and then the SiC is turned off.

4. The circuit breaker phase control switching system according to claim 1, characterized in that: The multi-parameter protection decision system builds a logic matrix based on the fault type and detection parameters. When di / dt>5kA / ms and a sudden increase in vibration is detected, the solid-state priority disconnection strategy is triggered. The disconnection time is < .

5. The circuit breaker phase control switching system according to claim 1, characterized in that: The digital twin operation and maintenance platform is built based on ANSYS Twin Builder and includes electromagnetic field models, multi-body dynamics models and thermal network models. By comparing sensor data with the digital twin model, it can achieve health scoring, remaining life prediction and maintenance recommendation generation.

6. A circuit breaker control unit, using the circuit breaker phase control switch system according to any one of claims 1 to 5, characterized in that: It includes an AI-enhanced phase prediction control unit, an ultra-fast solid-state hybrid circuit breaker unit, a multi-physical quantity fusion protection unit, a digital twin and remote operation and maintenance unit, and a self-powered unit; the AI-enhanced phase prediction control unit uses an edge computing module to run a lightweight AI model, combined with Kalman filtering, to achieve phase control with a switching time error of <0.1ms.

7. The circuit breaker control unit according to claim 6, characterized in that: The ultra-fast solid-state hybrid circuit breaker unit consists of a mechanical contact main circuit, parallel SiC MOSFET, energy absorption module and FPGA control circuit, with a breaking time of < , the formula is: in, is the fault detection time, is the SiC on-time, is the mechanical contact separation time.

8. The circuit breaker control unit according to claim 6, wherein: The multi-physical quantity fusion protection unit adds local discharge, contact resistance and vibration signal monitoring dimensions, integrates multi-sensor data through federated learning, and has a protection accuracy rate of >99.9%.

9. The circuit breaker control unit according to claim 6, characterized in that: The digital twin and remote operation and maintenance unit realize data interaction between the physical layer and the digital twin layer through 5G / optical fiber, and perform real-time simulation and fault prediction based on ANSYS Twin Builder, reducing operation and maintenance costs by 40%.

10. The circuit breaker control unit according to claim 6, wherein: The self-powered unit includes a CT power supply module, a supercapacitor and a photovoltaic auxiliary module, achieving completely maintenance-free power supply and can perform more than three operations in extreme cases.

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