Loop impedance calculation method considering concentrator data

Abstract

The invention discloses a loop impedance calculation method considering concentrator data. The loop impedance calculation method comprises the following steps: acquiring electrical data at a user home and a transformer; detecting current data collected at a user, and obtaining a moment when the current change of adjacent measurement intervals is relatively large; extracting a user and temporarily neglecting the influence of other users, and establishing an equivalent formula of a circuit principle; a loop impedance result obtained through calculation is stored in a new data set; and processing impedance results at multiple moments based on an estimation method of normal distribution fitting. According to the established loop impedance model, the measurement values of the intelligent electric meters in the home of the user and at the transformer are combined, and the accuracy of the calculation result is improved; the measurement value at the moment when the user current at the adjacent measurement intervals changes significantly is selected to perform impedance calculation, and then impedance estimation is performed on a plurality of calculation results through statistical analysis based on normal distribution, so that calculation errors are reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of data analysis, and in particular relates to a loop impedance calculation method considering concentrator data. Background Art

[0002] Many distribution equipment, due to inherent factors, can cause overcurrent and tripping, resulting in damage to lines or equipment. Aging equipment, poor conductor contact, and excessive power loads are highly susceptible to overcurrent. Short circuits can lead not only to power outages but also to equipment damage and even spontaneous combustion, resulting in equipment damage and personal injury. Failure to promptly inspect and maintain distribution equipment is a major cause of failures. However, power companies have limited operations and maintenance personnel, making it difficult to adequately monitor lines. Manual inspections are typically limited, and increasingly, lines are being laid with buried cables, making problem lines even more difficult to detect. Furthermore, failure to promptly replace aging lines and equipment increases the risk of failures. Excessive impedance correlates with abnormalities such as line aging, damage, and cracking. Therefore, quickly and easily obtaining line impedance parameters is crucial for predicting line aging and assessing risks. While medium and high voltage line parameters are relatively comprehensive, grid parameter information for low voltage distribution networks is relatively fragmented, with missing and inaccurate line impedance values. Summary of the Invention

[0003] The present invention is proposed to solve the problems existing in the prior art, and its purpose is to provide a loop impedance calculation method taking into account concentrator data.

[0004] The technical solution of the present invention is: a loop impedance calculation method considering concentrator data, comprising the following steps:

[0005] First, electrical data is collected at the user's home and transformer;

[0006] Then, the current data collected at the user is tested to obtain the moments when the current changes greatly between adjacent measurement intervals;

[0007] Then, extract one user and temporarily ignore the influence of other users to establish the equivalent formula of the circuit principle;

[0008] Then, the calculated loop impedance results are stored in a new data set;

[0009] Finally, the impedance results at multiple moments are processed using an estimation method based on normal distribution fitting.

[0010] Furthermore, the moment when the current changes greatly between adjacent measurement intervals is obtained. The specific process is as follows:

[0011] First, impedance calculations are selectively performed when significant current changes occur between adjacent measurement cycles.

[0012] Voltage anomalies are then filtered out before processing to suppress measurement-induced distortion.

[0013] Going further, we can establish the equivalent formula of the circuit principle. The specific process is as follows:

[0014] First, extract a user and temporarily ignore the influence of other users in the area, and establish an equivalent formula based on circuit principles, as follows:

[0015] V s (t) = I m (t)Z w +V m (t) (1)

[0016] V s (t+1)=I m (t+1)Z w +V m (t+1) (2)

[0017] Among them, V s (t) and V s (t+1) represents the voltage measured by the concentrator before and after the adjacent measurement cycle, I m (t) and I m (t+1) The current measured by smart meter m before and after the adjacent measurement cycle, V m (t) and V m (t+1) represents the voltage measured by smart meter m before and after the adjacent measurement cycle;

[0018] Then, transform the above formula (2) to get:

[0019]

[0020] Then, subtracting formula (1) and formula (3), we have:

[0021]

[0022] Finally, the equivalent loop impedance of user m is calculated as:

[0023]

[0024] Furthermore, the calculated loop impedance results are stored in a new data set, as follows:

[0025] For the impedance results at multiple moments, a data set is established as shown in formula (6):

[0026] R(t i )={z i (t)} (6)

[0027] Among them, R(t i ) represents the real number set consisting of the loop impedance results of each user at multiple moments, z i (t) represents the calculation result of single loop impedance.

[0028] Furthermore, the estimation method based on normal distribution fitting processes the impedance results at multiple moments, including the above loop impedance dataset R(t i ) is processed and the result set is probability distributed so that it fits within the range of 2δ, then:

[0029] F(z i )=frequency(R,[z i -δ,z i +δ]) (7)

[0030] Among them, z i Indicates the center point of each user's loop impedance evaluation value.

[0031] Furthermore, for F(z i ) to fit the normal distribution, the data set {z i ,F(z i )}(i=1,2,…,N) should follow the function shown in formula (8):

[0032]

[0033] Among them, μ is the mean, which indicates the center position of the data; σ is the standard deviation, which represents the degree of dispersion of the data.

[0034] Furthermore, for F(r i ) to fit the normal distribution curve, which should satisfy the function shown in formula (9):

[0035]

[0036] Where J0 represents the sum of squared errors.

[0037] Furthermore, the parameters are substituted into formula (8) and the parameter estimation is completed by solving J0;

[0038] The corresponding parameters are calculated by the least squares method, as shown in formula (10):

[0039]

[0040] Among them, p1, p2, and p3 represent unknown coefficients.

[0041] Furthermore, the corresponding parameter estimates are as follows:

[0042]

[0043] μ=σ 2 p2 (12)

[0044]

[0045] Where A represents the maximum probability density value.

[0046] The beneficial effects of the present invention are as follows:

[0047] The present invention improves the accuracy of calculation results by combining the measurement values ​​of smart meters in users' homes and at transformers based on an established loop impedance model. The impedance calculation is performed by selecting measurement values ​​at moments when user current changes significantly between adjacent measurement intervals. Then, impedance estimation is performed on multiple calculation results through statistical analysis based on normal distribution, thereby reducing calculation errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of the flow of the loop impedance calculation method of the present invention;

[0049] Figure 2 Schematic diagram of the structure of the loop impedance calculation system of the present invention;

[0050] Figure 3 It is a structural diagram of the single-user loop impedance model of the low-voltage distribution network in the present invention. DETAILED DESCRIPTION

[0051] Hereinafter, the present invention will be described in detail with reference to the accompanying drawings and embodiments:

[0052] like Figures 1 to 3 As shown, a loop impedance calculation method considering concentrator data includes the following steps:

[0053] First, electrical data is collected at the user's home and transformer;

[0054] Then, the current data collected at the user is tested to obtain the moments when the current changes greatly between adjacent measurement intervals;

[0055] Then, extract one user and temporarily ignore the influence of other users to establish the equivalent formula of the circuit principle;

[0056] Then, the calculated loop impedance results are stored in a new data set;

[0057] Finally, the impedance results at multiple moments are processed using an estimation method based on normal distribution fitting.

[0058] Obtain the moment when the current changes most between adjacent measurement intervals. The specific process is as follows:

[0059] First, impedance calculations are selectively performed when significant current changes occur between adjacent measurement cycles.

[0060] Voltage anomalies are then filtered out before processing to suppress measurement-induced distortion.

[0061] To establish the equivalent formula of the circuit principle, the specific process is as follows:

[0062] First, extract a user and temporarily ignore the influence of other users in the area, and establish an equivalent formula based on circuit principles, such as Figure 3 As shown, the details are as follows:

[0063] V s (t) = I m (t)Z w +V m (t) (1)

[0064] V s (t+1)=I m (t+1)Z w +V m (t+1) (2)

[0065] Among them, V s (t) and V s (t+1) represents the voltage measured by the concentrator before and after the adjacent measurement cycle, I m (t) and I m (t+1) The current measured by smart meter m before and after the adjacent measurement cycle, V m (t) and V m (t+1) represents the voltage measured by smart meter m before and after the adjacent measurement cycle;

[0066] Then, transform the above formula (2) to get:

[0067]

[0068] Then, subtracting formula (1) and formula (3), we have:

[0069]

[0070] Finally, the equivalent loop impedance of user m is calculated as:

[0071]

[0072] The calculated loop impedance results are stored in a new data set as follows:

[0073] For the impedance results at multiple moments, a data set is established as shown in formula (6):

[0074] R(t i )={z i (t)} (6)

[0075] Among them, R(t i ) represents the real number set consisting of the loop impedance results of each user at multiple moments, z i (t) represents the calculation result of single loop impedance.

[0076] The estimation method based on normal distribution fitting processes the impedance results at multiple moments, including the above loop impedance dataset R(t i ) is processed and the result set is probability distributed so that it fits within the range of 2δ, then:

[0077] F(z i )=frequency(R,[z i -δ,z i +δ]) (7)

[0078] Among them, z i Indicates the center point of each user's loop impedance evaluation value.

[0079] F(z i ) to fit the normal distribution, the data set {z i ,F(z i )}(i=1,2,…,N) should follow the function shown in formula (8):

[0080]

[0081] Among them, μ is the mean, which indicates the center position of the data; σ is the standard deviation, which represents the degree of dispersion of the data.

[0082] F(r i ) to fit the normal distribution curve, which should satisfy the function shown in formula (9):

[0083]

[0084] Where J0 represents the sum of squared errors.

[0085] Substitute the parameters into formula (8) and complete the parameter estimation by solving J0;

[0086] The corresponding parameters are calculated by the least squares method, as shown in formula (10):

[0087]

[0088] Among them, p1, p2, and p3 represent unknown coefficients.

[0089] The corresponding parameter estimates are as follows:

[0090]

[0091] μ=σ 2 p2 (12)

[0092]

[0093] Where A represents the maximum probability density value.

[0094] Specifically, when detecting moments with large current changes between adjacent measurement intervals, impedance calculations are selectively performed only when significant current changes occur between adjacent measurement cycles. Voltage anomalies must also be filtered out to suppress distortion caused by the measurement. Therefore, the electrical data must simultaneously meet the following conditions:

[0095]

[0096] Among them, I m (t) and I m (t+1) The current measured by smart meter m before and after the adjacent measurement cycle, V m (t) and V m (t+1) represents the voltage measured by smart meter m before and after the adjacent measurement cycle, and I set 3A, U set is 2V.

[0097] Specifically, the rationality check for loop impedance calculation must meet the following requirements:

[0098]

[0099] Among them, φ1 is set to 0.1, φ2 is set to 0.2, and if it exceeds the set threshold, it is determined to be a line abnormality.

[0100] like Figure 2 As shown, the present invention discloses a loop impedance calculation system based on smart meter data, including: an acquisition module, a detection module, a calculation module, and a fitting processing module.

[0101] The acquisition module collects electrical data from the user's home and the transformer.

[0102] The detection module detects the collected electrical data and the moments when the current changes greatly between adjacent measurement intervals.

[0103] The calculation module forms a new data set based on the electrical data at the above moment; and calculates a single loop impedance value using the voltage and current data in the user's home and the voltage data of the concentrator in the data set.

[0104] The fitting processing module performs normal fitting processing on the loop impedance calculation results at multiple moments.

[0105] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A loop impedance calculation method considering concentrator data, characterized by: The following steps are involved: First, electrical data is collected at the user's home and transformer; Then, the current data collected at the user is tested to obtain the moments when the current changes greatly between adjacent measurement intervals; Then, extract one user and temporarily ignore the influence of other users to establish the equivalent formula of the circuit principle; Then, the calculated loop impedance results are stored in a new data set; Finally, the impedance results at multiple moments are processed using an estimation method based on normal distribution fitting.

2. The loop impedance calculation method considering concentrator data according to claim 1, characterized in that: Obtain the moment when the current changes most between adjacent measurement intervals. The specific process is as follows: First, impedance calculations are selectively performed when significant current changes occur between adjacent measurement cycles. Voltage anomalies are then filtered out before processing to suppress measurement-induced distortion.

3. The loop impedance calculation method considering concentrator data according to claim 1, characterized in that: To establish the equivalent formula of the circuit principle, the specific process is as follows: First, extract a user and temporarily ignore the influence of other users in the area, and establish an equivalent formula based on circuit principles, as follows: V s (t)=I m (t)Z w +V m (t) (1) V s (t+1)=I m (t+1)Z w +V m (t+1) (2) Among them, V s (t) and V s (t+1) represents the voltage measured by the concentrator before and after the adjacent measurement cycle, I m (t) and I m (t+1) The current measured by smart meter m before and after the adjacent measurement cycle, V m (t) and V m (t+1) represents the voltage measured by smart meter m before and after the adjacent measurement cycle; Then, transform the above formula (2) to get: Then, subtracting formula (1) and formula (3), we have: Finally, the equivalent loop impedance of user m is calculated as:

4. The loop impedance calculation method considering concentrator data according to claim 1, characterized in that: The calculated loop impedance results are stored in a new data set as follows: For the impedance results at multiple moments, a data set is established as shown in formula (6): R(t i )={z i (t)} (6) Among them, R(t i ) represents the real number set consisting of the loop impedance results of each user at multiple moments, z i (t) represents the calculation result of single loop impedance.

5. The loop impedance calculation method considering concentrator data according to claim 4, characterized in that: The estimation method based on normal distribution fitting processes the impedance results at multiple moments, including the above loop impedance dataset R(t i ) is processed and the result set is probability distributed so that it fits within the range of 2δ, then: F(z i )=frequency(R,[z i -δ,z i +δ]) (7) Among them, z i Indicates the center point of each user's loop impedance evaluation value.

6. The loop impedance calculation method considering concentrator data according to claim 5, characterized in that: F(z i ) to fit the normal distribution, the data set {z i ,F(z i )}(i=1,2,…,N) should follow the function shown in formula (8): Among them, μ is the mean, which indicates the center position of the data; σ is the standard deviation, which represents the degree of dispersion of the data.

7. The loop impedance calculation method considering concentrator data according to claim 6, characterized in that: F(r i ) to fit the normal distribution curve, which should satisfy the function shown in formula (9): Where J0 represents the sum of squared errors.

8. The loop impedance calculation method considering concentrator data according to claim 7, characterized in that: Substitute the parameters into formula (8) and complete the parameter estimation by solving J0; The corresponding parameters are calculated by the least squares method, as shown in formula (10): Among them, p1, p2, and p3 represent unknown coefficients.

9. The loop impedance calculation method considering concentrator data according to claim 8, characterized in that: The corresponding parameter estimates are as follows: μ = σ 2 p2 (12) Where A represents the maximum probability density value.

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