A power distribution network working state analysis method based on traveling wave and a power distribution monitoring system

Abstract

The application relates to a power distribution network working state analysis method based on a traveling wave and a power distribution monitoring system. The method comprises collecting waveform data at a signal collection position; obtaining trigger waveform data, and decomposing the trigger waveform data by using a wavelet decomposition mode to obtain a waveform data decomposition group; when an abnormal waveform appears in the waveform data decomposition group, comparing waveform data of a time period where the abnormal waveform is located and waveform data of a previous time period on a time sequence to obtain traveling wave data; and determining position calculation data according to the traveling wave data at multiple signal collection positions, obtaining multiple position calculation data within a local network coverage range, and determining a traveling wave data occurrence position point according to the multiple position calculation data. The power distribution network working state analysis method based on the traveling wave and the power distribution monitoring system disclosed by the application can accurately detect the traveling wave and the traveling wave occurrence position point by means of multi-point synchronous sampling analysis and reverse positioning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a power distribution network working state analysis method based on traveling wave and a power distribution monitoring system. BACKGROUND

[0002] In recent years, the power industry in China has been developing rapidly. With the increasing requirements of State Grid for the automation level of distribution networks and the deepening of intelligent operation and maintenance methods, digital devices are gradually entering the distribution network operation and maintenance environment. Under the condition of managing hundreds of distribution network lines in the jurisdiction, it is very time-consuming to manually check the faults one by one for line hazards and fault power outages. If the line fault is not handled in a timely manner, it will expand the power outage range, causing greater economic losses.

[0003] During normal operation of the line, line tripping and power outage caused by hazards generated by other uncertain factors and line faults caused by equipment aging cannot be avoided during long-term high-load operation of the distribution line.

[0004] Outdoor power distribution automation switchgear is an important part of power distribution automation, mainly serving the functions of rapid fault section positioning and isolation. However, after isolating the fault section, maintenance personnel still need to locate the fault by patrolling the line, which is relatively time-consuming. In addition, the primary and secondary manufacturers are relatively independent, and there are often compatibility and maintenance difficulties.

[0005] Although the traveling wave detection fault technology has the advantages of high precision and fast response, it still faces some challenges in practical application. For example, the traveling wave signal may be affected by factors such as line parameter non-uniformity, branch lines, and load changes during transmission, resulting in signal distortion and affecting positioning accuracy. SUMMARY

[0006] The present application provides a power distribution network working state analysis method based on traveling wave and a power distribution monitoring system. The method determines the fault location point through multi-point synchronous sampling analysis and reverse positioning, which can accurately detect the traveling wave and the location where the traveling wave occurs.

[0007] The above-mentioned object of the present application is achieved by the following technical solution:

[0008] In a first aspect, the present application provides a power distribution network working state analysis method based on traveling wave, comprising:

[0009] Collecting waveform data at signal collection positions, the number of signal collection positions being multiple;

[0010] Obtaining trigger waveform data, and decomposing the trigger waveform data using a wavelet decomposition method to obtain a waveform data decomposition group, the trigger waveform data including waveform data at a first signal acquisition position and waveform data at a last signal acquisition position in a time sequence;

[0011] When an abnormal waveform appears in the waveform data decomposition group, comparing waveform data in a time period in which the abnormal waveform is located and waveform data in a previous time period in the time sequence to obtain traveling wave data;

[0012] Determining position calculation data according to the traveling wave data at multiple signal acquisition positions, the position calculation data including a traveling wave data source direction and an attenuation rate;

[0013] Obtaining multiple position calculation data in a local network coverage range and determining a traveling wave data occurrence position point according to the multiple position calculation data.

[0014] In a possible implementation manner of the first aspect, determining that an abnormal waveform appears in the waveform data decomposition group includes:

[0015] Determining a start time and an end time of each waveform in the waveform data decomposition group;

[0016] Comparing waveforms that discontinuously appear in a time line with stored data in an abnormal waveform database to determine whether the waveforms are normal waveforms or abnormal waveforms;

[0017] When the waveforms are normal waveforms, performing secondary determination using an overall comparison method.

[0018] In a possible implementation manner of the first aspect, when the waveforms are normal waveforms, performing secondary determination using the overall comparison method includes:

[0019] Obtaining a peak curve and a trough curve of the trigger waveform data, and merging the peak curve and the trough curve to obtain a merged feature curve;

[0020] Decomposing the merged feature curve using a fast Fourier transform method to obtain multiple sub-merged feature curves;

[0021] Sequentially superimposing each sub-merged feature curve on a normal state reference waveform to obtain a result, the result including inclusion and exclusion;

[0022] When the exclusion appears in the result, determining that an abnormal waveform appears in the waveform data decomposition group.

[0023] In a possible implementation manner of the first aspect, comparing waveform data in a time period in which the abnormal waveform is located and waveform data in a previous time period in the time sequence includes:

[0024] The waveform data of the time period where the abnormal waveform is located and the waveform data of the previous time period are converted into frequency domain for decomposition and determination of the abnormal waveform, the starting time and the ending time of the previous time period are adjusted and determined again until the abnormal waveform is determined when the abnormal waveform cannot be determined;

[0025] The starting time of the abnormal waveform is determined, when the abnormal waveform completely appears in the time period where the abnormal waveform is located, the time period where the abnormal waveform is located is moved forward until the appearance time of the abnormal waveform is shorter than the time period where the abnormal waveform is located.

[0026] In a possible implementation manner of the first aspect, the method further includes:

[0027] The arrival time of the abnormal waveform at other signal collection positions is calculated according to the starting time of the abnormal waveform, and the abnormal waveform at the other signal collection positions is obtained according to the arrival time of the abnormal waveform.

[0028] The signal intensity attenuation rate is calculated according to the abnormal waveforms at the multiple signal collection positions, and the direction of the data source of the traveling wave is determined according to the intensity attenuation rate.

[0029] In a possible implementation manner of the first aspect, determining the position point where the traveling wave data occurs according to the data at the multiple positions includes:

[0030] The signal collection positions are marked in the virtual reality power distribution network.

[0031] The traveling wave propagation path is drawn according to the propagation line belonging to the signal collection position, and the number of the traveling wave propagation paths is one or more.

[0032] When the number of the traveling wave propagation paths is more than one, the length of the propagation line of the signal collection position is limited by using the attenuation rate, and the number of the traveling wave propagation paths is reduced to one.

[0033] In a possible implementation manner of the first aspect, limiting the length of the propagation line of the signal collection position by using the attenuation rate includes:

[0034] The measurement signal is transmitted in the traveling wave propagation path.

[0035] The measurement signal is received and the line reference attenuation rate of the measurement signal is calculated.

[0036] The reference attenuation rate is used to correct the attenuation rate.

[0037] A fault signal position is set, and the fault signal intensity is linearly enhanced or linearly weakened, and the length of the propagation line of the signal collection position is adjusted synchronously until the number of intersection points of the traveling wave propagation paths is reduced to one.

[0038] In a second aspect, the application provides a power distribution network working state analysis device based on a traveling wave, including:

[0039] a data acquisition device configured to acquire waveform data at a plurality of signal acquisition positions;

[0040] a first data processing device configured to acquire trigger waveform data, and decompose the trigger waveform data using a wavelet decomposition method to obtain a waveform data decomposition group, the trigger waveform data including waveform data at a first signal acquisition position and waveform data at a last signal acquisition position in a time sequence;

[0041] a second data processing device configured to, when an abnormal waveform appears in the waveform data decomposition group, compare waveform data in a time period in which the abnormal waveform is located and waveform data in a previous time period in the time sequence to obtain traveling wave data;

[0042] a third data processing device configured to determine position calculation data according to the traveling wave data at the plurality of signal acquisition positions, the position calculation data including a traveling wave data source direction and an attenuation rate;

[0043] a position determination unit configured to acquire a plurality of position calculation data within a local network coverage range and determine a traveling wave data occurrence position point according to the plurality of position calculation data.

[0044] In a third aspect, the present application provides a power distribution network working state monitoring system based on traveling waves, the system comprising:

[0045] one or more memories configured to store instructions; and

[0046] one or more processors configured to call and run the instructions from the memories, and perform the method as described in the first aspect and any possible implementation manner of the first aspect.

[0047] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium comprising:

[0048] a program, when the program is run by a processor, the method as described in the first aspect and any possible implementation manner of the first aspect is performed.

[0049] In a fifth aspect, the present application provides a computer program product, comprising program instructions, when the program instructions are run by a computing device, the method as described in the first aspect and any possible implementation manner of the first aspect is performed.

[0050] In a sixth aspect, the present application provides a chip system, the chip system comprising a processor configured to implement the functions involved in the above aspects, for example, generating, receiving, sending, or processing the data and / or information involved in the above method.

[0051] The chip system can be composed of a chip, or can include a chip and other discrete devices.

[0052] In a possible design, the chip system further includes a memory, which is configured to store necessary program instructions and data. The processor and the memory can be decoupled and arranged on different devices, and connected through a wired or wireless manner, or the processor and the memory can be coupled on the same device. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 is a step flow schematic block diagram of a power distribution network working state analysis method based on a traveling wave provided in the present application.

[0054] Figure 2 is a process schematic diagram of determining a waveform type provided in the present application.

[0055] Figure 3 is a schematic diagram of a plurality of traveling wave propagation paths provided in the present application.

[0056] Figure 4 is a schematic diagram of one traveling wave propagation path provided in the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the present application are further described in detail below with reference to the accompanying drawings.

[0058] The present application discloses a power distribution network working state analysis method based on a traveling wave. Please refer to Figure 1 In some examples, the power distribution network working state analysis method based on a traveling wave disclosed in the present application includes the following steps.

[0059] S101, collecting waveform data at a plurality of signal collection positions;

[0060] S102, obtaining trigger waveform data, and decomposing the trigger waveform data by using a wavelet decomposition manner to obtain a waveform data decomposition group, the trigger waveform data including waveform data at a first signal collection position and waveform data at a last signal collection position in a time sequence;

[0061] S103, when an abnormal waveform appears in the waveform data decomposition group, comparing waveform data in a time period where the abnormal waveform is located and waveform data in a previous time period in a time sequence to obtain traveling wave data;

[0062] S104, determining position calculation data according to the traveling wave data at the plurality of signal collection positions, the position calculation data including a traveling wave data source direction and an attenuation rate;

[0063] S105, obtaining multiple position calculation data in the local network coverage range and determining the traveling wave data occurrence position point according to the multiple position calculation data.

[0064] Overall, in steps S101 to S105, waveform data needs to be collected and analyzed at the signal collection position first, when there is an abnormal waveform in the waveform, the traveling wave data is found through data comparison, and then the traveling wave data occurrence position point is calculated through the traveling wave data, or described as the generation position of the traveling wave data.

[0065] In the above, the signal collection position is generally deployed in a unit multi-point collection manner, that is, multiple signal collection positions are deployed at the same position, and this type of signal collection position becomes a group of signal collection positions. Generally, the same group of signal collection positions is deployed on the same power cable or the detection cable led out by the power cable.

[0066] Then the trigger waveform data is obtained and decomposed using the wavelet decomposition method to obtain the waveform data decomposition group. For the trigger waveform data, the specific requirement is that the trigger waveform data includes the waveform data at the first signal collection position and the waveform data at the last signal collection position in the sequence sequence, because there is a certain time interval between the waveform data at the first signal collection position and the waveform data at the last signal collection position, which can avoid the trigger waveform data acquisition error caused by interference to a certain extent, and through the mutual verification of the two signal collection positions, it can be judged whether the trigger waveform data obtained is accurate.

[0067] Because in this application, the trigger waveform data needs to exist in the waveform data at the first signal collection position and the waveform data at the last signal collection position in the sequence sequence.

[0068] When an abnormal waveform appears in the waveform data decomposition group, the waveform data in the time period where the abnormal waveform is located and the waveform data in the previous time period are compared in the time sequence to obtain the traveling wave data. The abnormal waveform here refers to the waveform that is different from the normal waveform.

[0069] When the abnormal waveform is obtained, the normal waveform needs to be determined first. The normal waveform is the waveform collected by the power distribution network in the normal working state, which is referred to as the reference waveform here, and then whether there is an abnormal waveform in the waveform data decomposition group is deduced in reverse with the reference waveform as the reference.

[0070] For the benchmark reference waveform, the specific construction is to collect the waveform that each device terminal in the distribution network may generate when working normally, which is the basic data. For the processing of the basic data, the specific parameters of each waveform need to be obtained, including frequency, amplitude, occurrence time period, and energy proportion.

[0071] Then, the basic data is used to extract from the waveform data decomposition group. If the same or similar data as the basic data can be extracted, then the part of the content is deleted in the waveform data decomposition group. After the basic data is traversed, if there is still residual in the waveform data decomposition group, it is considered that the residual at this time is an abnormal waveform.

[0072] The specific way to extract the same or similar data as the basic data is to find the same or similar waveform in the waveform data decomposition group as the waveform in the basic data. At this time, frequency and amplitude are used for processing first. When the occurrence time period of a certain waveform is non-continuous, the occurrence time period also needs to be used for confirmation.

[0073] Finally, according to the energy proportion, it is determined whether the waveform found in the waveform data decomposition group is correct. The specific way is to compare the similarity between the waveform found in the waveform data decomposition group after synthesis processing and the basic data. The specific way includes direct waveform difference method, statistical error index method, and correlation coefficient method, etc.

[0074] Another specific way to determine the abnormal waveform in the waveform data decomposition group is as follows:

[0075] S201, determine the start time and end time of each waveform in the waveform data decomposition group;

[0076] S202, compare the waveform that appears non-continuously on the time line with the stored data in the abnormal waveform database to determine whether the waveform is a normal waveform or an abnormal waveform;

[0077] S203, when the waveform is a normal waveform, use the overall comparison method for secondary determination.

[0078] The abnormal waveform database here refers to the waveform database composed of abnormal waveforms obtained in the previous data accumulation process. The purpose of determining the start time and end time of each waveform in the waveform data decomposition group is to find some non-continuous waveforms, which are probably abnormal waveforms.

[0079] Then, the waveform that appears non-continuously on the time line is compared with the stored data in the abnormal waveform database to determine whether the waveform is a normal waveform or an abnormal waveform. If an abnormal waveform is found at this time, the subsequent steps are continued.

[0080] The comparative mode actually superimposes the waveforms that appear discontinuously on the timeline on the stored data in the anomaly waveform database, and then views the superimposed result, that is, the deformation degree of the stored data in the anomaly waveform database. For the deformation degree, it is generally required that the area of the deformed region be greater than a set value, and it is considered that the stored data in the anomaly waveform database includes the superimposed waveforms that appear discontinuously on the timeline.

[0081] However, when the waveform is a normal waveform, the absence of the abnormal waveform cannot be determined, and the overall comparative mode is used for secondary determination. The specific process is as follows:

[0082] S301, the peak curve and the valley curve of the trigger waveform data are obtained, and the peak curve and the valley curve are merged to obtain a merged feature curve;

[0083] S302, the merged feature curve is decomposed using the fast Fourier transform mode to obtain a plurality of sub-merged feature curves;

[0084] S303, each sub-merged feature curve is sequentially superimposed on the normal state reference waveform to obtain a result, and the result includes inclusion and exclusion;

[0085] S304, when the exclusion appears in the result, it is determined that the abnormal waveform appears in the waveform data decomposition group.

[0086] In steps S301 to S304, the peak curve and the valley curve of the trigger waveform data are first obtained, the peak curve is a curve obtained by sequentially connecting and smoothing all peak points on the trigger waveform data, and the valley curve is a curve obtained by sequentially connecting and smoothing all valley points on the trigger waveform data.

[0087] The peak curve and the valley curve are merged to obtain a merged feature curve.

[0088] Then, the merged feature curve is decomposed using the fast Fourier transform mode, and then each sub-merged feature curve obtained is sequentially superimposed on the normal state reference waveform to obtain a result, and the result has two types, which are inclusion and exclusion. When the exclusion appears in the result, it is determined that the abnormal waveform appears in the waveform data decomposition group.

[0089] The normal state reference waveform here refers to the overall waveform collected when the power distribution network is working normally. The number of normal state reference waveforms is generally multiple. When the working state of the power distribution network changes, the collection of the normal state reference waveform is required. The change of the working state here refers to the change of the current, voltage, power data and equipment entering and exiting of the power distribution network.

[0090] The manner of superimposing the sub-merging characteristic curve onto the normal state reference waveform is described in the foregoing, and will not be described again here.

[0091] When the abnormal waveform appears, step S103 is performed, in which waveform data of the time period in which the abnormal waveform is located and waveform data of the previous time period are compared in time sequence to obtain traveling wave data, and position calculation data is determined according to the traveling wave data at the plurality of signal collection positions, the position calculation data including a traveling wave data source direction and an attenuation rate, that is, the content in step S104.

[0092] Finally, in step S105, a plurality of position calculation data are obtained within the local network coverage, and a traveling wave data occurrence position point is determined according to the plurality of position calculation data.

[0093] The specific manner of comparing the waveform data of the time period in which the abnormal waveform is located and the waveform data of the previous time period in time sequence is as follows:

[0094] The waveform data of the time period in which the abnormal waveform is located and the waveform data of the previous time period are both converted into the frequency domain for decomposition and determination of the abnormal waveform. When the abnormal waveform cannot be determined, the start time and the end time of the previous time period are adjusted and determined again until the abnormal waveform is determined.

[0095] The start time of the abnormal waveform is determined. When the abnormal waveform completely appears in the time period in which the abnormal waveform is located, the time period in which the abnormal waveform is located is moved forward until the appearance time of the abnormal waveform is shorter than the time period in which the abnormal waveform is located.

[0096] This manner determines the traveling wave data in the manner of time difference. The characteristic of the traveling wave is unidirectional propagation and temporary appearance. As mentioned in the foregoing, the number of signal collection positions is a plurality, and these signal collection positions all need to participate in the processing process recorded in the foregoing manner.

[0097] That is, the traveling wave data exists at each signal collection position, but the appearance time exists with differences before and after.

[0098] For the use manner of the plurality of signal collection positions, the following manner can also be used for processing:

[0099] The arrival time of the abnormal waveform at other signal collection positions is calculated according to the start time of the abnormal waveform, and the abnormal waveform at other signal collection positions is obtained according to the arrival time of the abnormal waveform.

[0100] The signal intensity attenuation rate is calculated according to the abnormal waveforms at the plurality of signal collection positions, and the traveling wave data source direction is determined according to the intensity attenuation rate.

[0101] In this way, the obtained traveling wave data is further determined whether accurate by combining prediction and actual analysis, and the source direction of the traveling wave data is also determined.

[0102] In the above, the specific way of determining the traveling wave data occurrence position point according to the plurality of position calculation data is as follows:

[0103] Marking the signal collection position in the virtual reality power distribution network;

[0104] Defining the traveling wave propagation path according to the propagation line belonging to the signal collection position, and the number of the traveling wave propagation path is one or more;

[0105] When the number of the traveling wave propagation path is more than one, the length of the propagation line of the signal collection position is limited by using the attenuation rate, and the number of the traveling wave propagation path is reduced to one.

[0106] In the above way, the position needs to be determined in combination with the virtual reality power distribution network, and in the specific process, first, the traveling wave propagation path is defined according to the propagation line belonging to the signal collection position, and the number of the traveling wave propagation path is one or more.

[0107] Generally, the number of the traveling wave propagation path is more than one, because the current power distribution network is relatively complex, and there is almost no single line structure. In addition, when the number of the traveling wave propagation path is more than one, the length of the propagation line of the signal collection position is limited by using the attenuation rate, and the number of the traveling wave propagation path is reduced to one, and the specific way is:

[0108] Transmitting the measurement signal in the traveling wave propagation path;

[0109] Receiving the measurement signal and calculating the line reference attenuation rate of the measurement signal;

[0110] Correcting the attenuation rate by using the reference attenuation rate;

[0111] Setting a fault signal position and driving the fault signal strength to linearly increase or linearly decrease, and synchronously adjusting the length of the propagation line of the signal collection position until the number of the intersection points of the traveling wave propagation path is reduced to one.

[0112] The measurement signal is an actually existing signal in reality, and the role is to calculate the line reference attenuation rate. After obtaining the line reference attenuation rate, the attenuation rate is corrected by using the reference attenuation rate, and finally a fault signal position is set and the fault signal strength is driven to linearly increase or linearly decrease, and the length of the propagation line of the signal collection position is synchronously adjusted until the number of the intersection points of the traveling wave propagation path is reduced to one, as shown in Figure 3 and Figure 4 .

[0113] The specific way of correcting the attenuation rate by using the reference attenuation rate is to divide the traveling wave propagation path into multiple segments, in which the reference attenuation rate can be used for calculation, the reference attenuation rate is used for calculation, and otherwise the attenuation rate is used for calculation.

[0114] Of course, there may be a special case where the number of traveling wave propagation paths cannot be one at this time, but the number of traveling wave propagation paths has been greatly reduced, which means that the fault range is more accurate.

[0115] When setting the fault signal position, the intersection of different traveling wave propagation paths is within the consideration range, and then the consistency of the fault signal strength at the fault signal position is checked, and of course a certain error range is allowed here.

[0116] The application also provides a power distribution network working state analysis device based on traveling wave, comprising:

[0117] The data acquisition device is used for acquiring waveform data at the signal acquisition position, and the number of signal acquisition positions is multiple;

[0118] The first data processing device is used for acquiring trigger waveform data and decomposing the trigger waveform data by using a wavelet decomposition method to obtain a waveform data decomposition group, and the trigger waveform data includes waveform data at the first signal acquisition position and waveform data at the last signal acquisition position in the sequence sequence;

[0119] The second data processing device is used for comparing the waveform data in the time period where the abnormal waveform is located and the waveform data in the previous time period in the time sequence when the abnormal waveform appears in the waveform data decomposition group, to obtain traveling wave data;

[0120] The third data processing device is used for determining position calculation data according to the traveling wave data at multiple signal acquisition positions, and the position calculation data includes the traveling wave data source direction and the attenuation rate;

[0121] The position determination unit is used for acquiring multiple position calculation data within the local network coverage range and determining the traveling wave data occurrence position point according to the multiple position calculation data.

[0122] Further, determining the abnormal waveform in the waveform data decomposition group comprises:

[0123] Determine the start time and end time of each waveform in the waveform data decomposition group;

[0124] Compare the waveforms that appear discontinuously on the time line with the stored data in the abnormal waveform database to determine whether the waveforms are normal waveforms or abnormal waveforms;

[0125] When the waveform is a normal waveform, the second determination is performed by using the overall comparison method.

[0126] Further, when the waveform is a normal waveform, the secondary determination using the overall comparison mode comprises:

[0127] obtaining a peak curve and a valley curve of the waveform data of the trigger waveform and merging the peak curve and the valley curve to obtain a merged feature curve;

[0128] decomposing the merged feature curve using a fast Fourier transform mode to obtain a plurality of sub-merged feature curves;

[0129] sequentially superimposing each sub-merged feature curve on a normal state reference waveform to obtain a result, the result including containing and not containing;

[0130] when the result contains not containing, determining that an abnormal waveform appears in the waveform data decomposition group.

[0131] Further, comparing the waveform data of the time period in which the abnormal waveform is located and the waveform data of the previous time period in time sequence comprises:

[0132] converting the waveform data of the time period in which the abnormal waveform is located and the waveform data of the previous time period into the frequency domain for decomposition and determining the abnormal waveform, when the abnormal waveform cannot be determined, adjusting the start time and end time of the previous time period and determining again until the abnormal waveform is determined;

[0133] determining the start time of the abnormal waveform, when the abnormal waveform appears completely in the time period in which the abnormal waveform is located, moving the time period in which the abnormal waveform is located forward until the appearance time of the abnormal waveform is shorter than the time period in which the abnormal waveform is located.

[0134] Further, it further comprises:

[0135] calculating the arrival time of the abnormal waveform at other signal collection positions according to the start time of the abnormal waveform and obtaining the abnormal waveform at the other signal collection positions according to the arrival time of the abnormal waveform;

[0136] calculating the signal intensity attenuation rate according to the abnormal waveforms at a plurality of signal collection positions and determining the direction of the traveling wave data source according to the intensity attenuation rate.

[0137] Further, determining the position point where the traveling wave data occurs according to the data of the plurality of positions comprises:

[0138] marking the signal collection positions in the virtual reality power distribution network;

[0139] drawing a traveling wave propagation path according to the propagation line belonging to the signal collection position, the number of the traveling wave propagation paths being one or more;

[0140] When the number of the traveling wave propagation paths is multiple, the length of the propagation line of the signal collection position is limited using the attenuation rate, and the number of the traveling wave propagation paths is reduced to one.

[0141] Further, the length of the propagation line of the signal collection position is limited using the attenuation rate, and the number of the traveling wave propagation paths is reduced to one.

[0142] The test signal is transmitted in the traveling wave propagation path;

[0143] The test signal is received and the line reference attenuation rate of the test signal is calculated;

[0144] The attenuation rate is corrected using the reference attenuation rate;

[0145] A fault signal position is set and the fault signal strength is linearly increased or linearly decreased, and the length of the propagation line of the signal collection position is adjusted synchronously until the number of the intersection points of the traveling wave propagation paths is reduced to one.

[0146] In one example, the units in any of the above apparatuses can be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASICs), or one or more digital signal processors (DSPs), or one or more field programmable gate arrays (FPGAs), or a combination of at least two of these integrated circuit forms.

[0147] Further, when the units in the apparatuses can be implemented in the form of a processing element scheduler, the processing element can be a general purpose processor, such as a central processing unit (CPU) or other processor that can invoke a program. Further, the units can be integrated together to implement a system-on-a-chip (SOC).

[0148] In the present application, various messages / information / devices / network elements / systems / apparatuses / actions / operations / processes / concepts, etc. of various objects that can appear in the present application are named, and it can be understood that these specific names do not constitute a limitation on the related objects, and the names assigned can be changed according to the scene, context or usage habits, etc. The technical meaning of the technical terms in the present application should be mainly determined from the function and technical effect embodied / implemented in the technical scheme.

[0149] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the system, device and unit described above can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0150] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or in other forms.

[0151] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0152] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0153] It should also be understood that in various embodiments of the present application, first, second, etc. are only to represent that a plurality of objects are different. For example, the first time window and the second time window are only to represent different time windows. The above first, second, etc. should not have any effect on the time window itself, and should not limit the embodiments of the present application.

[0154] It should also be understood that in various embodiments of the present application, the terms and / or descriptions of different embodiments have consistency and can be mutually referred to if there is no special description and logical conflict. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0155] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that make contributions to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a computer readable storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned computer readable storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0156] The present application also provides a power distribution network working state monitoring system based on a traveling wave, which comprises:

[0157] one or more memories for storing instructions; and

[0158] one or more processors for invoking and running the instructions from the memories to execute the methods as described in the above content.

[0159] The present application also provides a computer program product, which includes instructions that, when executed, cause the terminal device and the network device to perform the operations of the terminal device and the network device corresponding to the above methods.

[0160] The present application also provides a chip system, which includes a processor for implementing the functions involved in the above content, such as generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0161] The chip system can be composed of a chip, or can include a chip and other discrete devices.

[0162] The processor mentioned in any of the above can be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for controlling the execution of the programs of the above feedback information transmission method.

[0163] In a possible design, the chip system further includes a memory, which is configured to store necessary program instructions and data. The processor and the memory can be decoupled and arranged on different devices, and connected through wired or wireless means to support the chip system to implement various functions in the above embodiments. Alternatively, the processor and the memory can also be coupled on the same device.

[0164] Optionally, the computer instructions are stored in a memory.

[0165] Optionally, the memory is a storage unit within the chip, such as a register, a cache, etc. The memory can also be a storage unit outside the chip within the terminal, such as a ROM or other type of static storage device that can store static information and instructions, a RAM, etc.

[0166] It can be understood that the memory in the present application can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0167] The non-volatile memory can be a ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically EPROM (EEPROM), or a flash memory.

[0168] The volatile memory can be a RAM used as an external cache. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synch link DRAM (SLDRAM), and direct Rambus RAM (DRRAM).

[0169] The embodiments of the specific implementation are the preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, any equivalent changes made in the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method of power distribution network operating state analysis based on travelling waves, characterized in that, The method comprises the following steps: Collecting waveform data at signal collection positions, the number of signal collection positions being multiple; Obtaining trigger waveform data, and decomposing the trigger waveform data by using a wavelet decomposition method to obtain a waveform data decomposition group, the trigger waveform data comprising waveform data at a first signal collection position and waveform data at a last signal collection position in a time sequence; When an abnormal waveform appears in the waveform data decomposition group, comparing waveform data in a time period where the abnormal waveform is located and waveform data in a previous time period in the time sequence to obtain traveling wave data; Determining position calculation data according to the traveling wave data at the multiple signal collection positions, the position calculation data comprising a traveling wave data source direction and an attenuation rate; Obtaining multiple position calculation data within a local network coverage range and determining a traveling wave data occurrence position point according to the multiple position calculation data; The comparison of the waveform data in the time period where the abnormal waveform is located and the waveform data in the previous time period in the time sequence comprises: Converting the waveform data in the time period where the abnormal waveform is located and the waveform data in the previous time period into the frequency domain for decomposition to determine the abnormal waveform, adjusting a start time and an end time of the previous time period and determining again when the abnormal waveform cannot be determined, and determining the abnormal waveform until the abnormal waveform is determined; Determining a start time of the abnormal waveform, moving the time period where the abnormal waveform is located forward until the appearance time of the abnormal waveform is shorter than the time period where the abnormal waveform is located when the abnormal waveform completely appears in the time period where the abnormal waveform is located; The method further comprises the following steps: Calculating abnormal waveform arrival times at other signal collection positions according to the start time of the abnormal waveform, and obtaining abnormal waveforms at the other signal collection positions according to the abnormal waveform arrival times; Calculating a signal intensity attenuation rate according to the abnormal waveforms at the multiple signal collection positions, and determining a traveling wave data source direction according to the intensity attenuation rate; The determination of the traveling wave data occurrence position point according to the multiple position calculation data comprises the following steps: Marking signal collection positions in a virtual reality power distribution network; Defining a traveling wave propagation path according to a propagation line belonging to the signal collection position, the number of traveling wave propagation paths being one or multiple; When the number of traveling wave propagation paths is multiple, limiting the length of the propagation line of the signal collection position by using the attenuation rate, and reducing the number of traveling wave propagation paths to one; The limiting of the length of the propagation line of the signal collection position by using the attenuation rate comprises the following steps: Transmitting a measurement signal in the traveling wave propagation path; Receiving the measurement signal and calculating a line reference attenuation rate of the measurement signal; Correcting the attenuation rate by using the reference attenuation rate; Setting a fault signal position and driving the fault signal intensity to linearly increase or linearly decrease, and synchronously adjusting the length of the propagation line of the signal collection position until the number of intersection points of the traveling wave propagation paths is reduced to one.

2. A method of power distribution network operating state analysis based on travelling waves as defined in claim 1, characterised in that, The determination of the abnormal waveform appearing in the waveform data decomposition group comprises the following steps: Determining a start time and an end time of each waveform in the waveform data decomposition group; Comparing waveforms that do not continuously appear in a time line with stored data in an abnormal waveform database to determine whether the waveforms are normal waveforms or abnormal waveforms; When the waveforms are normal waveforms, performing secondary determination by using an overall comparison method.

3. A method of power distribution network operating state analysis based on travelling waves as defined in claim 2, characterised in that, The secondary determination by using the overall comparison method when the waveforms are normal waveforms comprises the following steps: The peak curve and the valley curve of the trigger waveform data are obtained and merged to obtain a merged feature curve; The merged feature curve is decomposed using a fast Fourier transform method to obtain a plurality of sub-merged feature curves; Each sub-merged feature curve is sequentially superimposed on a normal state reference waveform to obtain a result, which includes inclusion and exclusion; When the result includes exclusion, it is determined that an abnormal waveform appears in the waveform data decomposition group.

4. A power distribution network operating state analysis apparatus based on travelling waves, characterised in that, It comprises: a data acquisition device for acquiring waveform data at a signal acquisition position, the number of signal acquisition positions being multiple; a first data processing device for obtaining trigger waveform data and decomposing the trigger waveform data using a wavelet decomposition method to obtain a waveform data decomposition group, the trigger waveform data including waveform data at the first signal acquisition position and waveform data at the last signal acquisition position in the time sequence; a second data processing device for comparing waveform data in a time period where an abnormal waveform is located and waveform data in a previous time period in the time sequence when an abnormal waveform appears in the waveform data decomposition group to obtain traveling wave data; a third data processing device for determining position calculation data including the direction of the source of the traveling wave data and the attenuation rate according to the traveling wave data at multiple signal acquisition positions; a position determination unit for obtaining multiple position calculation data in a local network coverage range and determining a traveling wave data occurrence position point according to the multiple position calculation data; comparing waveform data in a time period where an abnormal waveform is located and waveform data in a previous time period in the time sequence includes: converting both the waveform data in the time period where the abnormal waveform is located and the waveform data in the previous time period into the frequency domain for decomposition and determining the abnormal waveform, adjusting the start time and end time of the previous time period and determining again when the abnormal waveform cannot be determined, until the abnormal waveform is determined; determining the start time of the abnormal waveform, moving the time period where the abnormal waveform is located forward until the occurrence time of the abnormal waveform is shorter than the time period where the abnormal waveform is located when the abnormal waveform appears completely in the time period where the abnormal waveform is located; further comprising: calculating the arrival time of the abnormal waveform at other signal acquisition positions according to the start time of the abnormal waveform and obtaining the abnormal waveform at other signal acquisition positions according to the arrival time of the abnormal waveform; calculating the signal intensity attenuation rate according to the abnormal waveform at multiple signal acquisition positions and determining the direction of the source of the traveling wave data according to the intensity attenuation rate; determining the traveling wave data occurrence position point according to the multiple position calculation data includes: marking the signal acquisition position in the virtual reality distribution network; defining the traveling wave propagation path according to the propagation line belonging to the signal acquisition position, the number of traveling wave propagation paths being one or more; when the number of traveling wave propagation paths is multiple, limiting the length of the propagation line of the signal acquisition position using the attenuation rate to reduce the number of traveling wave propagation paths to one; limiting the length of the propagation line of the signal acquisition position using the attenuation rate includes: emitting a measurement signal in the traveling wave propagation path; receiving the measurement signal and calculating the line reference attenuation rate of the measurement signal; correcting the attenuation rate using the reference attenuation rate; A fault signal position is set and the fault signal strength is linearly increased or linearly decreased, and the length of the propagation line of the signal collection position is synchronously adjusted until the number of intersection points of the traveling wave propagation path is reduced to one.

5. A power distribution monitoring system for operating conditions of a power distribution network based on travelling waves, characterized in that The system comprises: one or more memories for storing instructions; and one or more processors for calling and running the instructions from the memories to perform the method as claimed in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises: a program, when the program is run by a processor, the method as claimed in any one of claims 1 to 3 is performed.

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