Method, device and equipment for determining photovoltaic influence factors of line loss of power distribution area

Abstract

The embodiment of the invention provides a method, a device and equipment for determining photovoltaic influence factors of line loss of a power distribution area. The method comprises the following steps: firstly, acquiring first state data of at least one to-be-processed power distribution area, then determining first correlation between photovoltaic data in all the first state data and line loss data in all the first state data, and if it is detected that the correlation degree between photovoltaic penetration and line loss indicated by the first correlation is greater than a first threshold value, determining that at least one to-be-processed power distribution area exists. For each to-be-processed power distribution area, second correlation between weather data in the first state data and line loss data in the first state data is determined, and then at least one candidate power distribution area is determined from all the to-be-processed power distribution areas according to the second correlation; and finally, according to the first state data of the at least one candidate power distribution area, determining a photovoltaic influence factor corresponding to each to-be-processed power distribution area. According to the method provided by the invention, the accurate determination of the photovoltaic influence factors of the line loss of the power distribution area is realized.

Description

Technical Field

[0001] This application relates to the field of power technology, and in particular to a method, apparatus and equipment for determining photovoltaic influencing factors of distribution transformer area line loss. Background Technology

[0002] As the proportion of distributed photovoltaic (PV) installations in distribution substations increases, its impact on substation line losses becomes increasingly significant. The impact of PV on line losses is implicit, and the relationship between the two may be non-linear and non-monotonic, depending on factors such as the installed capacity, connection location, and operating output of the distributed PV system, requiring further analysis.

[0003] Existing methods for determining photovoltaic (PV) influencing factors in distribution transformer substation line losses typically rely on historical line loss data from substations with PV grid connections, and analyze the degree of impact of PV influencing factors on line losses based on manual experience.

[0004] However, existing methods for determining the photovoltaic influencing factors of distribution substation line loss have the problem of poor accuracy in identifying these factors. Summary of the Invention

[0005] This application provides a method, apparatus, and equipment for determining photovoltaic influencing factors of distribution transformer area line loss, so as to improve the accuracy of determining photovoltaic influencing factors of distribution transformer area line loss.

[0006] In a first aspect, embodiments of this application provide a method for determining photovoltaic influencing factors of distribution substation line loss, including:

[0007] Acquire first status data of at least one distribution transformer area to be processed, each first status data being used to indicate the status data of photovoltaic access in the distribution transformer area to be processed;

[0008] A first correlation is determined between photovoltaic data and line loss data in all first-state data. The first correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss.

[0009] If the correlation between photovoltaic penetration and line loss indicated by the first correlation is detected to be greater than the first threshold, for each distribution area to be processed, a second correlation is determined between the weather data in the first state data and the line loss data in the first state data. The second correlation is used to indicate the line loss correlation between different weather states in the distribution area to be processed.

[0010] Based on each second correlation, at least one candidate distribution area is determined from all distribution areas to be processed.

[0011] Based on the first state data of at least one candidate distribution area, determine the photovoltaic influencing factors corresponding to each distribution area to be processed.

[0012] In one or more embodiments, determining the second correlation between weather data in the first state data and line loss data in the first state data includes:

[0013] Based on the first state data, the first line loss data indicating a sunny day and the second line loss data indicating a non-sunny day are determined respectively;

[0014] Perform a t-test on the first line loss data and the second line loss data to determine the value of the first degree of freedom corresponding to the test result;

[0015] If the value of the first degree of freedom is less than the second threshold, the value of the first effect size between the first line loss data and the second line loss data is determined based on the first line loss data and the second line loss data.

[0016] The second correlation is determined based on the first effect size value.

[0017] In one or more embodiments, determining the first correlation between photovoltaic data in all first-state data and line loss data in all first-state data includes:

[0018] Determine the third correlation corresponding to each preset duration in all first state data, the third correlation being used to indicate the degree of correlation between photovoltaic penetration and line loss in the preset duration;

[0019] Based on the weather data corresponding to each preset duration, the third correlation corresponding to all preset durations is divided into a first set where the weather data indicates sunny days and a second set where the weather data indicates non-sunny days.

[0020] Perform a t-test on the first set and the second set to determine the value of the second degree of freedom corresponding to the test result;

[0021] If the value of the second degree of freedom is less than the third threshold, the first correlation is determined based on the value of the second degree of freedom and the third correlation in the first set.

[0022] In one or more embodiments, determining the first correlation based on the second degree of freedom value and the third correlation in the first set includes:

[0023] Determine the first proportion of positive data and the second proportion of negative data in the third correlation of the first set;

[0024] The first correlation is determined based on the first difference between the first ratio and the second ratio and the second degree of freedom value.

[0025] In one or more embodiments, determining the photovoltaic influencing factors corresponding to each distribution substation to be processed based on the first state data of at least one candidate distribution substation includes:

[0026] For each candidate distribution area, at least one photovoltaic feature data is determined based on the photovoltaic data in the first state data;

[0027] Based on the line loss data in the first state data, the fourth correlation between the line loss data and each photovoltaic feature data is determined respectively;

[0028] Photovoltaic feature data with a fourth correlation greater than the fourth threshold are identified as at least one target photovoltaic feature data.

[0029] Based on at least one target photovoltaic characteristic data and the line loss data in the first state data of the at least one distribution transformer area to be processed, determine the photovoltaic influencing factors corresponding to each distribution transformer area to be processed.

[0030] In one or more embodiments, determining the photovoltaic influencing factors corresponding to each distribution area to be processed based on at least one target photovoltaic characteristic data and line loss data in the first state data of the at least one distribution area to be processed includes:

[0031] Based on the line loss data and photovoltaic data in the first state data of the at least one distribution substation to be processed, classify the at least one distribution substation to be processed.

[0032] For each type of distribution area set to be processed and each target photovoltaic feature data, a fifth correlation is determined between the target photovoltaic feature data and the line loss data in the distribution area set to be processed;

[0033] The target photovoltaic feature data with a fifth correlation greater than the fifth threshold are identified as the photovoltaic influencing factors of each distribution area to be processed in the set of distribution areas to be processed.

[0034] In one or more embodiments, determining at least one candidate distribution area from all distribution areas to be processed based on various second correlations includes:

[0035] All distribution radio areas to be processed are arranged according to the magnitude of the second correlation to obtain the first sequence;

[0036] Based on the first sequence and the first ratio, at least one candidate distribution radio area is determined.

[0037] Secondly, embodiments of this application provide a device for determining photovoltaic influencing factors of distribution area line loss, comprising:

[0038] The acquisition module is used to acquire first status data of at least one distribution transformer area to be processed, and each first status data is used to indicate the status data of photovoltaic access in the distribution transformer area to be processed.

[0039] The first determining module is used to determine the first correlation between photovoltaic data and line loss data in all first state data, and the first correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss;

[0040] The second determining module is used to determine a second correlation between weather data in the first state data and line loss data in the first state data for each distribution area to be processed when the correlation between photovoltaic penetration and line loss indicated by the first correlation is greater than a first threshold. The second correlation is used to indicate the correlation between line loss in different weather states in the distribution area to be processed.

[0041] The third determining module is used to determine at least one candidate distribution radio area from all distribution radio areas to be processed based on each second correlation.

[0042] The fourth determination module is used to determine the photovoltaic influencing factors corresponding to each distribution area to be processed based on the first state data of at least one candidate distribution area.

[0043] In one or more embodiments, the second determining module determines a second correlation between weather data in the first state data and line loss data in the first state data, specifically for:

[0044] Based on the first state data, the first line loss data indicating a sunny day and the second line loss data indicating a non-sunny day are determined respectively;

[0045] Perform a t-test on the first line loss data and the second line loss data to determine the value of the first degree of freedom corresponding to the test result;

[0046] If the value of the first degree of freedom is less than the second threshold, the value of the first effect size between the first line loss data and the second line loss data is determined based on the first line loss data and the second line loss data.

[0047] The second correlation is determined based on the first effect size value.

[0048] In one or more embodiments, the first determining module is specifically used for:

[0049] Determine the third correlation corresponding to each preset duration in all first state data, the third correlation being used to indicate the degree of correlation between photovoltaic penetration and line loss in the preset duration;

[0050] Based on the weather data corresponding to each preset duration, the third correlation corresponding to all preset durations is divided into a first set where the weather data indicates sunny days and a second set where the weather data indicates non-sunny days.

[0051] Perform a t-test on the first set and the second set to determine the value of the second degree of freedom corresponding to the test result;

[0052] If the value of the second degree of freedom is less than the third threshold, the first correlation is determined based on the value of the second degree of freedom and the third correlation in the first set.

[0053] In one or more embodiments, the first determining module determines the first correlation based on the second degree of freedom value and the third correlation in the first set, specifically for:

[0054] Determine the first proportion of positive data and the second proportion of negative data in the third correlation of the first set;

[0055] The first correlation is determined based on the first difference between the first ratio and the second ratio and the second degree of freedom value.

[0056] In one or more embodiments, the fourth determining module is specifically used for:

[0057] For each candidate distribution area, at least one photovoltaic feature data is determined based on the photovoltaic data in the first state data;

[0058] Based on the line loss data in the first state data, the fourth correlation between the line loss data and each photovoltaic feature data is determined respectively;

[0059] Photovoltaic feature data with a fourth correlation greater than the fourth threshold are identified as at least one target photovoltaic feature data.

[0060] Based on at least one target photovoltaic characteristic data and the line loss data in the first state data of the at least one distribution transformer area to be processed, determine the photovoltaic influencing factors corresponding to each distribution transformer area to be processed.

[0061] In one or more embodiments, the fourth determining module determines the photovoltaic influencing factors corresponding to each distribution area to be processed, based on at least one target photovoltaic characteristic data and the line loss data in the first state data of the at least one distribution area to be processed, specifically for:

[0062] Based on the line loss data and photovoltaic data in the first state data of the at least one distribution substation to be processed, classify the at least one distribution substation to be processed.

[0063] For each type of distribution area set to be processed and each target photovoltaic feature data, a fifth correlation is determined between the target photovoltaic feature data and the line loss data in the distribution area set to be processed;

[0064] The target photovoltaic feature data with a fifth correlation greater than the fifth threshold are identified as the photovoltaic influencing factors of each distribution area to be processed in the set of distribution areas to be processed.

[0065] In one or more embodiments, the third determining module is specifically used for:

[0066] All distribution radio areas to be processed are arranged according to the magnitude of the second correlation to obtain the first sequence;

[0067] Based on the first sequence and the first ratio, at least one candidate distribution radio area is determined.

[0068] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0069] The memory stores computer-executed instructions;

[0070] The processor executes computer execution instructions stored in the memory, such that the processor, when executed, is used to implement the method described in the first aspect and any of the embodiments above.

[0071] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods described in the first aspect and any of the embodiments above.

[0072] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, is used to implement the method for determining photovoltaic influencing factors of distribution substation line loss as described in the first aspect and various possible implementations of the first aspect.

[0073] This application provides a method, apparatus, and device for determining photovoltaic influencing factors of distribution area line loss. The method first acquires first state data for at least one distribution area to be processed. Then, it determines a first correlation between photovoltaic data and line loss data in all the first state data. If the correlation between photovoltaic penetration and line loss, indicated by the first correlation, is greater than a first threshold, a second correlation is determined between weather data and line loss data in the first state data for each distribution area to be processed. Next, based on each second correlation, at least one candidate distribution area is determined from all the distribution areas to be processed. Finally, based on the first state data of at least one candidate distribution area, the photovoltaic influencing factors corresponding to each distribution area to be processed are determined. In the above method, by determining the first correlation between photovoltaic data and line loss data in all first-state data, the impact of photovoltaic penetration on the line loss of the distribution area can be clarified. If the first correlation is greater than the first threshold, it indicates that photovoltaic penetration has a significant impact on line loss, providing a basis for subsequent analysis of photovoltaic influencing factors of distribution area line loss. For each distribution area to be processed, by determining the second correlation between weather data and line loss data in the first-state data, using weather data to represent photovoltaic output differences, more dimensions of information can be provided for the confirmation of photovoltaic influencing factors, and the confusion of multiple photovoltaic influencing factors can be avoided, which helps to more accurately identify the key factors affecting the line loss of the distribution area. Through the various second correlations of each distribution area to be processed, at least one candidate distribution area can be screened from all the distribution areas to be processed, which helps to further clarify the areas with strong correlation with photovoltaic penetration and line loss changes, and improve the pertinence and efficiency of analyzing photovoltaic influencing factors. Based on the first-state data of at least one candidate distribution area, the photovoltaic influencing factors of each distribution area can be quickly and effectively determined. Attached Figure Description

[0074] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0075] Figure 1 A flowchart illustrating the method for determining photovoltaic influencing factors of distribution substation line loss provided in this application embodiment. Figure 1 ;

[0076] Figure 2 A flowchart illustrating the method for determining photovoltaic influencing factors of distribution substation line loss provided in this application embodiment. Figure 2 ;

[0077] Figure 3 A flowchart illustrating the method for determining photovoltaic influencing factors of distribution substation line loss provided in this application embodiment. Figure 3 ;

[0078] Figure 4 This is a schematic diagram of the photovoltaic access point location structure provided in an embodiment of this application;

[0079] Figure 5 A schematic diagram of the structure of the photovoltaic influencing factors determination device for distribution substation line loss provided in an embodiment of this application;

[0080] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0081] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0082] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0083] Before introducing the embodiments of this application, the application background of the embodiments of this application will be explained first:

[0084] As the proportion of distributed photovoltaic (PV) installations in distribution substations increases, its impact on substation line losses becomes increasingly significant. The impact of PV on line losses is implicit, and the relationship between the two may be non-linear and non-monotonic, depending on factors such as the installed capacity, connection location, and operating output of the distributed PV system, requiring further analysis.

[0085] Existing methods for determining photovoltaic (PV) influencing factors in distribution transformer substation line losses typically rely on historical line loss data from substations with PV grid connections, and analyze the degree of impact of PV influencing factors on line losses based on manual experience.

[0086] However, existing methods for determining the photovoltaic influencing factors of distribution substation line loss have the problem of poor accuracy in identifying these factors.

[0087] The method for determining photovoltaic (PV) influencing factors of distribution transformer substation line loss provided in this application aims to solve the aforementioned technical problems of the prior art. The inventive concept of this application is as follows: Existing methods for determining PV influencing factors of distribution transformer substation line loss suffer from poor accuracy in identifying these factors. These methods primarily determine the PV influencing factors by analyzing the degree of influence of various PV-related factors on line loss. However, if the degree of influence of these PV-related factors on line loss can be quantified, and the influence can be quantified through correlation, the PV influencing factors of distribution transformer substation line loss can be accurately determined. Therefore, this application first determines the first correlation between the photovoltaic data in the first state data of at least one distribution substation to be processed with photovoltaic access and the line loss data in all first state data. If the first correlation is greater than a first threshold, then for each distribution substation to be processed, the second correlation between the weather data in the first state data and the line loss data in the first state data is determined. Then, based on each second correlation, at least one candidate distribution substation is determined from all distribution substations to be processed. By analyzing the first state data of at least one candidate distribution substation, the photovoltaic influencing factors corresponding to each distribution substation to be processed are accurately determined.

[0088] The execution subject of this application embodiment is an electronic device, which can be a terminal device, such as a laptop, desktop computer, or tablet computer, or a server. In practical applications, whether the electronic device is a terminal device or a server can be determined according to the actual situation, and no specific limitation is imposed on it.

[0089] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0090] Figure 1 A flowchart illustrating the method for determining photovoltaic influencing factors of distribution substation line loss provided in this application embodiment. Figure 1 .like Figure 1 As shown, the method for determining the photovoltaic influencing factors of line loss in this distribution substation includes the following steps:

[0091] S110. Obtain the first status data of at least one distribution radio area to be processed;

[0092] Each first status data is used to indicate the status data of photovoltaic access in the distribution area to be processed.

[0093] In this step, the status data of photovoltaic access is determined from at least one distribution transformer area to be processed, and it is used as the first status data. In order to analyze and determine the photovoltaic influencing factors of the distribution transformer area with photovoltaic access, it is necessary to first obtain the first status data of at least one distribution transformer area to be processed.

[0094] For example, the status data includes the distribution area's line loss data, the distribution area's static parameters, the distribution area's power data, the distribution area's photovoltaic data, and the distribution area's weather data.

[0095] The line loss data for the distribution transformer area includes monthly and daily line loss data for the distribution transformer area;

[0096] The static parameters of a distribution transformer area include the rated capacity of the transformers, the electrical network topology, and the number of low-voltage users.

[0097] The power consumption data for the distribution area includes the total power consumption of the distribution area and the daily power consumption of users.

[0098] The photovoltaic data for a distribution substation includes the number of users connecting to photovoltaic systems, the installed photovoltaic capacity, the location of photovoltaic connections, and the photovoltaic power generation.

[0099] The weather data for the distribution area includes daily weather data for the distribution area.

[0100] In one possible implementation, before obtaining the first state data of at least one distribution transformer area to be processed, the state data of all distribution transformer areas to be processed can be cleaned and classified to obtain state data indicating that the distribution transformer area to be processed has photovoltaic access, i.e., the first state data.

[0101] Data cleaning processing is performed on the status data of all distribution substations to be processed, including: filling in missing values ​​and deleting or correcting outliers. For example, abnormal line loss data is deleted, and data with negative line loss values ​​or line loss data exceeding the line loss threshold is deleted.

[0102] The status data of all distribution stations to be processed is classified and processed, including: weather data is divided into sunny days and non-sunny days, and distribution stations are divided into those with photovoltaic access and those without photovoltaic access.

[0103] S120. Determine the first correlation between photovoltaic data and line loss data in all first-state data;

[0104] The first correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss.

[0105] In this step, the photovoltaic penetration rate is determined based on the photovoltaic data in all the first-state data, and then the first correlation between the photovoltaic data and the line loss data in all the first-state data is determined, which can indicate the degree of correlation between photovoltaic penetration and line loss.

[0106] For example, photovoltaic penetration rate is the percentage of the sum of photovoltaic installed capacity in a distribution substation area to the rated capacity of the distribution transformer in that substation area.

[0107] In one possible implementation, determining the first correlation can be done using at least one of Spearman's rank correlation coefficient method, distance correlation coefficient method, and grey relational analysis method.

[0108] 1) The Spearman rank correlation coefficient method converts variable values ​​into ranks (sortings), calculates the linear correlation between ranks, and measures the strength of the monotonic relationship. Its advantages include not requiring the data to follow a normal distribution, being insensitive to outliers, and being suitable for non-linear but monotonic scenarios.

[0109] 2) The distance correlation coefficient method measures the overall dependence between variables based on the Euclidean distance between sample points, and can detect both linear and nonlinear correlations. Its advantage is that the distance correlation coefficient is 0 if and only if the variables are independent, and it is sensitive to nonlinear relationships. It is suitable for scenarios where the data distribution is irregular and exhibits a curvilinear relationship (e.g., the line loss rate increases after photovoltaic penetration exceeds a threshold).

[0110] 3) Grey relational analysis measures the degree of correlation by comparing the geometric similarity of data sequence curves. It is suitable for scenarios with small samples and unknown data distribution. The steps are to determine the reference data sequence (e.g., line loss rate) and the comparison data sequence (photovoltaic penetration rate), perform dimensionless processing on the data, and calculate the correlation coefficient and correlation degree (the larger the correlation degree value, the stronger the correlation). The advantage is that it does not depend on the data distribution and is suitable for nonlinear and small sample sizes.

[0111] S130. If the correlation between photovoltaic penetration and line loss indicated by the first correlation is greater than the first threshold, for each distribution area to be processed, determine the second correlation between the weather data in the first state data and the line loss data in the first state data.

[0112] The second correlation is used to indicate the line loss correlation between different weather conditions in the distribution area to be processed.

[0113] In this step, if the correlation between photovoltaic penetration and line loss indicated by the first correlation is greater than the first threshold, then for each distribution area to be processed corresponding to the first correlation being greater than the first threshold, the second correlation between the weather data in the first state data and the line loss data in the first state data is further determined, which can indicate the line loss correlation between different weather conditions in the distribution area to be processed.

[0114] For example, weather data can be used as proxy data to represent the photovoltaic output level of a distribution area to be processed with photovoltaic access. That is, determining the second correlation between the weather data in the first state data and the line loss data in the first state data can indicate the line loss correlation between different weather conditions in the distribution area to be processed, and can also indicate the degree of correlation between photovoltaic penetration and line loss.

[0115] In one possible implementation, determining the second correlation between weather data in the first state data and line loss data in the first state data further includes the following steps:

[0116] Step 1: Based on the first state data, determine the first line loss data indicating sunny weather and the second line loss data indicating non-sunny weather.

[0117] For example, if the weather data is divided into weather data indicating sunny days and weather data indicating non-sunny days, then based on the first state data, the line loss data indicating sunny days in the first state data is determined as the first line loss data, and the line loss data indicating non-sunny days in the second state data is determined as the second line loss data.

[0118] Step 2: Perform a t-test on the first and second line loss data to determine the first degree of freedom value corresponding to the test results.

[0119] In one possible implementation, if the first line loss data and the second line loss data conform to a normal distribution, a t-test is performed on the first line loss data and the second line loss data to determine the value of the first degree of freedom corresponding to the test result.

[0120] If the first and second line loss data do not conform to a normal distribution, then a Mann-Whitney nonparametric test can be performed on the first and second line loss data to determine the value of the first degree of freedom corresponding to the test result.

[0121] Step 3: If the value of the first degree of freedom is less than the second threshold, determine the value of the first effect size between the first line loss data and the second line loss data based on the first line loss data and the second line loss data.

[0122] For example, the first effect size value is used to indicate the strength and directionality of the correlation between the first line loss data and the second line loss data.

[0123] The first line loss data includes the total number of first line loss samples, the first mean of the first line loss data, and the first standard deviation of the first line loss data. The second line loss data includes the total number of second line loss samples, the second mean of the second line loss data, and the second standard deviation of the second line loss data.

[0124] In one possible implementation, if the first degree of freedom value p is less than the second threshold of 0.05, the first effect size value can be calculated according to the following formula:

[0125]

[0126] Where d represents the first effect size, s pooled Let μ1 represent the first mean of the first line loss data, μ2 represent the second mean of the second line loss data, n1 represent the total number of the first line loss samples, n2 represent the total number of the second line loss samples, and σ1 represent the first standard deviation of the first line loss data and σ2 represent the second standard deviation of the second line loss data.

[0127] When d is greater than 0, it indicates a negative correlation (for example, high photovoltaic output means low line loss). When d is less than 0, it indicates a positive correlation. If |d|≥0.2, it can indicate a small effect. If |d|≥0.5, it can indicate a medium effect. If |d|≥0.8, it can indicate a large effect.

[0128] Step 4: Determine the second correlation based on the first effect size value.

[0129] For example, based on the first effect size value, the second correlation can be determined by combining the first degree of freedom value and the difference between the first mean and the second mean.

[0130] In one possible implementation, the second correlation can be calculated according to the following formula:

[0131] Q = d × sign(μ2 - μ1) × (1 - p1)

[0132] Where Q represents the second correlation, d represents the first effect size, p1 represents the first degree of freedom, μ1 represents the first mean of the first line loss data, and μ2 represents the second mean of the second line loss data.

[0133] S140. Based on each second correlation, determine at least one candidate distribution area from all distribution areas to be processed.

[0134] In one possible implementation, step S140 may further include the following steps:

[0135] Step 1: Sort all distribution radio areas to be processed according to the magnitude of the second correlation to obtain the first sequence;

[0136] For example, all distribution radio areas to be processed are arranged according to the magnitude of the second correlation to obtain the first sequence corresponding to the distribution radio areas to be processed.

[0137] For example, if the second correlation 1 corresponding to distribution area 1 to be processed is 0.3, the second correlation 2 corresponding to distribution area 2 to be processed is 0.7, and the second correlation 3 corresponding to distribution area 3 to be processed is 0.5, then the first sequence obtained by arranging according to the size of the second correlation is "distribution area 2 to be processed, distribution area 3 to be processed, distribution area 1 to be processed".

[0138] Step 2: Determine at least one candidate distribution radio area based on the first sequence and the first ratio.

[0139] For example, based on a first ratio, at least one distribution radio area corresponding to the first ratio is determined from the beginning of the first sequence as a candidate distribution radio area.

[0140] For example, at least one distribution area from the first 30% of the distribution areas to be processed can be selected as candidate distribution areas.

[0141] S150. Based on the first state data of at least one candidate distribution area, determine the photovoltaic influencing factors corresponding to each distribution area to be processed.

[0142] In this step, at least one factor with a high degree of correlation with photovoltaics and affecting line loss is determined based on the first state data of at least one candidate distribution substation. The at least one factor affecting line loss is then screened to determine the photovoltaic influencing factors corresponding to each distribution substation to be processed.

[0143] In one possible implementation, at least one factor that is highly correlated with photovoltaics and affects line loss can be identified. Correlation analysis is then performed on the at least one factor affecting line loss to identify at least one major factor affecting line loss. Subsequently, among the at least one major factor affecting line loss, the major factor with the highest correlation is identified as the photovoltaic influencing factor corresponding to each distribution area to be processed.

[0144] The method for determining photovoltaic influencing factors of distribution area line loss provided in this application embodiment first obtains first state data of at least one distribution area to be processed. Then, it determines the first correlation between photovoltaic data and line loss data in all first state data. If the correlation between photovoltaic penetration and line loss indicated by the first correlation is detected to be greater than a first threshold, a second correlation is determined between weather data and line loss data in the first state data for each distribution area to be processed. Then, based on each second correlation, at least one candidate distribution area is determined from all distribution areas to be processed. Finally, based on the first state data of at least one candidate distribution area, the photovoltaic influencing factors corresponding to each distribution area to be processed are determined. In this embodiment, by determining the first correlation between photovoltaic data and line loss data in all first-state data, the impact of photovoltaic penetration on the line loss of the distribution area can be clarified. If the first correlation is greater than a first threshold, it indicates that photovoltaic penetration has a significant impact on line loss, providing a basis for subsequent analysis of photovoltaic influencing factors of distribution area line loss. For each distribution area to be processed, by determining the second correlation between weather data and line loss data in the first-state data, using weather data to represent photovoltaic output differences, more dimensions of information can be provided for the confirmation of photovoltaic influencing factors, and the confusion of multiple photovoltaic influencing factors can be avoided, which helps to more accurately identify the key factors affecting the line loss of the distribution area. Through the various second correlations of each distribution area to be processed, at least one candidate distribution area can be screened from all the distribution areas to be processed, which helps to further clarify the areas with strong correlation to photovoltaic penetration and line loss changes, and improve the pertinence and efficiency of analyzing photovoltaic influencing factors. Based on the first-state data of at least one candidate distribution area, the photovoltaic influencing factors of each distribution area can be quickly and effectively determined.

[0145] Based on the above embodiments, Figure 2 A flowchart illustrating the method for determining photovoltaic influencing factors of distribution substation line loss provided in this application embodiment. Figure 2 .like Figure 2 As shown, a possible implementation of step S120 above also includes the following steps:

[0146] S210. Determine the third correlation corresponding to each preset duration in all first state data;

[0147] The third correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss over a preset time period.

[0148] In this step, based on the photovoltaic data and line loss data in the first state data, a third correlation is determined for each preset time period in all the first state data, which is used to indicate the degree of correlation between photovoltaic penetration and line loss in the preset time period.

[0149] In one possible implementation, for each preset duration, at least one of the Spearman rank correlation coefficient method, distance correlation coefficient method, and grey relational analysis method can be used to analyze the degree of correlation between photovoltaic penetration and line loss, and determine the third correlation corresponding to each preset duration.

[0150] In addition, a dictionary approach can be used to store each preset duration and its corresponding third correlation as key-value pairs, where the key of the dictionary is each preset duration and the value is the third correlation corresponding to that preset duration.

[0151] S220. Based on the weather data corresponding to each preset duration, divide the third correlation corresponding to all preset durations into a first set where the weather data indicates sunny days and a second set where the weather data indicates non-sunny days.

[0152] In this step, the weather data can be divided into weather data indicating sunny days and weather data indicating non-sunny days. Based on the weather data corresponding to each preset duration, the third correlation corresponding to all preset durations is divided to obtain a first set of weather data indicating sunny days and a second set of weather data indicating non-sunny days.

[0153] In one possible implementation, weather data can include terms such as "sunny, light rain, cloudy, and heavy rain". In this case, "sunny" corresponds to a sunny weather data indication, while "light rain, cloudy, and heavy rain" corresponds to a non-sunny weather data indication.

[0154] S230. Perform a t-test on the first set and the second set to determine the value of the second degree of freedom corresponding to the test result.

[0155] In one possible implementation, if the third correlation in the first set and the second set conforms to a normal distribution and homogeneity of variance, then an independent samples t-test is used to determine the value of the second degree of freedom corresponding to the test result.

[0156] If the third correlation in the first and second sets does not conform to homogeneity of variance, then the Welch-corrected t-test is used to determine the value of the second degree of freedom corresponding to the test result.

[0157] S240. If the value of the second degree of freedom is less than the third threshold, determine the first correlation based on the value of the second degree of freedom and the third correlation in the first set.

[0158] In this step, if the value of the second degree of freedom is less than the third threshold, the first set corresponding to the value of the second degree of freedom is determined, and then the first correlation is determined based on the value of the second degree of freedom and the third correlation in the first set.

[0159] In one possible implementation, determining the first correlation based on the second degree of freedom value and the third correlation in the first set further includes the following steps:

[0160] Step 1: Determine the first proportion of positive data and the second proportion of negative data in the third correlation of the first set.

[0161] For example, positive data can indicate that the third correlation is positive, meaning that as photovoltaic penetration increases over a preset period of time, the corresponding line loss will also increase.

[0162] Negative data can indicate that the third correlation is negative, meaning that as photovoltaic penetration increases during the preset time period, the corresponding line loss decreases.

[0163] In one possible implementation, the positive data in the third correlation of the first set is divided by all data to obtain a first ratio, and the negative data in the third correlation of the first set is divided by all data to obtain a second ratio. The first and second ratios can be used to illustrate the strength and directionality of the correlation between the positive and negative data in the third correlation.

[0164] Step 2: Determine the first correlation based on the first difference and the second degree of freedom value between the first ratio and the second ratio.

[0165] For example, the first correlation can be calculated according to the following formula:

[0166] P = (1-p2) × (B1-B2) / 100

[0167] Where P represents the first correlation, p2 represents the second degree of freedom, B1 represents the first proportion, and B2 represents the second proportion.

[0168] The method for determining photovoltaic influencing factors of distribution area line loss provided in this application embodiment first determines the third correlation corresponding to each preset duration in all first state data; wherein, the third correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss in the preset duration. Then, according to the weather data corresponding to each preset duration, the third correlations corresponding to all preset durations are divided into a first set where the weather data indicates sunny days and a second set where the weather data indicates non-sunny days. Then, a t-test is performed on the first set and the second set to determine the second degree of freedom value corresponding to the test result. Finally, if the second degree of freedom value is less than a third threshold, the first correlation is determined according to the second degree of freedom value and the third correlation in the first set. In this embodiment, by determining the third correlation corresponding to each preset duration in all first state data, the degree of correlation between photovoltaic penetration and line loss within the preset duration can be indicated, quantifying the impact of photovoltaic penetration on line loss in different time periods. Based on the weather data corresponding to each preset duration, the third correlation is divided into sunny and non-sunny conditions, resulting in a first set of weather data indicating sunny days and a second set indicating non-sunny days. A t-test is performed on the first and second sets to confirm whether the relationship between photovoltaic penetration and line loss differs significantly under different weather conditions. The degrees of freedom obtained from the test results are then compared with a third threshold to determine whether the relationship between photovoltaic penetration and line loss is significant under different weather conditions. If the second degree of freedom value is less than the third threshold, the first correlation is determined based on the second degree of freedom value and the third correlation in the first set. This multi-level correlation analysis provides a basis for accurately and efficiently determining the photovoltaic influencing factors of distribution area line loss.

[0169] Based on the above embodiments, Figure 3 A flowchart illustrating the method for determining photovoltaic influencing factors of distribution substation line loss provided in this application embodiment. Figure 3 .like Figure 3 As shown, a possible implementation of step S150 above also includes the following steps:

[0170] S310. For each candidate distribution area, determine at least one photovoltaic characteristic data based on the photovoltaic data in the first state data.

[0171] In this step, for each candidate distribution area, in order to analyze and determine the photovoltaic influencing factors, at least one photovoltaic characteristic data can be determined based on the photovoltaic data in the first state data.

[0172] For example, photovoltaic characteristic data includes photovoltaic penetration rate characteristics, photovoltaic power generation to total electricity consumption ratio characteristics, photovoltaic dispersion characteristics, and photovoltaic grid connection point location.

[0173] The photovoltaic penetration rate is the percentage of the sum of photovoltaic installed capacity in a distribution substation area to the rated capacity of the distribution transformer in that area.

[0174] The ratio of photovoltaic power generation to total electricity consumption can reflect the photovoltaic power generation situation;

[0175] The dispersion characteristics of photovoltaics can reflect the number of photovoltaic users and photovoltaic capacity, and thus affect the line loss of the distribution transformer area by influencing the power flow distribution of the distribution transformer area;

[0176] The location characteristics of photovoltaic access points reflect the spatial impact of the location of photovoltaic access points on the line loss of the distribution area. The larger the value of the location characteristics of photovoltaic access points, the more dispersed the distribution of photovoltaic access points or the farther away the photovoltaic access points are from the head end.

[0177] Figure 4 This is a schematic diagram of the photovoltaic access point location structure provided in the embodiments of this application, as shown below. Figure 4 As shown, the distances from the access points of photovoltaic PV1, photovoltaic PV2 and photovoltaic PV3 to the head end are L1, L2 and L3 respectively, and the capacities of photovoltaic PV1, photovoltaic PV2 and photovoltaic PV3 are S1, S2 and S3 respectively.

[0178] S320. Based on the line loss data in the first state data, determine the fourth correlation between the line loss data and each photovoltaic characteristic data.

[0179] In this step, based on the line loss data in the first state data and each photovoltaic feature data, the fourth correlation between the line loss data and each photovoltaic feature data can be determined.

[0180] In one possible implementation, the line loss data and photovoltaic feature data corresponding to each preset duration can be combined to obtain the feature matrix of each candidate distribution area. For each row or column in the feature matrix, the Pearson coefficient value between the line loss data and photovoltaic feature data corresponding to each preset duration is determined. Then, the Pearson coefficient value is determined as the fourth correlation between the line loss data and each photovoltaic feature data.

[0181] S330. Photovoltaic feature data with a fourth correlation greater than the fourth threshold are identified as at least one target photovoltaic feature data.

[0182] In this step, photovoltaic feature data with high relevance can be screened based on the fourth threshold. Photovoltaic feature data with a fourth relevance greater than the fourth threshold are then identified as at least one target photovoltaic feature data.

[0183] For example, the greater the fourth correlation, the greater the correlation between photovoltaic feature data and line loss data. The fourth threshold can be set to 0.3, and photovoltaic feature data with a fourth correlation greater than 0.3 can be identified as at least one target photovoltaic feature data. The target photovoltaic feature data is the feature data that has a major impact on the line loss data.

[0184] S340. Based on the line loss data in the first state data of at least one target photovoltaic characteristic data and at least one distribution transformer area to be processed, determine the photovoltaic influencing factors corresponding to each distribution transformer area to be processed.

[0185] In this step, based on the line loss data in the first state data of at least one distribution transformer area to be processed, at least one distribution transformer area to be processed can be classified. For at least one distribution transformer area to be processed corresponding to each type, the degree of correlation between at least one target photovoltaic characteristic data and line loss data is determined. Based on the magnitude of the correlation, the photovoltaic influencing factors corresponding to each distribution transformer area to be processed are determined.

[0186] In one possible implementation, step S340 above may further include the following steps:

[0187] Step 1: Classify at least one distribution substation to be processed based on the line loss data and photovoltaic data in the first state data of at least one distribution substation to be processed.

[0188] For example, based on the magnitude of the line loss data and the magnitude of the photovoltaic data in the first state data of at least one distribution substation to be processed, at least one distribution substation to be processed can be classified to obtain at least one type of distribution substation to be processed.

[0189] In one possible implementation, the K-means clustering method can be used to perform cluster analysis on at least one distribution radio station to be processed, and the number of types to be classified for at least one distribution radio station to be processed can be determined according to the elbow method.

[0190] Based on the line loss data and photovoltaic data in the first state data of at least one distribution transformer area to be processed, the sum of squared errors between each line loss data and photovoltaic data and the line loss data and photovoltaic data corresponding to the initial cluster center can be determined. Different K values ​​can be set, and the corresponding sum of squared errors can be calculated under different K values. The K value where the sum of squared errors changes significantly is determined as the number of types for classifying at least one distribution transformer area to be processed.

[0191] Step 2: For each type of distribution area set to be processed and each target photovoltaic feature data, determine the fifth correlation between the target photovoltaic feature data and the line loss data in the distribution area set to be processed.

[0192] For example, for each type of distribution area set to be processed and each target photovoltaic feature data, a fifth correlation between the target photovoltaic feature data and the line loss data in the distribution area set to be processed can be determined by using at least one of the Spearman rank correlation coefficient method, the distance correlation coefficient method, and the grey relational analysis method.

[0193] Step 3: Identify the target photovoltaic feature data with a fifth correlation greater than the fifth threshold as the photovoltaic influencing factors of each distribution area to be processed in the set of distribution areas to be processed.

[0194] In one possible implementation, the fifth correlation can be sorted in order of magnitude, and the target photovoltaic feature data corresponding to the fifth correlation greater than the fifth threshold can be determined as the photovoltaic influencing factors of each distribution area to be processed in the set of distribution areas to be processed.

[0195] The method for determining photovoltaic (PV) influencing factors of distribution area line loss provided in this application embodiment firstly determines at least one PV feature data for each candidate distribution area based on PV data in the first state data; then, based on the line loss data in the first state data, it determines the fourth correlation between the line loss data and each PV feature data; subsequently, it determines PV feature data with a fourth correlation greater than a fourth threshold as at least one target PV feature data; finally, it determines the PV influencing factors corresponding to each distribution area to be processed based on at least one target PV feature data and the line loss data in the first state data of at least one distribution area to be processed. In this embodiment, for each candidate distribution area, at least one PV feature data is determined based on the PV data in the first state data, and the fourth correlation between the line loss data and each PV feature data is analyzed and determined. This can accurately identify the actual impact of PV feature data on line loss, and determine PV feature data with a correlation greater than the fourth threshold as target PV feature data. This can quantify and screen PV feature data that have a significant impact on line loss. By selecting target PV feature data with significant correlation, the PV influencing factors of each distribution area to be processed can be determined more accurately.

[0196] Based on the above embodiments, the following is a photovoltaic influencing factor determination device for distribution area line loss provided in this application embodiment, which can execute the method provided in the above method embodiments.

[0197] Figure 5 This is a schematic diagram of the photovoltaic influencing factors determination device for distribution area line loss provided in an embodiment of this application. Figure 5 As shown, the photovoltaic influencing factor determination device 500 for the distribution area line loss includes:

[0198] The acquisition module 510 is used to acquire first status data of at least one distribution transformer area to be processed, wherein each first status data is used to indicate the status data of photovoltaic access in the distribution transformer area to be processed.

[0199] The first determining module 520 is used to determine the first correlation between photovoltaic data and line loss data in all first state data, wherein the first correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss;

[0200] The second determining module 530 is used to determine a second correlation between weather data in the first state data and line loss data in the first state data for each distribution area to be processed when the degree of correlation between photovoltaic penetration and line loss indicated by the first correlation is greater than a first threshold. The second correlation is used to indicate the correlation between line loss in different weather states in the distribution area to be processed.

[0201] The third determining module 540 is used to determine at least one candidate distribution radio area from all distribution radio areas to be processed based on each second correlation.

[0202] The fourth determining module 550 is used to determine the photovoltaic influencing factors corresponding to each distribution area to be processed based on the first state data of at least one candidate distribution area.

[0203] In one or more embodiments, the second determining module 530 determines a second correlation between weather data in the first state data and line loss data in the first state data, specifically for:

[0204] Based on the first state data, the first line loss data indicating sunny days and the second line loss data indicating non-sunny days are determined respectively;

[0205] Perform a t-test on the first and second line loss data to determine the value of the first degree of freedom corresponding to the test results;

[0206] If the value of the first degree of freedom is less than the second threshold, the value of the first effect size between the first line loss data and the second line loss data is determined based on the first line loss data and the second line loss data.

[0207] The second correlation is determined based on the first effect size value.

[0208] In one or more embodiments, the first determining module 520 is specifically used for:

[0209] Determine the third correlation corresponding to each preset duration in all first state data, wherein the third correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss in the preset duration;

[0210] Based on the weather data corresponding to each preset duration, the third correlation corresponding to all preset durations is divided into a first set where the weather data indicates sunny days and a second set where the weather data indicates non-sunny days.

[0211] Perform a t-test on the first set and the second set to determine the value of the second degree of freedom corresponding to the test result;

[0212] If the value of the second degree of freedom is less than the third threshold, the first correlation is determined based on the value of the second degree of freedom and the third correlation in the first set.

[0213] In one or more embodiments, the first determining module 520 determines the first correlation based on the second degree of freedom value and the third correlation in the first set, specifically for:

[0214] Determine the first proportion of positive data and the second proportion of negative data in the third correlation of the first set;

[0215] The first correlation is determined based on the first difference and the second degree of freedom value between the first ratio and the second ratio.

[0216] In one or more embodiments, the fourth determining module 550 is specifically used for:

[0217] For each candidate distribution area, at least one photovoltaic characteristic data is determined based on the photovoltaic data in the first state data;

[0218] Based on the line loss data in the first state data, the fourth correlation between the line loss data and each photovoltaic characteristic data is determined respectively;

[0219] Photovoltaic feature data with a fourth correlation greater than the fourth threshold are identified as at least one target photovoltaic feature data.

[0220] Based on the line loss data in at least one target photovoltaic characteristic data and at least one distribution transformer area to be processed in the first state data, determine the photovoltaic influencing factors corresponding to each distribution transformer area to be processed.

[0221] In one or more embodiments, the fourth determining module 550 determines the photovoltaic influencing factors corresponding to each distribution area to be processed based on at least one target photovoltaic characteristic data and line loss data in the first state data of at least one distribution area to be processed, specifically for:

[0222] Classify at least one distribution substation to be processed based on the line loss data and photovoltaic data in the first state data of at least one distribution substation to be processed.

[0223] For each type of distribution area set to be processed and each target photovoltaic feature data, a fifth correlation is determined between the target photovoltaic feature data and the line loss data in the distribution area set to be processed;

[0224] The target photovoltaic feature data with a fifth correlation greater than the fifth threshold are identified as the photovoltaic influencing factors of each distribution area to be processed in the set of distribution areas to be processed.

[0225] In one or more embodiments, the third determining module 540 is specifically used for:

[0226] All distribution radio areas to be processed are arranged according to the magnitude of the second correlation to obtain the first sequence;

[0227] Based on the first sequence and the first ratio, at least one candidate distribution radio area is determined.

[0228] Based on the above embodiments, Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 600 includes: a processor 610, a memory 620, and a bus 630;

[0229] The memory 620 is used to store the computer-executed instructions of the processor 610;

[0230] The processor 610 is configured to execute the technical solutions of any of the foregoing method embodiments by executing computer execution instructions.

[0231] Optionally, the memory 620 can be either standalone or integrated with the processor 610.

[0232] Optionally, memory 620 may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0233] Bus 630 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, only one thick line is used to represent a bus in the accompanying drawings of this application, but this does not imply that there is only one bus or one type of bus.

[0234] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0235] The electronic device is used to execute the technical solution of any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0236] This application also provides a computer-readable storage medium storing computer-executable instructions thereon, which, when executed by a processor, are used to implement the technical solutions provided in any of the above method embodiments.

[0237] This application also provides a computer program product, including a computer program, which includes computer instructions stored in a computer-readable storage medium. When the computer program is executed by a processor, it is used to implement the technical solutions provided in any of the above method embodiments.

[0238] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0239] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0240] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0241] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0242] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0243] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0244] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0245] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0246] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for determining photovoltaic influencing factors of distribution transformer area line loss, characterized in that, include: Acquire first status data of at least one distribution transformer area to be processed, each first status data being used to indicate the status data of photovoltaic access in the distribution transformer area to be processed; A first correlation is determined between photovoltaic data and line loss data in all first-state data. The first correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss. If the correlation between photovoltaic penetration and line loss indicated by the first correlation is detected to be greater than the first threshold, for each distribution area to be processed, a second correlation is determined between the weather data in the first state data and the line loss data in the first state data. The second correlation is used to indicate the line loss correlation between different weather states in the distribution area to be processed. Based on each second correlation, at least one candidate distribution area is determined from all distribution areas to be processed. Based on the first state data of at least one candidate distribution area, determine the photovoltaic influencing factors corresponding to each distribution area to be processed.

2. The method according to claim 1, characterized in that, Determining the second correlation between the weather data in the first state data and the line loss data in the first state data includes: Based on the first state data, the first line loss data indicating a sunny day and the second line loss data indicating a non-sunny day are determined respectively; Perform a t-test on the first line loss data and the second line loss data to determine the first degree of freedom value corresponding to the test result; If the value of the first degree of freedom is less than the second threshold, the value of the first effect size between the first line loss data and the second line loss data is determined based on the first line loss data and the second line loss data. The second correlation is determined based on the first effect size value.

3. The method according to claim 1 or 2, characterized in that, The determination of the first correlation between photovoltaic data and line loss data in all first-state data includes: Determine the third correlation corresponding to each preset duration in all first state data, the third correlation being used to indicate the degree of correlation between photovoltaic penetration and line loss in the preset duration; Based on the weather data corresponding to each preset duration, the third correlation corresponding to all preset durations is divided into a first set where the weather data indicates sunny days and a second set where the weather data indicates non-sunny days. Perform a t-test on the first set and the second set to determine the value of the second degree of freedom corresponding to the test result; If the value of the second degree of freedom is less than the third threshold, the first correlation is determined based on the value of the second degree of freedom and the third correlation in the first set.

4. The method according to claim 3, characterized in that, The step of determining the first correlation based on the second degree of freedom value and the third correlation in the first set includes: Determine the first proportion of positive data and the second proportion of negative data in the third correlation of the first set; The first correlation is determined based on the first difference between the first ratio and the second ratio and the second degree of freedom value.

5. The method according to claim 1 or 2, characterized in that, The step of determining the photovoltaic influencing factors corresponding to each distribution area to be processed based on the first state data of at least one candidate distribution area includes: For each candidate distribution area, at least one photovoltaic feature data is determined based on the photovoltaic data in the first state data; Based on the line loss data in the first state data, the fourth correlation between the line loss data and each photovoltaic feature data is determined respectively; Photovoltaic feature data with a fourth correlation greater than the fourth threshold are identified as at least one target photovoltaic feature data. Based on at least one target photovoltaic characteristic data and the line loss data in the first state data of the at least one distribution transformer area to be processed, determine the photovoltaic influencing factors corresponding to each distribution transformer area to be processed.

6. The method according to claim 5, characterized in that, The step of determining the photovoltaic influencing factors corresponding to each distribution area to be processed based on at least one target photovoltaic characteristic data and the line loss data in the first state data of the at least one distribution area to be processed includes: Based on the line loss data and photovoltaic data in the first state data of the at least one distribution substation to be processed, classify the at least one distribution substation to be processed. For each type of distribution area set to be processed and each target photovoltaic feature data, a fifth correlation is determined between the target photovoltaic feature data and the line loss data in the distribution area set to be processed; The target photovoltaic feature data with a fifth correlation greater than the fifth threshold are identified as the photovoltaic influencing factors of each distribution area to be processed in the set of distribution areas to be processed.

7. The method according to any one of claims 1-2, characterized in that, The step of determining at least one candidate distribution area from all distribution areas to be processed based on each second correlation includes: All distribution radio areas to be processed are arranged according to the magnitude of the second correlation to obtain the first sequence; Based on the first sequence and the first ratio, at least one candidate distribution radio area is determined.

8. A device for determining photovoltaic influencing factors of distribution area line loss, characterized in that, include: The acquisition module is used to acquire first status data of at least one distribution transformer area to be processed, and each first status data is used to indicate the status data of photovoltaic access in the distribution transformer area to be processed. The first determining module is used to determine the first correlation between photovoltaic data and line loss data in all first state data, and the first correlation is used to indicate the degree of correlation between photovoltaic penetration and line loss; The second determining module is used to determine a second correlation between weather data in the first state data and line loss data in the first state data for each distribution area to be processed when the correlation between photovoltaic penetration and line loss indicated by the first correlation is greater than a first threshold. The second correlation is used to indicate the correlation between line loss in different weather states in the distribution area to be processed. The third determining module is used to determine at least one candidate distribution radio area from all distribution radio areas to be processed based on each second correlation. The fourth determination module is used to determine the photovoltaic influencing factors corresponding to each distribution area to be processed based on the first state data of at least one candidate distribution area.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

Images