CN113281625A - Low-voltage user non-invasive arc fault accurate detection method and system - Google Patents
Low-voltage user non-invasive arc fault accurate detection method and system Download PDFInfo
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Abstract
The invention discloses a non-invasive arc fault accurate detection method for low-voltage users, which comprises the following steps: presetting a full-characteristic database of all electric appliances containing normal and fault samples; initializing relevant parameters; collecting total current and terminal voltage of a user at home and performing noise reduction treatment; capturing a steady-state time window of a total current signal, calculating CRC (cyclic redundancy check) characteristics, and detecting a fault arc in real time by combining a fault arc characteristic sample; if the fault arc exists, the load is decomposed by utilizing the current mode matching to obtain the load composition; if the time window steady-state characteristics change and the load composition is known, calculating ZCP characteristics, and listing a full-characteristic sample matrix of the running electric appliance according to the user load composition and the CRC and ZCP characteristics; and (4) taking the optimal matching result of the optimal decomposition of the full feature matrix as an arc current decomposition result to obtain a fault electric appliance, realizing fault branch positioning and further removing faults. The invention can realize fault arc monitoring according to the current of the user main interface, identify the fault branch and eliminate the fire hazard caused by the arc fault.
Description
Technical Field
The invention relates to the field of power system fault arc monitoring, in particular to a non-invasive arc fault accurate detection method for low-voltage users.
Background
The electrical fire is ranked the first of all fire types, according to the annual book of Chinese fire statistics, in recent years, the electrical fire in China accounts for about 30% of the total number of the fire and is on the trend of rising year by year, and the arc fault is the main cause of the electrical fire. The phenomenon of plasma discharge of a low-voltage distribution circuit caused by loosening of a wiring terminal, long-term overload of the circuit, insulation aging and the like is an arc fault, and a fire hazard is often generated along with local high-temperature sputtering of an electrode. Therefore, the research on the fault characteristics of the arc current and voltage signals has important significance, and theoretical guidance can be provided for the design of the arc fault protection device, so that the safety and reliability of power utilization are guaranteed.
Arc faults can be classified into parallel arc faults and series arc faults. The current waveforms of the parallel arc faults at different positions have strong commonality and are generally larger than the load current. The current waveform of a series arc fault is affected by the load of the downstream electrical appliance, and the amplitude is generally smaller than the load current. Conventional protection devices, such as fuses, circuit breakers, etc., that are triggered by an overcurrent, can effectively detect and protect parallel arc faults, but cannot detect series arc faults. When different electric appliances have series arc faults, the arc current waveform has the following characteristics: the waveform contains harmonic components; the current amplitude is lower than the normal load current due to the voltage drop; the current rise rate is higher than the normal current; the energy-saving lamp is extinguished before a zero crossing point and is ignited again after the zero crossing point to form a 'flat shoulder', namely a zero-rest time period; it is random, usually intermittent, with a normal current waveform in between.
Non-invasive Load Monitoring (NILM) was proposed by Hart of MIT in the 80's of the 20 th century. The power consumption and the running state of each electric appliance in a family can be monitored only by installing a sensor at a client entrance and collecting and analyzing the total current and the terminal voltage, so that the detailed power consumption condition of each electric appliance in the family is known.
At present, the detection method for series fault arc mainly has the following three aspects: firstly, arc fault detection based on arc light, heat, electromagnetic radiation and the like; secondly, based on an arc mathematical model; and thirdly, based on the arc voltage current waveform. A large number of fault arc detection methods and criteria are researched, and the time domain and frequency domain characteristics of arc current and voltage signals with high sampling rates are mostly focused. However, due to the practical installation scene of the line and the sampling frequency of the equipment, the voltage waveform of a single load terminal is difficult to obtain, and an algorithm with high requirement on the sampling frequency is difficult to popularize and apply. The invention aims to integrate the research results of the two fields to find a more effective method for detecting and identifying the series arc fault, and locate the branch circuit where the arc fault is located by researching the arc characteristic difference relative to the operation of different electrical appliances so as to eliminate the fire and the potential safety hazard of the fault arc more quickly and timely.
Disclosure of Invention
In view of the defects of the prior art, the invention provides a non-invasive accurate arc fault detection system and method for low-voltage users by combining a supervised non-invasive load monitoring technology, and aims to solve the problem of fault arc detection of the low-voltage users in order to further promote the popularization and application of fault arc detection to actual low-voltage users and realize fire early warning of the users and positioning and elimination of hidden line troubles. The invention can realize fault arc monitoring according to the current of the user main port, and identify the circuit branch where the arc fault is positioned, namely the branch where the fault occurs on which electric appliance is positioned, so that the fire and the safety hidden trouble caused by the arc fault can be eliminated in time.
In order to solve the technical problem, the invention provides a non-intrusive arc fault accurate detection method for a low-voltage user, which comprises the following steps:
step 1: presetting a full-characteristic sample database of all electric appliances, wherein the full-characteristic sample database of all electric appliances comprises a normal characteristic sample database and a fault arc characteristic database of all electric appliances; simultaneously initializing preset related parameters including steady-state characteristics of an initial time window and initial user load composition;
step 2: collecting the total user current and terminal voltage at the user entrance, and preprocessing the collected current data by adopting a wavelet packet threshold denoising method;
and step 3: capturing a steady state time window aiming at a user total current signal, performing characteristic calculation, including a current change rate and effective value ratio (CRC), and detecting a fault arc in real time according to the current change rate and effective value ratio (CRC) characteristics and in combination with the fault arc characteristic database;
and 4, step 4: judging whether a fault arc exists, if so, executing the step 5, otherwise, executing the step 6;
and 5: judging whether the steady-state characteristics of the current time window change, if so, returning to the step 2, otherwise, executing the step 8;
step 6: the method comprises the steps that a current mode matching method is utilized to realize user load decomposition to obtain user load constitution, wherein the user load constitution refers to various working states of electric switches in a low-voltage distribution system;
and 7: recording the user load composition of the current time window and the steady-state characteristics at the moment, and returning to the step 2;
and 8: judging whether the load composition of the current time window is known or not, if so, executing the step 9, otherwise, returning to the step 2;
and step 9: calculating current zero-rest time (ZCP) characteristics according to the user load constitution obtained in the step 6, and listing a full-characteristic sample matrix of the electric appliance in the running state according to the current change rate-effective value ratio (CRC) and the current zero-rest time (ZCP) characteristics; obtaining the total current of the corresponding operating electric appliance according to the full-characteristic sample database of the operating electric appliance; comparing the similarity of the total current of different electric appliance fault combinations with the real total current waveform of the user at the moment, wherein the optimal matching result is the arc current decomposition result;
step 10: obtaining an electric appliance with an arc fault according to the arc current decomposition result;
step 11: according to the electric appliance with the arc fault, the circuit branch where the fault is located is traced back, and the branch location where the arc fault is located is realized;
step 12: and (4) sending fault alarm and fault branch information, triggering related circuit breakers to act to cut off the branch where the arc fault is located, and returning to the step (2) or ending the detection.
Further, the invention provides a method for accurately detecting a non-invasive arc fault of a low-voltage user, wherein the method comprises the following steps:
and (2) presetting a normal characteristic sample database, a fault arc characteristic database and a full characteristic sample database of operating electric appliances of all the electric appliances, wherein the electric appliances refer to common electric appliances of low-voltage users and comprise an electric kettle, an electric water heater, an air conditioner, an induction cooker, an incandescent lamp and an electric hair drier.
For step 3, the rule adopted for realizing the capture of the steady-state time window is a rule that the power range of 14 continuous half-wave power is smaller than a specified threshold value through a time sequence.
For step 4, the method for judging whether the fault arc exists is as follows: according to the GB-14287.4-1014 standard, if the current change rate and effective value ratio (CRC) of 14 continuous half waves exceeds 10 times of the root mean square value of the index, the arc fault is judged to exist at the moment.
For step 7, the steady-state characteristics include the total average power, the total current effective value and the total current fundamental effective value of the user load in the current time window.
For step 9, the method for comparing the similarity between the total current of different electrical appliance fault combinations and the real total current waveform of the user at the time is as follows: and comparing similarity calculation between the total current and the real total current of different electrical appliance fault combinations obtained by full-characteristic sample matrix decomposition by adopting the Euclidean distance between time sequences.
Meanwhile, the invention also provides a system for realizing the non-invasive arc fault accurate detection method for the low-voltage user, and the system comprises a parameter presetting module, an electrical information acquisition and preprocessing module, a steady-state time window arc characteristic extraction and fault judgment module, a non-invasive normal current decomposition module, a current time window load composition and steady-state characteristic recording and judgment module, an electrical component known arc current decomposition and branch positioning module, a full-characteristic sample database module of all electrical appliances, a data information storage module, a data transmission and information communication module and an external interaction function module;
the parameter presetting module is used for presetting a full-characteristic sample database of all the electric appliances, including a normal characteristic sample database and a fault arc characteristic database of all the electric appliances, and initializing relevant parameters needing to be preset in different functional modules;
the electrical information acquisition and preprocessing module is used for acquiring voltage and total current signals at a monitoring point according to system setting and carrying out noise reduction data preprocessing on the acquired voltage and current signals;
the steady state time window arc characteristic extraction and fault judgment module captures a steady state time window aiming at the total current of a user, performs characteristic calculation, comprises a ratio of a current change rate to an effective value and current zero-break time, and judges whether a fault arc exists in real time by combining the fault arc characteristic database module according to the ratio of the current change rate to the effective value;
the non-invasive normal current decomposition module is used for analyzing and collecting total current and terminal voltage of a load, extracting load characteristics, carrying out current mode matching by using the load characteristics of equipment current according to a normal characteristic sample library of all electrical appliances, realizing load decomposition and obtaining the working state of each electrical appliance in the low-voltage distribution system;
the current time window load composition and steady state feature recording and judging module is used for recording the user load composition of the current time window and the steady state feature at the moment; meanwhile, the method is used for judging whether the steady-state characteristics of the current time window change or not and whether the load composition of the current time window is known or not;
the electric appliance component known arc current decomposition and branch positioning module utilizes the user load composition obtained by the non-invasive normal current decomposition module after the total current detects the arc fault, lists the full-characteristic sample matrix of the electric appliance in the running state, compares the similarity of the total current of different combinations and the real total current waveform of the user at the moment to obtain the best matching result, and backtracks the circuit branch where the arc fault is located to realize the arc fault branch positioning;
the all-electric-appliance full-characteristic sample database module comprises a normal characteristic sample database and a fault arc characteristic database of all electric appliances and is used for storing and managing characteristic fingerprint data of different electric appliances in normal and arc fault operation;
the data information storage module stores the results of signal analysis and data processing of other functional modules according to requirements and provides data access interfaces for the other functional modules;
the external interaction function module is used for performing necessary data information interaction between the low-voltage user non-invasive arc fault accurate detection system and the outside, and comprises but is not limited to display and output of monitoring and analysis results and input and output of regulation and control commands;
the data transmission and information communication module is used for data and information interaction between different functional modules in the system according to requirements.
Compared with the prior art, the invention has the beneficial effects that:
the invention applies a non-invasive load decomposition technical method to fault arc detection, establishes a low-voltage user non-invasive arc fault accurate detection system and a method, can realize user load decomposition and arc fault detection under the condition of not invading the interior of a low-voltage user, and determines the location of a fault electrical appliance and a branch where the fault is located. The method is beneficial to realizing real-time arc fault monitoring and early warning, can position the fault branch, is convenient for a user to timely overhaul and replace electrical equipment, greatly reduces the maintenance cost, reduces unnecessary economic and personal and property losses, and improves the electricity utilization safety of low-voltage users.
Drawings
FIG. 1 is a schematic diagram of a low-voltage user non-intrusive arc fault accurate detection system of the present invention;
FIG. 2 is a flow chart of a method for accurately detecting a non-intrusive arc fault for a low voltage user according to the present invention;
FIG. 3 is a comparison of current waveforms before (left) and after (right) noise reduction for normal and fault conditions of the electric kettle;
FIG. 4 is the calculation results of CRC and ZCP for electric kettles;
FIG. 5 is a schematic diagram of a full feature sample database for operating an appliance, wherein: symbol-representative electric blowing air-cooled wind powerArc fault, symbol x represents normal operation of hair dryer; symbol □ represents a hair dryer hot air arc fault; symbol o represents the normal operation of the electric hair drier by hot air; symbol
Representing an air conditioner arc fault; the symbol is supposed to represent the normal operation of the air conditioner; the symbol o represents an electric kettle arc fault; symbol
Representing the normal operation of the electric kettle;
FIG. 6 is a waveform of appliance operating current under normal operation and arc fault;
fig. 7 is a diagram of the actual total current segment of the low-voltage user and the calculation result of the CRC characteristic thereof.
Detailed Description
The invention will be further described with reference to the following figures and specific examples, which are not intended to limit the invention in any way.
As shown in fig. 1, the system for implementing the non-intrusive arc fault accurate detection method for the low-voltage user mainly includes the following 10 functional modules, and each functional module has the following functions:
(1) and the parameter presetting module is used for presetting a full characteristic sample database of all the electric appliances including a normal characteristic sample database and a fault arc characteristic database of all the electric appliances and initializing relevant parameters needing to be preset in different functional modules.
(2) The electrical information acquisition and preprocessing module is used for acquiring voltage and total current signals at a monitoring point according to system setting and carrying out noise reduction data preprocessing on the acquired voltage and current signals; the method has the specific functions of collecting strong voltage and large current analog signals at the mounting point, converting the strong voltage and large current analog signals into weak voltage and/or small current analog signals, and digitizing the weak voltage and/or small current analog signals. In the implementation process, the voltage and current signals can be collected by using a voltage and current transformer, and the current transformer can be a 'clamp type' transformer for convenience. And the collected voltage and current signals are subjected to data preprocessing of noise reduction, and the wavelet packet threshold denoising method is selected to carry out preprocessing on the collected current data.
(3) The steady state time window electric arc characteristic extraction and fault judgment module captures a steady state time window aiming at the total current of a user, performs characteristic calculation, comprises a current change rate and effective value ratio (CRC) and current zero-break time (ZCP), and judges whether a fault electric arc exists in real time by combining the fault electric arc characteristic database module according to the CRC; the two features of CRC and ZCP are defined as follows. The CRC, i.e., the difference between two adjacent samples, divided by the sampling interval between two samples, yields the rate of change. The sampling interval is a fixed value in this embodiment, i.e., the inverse of the sampling frequency, and the specific calculation is omitted here. In order to ensure real-time monitoring, the effective value of the previous current cycle is divided on the basis, and the specific formula is shown as (1). The Zero Current Period (ZCP) is device specific or determined by the device. For common electrical loads, the absolute value of the sampling point in each current cycle is less than five percent of the maximum value; for electrical appliances, such as hair dryers, where the instantaneous value of the current at the "flat shoulder" is not near zero, they follow the same definition after the additional adjustment of the absolute value of the "flat shoulder" to zero.
Wherein, IrmsIs the effective value of the previous current cycle, IjAnd Ij+1The current values of adjacent sampling points are respectively.
(4) The non-invasive normal current decomposition module is used for analyzing and collecting total current and terminal voltage of a load, extracting load characteristics, carrying out current mode matching by using the load characteristics of equipment current according to a normal characteristic sample library of all electrical appliances, realizing load decomposition and obtaining the working state of each electrical appliance in the low-voltage distribution system; a Non-invasive Load Monitoring current mode Matching Method provided by documents [ Yu Yi xin, Liu Bo, Wang Long Current Matching Method for Non-invasive Power Load Monitoring and differentiation: US2015309092[ P ].2015-10-29 ] can be adopted as a related technical Method of the module, and the harmonic characteristics of the equipment current are utilized for current mode Matching to realize Load decomposition.
(5) The current time window load composition and steady state feature recording and judging module is used for recording the user load composition of the current time window and the steady state feature at the moment; meanwhile, the method is used for judging whether the steady-state characteristics of the current time window change or not and whether the load composition of the current time window is known or not;
(6) the electric appliance component known arc current decomposition and branch positioning module utilizes a user load composition obtained by a non-invasive normal current decomposition module after the total current detects an arc fault, lists a full-characteristic sample matrix of the electric appliance in a running state, compares the similarity of the total current of different combinations and the real total current waveform of a user at the moment to obtain an optimal matching result, and backtracks the circuit branch where the arc fault is located to realize arc fault branch positioning;
(7) the all-electric-appliance full-characteristic sample database module comprises a normal characteristic sample database and a fault arc characteristic database of all electric appliances and is used for storing and managing characteristic fingerprint data of different electric appliances in normal and arc fault operation; in the later data processing process, the steady-state time window arc characteristic extraction and fault judgment module reads current characteristic sample data of the required electric appliance during arc fault operation from the all-electric-appliance full-characteristic sample database module; the non-invasive normal current decomposition module reads current characteristic sample data of the required electric appliance in normal operation from the full characteristic sample database module of all the electric appliances; and the electric appliance component known arc current decomposition and branch positioning module reads current characteristic sample data of the required electric appliance in normal and arc fault operation from the full characteristic sample database module of all the electric appliances.
(8) The data information storage module is used for storing the results of signal analysis and data processing of other functional modules according to the needs and providing data access interfaces for the other functional modules; in particular, the amount of the solvent to be used,
the parameter presetting module, the electrical information acquisition and preprocessing module, the steady state time window arc characteristic extraction and fault judgment module, the non-invasive normal current decomposition module, the current time window load composition and steady state characteristic recording and judgment module, the processing result of the known arc current decomposition and branch positioning module of the electrical components, and the input information of the external interaction function module can be stored in the data information storage module, and the parameter presetting module, the electrical information acquisition and preprocessing module, the steady state time window arc characteristic extraction and fault judgment module, the non-invasive normal current decomposition module, the current time window load composition and steady state characteristic recording and judgment module, the known arc current decomposition and branch positioning module of the electrical components, and the external interaction function module realize the definition function, the data information storage module may be accessed to retrieve the desired data therefrom.
(9) The external interaction function module is used for performing necessary data information interaction between the low-voltage user non-invasive arc fault accurate detection system and the outside, and comprises but is not limited to display and output of monitoring and analysis results and input and output of regulation and control commands;
(10) and the data transmission and information communication module is used for data and information interaction between different functional modules in the system according to the requirement. The "one-way" or "two-way" arrows in fig. 1 represent data transfer and information communication modules.
As shown in fig. 2, the steps of implementing accurate detection of the low-voltage user non-intrusive arc fault by using the system are as follows:
step 1: presetting a full-characteristic sample database of all electric appliances, which comprises a normal characteristic sample database of all electric appliances and a fault arc characteristic database, by using a parameter presetting module and a full-characteristic sample database module of all electric appliances; and simultaneously initializing relevant parameters needing to be preset in different functional modules, such as: the steady state characteristics of the initial time window and the initial user load. The system comprises a normal characteristic sample database, a fault arc characteristic database and a full characteristic sample database of operating electric appliances, wherein the normal characteristic sample database, the fault arc characteristic database and the full characteristic sample database of all preset electric appliances comprise common electric appliances of low-voltage users, such as an electric kettle, an electric water heater, an air conditioner, an induction cooker, an incandescent lamp, an electric hair drier and the like. The embodiment shows a full-characteristic sample database of operating appliances, as shown in fig. 5, samples of the appliances are well clustered under normal operation and arc fault, and the correlation between the arc characteristics and the appliances is strongly verified.
Because the arc characteristics are related to different types of electric appliances, several electric appliances which are common to low-voltage users are selected in the embodiment, including an electric heating kettle, an air conditioner, an electric hair drier (cold air) and an electric hair drier (hot air), which are respectively a resistance type electric appliance, an inductance type electric appliance, a motor type electric appliance and a motor-resistance type electric appliance, and the running current waveforms of each electric appliance in normal running and arc faults are collected. As shown in FIG. 6, each appliance included 100 samples for normal operation and 100 samples for arc fault operation, with a data sampling frequency of 25000 Hz.
Step 2: collecting the total user current and terminal voltage at the user entrance position by using an electrical information collecting and preprocessing module, and preprocessing the collected current data by adopting a wavelet packet threshold denoising method; specifically, the db1 wavelet is used to perform 3-layer decomposition on the original signal and denoise the original signal according to a given soft threshold. In the embodiment, a db1 wavelet is adopted to perform 3-layer decomposition on an original signal and perform noise reduction processing according to a given soft threshold, and as shown in fig. 3, a current waveform comparison graph before and after noise reduction under normal and fault conditions of the electric kettle is shown.
And step 3: capturing a steady state time window by utilizing a steady state time window electric arc characteristic extraction and fault judgment module aiming at a user total current signal, performing characteristic calculation, including CRC, and detecting a fault electric arc in real time according to the CRC characteristic and in combination with the fault electric arc characteristic database; the capturing of the steady-state time window is specifically realized by a rule that the power range of 14 continuous half-waves in the time sequence is smaller than a specified threshold value. The embodiment lists the calculation results of the CRC and ZCP characteristics when the electric kettle is in normal operation and in arc fault, and is shown in FIG. 4; and detecting the fault arc in real time by combining the fault arc characteristic database. This embodiment intercepts one segment of the actual low voltage user total current and calculates its CRC signature, as shown in the left graph of fig. 7, with the abscissa being relative time. Wherein the electric kettle and the blower (hot air) normally operate from the beginning, the air conditioner normally operates at 0.12s, and then the air conditioner has a series arc fault at 0.24s to 0.38 s. And (4) carrying out arc fault detection on the total current according to the CRC characteristics, wherein the calculation result is shown in the right curve of fig. 7, the arc fault duration is 0.24-0.38 s, the fault duration lasts 14 half-waves, and the arc fault is judged and an alarm is given.
And 4, step 4: judging whether a fault arc exists, if so, executing the step 5, otherwise, executing the step 6; specifically, according to the GB-14287.4-1014 standard, if 14 continuous half-waves exist and CRC exceeds 10 times of the root mean square value of the index, the arc fault exists at the moment.
And 5: and (3) judging whether the steady-state characteristics of the current time window change, calculating and tracking the total power according to the total load current and the terminal voltage, if the total power variation is smaller than a specified threshold value of 20W, determining that the steady-state characteristics change, executing the step 2, and otherwise, executing the step 8.
Step 6: the Method comprises the steps of utilizing a Non-invasive normal current decomposition module and a current time window Load composition and steady state characteristic recording and judging module, utilizing a current mode Matching Method [ Yu Yi xi, Liu Bo, Wang big. Current Pattern Matching Method for Non-intuitive Power Load Monitoring and diagnosis: US2015309092[ P ].2015-10-29 ] to realize user Load decomposition to obtain user Load composition, wherein the user Load composition refers to the working state of each electric appliance in a low-voltage distribution system, namely a plurality of working modes included by each electric appliance switch.
And 7: recording the user load composition of the current time window and the steady-state characteristics at the moment, wherein the steady-state characteristics specifically comprise the total average power, the total current effective value and the total current fundamental wave effective value of the user load of the current time window; and returning to the step 2.
And 8: judging whether the load composition of the current time window is known or not, if so, executing the step 9, otherwise, returning to the step 2;
and step 9: calculating ZCP characteristics according to the user load composition obtained in the step 6 by utilizing an electric appliance component known arc current decomposition and branch positioning module, and listing a full characteristic sample matrix of the electric appliance in the running state according to the CRC and ZCP characteristics; obtaining the total current of the corresponding operating electric appliance according to the full-characteristic sample database of the operating electric appliance; comparing the similarity of the total current of different electric appliance fault combinations with the real total current waveform of the user at the moment, wherein the best matching result is the arc current decomposition result; and calculating the similarity between the total current and the real total current of different electrical appliance fault combinations obtained by comparing the total characteristic matrix decomposition by adopting the Euclidean distance between the time sequences. The Euclidean distance d between time sequences can be used for comparison, and the specific calculation method is shown in the formula (2).
Wherein the content of the first and second substances,
the total current sequences are combined for different electrical appliance faults, x is a sequence between real total currents, i is a sequence point serial number, and n is a sequence length.
After an arc fault is detected, the arc fault waveform of a specific device is combined with the normal operation waveforms of other operation devices based on the full-characteristic sample database of the operation electrical appliance. And listing all possible combinations, calculating Euclidean distance between the current waveform of each combination and the original total current, expressing the similarity by the Euclidean distance, and selecting the combination with the maximum similarity as the optimal decomposition.
Step 10: obtaining an electric appliance with an arc fault according to the arc current decomposition result;
step 11: according to the electric appliance with the arc fault, the circuit branch where the fault is located is traced back, and the branch location where the arc fault is located is realized;
step 12: and (4) sending fault alarm and fault branch information, triggering related circuit breakers to act to cut off the branch where the arc fault is located, and returning to the step (2) or ending the detection.
The data information storage module, the external interaction function module and the data transmission and information communication module are used for data storage, communication and external interaction, and correspond to the steps.
For an actual total current of a certain user intercepted in this embodiment, all the combination results and similarity calculation results are shown in table 1, and thus it can be seen that the combination with the minimum euclidean distance or the maximum similarity is the optimal decomposition combination, that is, the detected arc fault is related to the air conditioner, and the arc fault is located on the power supply branch where the air conditioner is located.
TABLE 1 results of all combinations and similarity calculations
According to the embodiment, the invention can realize user load decomposition and arc fault detection under the condition of not invading the interior of a low-voltage user, and determine the fault electric appliance and the branch location where the fault is positioned. Therefore, the invention can realize real-time arc fault monitoring and early warning, can position the fault branch, is convenient for users to overhaul and replace electrical equipment in time, greatly reduces the maintenance cost, reduces unnecessary economic and personal and property losses, and improves the electricity utilization safety of low-voltage users.
While the present invention has been described with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are illustrative only and not restrictive, and many modifications may be made by those skilled in the art without departing from the spirit of the present invention, within the scope of the appended claims.
Claims (10)
1. A non-intrusive arc fault accurate detection method for a low-voltage user is characterized by comprising the following steps:
step 1: presetting a full-characteristic sample database of all electric appliances, wherein the full-characteristic sample database of all electric appliances comprises a normal characteristic sample database and a fault arc characteristic database of all electric appliances; simultaneously initializing preset related parameters including steady-state characteristics of an initial time window and initial user load composition;
step 2: collecting the total user current and terminal voltage at the user entrance, and preprocessing the collected current data by adopting a wavelet packet threshold denoising method;
and step 3: capturing a steady state time window aiming at a user total current signal, performing characteristic calculation, including a current change rate and effective value ratio (CRC), and detecting a fault arc in real time according to the current change rate and effective value ratio (CRC) characteristics and in combination with the fault arc characteristic database;
and 4, step 4: judging whether a fault arc exists, if so, executing the step 5, otherwise, executing the step 6;
and 5: judging whether the steady-state characteristics of the current time window change, if so, returning to the step 2, otherwise, executing the step 8;
step 6: the method comprises the steps that a current mode matching method is utilized to realize user load decomposition to obtain user load constitution, wherein the user load constitution refers to various working states of electric switches in a low-voltage distribution system;
and 7: recording the user load composition of the current time window and the steady-state characteristics at the moment, and returning to the step 2;
and 8: judging whether the load composition of the current time window is known or not, if so, executing the step 9, otherwise, returning to the step 2;
and step 9: calculating current zero-rest time (ZCP) characteristics according to the user load composition obtained in the step 6, and listing a full-characteristic sample matrix of the electric appliance in the running state according to the current change rate and effective value ratio (CRC) and the current zero-rest time (ZCP) characteristics; obtaining the total current of the corresponding operating electric appliance according to the full-characteristic sample database of the operating electric appliance; comparing the similarity of the total current of different electric appliance fault combinations with the real total current waveform of the user at the moment, wherein the optimal matching result is the arc current decomposition result;
step 10: obtaining an electric appliance with an arc fault according to the arc current decomposition result;
step 11: according to the electric appliance with the arc fault, the circuit branch where the fault is located is traced back, and the branch where the arc fault is located is positioned;
step 12: and (4) sending fault alarm and fault branch information, triggering related circuit breakers to act to cut off the branch where the arc fault is located, and returning to the step (2) or ending the detection.
2. The method for accurately detecting the non-invasive arc fault of the low-voltage user according to claim 1, wherein for step 1, a normal characteristic sample database, a fault arc characteristic database and a full characteristic sample database of operating electric appliances of all preset electric appliances are preset, wherein the electric appliances refer to common electric appliances of the low-voltage user and comprise an electric kettle, an electric water heater, an air conditioner, an induction cooker, an incandescent lamp and an electric hair drier.
3. The method for accurately detecting the non-intrusive arc fault of the low-voltage user as recited in claim 1, wherein for step 3, the rule adopted for realizing the capture of the steady-state time window is a rule that the power difference of 14 continuous half-wave power is smaller than a specified threshold value through a time sequence.
4. The method for accurately detecting the non-intrusive arc fault of the low-voltage user according to claim 1, wherein for step 4, the method for judging whether the fault arc exists is as follows: according to the GB-14287.4-1014 standard, if 14 continuous half waves exist and the ratio of the current change rate to the effective value (CRC) exceeds 10 times of the root mean square value of the index, the arc fault exists at the moment.
5. The method according to claim 1, wherein the steady state characteristics include total average power, total current effective value, and total current fundamental effective value of the user load for the current time window for step 7.
6. The method for accurately detecting the non-invasive arc fault of the low-voltage user according to claim 1, wherein for step 9, the method for comparing the similarity between the combined total current of different appliance faults and the real total current waveform of the user at the moment is as follows: and comparing similarity calculation between the total current and the real total current of different electrical appliance fault combinations obtained by full-characteristic sample matrix decomposition by adopting the Euclidean distance between time sequences.
7. The system is characterized by comprising a parameter presetting module, an electrical information acquisition and preprocessing module, a steady-state time window arc characteristic extraction and fault judgment module, a non-invasive normal current decomposition module, a current time window load composition and steady-state characteristic recording and judgment module, an electrical component known arc current decomposition and branch positioning module, a full-characteristic sample database module of all electrical appliances, a data information storage module, a data transmission and information communication module and an external interaction function module;
the parameter presetting module is used for presetting a full-characteristic sample database of all the electric appliances, including a normal characteristic sample database and a fault arc characteristic database of all the electric appliances, and initializing relevant parameters needing to be preset in different functional modules;
the electrical information acquisition and preprocessing module is used for acquiring voltage and total current signals at a monitoring point according to system setting and carrying out noise reduction data preprocessing on the acquired voltage and current signals;
the steady state time window arc characteristic extraction and fault judgment module captures a steady state time window aiming at the total current of a user, performs characteristic calculation, comprises a current change rate and effective value ratio and current zero-break time, and judges whether a fault arc exists in real time by combining the fault arc characteristic database module according to the current change rate and effective value ratio;
the non-invasive normal current decomposition module is used for analyzing and collecting total current and terminal voltage of a load, extracting load characteristics, carrying out current mode matching by using the load characteristics of equipment current according to a normal characteristic sample library of all electrical appliances, realizing load decomposition and obtaining the working state of each electrical appliance in the low-voltage distribution system;
the current time window load composition and steady state feature recording and judging module is used for recording the user load composition of the current time window and the steady state feature at the moment; meanwhile, the method is used for judging whether the steady-state characteristics of the current time window change or not and whether the load composition of the current time window is known or not;
the electric appliance component known arc current decomposition and branch positioning module utilizes the user load composition obtained by the non-invasive normal current decomposition module after the total current detects the arc fault, lists the full-characteristic sample matrix of the electric appliance in the running state, compares the similarity of the total current of different combinations and the real total current waveform of the user at the moment to obtain the best matching result, and backtracks the circuit branch where the arc fault is located to realize the arc fault branch positioning;
the all-electric-appliance full-characteristic sample database module comprises a normal characteristic sample database and a fault arc characteristic database of all electric appliances and is used for storing and managing characteristic fingerprint data of different electric appliances in normal and arc fault operation;
the data information storage module stores the results of signal analysis and data processing of other functional modules according to requirements and provides data access interfaces for the other functional modules;
the external interaction function module is used for the low-voltage user non-invasive arc fault accurate detection system to perform necessary data information interaction with the outside, including but not limited to display and output of monitoring and analysis results and input and output of regulation and control commands;
the data transmission and information communication module is used for data and information interaction between different functional modules in the system according to requirements.
8. The system for realizing the accurate detection method of the non-intrusive arc fault of the low-voltage user as claimed in claim 5, wherein the electrical information acquisition and preprocessing module is used for acquiring a strong voltage and large current analog signal at a mounting point, converting the strong voltage and large current analog signal into a weak voltage and/or small current analog signal, and then digitizing the weak voltage and/or small current analog signal.
9. The system for realizing the non-intrusive arc fault accurate detection method for the low-voltage users according to claim 5, wherein the steady-state time window arc feature extraction and fault judgment module reads current feature sample data of required electric appliance during arc fault operation from the full-feature sample database module of all electric appliances;
the non-invasive normal current decomposition module reads current characteristic sample data of the required electric appliance in normal operation from the full characteristic sample database module of all the electric appliances;
and the electric appliance component known arc current decomposition and branch positioning module reads current characteristic sample data required by normal and arc fault operation of the electric appliance from the full characteristic sample database module of all the electric appliances.
10. The system of claim 5, wherein the processing results of the parameter presetting module, the electrical information collecting and preprocessing module, the steady state time window arc characteristic extracting and fault judging module, the non-invasive normal current decomposing module, the current time window load forming and steady state characteristic recording and judging module, the known arc current decomposition and branch circuit positioning module of electrical components, and the input information of the external interaction function module are stored in the data information storage module, and the parameter presetting module, the electrical information collecting and preprocessing module, the steady state time window arc characteristic extracting and fault judging module, the non-invasive normal current decomposing module, the current time window load forming and steady state characteristic recording and judging module, the known arc current decomposition and branch circuit positioning module of electrical components, and the external interaction function module accesses the data information storage module to acquire the required data from the data information storage module in order to realize the definition function.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112505511A (en) * | 2020-12-14 | 2021-03-16 | 天津求实智源科技有限公司 | Non-invasive low-voltage fault arc detection and positioning method and system |
| CN113985121A (en) * | 2021-10-29 | 2022-01-28 | 天津大学 | Unsupervised non-invasive load identification method and system based on power subject discovery |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2460828A1 (en) * | 2004-03-03 | 2005-09-03 | Daniel J. Aubertin | Portable battery operated electronic interactive electric arc detector |
| CN102721896A (en) * | 2012-07-05 | 2012-10-10 | 天津市鸿远电气设备有限公司 | Stoppage arc detection method |
| CN102749533A (en) * | 2012-04-23 | 2012-10-24 | 华侨大学 | Low-voltage arc fault detection method |
| CN103001230A (en) * | 2012-11-16 | 2013-03-27 | 天津大学 | Non-invasive power load monitoring and decomposing current mode matching method |
| CN103812081A (en) * | 2014-02-27 | 2014-05-21 | 南京航空航天大学 | Direct-current arc fault detection protection method and method and detection circuit |
| CN104280668A (en) * | 2014-11-05 | 2015-01-14 | 广东电网有限责任公司佛山供电局 | Failure type identifying method and system of power distribution network |
| CN204330981U (en) * | 2014-11-05 | 2015-05-13 | 兴义供电局 | A kind of non-intrusion type load switch of transformer mechanical fault on-line measuring device |
| CN107505528A (en) * | 2017-10-18 | 2017-12-22 | 福州大学 | The Intelligentized test system and method for testing of electric arc fault protection electrical equipment |
| CN108258804A (en) * | 2017-12-29 | 2018-07-06 | 北京国电通网络技术有限公司 | A kind of Intelligent household energy source gateway and energy management system |
| WO2018159914A1 (en) * | 2017-03-02 | 2018-09-07 | 주식회사 웰림 | Complex multifunctional relay system and control method therefor |
| CN211606196U (en) * | 2020-03-25 | 2020-09-29 | 华北电力大学 | Load decomposition device of remote monitoring electric equipment |
| CN112505511A (en) * | 2020-12-14 | 2021-03-16 | 天津求实智源科技有限公司 | Non-invasive low-voltage fault arc detection and positioning method and system |
-
2021
- 2021-05-25 CN CN202110571290.5A patent/CN113281625B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2460828A1 (en) * | 2004-03-03 | 2005-09-03 | Daniel J. Aubertin | Portable battery operated electronic interactive electric arc detector |
| CN102749533A (en) * | 2012-04-23 | 2012-10-24 | 华侨大学 | Low-voltage arc fault detection method |
| CN102721896A (en) * | 2012-07-05 | 2012-10-10 | 天津市鸿远电气设备有限公司 | Stoppage arc detection method |
| CN103001230A (en) * | 2012-11-16 | 2013-03-27 | 天津大学 | Non-invasive power load monitoring and decomposing current mode matching method |
| CN103812081A (en) * | 2014-02-27 | 2014-05-21 | 南京航空航天大学 | Direct-current arc fault detection protection method and method and detection circuit |
| CN104280668A (en) * | 2014-11-05 | 2015-01-14 | 广东电网有限责任公司佛山供电局 | Failure type identifying method and system of power distribution network |
| CN204330981U (en) * | 2014-11-05 | 2015-05-13 | 兴义供电局 | A kind of non-intrusion type load switch of transformer mechanical fault on-line measuring device |
| WO2018159914A1 (en) * | 2017-03-02 | 2018-09-07 | 주식회사 웰림 | Complex multifunctional relay system and control method therefor |
| CN107505528A (en) * | 2017-10-18 | 2017-12-22 | 福州大学 | The Intelligentized test system and method for testing of electric arc fault protection electrical equipment |
| CN108258804A (en) * | 2017-12-29 | 2018-07-06 | 北京国电通网络技术有限公司 | A kind of Intelligent household energy source gateway and energy management system |
| CN211606196U (en) * | 2020-03-25 | 2020-09-29 | 华北电力大学 | Load decomposition device of remote monitoring electric equipment |
| CN112505511A (en) * | 2020-12-14 | 2021-03-16 | 天津求实智源科技有限公司 | Non-invasive low-voltage fault arc detection and positioning method and system |
Non-Patent Citations (2)
| Title |
|---|
| RENATO G. FERRAZ等: "《Arc fault location: A nonlinear time varying fault model and frequency domain parameter estimation approach》", 《INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS》 * |
| 王尧: "《基于电弧电流高频分量的串联交流电弧故障检测方法》", 《电力自动化设备》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112505511A (en) * | 2020-12-14 | 2021-03-16 | 天津求实智源科技有限公司 | Non-invasive low-voltage fault arc detection and positioning method and system |
| CN112505511B (en) * | 2020-12-14 | 2022-06-07 | 天津求实智源科技有限公司 | Non-invasive low-voltage fault arc detection and positioning method and system |
| CN113985121A (en) * | 2021-10-29 | 2022-01-28 | 天津大学 | Unsupervised non-invasive load identification method and system based on power subject discovery |
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|---|---|
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