Fault monitoring method and system applied to digital distribution network

Abstract

The present invention relates to the technical field of distribution network fault monitoring, and in particular to a fault monitoring method and system applied to a digital distribution network. The present invention first divides each fault time period from the time period to be filtered; according to the differences in the fault responsiveness of all monitoring points in each fault time period, obtains the noise measurement of the digital distribution network in the time period to be filtered; adjusts the smoothing coefficient according to the noise measurement to obtain the adjusted smoothing coefficient of the digital distribution network in the time period to be filtered; uses the adjusted smoothing coefficient to filter the data set in the time period to be filtered to obtain the filtered data set; and performs fault detection based on the filtered data set. The present invention improves the noise reduction effect by reasonably setting the adjusted smoothing coefficient of the digital distribution network in the time period to be filtered, so as to ensure accurate fault monitoring of the digital distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network fault monitoring, and in particular to a fault monitoring method and system applied to a digital distribution network. Background Art

[0002] The digital distribution network is a crucial component of the power system, delivering electricity to end-user devices or consumer equipment. Within the distribution network, electricity flows from the transmission grid to transformer stations, where it is converted from high-voltage to low-voltage. This low-voltage electricity is then delivered to consumer equipment via high-voltage cables, wires, and cable troughs. To promptly detect potential faults within the distribution network, it is necessary to monitor the operating status of monitoring points within the digital distribution network. Current and voltage are key power parameters that reflect the operating status of the distribution network. By monitoring changes in these parameters at monitoring points, abnormalities within the distribution network, such as overloads, short circuits, and ground faults, can be detected promptly.

[0003] In digital distribution networks, the power parameters collected by monitoring points are often subject to noise interference, resulting in inaccurate data. To obtain more accurate data, the collected current and voltage values need to be filtered. The exponentially weighted moving average method is a common data filtering algorithm. Existing technologies use this method to filter data. This method sets a fixed smoothing coefficient based on empirical values and fails to fully consider the varying levels of noise interference in distribution networks over different time periods. This results in poor noise reduction and makes it difficult to ensure accurate fault monitoring in digital distribution networks. Summary of the Invention

[0004] In order to solve the problem that the existing technology has poor data noise reduction effect and is difficult to ensure accurate fault monitoring of digital distribution networks, the purpose of the present invention is to provide a fault monitoring method and system for digital distribution networks. The technical solutions adopted are as follows:

[0005] A fault monitoring method applied to a digital distribution network, the method comprising:

[0006] Acquire a data set of the digital distribution network in the time period to be filtered; the data set includes current value data and voltage value data of each monitoring point;

[0007] Based on the fluctuation of the current and voltage values corresponding to the monitoring point at the sampling moment along with the time sequence, and the correlation between the current value data and voltage value data corresponding to the preset reference time period at the sampling moment of the monitoring point, the fault responsiveness of the monitoring point at the sampling moment is obtained; based on the fault responsiveness of all monitoring points at each sampling moment in the time period to be filtered, each fault time period is divided from the time period to be filtered; based on the differences in the fault responsiveness of all monitoring points in each fault time period, the noise measurement of the digital distribution network in the time period to be filtered is obtained;

[0008] The smoothing coefficient is adjusted according to the noise measurement to obtain the adjusted smoothing coefficient of the digital distribution network in the filtering time period; the data set in the filtering time period is filtered using the adjusted smoothing coefficient to obtain a filtered data set; and fault detection is performed based on the filtered data set.

[0009] Furthermore, the method for obtaining the fault responsiveness includes:

[0010] For any monitoring point, the current value data and voltage value data of the monitoring point are used as the power parameter data of the monitoring point; according to the average value of the upper envelope and the lower envelope of the power parameter data, the baseline data corresponding to the power parameter data is obtained; the current value data corresponding to the baseline data is used as the current baseline data; the voltage value data corresponding to the baseline data is used as the voltage baseline data;

[0011] Obtaining a first fault indicator at the sampling moment based on a difference between the sampling moment and the corresponding baseline data of the preset surrounding time period;

[0012] Obtaining a second fault indicator at the sampling moment based on a difference between the baseline data corresponding to the sampling moment and the previous sampling moment;

[0013] Obtaining a third fault indicator at the sampling moment according to the absolute value of the Pearson correlation coefficient of the current baseline data and the voltage baseline data corresponding to the preset reference time period of the monitoring point at the sampling moment;

[0014] The first fault indicator, the second fault indicator, and the third fault indicator are forwardly integrated to obtain the fault responsiveness of the monitoring point at the sampling time.

[0015] Furthermore, the method for obtaining the first fault indicator includes:

[0016] For any power parameter data corresponding to the baseline data, calculate the mean of all baseline data within the preset time period around the sampling moment to obtain the overall value around the sampling moment; calculate the absolute value of the difference between the baseline data at the sampling moment and the overall value around the sampling moment to obtain the local difference value of the baseline data at the sampling moment;

[0017] The average of the local difference values of the current baseline data and the voltage baseline data at the sampling moment is calculated to obtain the first fault indicator at the sampling moment.

[0018] Furthermore, the method for obtaining the second fault indicator includes:

[0019] The second fault indicator is obtained according to the second fault indicator acquisition formula, and the second fault indicator acquisition formula includes:

[0020] Among them, Y iis the second fault indicator at sampling time i; ΔU i is the difference value of the voltage baseline data at sampling time i; ΔI i is the difference value of the current baseline data at sampling time i; i-1 is the sampling time before sampling time i; ΔU i-1 is the difference value of the voltage baseline data at sampling time i-1; ΔI i-1 is the differential value of the current baseline data at sampling time i-1; || is the absolute value symbol; exp() is the exponential function with the natural number e as the base; ε is the preset denominator adjustment value.

[0021] Furthermore, the method for obtaining the noise metric includes:

[0022] The maximum value of the fault response intensity of the monitoring point in the fault time period is used as the fault value to be analyzed of the monitoring point in the fault time period; for any fault time period, all the fault values to be analyzed are sorted in descending order according to the fault values to be analyzed of the monitoring point to obtain the fault monitoring point sequence number of the fault value to be analyzed; all the fault values to be analyzed are counted in order according to the fault monitoring point sequence number to obtain the fault value sequence to be analyzed of the fault time period;

[0023] In the sequence of fault values to be analyzed in all fault time periods, the attention weight of the fault value to be analyzed is obtained according to the outlier situation of the fault value to be analyzed;

[0024] Obtain the time period weight of the fault time period according to the differences in the fault value sequences to be analyzed in all fault time periods;

[0025] Forward fusion of the attention weight of the fault value to be analyzed and the time period weight of the fault time period to which it belongs to obtain the target weight of the fault value to be analyzed;

[0026] Obtaining a target fault value sequence of the digital distribution network according to target weights of all fault values to be analyzed in the fault value sequence to be analyzed in all fault time periods;

[0027] According to the DTW distance between the target fault value sequence of the digital distribution network and the preset standard fault attenuation sequence, the noise measurement of the digital distribution network in the filtering time period is obtained.

[0028] Furthermore, the method for obtaining the attention weight includes:

[0029] Based on a two-dimensional coordinate system, the horizontal axis of the two-dimensional coordinate system is the fault monitoring point number, and the vertical axis of the two-dimensional coordinate system is the fault value to be analyzed. All fault values to be analyzed are mapped into the two-dimensional coordinate system; using the LOF algorithm, the outlier factor of the fault value to be analyzed in the two-dimensional coordinate system is calculated and negative correlation mapping is performed to obtain the attention weight of the fault value to be analyzed.

[0030] Furthermore, the method for obtaining the time period weight includes:

[0031] Calculate the mean of all fault values to be analyzed corresponding to the same fault monitoring point number to obtain the reference fault value of the fault monitoring point number; count all reference fault values in sequence according to the order of the fault monitoring point numbers to obtain the reference fault value sequence of the fault time period;

[0032] The DTW distance between the fault value sequence to be analyzed and the reference fault value sequence in each fault time period is calculated and negative correlation mapping is performed to obtain the time period weight of each fault time period.

[0033] Furthermore, the method for obtaining the target fault value sequence includes:

[0034] For all fault values to be analyzed corresponding to the same fault monitoring point number, the target weight is used to perform weighted averaging on all fault values to be analyzed to obtain the target fault value corresponding to each fault monitoring point number; all target fault values are counted in sequence according to the order of the fault monitoring point numbers to obtain the target fault value sequence of the digital distribution network.

[0035] Furthermore, the method for obtaining the adjusted smoothing coefficient includes:

[0036] The product of the negative correlation mapping result of the noise metric and the preset adjustment index is calculated to obtain the index to be adjusted; the product of the index to be adjusted and the smoothing coefficient is calculated to obtain the adjusted smoothing coefficient.

[0037] The present invention proposes a fault monitoring system for a digital distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, steps of a fault monitoring method for a digital distribution network are implemented.

[0038] The present invention has the following beneficial effects:

[0039] To properly filter the data set during the filtering period, we first need to analyze the noise performance of the monitoring points during the filtering period. Given that fault signals in distribution networks attenuate with increasing transmission distance, noise can interfere with the transmission and detection of fault signals, leading to abnormal attenuation of fault signals. First, the fault responsivity is used to reflect the likelihood of a fault at the monitoring point at the sampling moment. The filtering period is then divided into fault time periods, which reflect the time intervals corresponding to the fault signal transmission. Based on the abnormal attenuation of the fault signal, the noise level can be assessed to obtain a noise metric for the digital distribution network during the filtering period. A higher noise metric indicates a greater likelihood of noise in the digital distribution network during the filtering period, and thus a greater need for noise reduction. The noise metric reflects the likelihood of noise in the digital distribution network during the filtering period, allowing for adjustments to the smoothing coefficient. The adjusted smoothing coefficient is then appropriately set to improve noise reduction and ensure accurate fault monitoring of the digital distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 A flowchart of a fault monitoring method applied to a digital distribution network provided by one embodiment of the present invention;

[0042] Figure 2 A flow chart of a method for obtaining fault responsiveness provided by one embodiment of the present invention;

[0043] Figure 3 A flow chart of a method for obtaining noise metrics provided by one embodiment of the present invention;

[0044] Figure 4 A structural diagram of a fault monitoring system applied to a digital distribution network provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0045] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail a fault monitoring method and system for a digital distribution network according to the present invention, including its specific implementation, structure, features, and effectiveness. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0046] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0047] The following describes in detail a specific scheme of a fault monitoring method and system for a digital power distribution network provided by the present invention with reference to the accompanying drawings.

[0048] The present invention provides a method and system for fault monitoring in a digital power distribution network. Figure 1 , which shows a flow chart of a fault monitoring method applied to a digital distribution network provided by one embodiment of the present invention, the method comprising the following steps:

[0049] Step S1: Acquire a data set of the digital distribution network in the time period to be filtered; the data set includes current value data and voltage value data of each monitoring point.

[0050] In order to ensure the effect of subsequent data filtering, it is first necessary to obtain the data set of the digital distribution network during the filtering period to provide data support for subsequent data analysis.

[0051] From the monitoring system of the digital distribution network, a data set of the digital distribution network during the time period to be filtered is obtained. Considering that in the distribution network, electric energy is input from the transmission network to the transformer station, the transformer converts high voltage electricity into low voltage electricity, and then transmits it to the user equipment through high-voltage cables, wires, cable troughs, etc. Each monitoring point is installed with a current sensor and a voltage sensor to collect the current and voltage values of the monitoring point. For any monitoring point, the current value corresponding to all sampling moments of the monitoring point during the time period to be filtered is counted to obtain the current value data of the monitoring point; the voltage value corresponding to all sampling moments of the monitoring point during the time period to be filtered is counted to obtain the voltage value data of the monitoring point. The current value data and voltage value data of each monitoring point in the digital distribution network during the time period to be filtered are counted to obtain the data set of the digital distribution network during the time period to be filtered.

[0052] It should be noted that, in one embodiment of the present invention, synchronous sampling is performed according to a preset frequency, each sampling is taken as a sampling moment, the preset frequency is 10 seconds / time, and the filtering time period is from the beginning to the end of one hour. It should be noted that, in order to facilitate calculations, all indicator data involved in the calculations in the embodiment of the present invention are subjected to data preprocessing to eliminate the dimension effect. The specific means of removing the dimension effect are technical means well known to those skilled in the art and are not limited here.

[0053] Considering that in actual application scenarios, the distribution network is subject to different degrees of noise interference in different time periods. For example, in relatively quiet time periods such as at night or in the early morning, the distribution network is less subject to noise interference; in time periods with severe weather, the distribution network is more subject to noise interference. If the same smoothing coefficient is used to filter the data of different time periods in the distribution network, the noise reduction effect may not be ideal for time periods with greater noise interference; and for time periods with less noise interference, useful signal information may be lost due to over-filtering. The present invention analyzes the degree of noise performance in the filtering time period and then adjusts the smoothing coefficient to improve the noise reduction effect, thereby ensuring accurate fault monitoring of the digital distribution network.

[0054] Step S2: Based on the fluctuation of the current value and voltage value corresponding to the monitoring point at the sampling moment along with the time sequence, and the correlation between the current value data and voltage value data corresponding to the preset reference time period of the monitoring point at the sampling moment, the fault responsiveness of the monitoring point at the sampling moment is obtained; based on the fault responsiveness of all monitoring points at each sampling moment in the time period to be filtered, each fault time period is divided from the time period to be filtered; based on the difference in the fault responsiveness of all monitoring points in each fault time period, the noise measurement of the digital distribution network in the time period to be filtered is obtained.

[0055] To properly filter the data set within the filtering time period, we first need to analyze the noise performance of the monitoring points during the filtering time period. Considering that fault signals in distribution networks attenuate with increasing transmission distance, noise can interfere with the transmission and detection of fault signals, leading to abnormal attenuation of fault signals. First, the fault responsiveness is used to reflect the degree of fault performance at the monitoring point at the sampling moment. Fault time periods are then divided from the filtering time period. These time periods reflect the time intervals corresponding to the transmission of fault signals in the digital distribution network. Based on the abnormal attenuation of the fault signals, the noise level can be assessed and a noise metric for the digital distribution network during the filtering time period can be obtained. The larger the noise metric, the greater the likelihood of noise in the digital distribution network during the filtering time period, and the greater the need for noise reduction.

[0056] To analyze the degree of fault manifestation of the monitoring point at the sampling time, refer to Figure 2, which shows a flow chart of a method for obtaining fault responsiveness in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining fault responsiveness includes:

[0057] Step S201: For any monitoring point, the current value data and voltage value data of the monitoring point are used as the power parameter data of the monitoring point; according to the average of the upper envelope and the lower envelope of the power parameter data, the baseline data corresponding to the power parameter data is obtained; the current value data corresponding to the baseline data is used as the current baseline data; the voltage value data corresponding to the baseline data is used as the voltage baseline data.

[0058] Power parameter data reflects the normal characteristics of the power parameters at the monitoring point. Baseline data can more easily identify abnormal fluctuations or changes in power parameters, thereby discovering potential faults in a timely manner.

[0059] In one embodiment of the present invention, it should be noted that obtaining the upper and lower envelopes of data is a fundamental operation in the field of signal processing and analysis, which is well known to those skilled in the art and will not be described in detail here. The mean of the upper and lower envelopes of the power parameter data is calculated to obtain baseline data corresponding to the power parameter data; the baseline data corresponding to the current value data is used as the current baseline data; and the baseline data corresponding to the voltage value data is used as the voltage baseline data.

[0060] Step S202: obtaining a first fault indicator at the sampling moment according to a difference between the sampling moment and the baseline data corresponding to the preset surrounding time period.

[0061] Considering that the current and voltage values in the distribution network are stable during normal operation, but will fluctuate greatly when a fault occurs, the possibility of a fault at the sampling moment is evaluated by comparing the baseline data at the sampling moment with the average level of the baseline data in the preset surrounding time period, thereby obtaining the first fault indicator at the sampling moment.

[0062] In one embodiment of the present invention, for any power parameter data corresponding to baseline data, the mean of all baseline data is calculated within a preset time period around the sampling moment to obtain the overall value around the sampling moment; the absolute value of the difference between the baseline data at the sampling moment and the overall value around the sampling moment is calculated to obtain the local difference value of the baseline data at the sampling moment;

[0063] The mean of the local differences between the current baseline data and the voltage baseline data at the sampling moment is calculated to obtain the first fault indicator at the sampling moment. It should be noted that the method for obtaining the first fault indicator at any sampling moment is described here; the method for obtaining the first fault indicator at other sampling moments is the same. It should be noted that the present invention is described for a time period to be filtered in a digital distribution network where multiple fault signal transmissions occur.

[0064] In one embodiment of the present invention, a method for obtaining a preset time period surrounding a sampling moment includes: using the time period corresponding to the sampling moment and a preset number of sampling moments preceding the sampling moment as the preset time period surrounding the sampling moment. In one embodiment of the present invention, the preset number of surrounding times is 7, which can be set by the implementer according to the implementation scenario.

[0065] In the above steps, the overall surrounding value represents the average level of the baseline data in the time period surrounding the sampling moment. The absolute value of the difference between the baseline data at the sampling moment and the overall surrounding value is calculated to reflect the degree of deviation between the baseline data at the sampling moment and the average level of the baseline data in the surrounding time period, i.e., the local difference value of the baseline data at the sampling moment. For the current and voltage data, the local difference values of their corresponding baseline data at the sampling moment are calculated respectively. Then, the average of these local difference values is taken to obtain the first fault indicator at the sampling moment. The first fault indicator reflects the possibility of a fault at the sampling moment by integrating the local differences of the current and voltage baseline data.

[0066] Step S203: obtaining a second fault indicator at the sampling moment according to the difference between the baseline data corresponding to the sampling moment and the previous sampling moment.

[0067] Considering that when a fault occurs in the distribution network, there is a certain relationship between the fluctuation of voltage and current corresponding to the fault signal, and the noise is random, the changes in current and voltage corresponding to the noise signal are also random; therefore, the ratio of the changes in current and voltage is used to evaluate the possibility of a fault at the sampling moment and obtain the second fault indicator at the sampling moment.

[0068] In one embodiment of the present invention, a method for obtaining a second fault indicator includes:

[0069] The second fault indicator is obtained according to the second fault indicator acquisition formula, and the second fault indicator acquisition formula includes:

[0070] Among them, Y i is the second fault indicator at sampling time i; ΔU i is the difference value of the voltage baseline data at sampling time i; ΔI i is the difference value of the current baseline data at sampling time i; i-1 is the sampling time before sampling time i; ΔU i-1 is the difference value of the voltage baseline data at sampling time i-1; ΔI i-1 is the differential value of the current baseline data at sampling time i-1; || is the absolute value symbol; exp() is the exponential function with the natural number e as the base; ε is the preset denominator adjustment value.

[0071] In the formula, the differential value of the voltage baseline data reflects the voltage change; the differential value of the current baseline data reflects the current change; The smaller the value, the more likely a fault signal is present. The second fault indicator is ultimately obtained. The larger the second fault indicator, the more consistent the sampling time is with the fault signal. It should be noted that the present invention uses backward differencing to calculate the differential value: the data corresponding to the next sampling time minus the data corresponding to the current sampling time. To prevent the denominator from being zero, the present invention sets the denominator adjustment value to 0.001. This value can be customized by the implementer based on the implementation scenario.

[0072] Step S204: obtaining a third fault indicator at the sampling moment according to the absolute value of the Pearson correlation coefficient of the current baseline data and the voltage baseline data corresponding to the preset reference time period of the monitoring point at the sampling moment.

[0073] Considering that when a fault occurs in the distribution network, the changes in current and voltage are correlated, while the interference of noise is random, there is no relationship between the changes in voltage and current caused by noise, so the fault possibility at the sampling moment is evaluated and the third fault indicator at the sampling moment is obtained.

[0074] In one embodiment of the present invention, the absolute value of the Pearson correlation coefficient of the current baseline data and the voltage baseline data corresponding to the preset reference time period at the monitoring point at the sampling time is calculated to obtain the third fault indicator at the sampling time. It should be noted that the Pearson correlation coefficient is a technical means well known to those skilled in the art and will not be described in detail here.

[0075] In one embodiment of the present invention, a method for obtaining a preset reference time period for a sampling moment includes: using the time period corresponding to the sampling moment and a preset reference number of sampling moments before the sampling moment as the preset reference time period for the sampling moment. In one embodiment of the present invention, the preset reference number is 9, which can be set by the implementer based on the implementation scenario.

[0076] Step S205: forwardly fuse the first fault indicator, the second fault indicator, and the third fault indicator to obtain the fault responsiveness of the monitoring point at the sampling time.

[0077] Fault responsiveness comprehensively reflects the possibility of a fault signal existing at the monitoring point at the sampling time.

[0078] It should be noted that forward fusion is an existing technology well known to those skilled in the art, and forward fusion can adopt simple product, arithmetic mean or other suitable fusion methods. In one embodiment of the present invention, the product of the first fault indicator, the second fault indicator and the third fault indicator is calculated and normalized to obtain the fault responsiveness of the monitoring point at the sampling moment. It should be noted that the normalization method adopted is: normalization is performed using the norm normalization function to limit the numerical range to between 0 and 1. Normalization is a technical means well known to those skilled in the art, and the choice of normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0079] In order to analyze the time interval corresponding to the transmission of a fault signal in a digital distribution network, preferably, in one embodiment of the present invention, a method for obtaining the fault time period includes:

[0080] The average of all fault response degrees corresponding to each sampling moment is used as the overall fault degree at each sampling moment. The sampling moment at which the overall fault degree exceeds a preset fault threshold is used as the fault moment. The time period corresponding to a preset number of consecutive fault moments is used as the fault time period. In one embodiment of the present invention, the preset fault threshold is 0.59 and the preset number of faults is 6. Implementers can set these settings based on the implementation scenario.

[0081] In the above steps, a higher overall fault severity indicates a more severe distribution network fault at the sampling moment. The fault time reflects whether the distribution network fault was significant enough to be considered a fault signal at the corresponding sampling moment. Given that fault signal transmission lasts for a period of time, the time period corresponding to a predetermined number of consecutive fault moments is considered the fault time period. This division helps more accurately determine the actual duration of the fault.

[0082] Considering that fault signals in the distribution network will attenuate as the transmission distance increases, noise will interfere with the transmission and detection of fault signals, resulting in abnormal attenuation of fault signals. Noise measurement is used to reflect the possibility of noise in the digital distribution network during the filtering period. Figure 3 , which shows a flow chart of a method for obtaining a noise metric in one embodiment of the present invention. Preferably, in one embodiment of the present invention, the method for obtaining a noise metric includes:

[0083] Step S211: The maximum value of the fault response intensity of the monitoring point in the fault time period is used as the fault value to be analyzed of the monitoring point in the fault time period; for any fault time period, all the fault values to be analyzed are sorted in descending order according to the fault values to be analyzed of the monitoring point to obtain the fault monitoring point serial number of the fault value to be analyzed; all the fault values to be analyzed are counted in order according to the order of the fault monitoring point serial numbers to obtain the fault value sequence to be analyzed of the fault time period.

[0084] To analyze the propagation of fault signals, consider that in digital distribution networks, fault signals propagate from the fault source and gradually attenuate with increasing propagation distance. Monitoring points can capture the attenuation of fault signals at different locations and distances. The fault value to be analyzed is the maximum fault response intensity at the monitoring point during the fault period. This maximum value not only reflects the severity of the fault but also implicitly reflects the attenuation of the fault signal at that monitoring point. A small fault value to be analyzed at a particular monitoring point indicates that the monitoring point is far from the fault source and that the fault signal has experienced significant attenuation during propagation. By counting all fault values to be analyzed in order of the fault monitoring point numbers, a fault sequence to be analyzed can be obtained that reflects the attenuation of the fault signal.

[0085] Step S212: in the sequence of fault values to be analyzed in all fault time periods, obtaining the attention weight of the fault value to be analyzed according to the outlier condition of the fault value to be analyzed.

[0086] To eliminate the effects of noise and obtain a sequence that reflects the attenuation of digital distribution network fault signals, we consider that fault signals attenuate with transmission distance and have a low degree of outliers. However, due to the randomness of noise signals, the degree of outliers is high. Based on the outlier status of the fault value to be analyzed, an attention weight is obtained for the fault value to be analyzed. A larger attention weight indicates that the fault value to be analyzed more closely matches the characteristics of the fault signal.

[0087] In one embodiment of the present invention, a method for obtaining attention weight includes:

[0088] Based on a two-dimensional coordinate system, the horizontal axis of the two-dimensional coordinate system is the fault monitoring point number, and the vertical axis of the two-dimensional coordinate system is the fault value to be analyzed. All fault values to be analyzed are mapped into the two-dimensional coordinate system; using the LOF algorithm (Local Outlier Factor), the outlier factor of the fault value to be analyzed in the two-dimensional coordinate system is calculated and negatively correlated with the mapping to obtain the attention weight of the fault value to be analyzed. It should be noted that using the LOF algorithm to obtain the outlier factor is an existing technology well known to those skilled in the art and will not be described in detail here.

[0089] In order to visualize all the fault values to be analyzed in the coordinate system, a two-dimensional coordinate system is constructed. The fault value of each monitoring point is mapped to the corresponding position in the two-dimensional coordinate system, forming a series of data points. These data points can intuitively demonstrate the changing trend of the fault signal at different monitoring points. The outlier degree of each data point in the two-dimensional coordinate system is calculated. The LOF algorithm is a density-based outlier detection method that assesses the outlier degree of a data point by comparing the local density of a data point with other points in its neighborhood. The LOF algorithm is used to calculate the outlier factor of the fault value to be analyzed in the two-dimensional coordinate system. After obtaining the outlier factor of each data point, a negative correlation mapping is performed. Specifically, the smaller the outlier factor, the greater the attention weight assigned. This is because the attenuation characteristics of the fault signal during transmission cause it to exhibit a relatively consistent change trend in space, while the randomness of the noise signal causes it to exhibit a higher degree of outlier. Therefore, through negative correlation mapping, data points that are more consistent with the characteristics of the fault signal can be given a higher attention weight.

[0090] Step S213: obtaining the time period weight of the fault time period according to the differences of the fault value sequences to be analyzed in all fault time periods.

[0091] Considering that if the fault value sequence in a certain time period is significantly different from that in other time periods, it may mean that the time period is more affected by noise, so it will be given a lower weight. The larger the time period weight of the fault time period, the more consistent the fault signal transmission characteristics are with the fault time period.

[0092] In one embodiment of the present invention, a method for obtaining a time period weight includes:

[0093] Calculate the mean of all fault values to be analyzed corresponding to the same fault monitoring point number to obtain the reference fault value for the fault monitoring point number; count all reference fault values in order of the fault monitoring point number to obtain the reference fault value sequence for the fault time period; calculate the DTW distance between the fault value sequence to be analyzed and the reference fault value sequence for each fault time period and perform negative correlation mapping to obtain the time period weight for each fault time period. It should be noted that obtaining the DTW distance between two sequences is a prior art well known to those skilled in the art and will not be elaborated on here.

[0094] In the above steps, the reference fault value of the fault monitoring point number reflects the expected fault value at the propagation location under normal conditions. The reference fault value sequence reflects the attenuation of the fault signal within the overall digital distribution network. A smaller DTW distance indicates a higher similarity between the fault value sequence to be analyzed and the reference fault value sequence. In other words, the fault signal within this time period is more consistent with the overall transmission characteristics. The calculated DTW distance is negatively correlated. That is, the smaller the DTW distance, the greater the time period weight assigned. The larger the time period weight of the fault time period, the more consistent the fault signal transmission characteristics are.

[0095] Step S214: forwardly fuse the attention weight of the fault value to be analyzed and the time period weight of the fault time period to which it belongs to obtain the target weight of the fault value to be analyzed.

[0096] The greater the target weight, the more the fault value to be analyzed conforms to the fault signal performance characteristics.

[0097] It should be noted that forward fusion is an existing technology well known to those skilled in the art. Forward fusion can use simple multiplication, arithmetic mean, or other suitable fusion methods. In one embodiment of the present invention, a method for obtaining a target weight includes calculating the average of the attention weight of the fault value to be analyzed and the time period weights of the corresponding fault time period to obtain a target weight for the fault value to be analyzed.

[0098] For the above steps, the target weight is the result of a comprehensive consideration of the attention weight and time period weight. The attention weight reflects the degree to which the fault value being analyzed conforms to the fault signal's characteristics, while the time period weight reflects the degree to which the fault signal conforms to the overall transmission characteristics during the fault period. By combining these two weights, we obtain a target weight that more accurately reflects the true nature of the fault signal.

[0099] Step S215: obtaining a target fault value sequence of the digital distribution network according to the target weights of all the fault values to be analyzed in the fault value sequence to be analyzed in the fault time period.

[0100] The target fault value sequence can more effectively reflect the actual attenuation sequence of digital distribution network faults.

[0101] In one embodiment of the present invention, a method for obtaining a target fault value sequence includes:

[0102] For all fault values to be analyzed corresponding to the same fault monitoring point number, the target weight is used to perform weighted averaging on all fault values to be analyzed to obtain the target fault value corresponding to each fault monitoring point number; all target fault values are counted in sequence according to the order of the fault monitoring point numbers to obtain the target fault value sequence of the digital distribution network.

[0103] Based on the above steps, the calculated target weights are used to perform a weighted average of all fault values to be analyzed corresponding to each fault monitoring point number, resulting in a target fault value for each fault monitoring point number. This weighted average ensures that fault values with higher target weights account for a larger proportion of the final result, thereby improving the accuracy of the fault values. All target fault values are then counted in descending order of fault monitoring point numbers to obtain a target fault value sequence for the digital distribution network. This target fault value sequence more effectively reflects the actual attenuation of faults in the digital distribution network.

[0104] Step S216: obtaining a noise metric of the digital distribution network in the filtering time period according to the DTW distance between the target fault value sequence of the digital distribution network and the preset standard fault attenuation sequence.

[0105] Considering that the DTW distance between the target fault value sequence and the standard fault attenuation sequence is large, it means that the noise influence is large, so the noise metric value will be high.

[0106] In one embodiment of the present invention, a method for obtaining a noise metric includes:

[0107] The DTW distance between the target fault value sequence of the digital distribution network and the preset standard fault attenuation sequence is calculated and normalized to obtain the noise metric of the digital distribution network during the filtering period. It should be noted that the preset standard fault attenuation sequence is set based on empirical values. In one embodiment of the present invention, an engineer can select a target fault value sequence stored in a historical database as the preset standard fault attenuation sequence.

[0108] Based on the above steps, the target fault value sequence more effectively reflects the actual attenuation sequence of digital distribution network faults. The preset standard fault attenuation sequence reflects the expected attenuation trend of fault signals in the digital distribution network in the absence of noise interference. A larger noise metric indicates more severe noise interference with the fault signal; conversely, a smaller noise metric indicates that the fault signal transmission is closer to the ideal state.

[0109] Step S3: Adjust the smoothing coefficient according to the noise metric to obtain the adjusted smoothing coefficient of the digital distribution network in the filtering time period; use the adjusted smoothing coefficient to filter the data set in the filtering time period to obtain the filtered data set; and perform fault detection based on the filtered data set.

[0110] The noise measurement reflects the possibility of noise in the digital distribution network during the filtering period, and then adjusts the smoothing coefficient. The adjusted smoothing coefficient of the digital distribution network during the filtering period is reasonably set to improve the noise reduction effect and ensure accurate fault monitoring of the digital distribution network.

[0111] When the noise metric is large, it indicates that the noise interference is high. In this case, the smoothing coefficient needs to be reduced to reduce the smoothing processing of data details during the filtering process, thereby retaining more fault signal characteristics; when the noise metric is small, it indicates that the noise interference is low. In this case, the smoothing coefficient can be appropriately increased to further smooth the data and improve the readability of the data and the accuracy of the analysis. Preferably, in one embodiment of the present invention, the method for obtaining the adjusted smoothing coefficient includes:

[0112] The product of the negative correlation mapping result of the noise metric and the preset adjustment index is calculated to obtain the index to be adjusted; the product of the index to be adjusted and the smoothing coefficient is calculated to obtain the adjusted smoothing coefficient. In one embodiment of the present invention, the preset adjustment index is set to 1.2, and the smoothing coefficient is determined based on empirical values. In the present invention, it is set to 0.57, and the implementer can set it according to the implementation scenario. It should be noted that negative correlation mapping is a technical means well known to those skilled in the art. Negative correlation mapping can be in the form of inverse proportion or negative exponential power, which is not limited here.

[0113] In order to obtain filtered data, preferably, in one embodiment of the present invention, the method for obtaining a filtered data set includes:

[0114] In the data set of the digital distribution network during the filtering time period, based on the exponentially weighted moving average method, each power parameter data is filtered by using a reasonably set smoothing coefficient after adjustment of the digital distribution network during the filtering time period to obtain filtered power parameter data; all filtered power parameter data are counted to obtain a filtered data set. It should be noted that the exponentially weighted moving average method is an existing technology well known to those skilled in the art and will not be described in detail here.

[0115] Furthermore, the filtered data set is input into a trained neural network model to output fault detection results. The specific neural network model training process is a technical means well known to those skilled in the art and will not be described in detail here. Professional equipment engineers can also use the filtered data set to perform fault assessments on the digital distribution network during the filtering period.

[0116] In summary, the embodiments of the present invention provide a fault monitoring method and system for a digital distribution network. First, each fault time period is divided from the time period to be filtered; based on the differences in the fault responsiveness of all monitoring points in each fault time period, the noise measurement of the digital distribution network in the time period to be filtered is obtained; the smoothing coefficient is adjusted according to the noise measurement to obtain the adjusted smoothing coefficient of the digital distribution network in the time period to be filtered; the data set in the time period to be filtered is filtered using the adjusted smoothing coefficient to obtain the filtered data set; and fault detection is performed based on the filtered data set. The present invention improves the noise reduction effect by reasonably setting the adjusted smoothing coefficient of the digital distribution network in the time period to be filtered, thereby ensuring accurate fault monitoring of the digital distribution network.

[0117] The present invention also proposes a fault monitoring system for digital distribution network, see Figure 4 , which shows a structural diagram of a fault monitoring system applied to a digital distribution network provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a noise measurement module 102 and a fault detection module 103.

[0118] The data acquisition module 101 is used to acquire a data set of the digital distribution network in the time period to be filtered; the data set includes current value data and voltage value data of each monitoring point.

[0119] The noise measurement module 102 is used to obtain the fault responsiveness of the monitoring point at the sampling moment based on the timing fluctuations of the current value and voltage value corresponding to the monitoring point at the sampling moment, as well as the correlation between the current value data and voltage value data corresponding to the preset reference time period at the sampling moment of the monitoring point; divide the time period to be filtered into various fault time periods according to the fault responsiveness of all monitoring points at each sampling moment in the time period to be filtered; and obtain the noise measurement of the digital distribution network in the time period to be filtered according to the differences in the fault responsiveness of all monitoring points in each fault time period.

[0120] The fault detection module 103 is used to adjust the smoothing coefficient according to the noise measurement to obtain the adjusted smoothing coefficient of the digital distribution network in the time period to be filtered; use the adjusted smoothing coefficient to filter the data set in the time period to be filtered to obtain the filtered data set; and perform fault detection based on the filtered data set.

[0121] It should be noted that the system provided in the above embodiment is merely an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the fault monitoring system for a digital distribution network provided in the above embodiment and the fault monitoring method for a digital distribution network provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0122] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A fault monitoring method applied to a digital distribution network, characterized in that: The method comprises: Acquire a data set of the digital distribution network in the time period to be filtered; the data set includes current value data and voltage value data of each monitoring point; Obtaining the fault responsiveness of the monitoring point at the sampling moment based on the fluctuation of the current value and voltage value corresponding to the monitoring point at the sampling moment along the time sequence, and the correlation between the current value data and the voltage value data corresponding to the preset reference time period at the sampling moment; dividing the time period to be filtered into individual fault time periods based on the fault responsiveness of all monitoring points at each sampling moment in the time period to be filtered; and obtaining the noise metric of the digital distribution network in the time period to be filtered based on the differences in the fault responsiveness of all monitoring points in each of the fault time periods; Adjusting the smoothing coefficient according to the noise metric to obtain the adjusted smoothing coefficient of the digital distribution network in the filtering time period; filtering the data set in the filtering time period using the adjusted smoothing coefficient to obtain a filtered data set; and performing fault detection based on the filtered data set; Methods for obtaining noise metrics include: The maximum value of the fault response intensity of the monitoring point in the fault time period is used as the fault value to be analyzed of the monitoring point in the fault time period; for any fault time period, all the fault values to be analyzed are sorted in descending order of the fault values to be analyzed of the monitoring point to obtain the fault monitoring point sequence number of the fault value to be analyzed; all the fault values to be analyzed are counted in order according to the sequence of the fault monitoring point sequence numbers to obtain a sequence of fault values to be analyzed for the fault time period; In all the sequences of fault values to be analyzed in the fault time period, obtaining the attention weight of the fault value to be analyzed according to the outlier situation of the fault value to be analyzed; Obtaining a time period weight for the fault time period according to differences in the fault value sequences to be analyzed for all fault time periods; Forward fusing the attention weight of the fault value to be analyzed and the time period weight of the fault time period to which it belongs to obtain a target weight of the fault value to be analyzed; Obtaining a target fault value sequence of the digital distribution network according to target weights of all fault values to be analyzed in the fault value sequence to be analyzed in all fault time periods; According to the DTW distance between the target fault value sequence of the digital distribution network and a preset standard fault attenuation sequence, a noise metric of the digital distribution network in the filtering time period is obtained.

2. A fault monitoring method applied to a digital distribution network according to claim 1, characterized in that: The method for obtaining the fault responsiveness includes: For any monitoring point, the current value data and the voltage value data of the monitoring point are used as the power parameter data of the monitoring point; according to the average of the upper envelope and the lower envelope of the power parameter data, the baseline data corresponding to the power parameter data is obtained; the baseline data corresponding to the current value data is used as the current baseline data; and the baseline data corresponding to the voltage value data is used as the voltage baseline data; Obtaining a first fault indicator at the sampling moment based on a difference between the sampling moment and the baseline data corresponding to the preset surrounding time period; Obtaining a second fault indicator at the sampling moment according to a difference between the baseline data corresponding to the sampling moment and the previous sampling moment; Obtaining a third fault indicator at the sampling moment according to the absolute value of the Pearson correlation coefficient of the current baseline data and the voltage baseline data corresponding to the preset reference time period of the monitoring point at the sampling moment; The first fault indicator, the second fault indicator, and the third fault indicator are forwardly integrated to obtain the fault responsiveness of the monitoring point at the sampling moment.

3. The fault monitoring method applied to a digital distribution network according to claim 2, characterized in that: The method for obtaining the first fault indicator includes: For any power parameter data corresponding to the baseline data, within a preset time period around the sampling moment, the mean of all the baseline data is calculated to obtain the overall value around the sampling moment; the absolute value of the difference between the baseline data at the sampling moment and the overall value around the sampling moment is calculated to obtain the local difference value of the baseline data at the sampling moment; An average of the local difference values of the current baseline data and the voltage baseline data at a sampling moment is calculated to obtain a first fault indicator at the sampling moment.

4. A fault monitoring method applied to a digital distribution network according to claim 2, characterized in that: The method for obtaining the second fault indicator includes: The second fault indicator is obtained according to a second fault indicator obtaining formula, wherein the second fault indicator obtaining formula includes: Among them, Y i is the second fault indicator at sampling time i; ΔU i is the difference value of the voltage baseline data at sampling time i; ΔI i is the difference value of the current baseline data at sampling time i; i-1 is the sampling time before sampling time i; ΔU i-1 is the difference value of the voltage baseline data at sampling time i-1; ΔI i-1 is the differential value of the current baseline data at sampling time i-1; || is the absolute value symbol; exp() is an exponential function with the natural number e as the base; ε is a preset denominator adjustment value.

5. The fault monitoring method applied to a digital distribution network according to claim 1, characterized in that: The method for obtaining the attention weight includes: Based on a two-dimensional coordinate system, the horizontal axis of the two-dimensional coordinate system is the fault monitoring point number, and the vertical axis of the two-dimensional coordinate system is the fault value to be analyzed. All fault values to be analyzed are mapped into the two-dimensional coordinate system; the LOF algorithm is used to calculate the outlier factor of the fault value to be analyzed in the two-dimensional coordinate system and perform negative correlation mapping to obtain the attention weight of the fault value to be analyzed.

6. A fault monitoring method for a digital distribution network according to claim 1, characterized in that: The method for obtaining the time period weight includes: Calculating the mean of all the fault values to be analyzed corresponding to the same fault monitoring point number to obtain a reference fault value for the fault monitoring point number; and sequentially counting all the reference fault values in the order of the fault monitoring point numbers to obtain a reference fault value sequence for the fault time period; The DTW distance between the fault value sequence to be analyzed and the reference fault value sequence in each fault time period is calculated and negative correlation mapping is performed to obtain a time period weight of each fault time period.

7. A fault monitoring method for a digital distribution network according to claim 1, characterized in that: The method for obtaining the target fault value sequence includes: For all fault values to be analyzed corresponding to the same fault monitoring point number, all fault values to be analyzed are weighted and averaged using the target weight to obtain the target fault value corresponding to each fault monitoring point number; all the target fault values are counted in sequence according to the order of the fault monitoring point numbers to obtain the target fault value sequence of the digital distribution network.

8. A fault monitoring method for a digital distribution network according to claim 1, characterized in that: Methods for obtaining the adjusted smoothing coefficient include: The product of the negative correlation mapping result of the noise metric and the preset adjustment index is calculated to obtain the index to be adjusted; and the product of the index to be adjusted and the smoothing coefficient is calculated to obtain the adjusted smoothing coefficient.

9. A fault monitoring system for a digital power distribution network, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the fault monitoring method applied to a digital distribution network as claimed in any one of claims 1 to 8 are implemented.

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