Power grid state analysis method and power distribution monitoring system
By combining fuzzy position determination with fast Fourier transform and wavelet transform to decompose characteristic signals, the problems of low signal-to-noise ratio and signal attenuation in the power distribution monitoring system are solved, and the fault location is quickly and accurately positioned.
Patent Information
- Application Number
- CN202510546359.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-28
- Publication Date
- 2025-08-08
AI Technical Summary
In the existing distribution monitoring system, the low signal-to-noise ratio and signal attenuation problems lead to difficulties in fault positioning, and the traditional wavelet transform has limited effect in low signal-to-noise ratio environments.
The fuzzy position determination combined with screening method is used to decompose the characteristic signals through fast Fourier transform and wavelet transform, determine the continuous time of the basic waveform, and divide it into normal and abnormal waveforms, and analyze the traveling wave signals in the suspected abnormal characteristic signals for positioning.
Quickly and accurately locate fault location points in the distribution network, improve fault location speed and accuracy, and overcome the shortcomings of traditional methods in low signal-to-noise ratio environments.
Smart Images
Figure CN120449043A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a power grid status analysis method and a power distribution monitoring system. Background Art
[0002] Distribution monitoring systems play a key role in the power grid, ensuring safe, stable, and economical operation through real-time monitoring, intelligent analysis, and automated control. Using sensors, smart meters, and other devices, these systems collect real-time data on grid voltage, current, power, and equipment status. Using communications technology, these data is transmitted to a monitoring center, enabling global visualization of the grid's operating status. In the event of a fault (such as a short circuit or equipment anomaly), the system quickly locates the fault, automatically isolates the affected area, and restores power to non-faulty areas via backup lines, significantly shortening outage duration.
[0003] The main problems currently exist include low signal-to-noise ratio and signal attenuation. This is mainly because the characteristic signal amplitude generated by transient faults is small and easily submerged by background noise. At the same time, the loss caused by transmission distance is superimposed, making wave head identification difficult. Traditional wavelet transform has limited effect in low signal-to-noise ratio environments. This is because the parameters of the wavelet are fixed in one processing process and the number of effective processing times is limited. Summary of the Invention
[0004] The present application provides a power grid status analysis method and a power distribution monitoring system, which determines the time period through fuzzy location determination combined with a screening method, and then performs traveling wave analysis within the time period. This method can determine the location at a faster speed, thereby discovering the fault location point in the distribution network.
[0005] The above-mentioned purpose of this application is achieved through the following technical solutions:
[0006] In a first aspect, the present application provides a power grid status analysis method, comprising:
[0007] Obtaining characteristic signals in the power distribution network collected at a collection point, the characteristic signals including voltage signals and current signals;
[0008] Decompose the obtained characteristic signal to obtain the basic waveform;
[0009] Decompose the characteristic signal using the basic waveform to determine the continuous time of the basic waveform;
[0010] The basic waveform is divided into a normal basic waveform and an abnormal basic waveform according to the continuous time;
[0011] Using the abnormal basic waveform to determine the suspected abnormal time period and fusing the suspected abnormal time period to obtain an abnormal time period, where the number of the abnormal time period is one or more;
[0012] Intercept the characteristic signal corresponding to the abnormal time period and record it as a suspected abnormal characteristic signal;
[0013] Analyze the traveling wave signal in the suspected abnormal characteristic signal and use the traveling wave signal to locate the fault point;
[0014] The distribution network status analysis results are issued based on the fault point.
[0015] In a possible implementation of the first aspect, decomposing the obtained characteristic signal includes:
[0016] Determine all the peak points of the characteristic signal and connect all the peak points in sequence to obtain the peak characteristic line;
[0017] Determine all the trough points of the characteristic signal and connect all the trough points in sequence to obtain the trough characteristic line;
[0018] The peak characteristic line and the trough characteristic line are fused to obtain a composite characteristic line;
[0019] Subtract the synthetic characteristic line from the obtained characteristic signal to obtain an intermediate signal;
[0020] Determine whether the intermediate signal meets the requirements. When the intermediate signal meets the requirements, use the intermediate signal as a basic waveform. When the intermediate signal does not meet the requirements, repeat the above process until an intermediate signal that meets the requirements is obtained.
[0021] In a possible implementation manner of the first aspect, after obtaining a first intermediate signal that meets the requirements, a signal obtained by subtracting the first intermediate signal that meets the requirements from the characteristic signal is used as a new characteristic signal.
[0022] In a possible implementation of the first aspect, the condition for being unable to continue to obtain the basic waveform is that the frequencies included in the characteristic signal are all less than or equal to the set frequency or the power spectrum distribution of the characteristic signal tends to be uniform.
[0023] In a possible implementation of the first aspect, dividing the basic waveform into a normal basic waveform and an abnormal basic waveform according to the continuous time includes:
[0024] Selecting at least one characteristic signal located before the basic waveform and at least one characteristic signal located after the basic waveform in the time series, and recording them as reference characteristic signals;
[0025] The basic waveforms are divided into normal basic waveforms and suspected abnormal basic waveforms according to the continuous time;
[0026] superimposing the suspected abnormal basic waveform on the reference characteristic signal to determine whether the suspected abnormal basic waveform exists on the reference characteristic signal;
[0027] When a suspected abnormal basic waveform appears in the reference characteristic signal, the suspected abnormal basic waveform is determined to be an abnormal basic waveform.
[0028] In a possible implementation of the first aspect, analyzing and obtaining a traveling wave signal in the suspected abnormal characteristic signal includes:
[0029] Create a sliding window and drive the sliding window to move on the suspected abnormal characteristic signal to obtain a mean curve of the suspected abnormal characteristic signal, where the moving length of the sliding window is less than the length of the sliding window;
[0030] Calculate the local amplitude change rate on the mean value curve of the suspected abnormal characteristic signal and determine the suspected abnormal position point according to the local amplitude change rate;
[0031] Adjust the sliding window length according to the local amplitude change rate and check the suspected abnormal position points to obtain the abnormal position points. The sliding window length is negatively correlated with the local amplitude change rate;
[0032] Determine the abnormal position segment according to the abnormal position point;
[0033] The suspected abnormal characteristic signal corresponding to the abnormal position segment is analyzed to obtain the traveling wave signal.
[0034] In a possible implementation of the first aspect, adjusting the sliding window length according to the local amplitude change rate and verifying the suspected abnormal location point includes:
[0035] Record each suspected abnormal location point corresponding to the local amplitude change rate;
[0036] Calculate the frequency of occurrence of each suspected abnormal location point in different pairs of local amplitude change speeds;
[0037] Sort suspected abnormal location points according to their occurrence frequency. The occurrence frequency of suspected abnormal location points is negatively correlated with the sorting position.
[0038] The first suspected abnormal position point in the sequence is taken as the abnormal position point.
[0039] In a second aspect, the present application provides a power grid status analysis device, comprising:
[0040] A signal acquisition unit, configured to obtain characteristic signals in the power distribution network collected at a collection point, the characteristic signals including voltage signals and current signals;
[0041] A first signal analysis unit is used to decompose the obtained characteristic signal to obtain a basic waveform;
[0042] a second signal analysis unit, configured to decompose the characteristic signal using the basic waveform and determine the continuous time of the basic waveform;
[0043] a signal classification unit, for classifying a basic waveform into a normal basic waveform and an abnormal basic waveform according to continuous time;
[0044] A fusion processing unit, configured to use the abnormal basic waveform to determine a suspected abnormal time period and perform fusion processing on the suspected abnormal time period to obtain an abnormal time period, where the number of the abnormal time period is one or more;
[0045] A signal marking unit is used to intercept the characteristic signal corresponding to the abnormal time period and record it as a suspected abnormal characteristic signal;
[0046] An analysis and positioning unit is used to analyze the traveling wave signal in the suspected abnormal characteristic signal and use the traveling wave signal to locate the fault point;
[0047] The result sending unit is used to send the distribution network status analysis result according to the fault point.
[0048] In a third aspect, the present application provides a power distribution monitoring system, the system comprising:
[0049] one or more memories for storing instructions; and
[0050] One or more processors, configured to call and execute the instructions from the memory to perform the method as described in the first aspect and any possible implementation of the first aspect.
[0051] In a fourth aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium comprising:
[0052] The program, when the program is executed by a processor, the method described in the first aspect and any possible implementation of the first aspect is executed.
[0053] In a fifth aspect, the present application provides a computer program product, comprising program instructions. When the program instructions are executed by a computing device, the method described in the first aspect and any possible implementation of the first aspect is executed.
[0054] In a sixth aspect, the present application provides a chip system comprising a processor for implementing the functions involved in the above aspects, such as generating, receiving, sending, or processing the data and / or information involved in the above methods.
[0055] The chip system may be composed of chips, or may include chips and other discrete devices.
[0056] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and provided on different devices, connected via wired or wireless means, or the processor and the memory can be coupled on the same device. BRIEF DESCRIPTION OF THE DRAWINGS
[0057] Figure 1 This is a schematic block diagram of the steps of a power grid status analysis method provided in this application.
[0058] Figure 2 This is a schematic diagram of the working principle of the hardware involved in this application.
[0059] Figure 3 This is a schematic diagram of intercepting a characteristic signal corresponding to an abnormal time period provided by the present application.
[0060] Figure 4 This is a schematic diagram of the principle of decomposing the obtained characteristic signal provided by this application.
[0061] Figure 5 This is a schematic diagram of selecting a reference characteristic signal provided by this application.
[0062] Figure 6 This is a schematic diagram of the local amplitude change rate on the mean curve of a suspected abnormal characteristic signal provided by this application.
[0063] Figure 7 This is another schematic diagram of the local amplitude change rate on the mean curve of suspected abnormal characteristic signals provided by this application. DETAILED DESCRIPTION
[0064] The technical solution in this application is further described in detail below with reference to the accompanying drawings.
[0065] This application discloses a method for analyzing power grid status. Figure 1 In some examples, the power grid status analysis method disclosed in this application includes the following steps:
[0066] S101, obtaining characteristic signals in the power distribution network collected at a collection point, the characteristic signals including voltage signals and current signals;
[0067] S102, decomposing the obtained characteristic signal to obtain a basic waveform;
[0068] S103, decomposing the characteristic signal using the basic waveform to determine the continuous time of the basic waveform;
[0069] S104, dividing the basic waveform into a normal basic waveform and an abnormal basic waveform according to the continuous time;
[0070] S105, using the abnormal basic waveform to determine suspected abnormal time periods and fusing the suspected abnormal time periods to obtain abnormal time periods, where the number of abnormal time periods is one or more;
[0071] S106, intercepting the characteristic signal corresponding to the abnormal time period and recording it as a suspected abnormal characteristic signal;
[0072] S107, analyzing the traveling wave signal in the suspected abnormal characteristic signal and using the traveling wave signal to locate the fault point;
[0073] S108: Sending a distribution network status analysis result according to the fault point.
[0074] The power grid status analysis method disclosed in this application involves hardware including signal acquisition sensors and analysis servers, such as Figure 2 As shown, the signal acquisition sensor is deployed at a node position or a monitoring position on the distribution network, and is responsible for collecting signals transmitted on the distribution network and sending the collected signals to the analysis server for processing.
[0075] Signal acquisition sensors are divided into voltage and current types. Voltage sensors include electronic voltage transformers, fiber optic voltage sensors, and capacitive voltage sensors, while current sensors include Hall current sensors and Rogowski coils (Rogowski coils).
[0076] In step S101, the characteristic signal of the power distribution network collected at the collection point is first obtained. The obtaining here may be that the signal collection sensor actively sends it to the analysis server, or the analysis server requires the signal collection sensor to send it.
[0077] The analysis server may be a cloud-based analysis server or a combination of a main analysis server deployed in the cloud and an edge server deployed at a signal acquisition sensor. The specific composition of the analysis server is not limited here.
[0078] The characteristic signals include voltage signals and current signals. Both voltage signals and current signals are waveform signals and can be used in subsequent data processing.
[0079] In step S102, the obtained characteristic signal is decomposed to obtain a basic waveform. The decomposition method here generally uses a fast Fourier transform processing method, which is as follows:
[0080] Fast Fourier transform is an algorithm that calculates the discrete Fourier transform (DFT) or its inverse transform (IDFT) of a digital signal sequence. Fourier analysis converts a signal from its original domain (usually time or space) to a representation in the frequency domain, and vice versa.
[0081] The DFT is obtained by decomposing a sequence of values into components of different frequencies, and the FFT quickly computes this transform by decomposing the DFT matrix into a product of sparse (mostly zero) factors.
[0082] The length of the discrete time series signal to be transformed is n=2 m , group x(n) by odd or even:
[0083]
[0084] The above formula can be transformed into:
[0085]
[0086] make
[0087]
[0088] Where k is 0, 1, ..., N / 2-1, so:
[0089]
[0090] Since A(k) and B(k) are both N / 2-point DFTs, X(k) is an N-point DFT.
[0091] The result of FFT is frequency domain data, which is essentially a description of the frequency components of the signal and contains information about each frequency component (amplitude, frequency, and phase). The disadvantage of this method is that it has no time domain information.
[0092] Therefore, in step S103, the characteristic signal is decomposed using the basic waveform to determine the continuous time of the basic waveform. This step is based on the processing method of wavelet transform, and the basic waveform is used as the basic wave in the wavelet transform. At the same time, the amplitude and frequency are appropriately adjusted. The specific adjustment method is based on the existing amplitude and frequency. Generally, ±2%-±4% of the amplitude and frequency are selected as the adjusted interval end values. The advantage of this method is that it can quickly determine the basic waveform in the wavelet transform.
[0093] At this time, the continuous time of the basic waveform can be determined by wavelet transform. Then, in step S104, the basic waveform is divided into a normal basic waveform and an abnormal basic waveform according to the continuous time. The selection criterion of the abnormal basic waveform is that its appearance time is less than the duration of the corresponding characteristic signal.
[0094] In step S105, the abnormal basic waveform is used to determine the suspected abnormal time period. The suspected abnormal time period here refers to the length of time when the abnormal basic waveform appears. The number of suspected abnormal time periods can be one or more. When the number of suspected abnormal time periods is multiple, the suspected abnormal time periods are fused. The fusion processing method is to merge the repeated areas of the suspected abnormal time periods.
[0095] Then, in step S106, the characteristic signal corresponding to the abnormal time period is intercepted and recorded as a suspected abnormal characteristic signal, which can be regarded as a means of narrowing the scope, such as Figure 3 As shown, in step S107, the traveling wave signal in the suspected abnormal characteristic signal is analyzed and located using the traveling wave signal to obtain the fault point, and finally in step S108, the distribution network status analysis result is issued according to the fault point.
[0096] In some examples, the specific method of decomposing the obtained characteristic signal is as follows:
[0097] Determine all the peak points of the characteristic signal and connect all the peak points in sequence to obtain the peak characteristic line;
[0098] Determine all the trough points of the characteristic signal and connect all the trough points in sequence to obtain the trough characteristic line;
[0099] The peak characteristic line and the trough characteristic line are fused to obtain a composite characteristic line;
[0100] Subtract the synthetic characteristic line from the obtained characteristic signal to obtain an intermediate signal;
[0101] Determine whether the intermediate signal meets the requirements. When the intermediate signal meets the requirements, use the intermediate signal as a basic waveform. When the intermediate signal does not meet the requirements, repeat the above process until an intermediate signal that meets the requirements is obtained.
[0102] This method uses peak and trough features to obtain the basic waveform. The advantage of this method is that it overcomes the problem of non-adaptability of basis functions. For example, wavelet analysis requires the selection of a wavelet basis. The choice of wavelet basis has a great influence on the results of the entire wavelet analysis. Once the wavelet basis is determined, it cannot be changed during the entire analysis process. Even if the wavelet basis may be optimal globally, it may not be optimal in some local areas. Therefore, the basis function of wavelet analysis lacks adaptability.
[0103] In the method provided in this application, please refer to Figure 4 , the characteristic signal's own characteristics are used to decompose the characteristic signal. The specific conditions for judging whether the intermediate signal meets the requirements are:
[0104] The number of extreme points and the number of zero-crossing points must be equal or differ by no more than one;
[0105] At any time, the average value of the upper envelope formed by the local maximum point and the lower envelope formed by the local minimum point is zero, that is, the upper and lower envelopes are locally symmetrical with respect to the time axis.
[0106] After obtaining the first intermediate signal that meets the requirements, a signal obtained by subtracting the first intermediate signal that meets the requirements from the characteristic signal is used as a new characteristic signal.
[0107] Of course, a cutoff condition needs to be set here. The cutoff condition means that the basic waveform can no longer be obtained. The specific description is: the frequencies included in the characteristic signal are all less than or equal to the set frequency or the power spectrum distribution of the characteristic signal tends to be uniform.
[0108] The frequencies included in the characteristic signals are all less than or equal to the set frequency. This is because the traveling wave signal is generally a high-frequency signal. When there is no high-frequency signal in the frequencies included in the characteristic signal, the decomposition is performed at this time, and the content obtained is irrelevant to the traveling wave signal, so no further processing is performed.
[0109] When the power spectrum distribution of the characteristic signal tends to be uniform, it means that the traveling wave signal is not included at this time. This is because the instantaneous energy of the traveling wave signal is high and can directly affect the power spectrum distribution. The power spectrum distribution here tends to be uniform, which means that the difference between the maximum and minimum values on the power spectrum corresponding curve tends to zero.
[0110] In some examples, classifying the base waveform into a normal base waveform and an abnormal base waveform based on continuous time includes:
[0111] S201, selecting at least one characteristic signal located before a basic waveform and at least one characteristic signal located after the basic waveform in a time series, and recording them as reference characteristic signals;
[0112] S202, classifying the basic waveform into a normal basic waveform and a suspected abnormal basic waveform according to the continuous time;
[0113] S203, superimposing the suspected abnormal basic waveform on the reference characteristic signal to determine whether there is a suspected abnormal basic waveform on the reference characteristic signal;
[0114] S204 , when a suspected abnormal basic waveform appears in the reference characteristic signal, the suspected abnormal basic waveform is determined as an abnormal basic waveform.
[0115] In steps S201 to S204, first, at least one characteristic signal located before the basic waveform (characteristic signal) and at least one characteristic signal located after the basic waveform are selected in time series, that is, the characteristic signals before and after the characteristic signal mentioned in step S101, such as Figure 5 shown.
[0116] These additionally selected characteristic signals are recorded as reference characteristic signals.
[0117] Then, the basic waveform is divided into a normal basic waveform and a suspected abnormal basic waveform according to the continuous time. Here, the suspected abnormal basic waveform refers to a potential abnormal basic waveform that needs to be verified.
[0118] The verification process is carried out in step S203 by superimposing the suspected abnormal basic waveform on the reference characteristic signal to determine whether there is a suspected abnormal basic waveform on the reference characteristic signal. If the reference characteristic signal includes a suspected abnormal basic waveform, then after the suspected abnormal basic waveform is superimposed on the reference characteristic signal, the waveform of the reference characteristic signal will change.
[0119] When the suspected abnormal basic waveform is superimposed on the reference characteristic signal, the suspected abnormal basic waveform needs to be moved in the horizontal direction to achieve phase adjustment.
[0120] When a suspected abnormal base waveform appears in the reference characteristic signal, the suspected abnormal base waveform is determined to be an abnormal base waveform, which is the content of step S204. The reference characteristic signal is used here for verification purposes. This is because errors may occur during the data processing described in the above content. These errors mainly manifest as different time scales or frequency components being incorrectly assigned to the same base waveform, or the same frequency component being dispersed across multiple base waveforms.
[0121] This results in the final basic waveform possibly being wrong. In order to solve this problem, the present application adds a verification step. The condition for the appearance of a suspected abnormal basic waveform in the reference characteristic signal is that the increase in the amplitude of the change area on the vertical axis of the reference characteristic signal is at least 0.5-0.8 times the amplitude of the basic waveform.
[0122] In some examples, the specific method for analyzing and obtaining the traveling wave signal in the suspected abnormal characteristic signal is as follows:
[0123] S301, creating a sliding window and driving the sliding window to move on the suspected abnormal characteristic signal to obtain a mean curve of the suspected abnormal characteristic signal, wherein the moving length of the sliding window is less than the length of the sliding window;
[0124] S302, calculating the local amplitude change rate on the mean curve of the suspected abnormal characteristic signal and determining the suspected abnormal position point according to the local amplitude change rate;
[0125] S303, adjusting the sliding window length according to the local amplitude change rate and checking the suspected abnormal position point to obtain the abnormal position point, and the sliding window length is negatively correlated with the local amplitude change rate;
[0126] S304, determining an abnormal location segment according to the abnormal location point;
[0127] S305: Analyze the suspected abnormal characteristic signal corresponding to the abnormal position segment to obtain a traveling wave signal.
[0128] In steps S301 to S305, the suspected abnormal characteristic signal is first processed using a sliding processing method, where a mean curve of the suspected abnormal characteristic signal is obtained, and then the local amplitude change rate on the mean curve of the suspected abnormal characteristic signal is calculated and the suspected abnormal position point is determined according to the local amplitude change rate.
[0129] There are two cases for the local amplitude change speed on the mean curve of the suspected abnormal characteristic signal:
[0130] The first case is that the amplitude of a certain point on the mean curve of the suspected abnormal characteristic signal suddenly increases, exceeding the previous average amplitude in the time series, such as Figure 6 As shown;
[0131] The second case is that a reversal point appears in the local amplitude on the mean curve of the suspected abnormal characteristic signal. The direction of amplitude change (increase, decrease) at the reversal point is opposite to the direction of amplitude change (increase, decrease), such as Figure 7 shown.
[0132] The specific method of determining the abnormal position segment according to the abnormal position point is to determine the starting position point and the ending position point of the abnormal position segment according to the above two situations, and the starting position point and the ending position point both correspond to a suspected abnormal position point.
[0133] Of course, calibration is still needed here. The calibration method is to adjust the sliding window length according to the local amplitude change rate. The sliding window length is negatively correlated with the local amplitude change rate. When the suspected abnormal position point appears in each adjustment process or the number of occurrences exceeds the set proportion (generally greater than 95%), the suspected abnormal position point is considered to be an abnormal position point.
[0134] Finally, the suspected abnormal characteristic signal corresponding to the abnormal position segment is analyzed to obtain the traveling wave signal. The specific process of obtaining the traveling wave signal requires the use of a multi-point detection method. Here, two or three signal acquisition sensors are generally deployed at the same location.
[0135] In the above steps, the abnormal position segment is obtained, and the abnormal position segment has a starting time. Here, combined with the propagation characteristics of the traveling wave and the sequential position of the signal acquisition sensor, when the traveling wave propagates, it will pass through each signal acquisition sensor sequentially in a given direction. Here, by comparing whether the starting time of the abnormal position segment is consistent with the sequential position, it is possible to determine whether the suspected abnormal characteristic signal includes a traveling wave signal.
[0136] Then, the corresponding abnormal position segments belonging to different signal acquisition sensors are used for comparison. The same quantities are removed under time alignment to obtain different quantities. The same quantities refer to the waveforms that appear simultaneously in the corresponding abnormal position segments belonging to different signal acquisition sensors, and the different quantities refer to the waveforms that appear sequentially in the signal acquisition sensors.
[0137] After obtaining the traveling wave signal, the Hilbert transform method is used to analyze the instantaneous characteristics of the signal (such as amplitude, phase and frequency). Here, phase and frequency are matched (the frequency needs to be the same and the position needs to match the position).
[0138] After the matching is successful, the location of the traveling wave is calculated based on the amplitude attenuation. It should be noted here that the calculation of the fault point requires the use of multiple sets of signal acquisition sensors. This is because each set of signal acquisition sensors can only specify one range. When multiple sets of signal acquisition sensors all point to the same range, that is, the fault point, after obtaining the fault point, the traveling wave signal strength at the fault point needs to be used for verification.
[0139] If the fault point is accurate, then the initial signal strength calculated by reverse calculation based on amplitude attenuation should be consistent. Of course, the calculation error and position accuracy requirements need to be considered here. Generally, the ratio of any two signal strengths obtained by reverse calculation based on amplitude attenuation is required to be controlled within 0.97-1.03. Of course, this is only an example and is not a limitation of this application.
[0140] The specific method of adjusting the sliding window length according to the local amplitude change rate and checking the suspected abnormal location points is as follows:
[0141] Record each suspected abnormal location point corresponding to the local amplitude change rate;
[0142] Calculate the frequency of occurrence of each suspected abnormal location point in different pairs of local amplitude change speeds;
[0143] Sort suspected abnormal location points according to their occurrence frequency. The occurrence frequency of suspected abnormal location points is negatively correlated with the sorting position.
[0144] The first suspected abnormal position point in the sequence is taken as the abnormal position point.
[0145] The present application also provides a power grid status analysis device, comprising:
[0146] A signal acquisition unit, configured to obtain characteristic signals in the power distribution network collected at a collection point, the characteristic signals including voltage signals and current signals;
[0147] A first signal analysis unit is used to decompose the obtained characteristic signal to obtain a basic waveform;
[0148] a second signal analysis unit, configured to decompose the characteristic signal using the basic waveform and determine the continuous time of the basic waveform;
[0149] a signal classification unit, for classifying a basic waveform into a normal basic waveform and an abnormal basic waveform according to continuous time;
[0150] A fusion processing unit, configured to use the abnormal basic waveform to determine a suspected abnormal time period and perform fusion processing on the suspected abnormal time period to obtain an abnormal time period, where the number of the abnormal time period is one or more;
[0151] A signal marking unit is used to intercept the characteristic signal corresponding to the abnormal time period and record it as a suspected abnormal characteristic signal;
[0152] An analysis and positioning unit is used to analyze the traveling wave signal in the suspected abnormal characteristic signal and use the traveling wave signal to locate the fault point;
[0153] The result sending unit is used to send the distribution network status analysis result according to the fault point.
[0154] Furthermore, decomposing the obtained characteristic signal includes:
[0155] Determine all the peak points of the characteristic signal and connect all the peak points in sequence to obtain the peak characteristic line;
[0156] Determine all the trough points of the characteristic signal and connect all the trough points in sequence to obtain the trough characteristic line;
[0157] The peak characteristic line and the trough characteristic line are fused to obtain a composite characteristic line;
[0158] Subtract the synthetic characteristic line from the obtained characteristic signal to obtain an intermediate signal;
[0159] Determine whether the intermediate signal meets the requirements. When the intermediate signal meets the requirements, use the intermediate signal as a basic waveform. When the intermediate signal does not meet the requirements, repeat the above process until an intermediate signal that meets the requirements is obtained.
[0160] Furthermore, after obtaining the first intermediate signal that meets the requirements, a signal obtained by subtracting the first intermediate signal that meets the requirements from the characteristic signal is used as a new characteristic signal.
[0161] Furthermore, the condition for not being able to continue to obtain the basic waveform is that the frequencies included in the characteristic signal are all less than or equal to the set frequency or the power spectrum distribution of the characteristic signal tends to be uniform.
[0162] Furthermore, dividing the basic waveform into a normal basic waveform and an abnormal basic waveform according to the continuous time includes:
[0163] Selecting at least one characteristic signal located before the basic waveform and at least one characteristic signal located after the basic waveform in the time series, and recording them as reference characteristic signals;
[0164] The basic waveforms are divided into normal basic waveforms and suspected abnormal basic waveforms according to the continuous time;
[0165] superimposing the suspected abnormal basic waveform on the reference characteristic signal to determine whether the suspected abnormal basic waveform exists on the reference characteristic signal;
[0166] When a suspected abnormal basic waveform appears in the reference characteristic signal, the suspected abnormal basic waveform is determined to be an abnormal basic waveform.
[0167] Furthermore, the traveling wave signals in the suspected abnormal characteristic signals obtained by analysis include:
[0168] Create a sliding window and drive the sliding window to move on the suspected abnormal characteristic signal to obtain a mean curve of the suspected abnormal characteristic signal, where the moving length of the sliding window is less than the length of the sliding window;
[0169] Calculate the local amplitude change rate on the mean value curve of the suspected abnormal characteristic signal and determine the suspected abnormal position point according to the local amplitude change rate;
[0170] Adjust the sliding window length according to the local amplitude change rate and check the suspected abnormal position points to obtain the abnormal position points. The sliding window length is negatively correlated with the local amplitude change rate;
[0171] Determine the abnormal position segment according to the abnormal position point;
[0172] The suspected abnormal characteristic signal corresponding to the abnormal position segment is analyzed to obtain the traveling wave signal.
[0173] Furthermore, adjusting the sliding window length according to the local amplitude change rate and checking the suspected abnormal location points include:
[0174] Record each suspected abnormal location point corresponding to the local amplitude change rate;
[0175] Calculate the frequency of occurrence of each suspected abnormal location point in different pairs of local amplitude change speeds;
[0176] Sort suspected abnormal location points according to their occurrence frequency. The occurrence frequency of suspected abnormal location points is negatively correlated with the sorting position.
[0177] The first suspected abnormal position point in the sequence is taken as the abnormal position point.
[0178] In one example, the unit in any of the above devices can be one or more integrated circuits configured to implement the above method, 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.
[0179] For another example, when the units in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call programs. For another example, these units can be integrated together and implemented in the form of a system-on-a-chip (SOC).
[0180] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts 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 embodied / executed in the technical solutions.
[0181] Those skilled in the art will 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 aforementioned method embodiments and will not be repeated here.
[0182] In the several embodiments provided in this 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 schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0183] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.
[0184] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0185] It should also be understood that in various embodiments of this application, the terms "first," "second," and so on are merely used to indicate that multiple objects are distinct. For example, the terms "first time window" and "second time window" are merely used to indicate different time windows. They should not have any impact on the time windows themselves. The terms "first," "second," and so on should not limit the embodiments of this application in any way.
[0186] It should also be understood that in the various embodiments of the present application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.
[0187] If the 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 the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a computer-readable storage medium and includes a number of instructions for enabling 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 method described in each embodiment of the present application. The aforementioned computer-readable storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.
[0188] The present application also provides a power distribution monitoring system, the system comprising:
[0189] one or more memories for storing instructions; and
[0190] One or more processors are used to call and execute the instructions from the memory to perform the method as described above.
[0191] The present application also provides a computer program product, which includes instructions. When the instructions are executed, the terminal device and the network device perform operations of the terminal device and the network device corresponding to the above method.
[0192] 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 method.
[0193] The chip system may be composed of chips, or may include chips and other discrete devices.
[0194] The processor mentioned in any of the above may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits for executing a program for controlling the above-mentioned feedback information transmission method.
[0195] In one possible design, the chip system also includes a memory for storing necessary program instructions and data. The processor and the memory can be decoupled and provided on different devices, respectively, and connected via wired or wireless means to support the chip system in implementing the various functions of the above embodiments. Alternatively, the processor and the memory can be coupled on the same device.
[0196] Optionally, the computer instructions are stored in a memory.
[0197] Optionally, the memory is a storage unit within the chip, such as a register, cache, etc. The memory can also be a storage unit within the terminal located outside the chip, such as ROM or other types of static storage devices that can store static information and instructions, RAM, etc.
[0198] 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.
[0199] The non-volatile memory may be ROM, programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.
[0200] Volatile memory can be RAM, which is used as an external cache memory. There are many different types of RAM, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct memory bus RAM.
[0201] The embodiments of this specific implementation method are all preferred embodiments of the present application and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.
Claims
1. A method for analyzing power grid status, characterized in that: include: Obtaining characteristic signals in the power distribution network collected at a collection point, the characteristic signals including voltage signals and current signals; Decompose the obtained characteristic signal to obtain the basic waveform; Decompose the characteristic signal using the basic waveform to determine the continuous time of the basic waveform; The basic waveform is divided into a normal basic waveform and an abnormal basic waveform according to the continuous time; Using the abnormal basic waveform to determine the suspected abnormal time period and fusing the suspected abnormal time period to obtain an abnormal time period, where the number of the abnormal time period is one or more; Intercept the characteristic signal corresponding to the abnormal time period and record it as a suspected abnormal characteristic signal; Analyze the traveling wave signal in the suspected abnormal characteristic signal and use the traveling wave signal to locate the fault point; The distribution network status analysis results are issued based on the fault point.
2. The power grid status analysis method according to claim 1, characterized in that: Decomposing the obtained characteristic signal includes: Determine all the peak points of the characteristic signal and connect all the peak points in sequence to obtain the peak characteristic line; Determine all the trough points of the characteristic signal and connect all the trough points in sequence to obtain the trough characteristic line; The peak characteristic line and the trough characteristic line are fused to obtain a composite characteristic line; Subtract the synthetic characteristic line from the obtained characteristic signal to obtain an intermediate signal; Determine whether the intermediate signal meets the requirements. When the intermediate signal meets the requirements, use the intermediate signal as a basic waveform. When the intermediate signal does not meet the requirements, repeat the above process until an intermediate signal that meets the requirements is obtained.
3. The power grid status analysis method according to claim 2, characterized in that: After obtaining the first intermediate signal that meets the requirements, a signal obtained by subtracting the first intermediate signal that meets the requirements from the characteristic signal is used as a new characteristic signal.
4. The power grid status analysis method according to claim 2 or 3, characterized in that: The condition for not being able to continue to obtain the basic waveform is that the frequencies included in the characteristic signal are all less than or equal to the set frequency or the power spectrum distribution of the characteristic signal tends to be uniform.
5. The power grid status analysis method according to claim 1, characterized in that: The basic waveforms are divided into normal basic waveforms and abnormal basic waveforms according to continuous time, including: Selecting at least one characteristic signal located before the basic waveform and at least one characteristic signal located after the basic waveform in the time series, and recording them as reference characteristic signals; The basic waveforms are divided into normal basic waveforms and suspected abnormal basic waveforms according to the continuous time; superimposing the suspected abnormal basic waveform on the reference characteristic signal to determine whether the suspected abnormal basic waveform exists on the reference characteristic signal; When a suspected abnormal basic waveform appears in the reference characteristic signal, the suspected abnormal basic waveform is determined to be an abnormal basic waveform.
6. The power grid status analysis method according to claim 1, characterized in that: The traveling wave signals in the suspected abnormal characteristic signals obtained by analysis include: Create a sliding window and drive the sliding window to move on the suspected abnormal characteristic signal to obtain a mean curve of the suspected abnormal characteristic signal, where the moving length of the sliding window is less than the length of the sliding window; Calculate the local amplitude change rate on the mean value curve of the suspected abnormal characteristic signal and determine the suspected abnormal position point according to the local amplitude change rate; Adjust the sliding window length according to the local amplitude change rate and check the suspected abnormal position points to obtain the abnormal position points. The sliding window length is negatively correlated with the local amplitude change rate; Determine the abnormal position segment according to the abnormal position point; The suspected abnormal characteristic signal corresponding to the abnormal position segment is analyzed to obtain the traveling wave signal.
7. The power grid status analysis method according to claim 6, characterized in that: Adjust the sliding window length according to the local amplitude change rate and check the suspected abnormal location points including: Record each suspected abnormal location point corresponding to the local amplitude change rate; Calculate the frequency of occurrence of each suspected abnormal location point in different pairs of local amplitude change speeds; Sort suspected abnormal location points according to their occurrence frequency. The occurrence frequency of suspected abnormal location points is negatively correlated with the sorting position. The first suspected abnormal position point in the sequence is taken as the abnormal position point.
8. A power grid status analysis device, characterized in that: include: A signal acquisition unit, configured to obtain characteristic signals in the power distribution network collected at a collection point, the characteristic signals including voltage signals and current signals; A first signal analysis unit is used to decompose the obtained characteristic signal to obtain a basic waveform; a second signal analysis unit, configured to decompose the characteristic signal using the basic waveform and determine the continuous time of the basic waveform; a signal classification unit, configured to classify a basic waveform into a normal basic waveform and an abnormal basic waveform according to continuous time; A fusion processing unit, configured to use the abnormal basic waveform to determine a suspected abnormal time period and perform fusion processing on the suspected abnormal time period to obtain an abnormal time period, where the number of the abnormal time period is one or more; A signal marking unit is used to intercept the characteristic signal corresponding to the abnormal time period and record it as a suspected abnormal characteristic signal; An analysis and positioning unit is used to analyze the traveling wave signal in the suspected abnormal characteristic signal and use the traveling wave signal to locate the fault point; The result sending unit is used to send the distribution network status analysis result according to the fault point.
9. A power distribution monitoring system, 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 executed by a processor, executes the method according to any one of claims 1 to 7.