Traveling wave-based power distribution network working state analysis method and power distribution monitoring system

Abstract

The invention relates to a traveling wave-based power distribution network working state analysis method and a power distribution monitoring system. The method comprises the following steps: acquiring waveform data at a signal acquisition 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 in which the abnormal waveform is located with 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 the plurality of signal acquisition positions, acquiring a plurality of position calculation data in a local network coverage range, and determining a traveling wave data generation position point according to the plurality of position calculation data. According to the traveling wave-based power distribution network working state analysis method and the power distribution monitoring system disclosed by the invention, the fault position point is determined through a multi-point synchronous sampling analysis and reverse positioning mode, and the traveling wave and the traveling wave generation position can be accurately detected through the mode.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular, to a method for analyzing the working state of a distribution network based on traveling waves and a distribution monitoring system. Background Art

[0002] In recent years, China's power industry has continued to develop rapidly. With the improvement requirements of the State Grid for the level of distribution network automation and the continuous deepening of the intelligent operation and maintenance operation mode, digital devices are gradually flooding into the distribution network operation and inspection environment. When managing hundreds of distribution network lines in a jurisdiction at the same time, it is very time-consuming to manually check each pole for faults in case of line hidden dangers and fault power outages. If the line fault is not handled in time, the power outage range will be expanded, resulting in greater economic losses.

[0003] During the normal operation of the line, hidden dangers caused by other uncertain factors and line faults caused by equipment aging lead to line tripping and power outages, which are inevitable during the long-term high-load operation of the distribution network line.

[0004] Outdoor distribution automation switch equipment is an important part of distribution automation, mainly playing the role of quickly locating and isolating the fault section. However, after isolating the fault section, it generally still requires maintenance personnel to locate the fault through line patrols, etc., which takes a relatively long time, and the primary and secondary manufacturers are relatively independent, often having problems such as compatibility and maintenance difficulties.

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

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

[0007] The above object of the present application is achieved through the following technical solutions:

[0008] In a first aspect, the present application provides a method for analyzing the working state of a distribution network based on traveling waves, including:

[0009] Collect waveform data at signal acquisition positions, and the number of signal acquisition positions is multiple;

[0010] Obtain trigger waveform data, and decompose the trigger waveform data using wavelet decomposition to obtain a waveform data decomposition group. The trigger waveform data includes waveform data at the first signal acquisition position and the last signal acquisition position on the sequential sequence;

[0011] When an abnormal waveform appears in the waveform data decomposition group, compare the waveform data in the time period where the abnormal waveform is located with the waveform data in the previous time period in the time series to obtain traveling wave data;

[0012] Determine position calculation data based on the traveling wave data at multiple signal acquisition positions. The position calculation data includes the source direction and attenuation rate of the traveling wave data;

[0013] Obtain multiple position calculation data within the local network coverage range and determine the position point where the traveling wave data occurs based on 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] Determine the start time and end time of each waveform in the waveform data decomposition group;

[0016] Compare the waveforms that do not appear 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;

[0017] When the waveform is a normal waveform, use the overall comparison method for secondary determination.

[0018] In a possible implementation manner of the first aspect, when the waveform is a normal waveform, using the overall comparison method for secondary determination includes:

[0019] Obtain the peak curve and trough curve of the trigger waveform data and merge the peak curve and trough curve to obtain a merged characteristic curve;

[0020] Decompose the merged characteristic curve using the fast Fourier transform method to obtain multiple sub-merged characteristic curves;

[0021] Sequentially superimpose each sub-merged characteristic curve on the normal state reference waveform to obtain results, and the results include inclusion and non-inclusion;

[0022] When non-inclusion appears in the results, determine that an abnormal waveform appears in the waveform data decomposition group.

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

[0024] Transfer 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 determine the abnormal waveform. When the abnormal waveform cannot be determined, adjust the start time and end time of the previous time period and determine again until the abnormal waveform is determined;

[0025] Determine the start time of the abnormal waveform. When the abnormal waveform completely appears in the time period where the abnormal waveform is located, move 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.

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

[0027] Calculate the arrival time of the abnormal waveform at other signal acquisition positions according to the start time of the abnormal waveform and obtain the abnormal waveform at other signal acquisition positions according to the arrival time of the abnormal waveform;

[0028] Calculate the signal intensity attenuation rate according to the abnormal waveforms at multiple signal acquisition positions and determine the traveling wave data source direction according to the intensity attenuation rate.

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

[0030] Mark the signal acquisition positions in the virtual reality power distribution network;

[0031] Delimit the traveling wave propagation paths according to the transmission lines belonging to the signal acquisition positions, and the number of traveling wave propagation paths is one or more;

[0032] When the number of traveling wave propagation paths is multiple, use the attenuation rate to limit the length of the transmission lines of the signal acquisition positions and reduce the number of traveling wave propagation paths to one.

[0033] In a possible implementation manner of the first aspect, using the attenuation rate to limit the length of the transmission lines of the signal acquisition positions includes:

[0034] Transmit a measurement signal within the traveling wave propagation path;

[0035] Receive the measurement signal and calculate the line reference attenuation rate of the measurement signal;

[0036] Use the reference attenuation rate to correct the attenuation rate;

[0037] Set a fault signal position and drive the fault signal intensity to linearly increase or linearly decrease, and synchronously adjust the length of the transmission lines of the signal acquisition positions until the number of intersection points of the traveling wave propagation paths is reduced to one.

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

[0039] A data acquisition device for acquiring waveform data at signal acquisition positions, where the number of signal acquisition positions is multiple;

[0040] A first data processing device for obtaining trigger waveform data and decomposing the trigger waveform data using wavelet decomposition to obtain a waveform data decomposition group, where the trigger waveform data includes waveform data at the first signal acquisition position and the last signal acquisition position in a sequential sequence;

[0041] A second data processing device for, when an abnormal waveform appears in the waveform data decomposition group, comparing the waveform data in the time period where the abnormal waveform is located with the waveform data in the previous time period in the time series to obtain traveling wave data;

[0042] A third data processing device for determining position calculation data based on the traveling wave data at multiple signal acquisition positions, where the position calculation data includes the source direction and attenuation rate of the traveling wave data;

[0043] A position determination unit for obtaining multiple position calculation data within the local network coverage range and determining the position point where the traveling wave data occurs based on the multiple position calculation data.

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

[0045] One or more memories for storing instructions; and

[0046] One or more processors for calling and running the instructions from the memory to execute the method described in the first aspect and any possible implementation manners of the first aspect.

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

[0048] A program, when the program is run by a processor, the method described in the first aspect and any possible implementation manners of the first aspect is executed.

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

[0050] In a sixth aspect, the present application provides a chip system, which includes a processor for implementing 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 chips or can include chips and other discrete devices.

[0052] In a possible design, the chip system further includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and separately disposed on different devices and connected by a wired or wireless manner, or the processor and the memory can also be coupled on the same device. Description of the Drawings

[0053] Figure 1 It is a schematic block diagram of the steps of a method for analyzing the working state of a distribution network based on traveling waves provided by this application.

[0054] Figure 2 It is a schematic diagram of the process for determining the waveform type provided by this application.

[0055] Figure 3 It is a schematic diagram showing that the number of traveling wave propagation paths is multiple provided by this application.

[0056] Figure 4 It is a schematic diagram showing that the number of traveling wave propagation paths is one provided by this application. Detailed Description of the Embodiments

[0057] The following further elaborates on the technical solutions in this application with reference to the accompanying drawings.

[0058] This application discloses a method for analyzing the working state of a distribution network based on traveling waves. Please refer to Figure 1 , in some examples, the method for analyzing the working state of a distribution network based on traveling waves disclosed in this application includes the following steps:

[0059] S101, collect waveform data at signal acquisition positions, and the number of signal acquisition positions is multiple;

[0060] S102, obtain trigger waveform data and decompose the trigger waveform data using wavelet decomposition to obtain a waveform data decomposition group. The trigger waveform data includes the waveform data at the first signal acquisition position and the waveform data at the last signal acquisition position in the sequential sequence;

[0061] S103, when abnormal waveforms appear in the waveform data decomposition group, compare the waveform data in the time period where the abnormal waveforms are located with the waveform data in the previous time period in the time series to obtain traveling wave data;

[0062] S104, determine position calculation data according to the traveling wave data at multiple signal acquisition positions. The position calculation data includes the traveling wave data source direction and the attenuation rate;

[0063] S105. Obtain multiple location calculation data within the local network coverage range and determine the position point where the traveling wave data occurs based on the multiple location calculation data.

[0064] Overall, in steps S101 to S105, it is necessary to first collect and analyze waveform data at the signal acquisition location. When there is abnormal waveform in the waveform, the traveling wave data is discovered through data comparison, and then the position point where the traveling wave data occurs, or described as the generation position of the traveling wave data, is calculated through the traveling wave data.

[0065] In the above content, the signal acquisition location is generally deployed by the method of single - location multi - point acquisition, that is, multiple signal acquisition locations are deployed at the same position. This type of signal acquisition location is called a group of signal acquisition locations. Generally, the same group of signal acquisition locations is deployed on the same power supply cable or on the detection cable led out from the power supply cable.

[0066] Then obtain the trigger waveform data and decompose the trigger waveform data using the wavelet decomposition method to obtain a 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 acquisition location and the waveform data at the last signal acquisition location in the sequential sequence. This is because there is a certain time interval between the waveform data at the first signal acquisition location and the waveform data at the last signal acquisition location, which can avoid the error in obtaining the trigger waveform data caused by interference to a certain extent. In addition, through the mutual verification of the two signal acquisition locations, it can be judged whether the obtained trigger waveform data is accurate.

[0067] Because in this application, the trigger waveform data needs to exist simultaneously in the waveform data at the first signal acquisition location and the waveform data at the last signal acquisition location in the sequential sequence.

[0068] When there is an abnormal waveform in the waveform data decomposition group, compare the waveform data in the time period where the abnormal waveform is located with the waveform data in the previous time period in the time series to obtain the traveling wave data. Here, the abnormal waveform refers to the waveform that is different from the normal waveform.

[0069] When an abnormal waveform is obtained, it is first necessary to determine the normal waveform. The normal waveform is the waveform collected when the power distribution network is in a normal working state. Here, it is called the reference waveform. Then, with the reference waveform as a reference, reverse - infer whether there is an abnormal waveform in the waveform data decomposition group.

[0070] For the reference waveform, the specific construction method is to collect the waveforms that may be generated by each device terminal in the distribution network during normal operation. This 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 ratio.

[0071] Then, use the basic data to extract from the waveform data decomposition group. If data identical or similar to the basic data can be extracted, then this part of the content will be deleted from the waveform data decomposition group. After traversing all the basic data, if there are still residues in the waveform data decomposition group, then the residues at this time are considered abnormal waveforms.

[0072] The specific method for extracting data identical or similar to the basic data is to find waveforms in the waveform data decomposition group that are the same or similar to the waveforms in the basic data. At this time, the frequency and amplitude are first used for processing. When the occurrence time period of a certain waveform is discontinuous, the occurrence time period also needs to be used for confirmation.

[0073] Finally, determine whether the waveforms found in the waveform data decomposition group are correct according to the energy ratio. The specific method is to perform synthesis processing on the waveforms found in the waveform data decomposition group and then compare the similarity with the basic data. The specific methods include the direct waveform difference method, the statistical error index method, and the correlation coefficient method, etc.

[0074] Another specific method for determining abnormal waveforms 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 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;

[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 during 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 waveforms that appear discontinuously, and these waveforms are probably abnormal waveforms.

[0079] Then, 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. If abnormal waveforms are found at this time, then continue to execute the subsequent steps.

[0080] In the comparison method, waveforms that do not appear continuously on the time line are actually superimposed on the data stored in the abnormal waveform database, and then the superimposition result is viewed, that is, the degree of deformation of the data stored in the abnormal waveform database. For the degree of deformation, generally, it is considered that the data stored in the abnormal waveform database includes the waveforms that do not appear continuously on the time line when the area of the deformed region is greater than a set value.

[0081] However, when the waveform is a normal waveform, the non-existence of abnormal waveforms still cannot be confirmed. At this time, the overall comparison method is used for secondary determination. The specific process is as follows:

[0082] S301, Obtain the peak curve and trough curve of the triggered waveform data and merge the peak curve and trough curve to obtain a merged characteristic curve;

[0083] S302, Decompose the merged characteristic curve using the fast Fourier transform method to obtain multiple sub-merged characteristic curves;

[0084] S303, Sequentially superimpose each sub-merged characteristic curve on the normal state reference waveform to obtain results, including inclusion and non-inclusion;

[0085] S304, When non-inclusion appears in the results, it is determined that abnormal waveforms appear in the waveform data decomposition group.

[0086] In steps S301 to S304, first, obtain the peak curve and trough curve of the triggered waveform data. The peak curve is a curve obtained by sequentially connecting all the peak points on the triggered waveform data and performing smoothing processing. The trough curve is a curve obtained by sequentially connecting all the trough points on the triggered waveform data and performing smoothing processing.

[0087] Merge the peak curve and trough curve to obtain a merged characteristic curve.

[0088] Then, decompose the merged characteristic curve using the fast Fourier transform method, and then sequentially superimpose each obtained sub-merged characteristic curve on the normal state reference waveform to obtain results. There are two results, namely inclusion and non-inclusion. When non-inclusion appears in the results, it is determined that abnormal waveforms appear 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, normal state reference waveforms need to be collected. The change in the working state here refers to changes in the current, voltage, power data, and equipment entry and exit of the power distribution network.

[0090] The method of superimposing the sub - merged feature curve on the normal - state reference waveform has been described above and will not be elaborated here.

[0091] When an abnormal waveform appears, step S103 is executed. In this step, the waveform data in the time period where the abnormal waveform is located is compared with the waveform data in the previous time period in the time series to obtain traveling - wave data. Combining the position - calculation data determined according to the traveling - wave data at multiple signal - acquisition positions, the position - calculation data includes the source direction and attenuation rate of the traveling - wave data, which is the content in step S104.

[0092] Finally, in step S105, multiple position - calculation data are obtained within the local network coverage range and the position point where the traveling - wave data occurs is determined according to the multiple position - calculation data.

[0093] The specific method of comparing the waveform data in the time period where the abnormal waveform is located with the waveform data in the previous time period in the time series is as follows:

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

[0095] Determine the start time of the abnormal waveform. When the abnormal waveform completely appears in the time period where the abnormal waveform is located, move 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.

[0096] This method determines the traveling - wave data through the time - difference method. The characteristics of the traveling - wave are one - way propagation and temporary appearance. As mentioned above, the number of signal - acquisition positions is multiple, and all these signal - acquisition positions need to participate in the processing process described above.

[0097] That is, the traveling - wave data will exist at each signal - acquisition position, but there will be a time difference in the appearance time.

[0098] For the usage method of multiple signal - acquisition positions, the following method can also be used for processing:

[0099] Calculate the arrival time of the abnormal waveform at other signal - acquisition positions according to the start time of the abnormal waveform and obtain the abnormal waveform at other signal - acquisition positions according to the arrival time of the abnormal waveform;

[0100] Calculate the signal - intensity attenuation rate according to the abnormal waveforms at multiple signal - acquisition positions and determine the source direction of the traveling - wave data according to the intensity attenuation rate.

[0101] In this way, the obtained traveling wave data is further determined to be accurate by combining prediction and actual analysis, and at the same time, the source direction of the traveling wave data can be determined.

[0102] In the above content, the specific method for determining the occurrence position point of the traveling wave data according to the data calculated at the multiple positions is as follows:

[0103] Mark the signal acquisition positions in the virtual reality power distribution network;

[0104] Define the traveling wave propagation paths according to the transmission lines belonging to the signal acquisition positions, and the number of traveling wave propagation paths is one or more;

[0105] When the number of traveling wave propagation paths is multiple, use the attenuation rate to limit the length of the transmission lines of the signal acquisition positions, and reduce the number of traveling wave propagation paths to one.

[0106] In the above method, it is necessary to combine with the virtual reality power distribution network to determine the position. In the specific process, first, define the traveling wave propagation paths according to the transmission lines belonging to the signal acquisition positions, and the number of traveling wave propagation paths is one or more.

[0107] Generally speaking, the number of traveling wave propagation paths is multiple because the current power distribution networks are relatively complex and almost no single-line structures exist. In addition, when the number of traveling wave propagation paths is multiple, use the attenuation rate to limit the length of the transmission lines of the signal acquisition positions, and reduce the number of traveling wave propagation paths to one. The specific method is as follows:

[0108] Transmit a measurement signal within the traveling wave propagation path;

[0109] Receive the measurement signal and calculate the line reference attenuation rate of the measurement signal;

[0110] Use the reference attenuation rate to correct the attenuation rate;

[0111] Set a fault signal position and drive the fault signal intensity to linearly increase or linearly decrease, and synchronously adjust the length of the transmission lines of the signal acquisition positions until the number of intersection points of the traveling wave propagation paths is reduced to one.

[0112] The measurement signal is a signal that actually exists in reality, and its function is to calculate the line reference attenuation rate. After obtaining the line reference attenuation rate, use the reference attenuation rate to correct the attenuation rate. Finally, set a fault signal position and drive the fault signal intensity to linearly increase or linearly decrease, and synchronously adjust the length of the transmission lines of the signal acquisition positions until the number of intersection points of the traveling wave propagation paths is reduced to one, as Figure 3 and Figure 4 shown.

[0113] The specific way to correct the attenuation rate using the reference attenuation rate is to divide the traveling wave propagation path into multiple segments. For those segments where the reference attenuation rate can be used for calculation, the reference attenuation rate is used for calculation; otherwise, the attenuation rate is used for calculation.

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

[0115] When setting the position of the fault signal, the intersection points of different traveling wave propagation paths are all taken into consideration, and then it is checked whether the intensity of the fault signal at the position of the fault signal can be kept consistent. Of course, a certain error range is allowed here.

[0116] This application also provides a device for analyzing the working state of a distribution network based on traveling waves, including:

[0117] A data acquisition device, which is used to acquire waveform data at the signal acquisition positions, and the number of signal acquisition positions is multiple;

[0118] A first data processing device, which is used to obtain the triggered waveform data and decompose the triggered waveform data using the wavelet decomposition method to obtain a waveform data decomposition group. The triggered waveform data includes the waveform data at the first signal acquisition position and the last signal acquisition position in the sequential sequence;

[0119] A second data processing device, which is used to compare the waveform data in the time period where the abnormal waveform appears with the waveform data in the previous time period in the time series when an abnormal waveform appears in the waveform data decomposition group to obtain traveling wave data;

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

[0121] A position determination unit, which is used to obtain multiple position calculation data within the local network coverage range and determine the occurrence position point of the traveling wave data according to the multiple position calculation data.

[0122] Further, determining that an abnormal waveform appears in the waveform data decomposition group includes:

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

[0124] Comparing the waveforms that do not appear 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;

[0125] When the waveform is a normal waveform, a secondary determination is made using the overall comparison method.

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

[0127] Obtain the peak curve and trough curve of the triggered waveform data and merge the peak curve and trough curve to obtain a merged characteristic curve;

[0128] Decompose the merged characteristic curve using the fast Fourier transform method to obtain multiple sub-merged characteristic curves;

[0129] Sequentially superimpose each sub-merged characteristic curve on the normal state reference waveform to obtain results, where the results include inclusion and non-inclusion;

[0130] When non-inclusion appears in the results, it is determined that an abnormal waveform appears in the waveform data decomposition group.

[0131] Further, comparing the waveform data in the time period where the abnormal waveform is located with the waveform data in the previous time period includes:

[0132] Transfer 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 determine the abnormal waveform. When the abnormal waveform cannot be determined, adjust the start time and end time of the previous time period and determine again until the abnormal waveform is determined;

[0133] Determine the start time of the abnormal waveform. When the abnormal waveform completely appears in the time period where the abnormal waveform is located, move 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.

[0134] Further, it also includes:

[0135] Calculate the arrival time of the abnormal waveform at other signal acquisition positions according to the start time of the abnormal waveform and obtain the abnormal waveform at other signal acquisition positions according to the arrival time of the abnormal waveform;

[0136] Calculate the signal intensity attenuation rate according to the abnormal waveforms at multiple signal acquisition positions and determine the source direction of the traveling wave data according to the intensity attenuation rate.

[0137] Further, determining the occurrence position point of the traveling wave data according to the data calculated at the multiple positions includes:

[0138] Mark the signal acquisition positions in the virtual reality power distribution network;

[0139] Delimit the traveling wave propagation path according to the propagation lines belonging to the signal acquisition positions, and the number of traveling wave propagation paths is one or more;

[0140] When the number of traveling wave propagation paths is multiple, the attenuation rate is used to limit the length of the propagation line at the signal acquisition position, reducing the number of traveling wave propagation paths to one.

[0141] Further, using the attenuation rate to limit the length of the propagation line at the signal acquisition position includes:

[0142] Transmitting a measurement signal within the traveling wave propagation path;

[0143] Receiving the measurement signal and calculating the line reference attenuation rate of the measurement signal;

[0144] Using the reference attenuation rate to correct the attenuation rate;

[0145] 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 at the signal acquisition position until the number of intersection points of the traveling wave propagation paths is reduced to one.

[0146] In one example, the units in any of the above devices may be one or more integrated circuits configured to implement the above methods. For example: 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] Again, when the units in the device can be implemented in the form of a processing element scheduler, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call programs. Again, these units may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0148] In this application, various objects such as various messages / information / devices / network elements / systems / devices / actions / operations / processes / concepts, etc. may be named. It can be understood that these specific names do not constitute a limitation on the relevant objects, and the assigned names may change with factors such as the scenario, context, or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects they embody / perform in the technical solution.

[0149] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0150] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0151] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this 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 these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0153] It should also be understood that in each embodiment of the present application, the first, second, etc. are only used to indicate that multiple objects are different. For example, the first time window and the second time window are only used to indicate different time windows. And it should not have any impact on the time window itself. The above first, second, etc. should not impose any restrictions on the embodiments of the present application.

[0154] It should also be understood that in each embodiment of the present application, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments are consistent and can be mutually referred to, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0155] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several 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 various embodiments of this application. The aforementioned computer-readable storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical discs that can store program codes.

[0156] This application also provides a power distribution monitoring system for the operating state of a distribution network based on traveling waves. The system includes:

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

[0158] One or more processors for calling and running the instructions from the memory and executing the methods described in the above content.

[0159] This application also provides a computer program product. This computer program product 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] This application also provides a chip system. This chip system includes a processor for implementing the functions involved in the above content. For example, generating, receiving, sending, or processing the data and / or information involved in the above methods.

[0161] This chip system can be composed of chips or can also include chips and other discrete devices.

[0162] The processor mentioned anywhere above can be a CPU, a microprocessor, an ASIC, or an integrated circuit for controlling the execution of one or more programs of the above methods for transmitting feedback information.

[0163] In a possible design, this chip system further includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and are respectively arranged on different devices and connected by wired or wireless means to support the chip system in implementing various functions in the above embodiments. Or, 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, cache, etc. The memory can also be a storage unit outside the chip within the terminal, such as a ROM or other types of static storage devices that can store static information and instructions, a RAM, etc.

[0166] It can be understood that the memory in this 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 erasable PROM (EEPROM), or a flash memory.

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

[0169] The embodiments of this specific implementation manner are all preferred embodiments of this application, and do not limit the protection scope of this application. Therefore, all equivalent changes made according to the structure, shape, and principle of this application should be covered within the protection scope of this application.

Claims

1. A method for analyzing the working state of a distribution network based on traveling waves, characterized in that: include: Collecting waveform data at a signal collection position, where the number of signal collection positions is multiple; Acquire trigger waveform data, and decompose the trigger waveform data using a wavelet decomposition method to obtain a waveform data decomposition group, wherein the trigger waveform data includes waveform data at the first signal acquisition position and waveform data at the last signal acquisition position in the sequential sequence; When an abnormal waveform appears in the waveform data decomposition group, the waveform data of the time period where the abnormal waveform is located is compared with the waveform data of the previous time period in the time series to obtain the traveling wave data; Determine position calculation data according to the traveling wave data at the plurality of signal collection positions, the position calculation data including the source direction and attenuation rate of the traveling wave data; A plurality of position calculation data are acquired within the coverage area of ​​the local network, and a location point where the traveling wave data occurs is determined according to the plurality of position calculation data.

2. The method for analyzing the working state of a distribution network based on traveling waves according to claim 1, characterized in that: Determine that abnormal waveforms appear in the waveform data decomposition group include: Determine the start time and end time of each waveform in the waveform data decomposition group; Compare the waveform that appears non-continuously on the timeline with the stored data in the abnormal waveform database to determine whether the waveform is a normal waveform or an abnormal waveform; When the waveform is normal, a secondary determination is performed using an overall comparison method.

3. The method for analyzing the working state of a distribution network based on traveling waves according to claim 2, characterized in that: When the waveform is normal, the overall comparison method is used for secondary determination, including: Acquire the peak curve and the trough curve of the trigger waveform data and merge the peak curve and the trough curve to obtain a merged characteristic curve; Decomposing the merged characteristic curve by using a fast Fourier transform method to obtain multiple sub-merged characteristic curves; Sequentially superimpose each sub-merged characteristic curve onto the normal state reference waveform to obtain a result, which includes inclusion and non-inclusion; When "not included" appears in the result, it is determined that an abnormal waveform appears in the waveform data decomposition group.

4. The method for analyzing the working state of a power distribution network based on traveling waves according to claim 1, characterized in that: Comparing the waveform data of the time period where the abnormal waveform is located with the waveform data of the previous time period in the time series includes: The waveform data of the time period where the abnormal waveform is located and the waveform data of the previous time period are transferred to the frequency domain for decomposition and determination of the abnormal waveform. When the abnormal waveform cannot be determined, the start time and end time of the previous time period are adjusted and determined again until the abnormal waveform is determined. The start time of the abnormal waveform is determined, and when the abnormal waveform appears completely 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.

5. The method for analyzing the working state of a distribution network based on traveling waves according to claim 4, characterized in that: Also includes: 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 other signal collection positions according to the abnormal waveform arrival time; The signal intensity attenuation rate is calculated according to the abnormal waveforms at multiple signal collection positions, and the source direction of the traveling wave data is determined according to the intensity attenuation rate.

6. The method for analyzing the working state of a distribution network based on traveling waves according to claim 5, characterized in that: Determining the location point where the traveling wave data occurs according to the plurality of location calculation data comprises: Mark the signal collection location in the virtual reality distribution network; Delineating a traveling wave propagation path according to a propagation line belonging to a signal collection position, wherein the number of the traveling wave propagation paths is one or more; When there are multiple traveling wave propagation paths, the attenuation rate is used to limit the length of the propagation line at the signal collection position, thereby reducing the number of traveling wave propagation paths to one.

7. The method for analyzing the working state of a distribution network based on traveling waves according to claim 6, characterized in that: Using the attenuation rate to limit the length of the propagation line at which the signal is collected involves: transmitting a measurement signal within a traveling wave propagation path; receiving a measurement signal and calculating a line reference attenuation rate of the measurement signal; Correct the decay rate using a reference decay rate; A fault signal position is set and the fault signal strength is driven to be linearly increased or linearly decreased, and the length of the propagation line of the signal collection position is synchronously adjusted until the number of intersections of the traveling wave propagation path is reduced to one.

8. A distribution network working state analysis device based on traveling waves, characterized in that: include: A data acquisition device, used for acquiring waveform data at a signal acquisition position, wherein the number of signal acquisition positions is multiple; A first data processing device is used to obtain trigger waveform data and decompose the trigger waveform data using a wavelet decomposition method to obtain a waveform data decomposition group, wherein the trigger waveform data includes waveform data at a first signal acquisition position and waveform data at a last signal acquisition position in a sequential sequence; The second data processing device is used for comparing the waveform data of the time period where the abnormal waveform is located with the waveform data of the previous time period in time series to obtain traveling wave data when an abnormal waveform appears in the waveform data decomposition group; A third data processing device, used to determine position calculation data based on the traveling wave data at the plurality of signal collection positions, the position calculation data including the source direction and attenuation rate of the traveling wave data; The position determination unit is used to obtain a plurality of position calculation data within the coverage area of ​​the local network and determine the location point where the traveling wave data occurs according to the plurality of position calculation data.

9. A distribution network working status distribution monitoring system based on traveling waves, characterized in that: The system comprises: one or more memories for storing instructions; and One or more processors, configured to call and execute the instructions from the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium comprises: The program, when the program is executed by a processor, the method according to any one of claims 1 to 7 is executed.

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