Harmonic source identification method and device and electronic equipment

Abstract

The invention provides a harmonic source identification method and apparatus, and an electronic device. The harmonic source identification method comprises the steps of obtaining electric power of a target user at each moment in the whole day and a plurality of harmonic current monitoring indexes of a common connection point; clustering the electricity consumption power of the target user at each moment in the whole day to obtain a plurality of clusters; the minimum value is selected from the numerical values corresponding to the clustering center of each cluster to serve as a power lower limit value, a working condition switching time point is determined according to the power lower limit value, and the working condition switching time point represents the time point when the working mode of the electric equipment changes; determining time period division points according to the working condition switching time points, and performing time period division on the whole day based on the time period division points to obtain a plurality of time periods; determining a correlation coefficient between the electric power in each time period and the harmonic current monitoring index of the common connection point; and selecting the harmonic current monitoring index of which the correlation coefficient is greater than a first threshold value as an identification result of the harmonic source of the target user.

Description

Technical Field

[0001] The present application relates to the field of electric power, and in particular to a method, device, electronic device, storage medium and program product for identifying a harmonic source. Background Art

[0002] With the rapid growth of industrial users and the widespread application of power electronic equipment, the types and quantity of loads in the power system show a significant growth trend. This change not only increases the complexity of the power grid, but also brings unprecedented challenges to the power quality of the Point of Common Coupling (PCC). In recent years, with the development of smart grid technology, advanced equipment such as power quality monitoring terminals and smart meters have been widely deployed in power networks. These devices can not only collect power grid operation data in real time, but also provide rich data support for the power quality analysis of PCC. In the power system, harmonics will have an adverse effect on the normal operation of the power system. Therefore, when analyzing the power quality of PCC, accurately identifying the harmonic sources that generate harmonics is crucial to ensure the normal operation of the power system.

[0003] In some scenarios, the all-weather correlation analysis method is often used to capture the actual changes in the user's harmonic load. The all-weather correlation analysis method directly correlates the user's harmonic load conditions throughout the entire day. However, the harmonic load conditions of many users have a certain working mode, that is, they are active in certain time periods and may be closed in other time periods. Such operating rules result in the inability of traditional all-weather correlation analysis methods to accurately capture the actual changes in the user's harmonic load under different states, which will cause the omission and misjudgment of harmonic characteristics, which is prone to omission or misjudgment when identifying harmonic sources, resulting in low accuracy in identifying harmonic sources. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a method, device, electronic device, storage medium and program product for identifying a harmonic source, so as to solve the problem of low accuracy in identifying a harmonic source.

[0005] In order to solve the above technical problems, the embodiments of the present application are implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for identifying a harmonic source, and the method for identifying a harmonic source includes: obtaining the power consumption of a target user at all times throughout the day and multiple harmonic current monitoring indicators of a common connection point, where the common connection point is the connection point where the target user's electrical equipment is connected to the power grid; clustering the power consumption of the target user at all times throughout the day to obtain multiple clusters; selecting the minimum value from the numerical values ​​corresponding to the cluster centers of each cluster as the lower power limit, and determining the operating condition switching time point based on the lower power limit, the operating condition switching time point representing the time point at which the working mode of the electrical equipment changes; determining the time period division point based on the operating condition switching time point, and dividing the whole day into time periods based on the time period division point to obtain multiple time periods; determining the correlation coefficient between the power consumption in each time period and the harmonic current monitoring indicator of the common connection point; selecting the harmonic current monitoring indicator with a correlation coefficient greater than a first threshold as the identification result of the harmonic source of the target user.

[0007] In a second aspect, an embodiment of the present application provides a device for identifying a harmonic source, and the device for identifying a harmonic source includes: an acquisition module, which is used to acquire the power consumption of a target user at all times throughout the day and multiple harmonic current monitoring indicators of a common connection point, where the common connection point is the connection point where the target user's power equipment is connected to the power grid; a clustering module, which is used to cluster the power consumption of the target user at all times throughout the day to obtain multiple clusters; a determination module, which is used to select a minimum value from the numerical values ​​corresponding to the cluster centers of each cluster as a power lower limit value, and determine a working condition switching time point according to the power lower limit value, the working condition switching time point characterizing the time point at which the working mode of the power equipment changes; a division module, which is used to determine a time period division point according to the working condition switching time point, and divide the whole day into time periods based on the time period division point to obtain multiple time periods; the determination module is also used to determine the correlation coefficient between the power consumption in each time period and the harmonic current monitoring indicator of the common connection point; an identification module, which is used to select a harmonic current monitoring indicator with a correlation coefficient greater than a first threshold as the identification result of the harmonic source of the target user.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, including a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to implement the steps of the harmonic source identification method mentioned in the first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for identifying a harmonic source as mentioned in the first aspect are implemented.

[0010] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the steps of the harmonic source identification method mentioned in the first aspect.

[0011] Through the technical solution disclosed in the embodiment of the present application, the target user's power consumption at all times of the day and multiple harmonic current monitoring indicators of the public connection point are first obtained. The public connection point is the connection point where the target user's electrical equipment is connected to the power grid. Then the target user's power consumption at all times of the day is clustered to obtain multiple clusters. Secondly, the minimum value is selected from the numerical values ​​corresponding to the cluster centers of each cluster as the power lower limit value, and the operating condition switching time point is determined according to the power lower limit value. The operating condition switching time point represents the time point when the working mode of the electrical equipment changes. Then, the time period division point is determined according to the operating condition switching time point, and the whole day is divided into time periods based on the time period division point to obtain multiple time periods. And determine the correlation coefficient between the power consumption in each time period and the harmonic current monitoring indicator of the public connection point. Finally, the harmonic current monitoring indicator with a correlation coefficient greater than the first threshold is selected as the identification result of the harmonic source of the target user.

[0012] In this way, the embodiment of the present application can divide the whole day into time periods according to the time points when the working mode of the electrical equipment changes, and perform time series correlation analysis on the user power and harmonic current monitoring indicators based on the segmentation results, and identify the user's harmonic source based on the correlation coefficient of the correlation analysis. In this way, the actual changes of the user's harmonic load under different working modes can be accurately captured, avoiding the omission and misjudgment of harmonic characteristics, thereby reducing the probability of omission or misjudgment when identifying harmonic sources. The identification accuracy of harmonic sources is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0014] Figure 1 A schematic diagram of a flow chart of a method for identifying a harmonic source provided in an embodiment of the present application;

[0015] Figure 2 A schematic diagram of a module composition of a harmonic source identification device provided in an embodiment of the present application;

[0016] Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The purpose of the embodiments of the present application is to provide a method, device, electronic device, storage medium and program product for identifying a harmonic source, which improves the low accuracy of identifying a harmonic source.

[0018] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0019] For example, Figure 1 As shown, an embodiment of the present application provides a method for identifying a harmonic source. The execution subject of the method may be a server. The method for identifying a harmonic source may specifically include the following steps:

[0020] In step S101, the power consumption of the target user at all times throughout the day and multiple harmonic current monitoring indicators of the public connection point are obtained.

[0021] Among them, the public connection point is the connection point where the target user's electrical equipment is connected to the power grid.

[0022] Specifically, the types of target users may be industrial electricity users, household electricity users, school teaching electricity users, and non-residential lighting users. Among them, the power consumption may be the active power of the electrical equipment used by the target user, and users with larger power consumption have a greater impact on the harmonic pollution at the public connection point. In the embodiment of the present application, the target user can be determined by screening the type of users connected to the bus and the operating capacity. For example, the power consumption and operating capacity of industrial electricity users are both large, so industrial users can be selected as target users. The connection point where the target user's electrical equipment is connected to the power grid may be the connection point where the target user's electrical equipment is connected to the bus. In order to facilitate subsequent data processing and analysis, the power consumption and harmonic current monitoring indicators of each target user at all times of the day can be used as columns, and the identifier of each target user can be used as a row to form a multi-user-multi-data multi-dimensional data set.

[0023] Furthermore, harmonic current monitoring indicators include but are not limited to 2nd harmonic current monitoring indicators, 3rd harmonic current monitoring indicators, 4th harmonic current monitoring indicators and 5th harmonic current monitoring indicators.

[0024] In step S102, the power consumption of the target user at each time of the day is clustered to obtain a plurality of clusters.

[0025] Specifically, clustering the target user's power consumption at various times throughout the day is to cluster the similar power consumption of each target user, and each user corresponds to multiple clusters. When clustering the power consumption of the target user at various times throughout the day, the K-means clustering algorithm can be used. In the embodiment of the present application, it is assumed that the user's power consumption is the total active power, and the total active power sequence of user m is P m , which can be expressed as follows:

[0026] P m ={p m,1 ,p m,2 ,...,p m,i ,...,p m,n}

[0027] In the above formula, p m,1 represents the first point in the total power sequence of user m, p m,2 represents the second point in the total power sequence of user m, p m,i represents the i-th point in the total power sequence of user m, p m,n Represents the nth point in the total power sequence of user m.

[0028] Furthermore, when clustering is performed according to the total active power sequence of each target user, K points are first randomly selected as the centers of the clusters. Then the total active power sequence is calculated as P m The Euclidean distance from each value to each cluster center is calculated, and then the value is assigned to the nearest cluster center, thus forming K clusters. The calculation formula of the Euclidean distance is as follows:

[0029]

[0030] In the above formula, p m,i Represents the i-th point in the total power sequence of user m. j is the jth randomly selected cluster center. i represents the i-th point in the total power sequence, j represents the j-th cluster center, n represents the number of data points in the total power sequence, and K is the number of cluster centers.

[0031] Furthermore, after forming K clusters, the mean of all data points in each cluster is calculated and used as the centroid of each cluster, which is then used as the new cluster center. Repeat the above steps until the position of the centroid no longer changes or the set number of iterations is reached, and K new cluster centers are obtained.

[0032] Furthermore, the optimal number of cluster centers K can be determined by the elbow method, which uses the sum of squared errors to evaluate the optimal number of cluster centers K. The calculation formula is as follows:

[0033]

[0034] Among them, SSE refers to the clustering error of all samples, which represents the quality of clustering effect. The image of SSE curve will have an inflection point where the decline rate slows down significantly. The number of clusters corresponding to this inflection point is the optimal number of clusters. i is the i-th cluster. p is C i The sample points in m i It is C i The cluster center of .

[0035] In step S103, a minimum value is selected from the values ​​corresponding to the cluster centers of each cluster as the power lower limit value, and the operating mode switching time point is determined according to the power lower limit value.

[0036] The operating mode switching time point represents the time point at which the operating mode of the electrical equipment changes.

[0037] Specifically, after the clusters of each target user are obtained by the aforementioned clustering process, the minimum value is selected from the cluster center of each target user's cluster as the power lower limit value, which can be the minimum value in each cluster as the power lower limit value, to obtain a series of power lower limit value sequences of the target user. The power lower limit value sequence can be expressed by the following formula:

[0038] P l ={p l,1 ,p l,2 ,...,p l,i ,...,p l,n}

[0039] In the above formula, P l is the selected power lower limit sequence. l,1 is the first power lower limit value in the screened power sequence, p l,2 is the second lower power limit value in the selected power sequence, p l,i is the i-th power lower limit value in the screened power sequence, p l,n It is the nth power lower limit value in the screened power sequence.

[0040] Furthermore, when determining the time point for operating condition switching, first determine a first average power consumption at multiple time points before the time point corresponding to the lower power limit value, and a second average power consumption at multiple time points after the time point corresponding to the lower power limit value; then, when the power difference between the first average power consumption and the time point corresponding to the lower power limit value is greater than a second threshold or the power difference between the second average power consumption and the time point corresponding to the lower power limit value is greater than the second threshold, determine the time point corresponding to the lower power limit value as the operating condition switching time point.

[0041] Specifically, the first average power consumption at multiple time points before the time point corresponding to the power lower limit value may be the average power consumption at three time points before the time point corresponding to the power lower limit value, and the second average power consumption at multiple time points after the time point corresponding to the power lower limit value may be the average power consumption at three time points after the time point corresponding to the power lower limit value. The second threshold value may be selected according to actual conditions, and is calculated in the embodiment of the present application by means of the first-order difference mean and standard deviation.

[0042] Furthermore, when determining the second threshold, the following method can be used: first calculate the first-order difference of the target user's electricity consumption at all times throughout the day to obtain the first-order difference data of the electricity consumption at each time; then calculate the mean and standard deviation of the first-order difference data; finally, determine the sum of the mean and standard deviation as the threshold.

[0043] Specifically, for the function y=y(x), the definition of its first-order difference is as follows:

[0044] Δy x =y(x+1)-y(x),(x=0,1,2...)

[0045] In the above formula, Δy x It is called the first-order difference of the function y(x) at point x. x can be the active power in the active power sequence. The first-order difference data is obtained by calculating the first-order difference of the user's active power sequence as a whole. The first-order difference data of the user's active power can be expressed as follows:

[0046] ΔP m ={Δp m,1 ,Δp m,2 ,...,Δp m,n}

[0047] In the above formula, ΔP m Represents the first-order difference data sequence of the user's active power, Δp m,1 The first first-order difference data in the first-order difference data sequence representing the active power of user m, Δp m,2 The second first-order difference data in the first-order difference data sequence representing the active power of user m, Δpm,n The nth first-order difference data in the first-order difference data sequence representing the active power of user m.

[0048] Furthermore, the mean μ and standard deviation σ in the first-order difference data sequence of the user's active power are calculated. The calculation formula of the mean μ is as follows:

[0049]

[0050] In the above formula, n represents the nth first-order difference data in the first-order difference data sequence of the active power of user m, ΔP m Represents the first-order difference data sequence of the user's active power.

[0051] Furthermore, the calculation formula of the standard deviation σ is as follows:

[0052]

[0053] Furthermore, μ+σ, that is, the mean plus one standard deviation, is taken as the second threshold.

[0054] In step S104, a time period division point is determined according to the working condition switching time point, and the whole day is divided into time periods based on the time period division point to obtain multiple time periods.

[0055] Specifically, through the above embodiments of the present application, a data set O consisting of the working condition switching time points of each user is obtained, and a set O of working condition switching time points of all users is obtained. It is specifically expressed by the following formula:

[0056]

[0057] In the above formula, O 1 The data set composed of the first user's working condition switching time point, O m is the data set composed of the working condition switching time points of the mth user, t m,j is the j-th working condition switching time point of user m. It is worth noting that the Chinese meanings of the other parameters in this formula can refer to t m,j , the embodiments of the present application will not be described in detail here.

[0058] Furthermore, when determining the time period division point, first determine the difference between the operating condition switching time points that are adjacent in time sequence; then, when the difference is less than the tolerance threshold, merge the adjacent operating condition switching time points to obtain a new operating condition switching time point, and calculate the difference between the new operating condition switching time point and its adjacent operating condition switching time point, and repeat the process until the difference between the new operating condition switching time point and its adjacent operating condition switching time point is greater than or equal to the tolerance threshold; when the difference is greater than or equal to the tolerance threshold, determine any time point between the adjacent operating condition switching time points as the time period division point.

[0059] Specifically, the tolerance threshold can be determined according to the actual situation. In the embodiment of the present application, the value is 45 minutes. For example, for monitoring data with a sampling frequency of 15 minutes / time, in order to avoid losing the trend characteristics of the time series data due to too fine time division, the tolerance threshold T lim Set to 45 minutes, then for the time points of working condition switching between different users within 45 minutes, take the value as the time period division point. 1 ~O m Perform the above steps and take the starting time as t 0 Finally, we get T+1 time segmentation points, denoted as O′={t 0 ,t 1 ,...,t i ,...,t T}, where the user's power consumption data and the harmonic current monitoring index at the PCC are segmented according to the division result to obtain the user power data set and the harmonic current monitoring index data set. The user power data set is represented by the following formula:

[0060]

[0061] In the above formula, P' represents the user power data set of all users after data segmentation, P' m represents the user power dataset of user m after data segmentation, p' m,j represents the power consumption sequence of the ith period after the user m data is segmented. It is worth noting that the meanings of the other parameters in the above formula can refer to P m 'with p' m,j , the embodiments of the present application will not be described in detail here.

[0062] Furthermore, the harmonic current monitoring index data set can be expressed as follows:

[0063] I' h ={I' h,1 ,I' h,2 ,...,I' h,i ,...,I' h,T}

[0064] In the above formula, I' h It represents the harmonic current monitoring index data set after the hth harmonic current is segmented, I' h,i It is the harmonic current monitoring indicator sequence of the jth time period after the hth harmonic current is segmented.

[0065] Furthermore, after data segmentation, for one time period k, the power consumption sequence of user m is The harmonic current monitoring indicator sequence is: Among them, P m,k represents the power consumption sequence of user m in the kth period, P m,0 represents the first power consumption in the power consumption sequence of user m in the kth period, P m,1 represents the second power consumption in the power consumption sequence of user m in the kth period, represents the Nth in the power consumption sequence of user m in the kth period 1 The power consumption. h,，k represents the harmonic current monitoring index sequence of user m in the kth period, I h,，1 It represents the first harmonic current monitoring index in the harmonic current monitoring index sequence of user m in the kth period, I h,，2 It represents the second harmonic current monitoring index in the harmonic current monitoring index sequence of user m in the kth period. Indicates the Nth harmonic current monitoring index sequence of user m in the kth period 2 A harmonic current monitoring indicator.

[0066] In step S105, correlation coefficients between harmonic current monitoring indicators of the power common connection point in each time period are determined.

[0067] Specifically, the correlation coefficient refers to the degree of correlation between the user's power consumption and the harmonic current monitoring index at the public connection point. When determining the correlation coefficient, first calculate the Euclidean distance between the power consumption and the harmonic current monitoring index at each moment in each time period; then calculate the distance coefficient between the power consumption and the harmonic current monitoring index in each time period based on each Euclidean distance; then normalize the distance coefficient of each time period based on the maximum and minimum values ​​of the distance coefficient of each time period to obtain the initial correlation coefficient; finally determine the correlation coefficient based on the initial correlation coefficient and the number of time points in each time period.

[0068] Specifically, for each user, the Euclidean distance between the power consumption and the harmonic current monitoring index at each time in each period can be expressed as 1 ×N 2 The matrix D is represented by the Euclidean distance between the power consumption and the harmonic current monitoring index at each moment. The matrix D can be represented by the following formula:

[0069]

[0070] In the above formula, d(P' m,i ,I' h,j ) is the Euclidean distance between the power consumption and the harmonic current monitoring index. P' m,iis the power sequence P of user m m,k The i-th element in , 0 <i<N 1 I' h,j is the harmonic current monitoring index sequence I h,，k The jth element of <j<N 2 .

[0071] Furthermore, when calculating the distance coefficient, first determine the minimum Euclidean distance between the current power consumption at the current moment in the current time period and the harmonic current monitoring index at each moment in the current time period; then iteratively accumulate the minimum Euclidean distance corresponding to each moment in the current time period to obtain the overall cumulative distance of the current time period, and finally use the overall cumulative distance as the distance coefficient of the current time period.

[0072] Specifically, in the embodiment of the present application, the minimum Euclidean distance corresponding to each moment is iteratively accumulated, and the overall accumulated distance obtained is used as the distance coefficient of the current period. 1 ×N 2 The cumulative distance matrix S[i,j] is expressed as follows:

[0073]

[0074] In the above formula, S[i,j] represents the power consumption sequence P of user m in the kth period m,k The i-th element in the harmonic current monitoring index sequence I h,，k The minimum Euclidean distance between the jth elements of . The initial value can be set to S[1,1] = D[1,1] = d(P' m,1 ,I' h,1 ).

[0075] Furthermore, the above formula is continuously iterated until i=N 1 , j = N 2 , at this time S[N 1 ,N 2 ] is the above power sequence P m,k The overall cumulative distance from the harmonic current monitoring index sequence is recorded as the distance coefficient d(P' m,k ,I' h,k ). This parameter is used to characterize the power sequence P m,k The smaller the similarity between the change trends of the harmonic current monitoring indicator sequence, the smaller the cumulative distance between the two curves in the time series, that is, the more similar the change trends of the two in this period are. The above formula is used to calculate the distance coefficients between m users and M harmonic current monitoring indicators in T time periods, and the distance coefficient matrix W is obtained. i . It is specifically expressed by the following formula:

[0076]

[0077] In the above formula, d(P' i,k ,I' j,k ) represents the distance coefficient between the power consumption data of user i and the harmonic current monitoring index j in the kth period. It is worth noting that the meaning of the other parameters in the above formula can refer to d(P' i,k ,I' j,k ), the embodiments of the present application will not be described in detail here.

[0078] Furthermore, after obtaining the distance coefficient, the following method can be used to calculate the initial correlation coefficient: first, calculate the first difference between the maximum value in the distance coefficient of the current time period and the distance coefficient of the current time period, and the second difference between the maximum value and the minimum value in the distance coefficient of the current time period; then determine the ratio of the first difference to the second difference as the initial correlation coefficient.

[0079] Specifically, the initial correlation coefficient can be expressed as follows:

[0080]

[0081] In the above formula, d′(P i,k ,I j,k ) is the initial correlation coefficient. The larger its value is, the stronger the correlation between the user's power consumption and the harmonic current monitoring index in the k period is. max(W i ) represents the maximum value of the distance coefficient in the current time period, min(W i ) represents the minimum value of the distance coefficient in the current time period.

[0082] Furthermore, after the above normalization process, the initial correlation coefficient matrix W is obtained. i ′, specifically expressed by the following formula:

[0083]

[0084] It is worth noting that the above initial correlation coefficient matrix W i ′, the Chinese meaning of each element can refer to d′(P i,k ,I j,k ), the embodiments of the present application will not be described in detail here.

[0085] Furthermore, when determining the correlation coefficient, as an optional embodiment of the present application, first determine the number of target time periods in which the initial correlation coefficient corresponding to the current harmonic current monitoring index is greater than the third threshold, and the maximum number of target time periods in which the initial correlation coefficient corresponding to each harmonic current monitoring index is greater than the third threshold; then calculate a first superposition value of the initial correlation coefficient of the target time period, and calculate a second superposition value of the number of time points in the target time period; then determine the ratio of the number of time points in the current target time period to the second superposition value; finally, determine the product of the first superposition value and the ratio as the correlation coefficient.

[0086] Specifically, the third threshold value can be determined according to actual conditions, and in the embodiment of the present application, the value is 0.75. i,j It can be expressed as follows:

[0087]

[0088] In the above formula, N j is the number of target time periods in which the initial correlation coefficient of the jth harmonic current monitoring indicator is greater than 0.75. k is the number of monitoring points corresponding to the kth period, that is, the number of time points in the kth period. q is the maximum number of time periods in which the initial correlation coefficient with each user is greater than 0.75 for the harmonic current monitoring indicator. i,k ,I j,k ) is the initial correlation coefficient.

[0089] In step S106, the harmonic current monitoring index with a correlation coefficient greater than the first threshold is selected as the identification result of the harmonic source of the target user.

[0090] Specifically, the first threshold value can be set according to actual conditions, and in the embodiment of the present application, the value is 0.75. i,j When it is greater than 0.75, it is considered that the two are strongly correlated, and the harmonic current monitoring index is used as the identification result of the harmonic source of the target user. Furthermore, when the correlation coefficient is between 0.25 and 0.75, it can be considered that there is a weak correlation. When the correlation coefficient is less than 0.25, it can be considered that there is no correlation. Therefore, the harmonic current monitoring index with a correlation coefficient greater than 0.75 is screened as the identification result of the harmonic source of the target user. For example, the correlation coefficient between the 5th harmonic current monitoring index and the user's electric power consumption is greater than 0.75, and the correlation coefficient between the 2nd harmonic current monitoring index and the user's electric power consumption is less than 0.75, then the 5th harmonic current monitoring index can be used as the identification result of the harmonic source of the target user.

[0091] The embodiment of the present application can divide the whole day into time periods according to the time points when the working modes of the electrical equipment change, and perform time series correlation analysis on the user power and harmonic current monitoring indicators based on the segmentation results, and identify the user's harmonic source based on the correlation coefficient of the correlation analysis. In this way, the actual changes of the user's harmonic load under different working modes can be accurately captured, and the omission and misjudgment of harmonic characteristics can be avoided, thereby reducing the probability of omission or misjudgment when identifying harmonic sources. The recognition accuracy of harmonic sources is improved.

[0092] Corresponding to the harmonic source identification method provided in the above embodiment, based on the same technical concept, the embodiment of the present application also provides a harmonic source identification device, Figure 2 A schematic diagram of the module composition of a harmonic source identification device provided in an embodiment of the present application, wherein the harmonic source identification device is used to perform Figure 1 The identification method of harmonic sources described in Figure 2 As shown, the harmonic source identification device 200 includes: an acquisition module 201, which is used to obtain the target user's power consumption at all times throughout the day and multiple harmonic current monitoring indicators of a common connection point, where the common connection point is the connection point where the target user's power equipment is connected to the power grid; a clustering module 202, which is used to cluster the target user's power consumption at all times throughout the day to obtain multiple clusters; a determination module 203, which is used to select the minimum value from the values ​​corresponding to the cluster centers of each cluster as the power lower limit value, and determine the operating condition switching time point according to the power lower limit value, the operating condition switching time point represents the time point when the working mode of the power equipment changes; a division module 204, which is used to determine the time period division point according to the operating condition switching time point, and divide the whole day into time periods based on the time period division point to obtain multiple time periods; the determination module 203 is also used to determine the correlation coefficient between the power consumption in each time period and the harmonic current monitoring indicator of the common connection point; an identification module 205, which is used to select the harmonic current monitoring indicator with a correlation coefficient greater than a first threshold as the identification result of the harmonic source of the target user.

[0093] The embodiment of the present application can divide the whole day into time periods according to the time points when the working modes of the electrical equipment change, and perform time series correlation analysis on the user power and harmonic current monitoring indicators based on the segmentation results, and identify the user's harmonic source based on the correlation coefficient of the correlation analysis. In this way, the actual changes of the user's harmonic load under different working modes can be accurately captured, and the omission and misjudgment of harmonic characteristics can be avoided, thereby reducing the probability of omission or misjudgment when identifying harmonic sources. The recognition accuracy of harmonic sources is improved.

[0094] Optionally, the determination module 203 is also used to determine a first average power consumption at multiple time points before the time point corresponding to the power lower limit value, and a second average power consumption at multiple time points after the time point corresponding to the power lower limit value; when the power difference between the first average power consumption and the time point corresponding to the power lower limit value is greater than a second threshold or the power difference between the second average power consumption and the time point corresponding to the power lower limit value is greater than the second threshold, the time point corresponding to the power lower limit value is determined to be the operating condition switching time point.

[0095] Optionally, the determination module 203 is also used to calculate the first-order difference of the target user's power consumption at all times throughout the day to obtain the first-order difference data of the power consumption at each time; calculate the mean and standard deviation of the first-order difference data; and determine the sum of the mean and standard deviation as a threshold.

[0096] Optionally, the determination module 203 is also used to determine the difference between operating condition switching time points that are adjacent in time sequence; when the difference is less than a tolerance threshold, the adjacent operating condition switching time points are merged to obtain a new operating condition switching time point, and the difference between the new operating condition switching time point and its adjacent operating condition switching time point is calculated, and the cycle is repeated until the difference between the new operating condition switching time point and its adjacent operating condition switching time point is greater than or equal to the tolerance threshold; when the difference is greater than or equal to the tolerance threshold, any time point between the adjacent operating condition switching time points is determined as a time period division point.

[0097] Optionally, the determination module 203 is also used to calculate the Euclidean distance between the electric power consumption and the harmonic current monitoring index at each moment in each time period; calculate the distance coefficient between the electric power consumption and the harmonic current monitoring index in each time period according to each Euclidean distance; normalize the distance coefficient of each time period according to the maximum and minimum values ​​of the distance coefficient of each time period to obtain an initial correlation coefficient; determine the correlation coefficient based on the initial correlation coefficient and the number of time points in each time period.

[0098] Optionally, determination module 902 is also used to determine the minimum Euclidean distance between the electric power consumption at the current moment in the current time period and the harmonic current monitoring index at each moment in the current time period; iteratively accumulate the minimum Euclidean distance corresponding to each moment in the current time period to obtain the overall cumulative distance of the current time period, and use the overall cumulative distance as the distance coefficient of the current time period.

[0099] Optionally, the determination module 902 is also used to calculate a first difference between the maximum value in the distance coefficient of the current time period and the distance coefficient of the current time period, and a second difference between the maximum value and the minimum value in the distance coefficient of the current time period; and determine the ratio of the first difference to the second difference as the initial correlation coefficient.

[0100] Optionally, the determination module 902 is further used to determine the number of target time periods in which the initial correlation coefficient corresponding to the current harmonic current monitoring index is greater than the third threshold, and the maximum number of target time periods in which the initial correlation coefficient corresponding to each harmonic current monitoring index is greater than the third threshold; calculate a first superposition value of the initial correlation coefficient of the target time period, and calculate a second superposition value of the number of time points in the target time period; determine the ratio of the number of time points in the current target time period to the second superposition value;

[0101] The product of the first superposition value and the ratio is determined as the correlation coefficient.

[0102] It should be noted that the harmonic source identification device provided in the embodiment of the present application and the harmonic source identification method provided in the embodiment of the present application are based on the same application concept. Therefore, the specific implementation of this embodiment can refer to the implementation of the aforementioned harmonic source identification method, and has the same or similar beneficial effects, and the repeated parts will not be repeated.

[0103] Corresponding to the harmonic source identification method provided in the above embodiment, based on the same technical concept, the embodiment of the present application further provides an electronic device, which is used to perform the above harmonic source identification method. Figure 3 A schematic diagram of the structure of an electronic device for implementing each embodiment of the present application is shown in FIG. Figure 3 As shown. The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors 301 and memory 302, and the memory 302 may store one or more storage applications or data. Among them, the memory 302 may be a temporary storage or a permanent storage. The application stored in the memory 302 may include one or more modules (not shown in the figure), and each module may include a series of computer executable instructions in the electronic device.

[0104] Furthermore, the processor 301 can be configured to communicate with the memory 302 to execute a series of computer executable instructions in the memory 302 on the electronic device. The electronic device can also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input and output interfaces 305, and one or more keyboards 306.

[0105] Specifically in this embodiment, the electronic device includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to achieve the above Figure 1 The various steps in the method embodiment are similar to those in the method embodiment, and have the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present application will not be described again here.

[0106] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the above Figure 1 Each step in the method embodiment has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present application will not be described again here.

[0107] The present application provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the above Figure 1 Each step in the method embodiment has the beneficial effects of the above method embodiments. To avoid repetition, the embodiments of the present application will not be described again here.

[0108] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0109] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] In a typical configuration, an electronic device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0111] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0112] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0114] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for identifying a harmonic source, characterized in that: The method for identifying the harmonic source comprises: Obtaining the target user's power consumption at all times throughout the day and multiple harmonic current monitoring indicators of a public connection point, where the public connection point is the connection point where the target user's power equipment is connected to the power grid; Clustering the power consumption of the target user at all times throughout the day to obtain multiple clusters; Selecting a minimum value from the values ​​corresponding to the cluster centers of the clusters as the lower power limit, and determining a working condition switching time point according to the lower power limit, wherein the working condition switching time point represents a time point at which the working mode of the electrical device changes; Determine a time period division point according to the working condition switching time point, and divide the whole day into time periods based on the time period division point to obtain multiple time periods; Determining the correlation coefficient between the power consumption in each of the time periods and the harmonic current monitoring index of the common connection point; The harmonic current monitoring index having the correlation coefficient greater than the first threshold is selected as the identification result of the harmonic source of the target user.

2. The method for identifying harmonic sources according to claim 1, characterized in that: Determining the operating mode switching time point according to the power lower limit value includes: Determine a first average power consumption at a plurality of time points before the time point corresponding to the lower power limit value, and a second average power consumption at a plurality of time points after the time point corresponding to the lower power limit value; When the power difference between the first average power consumption and the time point corresponding to the power lower limit is greater than a second threshold or the power difference between the second average power consumption and the time point corresponding to the power lower limit is greater than a second threshold, the time point corresponding to the power lower limit is determined as the operating condition switching time point.

3. The method for identifying harmonic sources according to claim 2, characterized in that: When the power difference between the first average power consumption and the time point corresponding to the power lower limit is greater than a second threshold or the power difference between the second average power consumption and the time point corresponding to the power lower limit is greater than a second threshold, determining that the time point corresponding to the power lower limit is before the operating mode switching time point, the method further includes: Calculating the first-order difference of the power consumption of the target user at each time of the day to obtain the first-order difference data of the power consumption at each time; Calculate the mean and standard deviation of the first-order difference data; The sum of the mean and the standard deviation is determined as the threshold.

4. The method for identifying harmonic sources according to claim 1, characterized in that: Determining the time period division point according to the working condition switching time point includes: Determine the difference between the operating mode switching time points that are adjacent in time sequence; In the case where the difference is less than the tolerance threshold, the adjacent operating condition switching time points are merged to obtain a new operating condition switching time point, and the difference between the new operating condition switching time point and its adjacent operating condition switching time point is calculated, and the process is repeated until the difference between the new operating condition switching time point and its adjacent operating condition switching time point is greater than or equal to the tolerance threshold; When the difference is greater than or equal to the tolerance threshold, any time point between the adjacent operating mode switching time points is determined as the time period division point.

5. The method for identifying harmonic sources according to any one of claims 1 to 4, characterized in that: The determination of the correlation coefficient between the power consumption in each time period and the harmonic current monitoring index of the common connection point includes: Calculating the Euclidean distance between the power consumption at each moment in each time period and the harmonic current monitoring index; Calculate the distance coefficient between the power consumption and the harmonic current monitoring index in each time period according to each of the Euclidean distances; Normalizing the distance coefficients of each time period according to the maximum value and the minimum value of the distance coefficients of each time period to obtain an initial correlation coefficient; The correlation coefficient is determined based on the initial correlation coefficient and the number of time points within each of the time periods.

6. The method for identifying harmonic sources according to claim 5, characterized in that: The distance coefficient between the power consumption and the harmonic current monitoring index in each time period calculated according to each Euclidean distance includes: Determine the minimum Euclidean distance between the current moment power consumption in the current time period and the harmonic current monitoring index at each moment in the current time period; The minimum Euclidean distances corresponding to the moments in the current time period are iteratively accumulated to obtain an overall accumulated distance of the current time period, and the overall accumulated distance is used as a distance coefficient of the current time period.

7. The method for identifying harmonic sources according to claim 5, characterized in that: The normalizing of the distance coefficients of each time period according to the maximum value and the minimum value of the distance coefficients of each time period to obtain the initial correlation coefficient includes: Calculate a first difference between a maximum value of the distance coefficients of the current time period and the distance coefficient of the current time period, and a second difference between the maximum value of the distance coefficients of the current time period and the minimum value; A ratio of the first difference to the second difference is determined as the initial correlation coefficient.

8. The method for identifying harmonic sources according to claim 1, characterized in that: The determining the correlation coefficient based on the initial correlation coefficient and the number of time points in each of the time periods comprises: Determine the number of target time periods in which the initial correlation coefficient corresponding to the current harmonic current monitoring index is greater than the third threshold, and the maximum number of target time periods in which the initial correlation coefficient corresponding to each of the harmonic current monitoring indicators is greater than the third threshold; Calculating a first superposition value of an initial correlation coefficient of the target period, and calculating a second superposition value of a number of time points of the target period; Determine a ratio of the number of time points in the current target period to the second superposition value; The product of the first superposition value and the ratio is determined as the correlation coefficient.

9. A harmonic source identification device, characterized in that: include: An acquisition module, used to acquire the target user's power consumption at all times throughout the day and multiple harmonic current monitoring indicators of a public connection point, where the public connection point is a connection point where the target user's power equipment is connected to the power grid; A clustering module, used to cluster the power consumption of the target user at various times throughout the day to obtain multiple clusters; A determination module, configured to select a minimum value from the values ​​corresponding to the cluster centers of the clusters as a lower power limit, and determine a working condition switching time point according to the lower power limit, wherein the working condition switching time point represents a time point at which the working mode of the electrical device changes; A division module, used to determine a time period division point according to the working condition switching time point, and divide the whole day into time periods based on the time period division point to obtain multiple time periods; The determination module is further used to determine the correlation coefficient between the power consumption in each of the time periods and the harmonic current monitoring index of the common connection point; The identification module is used to select the harmonic current monitoring index whose correlation coefficient is greater than the first threshold as the identification result of the harmonic source of the target user.

10. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus; wherein the processor, the communication interface and the memory communicate with each other through the communication bus; the memory is used to store computer programs; the processor is used to execute the programs stored in the memory to implement the steps of the harmonic source identification method as described in any one of claims 1 to 8.

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