A real-time evaluation method and system for the health status of a distribution transformer

Abstract

The present invention provides a method and system for real-time evaluation of the health status of a distribution transformer. The method of the present invention includes uniformly describing the active power function and reactive power function of the distribution transformer through a linear function model; fitting to obtain the slope and intercept of the linear function model; obtaining the historical data of active power, reactive power, and load rate for the first 30 days and the most recent 30 days since the day after the distribution transformer to be evaluated is put into operation; calculating the power matrix; calculating the composite deviation degree and taking the consistency of the composite deviation degree sequence labels as the first judgment index; calculating the maximum change rate of the composite deviation degree as the second judgment index; and performing a health status evaluation on the distribution transformer to be evaluated based on the first and second judgment indexes. By using the real-time power of the distribution transformer to fit the real-time values of the evaluation parameters and constructing two-level evaluation indexes, the present invention realizes the quantitative evaluation of the health status of the transformer, and solves the problem that some evaluation indexes in the traditional evaluation method cannot be quantitatively evaluated.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical equipment status evaluation, and particularly relates to a method and system for real-time evaluation of the health status of distribution transformers. Background Art

[0002] Distribution transformers are the core equipment connecting the distribution network and users. For a long time, the maintenance methods of regular maintenance and after-failure maintenance have been adopted. However, due to the large workload of regular maintenance, maintenance personnel are often exhausted. Coupled with the influence of on-site conditions and personnel quality, the phenomenon of "getting worse after repair" also occurs from time to time. After-failure maintenance is a passive maintenance method. Although it saves unnecessary workload, it sacrifices power supply reliability. With the development of science and technology and the accumulation of operation experience, condition-based maintenance, as a new maintenance method, has gradually been applied to the maintenance work of the power system, minimizing the disadvantages of traditional maintenance methods to the greatest extent. The basis of condition-based maintenance is to accurately evaluate the health status of equipment, so as to be able to implement precise measures and achieve "repair when it should be repaired".

[0003] Currently, the methods for evaluating the health status of transformers are all evaluated by selecting transformer status quantity data and establishing an evaluation index system. However, most of the indicators selected by these methods cannot quantitatively evaluate the impact on the health status of distribution transformers, resulting in subjectivity and uncertainty inevitably existing when using any analysis method, and unable to essentially characterize the health status of distribution transformers under various operating conditions. Summary of the Invention

[0004] In view of this, the present invention aims to solve the problem that most of the indicators selected by the existing transformer health status evaluation methods cannot quantitatively evaluate the impact on the health status of distribution transformers and cannot essentially characterize the health status of distribution transformers under various operating conditions.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] In the first aspect, a method for real-time evaluation of the health status of a distribution transformer includes the following steps:

[0007] Unify the description of the active power function and reactive power function of the distribution transformer through a linear function model;

[0008] Fit to obtain the slope and intercept of the linear function model;

[0009] Obtain the historical data of active power, reactive power, and load rate for the first 30 days and the most recent 30 days since the distribution transformer to be evaluated was put into operation;

[0010] Based on the historical data of active power, reactive power, and load factor, use a linear function model to calculate the power matrices for the first 30 days and the most recent 30 days respectively;

[0011] Calculate the composite deviation degree based on the power matrix and use the consistency of the composite deviation degree sequence numbers as the first judgment index;

[0012] Calculate the maximum change rate of the composite deviation degree based on the composite deviation degree as the second judgment index;

[0013] Conduct a health status evaluation of the distribution transformer to be evaluated based on the first judgment index and the second judgment index.

[0014] Furthermore, the expression of the power matrix for the first 30 days is specifically as follows:

[0015]

[0016] In the formula, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the first 30 days.

[0017] Furthermore, the expression of the power matrix for the most recent 30 days is specifically as follows:

[0018]

[0019] In the formula, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the most recent 30 days.

[0020] Furthermore, calculating the composite deviation degree based on the power matrix and using the consistency of the composite deviation degree sequence numbers as the first judgment index specifically includes:

[0021] Calculate the composite deviation degree V1 of no-load active power loss for the first 30 days according to the power matrices of the first 30 days and the most recent 30 days respectively 1 , the composite deviation degree of rated load loss the composite deviation degree of no-load reactive power loss the composite deviation degree of rated load leakage magnetic power and the composite deviation degree V1 of no-load active power loss for the most recent 30 days 2 , the composite deviation degree of rated load loss the composite deviation degree V3 of no-load reactive power loss 2 , the composite deviation degree of rated load leakage magnetic power

[0022] For V1 1 , Sort from large to small to form a standard sequence of composite deviation degrees (V i 1 ), for V1 2 , V3 2 , Sort from large to small to form a standard sequence of composite deviation degrees Define the digital sorting of i and j as the consistency of composite deviation degree labels, and use whether they are consistent as the first judgment index.

[0023] Furthermore, the calculation formula of the maximum change rate of composite deviation degree is specifically as follows:

[0024]

[0025] In the formula, λ is the maximum change rate of composite deviation degree.

[0026] In the second aspect, the present invention provides a real-time evaluation system for the health status of a distribution transformer, including:

[0027] A fitting module, used to uniformly describe the active power function and reactive power function of a distribution transformer through a linear function model; also used to fit and obtain the slope and intercept of the linear function model;

[0028] A data acquisition module, used to acquire historical data of active power, reactive power, and load rate of the distribution transformer to be evaluated from the first day after it is put into operation for the first 30 days and the most recent 30 days;

[0029] A power matrix calculation module, used to calculate the power matrices of the first 30 days and the most recent 30 days respectively based on the historical data of active power, reactive power, and load rate, using the linear function model;

[0030] An index calculation module, configured to calculate a composite deviation degree according to a power matrix and use the consistency of the composite deviation degree sequence label as a first judgment index; and is further configured to calculate a maximum change rate of the composite deviation degree according to the composite deviation degree as a second judgment index.

[0031] A state evaluation module, configured to perform a health state evaluation on a distribution transformer to be evaluated based on the first judgment index and the second judgment index.

[0032] Furthermore, the expression of the power matrix for the first 30 days is specifically as follows:

[0033]

[0034] In the formula, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the first 30 days.

[0035] Furthermore, the expression of the power matrix for the most recent 30 days is specifically as follows:

[0036]

[0037] In the formula, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the most recent 30 days.

[0038] Furthermore, the index calculation module calculates the first judgment index specifically including:

[0039] Respectively obtain the no-load active power loss composite deviation degree V1 of the first 30 days according to the power matrices of the first 30 days and the most recent 30 days 1 , the rated load loss composite deviation degree the no-load reactive power loss composite deviation degree the rated load leakage magnetic power composite deviation degree and the composite deviation degree V1 of no-load active power loss in the most recent 30 days 2 , the composite deviation degree of rated load loss The composite deviation degree V3 of no-load reactive power loss 2 , the composite deviation degree of rated load leakage magnetic power

[0040] For V1 1 , , sort from large to small to form a composite deviation degree standard sequence (V i 1 ), for V1 2 , V3 2 , , sort from large to small to form a composite deviation degree standard sequence Define the digital sorting of i and j as the consistency of the composite deviation degree label, and use whether it is consistent as the first judgment index.

[0041] Furthermore, the index calculation module calculates the maximum change rate of the composite deviation degree specifically according to the following calculation formula:

[0042]

[0043] In the formula, λ is the maximum change rate of the composite deviation degree.

[0044] In summary, the present invention provides a method and system for real-time evaluation of the health status of a distribution transformer. The method of the present invention includes uniformly describing the active power function and reactive power function of the distribution transformer through a linear function model; fitting to obtain the slope and intercept of the linear function model; obtaining the historical data of active power, reactive power and load rate of the distribution transformer to be evaluated from the first day after commissioning to the first 30 days and the most recent 30 days; calculating the power matrix; calculating the composite deviation degree and using the consistency of the composite deviation degree sequence label as the first judgment index; calculating the maximum change rate of the composite deviation degree as the second judgment index; and evaluating the health status of the distribution transformer to be evaluated based on the first and second judgment indexes. The present invention realizes the quantitative evaluation of the health status of the transformer by using the real-time power fitting evaluation parameter real value of the distribution transformer and constructing a two-level evaluation index, and solves the problem that some evaluation indexes in the traditional evaluation method cannot be quantitatively evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic flowchart of a method for real-time evaluation of the health status of a distribution transformer provided by an embodiment of the present invention;

[0047] Figure 2 It is a simplified flowchart of a method for real-time evaluation of the health status of a distribution transformer provided by an embodiment of the present invention. Specific embodiments

[0048] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0049] The distribution transformer is the core equipment connecting the distribution network and users. For a long time, the operation and maintenance methods of regular maintenance and post-fault maintenance have been adopted. Due to the large size of the distribution transformer, regular maintenance has great blindness, resulting in a waste of a lot of unnecessary manpower and costs. Due to the large workload of regular maintenance, maintenance personnel are often exhausted. Coupled with the influence of on-site conditions and personnel quality, the phenomenon of "getting worse after repair" also occurs from time to time. Post-fault maintenance is a passive operation and maintenance method. Although it saves unnecessary workload, it is at the cost of sacrificing power supply reliability. With the development of science and technology and the accumulation of operation experience, a relatively complete set of equipment condition monitoring means and analysis and judgment methods have been formed at present. There is sufficient technical guarantee for carrying out condition-based maintenance. As a new maintenance method, condition-based maintenance is gradually applied to the maintenance work of the power system, minimizing the disadvantages of traditional maintenance methods to the greatest extent. The basis of condition-based maintenance is to accurately evaluate the health status of equipment, so as to be able to make precise policies and achieve "repair when it should be repaired". Real-time evaluation of the health status of distribution transformers can timely detect some potential hidden dangers during operation and nip minor problems in the bud, thus effectively reducing power outages caused by distribution transformer failures and effectively improving power supply reliability.

[0050] At present, there are many methods for evaluating the health status of transformers, and the achievements are also relatively rich. For distribution transformers, big data analysis methods such as the fuzzy comprehensive evaluation method, cloud model function method, evidence theory method, fuzzy membership function method, analytic hierarchy process, entropy weight method, and principal component analysis method are used. However, in engineering practice, good results cannot be obtained. The reason is that the existing methods for evaluating the health status of distribution transformers have a common feature, that is, there are obvious drawbacks in the scientific nature of the established evaluation index system. The establishment of its evaluation index system uniformly refers to the "Preventive Test Regulations for Electric Equipment" and the "Operating Regulations for Power Transformers". The selected state quantity data includes quasi-dynamic indicators such as grounding resistance, winding DC resistance, insulation resistance, body integrity, tap-changer performance, internal abnormal noise, oil leakage, tank pressure, and insulating oil color, as well as dynamic indicators such as average load rate, three-phase unbalance rate, tank temperature, winding joint temperature, and low-voltage side voltage quality, which meet the requirements of timeliness and feasibility. However, there are drawbacks in practicality and scientific nature because most of these indicators cannot quantitatively evaluate the impact on the health status of distribution transformers, resulting in subjectivity and uncertainty inevitably existing when using any analysis method, and unable to essentially characterize the health status of distribution transformers under various operating conditions.

[0051] From the essence of a distribution transformer as a power transmission device, the loss directly reflects the power transmission efficiency of the distribution transformer, that is, the loss represents the health status of the distribution transformer under various operating conditions. In view of the obvious drawbacks of the existing methods for evaluating the health status of distribution transformers at the present stage, the present invention establishes an evaluation index system for the health status of distribution transformers including four evaluation indicators: no-load active power loss, rated load loss, no-load reactive power loss, and rated load leakage magnetic power, and proposes a method for fitting and calculating the real-time value of the indicators, a calculation method for two-level judgment indicators for result abnormal alarm, and a method and system for evaluating the health status of distribution transformers using the same.

[0052] The following is a detailed introduction to an embodiment of a real-time evaluation method for the health status of a distribution transformer according to the present invention.

[0053] Please refer to Figure 1 and 2 , this embodiment provides a real-time evaluation method for the health status of a distribution transformer, including the following steps:

[0054] S100: Uniformly describe the active power function and reactive power function of the distribution transformer through a linear function model.

[0055] According to the basic circuit principle and the power function of the distribution transformer, it can be described by the following expression:

[0056] Active power function: ΔP = P1 - P2 = P0 + 1.05β2 P k

[0057] Reactive power function: ΔQ = Q1 - Q2 = Q0 + 1.05β 2 Q k

[0058] Where ΔP represents the difference in active power between the high and low voltage sides of the distribution transformer, P1 represents the active power on the high voltage side, P2 represents the active power on the low voltage side, P0 represents the no-load active loss, P k represents the rated load loss, β represents the load factor, ΔQ represents the difference in reactive power between the high and low voltage sides of the distribution transformer, Q1 represents the reactive power on the high voltage side, Q2 represents the reactive power on the low voltage side, Q0 represents the no-load reactive loss, Q k represents the rated load loss, β represents the load factor.

[0059] Therefore, the active power function and reactive power function of the distribution transformer can be uniformly described by a linear function model as follows:

[0060] y = kx + b

[0061] Where k represents the slope of the linear function model and b represents the intercept of the linear function model.

[0062] S200: Fitting to obtain the slope and intercept of the linear function model.

[0063] Let the sample points be (x i y i ), i = 1, 2,..., n. Fitting the linear function model through these sample points, let the fitted value y i ' = kx i + b, then there is:

[0064]

[0065] Using L to represent the loss function of fitting, then

[0066] Now we need to find k and b to make L take the minimum value to achieve the optimal fitting. Therefore, there is:

[0067]

[0068] From the above expressions, we can obtain:

[0069]

[0070] From the above expressions, we can obtain:

[0071]

[0072] From the above expressions, we can obtain:

[0073]

[0074] Thus, there is the slope of the fitting first-order function model:

[0075]

[0076] Similarly, the intercept of the fitting first-order function model can be obtained:

[0077]

[0078] S300: Obtain the historical data of active power, reactive power, and load rate for the first 30 days starting from the day after the distribution transformer to be evaluated is put into operation and the most recent 30 days.

[0079] The historical data of active power, reactive power, and load rate for the first 30 days starting from the day after the distribution transformer to be evaluated is put into operation and the most recent 30 days are obtained from the synchronized phasor measurement device. The time interval for data storage is 5 minutes, that is, there are 144 measurement points of data available for each state quantity per day.

[0080] S400: Based on the historical data of active power, reactive power, and load rate, use the first-order function model to calculate the power matrices for the first 30 days and the most recent 30 days respectively.

[0081] Taking 24 hours as the time window pane, by using the method of sliding time window pane, substitute the historical data of active power, reactive power, and load rate for the first 30 days obtained in S300 into the slope and intercept expressions obtained in S200 respectively, and combine with the active power function and reactive power function of the distribution transformer obtained in S100 to obtain the power matrix A for the first 30 days starting from the day after the distribution transformer to be evaluated is put into operation, which is defined as the standard matrix.

[0082]

[0083] In the formula represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the first 30 days.

[0084]

[0085]

[0086]

[0087]

[0088] wherein represents the load rate value at the j-th measurement point (j = 1, 2, 3,..., 144) on the i-th day (i = 1, 2, 3,..., 30) in the first 30 days, represents the active power value on the high voltage side of the distribution transformer at the j-th measurement point (j = 1, 2, 3,..., 144) on the i-th day (i = 1, 2, 3,..., 30) in the first 30 days, represents the active power value on the low voltage side of the distribution transformer at the j-th measurement point (j = 1, 2, 3,..., 144) on the i-th day (i = 1, 2, 3,..., 30) in the first 30 days, represents the reactive power value on the high voltage side of the distribution transformer at the j-th measurement point (j = 1, 2, 3,..., 144) on the i-th day (i = 1, 2, 3,..., 30) in the first 30 days, represents the reactive power value on the low voltage side of the distribution transformer at the j-th measurement point (j = 1, 2, 3,..., 144) on the i-th day (i = 1, 2, 3,..., 30) in the first 30 days.

[0089] Taking 24 hours as the time window pane, the historical data of active power, reactive power, and load rate for the most recent 30 days obtained in S3 are respectively substituted into the slope and intercept expressions obtained in S2 by using the method of sliding time window pane, and combined with the active power function and reactive power function of the distribution transformer obtained in S1, to obtain the power matrix B for the most recent 30 days of the distribution transformer to be evaluated, which is defined as the judgment matrix.

[0090]

[0091] wherein represents the no-load active power loss obtained by fitting with the data on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days, represents the rated load loss obtained by fitting with the data on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days, the no-load reactive power loss obtained by fitting with the data on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days, the rated load leakage magnetic power obtained by fitting with the data on the i-th day in the most recent 30 days.

[0092]

[0093]

[0094]

[0095]

[0096] where represents the load rate value of the j-th measurement point on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days, represents the active power value on the high-voltage side of the distribution transformer at the j-th measurement point on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days, represents the active power value on the low-voltage side of the distribution transformer at the j-th measurement point on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days, represents the reactive power value on the high-voltage side of the distribution transformer at the j-th measurement point on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days, represents the reactive power value on the low-voltage side of the distribution transformer at the j-th measurement point on the i-th day (i = 1, 2, 3,..., 30) in the most recent 30 days.

[0097] S500: Calculate the composite deviation degree based on the power matrix and use the consistency of the composite deviation degree sequence numbers as the first judgment index.

[0098] Respectively obtain the no-load active power loss composite deviation degree V1 of the first 30 days according to the standard matrix A 1 and the rated load loss composite deviation degree the no-load reactive power loss composite deviation degree the rated load leakage magnetic power composite deviation degree as follows:

[0099]

[0100]

[0101]

[0102]

[0103] Similarly, respectively obtain the no-load active power loss composite deviation degree V1 of the most recent 30 days according to the judgment matrix B 2 and the rated load loss composite deviation degree the no-load reactive power loss composite deviation degree V3 2 and the rated load leakage magnetic power composite deviation degree as follows:

[0104]

[0105]

[0106]

[0107]

[0108] For V1 1 and sort them from large to small to form a composite deviation standard sequence (V i 1 ). For V1 2 and V3 2 and sort them from large to small to form a composite deviation standard sequence Define the digital sorting of i and j as the consistency of the composite deviation label, and use whether they are consistent as the first judgment index. That is, when the sequence numbers are sorted consistently, it is judged that the distribution transformer is in normal state; when they are inconsistent, it is judged as abnormal.

[0109] S600: Calculate the maximum change rate of the composite deviation as the second judgment index according to the composite deviation.

[0110] The calculation method of the maximum change rate λ of the composite deviation is as follows:

[0111]

[0112] S700: Carry out the health state evaluation of the distribution transformer to be evaluated based on the first judgment index and the second judgment index.

[0113] Determine the value of λ according to experience. In this embodiment, the value is 10%.

[0114] That is, when λ≥10%, it is considered that the transformer is abnormal, and at this time, an abnormal alarm message is generated.

[0115] In addition, as Figure 2 shown, when the abnormal standard is not met, return to execute the step of obtaining the real-time power data of the distribution transformer to be evaluated, and obtain the updated active power and reactive power data for the most recent 30 days (discard the data of the first day in the original data and add the data of the latest 1 day), so as to realize the real-time evaluation of the transformer.

[0116] The above is a detailed introduction to an embodiment of a real-time evaluation method for the health state of a distribution transformer of the present invention. Next, a detailed introduction to an embodiment of a real-time evaluation system for the health state of a distribution transformer of the present invention will be given.

[0117] This embodiment provides a real-time evaluation system for the health status of distribution transformers, including:

[0118] A fitting module, which is used to uniformly describe the active power function and reactive power function of the distribution transformer through a linear function model; and is also used to fit and obtain the slope and intercept of the linear function model.

[0119] A data acquisition module, which is used to acquire the historical data of active power, reactive power, and load rate for the first 30 days and the most recent 30 days since the day after the distribution transformer to be evaluated was put into operation.

[0120] A power matrix calculation module, which is used to calculate the power matrices for the first 30 days and the most recent 30 days respectively based on the historical data of active power, reactive power, and load rate by using the linear function model.

[0121] In this embodiment, the expression of the power matrix for the first 30 days is specifically as follows:

[0122]

[0123] In the formula, represents the no-load active power loss fitted by using the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, represents the rated load loss fitted by using the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the no-load reactive power loss fitted by using the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the rated load leakage magnetic power fitted by using the data of the i-th day in the first 30 days.

[0124] The expression of the power matrix for the most recent 30 days is specifically as follows:

[0125]

[0126] In the formula, represents the no-load active power loss fitted by using the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, represents the rated load loss fitted by using the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the no-load reactive power loss fitted by using the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the rated load leakage magnetic power fitted by using the data of the i-th day in the most recent 30 days.

[0127] An index calculation module, configured to calculate a composite deviation degree according to a power matrix and use the consistency of the composite deviation degree sequence numbers as a first judgment index; and is also configured to calculate the maximum change rate of the composite deviation degree according to the composite deviation degree as a second judgment index.

[0128] In this embodiment, the specific steps for the index calculation module to calculate the first judgment index include:

[0129] Respectively obtain the no-load active power loss composite deviation degree V1 of the first 30 days according to the power matrices of the first 30 days and the most recent 30 days 1 , the rated load loss composite deviation degree the no-load reactive power loss composite deviation degree the rated load leakage magnetic power composite deviation degree and the no-load active power loss composite deviation degree V1 of the most recent 30 days 2 , the rated load loss composite deviation degree the no-load reactive power loss composite deviation degree V3 2 , the rated load leakage magnetic power composite deviation degree

[0130] For V1 1 , Sort them from large to small to form a composite deviation degree standard sequence (V i 1 ), for V1 2 , V3 2 , Sort them from large to small to form a composite deviation degree standard sequence Define the digital sorting of i and j as the consistency of the composite deviation degree labels, and use whether they are consistent as the first judgment index.

[0131] The index calculation module calculates the maximum change rate of the composite deviation degree specifically according to the following calculation formula:

[0132]

[0133] In the formula, λ is the maximum change rate of the composite deviation degree.

[0134] A state evaluation module, configured to perform a health state evaluation on the distribution transformer to be evaluated based on the first judgment index and the second judgment index.

[0135] It should be noted that the real-time evaluation system provided in this embodiment is used to implement the real-time evaluation method provided in the foregoing embodiment. The specific settings of each module are all based on the complete implementation of this method, and will not be elaborated here.

[0136] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A real-time evaluation method for the health status of a distribution transformer, characterized in that, Including the following steps: Unify the description of the active function and reactive function of the distribution transformer through a linear function model; the expressions of the active function, reactive function, and linear function model are as follows: Active function: ΔP = P1 - P2 = P0 + 1.05β 2 P k Reactive function: ΔQ = Q1 - Q2 = Q0 + 1.05β 2 Q k Linear function model: y = kx + b Where, ΔP represents the active power difference between the high and low voltage sides of the distribution transformer, P1 represents the active power on the high voltage side, P2 represents the active power on the low voltage side, P0 represents the no-load active power loss, P k represents the rated load loss, β represents the load factor, ΔQ represents the reactive power difference between the high and low voltage sides of the distribution transformer, Q1 represents the reactive power on the high voltage side, Q2 represents the reactive power on the low voltage side, Q0 represents the no-load reactive power loss, Q k represents the rated load loss, β represents the load factor; k represents the slope of the linear function model, b represents the intercept of the linear function model; Fit to obtain the slope and intercept of the linear function model; Obtain the historical data of active power, reactive power, and load rate for the first 30 days and the most recent 30 days starting from the day after the distribution transformer to be evaluated is put into operation; Based on the historical data of active power, reactive power, and load rate, use the linear function model to calculate the power matrices for the first 30 days and the most recent 30 days respectively; the expressions of the power matrices for the first 30 days and the most recent 30 days are as follows: Power matrix for the first 30 days: Wherein, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the first 30 days; Power matrix for the most recent 30 days: In the formula, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the most recent 30 days; Calculate the composite deviation degree according to the power matrix and use the consistency of the composite deviation degree sequence numbers as the first judgment index, specifically including: Calculate the composite deviation degree of no-load active power loss for the first 30 days based on the power matrices of the first 30 days and the most recent 30 days respectively Composite deviation degree of rated load loss Composite deviation degree of no-load reactive power loss Composite deviation degree of rated load leakage magnetic power And the composite deviation degree of no-load active power loss for the most recent 30 days Composite deviation degree of rated load loss Composite deviation degree of no-load reactive power loss Composite deviation degree of rated load leakage magnetic power Pair Sort from largest to smallest to form a composite deviation standard sequence Pair Sort from largest to smallest to form a composite deviation standard sequence Define the digital sorting of i and j as the consistency of the composite deviation label, and use whether they are consistent as the first judgment index; Calculate the maximum change rate of the composite deviation degree as the second judgment index according to the composite deviation degree; the calculation formula of the maximum change rate of the composite deviation degree is as follows: In the formula, λ is the maximum change rate of the composite deviation degree; Conduct a health status evaluation of the distribution transformer to be evaluated based on the first judgment index and the second judgment index.

2. A real-time evaluation system for the health status of a distribution transformer, characterized in that, Including: A fitting module, which is used to unify the description of the active function and reactive function of the distribution transformer through a linear function model; it is also used to fit and obtain the slope and intercept of the linear function model; the expression of the active function is as follows: Active function: ΔP = P1 - P2 = P0 + 1.05β 2 P k Reactive function: ΔQ = Q1 - Q2 = Q0 + 1.05β 2 Q k Linear function model: y = kx + b Where, ΔP represents the active power difference between the high and low voltage sides of the distribution transformer, P1 represents the active power on the high voltage side, P2 represents the active power on the low voltage side, P0 represents the no-load active power loss, P k represents the rated load loss, β represents the load factor, ΔQ represents the reactive power difference between the high and low voltage sides of the distribution transformer, Q1 represents the reactive power on the high voltage side, Q2 represents the reactive power on the low voltage side, Q0 represents the no-load reactive power loss, Q k represents the rated load loss, β represents the load factor; k represents the slope of the linear function model, b represents the intercept of the linear function model; A data acquisition module, which is used to obtain the historical data of active power, reactive power, and load rate for the first 30 days and the most recent 30 days starting from the day after the distribution transformer to be evaluated is put into operation; A power matrix calculation module, which is used to calculate the power matrices for the first 30 days and the most recent 30 days respectively based on the historical data of active power, reactive power, and load rate by using the linear function model; the expressions of the power matrices for the first 30 days and the most recent 30 days are as follows: Power matrix for the first 30 days: In the formula, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the first 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the first 30 days; Power matrix for the most recent 30 days: Wherein, represents the no-load active power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, represents the rated load loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the no-load reactive power loss obtained by fitting the data of the i-th (i = 1, 2, 3,..., 30) day in the most recent 30 days, the rated load leakage magnetic power obtained by fitting the data of the i-th day in the most recent 30 days; An index calculation module, which is used to calculate the composite deviation degree according to the power matrix and use the consistency of the composite deviation degree sequence numbers as the first judgment index, specifically including: Calculate the composite deviation degree of no-load active power loss for the first 30 days based on the power matrices of the first 30 days and the most recent 30 days respectively Composite deviation degree of rated load loss Composite deviation degree of no-load reactive power loss Composite deviation degree of rated load leakage magnetic power And the composite deviation degree of no-load active power loss for the most recent 30 days Composite deviation degree of rated load loss Composite deviation degree of no-load reactive power loss Composite deviation degree of rated load leakage magnetic power Pair Sort from largest to smallest to form a composite deviation standard sequence Pair Sort from largest to smallest to form a composite deviation standard sequence Define the numerical sorting of i and j as the consistency of the composite deviation label, and use whether they are consistent as the first judgment index; it is also used to calculate the maximum change rate of the composite deviation according to the composite deviation as the second judgment index; the calculation formula of the maximum change rate of the composite deviation is specifically as follows: In the formula, λ is the maximum change rate of the composite deviation degree; A status evaluation module, which is used to conduct a health status evaluation of the distribution transformer to be evaluated based on the first judgment index and the second judgment index.

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