A distributed photovoltaic influence detection method and system for line loss rate of a power distribution area

Abstract

A distributed photovoltaic influence detection method and system for detecting the influence of a district line loss rate, characterized in that the method comprises the following steps: step 1, collecting the load curve of a district power user, and using a clustering algorithm to analyze the load curve to realize primary district classification; step 2, based on the number of distributed photovoltaic access points in each district, equally dividing the district to obtain secondary district classification; step 3, based on the primary district classification and the secondary district classification, constructing a correlation between the district line loss rate and the photovoltaic power ratio of all districts in the district classification, and finding a line loss rate inflection point from the correlation; step 4, obtaining the confidence interval of the line loss rate inflection point, and detecting the line loss rate trend of the to-be-tested district according to the confidence interval. The method accurately defines the influence of the distributed photovoltaic device on the district line loss rate, and points the direction for the grid-connected mode of the distributed photovoltaic and the simplified calculation mode of the line loss rate after the photovoltaic is connected to the grid.

Description

Technical Field

[0001] This invention relates to the field of power systems, and more specifically, to a method and system for detecting the impact of distributed photovoltaic power generation on the line loss rate of transformer substations. Background Technology

[0002] Currently, line loss rate is an important indicator for measuring the technical and economic efficiency of power grids. It is a comprehensive technical and economic indicator that reflects the planning, design, operation, and management level of power systems. At the same time, power line loss is also a very important aspect of internal management within the power industry, and the quality and level of power line loss management have a crucial impact on the overall energy efficiency of the power grid.

[0003] In recent years, distributed photovoltaic (PV) power has developed rapidly, with a significant increase in residential PV installations. The impact of distributed PV on low-voltage distribution area line losses is closely related to the PV grid connection location and installed capacity. When the PV output of a distribution area can be absorbed locally, the line loss decreases; when a large amount of PV output is fed back to the grid, the line loss increases. A certain provincial power grid company has more than 500,000 public distribution areas. The electricity load distribution, time and space distribution, distributed PV grid connection location, and installed capacity of each area are different, making it impossible to effectively assess the impact of distributed PV on the line loss of each area.

[0004] It is evident that current technologies cannot accurately determine the specific impact of distributed photovoltaic (PV) systems on grid line loss rates based on the grid connection method and location. As the scale of distributed PV grid connection gradually increases, its impact on grid line loss rates becomes increasingly apparent. If the implications of this impact cannot be measured reasonably, it will not only hinder the grid connection of distributed PV technology but also make it difficult for the power grid to operate safely according to pre-set patterns.

[0005] To address the aforementioned issues, there is an urgent need for a detection method and system for assessing the impact of distributed photovoltaic power generation on the line loss rate of transformer substations. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method and system for detecting the impact of distributed photovoltaic (PV) on the line loss rate of a transformer substation. The method classifies all transformer substations based on clustering algorithms and the number of access points, and interprets the relationship between the line loss rate of each substation and the proportion of PV power generation, thereby accurately determining the trend of the line loss rate of the substation under test.

[0007] The present invention adopts the following technical solution.

[0008] The first aspect of this invention relates to a method for detecting the impact of distributed photovoltaic (PV) power on the line loss rate of a transformer substation. The method includes the following steps: Step 1, collecting load curves of electricity users in the transformer substation and analyzing the load curves using a clustering algorithm to achieve primary substation classification; Step 2, dividing the substation into equal-frequency substations based on the number of distributed PV access points in each substation to obtain secondary substation classification; Step 3, based on the primary and secondary substation classifications, constructing a correlation between the line loss rate of all substations under a substation classification and the proportion of PV power generation, and finding the inflection point of the line loss rate from the correlation; In this invention, the proportion of PV power generation is the ratio of the power generation of distributed PV in the substation to the total electricity consumption of users in the corresponding substation; Step 4, obtaining the confidence interval of the line loss rate inflection point, and detecting the line loss rate trend of the substation under test based on the confidence interval.

[0009] Preferably, the clustering algorithm is the K-centroid clustering algorithm, and the evaluation of the clustering algorithm and the optimization of the number of clusters are based on the Davidson-Bolding index. Specifically, the load discrete data corresponding to the load curve of each power user in the full distribution area are input multiple times into clustering algorithms with different numbers of clusters to obtain clustering results. The evaluation of the clustering results is based on the Davidson-Bolding index. Based on the evaluation, the optimal clustering result is selected, and the optimal number of clusters is determined.

[0010] Preferably, step 2 further includes: step 2.1, counting the number of distributed photovoltaic access points in each transformer area, and sorting all transformer areas according to the number of access points; step 2.2, based on the principle of equal frequency algorithm, dividing the classification interval of secondary transformer area classification according to the number of access points, thereby realizing the secondary classification of all transformer areas.

[0011] Preferably, all distribution areas belonging to both the same primary distribution area classification and the same secondary distribution area classification are considered as a distribution area set; for each distribution area under the distribution area set, the correlation between the line loss rate and the proportion of photovoltaic power generation is calculated.

[0012] Preferably, the load curves of electricity users and the output curves of distributed photovoltaic (PV) power in the current typical day are collected. Based on the above curves, the transformer area is set to operate at different PV access capacities, and the line loss rate of the transformer area on the current typical day under different PV access capacities is calculated, as well as the proportion of distributed PV power generation on the current typical day corresponding to the line loss rate of the transformer area on the current typical day. Based on the line loss rate of the transformer area and the proportion of PV power generation on the current typical day, a correlation curve is constructed to obtain the correlation between the line loss rate of the transformer area and the proportion of PV power generation.

[0013] Preferably, the slope of each point in the correlation curve is calculated, and the inflection point of the line loss rate of the transformer area is obtained based on the comparison between the range of slope values ​​and the reference value; wherein the inflection point of the line loss rate includes the line loss rate value and the photovoltaic power generation ratio value at the inflection point.

[0014] Preferably, the probability distribution of the inflection point of the line loss rate for each transformer area in the transformer area set is obtained, and the confidence interval of the inflection point of the line loss rate under the probability distribution is estimated; the confidence interval is the range of values ​​for the proportion of photovoltaic power generation.

[0015] Preferably, the current photovoltaic power generation ratio of the tested area is calculated. If the current photovoltaic power generation ratio falls within the confidence interval, the tested area is determined to be at the inflection point of the line loss rate. If the current photovoltaic power generation ratio of the tested area is on the left side of the confidence interval, the line loss rate of the tested area is determined to be on a downward trend. If the current photovoltaic power generation ratio of the tested area is on the right side of the confidence interval, the line loss rate of the tested area is determined to be on an upward trend.

[0016] Preferably, the attributes of each transformer substation in the current transformer substation set are extracted, and a virtual transformer substation belonging to the current transformer substation set is constructed based on the extracted attributes; wherein, other typical photovoltaic access schemes are adopted to replace the photovoltaic access schemes of each transformer substation in the current transformer substation set to construct new virtual transformer substations.

[0017] Preferably, each virtual transformer substation corresponding to the current transformer substation set is simulated to obtain the correlation between the substation's line loss rate and the proportion of photovoltaic power generation; the correlation is then added to the correlation of the current transformer substation set to update the confidence interval.

[0018] The second aspect of this invention relates to a detection system for the impact of distributed photovoltaic (PV) power on the line loss rate of a transformer substation. The system includes a primary classification module, a secondary classification module, a construction module, and a detection module. The primary classification module collects load curves of electricity users in the transformer substation and analyzes these curves using a clustering algorithm to achieve primary substation classification. The secondary classification module divides the substation into equal-frequency substations based on the number of distributed PV access points in each substation to obtain a secondary substation classification. The construction module constructs a correlation between the line loss rate and the proportion of PV power generation for all substations under a given substation classification, based on the primary and secondary classifications, and identifies the inflection point of the line loss rate from this correlation. The detection module obtains the confidence interval of the line loss rate inflection point and detects the line loss rate trend of the substation under test based on the confidence interval.

[0019] The beneficial effects of this invention are that, compared with the prior art, the method and system for detecting the impact of distributed photovoltaic (PV) on the line loss rate of a transformer substation in this invention classifies all transformer substations based on clustering algorithms and the number of connection points, and interprets the relationship between the line loss rate of the substation and the proportion of PV power generation under each category, thereby accurately determining the trend of the line loss rate of the substation under test. This invention precisely defines the impact of distributed PV equipment on the line loss rate of a transformer substation, pointing the way for grid connection methods of distributed PV and simplified calculation methods for the line loss rate after grid connection.

[0020] The beneficial effects of the present invention also include:

[0021] 1. Using the method in this invention, distribution areas can be accurately and reasonably classified through multiple different dimensions. This classification method allows distribution areas to be divided according to the location of photovoltaic access and the overall power generation ratio of the distribution area, thereby ensuring that the classified distribution areas can represent a certain type of distribution area to a certain extent, and ensuring the effectiveness of subsequent correlation calculations.

[0022] 2. This invention uses the photovoltaic power generation ratio as a parameter to characterize the line loss rate of a transformer substation. Through reasonable substitution, the line loss rate of the transformer substation no longer needs to be based on a large amount of data collection and calculation according to the actual situation. Instead, by collecting the photovoltaic power generation ratio, the current trend of the line loss rate of the transformer substation can be accurately estimated. This greatly reduces the amount of calculation while still ensuring the accuracy of the line loss rate estimation.

[0023] 3. The method in this invention can quickly obtain the line loss rate of the transformer substation, and therefore can also be applied to predict and adjust the power supply and consumption of the transformer substation. By reasonably adjusting the photovoltaic access and the output power of the transformer substation, the stable, safe and reliable operation of the transformer substation is ensured. Attached Figure Description

[0024] Figure 1 This is a schematic diagram illustrating the steps of a method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer area according to the present invention.

[0025] Figure 2 This is a schematic diagram of the load curve of the electricity user in the distribution area in the detection method of the impact of distributed photovoltaic on the line loss rate of the distribution area according to the present invention;

[0026] Figure 3 This is a schematic diagram of the photovoltaic output curve per unit capacity of a distribution area in the detection method for the impact of distributed photovoltaic on the line loss rate of a distribution area according to the present invention;

[0027] Figure 4 This is a schematic diagram of the correlation curve between the line loss rate of a distribution photovoltaic area and the proportion of photovoltaic power generation in the detection method for the impact of distributed photovoltaic on the line loss rate of a distribution area according to the present invention.

[0028] Figure 5 This is a schematic diagram of the architecture of a detection system for the impact of distributed photovoltaic power generation on the line loss rate of a transformer area, according to the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this invention are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments not described in this invention obtained by those skilled in the art based on the embodiments described in this invention without creative effort should fall within the protection scope of this invention.

[0030] Figure 1 This is a schematic diagram illustrating the steps of a method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer area, according to the present invention. Figure 1 As shown, in a first aspect, the present invention relates to a method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation, the method comprising steps 1 to 4.

[0031] Step 1: Collect the load curves of electricity users in the transformer area, and use a clustering algorithm to analyze the load curves to achieve primary transformer area classification.

[0032] It is understandable that, in order to obtain the correlation between the line loss rate of the distribution area and the proportion of photovoltaic power generation, and thus obtain a simplified method for determining the line loss rate of the distribution area and the substantial impact of distributed photovoltaic grid connection on the line loss rate of the distribution area, the distribution area needs to be classified according to its attributes first.

[0033] Since this division primarily considers the similarities between transformer substations, and is based solely on the similarity of overall factors such as substation output, intelligent algorithms can be employed. In one embodiment of this invention, a clustering algorithm is selected to implement the substation division.

[0034] Preferably, the clustering algorithm is the K-centroid clustering algorithm, and the evaluation of the clustering algorithm and the optimization of the number of clusters are based on the Davidson-Bolding index. Specifically, the load discrete data corresponding to the load curve of each power user in the full distribution area are input multiple times into clustering algorithms with different numbers of clusters to obtain clustering results. The evaluation of the clustering results is based on the Davidson-Bolding index. Based on the evaluation, the optimal clustering result is selected, and the optimal number of clusters is determined.

[0035] It is understood that the clustering algorithm in this invention is the K-centroid clustering algorithm. The K-centroid algorithm is a commonly used clustering algorithm. The basic idea of ​​K-centroid clustering is similar to that of K-Means, and it is essentially an optimization and improvement of the K-means algorithm.

[0036] In K-means, outliers can significantly impact the algorithm's process. During K-means execution, initial centroids can be randomly selected. Only the initial randomly generated centroids are the actual centers of the clusters to be formed. Subsequent iteratively generated centroids may not actually be within the clusters. If some outliers are significantly distant from the centroids, the recalculated centroids may deviate from the true centers of the clusters.

[0037] The K-center clustering algorithm calculates the point with the minimum sum of distances to all other points. By using the shortest distance calculation method, it can reduce the impact of isolated data on the clustering process, thus making the final result closer to the true partitioning.

[0038] Specifically, the data input to the clustering algorithm in this invention can be the load data of the entire distribution network, for example:

[0039]

[0040] Where n is the total number of transformer substations, j is the substation number, and p j,i Let be the total load of the j-th transformer substation during the i-th time period of the day.

[0041] A normalization algorithm can be used to normalize the above data before performing cluster analysis. That is, the load data can be calculated as follows:

[0042]

[0043] in, In the above formula, p j,i_max p is the maximum value of the load for all transformer areas. j,i_min This represents the minimum load across all transformer substations.

[0044] In this invention, cluster analysis is performed on the normalized loading data P′ to obtain the final clustering results. It should be noted that during the calculation process, the number of clusters can be set to any natural number between 2 and a certain natural number N. After obtaining the clustering results by solving for different numbers of clusters, the Davidson-Bourdin index is used to evaluate the clustering results. The Davidson-Bourdin index, also known as the classification accuracy index, is a metric proposed by David L. Davis and Donald Bouldin to evaluate the performance of clustering algorithms. This index calculates the sum of the intra-cluster mean squares between any two clusters and the distance between the two cluster centers. Using this calculation method, a smaller Davidson-Bourdin index (DBI) indicates a smaller intra-cluster distance and a larger inter-cluster distance.

[0045] Therefore, this invention uses this index to evaluate the optimal number of clusters, thereby optimizing the clustering model.

[0046] Step 2: Based on the number of distributed photovoltaic access points in each distribution area, the distribution areas are divided into equal-frequency groups to obtain secondary distribution area classification.

[0047] Of course, this invention also supports other methods for dividing transformer substations. Since the location and number of photovoltaic (PV) connection points within a substation are crucial to the impact of PV on line loss rate that needs to be analyzed in this invention, another method is also used to classify substations in this invention.

[0048] It should be noted that the number of distributed photovoltaic (PV) access points mentioned in this invention differs from the usual meaning of access point number in existing technologies. It does not refer to the actual number of distributed PV units connected to a single distribution area, but rather to the total number of different locations within the distribution network lines supporting distributed PV access in that area. At each location supporting PV access, more than one PV inverter may be connected. The number of PV access points mentioned below will all use this meaning.

[0049] Preferably, step 2 further includes: step 2.1, counting the number of distributed photovoltaic access points in each transformer area, and sorting all transformer areas according to the number of access points; step 2.2, based on the principle of equal frequency algorithm, dividing the classification interval of secondary transformer area classification according to the number of access points, thereby realizing the secondary classification of all transformer areas.

[0050] It is understood that the classification method implemented in this invention is as described above. Since the number of photovoltaic access points in each distribution area is different, this number can be sorted to obtain the arrangement order of the distribution areas. For example, distribution areas with only one photovoltaic access point are ranked higher, and those with more access points are ranked lower.

[0051] In this way, photovoltaic power stations can be arranged in a reasonable order. Then, an equal-frequency algorithm can be used to divide this order into multiple intervals. After obtaining all the power stations in each interval, one type of result in the secondary power station classification is obtained.

[0052] Step 3: Based on the primary and secondary distribution area classifications, construct the correlation between the line loss rate and the proportion of photovoltaic power generation for all distribution areas under a single distribution area classification, and find the inflection point of the line loss rate from the correlation. Here, the proportion of photovoltaic power generation is the ratio of the power generation of distributed photovoltaic in the distribution area to the total electricity consumption of the corresponding distribution area users.

[0053] Preferably, all distribution areas belonging to both the same primary distribution area classification and the same secondary distribution area classification are considered as a distribution area set; for each distribution area under the distribution area set, the correlation between the line loss rate and the proportion of photovoltaic power generation is calculated.

[0054] It is understandable that the transformer substation classification in this invention can be divided into different dimensions according to the two different methods mentioned above. After dividing into two dimensions, a sub-category can be a set of transformer substations in this invention. The algorithm in this invention is based on statistical analysis of all transformer substations under this sub-category to obtain the variation pattern of line loss rate under this sub-category.

[0055] Preferably, the load curves of electricity users and the output curves of distributed photovoltaic (PV) power in the transformer substation on a typical day are collected. Based on these curves, the transformer substation is set to operate at different PV access capacities, and the line loss rate of the transformer substation on a typical day under different PV access capacities is calculated, as well as the proportion of distributed PV power generation on the typical day corresponding to the line loss rate of the transformer substation on the typical day. Based on the line loss rate and the proportion of PV power generation on the typical day, a correlation curve is constructed to obtain the correlation between the line loss rate and the proportion of PV power generation in the transformer substation.

[0056] It is understood that the present invention can comprehensively determine the relationship between the line loss of the distribution area and the proportion of photovoltaic power generation based on the user load, the output of distributed photovoltaic power, and other factors.

[0057] In this invention, the photovoltaic (PV) power ratio refers to the proportion between PV power generation and the electricity consumption of users in the distribution area. Therefore, to obtain the correlation between the line loss rate and the PV power ratio, this invention can select the load and PV output curves of one or more typical daily distribution area users from the aforementioned curves. Based on this, the PV power ratio curves at different grid-connected capacities within the distribution area can be constructed accordingly. Subsequently, the actual line loss rate of the distribution area is calculated based on the total load of the distribution area users and the total output in the distribution area, such as the power supply of all equipment in the distribution area at the current moment, thus obtaining the line loss rate at the current moment. After calculating the line loss rate and PV power ratio at the corresponding moment using the above method, a correlation curve can be constructed with the different PV power ratios as the horizontal axis and the line loss rate as the vertical axis. During the construction of the correlation curve, different PV power ratio values ​​can be considered, and data points on the curve can be sampled and the curve fitted using a constant step size.

[0058] Figure 2 This is a schematic diagram of the load curves of electricity users in a distribution area, as described in the present invention's method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a distribution area. Figure 2 As shown, the user load situation in this invention can be as follows: Figure 2 As shown, Figure 2 The image shows the load status of all users in a certain distribution area at 24:00 on a certain day in 2022.

[0059] Figure 3 This is a schematic diagram of the photovoltaic output curve per unit capacity of a distribution area in the detection method for the impact of distributed photovoltaic power generation on the line loss rate of a distribution area according to the present invention. Figure 3 As shown, the photovoltaic output curve per unit capacity also includes the ratio of photovoltaic output to the total electricity consumption of all users in the area over a 24-hour period, with a value ranging from 0 to 1.

[0060] Figure 4 This is a schematic diagram illustrating the correlation between the line loss rate of a distribution photovoltaic (PV) area and the proportion of PV power generation in the detection method for the impact of distributed PV on the line loss rate of a distribution area according to the present invention. Figure 4 As shown, using the above data, this invention can obtain the line loss rate of all distribution areas within the distribution area set when the proportion of photovoltaic power generation is different. In this way, the correlation between the line loss rate of a distribution area and the proportion of photovoltaic power generation can be established. Figure 4 In this process, the proportion of photovoltaic power generation can be calculated in 2% increments, and the line loss rate of the distribution area can be calculated for each 2% increment.

[0061] The statistical results can be recorded with the horizontal axis representing the proportion of photovoltaic power generation and the vertical axis representing the line loss rate of the distribution area. Figure 4 Thus, a schematic diagram of the line loss rate is obtained.

[0062] Preferably, the slope of each point in the correlation curve is calculated, and the inflection point of the line loss rate of the transformer area is obtained based on the comparison between the range of slope values ​​and the reference value; wherein the inflection point of the line loss rate includes the line loss rate value and the photovoltaic power generation ratio value at the inflection point.

[0063] Using the above method, the slope of each point in the correlation curve can be calculated, or the inflection point of the transformer area line loss rate can be obtained intuitively. This inflection point can also be called the loss increase inflection point. At the inflection point, different slopes may appear, and... Figure 4 As shown, the line loss rate gradually increases to the right of the inflection point and gradually decreases to the left of the inflection point.

[0064] In one embodiment of the present invention, namely Figure 4 In this case, the line loss rate at the inflection point of the line loss increase in this area is about 1.9%, and the photovoltaic power generation corresponding to this inflection point is 12%, which means that the photovoltaic power generation and electricity consumption account for 12% of the total power consumption on that day.

[0065] Step 4: Obtain the confidence interval of the inflection point of the line loss rate, and detect the trend of the line loss rate of the test area based on the confidence interval.

[0066] After obtaining the inflection point of the line loss rate for each transformer area, the confidence range of the inflection point of the line loss rate can be constructed.

[0067] Preferably, the probability distribution of the inflection point of the line loss rate for each transformer area in the transformer area set is obtained, and the confidence interval of the inflection point of the line loss rate under the probability distribution is estimated; the confidence interval is the range of values ​​for the proportion of photovoltaic power generation.

[0068] It is understandable that the probability distribution of the inflection point of the line loss rate for each transformer substation in the set can be distributed according to the value of the photovoltaic power generation ratio. In this invention, the range in which the 95% line loss rate falls is selected to finally obtain the confidence interval.

[0069] In the above embodiment, the confidence interval of this set is [28.3%, 71.5%].

[0070] Preferably, the current photovoltaic power generation ratio of the tested area is calculated. If the current photovoltaic power generation ratio falls within the confidence interval, the tested area is determined to be at the inflection point of the line loss rate. If the current photovoltaic power generation ratio of the tested area is on the left side of the confidence interval, the line loss rate of the tested area is determined to be on a downward trend. If the current photovoltaic power generation ratio of the tested area is on the right side of the confidence interval, the line loss rate of the tested area is determined to be on an upward trend.

[0071] Using the aforementioned interval as a dividing range, the trend characteristics of the current line loss for all transformer areas within the current set are extracted. Specifically, as follows: Figure 4 As shown, the trends of increasing, inflection point, and decreasing line loss rates for the test areas belonging to this set can be obtained respectively.

[0072] Preferably, the attributes of each transformer substation in the current transformer substation set are extracted, and a virtual transformer substation belonging to the current transformer substation set is constructed based on the extracted attributes; wherein, other typical photovoltaic access schemes are adopted to replace the photovoltaic access schemes of each transformer substation in the current transformer substation set to construct new virtual transformer substations.

[0073] It is understood that, in one embodiment of the present invention, more than 500 typical photovoltaic grid connection schemes for distribution substations were used for simulation calculations. These typical photovoltaic grid connection schemes can be obtained in various ways. For example, since existing distribution substations cannot cover all different photovoltaic grid connection methods, various methods can be used to reasonably and similarly adjust parameters such as location and grid capacity for photovoltaic grid connection. By inputting real electricity consumption data into the simulation model, the operation of the power grid is simulated, and the relationship between line loss rate and photovoltaic power generation ratio is calculated.

[0074] Preferably, each virtual transformer substation corresponding to the current transformer substation set is simulated to obtain the correlation between the substation's line loss rate and the proportion of photovoltaic power generation; the correlation is then added to the correlation of the current transformer substation set to update the confidence interval.

[0075] It is understood that the confidence interval in this invention can also be obtained by adding such simulated virtual stations as samples, thereby obtaining a more reliable confidence interval, eliminating the influence of real special stations, and optimizing the number of samples.

[0076] In one embodiment of the present invention, for the above-mentioned type of distribution area, if the ratio of photovoltaic power generation to user electricity consumption in a certain distribution area on a certain day is greater than 71.5%, the photovoltaic increase or loss in that distribution area on that day can be estimated; if the ratio is less than 28.3%, the photovoltaic decrease or loss in that distribution area on that day can be estimated.

[0077] Figure 5 This is a schematic diagram of the architecture of a detection system for the impact of distributed photovoltaic power generation on the line loss rate of a transformer area, according to the present invention. Figure 5 As shown, in a second aspect, this invention relates to a detection system for the impact of distributed photovoltaic (PV) power on the line loss rate of a transformer substation. The system includes a primary classification module, a secondary classification module, a construction module, and a detection module. The primary classification module collects load curves of electricity users in the transformer substation and analyzes these curves using a clustering algorithm to achieve primary substation classification. The secondary classification module divides the substation into equal-frequency substations based on the number of distributed PV access points in each substation to obtain a secondary substation classification. The construction module constructs a correlation between the line loss rate and the proportion of PV power generation for all substations under a given substation classification, based on the primary and secondary classifications, and identifies the inflection point of the line loss rate from this correlation. The detection module obtains the confidence interval of the line loss rate inflection point and detects the line loss rate trend of the substation under test based on the confidence interval.

[0078] It is understood that, in order to implement the various functions in the methods provided in the embodiments of this application, the detection system includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0079] This application embodiment can divide the detection system into functional modules based on the above method example. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or as software functional modules. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0080] The system includes at least one processor, a bus system, and at least one communication interface. The processor may consist of a central processing unit (CPU), a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or other hardware. The memory may consist of read-only memory (ROM), random access memory (RAM), etc. The memory may be independent and connected to the processor via a bus. Alternatively, the memory may be integrated with the processor. The hard disk may be a mechanical hard disk (HDD) or a solid-state drive (SSD), etc. This embodiment of the invention does not limit the specific implementation. The above embodiments are typically implemented using software and hardware. When implemented using software programs, they can be implemented in the form of a computer program product. This computer program product includes one or more computer instructions.

[0081] When computer program instructions are loaded and executed on a computer, the corresponding functions are implemented according to the process provided in the embodiments of this invention. The computer program instructions involved may be assembly instructions, machine instructions, or code written in a programming language, etc.

[0082] The beneficial effects of this invention are that, compared with the prior art, the method and system for detecting the impact of distributed photovoltaic (PV) on the line loss rate of a transformer substation in this invention classifies all transformer substations based on clustering algorithms and the number of connection points, and interprets the relationship between the line loss rate of the substation and the proportion of PV power generation under each category, thereby accurately determining the trend of the line loss rate of the substation under test. This invention precisely defines the impact of distributed PV equipment on the line loss rate of a transformer substation, pointing the way for grid connection methods of distributed PV and simplified calculation methods for the line loss rate after grid connection.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation, characterized in that, The method includes the following steps: Step 1: Collect the load curves of electricity users in the transformer area, and use a clustering algorithm to analyze the load curves to achieve a primary transformer area classification; Step 2: Based on the number of distributed photovoltaic access points in each distribution area, the distribution area is divided into equal-frequency sections to obtain a secondary distribution area classification; the number of distributed photovoltaic access points is the total number of different locations in the distribution network lines under the distribution area that support the access of distributed photovoltaics. Step 3: Based on the primary and secondary distribution area classifications, construct the correlation between the line loss rate and the photovoltaic power generation ratio of all distribution areas under a distribution area classification, and find the inflection point of the line loss rate from the correlation; the photovoltaic power generation ratio is the ratio of the power generation of distributed photovoltaic in the distribution area to the total electricity consumption of the corresponding distribution area users; Step 4: Obtain the confidence interval of the inflection point of the line loss rate, and detect the trend of the line loss rate of the test area based on the confidence interval.

2. The method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer area as described in claim 1, characterized in that: The clustering algorithm is a K-centroid clustering algorithm, and the evaluation of the clustering algorithm and the optimization of the number of clusters are based on the Davidson-Bolding index. In this process, the load discrete data corresponding to the load curve of each power user in the full distribution area is input multiple times into the clustering algorithm with different numbers of clusters to obtain clustering results. The clustering results are evaluated based on the Davidson-Bolding index. Based on the evaluation, the optimal clustering result is selected, and the optimal number of clusters is determined.

3. The method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer area as described in claim 2, characterized in that: Step 2 also includes: Step 2.1: Count the number of distributed photovoltaic access points in each transformer area, and sort all transformer areas according to the number of access points; Step 2.2: Based on the principle of equal frequency algorithm, the classification interval of secondary transformer area classification is divided according to the number of access points, thereby realizing the secondary classification of all transformer areas.

4. The method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation as described in claim 3, characterized in that: All transformer areas that belong to both the same primary transformer area classification and the same secondary transformer area classification are considered as a transformer area set. For each distribution area under the aforementioned distribution area set, the correlation between the line loss rate and the proportion of photovoltaic power generation in that distribution area is calculated.

5. The method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer area according to claim 4, characterized in that: The load curves of electricity users and the output curves of distributed photovoltaic power generation in the current typical day of the data collection area are used. Based on the above curves, the transformer area is set to operate at different photovoltaic access capacities, and the line loss rate of the transformer area on the current typical day under different photovoltaic access capacities is calculated, as well as the proportion of distributed photovoltaic power generation on the current typical day corresponding to the line loss rate of the transformer area on the current typical day. Based on the line loss rate of the transformer area and the photovoltaic power generation ratio on a typical day, a correlation curve is constructed to obtain the correlation between the line loss rate of the transformer area and the photovoltaic power generation ratio.

6. The method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation as described in claim 5, characterized in that: Calculate the slope of each point in the correlation curve, and obtain the inflection point of the line loss rate of the transformer area based on the comparison between the range of the slope and the reference value; The line loss rate inflection point includes the line loss rate value and the photovoltaic power generation ratio value at the inflection point.

7. The method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer area as described in claim 6, characterized in that: Obtain the probability distribution of the line loss rate inflection point for each transformer area in the transformer area set, and estimate the confidence interval of the line loss rate inflection point under the probability distribution; The confidence interval is the range of values ​​for the photovoltaic power generation ratio.

8. The method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation as described in claim 7, characterized in that: Calculate the current photovoltaic power generation ratio of the tested area. If the current photovoltaic power generation ratio falls within the confidence interval, the tested area is determined to be at the inflection point of the line loss rate. If the current photovoltaic power generation ratio of the tested area is located to the left of the confidence interval, then the line loss rate of the tested area is determined to be in a downward trend. If the current photovoltaic power generation ratio of the tested area is located to the right of the confidence interval, then the line loss rate of the tested area is determined to be on an upward trend.

9. A method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation as described in claim 8, characterized in that: Extract the attributes of each transformer in the current transformer set, and simulate and construct a virtual transformer belonging to the current transformer set based on the extracted attributes; This involves replacing the photovoltaic access scheme of each transformer area in the current transformer area set with other typical photovoltaic access schemes to construct new virtual transformer areas.

10. A method for detecting the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation as described in claim 9, characterized in that: Simulations are performed on each virtual distribution area corresponding to the current distribution area set to obtain the correlation between the line loss rate of the distribution area and the proportion of photovoltaic power generation. The association is added to the association of the current transformer area set to update the confidence interval.

11. A detection system for the impact of distributed photovoltaic power generation on the line loss rate of a transformer substation, characterized in that: The system includes a primary classification module, a secondary classification module, a construction module, and a detection module; wherein, The primary classification module is used to collect the load curves of electricity users in the transformer area and use a clustering algorithm to analyze the load curves to achieve primary transformer area classification. The secondary classification module is used to perform equal-frequency division of the distribution area based on the number of distributed photovoltaic access points in each distribution area to obtain a secondary distribution area classification; the number of distributed photovoltaic access points is the total number of different locations in the distribution network line under the distribution area that support the access of distributed photovoltaic. The construction module is used to construct the correlation between the line loss rate and the proportion of photovoltaic power generation for all transformer substations under a transformer substation classification based on primary and secondary transformer substation classification, and to find the inflection point of the line loss rate from the correlation; the proportion of photovoltaic power generation is the ratio of the power generation of distributed photovoltaic in the transformer substation to the total electricity consumption of users in the corresponding transformer substation. The detection module is used to obtain the confidence interval of the inflection point of the line loss rate and detect the line loss rate trend of the test area based on the confidence interval.

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