WO2023045008A1 - 基于小波分解的智能自适应电弧检测方法及其应用装置 - Google Patents

基于小波分解的智能自适应电弧检测方法及其应用装置 Download PDF

Info

Publication number
WO2023045008A1
WO2023045008A1 PCT/CN2021/126153 CN2021126153W WO2023045008A1 WO 2023045008 A1 WO2023045008 A1 WO 2023045008A1 CN 2021126153 W CN2021126153 W CN 2021126153W WO 2023045008 A1 WO2023045008 A1 WO 2023045008A1
Authority
WO
WIPO (PCT)
Prior art keywords
signal
arc
data
wavelet decomposition
detection method
Prior art date
Application number
PCT/CN2021/126153
Other languages
English (en)
French (fr)
Inventor
周顺新
崔斌
罗怀林
康会彬
卢云
向小民
望坤
Original Assignee
湖北创全电气有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 湖北创全电气有限公司 filed Critical 湖北创全电气有限公司
Publication of WO2023045008A1 publication Critical patent/WO2023045008A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
    • G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y04S10/52—Outage or fault management, e.g. fault detection or location

Definitions

  • the invention relates to the field of power quality detection, in particular to an intelligent adaptive arc detection method based on wavelet decomposition and an application device thereof.
  • the insulation layer damage is easy to occur arc, which can easily cause a fire.
  • the protectors on the market such as miniature short circuit MCB or fuse, can protect against overcurrent or leakage current, but there is another problem that if an arc occurs at this time, but the current is less than the rated current, these protectors cannot To protect the circuit, arcing from damaged cables that can cause fires cannot be effectively protected by these traditional circuit breakers.
  • the technical problem to be solved by the present invention is to provide an intelligent self-adaptive arc detection method based on wavelet decomposition and its application device, which uses a deeply optimized wavelet transform algorithm to accurately identify the current signal of the fault arc from the load current, and The signal sorting is compared and analyzed with the historical data in the database to eliminate the interference of the arc generated by the normal operation of the electrical equipment, so as to accurately determine the occurrence of the fault arc.
  • An intelligent self-adaptive arc detection method based on wavelet decomposition the detection steps include:
  • Step 1 signal acquisition, establish power supply and different load connection lines under standard working conditions, and simulate signal waveforms under various abnormal conditions, set up sampling circuits on the line, collect power supply signals and amplify them, and intercept and intercept power supply according to the power supply frequency A waveform of a periodic signal to obtain a set of data D1[N];
  • Step 2 signal denoising, the obtained data D1[N] is denoised by the Mallat algorithm, and the original signal is decomposed in multiple layers, and the decomposed data is reconstructed to obtain the signal D2[N];
  • Step 3 signal normalization, normalize the reconstructed signal D2[N] under different loads or abnormal conditions to obtain D3[N];
  • Step 4 data processing, detect the obtained D3[N] signal, judge the number of voltage or current zero crossings under different loads and abnormal conditions, and judge through multiple detections, select the largest zero crossing data under various conditions value M;
  • Step 5 Judgment of arc faults.
  • the zero-crossing data of the target sampling data after the analysis of the above steps 1 to 4 is N, and the error threshold coefficient K is set. If N>K*M, it meets the characteristics of arc faults.
  • the signals of multiple cycles are selected for repeated judgment and comparison. In the preset measurement interval, when the accumulated data conforming to the characteristic value of fault arc judgment is greater than the set value, it is judged that an arc has occurred.
  • the Mallat algorithm is used to decompose the data D1[N] into the high and low frequency coefficients of the fourth layer, and then remove some noise from the coefficients of each layer through the threshold method, and preset a value ⁇ , and the ⁇ value decreases layer by layer, and the processed high and low frequency coefficients are substituted into the formula:
  • the device includes a processor MCU, a sampling amplifier circuit, a display module, an alarm module, a wireless communication module and an interactive module, and a signal sensor is arranged in the sampling amplifier circuit , the signal sensor is connected to the target detection circuit, the output end of the sampling amplifier circuit is electrically connected to the input end of the processor MCU, and the output end of the processor MCU is connected to the alarm module.
  • the above-mentioned signal sensor adopts a current transformer.
  • the present invention provides an intelligent self-adaptive arc detection method based on wavelet decomposition and its application device. After amplifying the sampling signal of the target circuit, denoising and normalizing are carried out, and a detection value of one period is intercepted within the frequency of the power supply for wavelet analysis. Decompose and reconstruct, and judge whether there is an arc fault according to the number of zero-point data of the processed signal.
  • the method and device use wavelet decomposition to process the frequency spectrum and time spectrum on the finite-length waveform.
  • the short-time Fourier transform has a specific time response, which is convenient for instantaneous waveform detection such as arc, and the detection data is simple and practical, which reduces the requirements for hardware and is suitable for popularization and application in power transmission, household power supply and other power industries.
  • Fig. 1 is a schematic diagram of a sampling circuit of the present invention
  • Fig. 2 is a schematic diagram of mallat algorithm denoising in step 2;
  • Fig. 3 is the decomposition diagram of wavelet algorithm in step 2 on MATLAB;
  • Fig. 4 is a schematic flow sheet of the method of the present invention.
  • Fig. 5 is a structural block diagram of the application device of the present invention.
  • an intelligent adaptive arc detection method based on wavelet decomposition the detection steps include:
  • Step 1 signal acquisition, establish power supply and different load connection lines under standard working conditions, and simulate signal waveforms under various abnormal conditions, set up sampling circuits on the line, collect power supply signals and amplify them, and intercept and intercept power supply according to the power supply frequency A waveform of a periodic signal to obtain a set of data D1[N];
  • Step 2 signal denoising, the obtained data D1[N] is denoised by the Mallat algorithm, and the original signal is decomposed in multiple layers, and the decomposed data is reconstructed to obtain the signal D2[N];
  • Step 3 signal normalization, normalize the reconstructed signal D2[N] under different loads or abnormal conditions to obtain D3[N];
  • Step 4 data processing, detect the obtained D3[N] signal, judge the number of voltage or current zero crossings under different loads and abnormal conditions, and judge through multiple detections, select the largest zero crossing data under various conditions value M;
  • Step 5 Judgment of arc faults.
  • the zero-crossing data of the target sampling data after the analysis of the above steps 1 to 4 is N, and the error threshold coefficient K is set. If N>K*M, it meets the characteristics of arc faults.
  • the signals of multiple cycles are selected for repeated judgment and comparison. In the preset measurement interval, when the accumulated data conforming to the characteristic value of fault arc judgment is greater than the set value, it is judged that an arc has occurred.
  • the Mallat algorithm is used to decompose the data D1[N] into the high and low frequency coefficients of the fourth layer, and then remove some noise from the coefficients of each layer through the threshold method, and preset a value ⁇ , and the ⁇ value decreases layer by layer, and the processed high and low frequency coefficients are substituted into the formula:
  • the collected signals have different characteristics for loads with different impedance properties and different load currents.
  • the signal data after denoising processing and filtering out clutter are normalized
  • step 3 since the normal and abnormal signal waveforms collected under different loads are very different, the collected signals have different characteristics for loads with different impedance properties and different load currents. Therefore, the signal data after denoising and clutter removal can be normalized to cover the arc detection in various situations more completely.
  • the detection principle of the arc in step 4 is: through the analysis of the mechanism of the arc, when the AC voltage crosses the zero point in the circuit, the voltage is not enough to generate an arc, so there is a certain no-current area near the zero point, that is, the current is zero. situation. Through repeated tests of the load current under normal conditions and arc fault conditions, it is found that in the D3[i] array, there are at most m zero point data under normal conditions, while under arc fault conditions, D3[i] The zero point data in the array is much larger than this value.
  • this device uses multiple measurement calculations. In the preset measurement interval, when the accumulated data that meets the characteristic value of fault arc judgment is greater than the set value, it is determined that the line has failed at this time. At the same time, the fault arc data is stored to form a historical record.
  • the application device using the above-mentioned intelligent adaptive arc detection method based on wavelet decomposition includes a processor MCU, a sampling amplifier circuit, a display module, an alarm module, a wireless communication module and an interaction module
  • a signal sensor is arranged in the sampling amplifier circuit, the signal sensor is connected with the target detection circuit, the output terminal of the sampling amplifier circuit is electrically connected with the input terminal of the processor MCU, and the output terminal of the processor MCU is connected with the alarm module.
  • the above-mentioned signal sensor adopts a current transformer.
  • the device can set the relevant parameters of the fault arc judgment conditions through the interactive module to realize human-computer interaction, so as to realize the user-customized fault arc monitoring mode and meet the needs of different users.
  • the signal sensor of this device adopts a high magnetic induction Hall element, which can detect various irregular current signals such as AC, DC and pulse, and is used to capture weak signals caused by fault arcs, and uses low-drift, low-noise, high-speed operational amplifiers to capture faults
  • the weak signal of the arc is amplified and isolated, and the zero point drift is eliminated.
  • the characteristic value of the fault arc is extracted for the calculation of the fault arc intensity, and the interference of the arc generated during the normal operation of the electrical equipment is eliminated.
  • output the control signal of real fault arc production avoid false positives and false positives;
  • MCU When MCU calculates and analyzes, if it judges that there is a real fault arc, it will output corresponding data and control signals.
  • the data output by the MCU is used to refresh the display module, and the relevant data of the fault arc is displayed on the display module.
  • the control signal output by the MCU is used to start the alarm module, which drives the local warning light and buzzer, prompting the user to overhaul the circuit , the alarm module can set the working mode of the warning light and the buzzer by operating the interactive module of the device according to the user's requirements, so as to meet the user's needs.
  • the MCU starts the wireless communication module at the same time, and transmits the results of arc fault detection and analysis to the user through the network, which is convenient for the user to view and grasp the relevant fault information in time, and can modify and adjust the device parameters through remote control according to the user's needs;
  • the interactive module used in this device realizes human-computer interactive operation through physical buttons, and completes the setting of fault arc detection function and condition parameters of this device to meet the needs of users for customized services;
  • the power module used in this device uses a rechargeable battery, which provides high-reliability power for the core circuit and each functional module through a voltage-stabilizing isolation circuit. Ensure that each module can be powered independently without affecting its normal operation;
  • the circuit structure of the arc detection device is:
  • the signal sensor is connected to the input end of the sampling circuit, the schematic diagram is shown in Figure 1, the output end of the sampling circuit is connected to the port with ADC function of the MCU, the display module and the wireless communication module are connected to the MCU through the serial port, the alarm module, the interactive module and the fault
  • the positioning module completes the transfer of model instructions through the I/O port of the MCU.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing Electric Properties And Detecting Electric Faults (AREA)
  • Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)

Abstract

基于小波分解的智能自适应电弧检测方法及其应用装置，包括步骤一、信号采集；步骤二、信号去噪；步骤三、信号归一化；步骤四、小波分解重构；步骤五、目标数据采集比较；通过对目标电路的采样信号放大后进行去噪和归一化，在电源频率内截取一个周期的检测值进行小波分解并重构，根据处理后信号的零点数据个数来判断是否有电弧故障发生，该方法及装置利用小波分解在有限长波形上处理同时得到频谱和时谱，相比单纯傅里叶变换或短时傅里叶变换，具有特定的时间响应，便于进行电弧这种瞬时的波形检测，且检测数据简单实用，降低了对硬件的要求，适应在电力传输、居家电源等电力行业推广应用。

Description

基于小波分解的智能自适应电弧检测方法及其应用装置 技术领域
本发明涉及电能质量检测领域，具体涉及一种基于小波分解的智能自适应电弧检测方法及其应用装置。
背景技术
随着国民经济的快速发展科技的飞速进步，整个社会的电气化水平也越来越高，各式各样的家电走进了各家各户，大到城市小到较为偏远的乡村电气化普及度也越来越高。现如今在人们生活体验感幸福感更高的时代下，同时也面临着巨大的风险和挑战，住宅中因为电气发生的火灾比例居高不下，对生命和经济造成的损失也难以估量。引起电气火灾的原因主要有：①过流。因为过流导致导线发热而引发的火灾；②电弧。因接触不良或运行条件不好的情况导致电弧产生进而引发火灾。对于电弧产生火灾的原因主要有：因为线缆折叠或人为因素导致绝缘保护层破损、使用时间变长绝缘劣化、使用条件不当、电器损坏、环境恶劣或动物啃咬等原因导致绝缘层破坏易发生电弧，进而容易引发火灾。市面上的保护器如微型短路器MCB或熔断器等能对过电流或漏电流进行保护，但是这样还有一个问题就是，如果此时发生了电弧，但是电流小于额定电流，这些保护器就不能对电路造成保护，能够导致火灾产生的破损线缆产生的电弧不能在这些传统断路器得到有效的保护。
现有技术中也有对电弧进行检测的装置，例如中国专利文献CN107064752A记载了一种航空故障电弧检测的判别算法，首先，采集实验平台上不同负载下的故障电弧电流信号；然后，判断故障电弧电流信号为直流故障电弧电流还是交流故障电弧电流，并分别提取兼具时域和频域的特征量；针对直流故障电弧的小波能量，信息熵和电流变化率以及交流故障电弧的小波能量，信息熵和经验模态分解的第四个本征模函数值分别作为训练样本，训练支持向量机预测模型；最后，利用两个支持向量机预测模型分别辨别电弧的故障与正常状态，选取多个特征量，减少了故障特征的偶然性，增加了判别的准确性；但是由于需要对故障电弧采取小波能量、信息熵和电流变化率等多重变换，在实际应用中需要强大的硬件支撑来支撑其应用，对技术的推广不利，
发明内容
本发明所要解决的技术问题是提供一种基于小波分解的智能自适应电弧检测方法及其应用装置，采用深度优化的小波变换算法，从负载电流中准确识别出故障电弧的电流信号，并对此信号排序，与数据库中历史数据进行对比分析，以排除用电设备正常工作产生的电弧的干扰，从而准确地判断出故障电弧的产生。
为解决上述技术问题，本发明所采用的技术方案是：
一种基于小波分解的智能自适应电弧检测方法，检测的步骤包括：
步骤一、信号采集，建立标准工况下的电源及不同负载连接线路，并进行各种非正常条件的信号波形模拟，在线路上设置采样电路，采集电源信号并进行放大，根据电源频率截取截取电源一个周期信号的波形，得到一组数据D1[N]；
步骤二、信号去噪，将得到的数据D1[N]通过Mallat算法进行去噪，并对原始信号进行多层分解，分解处理后的数据重构得到信号D2[N]；
步骤三、信号归一化，将重构后的不同负载或非正常条件下信号D2[N]进行归一化处理得到D3[N]；
步骤四、数据处理，对得到的D3[N]信号进行检测，判断不同负载和非正常情况下，电压或者电流过零点的次数，并通过多次检测判断，选取各种情况下过零点数据最大值M；
步骤五、电弧故障判断，对目标采样数据经过上述步骤一至步骤四分析后的过零点数据为N，设定误差阈值系数K，若N>K*M时，则符合电弧故障特征，再多次选定多个周期的信号重复进行判断比较，在预设的测量区间内，符合故障电弧判定特征值的累积数据大于设定值时，判定产生了电弧。
上述的步骤二中使用Mallat算法对数据D1[N]进行多层分解后到第四层的高低频系数中，然后通过阈值法对每层系数除掉一些噪声，在每一层中预设一个值δ，且δ值逐层递减，将处理后的高低频系数代入公式：
Figure PCTCN2021126153-appb-000001
得到信号D2[N]。
上述的步骤三中信号归一化满足：
Figure PCTCN2021126153-appb-000002
使用上述的一种基于小波分解的智能自适应电弧检测方法的应用装置，装置包括处理器MCU、采样放大电路、显示模块、报警模块、无线通讯模块及交互模 块，采样放大电路内设有信号传感器，信号传感器与目标检测电路连接，采样放大电路输出端与处理器MCU输入端电连接，处理器MCU输出端连接报警模块。
上述的信号传感器采用电流互感器。
本发明提供的一种基于小波分解的智能自适应电弧检测方法及其应用装置，通过对目标电路的采样信号放大后进行过去噪和归一化，在电源频率内截取一个周期的检测值进行小波分解并重构，根据处理后信号的零点数据个数来判断是否有电弧故障发生，该方法及装置利用小波分解在有限长波形上处理同事得到频谱和时谱，相比单纯傅里叶变换或短时傅里叶变换，具有特定的时间响应，便于进行电弧这种瞬时的波形检测，且检测数据简单实用，降低了对硬件的要求，适应在电力传输、居家电源等电力行业推广应用。
附图说明
下面结合附图和实施例对本发明作进一步说明：
图1为本发明的采样电路示意图；
图2为步骤二中mallat算法去噪示意图；
图3为步骤二中小波算法在MATLAB上的分解示意图；
图4为本发明方法的流程示意图；
图5为本发明应用装置的结构框图。
具体实施方式
如图4中所示，一种基于小波分解的智能自适应电弧检测方法，检测的步骤包括：
步骤一、信号采集，建立标准工况下的电源及不同负载连接线路，并进行各种非正常条件的信号波形模拟，在线路上设置采样电路，采集电源信号并进行放大，根据电源频率截取截取电源一个周期信号的波形，得到一组数据D1[N]；
步骤二、信号去噪，将得到的数据D1[N]通过Mallat算法进行去噪，并对原始信号进行多层分解，分解处理后的数据重构得到信号D2[N]；
步骤三、信号归一化，将重构后的不同负载或非正常条件下信号D2[N]进行归一化处理得到D3[N]；
步骤四、数据处理，对得到的D3[N]信号进行检测，判断不同负载和非正常情况下，电压或者电流过零点的次数，并通过多次检测判断，选取各种情况下过零点 数据最大值M；
步骤五、电弧故障判断，对目标采样数据经过上述步骤一至步骤四分析后的过零点数据为N，设定误差阈值系数K，若N>K*M时，则符合电弧故障特征，再多次选定多个周期的信号重复进行判断比较，在预设的测量区间内，符合故障电弧判定特征值的累积数据大于设定值时，判定产生了电弧。
上述的步骤二中使用Mallat算法对数据D1[N]进行多层分解后到第四层的高低频系数中，然后通过阈值法对每层系数除掉一些噪声，在每一层中预设一个值δ，且δ值逐层递减，将处理后的高低频系数代入公式：
Figure PCTCN2021126153-appb-000003
得到信号D2[N]。
如图2中所示，在经过去噪后，在单个周期范围内，波形变换过程中的正常波动被消除，避免了波形的范围波动影响后续的计算结果。
上述的步骤三中，由于不一样的负载下采集的正常与非正常的信号波形都有很大不一样，对于不同阻抗性质的负载以及负载电流大小不同的情况下，采集的信号都具有不一样的特点，为此将经过去噪处理滤除杂波后的信号数据进行归一
化处理：
Figure PCTCN2021126153-appb-000004
步骤三中，由于不一样的负载下采集的正常与非正常的信号波形都有很大不一样，对于不同阻抗性质的负载以及负载电流大小不同的情况下，采集的信号都具有不一样的特点，为此将经过去噪处理滤除杂波后的信号数据进行归一化处理后能够更全的覆盖各种情况下的电弧检测。
步骤四中电弧的检测原理为：通过对电弧产生机理的分析，在电路中交流电压在过零点时由于电压不足以发生电弧，所以在零点附近，存在一定的无电流区域，即电流为零的情形。通过对于正常情况下以及故障电弧情况下负载电流的多次重复试验，发现在D3[i]数组中，正常情况下存在的零点数据最多只有m个，而在故障电弧情况下，D3[i]数组中的零点数据要远大于该值。据此设定一个适当的阈值n(n>m)，通过比较D3[i]数组中的零点数据的个数是否超过该阈值，并对大于设定阈值的信号计数累加，至此，单次测量-处理-计算结束，为防止误判，本装置采用多次测量计算，在预设的测量区间内，符合故障电弧判定特征值的累计数据大于设定值时，则判定此时线路已经产生故障电弧，同时对故障电弧数据进 行存储，形成历史记录。
如图3中所示，一个周期内，在小波变换的过程中，与正常的信号相比，含电弧的信号经过小波变换后，从D1至D4层中，D1先将信号中的电弧信号分解出来，且包含的周期内的时间信息，在经过过D2至D4中阈值的极大值的不断调整，使得结果越来越精准，到D4时可以得到精确的电弧波形，可以看到，其中每次电弧产生时，其波形多次过零点，明显不同于正常波形，因此应用此种方法，可以通过简单的计算即可分辨电弧信号，可以降低对硬件的要求，适合在家居领域推广使用。
如图1和5中所示，使用上述的一种基于小波分解的智能自适应电弧检测方法的应用装置，装置包括处理器MCU、采样放大电路、显示模块、报警模块、无线通讯模块及交互模块，采样放大电路内设有信号传感器，信号传感器与目标检测电路连接，采样放大电路输出端与处理器MCU输入端电连接，处理器MCU输出端连接报警模块。
上述的信号传感器采用电流互感器。
本装置可通过交互模块对故障电弧判定条件的相关参数进行设置，实现人机交互，以此实现用户定制的故障电弧监测模式，满足不同用户的需求。
本装置信号传感器采用高磁感应霍尔元件，能检测交直流及脉冲等各种不规则电流信号，用来捕捉因故障电弧引起的微弱信号，并采用低漂移低噪高速运算放大器将捕捉到的故障电弧的微弱信号进行放大和隔离，消除零点漂移后进入到MCU进行小波变换，提取故障电弧的特征值用于故障电弧强度计算，排除用电设备正常工作时产生的电弧的干扰，通过与数据库历史数据对比及分析，输出真正故障电弧生产的控制信号，避免误报漏报；
当MCU通过计算分析，如果判断有真实的故障电弧产生，则输出相应数据和控制信号。MCU输出的数据用以刷新显示模块，将故障电弧的相关数据显示在显示模块上，MCU输出的控制信号用以启动报警模块，报警模块驱动本地示警灯和蜂鸣器，提示用户对电路进行检修，报警模块可根据用户的要求，通过操作本装置的交互模块来设置示警灯和蜂鸣器的工作模式，以满足用户的需求。
MCU同时启动无线通讯模块，将故障电弧检测分析的结果通过网络传给用户，方便用户及时查看和掌握相关故障信息，并可以根据用户的需求，通过远程控制 对装置参数进行修正和调整；
本装置所用交互模块通过实体按键实现人机交互操作，完成对本装置故障电弧检测功能及条件参数进行设定，以满足用户定制化服务的需求；
本装置所用电源模块采用充电电池，通过稳压隔离电路为核心电路及各功能模块提供高可靠性电源,为防止各模块信号通过电源相互造成干扰，电源模块的供电回路通过光电隔离多路输出，保证对各模块能够独立供电不影响其正常工作；
电弧检测装置电路结构为：
信号传感器连接在采样电路的输入端，其原理图如图1，采样电路的输出端连接在MCU具有ADC功能的端口，显示模块和无线通讯模块通过串口与MCU链接，报警模块，交互模块以及故障定位模块通过MCU的I/O口完成型号指令的传递工作。

Claims (5)

  1. 基于小波分解的智能自适应电弧检测方法，其特征在于，检测的步骤包括：
    步骤一、信号采集，建立标准工况下的电源及不同负载连接线路，并进行各种非正常条件的信号波形模拟，在线路上设置采样电路，采集电源信号并进行放大，根据电源频率截取电源一个周期信号的波形，得到一组数据D1[N]；
    步骤二、信号去噪，将得到的数据D1[N]通过Mallat算法进行去噪，并对原始信号进行多层分解，分解处理后的数据重构得到信号D2[N]；
    步骤三、信号归一化，将重构后的不同负载或非正常条件下信号D2[N]进行归一化处理得到D3[N]；
    步骤四、数据处理，对得到的D3[N]信号进行检测，判断不同负载和非正常情况下，电压或者电流过零点的次数，并通过多次检测判断，选取各种情况下过零点数据最大值M；
    步骤五、电弧故障判断，对目标采样数据经过上述步骤一至步骤四分析后的过零点数据为N，设定误差阈值系数K，若N>K*M时，则符合电弧故障特征，再多次选定多个周期的信号重复进行判断比较，在预设的测量区间内，符合故障电弧判定特征值的累积数据大于设定值时，判定产生了电弧。
  2. 根据权利要求1所述的基于小波分解的智能自适应电弧检测方法，其特征在于，所述的步骤二中使用Mallat算法对数据D1[N]进行多层分解后到第四层的高低频系数中，然后通过阈值法对每层系数除掉一些噪声，在每一层中预设一个值δ，且δ值逐层递减，将处理后的高低频系数代入公式：
    Figure PCTCN2021126153-appb-100001
    得到信号D2[N]。
  3. 根据权利要求1所述的基于小波分解的智能自适应电弧检测方法，其特征在于，所述的步骤三中信号归一化满足：
    Figure PCTCN2021126153-appb-100002
  4. 使用上述权利要求1-4中任一所述的基于小波分解的智能自适应电弧检测方法的应用装置，且特征在于，装置包括处理器MCU、采样放大电路、显示模块、报警模块、无线通讯模块及交互模块，采样放大电路内设有信号传感器，信号传感器与目标检测电路连接，采样放大电路输出端与处理器MCU输入端电连接，处 理器MCU输出端连接报警模块。
  5. 根据权利要求4所述的基于小波分解的智能自适应电弧检测装置，其特征在于，所述的信号传感器采用电流互感器。
PCT/CN2021/126153 2021-09-25 2021-10-25 基于小波分解的智能自适应电弧检测方法及其应用装置 WO2023045008A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202111124735.1A CN113917294B (zh) 2021-09-25 2021-09-25 基于小波分解的智能自适应电弧检测方法及其应用装置
CN202111124735.1 2021-09-25

Publications (1)

Publication Number Publication Date
WO2023045008A1 true WO2023045008A1 (zh) 2023-03-30

Family

ID=79235825

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/126153 WO2023045008A1 (zh) 2021-09-25 2021-10-25 基于小波分解的智能自适应电弧检测方法及其应用装置

Country Status (2)

Country Link
CN (1) CN113917294B (zh)
WO (1) WO2023045008A1 (zh)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116109017A (zh) * 2023-04-12 2023-05-12 国网信息通信产业集团有限公司 一种短路电流零点快速精准预测方法及系统
CN117930077A (zh) * 2023-12-25 2024-04-26 迪力普电子(常州)有限公司 一种基于小波分析的线束通断信号检测方法
CN118518998A (zh) * 2024-07-24 2024-08-20 山东奥斯登房车有限公司 一种新能源房车负载绝缘故障定位方法及系统

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8067942B2 (en) * 2007-09-28 2011-11-29 Florida State University Research Foundation Method for locating phase to ground faults in DC distribution systems
CN103543375A (zh) * 2013-08-26 2014-01-29 上海交通大学 基于小波变换和时域混合特征的交流故障电弧检测方法
CN103915818A (zh) * 2014-04-14 2014-07-09 河北工业大学 串联故障电弧检测方法及其专用装置
CN105954628A (zh) * 2016-06-23 2016-09-21 深圳市中智盛安安全技术有限公司 一种故障电弧检测方法及检测装置
CN106771798A (zh) * 2015-11-25 2017-05-31 山东建筑大学 一种基于小波系数均差值的故障电弧检测方法
CN112952753A (zh) * 2021-03-15 2021-06-11 国网福建省电力有限公司南平供电公司 一种基于阈值自适应调整的低压电网触电事故检测方法及系统

Family Cites Families (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7023196B2 (en) * 2003-12-12 2006-04-04 Leach International Corporation High level arc fault detector
US7492163B2 (en) * 2006-04-27 2009-02-17 Siemens Energy & Automation, Inc. Systems, devices, and methods for arc fault detection
US8054591B2 (en) * 2008-07-24 2011-11-08 General Electric Company Arc detection using discrete wavelet transforms
US8170816B2 (en) * 2008-12-29 2012-05-01 General Electric Company Parallel arc detection using discrete wavelet transforms
AT511790A3 (de) * 2011-07-26 2020-06-15 Eaton Gmbh Verfahren zur adaption eines lichtbogensensors
CN107294494A (zh) * 2017-07-03 2017-10-24 上海岩芯电子科技有限公司 光伏系统直流侧电弧故障检测装置及其检测方法
CN108020761B (zh) * 2017-12-04 2019-08-23 中国水利水电科学研究院 一种局部放电去噪方法
CN109917241A (zh) * 2019-03-19 2019-06-21 德力西电气有限公司 一种故障电弧的检测方法和故障电弧断路器
CN211785904U (zh) * 2020-02-26 2020-10-27 福建省润泽智能科技有限公司 一种基于电流过零值点的故障电弧检测装置
CN111626153B (zh) * 2020-05-13 2022-10-18 电子科技大学 一种基于集成学习的局放故障状态识别方法
CN111738128A (zh) * 2020-06-17 2020-10-02 山东卓文信息科技有限公司 一种基于形态学滤波和mmg的串联故障电弧检测方法
CN113156261B (zh) * 2021-03-15 2022-03-04 国网湖北省电力有限公司电力科学研究院 一种串联电弧故障检测、定位方法及系统
CN113009271A (zh) * 2021-04-22 2021-06-22 威胜集团有限公司 故障电弧检测及定位方法、装置及计算机可读存储介质
CN113376551B (zh) * 2021-06-23 2022-11-08 北京市腾河智慧能源科技有限公司 串联故障电弧的检测方法及系统、设备、存储介质

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8067942B2 (en) * 2007-09-28 2011-11-29 Florida State University Research Foundation Method for locating phase to ground faults in DC distribution systems
CN103543375A (zh) * 2013-08-26 2014-01-29 上海交通大学 基于小波变换和时域混合特征的交流故障电弧检测方法
CN103915818A (zh) * 2014-04-14 2014-07-09 河北工业大学 串联故障电弧检测方法及其专用装置
CN106771798A (zh) * 2015-11-25 2017-05-31 山东建筑大学 一种基于小波系数均差值的故障电弧检测方法
CN105954628A (zh) * 2016-06-23 2016-09-21 深圳市中智盛安安全技术有限公司 一种故障电弧检测方法及检测装置
CN112952753A (zh) * 2021-03-15 2021-06-11 国网福建省电力有限公司南平供电公司 一种基于阈值自适应调整的低压电网触电事故检测方法及系统

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116109017A (zh) * 2023-04-12 2023-05-12 国网信息通信产业集团有限公司 一种短路电流零点快速精准预测方法及系统
CN116109017B (zh) * 2023-04-12 2023-06-09 国网信息通信产业集团有限公司 一种短路电流零点快速精准预测方法及系统
CN117930077A (zh) * 2023-12-25 2024-04-26 迪力普电子(常州)有限公司 一种基于小波分析的线束通断信号检测方法
CN118518998A (zh) * 2024-07-24 2024-08-20 山东奥斯登房车有限公司 一种新能源房车负载绝缘故障定位方法及系统

Also Published As

Publication number Publication date
CN113917294A (zh) 2022-01-11
CN113917294B (zh) 2023-10-13

Similar Documents

Publication Publication Date Title
Wang et al. ArcNet: Series AC arc fault detection based on raw current and convolutional neural network
WO2023045008A1 (zh) 基于小波分解的智能自适应电弧检测方法及其应用装置
WO2019127440A1 (zh) 一种直流电弧的处理方法及装置
CN104614608B (zh) 一种低压并联电弧故障检测装置及方法
Gu et al. High impedance fault detection in overhead distribution feeders using a DSP-based feeder terminal unit
CN101673930B (zh) 使用离散小波变换的电弧检测
CN112505511B (zh) 一种非侵入式低压故障电弧检测与定位方法及系统
CN110007197B (zh) 串联电弧故障检测系统及检测方法
CN105510760B (zh) 一种基于小波分析的短路故障数据检测方法
CN105629112B (zh) 一种故障电弧检测装置及方法
CN202133738U (zh) 一种故障电弧的检测保护装置
Jiang et al. Machine learning approach to detect arc faults based on regular coupling features
CN113191192A (zh) 基于小波分析与模糊神经网络算法的断路器故障检测方法
CN110007198A (zh) 一种新型的单相接地故障启动方法
CN206096341U (zh) 应用于配电网单相故障接地工况下的故障线路识别装置
CN113281625B (zh) 一种低压用户非侵入式电弧故障精准检测方法及系统
Gong et al. Series arc fault identification method based on wavelet transform and feature values decomposition fusion DNN
Zhang et al. Lightweight AC arc fault detection method by integration of event-based load classification
KR20220145582A (ko) 화재 위험도 예측을 위한 아크 감지기 및 이의 동작 방법
Zhang et al. Two terminals protection method for HVDC transmission lines based on maximal overlap discrete wavelet transform and cross-correlation coefficient of traveling wave
Lu et al. A new series arc fault identification method based on wavelet transform
CN113514728B (zh) 一种故障电弧检测装置及方法与三相电路故障检测方法
Zhao et al. Series arc fault diagnosis based on variational mode decomposition and random forest
CN109581160B (zh) 故障电弧检测方法
CN114034981A (zh) 一种交流输电线路故障检测方法及系统

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21958123

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE