KR101350618B1 - Apparatus and method for high impedance fault detecting - Google Patents

Abstract

The present invention relates to an apparatus and a method for detecting a high impedance fault. The apparatus for detecting a high impedance fault includes: a voltage and current conversion part for converting an analog signal inputted from a voltage and current measuring instrument into a digital signal; a signal calculation part for calculating a root-mean-square value and a positive phase component, a zero phase component and a negative phase component from the digital signal; an over current fault recognition part for recognizing over current fault generation through the root-mean-square value; a high impedance monitoring data calculation part for calculating monitoring data, by performing average discrete wavelet transformation of period unit for the digital signal; a high impedance fault recognition part for recognizing an arc fault through the monitoring data, or recognizing a ground fault through the zero phase component and negative phase component change; and a fault judgment part for judging fault condition according to the recognition result of the over current fault recognition part and the high impedance fault recognition part. [Reference numerals] (110) Voltage and current conversion part; (120) Signal calculation part; (130) Over current fault recognition part; (140) High impedance monitoring data calculation part; (151) Arc sensing part; (155) Image and reverse phase sensing part; (160) Interface unit; (170) Fault judgment part

Description

High resistance failure detection device and its method {APPARATUS AND METHOD FOR HIGH IMPEDANCE FAULT DETECTING}

The present invention relates to a high resistance failure detection device and method thereof, and more particularly, to a high resistance failure detection device and method for determining before and after an arc generation due to a high resistance failure by calculating the average unitary discrete wavelet transform and monitoring data. To provide.

Most detection methods applied to protection devices (substation relays, reclosers and fuses) for detecting distribution line failures include over current relay (OCR) and over current ground relay (OCGR). In this method, both the phase current and the neutral wire current are operated when the predetermined value is over.

However, for low current faults that are not recognized as overcurrent, for example, for fault currents where the "load current + fault current" is typically 400 A or less for phases and for "neutral unbalanced current after fault" There is a problem that does not work in the situation below 70A. This low current fault, which is not recognized as an overcurrent, is called a high resistance fault (HIF).

In the case of a non-grounded distribution system, even though a general ground fault occurs, the current flow between phase and ground is very small, so the boundary between general fault and high resistance ground fault is ambiguous. It is necessary to analyze the characteristics of high resistance ground fault that cannot be detected by overcurrent relay method. In particular, if high resistance fault current occurs at a certain level (less than 100A), it is likely to be accompanied by an arc. The arc has the characteristic of showing both odd and even harmonics at the same time in the harmonic analysis, and because the frequency component is included irregularly within 10kHz, the wavelet transform data can be processed to distinguish between before and after failure. To this end, a method for detecting an arc using a wavelet transform is mainly used as a method of obtaining an approximate coefficient from a high frequency component and an approximation coefficient from a low frequency component from a discrete wavelet transform, as disclosed in Japanese Patent Laid-Open No. 2010-154738. In this method, it takes a long time to obtain both the detailed coefficient and the similar coefficient by the wavelet transform, and even if only one coefficient is obtained, the processing performance of the detection apparatus is deteriorated when the time interval between the samples is considered.

The present invention has been invented to solve the above problems, by calculating the average discrete wavelet transform and monitoring data on a periodic basis, and by fixing the average and the reference value for a predetermined determination period by the user, the arc caused by high resistance failure It is an object of the present invention to provide a high resistance failure detection device and a method which can easily determine before and after occurrence.

In addition, the present invention by using the over-current failure information, high resistance arc failure information and image and reverse phase change information to accurately classify the failure situation by type, high resistance failure that can be quickly responded to the high resistance failure It is an object of the present invention to provide a detection apparatus and method thereof.

In addition, an object of the present invention is to provide a high resistance failure detection apparatus and method that can be applied to the terminal equipment for general power distribution rather than expensive measurement equipment or relay, which can minimize the hardware burden.

High resistance failure detection apparatus according to the present invention for achieving the above object is a voltage and current converter for converting an analog signal input from a voltage and current measuring instrument into a digital signal; A signal calculator for calculating an effective value and a normal, video, and inverse phase component from the digital signal; An overcurrent fault recognition unit for recognizing the occurrence of an overcurrent fault through the effective value; A high-resistance monitoring data calculator for performing periodic unit average discrete wavelet transformation on the digital signal to calculate monitoring data; A high resistance failure recognizing unit for recognizing the occurrence of an arc failure through the monitoring data and recognizing the occurrence of a ground fault through the image and reverse phase component change; And a failure determination unit that determines a failure situation according to a large current experience of the overcurrent failure recognition unit and a recognition result of the high resistance failure recognition unit.

In addition, the high resistance monitoring data calculation unit, after the wavelet transform for N periods after calculating the sum of the absolute value of the detail coefficient only when the sample data of the original signal is a specific multiple of the sample window length as shown in [Equation 1] below The monitoring data is calculated by dividing by N again.

[Equation 1]

(ω: wavelet detail coefficient, ｊ: Scale or level, k: {1.2.… kt}, kt: total number of samples, N: coefficient related to wavelet conversion period)

The high resistance failure recognizing unit may include: an arc detecting unit configured to detect before and after occurrence of the arc failure by comparing an input value with a reference value of the monitoring data during a predetermined determination period; And an image and a reverse phase detector for detecting the ground fault when the rate of change of the image and the reverse phase component is greater than or equal to a preset ratio.

The arc detector may set the variable to '1' when the input value of the monitoring data is greater than or equal to the reference value to fix the reference value for a preset determination period.

When the input value of the monitoring data is smaller than the reference value, the arc detection unit sets the variable to '0' and updates the reference value by applying a weighted average.

The arc detection unit may output a short term arc detection flag whenever the setting of the variable is changed, and output an arc detection signal when a predetermined number or more of the short term arc detection flags are set for a predetermined time.

In addition, the determination unit, when only the change of the image and the reverse phase component is recognized as "high resistance failure", and if only the occurrence of the arc failure is determined as "high resistance arc failure", the image and reverse phase component When the overcurrent fault is detected and the change is previously recognized as a 'high resistance single circuit fault', and when the arc fault occurrence and the change in the image and the reverse phase component are recognized at the same time as the experience of the overcurrent fault occurrence, the 'high resistance disconnection' Ground fault arc failure 'characterized in that the classification by determining the failure status.

The apparatus may further include an interface unit configured to transmit the determination result of the failure determination unit to a distribution automation system or an in-house computer network.

The high resistance failure detection method according to the present invention for achieving the above object comprises the steps of: converting an analog signal input from a voltage and current meter into a digital signal by a voltage and current converter; Calculating, by the signal calculator, an effective value and a normal, video, and inverse phase component from the digital signal; Recognizing, by an overcurrent fault recognition unit, occurrence of an overcurrent fault through the effective value; Calculating, by the high resistance monitoring data calculator, monitoring data by performing a period-averaged discrete wavelet transformation on the digital signal; Recognizing, by the high resistance failure recognizing unit, occurrence of an arc failure through the monitoring data; Recognizing a ground fault occurrence through the image and reverse phase component change by the high resistance failure recognizing unit; And determining, by the failure determination unit, a failure situation according to the recognition result.

In the calculating of the monitoring data, the sum of the absolute values of the detailed coefficients is obtained only when the sample data of the original signal is a specific multiple of the sample window length after the wavelet transform for N periods as shown in Equation 1 below. The monitoring data is then calculated by dividing by N again.

[Equation 1]

(ω: wavelet detail coefficient, ｊ: Scale or level, k: {1.2.… kt}, kt: total number of samples, N: coefficient related to wavelet conversion period)

In addition, the step of recognizing the arc failure through the monitoring data, if the input value of the monitoring data is greater than or equal to the reference value during the predetermined determination period by setting the variable '1' Set the reference value to fix the reference value for a preset determination period, and if the input value of the monitoring data is smaller than the reference value, set the variable to '0' and then apply the weighted average to update the reference value to determine the occurrence of the arc failure. Characterized by detecting the front and rear.

In addition, the step of recognizing the arc failure through the monitoring data, the short-term arc detection flag is sent out whenever the setting of the variable is changed, and if the predetermined number of short-term arc detection flag for a predetermined time or more, the arc detection signal. It characterized in that the output.

In addition, the step of recognizing the ground fault through the change of the image and the reverse phase component, characterized in that the detection of the ground fault occurs when the rate of change of the image and the reverse phase component is greater than or equal to a preset ratio.

The determining of the fault condition according to the recognition result may include determining that a high resistance fault occurs when only the change of the image and the reverse phase component is recognized, and a high resistance arc fault when only the occurrence of the arc fault is recognized. If the change of the image and the reverse phase component and the occurrence of the overcurrent failure was previously recognized as a "high resistance single-cell failure", the arc failure and the image and the reverse phase component at the same time as the experience of the occurrence of the overcurrent failure If the change is recognized, the failure status is classified by the 'high resistance disconnection ground arc fault' and characterized.

On the other hand, after the step of determining the failure situation in accordance with the recognition result, the interface unit, characterized in that it further comprises the step of transmitting the result of determining the failure situation to the distribution automation system or in-house computer network.

The high resistance failure detection apparatus and method according to the present invention having the above-described configuration calculates the periodic discrete wavelet transform and monitoring data on a periodic basis, and fixes the average and reference values for a predetermined determination period set by the user, thereby providing high resistance. It is easy to determine before and after the occurrence of an arc due to a failure, thereby improving the high resistance failure detection rate.

In addition, the present invention by using the over-current failure information, high resistance arc failure information and image minute, reverse phase change information to accurately classify the failure situation by type, there is an effect that can be quickly responded to the high resistance failure. .

In addition, the present invention can be applied to the terminal equipment for general distribution rather than the expensive measuring device or relay, there is an effect that can minimize the hardware burden.

1 is a diagram illustrating a wavelet transform time required when processing 64 samples / cycle.
2 is a diagram showing the result of change of the monitoring data in which the value of the detailed coefficient is seen in sample units.
3 is a view showing the configuration of a high resistance failure detection device according to an embodiment of the present invention.
4 and 5 are diagrams showing a period unit processing result according to Equation 1 for calculating period unit average discrete wavelet transform and monitoring data according to the present invention.
FIG. 6 is a diagram illustrating a change result of the monitoring data viewed in units of 1 cycle.
Fig. 7 is a diagram showing the result of change of the monitoring data as the average of 4 cycles.
FIG. 8 is a diagram illustrating a processing time for high-speed sampling of 64 samples / cycle as an average of 4 cycles as shown in FIG. 7.
FIG. 9 is a diagram illustrating a processing time for high-speed sampling of 128 samples / cycle with an average of 4 cycles as shown in FIG. 7.
10 is a flowchart illustrating a high resistance failure detection method according to the present invention.
11 is a flowchart illustrating a method of detecting before and after arc generation by a short-term arc detection unit employed in the high resistance failure detection method according to the present invention.
12 and 13 illustrate results before and after application of the short-term arc detector of FIG. 11.
14 is a view showing the low-cost low-voltage line arc detection device of another embodiment to which the high resistance failure detection device according to the present invention is applied.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings in order to facilitate a person skilled in the art to easily carry out the technical idea of the present invention. . First, in adding reference numerals to the constituents of the drawings, it is to be noted that the same constituents are denoted by the same reference symbols as possible even if they are displayed on different drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Hereinafter, a high resistance failure detection device and a method thereof according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a diagram showing a wavelet conversion time required when processing 64 samples / cycle, and FIG. 2 is a diagram showing a result of change of monitoring data in which sample values of detail coefficients are sampled. (How one-cycle window moves in samples)

Referring to FIG. 1, when processing the original signal of current at 64 samples per cycle, the time required for wavelet transformation in one cycle is estimated. The similarity and detail coefficients are obtained by the wavelet transformation. It takes 6.4 ms and similar coefficients and detail coefficients each takes 3.2 ms. Also, the time interval between samples is 0.259 ms (16.6 ms / cycle ÷ 64 samples / cycle). This result can also be applied to the high resistance failure detection apparatus according to the present invention described later.

In addition, there are methods for analyzing harmonics and using wavelet transformation to distinguish waveforms before and after high resistance failure. First of all, harmonic analysis has a problem that the exact frequency can be known, but due to the irregular nature of the arc, a number of theories must be applied to distinguish the features, and the level of the underlying hardware must be increased. On the other hand, the wavelet transform can not know the exact frequency, but if properly selected Wavelet Mother Function that determines the wavelet shape (HIF: High) Impedance Fault) can be distinguished before and after. Therefore, the arc characteristics of the arc generated at the time of high resistance failure are irregular in terms of frequency, but if they occur once, the duration is longer than that due to switching.

Therefore, when comparing the method of moving the wavelet transform coefficients in the sample unit while processing the wavelet transform coefficients for one period according to the present invention will be described later in FIG. We proved that there is no problem in distinguishing and presented the method of applying this principle and the estimation method of short-term high resistance fault arc detection criteria and suggested the functional configuration as a device.

3 is a view showing the configuration of a high resistance failure detection device according to an embodiment of the present invention.

Referring to Figure 3, the high resistance failure detection apparatus 100 according to the present invention is largely voltage and current conversion unit 110, signal calculation unit 120, overcurrent failure recognition unit 130, high resistance monitoring data The calculator 140 includes a high resistance failure recognizer 150 and a failure determiner 160.

The voltage and current converter 110 detects a voltage and current signal received from a voltage transformer (PT: Current Transformer), and then converts an analog signal into a digital signal.

The signal calculator 120 calculates a root mean square (RMS) and a symmetrical component, that is, an image, an inverse phase, and a normal component, from the digital signal converted from the voltage and current converter 9210.

The overcurrent failure recognition unit 130 recognizes the overcurrent failure through the effective value from the signal calculator 120. That is, the overcurrent fault recognition unit 130 compares the calculated rms value with a set value for determining an overcurrent fault, and when the rms value is greater than or equal to the set value, an overcurrent caused by phase or ground. It recognizes the occurrence of a failure and transmits it to the high resistance failure recognition unit 150 and the failure determination unit 160.

The high resistance monitoring data calculator 140 calculates the monitoring data by performing the average discrete wavelet transformation on a periodic basis with respect to the digital signal. At this time, the high resistance monitoring data calculation unit 140 converts the wavelet in cycle units for N cycles, and then the sample data of the original signal (voltage, current sequence value) is the length of the sample window as shown in Equation 1 below. Only in the case of a certain multiple (N), the sum of the absolute values for the details (Detail) is calculated and divided by N again to calculate the monitoring data. At this time, in the apparatus which takes the average of N cycles of the original signal from the beginning and performs high-speed sampling in 1 cycle, N of Equation 1 is applied as 1.

ω: Wavelet detail coefficient

ｊ: Scale or level

k: {1.2... kt}

kt: total number of samples

N: coefficient associated with wavelet transform unit (period)

(Multiple of N if unit is N cycle)

The result of the periodic processing and the change result of the monitoring data and the high speed sampling result by applying [Equation 1] will be described in detail with reference to FIGS. 4 to 9.

The high resistance failure detection unit 150 recognizes the occurrence of an arc failure, rather than an overcurrent failure through monitoring data, or recognizes the occurrence of a ground fault through image and reverse phase component changes. That is, the high resistance failure recognition unit 150 recognizes the occurrence of the arc failure through the monitoring data calculated from the high resistance monitoring data calculator 140 described above, and calculates the image and the reverse phase component calculated from the signal calculator 120. Through the change of the ground fault is recognized.

To this end, the high resistance failure detection unit 150 includes an arc detector 151 and an image and reversed phase detector 155. At this time, the arc is a kind of gas discharge, and if the discharge phenomenon persists to some extent, the electrode is overheated and evaporated due to the discharge phenomenon in which the current continues due to collision ionization even if external action is removed. kV) It has the characteristic of generating a fault current below the load current level during arc discharge due to ground fault of the distribution line. In the symmetric coordinate method for unbalanced circuit analysis, the image part refers to the symmetrical component of the same phase and magnitude at each unbalanced voltage and current, and the reverse phase refers to the symmetrical component in which the original voltage and the phase are reversed in the symmetrical coordinate method. .

The arc detecting unit 151 detects before and after occurrence of an arc failure by comparing an input value of monitoring data with a reference value during a predetermined determination period. In order to determine the arc due to high resistance ground fault using the periodic averaged discrete wavelet transform and monitoring data calculated above, it is necessary to continuously monitor the arc for a predetermined period. In addition, the arc generated through incomplete contact with the ground after the wire breakage due to high resistance ground fault has a characteristic that lasts irregularly for a long time unlike the arc generated within a few cycles due to switching system switching phenomenon. have.

Therefore, when the input value of the monitoring data is greater than or equal to the reference value, the arc detection unit 151 sets the variable to '1' to fix the reference value for a preset determination period, and when the input value of the monitoring data is smaller than the reference value. Set the variable to '0' and apply the weighted average to update the baseline to detect before and after the occurrence of an arc failure. This will be described later in detail.

In addition, since the arc detection unit 151 cannot guarantee the detection reliability with only the short-term arc monitoring flag considering only irregular persistence in the tens of cycles, the short-term arc detection flag is sent out every time the variable setting is changed, and the preset time is set. If the short-term arc detection flag is over, the arc detection signal is output.

The image and reverse phase detector 155 detects the occurrence of a ground fault when the rate of change of the image and reverse phase components is greater than or equal to a preset ratio.

The failure determination unit 160 determines a variety of failure conditions comprehensively based on the results of the overcurrent failure recognition unit 130 and the high resistance failure recognition unit 150. For example, when only the image and the reverse phase component change is recognized by the image and the reverse phase detection unit 155 of the high resistance failure recognition unit 150, it is determined as 'high resistance failure', and the high resistance failure recognition unit 150 If only the arc failure is detected by the arc detection unit 151 of the high-resistance arc failure is determined, and if the overcurrent failure has been previously detected by the overcurrent failure recognition unit 130 high resistance failure recognition unit When the image and reverse phase component change is recognized by the image and reverse phase detection unit 155 of 150, it is determined as a 'high resistance disconnection ground fault', and the overcurrent fault recognition unit 130 previously recognizes whether an overcurrent failure has occurred. When the arc failure is generated by the arc detection unit 151 of the high resistance failure detection unit 150 and the change of the image and the reverse phase component is recognized by the image and reverse phase detection unit 155, the high resistance disconnection ground arc failure Judging by '

The interface unit 170 transmits the determination result of the failure determination unit 160 to a distribution automation system or an in-house computer network. In this case, the interface unit 170 may provide failure situation information to distribution line operation personnel through a distribution automation system or an in-house computer network according to various types of physical communication connection methods.

4 and 5 are diagrams showing a cycle unit processing result according to Equation 1 which calculates a cycle unit average discrete wavelet transform and monitoring data in a sample unit according to the present invention, and FIG. 7 is a diagram showing a change result of the monitoring data viewed with a 4 cycle average, and FIG. 8 is a diagram showing a processing time for fast sampling of 64 samples / cycle with a 4 cycle average as shown in FIG. 9 is a diagram illustrating a processing time for high-speed sampling of 128 samples / cycle with an average of 4 cycles as shown in FIG. 7.

Referring to FIGS. 4 and 5, for the conceptual description of Equation 1 described above, it is assumed that the number of samples (sample window length) is 5 for 1 cycle, and FIG. 4 is the conventional method and FIG. 5 is an improvement. This is shown by comparing the method (when N is 1).

6 is a result of the change of the monitoring data processed by applying the discrete wavelet transformation and the equation 1 in units of 1 cycle using the actual experimental data, wherein 1 cycle is about 16.6 ms to be sampled at 64 samples / cycle as shown in FIG. Since the time required for the conversion of discrete wavelets in one cycle is 6.4 ms, real time processing is possible and there is no problem in determining before and after high resistance ground fault. 7 shows that there is no problem in determining before and after a high resistance ground fault as a result of the change of the monitoring data processed by the discrete wavelet transform and Equation 1 as the average of 4 cycles using the actual experimental data. As such, when the original signal is sampled at a high speed of 4 cycles from the beginning, as shown in FIG. 8, a time margin of about 83.1% (1-11.2 / 66.4 ms) based on 64 samples / cycle can be obtained. As shown, it is possible to have about 66.3% (1-22.4 / 66.4ms) spare time based on 128 samples / cycle.

10 is a flowchart showing a high resistance failure detection method according to the present invention.

Referring to FIG. 10, first, the voltage and current converter 110 converts an analog signal input from a voltage and current meter into a digital signal. (S100)

Next, the signal calculating unit 120 calculates the effective value and the normal, video, and reverse phase components from the digital signal (S200).

Next, the overcurrent failure recognition unit 130 recognizes the occurrence of the overcurrent failure through the effective value (S300). At this time, the overcurrent failure recognition unit 130 sets the calculated effective value and the set value for determining the overcurrent failure. When the effective value is greater than or equal to the set value, it is recognized as an overcurrent failure caused by phase or ground.

Next, the high resistance monitoring data calculation unit 140 performs unit-average discrete wavelet transformation on the digital signal to calculate monitoring data. (S400) At this time, the monitoring data is stored for N cycles. Absolute value for Detail only when the sample data of the original signal (voltage, current sequence value) is a certain multiple (N) of the sample window length as shown in [Equation 1] after converting the wavelet by period. Calculate the sum of and divide by N again.

Next, the high resistance failure recognition unit 150 recognizes the occurrence of the arc failure through the monitoring data (S500) and recognizes the ground fault occurrence through the image and the reverse phase component change (S600). At this time, in recognizing the occurrence of an arc failure, if the input value of the monitoring data is greater than or equal to the reference value, the variable is set to '1' to fix the reference value for a preset judgment period, and the input value of the monitoring data is smaller than the reference value. In this case, the variable is set to '0' and then the weighted average is applied to update the reference value to detect before and after occurrence of an arc failure, which will be described in detail later with reference to FIG. 11. In addition, in recognizing the occurrence of a ground fault, if the rate of change of the image and the reverse phase component is greater than or equal to the preset rate it is detected as the ground fault occurs.

Then, the failure determination unit 160 determines the failure situation according to the recognition result. (S700) In this case, when only the image and the reverse phase component change is recognized, it is determined as a 'high resistance failure', and only an arc failure occurs. If it is recognized as 'high resistance arc fault' and if overcurrent fault is recognized before and image and reverse phase change is recognized, it is judged as 'high resistance disconnection ground fault'. When the change of image and reverse phase component is recognized, classify the failure status as 'high resistance disconnected ground arc failure'.

Finally, the interface unit transmits the result of determining the failure situation to the distribution automation system or the in-house computer network.

11 is a flowchart illustrating a method of detecting before and after arc generation by a short-term arc detection unit employed in the high resistance failure detection method according to the present invention.

Referring to FIG. 11, first, an arithmetic mean of the monitoring data input value 'In' is calculated once during a specific determination period 'L' set by the user at the initial start part S500, and then the 'reference value' and the input value are obtained. ('In') is compared (S510). At this time, the 'reference value' is a multiple of the average value, the multiple can be set by the user.

Next, if 'In (t)' is greater than or equal to 'baseline' when comparing 'In (t)', the input value of the specific period 't', with 'baseline (t-1)' (t) 'variable is set to' 1 '(S511). In this case, it is counted how many times the comparison is performed using the variable 'fan' in order to prevent the reference value from being changed during the specific judgment period 'L' set by the user (S517). When ') is equal to or smaller than' L ', the' plate t 'is increased by 1 compared to the' plate t-1 '(S517). Maintaining the average and the 'baseline' as described above (S530) and then returns to step S510.

As such, when 'In (t)' is greater than or equal to the reference value (t-1), when the 'machine (t-1)' is already greater than the judgment period 'L' input by the user (S513) Will continue longer than 'L' to change the 'span (t)' variable to '1' and start from 1 again and maintain the baseline (S530) to repeat the process of preventing reference fluctuations during 'L'. will be.

Next, there are two situations where 'In (t)' is smaller than the 't-1'. After the first or the end of the judgment period, the input value 'In (t)' falls below the threshold and increases above the threshold and then decreases within the judgment period. At first, or after the determination period, the input value 'In (t)' enters below the reference value (S520), where the judgment (t-1) is 0 from the beginning, not between 1 and 'L', or the determination period. After the end, it becomes 0 after the end of 'Plant (t)' to 0 (S521) and 'Up (t)' to '0' (S523). A weighted average is performed as well to update the reference value which is a multiple thereof (S540).

In this case, the weighted average input 'In (t-1)' takes a weight L-1 greater than 'In (t)' in order to mitigate the change in the reference value associated with the average.

On the other hand, when the input period 'In (t)' decreases again than the reference value within the judgment period in a situation where the judgment period for maintaining the reference value is increased due to the increase above the reference value (S510), it is determined and the vending machine (t-1) is 1. If the value between them is determined by comparing (S520), the process of counting the 'panel' (S517) and maintaining the reference value (S530) will be repeated.

12 and 13 illustrate results before and after application of the short-term arc detector of FIG. 11.

12 is a case where the short-term arc monitoring unit of the present invention is not applied. X-axis is a period unit and 201 is monitoring data input value obtained by calculating the wavelet coefficient through the method proposed in the present invention. Is set to 30 cycles, the general arithmetic average is obtained every 30 cycles, and 203 shows an example of setting the reference value three times the arithmetic mean. As can be seen from the figure, it can be seen that it is very difficult to determine before and after occurrence of an arc failure due to high resistance because the 'reference value' itself changes according to the monitoring data input value.

On the other hand, Figure 13 is a case where the short-term arc monitor of the present invention is applied to the monitoring data input value is the same, except that 221 to 222 except for the initial value described above, that is, the 'reference value' to which the first calculated average is applied. The weighted average of Equation 2 is applied to the reference value. At 222, 'In (t)', which is larger than the 't', is entered, indicating that the average and the reference value are fixed for 30 cycles, which is a predetermined judgment period from 222 to 223. At 223, In (t), which is smaller than the reference value (t-1), is entered, and the mean and reference value are updated (increased gradually) by the method of [Equation 2] from 223 to 224. 'In (t)', which is larger than 't-1)', shows that the average and the reference value are fixed for 30 cycles, which is a predetermined period of judgment from 224 to 225. At 225, 'In (t)', which is smaller than 'baseline (t-1)', is returned, and the mean and baseline are updated (reduced gradually) from 225 to 226 (Equation 2). As In (t), which is greater than (t-1), comes in, it shows that the average and reference value are fixed for 30 cycles, which is a user-defined judgment period from 226 to 227. Accordingly, it can be seen that the reference value is fixed at the abnormal points 222, 224, and 226 while reflecting the trend of the monitoring data input value, thereby making it easier to determine before and after occurrence of an arc failure due to high resistance.

In the case of outputting an arc detection signal when a predetermined number of short-term arc detection flags are exceeded for a predetermined time, the condition is a portion that passes only when a value equal to or more than a reference value exceeds a predetermined rate for a predetermined determination period. Short-term arc detection flag is detected when the value between 222 and 223 of Eq. In this case, the situation where the value between 222 and 223 of FIG. 13 exceeds the reference value may occur when an arc fault occurs as well as an overcurrent or inrush condition.

14 is a view showing the low-cost low-voltage line arc detection device 300 of another embodiment to which the high resistance failure detection device 100 according to the present invention is applied.

Simplifying the high resistance failure detection device 100 proposed in the present invention as described above can be utilized as a low-cost low-voltage line arc detection device 300 on the secondary side of the power transformer. Basically, it is possible to add to the electricity meter function because the electricity meter is used.

Compared with the high resistance failure detection device 100 according to the present invention, the voltage and current converter 110 and the high resistance monitoring data calculator 140 exist, but the signal calculator calculates a symmetric component and an effective value ( 120 and the overcurrent failure detection unit 130 is excluded, the high resistance failure detection unit 150 is replaced by the arc detection unit 310 that has the same function as the arc detection unit 151 and the failure determination unit 160 is arc Using only the results of the detector 310 has been replaced by the short-term and long-term arc determination unit 320 to determine the short-term or long-term arc recognition. And although the interface unit 170 is the same, since the information is transmitted to the interface between the person and the machine, such as a near field power terminal monitoring terminal device can reduce the burden of communication hardware.

As described above, the high resistance failure detection device and the method according to the present invention calculate the average discrete wavelet transform and monitoring data on a periodic basis, and fix the average and reference values for a predetermined determination period by the user, thereby preventing the arc caused by the high resistance failure. The high resistance failure detection rate can be improved by easily determining before and after occurrence. In addition, the present invention by using the over-current failure information, high resistance arc failure information and image minute, reverse phase change information to accurately determine the failure situation by type, it is possible to quickly cope with the high resistance failure.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is evident that many alternatives, modifications, and variations will be apparent to those skilled in the art without departing from the scope of the appended claims. As will be understood by those skilled in the art.

100: high resistance failure detection device
110: voltage and current converter 120: signal calculator
130: overcurrent fault recognition unit 140: high resistance monitoring data calculation unit
150: high resistance failure recognition unit 160: failure determination unit
170: interface unit

Claims (15)

A voltage and current converter configured to convert an analog signal input from a voltage and current meter into a digital signal;
A signal calculator for calculating an effective value and a normal, video, and inverse phase component from the digital signal;
An overcurrent fault recognition unit for recognizing the occurrence of an overcurrent fault through the effective value;
A high-resistance monitoring data calculator for performing periodic unit average discrete wavelet transformation on the digital signal to calculate monitoring data;
A high resistance failure recognizing unit for recognizing the occurrence of an arc failure through the monitoring data and recognizing the occurrence of a ground fault through the image and reverse phase component change; And
And a failure determination unit determining a failure situation according to a recognition result of the overcurrent failure recognition unit and the high resistance failure recognition unit.
The failure determination unit,
When only the image and reverse phase component change is recognized, it is determined as 'high resistance failure', and when only the occurrence of the arc failure is recognized as 'high resistance arc failure,' the image and reverse phase component changes and the overcurrent previously If a fault is detected, it is judged as a 'high resistance disconnection ground fault', and when the occurrence of the arc fault and a change in the image and reverse phase component are recognized at the same time as the overcurrent fault recognition experience, the fault situation is referred to as a 'high resistance overcurrent arc fault'. High resistance failure detection device characterized in that the classification and determination. The method of claim 1,
The high resistance monitoring data calculation unit,
After the wavelet transform for N periods, as shown in Equation 1 below, when the sample data of the original signal is a specific multiple of the sample window length, the absolute value of the detail coefficient is summed and divided by N again to calculate the monitoring data. High resistance failure detection device, characterized in that.
[Equation 1]

(ω: wavelet detail coefficient, ｊ: Scale or level, k: {1.2.… kt}, kt: total number of samples, N: coefficient related to wavelet conversion period) The method of claim 1,
The high resistance failure detection unit,
An arc detector for detecting before and after occurrence of an arc failure by comparing an input value of the monitoring data with a reference value during a predetermined determination period; And
An image and reverse phase detection unit to detect the ground fault occurrence when the rate of change of the image and reverse phase components is greater than or equal to a preset ratio;
High resistance failure detection device comprising a. The method of claim 3, wherein
The arc detection unit, if the input value of the monitoring data is greater than or equal to the reference value high resistance failure detection device, characterized in that to set the variable to the first value and the reference value for a predetermined determination period. The method of claim 3, wherein
The arc detecting unit, if the input value of the monitoring data is less than the reference value high resistance failure detection device, characterized in that for setting the variable to a second value and applying a weighted average to update the reference value. 6. The method according to any one of claims 4 to 5,
The arc detector outputs a short term arc detection flag whenever the setting of the variable is changed, and outputs an arc detection signal when the short term arc detection flag is a predetermined number or more for a preset time. High resistance failure detection device characterized in that. delete The method of claim 1,
The high resistance failure detection apparatus further comprises an interface unit for transmitting the determination result of the failure determination unit to a distribution automation system or an in-house computer network. Converting, by the voltage and current converter, an analog signal input from the voltage and current meter into a digital signal;
Calculating, by the signal calculator, an effective value and a normal, video, and inverse phase component from the digital signal;
Recognizing, by an overcurrent fault recognition unit, occurrence of an overcurrent fault through the effective value;
Calculating, by the high resistance monitoring data calculator, monitoring data by performing a period-averaged discrete wavelet transformation on the digital signal;
Recognizing, by the high resistance failure recognizing unit, occurrence of an arc failure through the monitoring data, or a ground fault occurrence through the image and reverse phase component change; And
Determining, by the failure determination unit, a failure situation according to the recognition result;
Determining the failure situation according to the recognition result,
When only the image and reverse phase component change is recognized, it is determined as 'high resistance failure', and when only the occurrence of the arc failure is recognized as 'high resistance arc failure,' the image and reverse phase component changes and the overcurrent previously If a fault is recognized, it is determined as a 'high resistance single fault circuit', and when the occurrence of the arc fault and a change in the image and reverse phase components are recognized at the same time as the overcurrent fault recognition experience, a fault is identified as a 'high resistance disconnected ground fault fault'. A high resistance failure detection method characterized by classifying and determining the situation. The method of claim 9,
Computing the monitoring data,
After the wavelet transform for N periods, as shown in Equation 1 below, when the sample data of the original signal is a specific multiple of the sample window length, the absolute value of the detail coefficient is summed and divided by N again to calculate the monitoring data. High resistance failure detection method characterized in that.
[Equation 1]

(ω: wavelet detail coefficient, ｊ: Scale or level, k: {1.2.… kt}, kt: total number of samples, N: coefficient related to wavelet conversion period) The method of claim 9,
Recognizing an arc failure through the monitoring data,
Comparing the input value of the surveillance data with a reference value during a preset determination period, and if the input value of the surveillance data is greater than or equal to the reference value, setting the variable to a first value to fix the reference value for the preset determination period, And setting a variable to a second value when the input value of the monitoring data is smaller than a reference value, and then applying the weighted average to update the reference value to detect before and after occurrence of the arc failure. 12. The method of claim 11,
Recognizing an arc failure through the monitoring data,
High resistance is characterized in that the short-term arc detection flag (Flag) is sent out whenever the setting of the variable is changed, and if the short-term arc detection flag (Flag) is a predetermined number or more for a preset time, the arc detection signal is output. Fault detection method. The method of claim 9,
Recognizing a ground fault through the image and reverse component change,
And detecting a ground fault if the rate of change of the image and the reverse phase component is greater than or equal to a preset ratio. delete The method of claim 9,
After determining the failure situation according to the recognition result,
And transmitting, by an interface unit, a result of determining the fault condition to a distribution automation system or an in-house computer network.

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