Method for obtaining the influence of distribution transformer load on total load characteristics of distribution network

Abstract

The present invention discloses a method for obtaining the influence of the load of a distribution transformer on the total load characteristics of a distribution network, including: selecting an effective active load curve from the historical operating load curve of the distribution transformer, and obtaining the distribution line load curve of the distribution transformer on the corresponding line in the distribution network topology; extracting the characteristics and typical daily load curve of the distribution transformer from the effective active load curve; clustering the characteristics and typical daily load curves to obtain each type of feature label and each type of typical daily load curve; calculating the weight of each type of feature label, the proportion of each type of typical daily load curve and the correlation coefficient; calculating the influence of each type of typical daily load curve on the distribution line load curve according to the weight, the proportion of each type of load curve and the correlation coefficient. The purpose of the present invention is to provide a method for obtaining the influence of the load of a distribution transformer on the total load characteristics of a distribution network, and to obtain the influence of the load of a distribution transformer on the load characteristics of a distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power distribution, and in particular to a method for obtaining the influence of distribution transformer load on the total load characteristics of a distribution network. Background Art

[0002] With the complexity of the power grid structure and the increase in load demand, problems such as line overload, voltage overrun, and increased line loss are occurring more and more frequently. At the same time, as the terminal of the power supply system and the first handle for users, the distribution network plays a vital role in ensuring the safe power supply and economic operation of the distribution network. How to more comprehensively evaluate the impact of the load characteristics of the distribution network based on operating data, and even provide guidance for solving operating problems such as overload and voltage overrun, has always been a concern of the industry and academia, and is also of great significance to the safe and stable operation of the distribution network.

[0003] At present, due to the in-depth and extensive research conducted by scholars at home and abroad, there are some mature theoretical frameworks for the application of clustering methods in distribution data mining. Compared with other applications of clustering algorithms in distribution network data mining, existing methods often ignore the impact of distribution transformer load on the load characteristics of distribution network lines. Summary of the invention

[0004] The object of the present invention is to provide a method for obtaining the degree of influence of the distribution transformer load on the total load characteristics of the distribution network, so as to obtain the degree of influence of the distribution transformer load on the load characteristics of the distribution network.

[0005] The present invention is achieved through the following technical solutions:

[0006] The method for obtaining the influence degree of the distribution transformer load on the total load characteristics of the distribution network comprises the following steps:

[0007] S1: Selecting an effective active load curve from the historical operating load curve of the distribution transformer, and obtaining a distribution network line load curve of the distribution transformer on a corresponding line in the distribution network topology structure;

[0008] S2: extracting the characteristics of the distribution transformer and the typical daily load curve of the distribution transformer based on the effective active load curve;

[0009] S3: clustering the features and the typical daily load curves respectively to obtain feature labels of each type and typical daily load curves of each type;

[0010] S4: Calculate the weight of each type of feature label, the proportion of each type of typical daily load curve, and the correlation coefficient, where the correlation coefficient is the correlation coefficient between each type of typical daily load curve and the distribution network line load curve;

[0011] S5: Calculate the influence of each type of typical daily load curve on the distribution network line load curve according to the weight, the proportion of each type of load curve and the correlation coefficient.

[0012] Preferably, the step of selecting an effective active load curve from a historical operating load curve of a distribution transformer comprises the following steps:

[0013] Selecting an active load curve from the historical operating load curves of the distribution transformer;

[0014] Determining whether the amount of missing load data and / or the amount of abnormal load data of the active load curve exceeds 10%;

[0015] When the amount of missing load data and / or the amount of abnormal load data of the active load curve does not exceed 10%, determining whether the load data of the active load curve is abnormal;

[0016] When the load data of the active load curve is abnormal, correcting the abnormal load data of the active load curve;

[0017] The active load curve having no abnormality in the load data and the corrected active load curve is selected as the effective active load curve.

[0018] Preferably, the step of determining whether the data of the active load curve is abnormal comprises the following sub-steps:

[0019] Obtain the load change rate of each load data in the active load curve:

[0020]

[0021] Among them, δ i,t represents the load change rate of the active load curve at the tth load data, x i,t represents the tth load data of the active load curve of the ith item, x i,t+1 represents the t+1th load data of the active load curve of the ith item;

[0022] When the load change rate is greater than a threshold load change rate, it is determined that the load data on the active load curve is abnormal.

[0023] Preferably, the abnormal load data of the active load curve is corrected by the following formula:

[0024]

[0025] Among them, X i,t represents the corrected load data, x i,t-prepresents the tpth load data of the active load curve of the ith item, x i,t+q It represents the t+pth load data of the i-th active load curve.

[0026] Preferably, the S1 further comprises normalizing the load data in the effective active load curve and the load data in the distribution network line load curve:

[0027] P t * =P t / P max ;

[0028] Among them, P t * represents the normalized load data, P t is the load data at time t, P max The maximum load data.

[0029] Preferably, the characteristics include monthly load peak, monthly load peak-to-valley difference, monthly load average, daily load peak, daily load peak-to-valley difference, daily load average, weekday load peak, weekday load peak-to-valley difference, weekday load average, weekend load peak, weekend load peak-to-valley difference and weekend load average.

[0030] Preferably, the typical daily load curve is a daily load curve corresponding to a daily load rate that is closest to the monthly average daily load rate.

[0031] Preferably, the step of calculating the weight of each type of feature label comprises the following steps:

[0032] Establish a feature label matrix based on the categories of feature labels obtained by clustering:

[0033]

[0034] Among them, A represents the feature label matrix, x represents the feature type, y represents the cluster type, and A xy Indicates the cluster center value of the yth class in feature x.

[0035] Normalize the feature label matrix:

[0036]

[0037] Among them, B represents the normalized value, and M represents the number of cluster types.

[0038] Find the weight for each category:

[0039]

[0040] Among them, α represents the weight and N represents the number of feature types.

[0041] Preferably, the correlation coefficient is obtained by the following formula:

[0042]

[0043] Among them, Φ represents the correlation coefficient, P bt represents the load data at the tth moment in the effective active load curve, P lt represents the load data at the tth moment in the distribution network line load curve, represents the average value of the load data in the effective active load curve, Represents the average value of the load data of the distribution network line load curve.

[0044] Preferably, the influence degree is obtained by the following formula:

[0045] ε=β·(α×l×|Φ|);

[0046]

[0047] Among them, ε represents the influence, β represents the amplification factor, α represents the weight, l represents the proportion, and Φ represents the correlation coefficient.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] The influence of the active load curve of the distribution transformer and the horizontal and vertical similarity of the distribution network line load is comprehensively considered. Through FCM cluster analysis and curve similarity analysis, the intrinsic relationship between the distribution network feeder and the distribution transformer load change is obtained, providing new guidance for solving operational problems such as overload and voltage overlimit. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0051] Figure 1 It is a schematic diagram of the process of the present invention;

[0052] Figure 2 This is a schematic diagram of clustering results in Example 2 of the present invention;

[0053] Figure 3 It is a load diagram of 8 types of typical daily load curves in Example 2 of the present invention;

[0054] Figure 4 It is a schematic diagram of typical load curves corresponding to 7 distribution network lines in Example 2 of the present invention;

[0055] Figure 5 It is a schematic diagram of the conditions of each line drawn according to the matrix ε in embodiment 2 of the present invention. DETAILED DESCRIPTION

[0056] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0057] Example 1

[0058] This embodiment provides a method for obtaining the degree of influence of the distribution transformer load on the total load characteristics of the distribution network, such as Figure 1 As shown, the following steps are included:

[0059] S1: Selecting an effective active load curve from the historical operating load curve of the distribution transformer, and obtaining a distribution network line load curve of the distribution transformer on the corresponding line in the distribution network topology structure;

[0060] Since the data acquisition system and various external influences often lead to missing load data and the existence of bad data in the active load curve, in order to obtain accurate impact results, invalid load data must be eliminated and repaired.

[0061] Specifically, selecting an effective active load curve from the historical operating load curve of the distribution transformer includes the following steps:

[0062] Selecting an active load curve from the historical operating load curve of the distribution transformer;

[0063] Determine whether the amount of missing load data and / or the amount of abnormal load data of the active load curve exceeds 10%;

[0064] When the amount of missing load data and / or the amount of abnormal load data of a certain active load curve exceeds 10%, the active load curve is considered invalid and discarded; when the amount of missing load data and / or the amount of abnormal load data of a certain active load curve does not exceed 10%, it is determined whether the load data of the active load curve is abnormal. If the load data of the active load curve is not abnormal, it is retained as an effective active load curve; if the load data of the active load curve is abnormal, the abnormal load data in the active load curve is corrected. After the correction is completed, the active load curve is retained as an effective active load curve.

[0065] Wherein, judging whether the data of the active load curve is abnormal includes the following sub-steps:

[0066] Get the load change rate of each load data in the active load curve:

[0067]

[0068] Among them, δ i,t represents the load change rate of the ith active load curve at the tth load data, x i,t represents the tth load data of the ith active load curve, x i,t+1 Represents the t+1th load data of the i-th active load curve;

[0069] Determine the size of the load change rate and the threshold load change rate. When the load change rate is less than the threshold load change rate, it is determined that all the load data on the active load curve are normal; otherwise, it is determined that the load data on the active load curve is abnormal. The threshold load change rate is usually 0.5 to 0.8. Such abnormal load data can be corrected using the smoothing correction formula:

[0070]

[0071] Among them, X i,t represents the corrected load data, x i,t-p represents the tpth load data of the active load curve of the ith item, x i,t+q It represents the t+pth load data of the i-th active load curve.

[0072] Among them, it is worth mentioning that the distribution transformers are configured in each line of the power grid topology structure. Since the lines of the power grid topology structure are complex, in order to obtain more accurate results, this embodiment, during its specific implementation, analyzes each distribution line in turn. Therefore, the distribution transformers mentioned in this embodiment are all from the same distribution line.

[0073] Furthermore, considering that the active load curve patterns of users with different orders of magnitude may be the same, in this step, the load data in the effective active load curve and the load data in the distribution network line load curve are normalized to reflect the characteristics of the load curve:

[0074] P t * =P t / P max ;

[0075] Among them, P t * represents the normalized load data, P t is the load data at time t, P maxis the maximum load data. S2: Extract the characteristics of the distribution transformer and the typical daily load curve of the distribution transformer from the effective active load curve;

[0076] The characteristics include: monthly load peak, monthly load peak-to-valley difference, monthly load average, daily load peak, daily load peak-to-valley difference, daily load average, weekday load peak, weekday load peak-to-valley difference, weekday load average, weekend load peak, weekend load peak-to-valley difference and weekend load average.

[0077] The above characteristics can be divided into short-term power consumption characteristic indicators and long-term power consumption characteristic indicators, collectively referred to as variable time scale power consumption performance indicators. Among them, short-term power consumption characteristic indicators are mostly used to describe the daily load characteristics of distribution transformer loads, and long-term power consumption characteristic indicators are used to describe the monthly load characteristics of users. The advantage of using power consumption characteristics with variable time scales is that these characteristics can be described from different granularities.

[0078] The typical daily load curve of the distribution transformer can reflect the peak and valley changes of the user load, and is an important basis for orderly power consumption management and peak load regulation on the demand side. In this embodiment, the daily load curve corresponding to the daily load rate closest to the monthly average daily load rate is selected as the typical daily load curve of the month. The specific method is as follows:

[0079] Assume the load curve is P, and the load data at time t is P t , t=(1,2,…,T), the maximum load data of the whole day is P max , then the daily load rate DR is:

[0080]

[0081] Assuming that there are M days in each month, the monthly average daily load factor MR for that month is:

[0082]

[0083] The daily load curve corresponding to the daily load rate DR that is closest to the monthly average daily load rate MR is selected as the typical daily load.

[0084] S3: clustering the characteristic and typical daily load curves respectively to obtain each type of characteristic label and each type of typical daily load curve;

[0085] This embodiment selects the FCM clustering algorithm to cluster the transformers according to the characteristics of the distribution transformers and the typical daily load curves of the distribution transformers, so as to label the distribution transformers with different characteristics with different labels to facilitate subsequent analysis.

[0086] S4: Calculate the weight of each type of feature label, the proportion of each type of typical daily load curve, and the correlation coefficient, where the correlation coefficient is the correlation coefficient between each type of typical daily load curve and the distribution network line load curve;

[0087] Specifically:

[0088] Calculating the weight of each feature label includes the following steps:

[0089] Establish a feature label matrix based on the categories of feature labels obtained by clustering:

[0090]

[0091] Among them, A represents the feature label matrix, x represents the feature type, y represents the cluster type, and A xy Indicates the cluster center value of the yth class in feature x.

[0092] Normalize the feature label matrix:

[0093]

[0094] Among them, B represents the normalized value, and M represents the number of cluster types.

[0095] Find the weight for each category:

[0096]

[0097] Among them, α represents the weight and N represents the number of feature types.

[0098] The correlation coefficient between each type of typical daily load curve and the distribution network line load curve is obtained by the following formula:

[0099]

[0100] Among them, Φ represents the correlation coefficient, P bt Represents the load data at the tth moment in the effective active load curve, P lt Represents the load data at the tth moment in the distribution network line load curve, Indicates the average value of the load data in the effective active load curve. It represents the average value of the load data of the distribution network line load curve.

[0101] S5: Calculate the influence of each type of typical daily load curve on the distribution network line load curve based on the weight, proportion of each type of load curve and correlation coefficient:

[0102] ε=β·(α×l×|Φ|);

[0103]

[0104] Wherein, ε represents the influence, β represents the amplification factor, which is 100 in this embodiment, α represents the weight, l represents the proportion, m and n represent the type of clustering and the number of lines respectively, l mn It represents the proportion of type m load in line n, and Φ represents the correlation coefficient.

[0105] In this embodiment, the similarity between the active load curve of the distribution transformer and the load of the distribution network line in the horizontal and vertical directions is comprehensively considered. Through FCM cluster analysis and curve similarity analysis, the intrinsic relationship between the load changes of the distribution network feeder and the distribution transformer is obtained, providing a new guiding direction for solving operational problems such as overload and voltage overrun.

[0106] Example 2

[0107] This embodiment uses a specific example to illustrate the solution in Embodiment 1.

[0108] According to the load characteristics of 162 distribution transformers in a certain area of ​​Sichuan, 12 features are used to comprehensively characterize the power consumption characteristics. The establishment of a label system is an important step in realizing the clustering of distribution transformers. A 12-dimensional power consumption characteristic number is constructed for each distribution transformer (the distribution transformer load data has been normalized) to achieve a comprehensive and detailed characterization of the power consumption characteristics of each distribution transformer. Based on these dimensions that describe power consumption characteristics, the FCM clustering algorithm is used to cluster all users into 5 categories. The 12 labels in spring are shown in Table 1 below:

[0109] Table 1

[0110]

[0111] like Figure 2 The clustering finally resulted in five groups, and the numbers of distribution transformers in these five groups were 22, 36, 13, 41 and 50, respectively, as shown in Table 2.

[0112] Table 2

[0113]

[0114] After clustering the 12 features using the FCM algorithm, the labels of each class are shown in Table 3. Table 3 only gives the cluster center of each class for the indicators of the three-month data in spring (the data has been normalized).

[0115] Table 3 Power consumption characteristic labels of cluster centers

[0116]

[0117] The loads under these five categories are then clustered through daily load curves to obtain eight types of typical daily load curve loads, such as Figure 3 Table 4 shows the corresponding relationship between the 8 types of load curve clustering and the 5 types of characteristic index clustering.

[0118] Table 4 Correspondence between characteristic index clustering and load curve clustering

[0119] Number of characteristic index clusters 1 2 3 4 5 Load curve cluster number 1 2,3 4 5,6,7 8

[0120] Extract the typical load curves corresponding to the 7 distribution network lines as follows Figure 4 shown.

[0121] The Pearson correlation coefficient matrix Φ of the load curve and the line load curve is:

[0122]

[0123] The proportion matrix l of each type of load curve in the line load curve:

[0124]

[0125] The feature label matrix A and its load weight for each category are calculated as:

[0126]

[0127] The feature label matrix A and its load weight for each category are calculated as:

[0128]

[0129] α=[0.189 0.356 0.297 0.117 0.041]

[0130] The influence matrix ε is obtained

[0131]

[0132] According to the matrix ε, the lines are plotted as follows Figure 5 As shown in the figure. Among them, the second type of load in the characteristic clustering index of B and G lines has the greatest contribution to the line, and is also the root cause of the overload and heavy load of B and G lines. Therefore, the proportion of the second type of load should be reduced in future planning to improve the safety of distribution network operation. Most of the loads in S and P lines contribute less to the line, and the proportion of line loads can be appropriately increased during planning.

[0133] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for obtaining the degree of influence of distribution transformer load on total load characteristics of distribution network, characterized in that: The following steps are involved: S1: Selecting an effective active load curve from the historical operating load curve of the distribution transformer, and obtaining a distribution network line load curve of the distribution transformer on a corresponding line in the distribution network topology structure; S2: extracting the characteristics of the distribution transformer and the typical daily load curve of the distribution transformer from the effective active load curve; S3: clustering the features and the typical daily load curves respectively to obtain feature labels of each type and typical daily load curves of each type; S4: Calculate the weight of each type of feature label, the proportion of each type of typical daily load curve, and the correlation coefficient, where the correlation coefficient is the correlation coefficient between each type of typical daily load curve and the distribution network line load curve; The step of calculating the weight of each type of feature label comprises the following steps: Establish a feature label matrix based on the categories of feature labels obtained by clustering: Among them, A represents the feature label matrix, x represents the feature type, y represents the cluster type, and A xy Indicates the cluster center value of the yth class in feature x; Normalize the feature label matrix: Among them, B represents the normalized value, and M represents the number of cluster types; Find the weight for each category: Among them, α represents the weight, and N represents the number of feature types; The correlation coefficient is obtained by the following formula: Among them, Φ represents the correlation coefficient, P bt represents the load data at the tth moment in the effective active load curve, P lt represents the load data at the tth moment in the distribution network line load curve, represents the average value of the load data in the effective active load curve, Represents the average value of the load data of the distribution network line load curve; S5: Calculate the influence of each type of typical daily load curve on the distribution network line load curve according to the weight, the proportion of each type of typical daily load curve and the correlation coefficient; The influence is obtained by the following formula: ε=β.(α×l×|Φ|); Among them, ε represents the influence, β represents the amplification factor, α represents the weight, l represents the proportion, m and n represent the type of clustering and the number of lines respectively, l mn It represents the proportion of type m load in line n, and Φ represents the correlation coefficient.

2. The method for obtaining the degree of influence of the distribution transformer load on the total load characteristics of the distribution network according to claim 1, characterized in that: The step of selecting an effective active load curve from the historical operating load curve of the distribution transformer comprises the following steps: Selecting an active load curve from the historical operating load curves of the distribution transformer; Determining whether the amount of missing load data and / or the amount of abnormal load data of the active load curve exceeds 10%; When the amount of missing load data and / or the amount of abnormal load data of the active load curve does not exceed 10%, determining whether the load data of the active load curve is abnormal; When the load data of the active load curve is abnormal, correcting the abnormal load data of the active load curve; The active load curve having no abnormality in the load data and the corrected active load curve is selected as the effective active load curve.

3. The method for obtaining the influence of the distribution transformer load on the total load characteristics of the distribution network according to claim 2, characterized in that: The step of determining whether the data of the active load curve is abnormal comprises the following sub-steps: Obtain the load change rate of each load data in the active load curve: Among them, δ i,t represents the load change rate of the active load curve at the tth load data, x i,t represents the tth load data of the active load curve of the ith item, x i,t+1 represents the t+1th load data of the active load curve of the ith item; When the load change rate is greater than a threshold load change rate, it is determined that the load data on the active load curve is abnormal.

4. The method for obtaining the influence degree of distribution transformer load on the total load characteristics of distribution network according to claim 2, characterized in that: The abnormal load data of the active load curve is corrected by the following formula: Among them, X i,t represents the corrected load data, x i,t-p represents the tpth load data of the active load curve of the ith item, x i,t+q It represents the t+qth load data of the i-th active load curve.

5. The method for obtaining the influence degree of distribution transformer load on the total load characteristics of distribution network according to any one of claims 1 to 4, characterized in that: The S1 also includes normalizing the load data in the effective active load curve and the load data in the distribution network line load curve: Among them, P t * represents the normalized load data, P t is the load data at time t, P max The maximum load data.

6. The method for obtaining the influence of the distribution transformer load on the total load characteristics of the distribution network according to claim 1, characterized in that: The characteristics include monthly load peak, monthly load peak-to-valley difference, monthly load average, daily load peak, daily load peak-to-valley difference, daily load average, weekday load peak, weekday load peak-to-valley difference, weekday load average, weekend load peak, weekend load peak-to-valley difference and weekend load average.

7. The method for obtaining the influence degree of distribution transformer load on the total load characteristics of distribution network according to claim 1, characterized in that: The typical daily load curve is a daily load curve corresponding to a daily load rate that is closest to the monthly average daily load rate.

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