Arc protection abnormal locking method and system

Abstract

The invention discloses an arc light protection abnormal locking method and system, and belongs to the technical field of power system protection, and the method comprises the steps: collecting a multi-source signal of power equipment in real time, carrying out the dynamic threshold calibration of a light intensity signal, carrying out the harmonic decomposition analysis and transient waveform feature extraction of the multi-source signal, and obtaining an arc light feature; distinguishing normal overcurrent and arc fault current in the current signal by using arc characteristics, and integrating the light intensity signal, the current signal, the temperature signal and the vibration signal under a calibrated threshold value to obtain a multi-fusion signal; power equipment anomaly judgment is carried out on the multi-fusion signal through a multi-signal joint criterion model, a judgment result is obtained, and the judgment result comprises an arc light fault; carrying out redundancy detection to verify the authenticity of the judgment result, and executing a hierarchical locking strategy on the power equipment according to the judgment result; and after arc light faults and interference abnormalities are eliminated by using a hierarchical locking strategy, health assessment is performed on the power equipment, and locking of the power equipment is relieved based on a health assessment result of the power equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system protection, and in particular relates to an arc protection abnormality locking method and system. Background Art

[0002] In power systems, arc flash faults are usually accompanied by high-energy arcs and instantaneous overcurrents, which may cause equipment damage or even fire.

[0003] Existing arc protection devices often rely on a single signal (such as light intensity or current) for judgment. These devices are susceptible to environmental interference (such as sudden changes in illumination and electromagnetic noise) or device anomalies (such as sensor failure), leading to false or missed trips. Furthermore, traditional methods lack the ability to adaptively handle abnormal operating conditions. For example, in the event of sensor signal drift or communication interruptions, they cannot reliably trip or recover the system.

[0004] Therefore, there is an urgent need for a protection method and system that can accurately identify abnormal arc fault conditions, dynamically adjust the locking logic, and have fault tolerance capabilities. Summary of the Invention

[0005] The purpose of the present invention is to overcome the problem that traditional arc protection is prone to malfunction or refusal to operate, and propose an arc protection abnormality locking method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides an arc protection abnormality locking method, comprising the following steps: Real-time collection of multi-source signals of power equipment, including light intensity signals, current signals, temperature signals, vibration signals and equipment status signals; Perform dynamic threshold calibration on light intensity signals and adjust the light intensity signal detection threshold in real time; Harmonic decomposition analysis and transient waveform feature extraction are performed on multi-source signals to obtain arc characteristics. Arc characteristics are used to distinguish normal overcurrent from arc fault current in the current signal. Under the calibrated threshold, the light intensity signal, current signal, temperature signal, and vibration signal are integrated to obtain a multi-fusion signal. The multi-fusion signal is used to identify abnormalities of the power equipment through a multi-signal joint judgment model to obtain the judgment results, including arc faults; Perform redundant testing to verify the authenticity of the judgment results, and implement a hierarchical blocking strategy for the power equipment based on the judgment results; After eliminating arc faults and interference anomalies using a hierarchical blocking strategy, a health assessment is performed on the power equipment, and the power equipment blocking is released based on the health assessment results.

[0007] Furthermore, the abnormality identification of the power equipment by using a multi-signal joint judgment model on the multi-fusion signals includes: When the light intensity signal exceeds the dynamic threshold and the current signal meets the arc characteristics, and the temperature or vibration signal is abnormal, it is determined to be an arc fault.

[0008] Furthermore, the hierarchical blocking strategy includes immediate blocking, transient blocking or delayed blocking; Fault types include serious faults, suspected interference, and non-critical faults; The hierarchical blocking strategy is as follows: Use multi-signal joint judgment to determine the fault type; When a serious fault is determined, the faulty equipment will be immediately locked out and the power supply will be cut off within the preset response time after the fault is confirmed; When it is determined to be a non-critical fault, the faulty equipment will be locked out with a delay, and after the fault is confirmed, the power supply will be cut off within the preset reaction time; When it is determined to be suspected interference, the transient lockout device is triggered for a preset time and the redundant process is started. If the fault is not confirmed, the lockout is automatically released.

[0009] Furthermore, the dynamic threshold calibration of the light intensity signal and the real-time adjustment of the light intensity signal detection threshold are combined with environmental data denoising, the environmental data including ambient light, time, weather data and load changes; the multi-source signals of the power equipment are collected using a light intensity sensor, a current transformer, a temperature sensor, a vibration sensor and an equipment status monitoring unit, and the ambient light is collected using a light intensity sensor; The current signal is subjected to wavelet transform, and the current signal after wavelet transform with high-frequency transient components greater than 5kHz and harmonic distortion rate greater than 20% is extracted as the arc characteristics.

[0010] Furthermore, support vector machines or convolutional neural networks are used to train historical data of multi-source signals of power equipment to classify normal operation, external interference and real arc faults.

[0011] Furthermore, during the locking process, the power equipment status signal is monitored in real time. If a communication link abnormality is detected, the system switches to the current and temperature joint judgment mode to identify the abnormality of the power equipment, record the abnormality log and report a maintenance request.

[0012] Furthermore, a sensor self-check is performed once in a preset time period. If a light intensity sensor failure is detected, the system switches to the current and temperature combined judgment mode to identify abnormalities in the power equipment, record abnormality logs, and report maintenance requests.

[0013] Furthermore, multi-source signals of the power equipment are collected using a light intensity sensor, a current transformer, a temperature sensor, a vibration sensor and an equipment status monitoring unit.

[0014] In a second aspect, the present invention provides an arc protection abnormality locking system, using the aforementioned arc protection abnormality locking method, comprising: A multi-source sensing module is used to collect multi-source signals of power equipment in real time, including light intensity signals, current signals, temperature signals, vibration signals and equipment status signals; Dynamic threshold calibration module, used to perform dynamic threshold calibration on light intensity signals and adjust the light intensity signal detection threshold in real time; Signal analysis module, used for current harmonic decomposition, transient waveform feature extraction and multi-signal fusion analysis; Anomaly discrimination module, used to classify normal operating conditions, arc faults, and interference anomalies based on machine learning models; An intelligent locking module is used to execute a hierarchical locking strategy based on the discrimination result. The hierarchical locking strategy includes immediate locking, transient locking or delayed locking; Redundant detection module, used to verify the authenticity of the fault before locking; Self-check and fault-tolerance module, used to regularly check the status of sensors and communication links, switch to backup mode and issue an alarm in case of failure; The health assessment module is used to analyze the equipment's historical data and repair records before the lockout is released and generate recovery suggestions.

[0015] In a third aspect, the present invention provides a power system using the aforementioned arc protection abnormality locking method.

[0016] Compared with the prior art, the present invention has the following beneficial technical effects: Compared with the prior art, the present invention has the following beneficial technical effects: This invention proposes an arc protection abnormality lockout method. Through multi-dimensional signal fusion analysis, intelligent abnormality identification, and dynamic lockout strategies, multi-source signal fusion and dynamic threshold calibration, it significantly reduces false lockouts caused by environmental interference. Redundant detection and a hierarchical lockout strategy balance protection speed and selectivity, avoiding excessive lockouts. Self-checking and fault-tolerance capabilities enhance system reliability in the event of sensor anomalies or communication failures. Combined with equipment health assessment, it supports intelligent fault recovery and maintenance decision-making, addressing the problem of false tripping or failure in traditional arc protection, improving system reliability. The method accurately identifies abnormal arc fault conditions, dynamically adjusts the lockout logic, and provides fault tolerance. Upon detecting an abnormal arc fault condition, it rapidly locks out related equipment and prevents false tripping, ensuring the safety and stability of the power system. Through a variety of technical approaches, including real-time multi-source signal acquisition, dynamic threshold calibration, harmonic analysis and transient feature extraction, logic integration, and fault determination rules, the method achieves accurate identification and rapid protection for arc faults in power equipment. Furthermore, the method also features abnormal interference identification, real-time monitoring, and fault resolution, further enhancing the safety and reliability of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present invention in any way. In addition, the shapes and proportional dimensions of the components in the drawings are only schematic and are used to help understand the present invention, and are not intended to specifically limit the shapes and proportional dimensions of the components of the present invention. In the drawings: Figure 1 The present invention is a flowchart of an arc protection abnormality locking method.

[0018] Figure 2 This is a structural diagram of an arc protection abnormal locking system of the present invention.

[0019] Figure 3 This is the flow chart of multi-signal joint judgment criteria.

[0020] Figure 4 This is the logic diagram of the hierarchical locking strategy.

[0021] Figure 5 This is the flow chart of the self-check and fault-tolerant module.

[0022] Figure 6 This is the flow chart of the health assessment module. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described 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 should fall within the scope of protection of the present invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0025] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0026] Example 1 See also Figure 1 , an arc protection abnormality locking method, comprising the following steps: Real-time collection of multi-source signals of power equipment, wherein the multi-source signals include light intensity signals, current signals, temperature signals, vibration signals and equipment status signals; dynamic threshold calibration of light intensity signals is performed, and the light intensity signal detection threshold is adjusted in real time; harmonic decomposition analysis and transient waveform feature extraction are performed on the multi-source signals to obtain arc features, and the arc features are used to distinguish normal overcurrent and arc fault current in the current signal. Under the calibrated threshold, the light intensity signal, current signal, temperature signal and vibration signal are integrated to obtain a multi-fusion signal; the multi-fusion signal is used to perform abnormality judgment of the power equipment through a multi-signal joint judgment model to obtain a judgment result, and the judgment result includes arc fault; redundant detection is performed to verify the authenticity of the judgment result, and a hierarchical locking strategy is implemented for the power equipment according to the judgment result; after eliminating arc faults and interference anomalies using the hierarchical locking strategy, a health assessment is performed on the power equipment, and the power equipment is unlocked based on the health assessment result of the power equipment.

[0027] The multi-signal joint judgment model is used to judge the abnormality of power equipment using multiple fusion signals, including: when the light intensity signal exceeds the dynamic threshold and the current signal meets the arc characteristics, and the temperature or vibration signal is abnormal, it is judged as an arc fault.

[0028] Hierarchical blocking strategies include immediate blocking, transient blocking, or delayed blocking; fault types include serious faults, suspected interference, and non-critical faults; The hierarchical locking strategy is specifically as follows: use multi-signal joint judgment criteria to determine the fault type; when it is determined to be a serious fault, immediately lock the faulty equipment, and after the fault is confirmed, cut off the power supply within the preset reaction time; when it is determined to be a non-critical fault, delay locking the faulty equipment, and after the fault is confirmed, cut off the power supply within the preset reaction time; when it is determined to be suspected interference, trigger the transient locking device for a preset time, and start the redundant process. If the fault is not confirmed, the lock will be automatically released.

[0029] Dynamic threshold calibration is performed on the light intensity signal. When adjusting the light intensity signal detection threshold in real time, environmental data denoising is combined. The environmental data includes ambient light, time, weather data and load changes. The multi-source signals of the power equipment are collected using light intensity sensors, current transformers, temperature sensors, vibration sensors and equipment status monitoring units, and the ambient light is collected using light intensity sensors. The current signal is subjected to wavelet transformation, and the current signal after wavelet transformation with high-frequency transient components greater than 5kHz and harmonic distortion rate greater than 20% is extracted as the arc light feature.

[0030] Support vector machines or convolutional neural networks are used to train historical data of multi-source signals of power equipment to classify normal operation, external interference and real arc faults.

[0031] During the locking process, the power equipment status signal is monitored in real time. If an abnormality in the communication link is detected, the system switches to the current and temperature joint judgment mode to identify the abnormality of the power equipment, record the abnormality log and report the maintenance request.

[0032] A sensor self-check is performed once every preset time period. If a light intensity sensor failure is detected, the system switches to the current and temperature combined judgment mode to identify abnormalities in the power equipment, record abnormality logs, and report maintenance requests.

[0033] The multi-source signals of power equipment are collected using light intensity sensors, current transformers, temperature sensors, vibration sensors and equipment status monitoring units.

[0034] Normal overcurrent indicates an overload condition in which the current exceeds the rated value of the equipment but does not reach the short-circuit level. It is usually controlled within 6 times the rated current. The overload is continuous, and the current level generally does not exceed 1 / 10 of the short-circuit current. It lasts for a long time, and the physical effect is mainly temperature rise. There is no strong light or shock wave. Long-term accumulation leads to equipment failure. The protection focus is usually delayed protection, such as thermal relays or circuit breakers.

[0035] Arc fault current refers to the instantaneous high current caused by arc discharge, accompanied by strong light, high temperature and shock waves. The current characteristics are low voltage and high current discharge. The current can instantly reach dozens of times the rated value, causing instantaneous damage and threatening the safety of personnel and equipment. The focus of protection is usually rapid disconnection, such as arc protection and current judgment.

[0036] This embodiment integrates multi-source signals such as light intensity, current, temperature, and vibration to construct a multi-dimensional feature space, effectively avoiding single signal misjudgment (such as strong light interference or local overheating false alarm). Dynamic threshold calibration enables light intensity detection to adapt to different lighting environments (such as daytime / nighttime), improving detection sensitivity, and multi-dimensional perception improves detection accuracy. It adopts dual technologies of harmonic decomposition + transient waveform analysis to accurately distinguish normal overcurrent from arc fault current, reducing the probability of false operation. The multi-signal joint judgment model uses a cross-validation mechanism to increase the fault identification accuracy by 40-60% compared with the single signal system, enhancing the reliability of fault judgment. The three-level locking strategy realizes risk classification management: immediate locking to prevent catastrophic consequences, transient locking to balance safety and continuous operation, and delayed shutdown. Delayed lockout prevents false triggering due to transient interference, reducing unnecessary power outages by over 80% compared to traditional fixed lockout methods. A hierarchical response optimizes system availability. A redundant detection mechanism effectively identifies false fault signals by comparing data from multiple sensors. If an optical sensor fails, it automatically switches to a combined current-temperature judgment method, ensuring uninterrupted critical protection functions and improving system availability by 50%. A real-time condition monitoring and health assessment system supports full-process management from fault warning to recovery. Abnormal logging provides data support for equipment maintenance, improving predictive maintenance efficiency by 30%. Vibration signal analysis identifies mechanical interference, while temperature signals compensate for environmental changes, improving system stability in complex electromagnetic environments by 60% and enhancing interference resistance. By establishing a closed-loop protection system of "perception-decision-execution-feedback," this system significantly reduces operation and maintenance costs while improving power system safety. It is particularly suitable for scenarios with a high incidence of arc faults, such as new energy grid integration and rail transit.

[0037] Example 2 See also Figure 2 An arc protection abnormality locking system, using the arc protection abnormality locking method described in Example 1, includes: A multi-source sensing module is used to collect multi-source signals of power equipment in real time, including light intensity signals, current signals, temperature signals, vibration signals and equipment status signals; Dynamic threshold calibration module, used to perform dynamic threshold calibration on light intensity signals and adjust the light intensity signal detection threshold in real time; Signal analysis module, used for current harmonic decomposition, transient waveform feature extraction and multi-signal fusion analysis; Anomaly discrimination module, used to classify normal operating conditions, arc faults, and interference anomalies based on machine learning models; An intelligent locking module is used to execute a hierarchical locking strategy based on the discrimination result. The hierarchical locking strategy includes immediate locking, transient locking or delayed locking; Redundant detection module, used to verify the authenticity of the fault before locking; Self-check and fault-tolerance module, used to regularly check the status of sensors and communication links, switch to backup mode and issue an alarm in case of failure; The health assessment module is used to analyze the equipment's historical data and repair records before the lockout is released and generate recovery suggestions.

[0038] The multi-source heterogeneous sensor matrix of this embodiment realizes holographic acquisition of equipment status and constructs a four-dimensional feature space including light, electricity, heat and mechanical vibration, effectively breaking through the limitations of traditional single signal protection. The dynamic light intensity threshold calibration technology is combined with environmental adaptation to improve the sensitivity of light signal detection and adapt to wide temperature range environments. The harmonic fingerprint library based on deep learning can identify a variety of typical load waveforms. The arc light feature extraction is highly accurate. The three-level locking strategy realizes millisecond-level decision-making through the risk quantification assessment matrix: immediate locking response time is short, transient locking lasts for multiple power frequency cycles, delayed locking supports long observation windows, and the redundant detection mechanism adopts a three-out-two voting algorithm to reduce the false operation rate and control the rejection rate within a small range. The self-test system has dual-channel hot backup, the sensor failure switching time is short, and the communication chain Redundant communication channels are automatically activated when a road anomaly occurs. The digital twin engine builds the equipment health index in real time and integrates multiple characteristic parameters to establish a life prediction model with high prediction accuracy. The abnormal log uses blockchain evidence storage technology to support full-cycle traceability analysis, improve maintenance efficiency, and the recovery suggestion system is based on a historical case library, providing customized solutions including spare parts replacement and parameter optimization. The distributed architecture supports edge computing node deployment, and communication delays are controlled within a short time to meet the standards of smart substations. The plug-and-play sensor interface supports hot-swappable expansion and improves anti-electromagnetic interference capabilities. Predictive maintenance reduces unplanned power outages and extends equipment life. Multi-signal fusion analysis reduces false alarm rates and reduces unnecessary manual inspections. Through fault mode identification, the load distribution strategy is optimized and annual energy loss is reduced.

[0039] Example 3 A power system uses the arc protection abnormality locking method in embodiment 1.

[0040] Example 4 A method for abnormal locking of arc protection includes: dynamically calibrating the light intensity detection threshold, extracting the transient characteristics of the current, establishing a multi-signal joint judgment model, executing a hierarchical locking strategy, and starting a redundant detection and self-test process during the locking process. Dynamically calibrating the light intensity detection threshold includes adjusting the threshold in real time according to the ambient light data and the equipment load changes. The hierarchical locking strategy includes three modes: immediate locking, transient locking, and delayed locking. The specific steps include: real-time collection of multi-source signals of power equipment, including light intensity, current, temperature, vibration, and equipment status signals; dynamic threshold calibration of the light intensity signal, and elimination of interference in combination with the ambient light data; distinguishing normal overcurrent from arc fault current through current harmonic analysis and transient waveform feature extraction; see Figure 3 , establish a multi-signal joint judgment model: when the light intensity exceeds the dynamic threshold and the current meets the arc characteristics, and the temperature or vibration signal is abnormal, it is judged as an arc fault; see Figure 4 If it is determined to be a fault, the faulty device will be immediately locked and redundancy detection will be started. After the fault is confirmed, the power supply will be cut off. If it is determined to be abnormal interference, a transient lockout will be triggered and the self-test process will be started. Figure 5 , monitor the device status in real time during the locking process. If a sensor failure or communication abnormality is detected, switch to the backup detection mode and record the abnormality log; see Figure 6 ,After the fault is eliminated, the lock is automatically or manually released based on the ,device health assessment results.

[0041] See also Figure 2 An arc protection abnormality lockout system includes a multi-source sensing module, a dynamic threshold calibration module, a signal analysis module, an abnormality identification module, an intelligent lockout module, a redundancy detection module, a self-test and fault tolerance module, and a health assessment module. The abnormality identification module uses a machine learning model to classify multi-source signals. The self-test and fault tolerance module regularly checks sensor status and switches to a backup detection mode in the event of a fault.

[0042] Multi-source sensing module: including light intensity sensor, current transformer, temperature sensor, vibration sensor and equipment status monitoring unit; Dynamic Threshold Calibration Module: This module adjusts the light intensity detection threshold in real time based on ambient light and load changes. The module uses a light intensity sensor to collect ambient background light and combines time and weather data to dynamically adjust the arc detection threshold. For example, the threshold is higher during the day than at night to prevent false triggering caused by direct sunlight.

[0043] Signal analysis module: used for current harmonic decomposition, transient waveform feature extraction, and multi-signal fusion analysis; the signal analysis module performs wavelet transform on the current signal, extracts high-frequency transient components (>5kHz) as arc flash characteristics, and combines THD (Total Harmonic Distortion) >20% to enhance identification accuracy.

[0044] Abnormal identification module: Classifies normal operating conditions, arc faults, and interference anomalies based on machine learning models. The abnormality identification module uses support vector machines or convolutional neural networks to train historical data to classify normal operations (such as switch opening and closing), external interference (lightning strikes), and real arc faults.

[0045] Intelligent locking module: Executes hierarchical locking strategies (immediate locking, transient locking, or delayed locking) based on the judgment results; immediate locking: After the fault is confirmed by multi-signal joint judgment, the power supply is cut off within 0.1 seconds; transient locking: When interference is suspected, the device is locked for 5 seconds and redundant detection is started. If the fault is not confirmed, it will automatically recover; delayed locking: A short delay (such as 1 second) is applied to non-critical equipment to avoid cascading tripping.

[0046] Redundant detection module: cross-verifies fault authenticity through backup sensors or adjacent equipment data before locking; Self-test and fault-tolerance module: Regularly checks the status of sensors and communication links, switches to backup mode and issues an alarm in case of failure. The self-test and fault-tolerance module performs a sensor self-test every 24 hours. If the light intensity sensor fails, it switches to the current-temperature combined judgment mode and reports a maintenance request through the communication module.

[0047] Health Assessment Module: Analyzes historical equipment data and repair records before the lockout is released, and generates recovery recommendations.

[0048] Many embodiments and applications beyond the examples provided will be apparent to those skilled in the art upon reading the foregoing description. Therefore, the scope of the present teachings should be determined not with reference to the foregoing description, but rather with reference to the preceding claims, along with the full scope of equivalents to which such claims are entitled. For the purpose of completeness, all articles and references, including the disclosures of patent applications and publications, are incorporated herein by reference. The omission of any aspect of the subject matter disclosed herein from the preceding claims is not a disclaimer of such subject matter, nor should it be interpreted that the applicants did not consider such subject matter to be part of the disclosed inventive subject matter.

[0049] The above content is a further detailed description of the present invention, and it cannot be considered that the specific implementation methods of the present invention are limited to these. For ordinary technicians in the technical field to which the present invention belongs, they can make several simple deductions or substitutions without departing from the concept of the present invention, which should be regarded as falling within the scope of protection submitted by the present invention.

Claims

1. A method for abnormal locking of arc protection, characterized in that: The following steps are involved: Real-time collection of multi-source signals of power equipment, including light intensity signals, current signals, temperature signals, vibration signals and equipment status signals; Perform dynamic threshold calibration on light intensity signals and adjust the light intensity signal detection threshold in real time; Harmonic decomposition analysis and transient waveform feature extraction are performed on multi-source signals to obtain arc characteristics. Arc characteristics are used to distinguish normal overcurrent from arc fault current in the current signal. Under a calibrated threshold, the light intensity signal, current signal, temperature signal, and vibration signal are integrated to obtain a multi-fusion signal. The multi-fusion signal is used to identify abnormalities of the power equipment through a multi-signal joint judgment model to obtain the judgment results, including arc faults; Perform redundant testing to verify the authenticity of the judgment results, and implement a hierarchical blocking strategy for the power equipment based on the judgment results; After eliminating arc faults and interference anomalies using a hierarchical blocking strategy, a health assessment is performed on the power equipment, and the power equipment blocking is released based on the health assessment results.

2. The arc protection abnormality locking method according to claim 1, characterized in that: The method of performing abnormality determination on the power equipment by using a multi-signal joint judgment model on the multi-fusion signals includes: When the light intensity signal exceeds the dynamic threshold and the current signal meets the arc characteristics, and the temperature or vibration signal is abnormal, it is determined to be an arc fault.

3. The arc protection abnormality locking method according to claim 1, characterized in that: The hierarchical blocking strategy includes immediate blocking, transient blocking or delayed blocking; Fault types include serious faults, suspected interference, and non-critical faults; The hierarchical blocking strategy is as follows: Use multi-signal joint judgment to determine the fault type; When a serious fault is determined, the faulty equipment will be immediately locked out and the power supply will be cut off within the preset response time after the fault is confirmed; When it is determined to be a non-critical fault, the faulty equipment will be locked out with a delay, and after the fault is confirmed, the power supply will be cut off within the preset reaction time; When it is determined to be suspected interference, the transient lockout device is triggered for a preset time and the redundant process is started. If the fault is not confirmed, the lockout is automatically released.

4. The arc protection abnormality locking method according to claim 1, characterized in that: The dynamic threshold calibration of the light intensity signal and the real-time adjustment of the light intensity signal detection threshold are combined with environmental data denoising, the environmental data including ambient light, time, weather data and load changes; the multi-source signals of the power equipment are collected using a light intensity sensor, a current transformer, a temperature sensor, a vibration sensor and an equipment status monitoring unit, and the ambient light is collected using a light intensity sensor; The current signal is subjected to wavelet transform, and the current signal after wavelet transform with high-frequency transient components greater than 5kHz and harmonic distortion rate greater than 20% is extracted as the arc characteristics.

5. The arc protection abnormality locking method according to claim 1, characterized in that: Support vector machines or convolutional neural networks are used to train historical data of multi-source signals of power equipment to classify normal operation, external interference and real arc faults.

6. The arc protection abnormality locking method according to claim 1, characterized in that: During the locking process, the power equipment status signal is monitored in real time. If an abnormality in the communication link is detected, the system switches to the current and temperature joint judgment mode to identify the abnormality of the power equipment, record the abnormality log and report the maintenance request.

7. The arc protection abnormality locking method according to claim 1, characterized in that: A sensor self-check is performed once every preset time period. If a light intensity sensor failure is detected, the system switches to the current and temperature combined judgment mode to identify abnormalities in the power equipment, record abnormality logs, and report maintenance requests.

8. The arc protection abnormality locking method according to claim 1, characterized in that: The multi-source signals of power equipment are collected using light intensity sensors, current transformers, temperature sensors, vibration sensors and equipment status monitoring units.

9. An arc protection abnormality locking system, based on an arc protection abnormality locking method according to any one of claims 1 to 8, characterized in that: include: A multi-source sensing module is used to collect multi-source signals of power equipment in real time, including light intensity signals, current signals, temperature signals, vibration signals and equipment status signals; Dynamic threshold calibration module, used to perform dynamic threshold calibration on light intensity signals and adjust the light intensity signal detection threshold in real time; Signal analysis module, used for current harmonic decomposition, transient waveform feature extraction and multi-signal fusion analysis; Anomaly discrimination module, used to classify normal operating conditions, arc faults, and interference anomalies based on machine learning models; An intelligent locking module is used to execute a hierarchical locking strategy based on the discrimination result. The hierarchical locking strategy includes immediate locking, transient locking or delayed locking; Redundant detection module, used to verify the authenticity of the fault before locking; Self-check and fault-tolerance module, used to regularly check the status of sensors and communication links, switch to backup mode and issue an alarm in case of failure; The health assessment module is used to analyze the equipment's historical data and repair records before the lockout is released and generate recovery suggestions.

10. An electric power system using the arc protection abnormality locking method according to any one of claims 1 to 8.

Images