A method for identifying the pole line loss of a transformer area based on ridge regression algorithm and double-hook curve

Abstract

The present application relates to the field of electric power based on ridge regression algorithm and double hook curve of area benchmark line loss identification method, in the data preprocessing stage, through defining business logic rules and quantile threshold, eliminate obviously unreasonable and extreme fluctuation data point, ensure that the subsequent analysis is based on reliable data set, in the modeling stage, using polynomial fitting method is fitted to the area line loss data, find the best fitting curve, and combine ridge regression algorithm, through the regularity control model complexity, according to the fitting result, construct line loss double hook curve, obtain the representative line loss level in the normal line loss range, based on the constructed line loss double hook curve, real-time preprocessing is carried out to the newly collected data, provide clear target and reference for area line loss management, the present application has the characteristics of more fine data processing, high algorithm flexibility and strong area adaptation, solves the problems of insufficient accuracy and low efficiency in the existing area data processing method.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a method for identifying line losses of substation benchmark poles based on a ridge regression algorithm and a double-hook curve. Background Art

[0002] A transformer district (full name: transformer district) is a basic management unit in the power system, consisting of a distribution transformer and the low-voltage power grid within its power supply range. District data refers to various operational and management information related to the power transformer district (i.e., district). This data reflects the status and performance of the power system within the district, as well as customer electricity usage. In the operation and management of the power system, district data is an important basis for evaluating district operations and efficiency. However, because district operations are analyzed based on a large amount of real-time data, district data is subject to interference from various factors during actual collection, such as load fluctuations, equipment aging, and unexpected abnormal customer electricity usage, which can easily introduce noise data. The accumulation of noise data can lead to biased analysis results, affecting the assessment of district efficiency, power safety, and line health. For example, noise data can cause abnormal fluctuations in district line loss calculations, affecting the accuracy of district analysis and increasing system operating costs.

[0003] Therefore, to ensure the accuracy of line loss calculations, it is necessary to remove abnormal data from the substation data. Although existing substation data processing methods have certain data processing capabilities, they mainly focus on the collection and preliminary processing of basic data. There is still room for improvement in the accuracy and efficiency of intelligent identification and processing of abnormal data.

[0004] The above problems are worth solving. Summary of the Invention

[0005] In order to overcome the shortcomings of the existing technology, the present invention provides a substation benchmark line loss identification method based on the ridge regression algorithm and the double-hook curve. By constructing a double-hook curve of the line loss interval and identifying the benchmark line loss, the substation line loss is intelligently analyzed and processed to identify and eliminate noise data. The method of the present invention can significantly improve the accuracy of the substation data by automatically identifying and processing the line loss data that exceeds a reasonable range, thereby providing a reliable data basis for substation analysis.

[0006] The technical solution of the present invention is as follows:

[0007] A method for identifying line loss of a substation benchmark pole based on a ridge regression algorithm and a double-hook curve includes the following steps:

[0008] Step 1: Obtain original data from the power substation database;

[0009] Step 2, pre-process the original transformer area data to eliminate unreasonable data points and extreme value data points;

[0010] Step 3, determine the reasonable distribution range of line loss data;

[0011] Step 4, fit the data using polynomial fitting and ridge regression algorithm;

[0012] Use the polynomial fitting method to fit the transformer area data to find the best fitting curve; on the basis of polynomial fitting, increase the regularization term to constrain the complexity of the polynomial;

[0013] Step 5, according to the fitting result, construct a line loss double hook curve, the line loss double hook curve includes an upper limit curve and a lower limit curve, the upper limit curve and the lower limit curve are used to define the highest allowable value and the lowest allowable value of the transformer area line loss data;

[0014] Step 6, identify the benchmark line loss through the line loss double hook curve, and perform adaptive preprocessing on the transformer area data, and only output the data between the upper limit curve and the lower limit curve.

[0015] As a preferred technical scheme of the present application, in step 1, the original transformer area data includes daily transformer area power supply and transformer area power supply.

[0016] As a preferred technical scheme of the present application, the step 2 includes the following steps:

[0017] Step 201, eliminate unreasonable data points;

[0018] Define the power transformer area line loss rule, eliminate abnormal data that does not conform to the transformer area business logic, the abnormal data includes transformer area power supply data, transformer area power supply data and transformer area line loss rate data below 0%;

[0019] Step 202, eliminate extreme value data points;

[0020] Through the distribution analysis of the transformer area line loss data, set the upper threshold and the lower threshold, and eliminate the extreme values outside the threshold range.

[0021] Further, the upper threshold is set to 95% quantile, and the lower threshold is set to 5% quantile, then the daily data of the transformer area line loss power falling below 5% quantile and above 95% quantile is eliminated.

[0022] Further, the calculation formula of step 202 is as follows:

[0023]

[0024] Where, n is the total number of transformer area data, X is the line loss data set, iQ5 and i Q95 are the positions of the 5% quantile and the 95% quantile respectively; It means to take the largest integer less than or equal to the number in the brackets. Indicates the smallest integer greater than or equal to the number in the brackets.

[0025] As a preferred technical solution of the present invention, step 3 includes the following steps:

[0026] Step 301: Calculate the percentile value;

[0027] Based on the eliminated data, calculate the upper and lower quantiles of the data;

[0028] Step 302: Calculate the observation interval;

[0029] Calculate and count the minimum and maximum observation values, and determine the area data between the minimum and maximum observation values ​​as the observation interval.

[0030] Furthermore, in step 301, the lower quantile of the data is set to 25%, and the upper quantile is set to 75%. The calculation formula is as follows:

[0031]

[0032] Where n is the total number of days of data in the substation area, X is the line loss dataset, i Q25 and i Q75 The positions of the 25% and 75% quantiles respectively; It means to take the largest integer less than or equal to the number in the brackets. Indicates the smallest integer greater than or equal to the number in the brackets;

[0033] In step 302, the calculation formula for the minimum observation value is:

[0034] R min =Q25-(Q75-Q25);

[0035] The formula for calculating the maximum observation value is:

[0036] R max =Q75+(Q75-Q25).

[0037] As a preferred technical solution of the present invention, step 4 includes the following steps:

[0038] Step 401: polynomial fitting;

[0039] Select the data of the observation area within the observation interval, set X = {x i} is the line loss data set, T={t i} is the data time set, and the polynomial fitting method is used to fit the station area data in the selected interval; the polynomial fitting formula is:

[0040] f(t)=a n t n +a n-1 t n-1 +…+a1t+a0;

[0041] Among them, a n , a n-1 , ..., a0 is the fitting coefficient, and the fitting polynomial parameters are determined by the least squares method. The formula is as follows:

[0042]

[0043] Step 402: Ridge regression fitting calculation;

[0044] Using the ridge regression fitting formula:

[0045]

[0046] Here, γ is the regularization parameter used to control the size of the polynomial system.

[0047] As a preferred technical solution of the present invention, the formula of the upper bound curve in step 5 is:

[0048] L up (t) = f(t) + ∈;

[0049] The formula of the lower bound curve is:

[0050] L low (t) = f(t)-∈;

[0051] Among them, ∈ is the noise tolerance, which is used to allow a certain fluctuation range.

[0052] Preferably, the value range of ∈ is [2, 3].

[0053] Furthermore, in step 6, the line loss double-hook curve is used to identify the benchmark line loss, and the calculation formula is as follows:

[0054]

[0055] Among them, P x ' s (t) is the benchmark line loss.

[0056] Further, the step 6 outputs the data between the upper boundary curve and the lower boundary curve, and the specific steps are as follows: the data outside the upper boundary curve and the lower boundary curve is removed, that is, the data less than the upper boundary curve and the data greater than the lower boundary curve is removed; and the remaining data is output as the substation pre-processing.

[0057] The application also provides a computer storage medium, which stores a readable program, and when the program is executed, the program executes the substation benchmark line loss identification method based on the ridge regression algorithm and the double-hook curve.

[0058] The application also provides a device, which comprises one or more processors and a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more programs enable the one or more processors to execute the substation benchmark line loss identification method based on the ridge regression algorithm and the double-hook curve.

[0059] The application also provides a substation benchmark line loss identification system based on the ridge regression algorithm and the double-hook curve, which comprises:

[0060] A data acquisition module is configured to acquire original substation data from a database of a power substation;

[0061] A data preprocessing module is configured to preprocess the original substation data to remove unreasonable data points and extreme value data points;

[0062] A dynamic modeling module is configured to determine a reasonable distribution range of line loss data, fit the data by using a polynomial fitting method and a ridge regression algorithm, and construct a line loss double-hook curve according to a fitting result.

[0063] A substation data adaptive preprocessing module is configured to identify a benchmark line loss by using the line loss double-hook curve, adaptively preprocess the substation data, and output only data between the upper boundary curve and the lower boundary curve.

[0064] The application according to the above scheme has the following beneficial effects:

[0065] In the data preprocessing stage, the application defines business logic rules and quantile threshold values to remove obviously unreasonable and extreme fluctuation data points, so as to ensure that subsequent analysis is based on a reliable data set; by calculating quantile values and defining an observation interval, the application captures the real distribution characteristics of line loss data, effectively reduces noise interference, and provides an important reference standard for the model.

[0066] In the modeling stage, the application uses a polynomial fitting method to fit the substation line loss data, finds the best fitting curve, combines the ridge regression algorithm, controls the model complexity by regularization, enhances the robustness of the model, obtains a smoother and more stable fitting result, and further improves the reliability of the model.

[0067] During the data detection and processing phase, the present invention identifies benchmark line losses based on the double-hook curve and obtains a representative line loss level within the normal line loss range, providing a clear target and reference for line loss management in the substation area. Based on the constructed double-hook curve, newly collected data is pre-processed in real time. Any new line loss data that falls outside the double-hook curve is marked as abnormal. Conversely, data that falls within the double-hook curve is accepted as valid data and included in subsequent analysis.

[0068] Therefore, the present invention achieves more effective substation analysis data by fitting substation data, constructing a double-hook curve, and adaptively screening the substation data. Compared with the existing technology, it has the characteristics of more refined data processing, high algorithm flexibility and strong substation adaptability, which solves the problems of insufficient accuracy and low efficiency in existing substation data processing methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0070] To better understand the objectives, technical solutions, and technical effects of the present invention, the present invention is further explained below with reference to the accompanying drawings and embodiments. It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. It should also be noted that the embodiments described below are intended only to illustrate the present invention and are not intended to limit the present invention.

[0071] It should be noted that when an element is referred to as being "fixed on" or "disposed on" another element, it may be directly on the other element or there may also be an intermediate element; when an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be an intermediate element at the same time.

[0072] like Figure 1 As shown in FIG, the method for identifying the line loss of the substation benchmark pole based on the ridge regression algorithm and the double hook curve includes the following steps:

[0073] Step 1: Obtain original data from the power substation database;

[0074] In this embodiment, the original data of the substation area includes the daily substation area power supply p gr The power output of the area p gc .

[0075] Step 2: pre-process the original data to remove unreasonable data points and extreme data points;

[0076] Specifically, step 2 includes the following steps:

[0077] Step 201: Eliminate unreasonable data points;

[0078] Define the power line loss rules of the power substation area and eliminate abnormal data that does not conform to the business logic of the substation area. The abnormal data includes the power supply to the substation area is 0 (p gr =0) data, the power output of the area is 0 (i.e. p gc =0) and the line loss rate p xsl Less than 0% (i.e. ) data; p xs It is the line loss power in the substation area.

[0079] Step 202: Eliminate extreme value data points;

[0080] By analyzing the distribution of line loss data in the substation area, upper and lower thresholds are set to eliminate extreme values ​​outside the threshold range. In this embodiment, the upper threshold is set to the 95% quantile and the lower threshold is set to the 5% quantile. That is, extreme values ​​are eliminated based on the 5% quantile Q5 and the 95% quantile Q95, further eliminating abnormal data caused by accidental factors. The calculation formula is as follows:

[0081]

[0082] Where n is the total number of days of data in the substation area, X is the line loss dataset, i Q5 and i Q95 are the positions of the 5% quantile and the 95% quantile respectively; It means to take the largest integer less than or equal to the number in the brackets. Indicates taking the smallest integer greater than or equal to the number in the brackets. Therefore, this step eliminates the line loss p that meets the requirements of the area. xs <Q5和p xs >Daily data of Q95.

[0083] Step 3: Determine the reasonable distribution range of line loss data;

[0084] Specifically, step 3 includes the following steps:

[0085] Step 301: Calculate the percentile value;

[0086] Based on the data removed in the previous step, calculate the upper and lower quantiles of the data; the lower quantile of the data is set to 25% and the upper quantile is set to 75%. The calculation formula is as follows:

[0087]

[0088]

[0089] Where n is the total number of days of data in the substation area, X is the line loss dataset, i Q25 and i Q75 The positions of the 25% and 75% quantiles respectively; It means to take the largest integer less than or equal to the number in the brackets. Indicates the smallest integer greater than or equal to the number in the brackets;

[0090] In step 302, the observation interval is calculated;

[0091] Calculate and count the minimum observed value R min and the maximum observed value R max , and determine the station area data between the minimum observation value and the maximum observation value as the observation interval;

[0092] The formula for calculating the minimum observation value is:

[0093] R min =Q25-(Q75-Q25);

[0094] The formula for calculating the maximum observation value is:

[0095] R max =Q75+(Q75-Q25).

[0096] Step 4: Fit the data using polynomial fitting and ridge regression algorithm;

[0097] Use polynomial fitting method to fit the station data and find the best fitting curve; based on the polynomial fitting, add regularization terms to constrain the complexity of the polynomial;

[0098] Specifically, step 4 includes the following steps:

[0099] Step 401: polynomial fitting;

[0100] Select the data of the station area within the observation interval, that is, R min <Line loss power in the substation area p xs <R max The area data is set as X={x i} is the line loss data set, T={t i} is the data time set, and the polynomial fitting method is used to fit the station area data in the selected interval; the polynomial fitting formula is:

[0101] f(t)=a n t n +a n-1 t n-1 +…+a1t+a0;

[0102] Among them, a n , a n-1, …, a0is the fitting coefficient, the polynomial fitting parameters are determined by the least square method, and the formula is as follows:

[0103]

[0104] Step 402, ridge regression fitting calculation;

[0105] In order to avoid overfitting problem in high-order polynomial fitting, ridge regression is used for regularized fitting; on the basis of polynomial fitting, a regularization term is added to constrain the complexity of the polynomial, and the ridge regression fitting formula is:

[0106]

[0107] Wherein, γ is a regularization parameter, used to control the size of the polynomial system.

[0108] Step 5, according to the fitting result, the line loss double hook curve is constructed, the line loss double hook curve includes upper limit curve and lower limit curve, the upper limit curve and the lower limit curve are used to define the highest allowable value and the lowest allowable value of the line loss data of the transformer area respectively;

[0109] Specifically, step 5 is to construct the line loss double hook curve of the transformer area data according to the results of polynomial fitting and ridge regression, the curve is composed of upper limit curve and lower limit curve; and the formula of the upper limit curve is:

[0110] L up (t)=f(t)+∈;

[0111] The formula of the lower limit curve is:

[0112] L low (t)=f(t)-∈;

[0113] Wherein, ∈ is the noise tolerance, used to allow a certain fluctuation range. Preferably, ∈ takes the value range of [2, 3].

[0114] Step 6, identifying the benchmark line loss through the line loss double hook curve, and adaptively preprocessing the transformer area data, only outputting the data between the upper limit curve and the lower limit curve.

[0115] Specifically, the benchmark line loss is identified through the line loss double hook curve, and the calculation formula is as follows:

[0116]

[0117] Wherein, P x ′ s (t) is the benchmark line loss.

[0118] Adaptive preprocessing of the area data in step 6: Use the line loss double hook curve constructed by the area data to perform adaptive preprocessing on the area data. Any data falling outside the upper and lower bounds of the curve is regarded as line loss noise data, that is, the area line loss power p is eliminated. xs <L low (t) and p xs >L up (t) daily data, and the remaining data are used as the final output of the station area preprocessing.

[0119] In summary, unlike traditional line loss data processing methods, the present invention combines polynomial fitting with ridge regression algorithms, so that the processing process achieves a good balance between accuracy and robustness, avoiding the problems of overfitting and excessive model complexity. In traditional methods, the screening of line loss data often relies on fixed thresholds or simple empirical rules, while the present invention uses dynamically generated line loss double-hook curves to analyze the line loss interval boundaries and identify benchmark line losses, effectively improving the intelligence level and accuracy of the processing. When analyzing the conditions of different substations, compared with the traditional methods that require specific manual analysis, the present invention realizes the automation of the entire process from data screening, fitting to preprocessing, greatly improving efficiency and ensuring the objectivity and consistency of the processing process.

[0120] It can be seen that the present invention can accurately identify abnormal fluctuations in substation data by constructing a double-hook curve for line loss, thereby effectively reducing the impact of noise data on line loss calculations. The method of the present invention is applicable to all types of substations and can be adaptively adapted according to the power consumption characteristics of different substations. It ensures that it can be effectively applied in large-scale substations or newly built substations, thereby improving compatibility and practicality. The present invention utilizes a data-driven intelligent detection and correction mechanism to reduce the factors of human intervention and ensure the objectivity of data processing. The objectivity of data analysis is enhanced through automated analysis and processing based on the double-hook curve for line loss.

[0121] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0122] The above embodiments merely illustrate several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve is characterized by: The following steps are involved: Step 1: Obtain original data from the power substation database; Step 2: pre-process the original data to remove unreasonable data points and extreme data points; Step 3: Determine the reasonable distribution range of line loss data; Step 4: Fit the data using polynomial fitting and ridge regression algorithm; Use polynomial fitting method to fit the station data and find the best fitting curve; based on the polynomial fitting, add regularization terms to constrain the complexity of the polynomial; Step 5: construct a line loss double-hook curve based on the fitting results. The line loss double-hook curve includes an upper bound curve and a lower bound curve. The upper bound curve and the lower bound curve are used to define the maximum allowable value and the minimum allowable value of the line loss data in the substation area, respectively. Step 6: Identify the benchmark line loss through the line loss double-hook curve, and perform adaptive preprocessing on the substation data, and only output the data between the upper limit curve and the lower limit curve.

2. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 1 is characterized in that: In step 1, the original substation data includes the daily substation power supply and substation power supply.

3. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 1 is characterized in that: The step 2 comprises the following steps: Step 201: Eliminate unreasonable data points; Define power substation line loss rules and eliminate abnormal data that does not conform to the substation business logic. The abnormal data includes data with a substation power input of 0, data with a substation power output of 0, and data with a substation line loss rate below 0%; Step 202: Eliminate extreme value data points; By analyzing the distribution of line loss data in the substation area, the upper and lower thresholds are set and extreme values ​​outside the threshold range are eliminated.

4. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 3 is characterized in that: The upper threshold is set to the 95% quantile, and the lower threshold is set to the 5% quantile, and the daily data with the line loss power falling below the 5% quantile and above the 95% quantile in the substation area are eliminated.

5. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 1 is characterized in that: The step 3 comprises the following steps: Step 301: Calculate the percentile value; Based on the eliminated data, calculate the upper and lower quantiles of the data; Step 302: Calculate the observation interval; Calculate and count the minimum and maximum observation values, and determine the area data between the minimum and maximum observation values ​​as the observation interval.

6. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 5 is characterized in that: In step 301, the lower quantile of the data is set to 25%, and the upper quantile is set to 75%. The calculation formula is as follows: Where n is the total number of days of data in the substation area, X is the line loss dataset, i Q25 and i Q75 The positions of the 25% and 75% quantiles respectively; It means to take the largest integer less than or equal to the number in the brackets. Indicates the smallest integer greater than or equal to the number in the brackets; In step 302, the calculation formula for the minimum observation value is: R min =Q25-(Q75-Q25); The formula for calculating the maximum observation value is: R max =Q75+(Q75-Q25)。 7. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 1 is characterized in that: The step 4 comprises the following steps: Step 401: polynomial fitting; Select the data of the observation area within the observation interval, set X = {x i } is the line loss data set, T={t i } is the data time set, and the polynomial fitting method is used to fit the station area data in the selected interval; the polynomial fitting formula is: f(t)=a n t n +a n-1 t n-1 +…+a1t+a0; Among them, a n , a n-1 , ..., a0 is the fitting coefficient, and the fitting polynomial parameters are determined by the least squares method. The formula is as follows: Step 402: Ridge regression fitting calculation; Using the ridge regression fitting formula: Here, γ is the regularization parameter used to control the size of the polynomial system.

8. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 1 is characterized in that: In step 5, the formula of the upper bound curve is: L up (t)=f(t)+∈; The formula of the lower bound curve is: L low (t)=f(t)-∈; Among them, ∈ is the noise tolerance, which is used to allow a certain fluctuation range.

9. The method for identifying line loss of substation benchmark poles based on ridge regression algorithm and double hook curve according to claim 8, characterized in that: In step 6, the line loss double-hook curve is used to identify the benchmark line loss, and the calculation formula is as follows: Among them, P x ′ s (t) is the benchmark line loss.

10. The method for identifying line loss of substation area benchmark poles based on ridge regression algorithm and double hook curve according to claim 9, characterized in that: In step 6, the data between the upper bound curve and the lower bound curve is outputted, specifically by the following steps: Eliminate the data that falls outside the upper and lower bounds, that is, eliminate the line loss electricity in the substation area that is less than the lower bound and the line loss electricity in the substation area that is greater than the upper bound; the remaining data is used as the substation preprocessing output.

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