Accident point calibration device and accident point calibration method

Abstract

To obtain a fault location device and fault location method that can appropriately distinguish between noise and surge rise even when the surge rise is gradual. [Solution] A fault location device for locating a fault in a power system using measurement information obtained from multiple measurement terminals installed in a power system, the fault location device comprising: a predicted waveform creation unit that creates a predicted waveform, which is the waveform during normal times, from measurement information during normal times; a surge arrival time calculation unit that compares the predicted waveform with the measurement information and considers the time when the difference between the predicted waveform and the measurement information exceeds a threshold as the fault surge arrival time at the measurement terminal; a location result calculation unit that calculates the fault location based on the fault surge arrival times obtained for each of the multiple measurement terminals; and a display unit that displays the fault location.

Description

Technical Field

[0004]

[0001] The present invention relates to an accident point calibration device and an accident point calibration method.

Background Art

[0002] The power system is a large-scale system constructed and operated by combining many means and methods for stable power supply. Among them, the distribution system has a wide area, so accidents such as ground faults may occur due to various external factors.

[0003] Ground faults are generally classified into two types: complete ground faults and high-resistance ground faults. A complete ground fault is an accident caused by the contact of metals, such as the contact of construction scaffolding hardware or the contact of a hanger due to nesting. On the other hand, a high-resistance ground fault is an accident caused by bird contact, tree contact, etc., and most of the accidents occurring in the distribution system are high-resistance ground faults.

[0004] When an accident occurs, the protection relay in the distribution system operates and the power supply is temporarily stopped. At that time, it is essential to start the restoration work promptly in order to improve the supply reliability. For this purpose, it is required to quickly grasp the accident details (occurrence location, cause, etc.). Currently, the accident point and the cause of the accident are identified by the patrol work of on-site workers. Especially when there is no information about the accident occurrence location, it is necessary to patrol a range of up to several kilometers, so the start of the restoration work is delayed by that much and the power outage state lasts longer.

[0005] In recent years, due to the progress of networks and digital signal processing, many technologies for estimating the system state using sensor measurement signals by installing sensors in the power system have been proposed. Among these, the technology for estimating the accident point is called accident point calibration, and accident point calibration methods such as the surge method and the frequency method have been proposed.

[0006] The surge method is a technique for locating fault locations by measuring the arrival time of surges propagating along transmission lines. Surges, also known as traveling waves, propagate at speeds close to the speed of light and are known to reflect at points where impedance mismatch occurs. A fault location method is known that uses a measurement waveform obtained by sampling the waveform of this surge with an AD converter to locate the fault location.

[0007] Patent Document 1 aims to provide a method for accurately locating the fault point even when surges generated during an accident are attenuated as they propagate along the line. The method involves "when an accident occurs in a power supply line, measuring and storing the surge waveforms at both ends of the line in synchronous manner, extracting the waveform that is most similar to the surge waveforms at both ends of the line, determining the time difference between the time of the surge waveform at one end and the time of the surge waveform at the other end, and determining the distance from one end of the line to the fault point based on that time difference to locate the fault point." [Prior art documents] [Patent Documents]

[0008] [Patent Document 1] Japanese Patent Publication No. 2012-108011 [Overview of the Initiative] [Problems that the invention aims to solve]

[0009] The technology described in Patent Document 1 is based on the principle of calculating the fault point by measuring the arrival time of surges generated and propagating at the fault point of a power system at multiple points along the line, and solving a relational equation based on the distance from the fault point to the measurement point, the difference in surge arrival time, and the surge propagation speed. However, the waveform of the measurement signal from the power system may be distorted due to noise, harmonics, line crosstalk, etc.

[0010] In particular, in the case of high-resistance ground faults, which account for the majority of accidents occurring in power distribution systems, it is known that the slope of the current or voltage measurement becomes gradual during the surge rise. In this case, it becomes difficult to distinguish between noise and the surge rise, which may lead to misdetection of the surge arrival time.

[0011] Therefore, the present invention aims to provide a fault location device and fault location method that can appropriately distinguish between noise and surge rise even when the surge rise is gradual. [Means for solving the problem]

[0012] Based on the above, the present invention is a fault location device that uses measurement information obtained from multiple measurement terminals installed in a power system to locate a fault location in a power system, the fault location device comprising: a predicted waveform creation unit that creates a predicted waveform, which is the waveform during normal times, from measurement information during normal times; a surge arrival time calculation unit that compares the predicted waveform with the measurement information and considers the time when the difference between the predicted waveform and the measurement information exceeds a threshold as the fault surge arrival time at the measurement terminal; a location result calculation unit that calculates the fault location based on the fault surge arrival time obtained for each of the multiple measurement terminals; and a display unit that displays the fault location.

[0013] Furthermore, the present invention is defined as "a fault location method for determining the fault location of a power system using a computer with measurement information obtained from multiple measurement terminals installed in a power system, wherein the computer creates a predicted waveform, which is the waveform during normal times, from measurement information during normal times, compares the predicted waveform with the measurement information, considers the time when the difference between the predicted waveform and the measurement information exceeds a threshold as the fault surge arrival time at the measurement terminal, and calculates the fault location based on the fault surge arrival time obtained for each of the multiple measurement terminals." [Effects of the Invention]

[0014] According to the present invention, even when the surge rise is gradual, the rise of noise and the rise of the surge can be appropriately distinguished. This results in the accurate calculation of the surge arrival time and the resulting improvement in localization accuracy. [Brief explanation of the drawing]

[0015] [Figure 1] This figure shows a schematic configuration example of an accident location device according to Embodiment 1 of the present invention. [Figure 2] A diagram illustrating the functional processing content of the CPU (arithmetic unit) 12. [Figure 3] Figure 3 shows an example of measurement information before and after the accident. [Figure 4] A diagram showing the processing flow of the predictive waveform generation unit 22. [Figure 5] A diagram showing the processing flow of the surge arrival time determination unit. [Figure 6] A diagram illustrating an example of determining the surge arrival time. [Figure 7] This figure shows a schematic configuration example of an accident location device according to Embodiment 2 of the present invention. [Modes for carrying out the invention]

[0016] The following describes embodiments of the present invention with reference to the drawings. However, the present invention is not limited by these embodiments. Furthermore, each embodiment can be appropriately combined as long as the processing content is not contradictory.

[0017] The following assumptions are made when describing the present invention. First, a power system consists of a distribution system and a transmission system. The following description of the present invention focuses on the distribution system, but the technology is also applicable to the transmission system.

[0018] In power systems, voltage and current changes can occur due to accidents such as ground faults, short circuits, and open circuits. Transient changes in voltage and current that occur immediately after an accident are called surges. Surges are also called traveling waves and can propagate along the lines at speeds as fast as light.

[0019] In the following, a surge generated when an accident is determined from changes in voltage and current using equipment installed in the power system will be referred to as an accident surge. Also, a surge that is not determined to be an accident but is generated due to the same cause as an accident will be referred to as a precursor surge.

[0020] Even if accident surges and precursor surges have differences such as wave height and duration, they may be generated by common factors and have common properties. The present invention is applicable to both. Therefore, in the following description, in some cases, the above-described accident surges and precursor surges may be simply described as surges as terms used interchangeably without distinction.

Example

[0021] A schematic configuration example of an accident point calibration device according to Example 1 of the present invention is shown in FIG. 1. FIG. 1 shows that a surge generated by an accident occurring in the distribution line 1 is measured by measurement devices 3 installed at both ends of the accident point 2, and the measured value 4 is input into the accident point calibration device 10 composed of a computer, and the position of the accident point 2 is specified.

[0022] In FIG. 1, it is assumed that an accident has occurred in the distribution line 1, and the occurrence position of the accident is the accident point 2. In the process of the surge generated at the accident point 2 propagating along the line (in this case, the distribution line is exemplified, but it may also be a transmission line), waveform data of the surge is collected using the measurement device 3 installed on the line 1.

[0023] The measurement device 3 is an electric circuit (which may include program processing) that combines a frequency filter, an AD converter, a memory, etc., and converts an analog signal related to the input voltage and current into a digital signal and outputs it. Also, time information can be added using GNSS (Global Navigation Satellite System, Global Navigation Satellite System), etc. Thus, in addition to the waveform data of the surge, the waveform collection location (the measurement device where the waveform was collected), the waveform collection time, etc. can be associated and treated as the measurement information 4 of the surge.

[0024] The measurement information 4 collected by each measuring instrument 3 is sent to the fault location device 10 via communication. The fault location device 10, which is composed of a computer, consists of a communication device 11, a CPU (processing unit) 12, memory 13, an input device 14, a display device 15, and a storage device 16.

[0025] Figure 2 is a diagram illustrating the functional processing content of the CPU (processing unit) 12. As shown in Figure 2, the fault location device 10 includes a measurement information extraction unit 21, a predicted waveform creation unit 22, a surge arrival time determination unit 23, a location result calculation unit 24, and a screen output unit 25. However, in addition to the functional units shown in Figure 1, the fault location device 10 may also include, for example, an input unit for inputting input data, a communication interface unit for communicating with other terminals, and so on.

[0026] The measurement information extraction unit 21 receives measurement information 31 from each measuring instrument 3 as external input for processing. Note that the measurement information 31 includes information from the measuring instrument 3 on the right side of the fault point 2 in Figure 1 and information from the measuring instrument 3 on the left side of the fault point 2 in Figure 1. However, they are basically the same type of information, and unless it is necessary to distinguish between them in the explanation, they will simply be referred to as measurement information 31.

[0027] Measurement information 31 is information that associates the measured voltage and current waveforms with the waveform acquisition location, waveform acquisition time, etc. An example of measurement information before and after an accident is shown in Figure 3. In Figure 3, the horizontal axis represents the number of data counts corresponding to time, and the vertical axis represents, for example, current. According to this, the measurement information 31 before the accident (for example, in the region where the number of data counts is less than 10,000) is a very small AC current value, so it is considered to be the measurement waveform before the surge arrived, i.e., during normal conditions. In contrast, the measurement information 31 after the accident (for example, in the region where the number of data counts is 10,000 or more) is a large AC current value, so it is considered to be the measurement waveform after the surge arrived, i.e., at the time of the accident.

[0028] Given that an example of measurement information 31 is shown in Figure 3, the measurement information extraction unit 21 in Figure 2 splits the measurement information 31 into normal measurement information 32 and accident measurement information 33, with the splitting time 43 as the dividing point, and outputs them.

[0029] Specifically, for example, the measurement information extraction unit 21 divides the aforementioned measurement information 31 into a measurement waveform 32 during normal operation and a measurement waveform 33 during an accident. To do this, it uses a threshold value 41 for the current, which is the measured value, to calculate the time 42 when the measurement information 31 exceeds the threshold value 41. Next, it calculates a splitting time 43, which is a certain time before time 42. The part before this splitting time 43 is designated as normal operation measurement information 32, and the part after is designated as accident measurement information 33.

[0030] Here, the threshold value 41 may be predetermined, or it may be dynamically determined according to the measurement information 31, such as half the maximum value of the measurement information 31. Similarly, the value of the difference between the time 42 when the measured value exceeds the threshold 41 and the separation time 43 may also be predetermined, or it may be dynamically determined according to the measurement information 31.

[0031] The reason for setting the separation time 43 to a time before the time 42 when the measured value exceeded the threshold 41 is that in subsequent processing, the normal measurement information 32 will be used to create a predicted waveform, and the accident measurement information 33 will be used to determine the surge arrival time. For this reason, the normal measurement information 32 is intended to consist only of waveforms from times when a surge has not arrived, while the accident measurement information 33 is intended to include the surge arrival time, i.e., the time when the system transitions from a normal state to an accident state.

[0032] As a method for calculating the time 42 when the measured value exceeds the threshold 41, in addition to the method using the threshold 41 as described above, another method is to create a waveform that simulates the ideal state at the time of the accident and shape-match it so that the difference with the measured information 31 is minimized. For example, a step waveform can be used as a simulated waveform at the time of the accident.

[0033] Returning to the explanation of Figure 2, the next unit, the predictive waveform generation unit 22, takes the normal measurement information 32 and the noise period parameter 34 as input and outputs a predictive waveform 35 for normal conditions. In other words, the predictive waveform 35 is a prediction of the measurement waveform if no accident occurred (and therefore for normal conditions). The noise period parameter 34 is a list of candidate values ​​that can be considered as the period of the noise contained in the normal measurement information 32, such as 5, 10, 15...300.

[0034] Figure 4 shows the processing flow of the predicted waveform generation unit 22. First, in processing step S51, a certain value (for example, 300) is experimentally selected from the input data, which is the noise period parameter 34, and this value is set as the noise period a (microseconds: μs).

[0035] Next, in processing step S52, the normal measurement information 32 is divided by the noise period a (μs), and each waveform after division is defined as a waveform block. Therefore, the data length of the waveform block and the value of the noise period a are the same. For example, when the data length of the predicted waveform creation unit 22 is 1000 (indicated by the data count on the horizontal axis in Figure 3) and the noise period a is 300, waveform blocks of 300 data length are extracted from the beginning of the predicted waveform creation unit 22, resulting in the generation of four waveform blocks (four sets of data lengths of 300, 300, 300, and 100). In the following description, the subscripts i=1, 2...n are added to the end of each waveform block in chronological order to distinguish them from each other. In the above example, it is defined that waveform block 1 is extracted from the data 0-300 of the normal measurement information 32 with a data length of 1000, waveform block 2 is extracted from the data 300-600, and waveform block 3 is extracted from the data 600-900. In this case, the predictive waveform generation unit 22 contains a portion that does not belong to any waveform block (i.e., the portion of data from 900 to 1000), but this is not a particular problem (this portion does not need to be used in the present invention).

[0036] Next, in processing step S53, the sum of the differences between waveform block i and waveform block i+1 (where i <= n-1) in the time direction is calculated and this is taken as the total difference value si. For example, in the case of the normal measurement information 32 with a data length of 1000 as described above, the total difference value s1 between waveform blocks 1 and 2 and the total difference value s2 between waveform blocks 2 and 3 are calculated.

[0037] Next, in processing step S54, the average value save of the sum of difference values ​​si of the waveform blocks calculated in processing step S53 is calculated. For example, if s1 calculated above was 100 and s2 was 200, save will be 150.

[0038] Next, in processing step S55, it is checked whether processing steps S52 to S54 have been performed for all noise period parameters. If not, the process returns to processing step S51, the setting value of noise period a is changed to a different value, and processing steps S52 to S54 are performed again. If processing steps S52 to S54 have been performed for all noise period parameters, the process proceeds to processing step S56.

[0039] In processing step S56, the saved values ​​calculated for multiple noise period a settings are compared to search for the noise period a setting that minimizes the saved value. This setting will be referred to as a' from now on. For example, consider the case where there are three types of noise period parameters: 100, 200, and 300. If the saved value is 600 when noise period a is set to 100, 450 when set to 200, and 150 when set to 300, then the adopted noise period a' will be 300.

[0040] The noise period a' selected through the above process coincides with the lowest frequency, i.e., the longest period, of the noise frequencies present in the normal measurement information 32. For example, if the normal measurement information 32 is a combination of noise of three different frequencies, with noise periods of 50 data, 100 data, and 300 data respectively, then the noise period a' selected through the above process is estimated to be 300. This noise period a' can be considered to correspond to the period of the fundamental wave of the AC measurement waveform observed under normal conditions.

[0041] In processing step S57, the predicted waveform 35 is created by connecting multiple waveform blocks obtained by dividing using a' calculated above. At this time, one waveform block may be selected and multiple copies of it may be connected as is, or the average value of all waveform blocks may be calculated at each time step, and the resulting waveforms may be connected.

[0042] Returning to the explanation of Figure 2, the surge arrival time determination unit 23 takes normal measurement information 32, fault measurement information 33, and predicted waveform 35 as input and outputs the surge arrival time 36. The surge arrival time 36 is assumed to output different surge arrival times for each measuring device. For example, if two measuring devices A and B are installed on the power distribution line where the fault occurred, the surge arrival time for measuring device A and the surge arrival time for measuring device B will be output.

[0043] Figure 5 shows the processing flow of the surge arrival time determination unit 23. Figure 6 shows an example of surge arrival time determination. In Figure 6, the horizontal axis represents the number of data counts corresponding to time, and the vertical axis represents the magnitude of the predicted waveform estimated by the predicted waveform creation unit 22. In this figure, the period of the predicted waveform is determined as described in Figure 4, but the surge arrival time determination unit 23 assumes that the magnitude is within a certain width 71. In other words, for the waveform 35 at the estimated period, its magnitude is determined by the width 71 and is estimated to be within the range of the upper and lower limit waveforms 72 and 73. Then, the time when the observed waveform deviates from the upper and lower limit waveforms 72 and 73 is determined as the surge arrival time. In the illustrated example, 36 is determined as the surge arrival time. This determination is made individually for the surge arrival time at measuring instrument A and the surge arrival time at measuring instrument B if two measuring instruments A and B are installed on the power distribution line where the accident occurred.

[0044] As a specific example of the process for this determination, Figure 5 shows that in processing step S61, the normal measurement information 32 and the predicted waveform 35 are compared at each time step, and the difference is calculated.

[0045] Next, in processing step S62, the fluctuation range 71 of the normal measurement waveform is calculated based on the difference calculated above. This fluctuation range 71 may be, for example, a range of ±3σ from the predicted waveform 35, using the value of the standard deviation σ calculated from the difference above.

[0046] Next, in processing step S63, the time at which the accident measurement information 33 begins to deviate from the fluctuation range 71 is calculated and this is set as the surge arrival time 36.

[0047] Note that when comparing the example of determining the surge arrival time in Figure 6 with that in Figure 3, both the vertical and horizontal axes are on a smaller scale, and the figure is enlarged compared to Figure 3. In Figure 6, the thick black line represents the measurement information at the time of the accident 33, and the solid gray line represents the predicted waveform 35. Above and below the predicted waveform 35 are the upper limit 71 and lower limit 72 of the fluctuation range 71. The time when the measurement information at the time of the accident 33 begins to deviate from the fluctuation range 71, i.e., the surge arrival time 36, is shown by the solid vertical line.

[0048] Through the above processing, the time at which the predicted waveform 35 and the fault measurement information 33 begin to diverge can be calculated as the surge arrival time 36. This allows for accurate calculation of the surge arrival time 36 even when the slope of the surge rise is small due to high-resistance ground faults, etc.

[0049] Returning to the explanation of Figure 2, the location result calculation unit 24 takes the surge arrival time 36 as input and outputs the location result 37. An example of the location result 37 is assumed to be "an accident occurred 500m from the substation". The method for calculating the location result 37 may be to set up a system of equations with three variables: the accident location, the accident time, and the surge propagation speed, based on the surge arrival time 36, and solve it. Alternatively, the system of equations may be solved by assuming that some of the above three variables are fixed values.

[0050] The screen output unit 25 has the function of displaying a screen for the purpose of communicating the measurement result calculation unit 24 to the field worker 26. [Examples]

[0051] Figure 7 shows the logic configuration diagram for Example 2. In Example 1, the predicted waveform 35 was created based on normal measurement information 32, which is part of the measurement information 31 acquired at the time of the accident. In Example 2, however, the predicted waveform creation unit 83 creates the predicted waveform 35 based on past measurement information 81 and stores it in advance. This storage allows multiple predicted waveforms to be stored according to past conditions. Therefore, multiple patterns of predicted waveforms 35 can be created in advance, and when an accident occurs, it becomes possible to select a predicted waveform 35 that is similar to the one in the previous calculation.

[0052] The internal processing of the waveform prediction unit 83 in Figure 7 may use the same processing as in Example 1, or it may use processing that utilizes machine learning. [Explanation of Symbols]

[0053] 1: Power distribution line 2: Accident point: 3: Measuring instruments: 4: Measured values 10: Accident point location device 21: Measurement information extraction section 22: Predicted waveform generation section 23: Surge arrival time determination unit 24: Orientation result calculation part 25: Screen output section

Claims

1. A fault location device that uses measurement information obtained from multiple measurement terminals installed in a power system to locate fault locations in a power system, The accident location location device is characterized by comprising: a predicted waveform creation unit that creates a predicted waveform, which is a waveform during normal conditions, from measurement information during normal conditions; a surge arrival time calculation unit that compares the predicted waveform with the measurement information and considers the time when the difference between the predicted waveform and the measurement information exceeds a threshold as the accident surge arrival time at the measurement terminal; a location result calculation unit that calculates the accident location based on the accident surge arrival times obtained for each of the multiple measurement terminals; and a display unit that displays the accident location.

2. An accident location device according to claim 1, The fault location device is characterized in that the threshold value in the surge arrival time calculation unit is calculated based on the difference between the predicted waveform and the measurement information during normal operation, and the measurement information during normal operation is a portion of the measurement information extracted from the portion before the fault surge arrives.

3. An accident location device according to claim 1, The fault location device is characterized in that the predictive waveform generation unit generates the predictive waveform based on the measurement information during normal operation and noise period parameters.

4. An accident location device according to claim 3, The fault location device is characterized in that the predictive waveform generation unit generates a predictive waveform by connecting multiple waveform blocks extracted from the measurement information during normal operation.

5. An accident location device according to claim 4, The data length of the waveform block is determined by the noise period parameter, and the method for determining this is characterized by selecting the candidate from among multiple candidates for the noise period parameter that minimizes the total difference value when the measurement information under normal conditions is divided and the difference between the divided waveforms is calculated.

6. An accident location device according to claim 1, The fault location device is characterized in that the predictive waveform generation unit divides the measurement information during normal operation according to the noise period to obtain a plurality of waveform blocks, calculates the difference between the plurality of waveform blocks, and selects the noise period with the smallest difference among the plurality of noise periods to obtain the predictive waveform.

7. An accident location device according to claim 1, The accident point location device is characterized in that the predictive waveform generation unit stores a plurality of predictive waveforms obtained using measurement information from past operating conditions as measurement information during normal operation, and the surge arrival time calculation unit selects a predictive waveform from the stored plurality of predictive waveforms that is similar to the measurement information during normal operation to determine the arrival time of the accident surge.

8. A fault location method for determining the fault location of a power system using a computer based on measurement information obtained from multiple measurement terminals installed in the power system, The accident location method is characterized in that the computer creates a predicted waveform, which is the waveform during normal conditions, from measurement information during normal conditions, compares the predicted waveform with the measurement information, considers the time when the difference between the predicted waveform and the measurement information exceeds a threshold as the time of arrival of the accident surge at the measurement terminal, and calculates the accident location based on the time of arrival of the accident surge obtained for each of the multiple measurement terminals.

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