CN111579947B - Series arc multi-feature detection applied to direct current system - Google Patents
Series arc multi-feature detection applied to direct current system Download PDFInfo
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Abstract
The invention relates to direct current system series arc discharge fault detection based on multi-dimensional sensing, which utilizes a multi-dimensional characteristic construction model of a fault arc to diagnose a fault. The power supply and load unit comprises a high-voltage direct-current power supply and a load, the multidimensional characteristic data processing unit constructs a fault arc diagnosis model according to the electric, optical and acoustic three-dimensional fault characteristics detected by the fault information measuring unit, and a foundation is laid for stable and reliable series arc fault diagnosis for the direct-current system. The series arc discharge fault detection method provided by the invention is suitable for a direct current system, provides support for ensuring the normal operation of the direct current system, and makes up for the defect of single characteristic of the traditional fault arc.
Description
Technical Field
The invention relates to direct current system series arc discharge fault detection based on multi-dimensional sensing, and belongs to the technical field of on-line monitoring and fault detection.
Background
Arc discharge is a vigorous gas self-sustaining discharge that occurs under conditions where there is a sufficient voltage difference across the discharge electrodes. The arc discharge phenomenon conforms to the basic gas discharge theory. With the increasing of the electrode distance (discharge gap), the contact surface between the arc and the electrode is gradually reduced, the current in the circuit passes through the narrow contact surface, the current density at the contact part is gradually increased, and the metal strongly heats. When the electrodes are separated, the metal at the contact melts to form a liquid metal bridge, some of which becomes vapor that enters the discharge gap between the electrodes, promoting thermal emission of electrons. At this time, since the distance between the electrodes is small, the electric field intensity between the gaps is large, and the electric field on the surface of the cathode is high, and many electrons are emitted, which is called high electric field emission. Thermal emission and high electric field emission cause a large number of electrons to enter the discharge gap of the arc, and under an applied voltage, the electric field ionizes to produce a large number of electrons and positive ions. Electrons move to the anode and are compounded with positive charges on the surface of the anode, energy is released to heat the surface of the anode, thermal emission of the anode is promoted, and positive ions move to the cathode and are further compounded with the electrons to release energy, so that electric arcs are formed.
In a direct current system, a series fault arc and a parallel fault arc can be classified according to different connection modes of the fault arc and a load. The arc current of the series fault arc flows through the live conductor and is connected in series with the load, and the current of the fault arc is less than the rated working current of the line due to the current limiting effect of the load. The parallel arc current flows through the live conductor and is connected with the load in parallel, and the fault current is larger than the rated working current of the line, which is equivalent to the short-circuit fault of the line. Series fault arcs are caused by broken lines or loose connections of lines in an electrical system, namely fault arcs caused by poor contact between electrodes belong to the series fault arcs. In a direct current system, due to the fact that a connecting plug or a series load connecting part is connected, corrosion oxidation, connection damage, cable insulation degradation, internal metal conductor breakage and the like are difficult to avoid, due to the fact that series arc current shows a descending trend along with time and does not have a zero crossing point, the series arc current cannot be detected by an existing fuse, a breaker and an overcurrent protection device, interruption is not easy to occur, high temperature of thousands of degrees centigrade is generated within a few seconds, equipment is damaged, and safety is endangered. In addition to series arc faults, the three faults can be quickly detected and eliminated by traditional relay protection devices such as a zero sequence current transformer, an air switch and a residual current circuit breaker. Therefore, research on series arc fault discharge of a dc system is necessary.
The direct current series arc detection at home and abroad has the problems of single characteristic and poor reliability. Therefore, the invention aims at detecting the multiple characteristics of the series direct current arc fault by means of a multi-dimensional detection means, and lays a foundation for establishing the direct current series arc fault protection with the multi-dimensional characteristics.
Disclosure of Invention
The purpose of the invention is as follows:
the present invention is directed to solve the above problems, and an object of the present invention is to provide an arc detection method for a dc system, which detects an arc fault in the dc system by a multidimensional sensing method with three dimensions of electricity, light, and sound. The detection technology for detecting the series arc fault of the direct current system through the multi-dimensional sensing is provided, and support is provided for ensuring normal operation of the direct current system.
The technical scheme is as follows:
in order to achieve the above object, the present invention provides a dc series arc fault testing and diagnosing system for a dc system, comprising: the system comprises an arc fault generator unit, a power supply and load unit, a fault information measuring unit and a multi-dimensional characteristic data processing unit;
the arc fault generation unit comprises a fixed end, a sliding block, an electrode, a screw rod and a stepping motor;
the power supply and load unit comprises a high-voltage direct-current power supply and a load;
the fault information measuring unit comprises a voltage sensing module, a current sensing module, an arc sound sensing module and an arc light sensing module;
the fixed end of the arc fault generation unit is connected with a power supply and a high-voltage direct-current power supply of the load unit, and the sliding block is connected with the load;
the data output of the voltage sensing module, the current sensing module, the arc sound sensing module and the arc light sensing module are respectively connected with the input interface of the multidimensional characteristic data processing unit;
the direct-current series arc fault testing and diagnosing system applied to the direct-current system is characterized by further comprising a multi-dimensional characteristic data processing unit and a classification model for diagnosing multi-dimensional arc fault data.
According to one aspect of the invention, the voltage sensing module performs the measurement through a secondary circuit connected to the electrical output.
According to one aspect of the invention, the arc sound sensing module is a non-coupling detection method, and has the characteristics of high precision and no influence of load.
According to one aspect of the invention, the light inlet of the arc sensing module is positioned within a range of 5cm to 20cm from the fault arc.
According to one aspect of the invention, the fault information measuring unit obtains the optical, acoustic and electrical characteristics of the direct current series arc, then a decision tree model is constructed according to the multi-dimensional series arc fault characteristics, a decision node of the decision tree model is a diagnosis condition of the multi-dimensional characteristics, a terminal node of the decision tree model is a diagnosis result of normal work of the direct current system or occurrence of the arc fault, and after the decision characteristic and the splitting criterion are determined, a preprocessing step is not required to be carried out on the multi-dimensional fault characteristics, and a classification model can be rapidly constructed for arc fault diagnosis.
According to the specific scheme of the arc detection method for the direct current system, the following technical effects can be actually achieved:
(1) fault diagnosis is carried out according to the multiple dimensionality information of the arc fault, so that the accuracy of fault detection is guaranteed;
(2) the acoustic characteristics in the multi-dimensional arc fault characteristics are not affected by the load in the detection environment, and the optical characteristics avoid electromagnetic interference and ensure the reliability of the arc detection result.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a schematic diagram of a DC series fault arc diagnosis unit according to the present embodiment;
FIG. 2 is a flow of DC arc data collection and fault diagnosis according to the present embodiment;
FIG. 3 is a circuit of the DC series arc fault experimental platform of the present embodiment;
FIG. 4 is a typical waveform of the experimental platform of this embodiment in normal state and in fault arc;
FIG. 5 is a CART classification decision tree structure employed in the present embodiment;
FIG. 6 shows the final CART classification decision tree result of this embodiment.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings.
The direct current series fault arc diagnosis unit of an embodiment related to the invention is composed as shown in fig. 1, and the diagnosis process is shown in fig. 2, wherein the direct current series arc information base is established on the basis of the direct current series arc fault test and diagnosis system of the direct current system shown in fig. 3, and the arc fault generation unit 1 in the invention comprises a fixed end 101, a slider 102, an electrode 103, a screw rod 104 and a stepping motor 105; the power supply and load unit 2 includes a high voltage dc power supply 201 and a load 202; the fault information measuring unit 3 comprises a voltage sensing module 301, a current sensing module 302, an arc sound sensing module 303 and an arc sensing module 304; the fixed end 101 of the arc fault generating unit 1 is connected with a high-voltage direct-current power supply 201 of the power supply and load unit 2, and the sliding block 102 is connected with a load 202; the data output of the voltage sensing module 301, the current sensing module 302, the arc sound sensing module 303 and the arc sensing module 304 are respectively connected with the input interface of the multidimensional characteristic data processing unit; the multidimensional feature data processing unit 4 also comprises a classification decision tree model for diagnosing multidimensional arc fault data.
The present invention is described below in a specific embodiment.
On the basis of a direct-current series arc fault testing and diagnosing system of a direct-current system, the sliding block is embedded with electrodes of different materials or diameters, and the distance between the electrodes is adjusted by controlling a stepping motor and a lead screw mechanism so as to simulate the fault arc condition which possibly occurs in a power system. The electrodes are closed in the state of the passage of the arc fault generator, then the motor is controlled to rotate at a constant speed, the distance between the electrodes is continuously increased, the process spans the arc front, the arc middle and the arc back, the arc length distribution characteristic of the arc can be directly obtained by collecting the data of the whole fault arc process, and the development state of the arc is researched. However, the characteristics of a dynamic arc event with a single arc length change under the current conditions are difficult to represent the characteristics of all arc discharges under the current conditions, so that multi-dimensional data of a fault arc needs to be collected continuously, and the detection range of an arc detection module in an electrical system should be in an ultraviolet region and cover the spectral wavelength of copper in consideration of the influence of sunlight interference and faults on an optical sensor including a cross point of the width of a light spectrum.
When an arc fault is triggered, different arc intervals are realized by controlling the distance between a fixed end and a sliding block in an arc fault generation module, arc data of 6 states including normal direct current series arc data and arc data with the arc intervals of 1nm, 2nm, 4nm, 6nm and 8nm are collected, 200 groups of data of each type are provided, 2000 data points in each group are provided, and the sampling frequency is 50 kHz.
The normal state measured by the sensor and the typical waveform when the fault arc with unchanged electrode distance occurs are shown in fig. 4, the voltage and current of the fault arc fluctuate, and the light signal and the sound signal have pulse waves, so that the signals are not regular. The occurrence of a fault arc raises the uncertainty of the signal. The invention utilizes multi-dimensional sensors to measure fault arcs together, the response of each sensor is regarded as a random variable system, when multi-dimensional information is correlated, the Gini gain is used as a standard for selecting segmentation attributes, a decision node with the maximum Gini gain is selected as the segmentation attribute of a current data set, arc data in 6 states are used as original data of a CART classification decision tree, and a CART classification decision tree model shown in figure 5 is constructed and used for classifying series fault arcs of a direct current system.
In order to determine the topological structure among the arc characteristic attributes, attribute selection measurement needs to be performed on the series fault arc signals, namely, decision characteristics and a splitting criterion are selected, and different branches are constructed at a certain node according to different divisions of a certain type of decision parameters. Aiming at the multi-dimensional arc characteristics, a decision tree is adopted to divide the multi-dimensional data with or without arc generation, and the decision characteristics are all the multi-dimensional sensors. The tree structure of the decision tree represents a process of classifying the examples based on the characteristics, and a decision tree model is established by utilizing the training data according to the principle of selecting Gini gain maximum.
The decision tree is constructed based on the signals, in order to determine the topological structure among the arc characteristic attributes, attribute selection measurement needs to be carried out on series fault arc signals, namely, decision characteristics and a splitting criterion are selected, and different branches are constructed at a certain node according to different divisions of a certain class of decision parameters. Aiming at the multi-dimensional arc characteristics, the CART decision tree is adopted to divide the multi-dimensional data with or without arc generation, and the decision characteristics are all the multi-dimensional sensors. The tree structure of the decision tree represents the process of classifying instances based on features. And during learning, establishing a decision tree model by utilizing training data according to the principle of selecting the maximum Gini gain. Training is carried out from a root node according to a multidimensional sensing information base of the fault arc, the following operations are carried out on each node recursively to construct a CART decision tree, and when the number of the samples of the fault arc is smaller than a preset threshold value and the Gini coefficient of a fault arc sample set is smaller than any one of the preset threshold values, the algorithm stops calculation and training. Otherwise, the algorithm is continuously operated according to the steps 1 to 5.
And 5, repeating the step 2 and the step 3 until a certain expected data purity level is reached, acquiring a terminal node, and generating a decision tree.
According to the steps, the threshold of the Kini index of the multi-dimensional fault features and the decision nodes in the CART decision tree shown in FIG. 6 are obtained, and arc fault diagnosis is carried out according to the threshold of most features and the diagnosis result represented by the terminal node under the corresponding threshold of the nodes on the CART classification tree.
According to the above arrangement of the present invention, the following technical effects can be actually obtained:
(1) fault diagnosis is carried out according to the multiple dimensionality information of the arc fault, so that the accuracy of fault detection is guaranteed;
(2) the acoustic characteristics in the multi-dimensional arc fault characteristics are not affected by the load in the detection environment, and the optical characteristics avoid electromagnetic interference and ensure the reliability of the arc detection result.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. It will be appreciated by those skilled in the art that a number of simple derivations or substitutions can be made without departing from the spirit of the invention and are intended to be within the scope of the invention.
Claims (4)
1. A DC series arc fault testing and diagnosing system applied to a DC system is characterized by comprising: the device comprises an arc fault generation unit (1), a power supply and load unit (2), a fault information measurement unit (3) and a multi-dimensional characteristic data processing unit (4); the arc fault generation unit (1) comprises a direct current arc generation device to be tested, and is an arc phenomenon caused by arc faults or insulation defects which are set manually; the power supply and load unit (2) comprises a high-voltage direct current power supply (201) and a load (202); the fault information measuring unit (3) comprises a voltage sensing module (301), a current sensing module (302), an arc sound sensing module (303) and an arc light sensing module (304), or any one of the voltage sensing module, the current sensing module, the arc sound sensing module and the arc light sensing module and a combination form thereof; the arc fault generating unit (1) is connected with a power supply and load unit (2); the data output of the voltage sensing module (301), the current sensing module (302), the arc sound sensing module (303) and the arc light sensing module (304) are respectively connected with the input of the multidimensional characteristic data processing unit;
the method comprises the following steps: step S1, the fault information measuring unit (3) detects the fault characteristics of the arc fault in three dimensions of electricity, light and sound, simultaneously obtains the data of the arc fault in the whole process before, during and after the arc, and also obtains the arc fault data in different degrees if the severity of the fault needs to be determined; step S2, under the condition that multi-dimensional fault feature data do not need to be preprocessed, the multi-dimensional feature data processing unit (4) builds a classification model, and establishes thresholds and diagnosis paths of all dimensional features in normal states and arc faults; step S3, judging the initial state of the DC system according to the threshold value and the diagnosis path of the voltage and current characteristic data in the classification model, if the DC system is in the normal state, detecting the possibility of arc fault according to the arc data, once the possibility of fault is found according to the arc data, repeating the step S3, if the DC system is in the fault state, further diagnosing the severity, and entering the step S4; step S4, comprehensively judging the severity of the arc fault according to the multidimensional threshold values of voltage, current, arc sound and arc light in the classification model and the diagnosis path, and the specific steps are as follows:
firstly, judging a first voltage characteristic, and obtaining a normal conclusion of a terminal node or judging a second voltage characteristic according to a first voltage characteristic judgment result; according to the second voltage characteristic judgment result, judging the first current characteristic or judging the third voltage characteristic; according to the first current characteristic judgment result, a conclusion that the arc interval d =8mm of the terminal node is obtained or fourth voltage characteristic judgment is carried out; according to the fourth voltage characteristic judgment result, fifth voltage characteristic judgment or first arc light characteristic judgment is carried out; according to the fifth voltage characteristic judgment result, a conclusion that the arc interval of the terminal node is d =1mm or d =6mm is obtained; obtaining a conclusion that the arc interval of the terminal node is d =1mm or d =2mm according to the first arc judgment result; according to the third voltage characteristic judgment result, second current characteristic judgment or sixth voltage characteristic judgment is carried out; according to the second current characteristic judgment result, third current characteristic judgment or seventh voltage characteristic judgment is carried out; according to the third current judgment result, a conclusion that the arc interval d =2mm of the terminal node is obtained or eighth voltage characteristic judgment is carried out; according to the eighth voltage characteristic judgment result, a conclusion that the arc interval of the terminal node is d =6mm or d =4mm is obtained; according to the seventh voltage characteristic judgment result, a conclusion that the terminal node d =1mm is obtained or ninth voltage characteristic judgment is carried out; obtaining a conclusion that the terminal node d =1mm or d =4mm according to the ninth voltage characteristic judgment result; according to a sixth voltage characteristic judgment result, obtaining a terminal node d =6mm or performing fourth current characteristic judgment; obtaining a conclusion that the terminal node d =6mm or d =4mm according to the fourth current characteristic judgment result;
constructing a CART classification decision tree model for classifying series fault arcs of a direct current system; when the CART classification decision tree model is learned, a decision tree model is established by utilizing training data according to the principle of selecting the maximum Gini gain; training is started from a root node according to a multi-dimensional sensing information base of the fault arc, the following operations are recursively carried out on each node to construct a CART decision tree, when the number of the samples of the fault arc is smaller than a preset threshold value and the Keyny coefficient of a fault arc sample set is smaller than any one of the preset threshold values, the algorithm stops calculating and training, otherwise, the algorithm is continuously carried out according to the following steps:
step 1, prediction: taking into account the characteristics of all possible splits;
step 2, selecting the best separation data providing prediction and splitting: the method comprises the steps that a kini index and a minimum partition attribute are used, so that the kini index and the minimum feature are used as the optimal partition feature, and the decision node is determined according to which sensor measures signals to be used as the feature;
step 3, splitting: obtaining the information gain of the sensor data, and dividing the data into subsets which meet and do not meet threshold conditions through a determined threshold of a known class;
step 4, for each subset, considering all possible segmentations of each characteristic;
and 5, repeating the step 2 and the step 3 until a certain expected data purity level is reached, acquiring a terminal node, and generating a decision tree.
2. The dc series arc fault testing and diagnosing system as claimed in claim 1, wherein the sensing module is installed at the dc bus or the junction of the dc bus and adopts mechanical direct contact.
3. The system as claimed in claim 1, wherein the arc sensing module is used to effectively detect the wavelength range of 300-330nm, and the arc sensing module is used to detect the optical characteristics of the arc fault, so as to determine the occurrence of the arc in the electrical system, and serve as a reference signal at the occurrence time of the arc fault; wherein the light signature is used in arc fault diagnostics to further determine the severity of the arc fault.
4. The direct-current series arc fault testing and diagnosing system applied to the direct-current system according to claim 1, wherein the combination of the arc sound sensing module (303) and the arc light sensing module (304) is adopted, and the acoustic characteristics and the optical characteristics detected by the arc sound sensing module (303) and the arc light sensing module (304) can comprehensively judge the starting time and the extinguishing time of the fault arc, and can be used as a reference for the duration of a single arc fault, namely the severity of the arc fault; wherein the acoustic signature is also used in arc fault diagnostics to further determine the severity of the arc fault.
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