A distributed power supply access unit remote precise positioning method and system

Abstract

This invention belongs to the field of remote precise positioning technology, specifically relating to a method and system for remote precise positioning of distributed power supply access units (DPSUs). The method includes collecting power timing data and bus loss timing data of DPSUs within a distribution area; identifying feeder branches of DPSUs through signal feature similarity analysis; performing line loss analysis based on the feeder branches, power timing data, and bus loss timing data to obtain a line loss sensitivity coefficient; obtaining the line distance of DPSUs within the feeder branches through power flow analysis based on the line loss sensitivity coefficient; and sorting the DPSUs within the feeder branches according to the line distance to obtain complete topology location information. This invention achieves end-to-end precise positioning from determining the affiliation of feeder branches to the node sorting of DPSUs within the feeder branches, solving the problem of existing methods' difficulty in accurately locating DPSUs.

Description

Technical Field

[0001] This invention belongs to the field of remote precise positioning technology, specifically relating to a method and system for remote precise positioning of a distributed power supply access unit. Background Technology

[0002] With the rapid development of distributed generation technology, the installed capacity of distributed power sources such as photovoltaics and wind power in low-voltage distribution substations continues to expand, and a large number of distributed power generation units are connected to the various feeder branches of the distribution network in a decentralized manner. Accurately determining the physical location of these connection units in the distribution network topology is an important foundation for carrying out refined management of substation line losses, reasonable power flow control, and rapid fault response.

[0003] However, historical records of low-voltage distribution networks are generally incomplete or outdated, and discrepancies between on-site wiring conditions and ledger records are common. Relying on manual inspections or offline record checks is becoming increasingly unsustainable with the continuous growth in the number of distributed power sources. Regarding automated identification, existing methods typically use power line carrier communication and other means to collect signal characteristics of access units to determine their topology affiliation. However, signals are affected by attenuation, noise, and multipath propagation in complex low-voltage lines, resulting in a certain degree of overlap in signal characteristics between access units at different locations. This makes accurate topology determination difficult, hindering the precise location of distributed power source access units within the distribution network topology. Summary of the Invention

[0004] (1) Technical problems to be solved The purpose of this invention is to provide a method and system for remote and precise positioning of distributed power supply access units, so as to solve the problem that existing automatic identification methods are unable to accurately determine the location of distributed power supply access units in the distribution network topology.

[0005] (2) Technical solution To achieve the above objectives, in one aspect, the present invention provides a method for remote and precise positioning of a distributed power supply access unit, the method comprising: Collect power timing data and bus loss timing data of distributed power supply access units within the transformer area; identify feeder branches of distributed power supply access units through signal feature similarity analysis.

[0006] The line loss sensitivity coefficient is obtained by performing line loss analysis based on the feeder branches of the distributed power access unit, the power timing data, and the bus loss timing data.

[0007] The line distance of the distributed power source access unit within the feeder branch is obtained through power flow analysis based on the line loss sensitivity coefficient.

[0008] Within the feeder branch, the distributed power access units are sorted according to the line distance to obtain the complete topology location information of the distributed power access units.

[0009] Furthermore, the method for collecting power timing data and bus loss timing data of distributed power access units within the distribution area includes: The power timing data and bus loss timing data of the distributed power access units in the transformer area are collected continuously. The bus loss timing data is calculated by the difference between the total power supply of the transformer area and the sum of the power of the distributed power access units in the transformer area.

[0010] Furthermore, the method for identifying feeder branches of a distributed power supply access unit through signal feature similarity analysis includes: The distribution area concentrator sends positioning detection frames to the distributed power supply access unit via power line carrier communication; it collects the received signal strength, signal-to-noise ratio, and relay hop count of the distributed power supply access unit; it constructs a signal feature vector based on the received signal strength, signal-to-noise ratio, and relay hop count of the distributed power supply access unit; it normalizes the signal feature vector; it calculates the cosine similarity between each pair of signal feature vectors of the distributed power supply access units to construct a cosine similarity matrix; and it clusters the cosine similarity matrix to obtain the feeder branches of the distributed power supply access unit.

[0011] Furthermore, the method for obtaining the line loss sensitivity coefficient by performing line loss analysis based on the feeder branch of the distributed power access unit, the power timing data, and the bus loss timing data includes: Acquire power timing data and transformer bus loss timing data of the distributed power access unit within the feeder branch; perform data preprocessing on the power timing data and transformer bus loss timing data to obtain denoised power timing data and transformer bus loss timing data; calculate the line loss sensitivity coefficient based on the denoised power timing data and transformer bus loss timing data; the line loss sensitivity coefficient... The calculation formula is: .

[0012] in, Distributed power access unit In time sequence The power; For the distributed power access unit in Average power of a continuous time series; For time sequence The bus loss in the station area; For the aforementioned transformer area Average bus loss over a continuous timing sequence; This represents the number of valid time-series data points after data preprocessing.

[0013] Furthermore, the method for preprocessing the power timing data and the transformer bus loss timing data to obtain denoised power timing data and transformer bus loss timing data includes: Calculate the standard deviation of power time series data When power deviation When the time is abnormal, the power timing data is marked as an outlier and removed to obtain normal power timing data.

[0014] When the normal power timing data is missing, power timing data before and after the normal power timing data are selected, and linear interpolation is performed to complete the data to obtain denoised power timing data.

[0015] The timestamps of the denoised power timing data and the transformer bus loss timing data are obtained; the timing deviation is calculated by the difference between the timestamps of the denoised power timing data and the transformer bus loss timing data; the transformer bus loss timing data whose timing deviation exceeds a preset time window are removed to obtain the denoised transformer bus loss timing data.

[0016] Furthermore, the method for obtaining the line distance of the distributed power generation access unit within the feeder branch through distribution network power flow analysis based on the line loss sensitivity coefficient includes: A relationship model between the line loss sensitivity coefficient and the line distance is established based on the power flow characteristics of the distribution network; the relationship model is as follows: .

[0017] in, Distributed power access unit The feeder branch to which it belongs The transformer area constant; Distributed power access unit The distance of the route.

[0018] Obtain the bus loss rate of the transformer area The target line loss rate of the feeder branch is calculated proportionally based on the power ratio of the feeder branch; the target line loss rate of the feeder branch The calculation formula is: .

[0019] in, The bus loss rate of the distribution area; feeder branch A collection of distributed power supply access units within the system; Distributed power access unit The average power.

[0020] In the feeder branch Within, according to the distributed power access unit within the feeder branch. average power and line loss sensitivity coefficient Establish theoretical line loss rate Regarding the transformer area constant The functional relationship is: .

[0021] in, This is the preset line loss rate scaling factor.

[0022] Based on the theoretical line loss rate and the target line loss rate, establish a target transformer area constant identification function: .

[0023] The line distance of the distributed power supply access unit is obtained based on the target transformer area constant identification function and the relationship model between the line loss sensitivity coefficient and the line distance.

[0024] Furthermore, the method for obtaining the line distance of the distributed power supply access unit based on the target transformer area constant identification function and the relationship model between the line loss sensitivity coefficient and the line distance includes: The target transformer area constant identification function is processed using the golden section method to obtain the actual transformer area constant; the steps of the golden section method include: The initial search interval is set based on the historical parameter data of the distribution network in the aforementioned area. .

[0025] Within the initial search interval, two interior points are selected according to the golden ratio. and And calculate the interior points of the two interior points. and Target area constant identification function value and .

[0026] when If so, the initial search interval is modified to .

[0027] when If so, the initial search interval is modified to .

[0028] Repeat the above steps until the length of the final search interval is less than the preset accuracy threshold; obtain the actual station area constant based on the final search interval; The line distance of the distributed power supply access unit is calculated based on the relationship model between the actual transformer area constant and the line loss sensitivity coefficient and the line distance.

[0029] Furthermore, the initial search interval is set based on the historical parameter data of the distribution network in the transformer area. The methods include: Obtain historical parameter data of the distribution network in the transformer substation; calculate the theoretical transformer substation constant based on the historical parameter data of the distribution network in the transformer substation; the theoretical transformer substation constant... The calculation formula is: .

[0030] in, The average power of the distribution network in the transformer area; The resistivity of the conductors in the distribution network of the transformer substation; This refers to the cross-sectional area of ​​the conductors in the distribution network of the transformer substation. This refers to the rated voltage of the distribution network in the transformer substation.

[0031] An initial search interval is generated based on the maximum and minimum values ​​of the theoretical plateau constant, combined with a preset search margin. .

[0032] Based on the same inventive concept, the present invention also provides a remote precise positioning system for distributed power supply access units, the system comprising: The data acquisition and identification module is used to collect power timing data and bus loss timing data of the distributed power supply access units in the transformer area; and to identify the feeder branches of the distributed power supply access units through signal feature similarity analysis.

[0033] The line loss sensitivity analysis module is used to perform line loss analysis based on the feeder branches of the distributed power access unit, the power timing data, and the bus loss timing data to obtain the line loss sensitivity coefficient.

[0034] The line distance calculation module is used to obtain the line distance of the distributed power supply access unit within the feeder branch through power flow analysis based on the line loss sensitivity coefficient.

[0035] The positioning module is used to sort the distributed power access units within the feeder branch according to the line distance to obtain the complete topological location information of the distributed power access units.

[0036] (3) Beneficial effects Compared with the prior art, the beneficial effects of the present invention are: 1. By combining feeder branch identification technology that utilizes communication signal characteristics with line distance calculation based on power flow analysis, an end-to-end positioning technology path is formed, which ranges from determining the affiliation of feeder branches to sorting the distributed power access unit nodes within the feeder branches.

[0037] 2. By establishing a model of line loss sensitivity coefficient and line distance, and using transformer area operation data to obtain actual transformer area constants, qualitative electrical correlations are transformed into quantifiable distance indicators, thereby achieving accurate topology sorting of distributed power access units within the same feeder branch. Attached Figure Description

[0038] Figure 1 This is a flowchart of a remote and precise positioning method for a distributed power access unit according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the module composition of a remote precision positioning system for a distributed power access unit according to Embodiment 2 of the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0040] Before providing examples, it is necessary to describe the application scenario of this invention. In a newly built residential community in a certain city, the transformer capacity of the distribution area is... The low-voltage distribution network adopts a radial structure with a total of 3 main feeders. The power line supplies electricity to 15 buildings. Fifteen households in the community have installed residential distributed photovoltaic (PV) power generation systems on their rooftops (each system is equipped with a distributed power access unit), with a grid-connected capacity of [missing information]. to Due to incomplete construction records and subsequent user self-application for access, the power grid system and distribution automation system lack an accurate "household-transformer-line" topology correspondence. This makes it impossible to determine which feeder the distributed power supply access unit is connected to, as well as the electrical distance and sequence on the feeder. This poses difficulties for issues such as refined management of distribution area line losses, configuration of anti-islanding protection, voltage over-limit management, and accurate fault diagnosis.

[0041] Example 1: As Figure 1 As shown in the figure, this embodiment provides a method for remote and precise positioning of a distributed power supply access unit, the method including: S1. Collect power timing data and bus loss timing data of the distributed power supply access units in the transformer area; identify the feeder branches of the distributed power supply access units through signal feature similarity analysis.

[0042] S2. Based on the feeder branch of the distributed power access unit, the power timing data, and the bus loss timing data, perform line loss analysis to obtain the line loss sensitivity coefficient.

[0043] S3. Based on the line loss sensitivity coefficient, the line distance of the distributed power supply access unit in the feeder branch is obtained through power flow analysis of the distribution network.

[0044] S4. Within the feeder branch, the distributed power access units are sorted according to the line distance to obtain the complete topology location information of the distributed power access units.

[0045] For example, power timing data and bus loss timing data of 15 distributed power supply access units are collected by a power line concentrator. The concentrator sends location detection frames to the 15 distributed power supply access units via power line carrier communication, and collects the received signal strength, signal-to-noise ratio, and relay hop count of the distributed power supply access units. Based on these signal characteristics, feature vectors are constructed and cosine similarity is calculated. Clustering algorithms are then used to form... (4 distributed power supply access units) (6 distributed power supply access units) (5 distributed power supply access units) Three feeder branches. Power timing data and bus loss timing data of the distributed power supply access units within each feeder branch are preprocessed, and then line loss sensitivity coefficients are calculated through line loss analysis. Taking a branch as an example, the line loss sensitivity coefficients of the six distributed power supply access units are 0.394, 0.425, 0.318, 0.276, 0.189, and 0.155, respectively. A model relating the line loss sensitivity coefficient to the line distance is established based on the power flow characteristics of the distribution network, and the golden section method is used to optimize and identify the transformer constants of the feeder branches. Taking the branch as an example, the actual transformer constant is obtained as follows: The calculated line distances for the six access units are as follows: , , , , , Within each feeder branch, the access units are arranged in ascending order based on the line distance and assigned node numbers. Taking branches as an example, they are ordered from closest to furthest: #10 ( Node 1), #9 ( Node 2), #8 Node 3), #7 ( Node 4), #5 ( Node 5), #6 ( (Node 6). and The same method is used for feeder branches to obtain complete topology location information. The final output of the complete topology location information for the distributed power supply access unit includes its feeder branch, line distance, and node number.

[0046] The method for collecting power timing data and bus loss timing data of distributed power supply access units within the transformer area includes: The power timing data and bus loss timing data of the distributed power access units in the transformer area are collected continuously. The bus loss timing data is calculated by the difference between the total power supply of the transformer area and the sum of the power of the distributed power access units in the transformer area.

[0047] For example, power timing data and bus loss timing data of distributed power access units within the distribution area are collected in 672 consecutive time-series acquisitions. It should be noted that the number of consecutive time-series acquisitions... The optimal data collection interval is 15 minutes, which captures power fluctuation characteristics without generating excessive data volume, and covers at least one complete daily cycle. Using 7 days of data better reflects the periodic power generation characteristics of distributed photovoltaic systems. At a given time series, the power supplied by the total meter reading for the distribution area is... The sum of the power of the 15 access units is The calculated bus loss for the transformer area is... Repeat the above calculations for 672 timing sequences to obtain complete bus loss timing data for the transformer substation.

[0048] The method for identifying feeder branches of a distributed power supply access unit through signal feature similarity analysis includes: The distribution area concentrator sends positioning detection frames to the distributed power supply access unit via power line carrier communication; it collects the received signal strength, signal-to-noise ratio, and relay hop count of the distributed power supply access unit; it constructs a signal feature vector based on the received signal strength, signal-to-noise ratio, and relay hop count of the distributed power supply access unit; it normalizes the signal feature vector; it calculates the cosine similarity between each pair of signal feature vectors of the distributed power supply access units to construct a cosine similarity matrix; and it clusters the cosine similarity matrix to obtain the feeder branches of the distributed power supply access unit.

[0049] For example, the distribution concentrator sequentially sends location detection frames to 15 distributed power supply access units (DPSUs) via power line carrier communication, collecting the response signal parameters of the DPSUs. For instance, the response signal parameters of DPSU #5 are: received signal strength... Signal-to-noise ratio The relay hop count is 1. Based on the received signal strength, signal-to-noise ratio, and relay hop count of distributed power access unit #5, a signal feature vector is constructed as follows: The signal feature vectors of all distributed power supply access units are normalized using a min-max method, mapping the value of each feature dimension to the [0,1] interval. Taking this embodiment as an example, the received signal strength range is... The signal-to-noise ratio ranges from [8, 25] dB, and the relay hop count ranges from [1, 3]. The original signal feature vector of distributed power access unit #5. After normalization, a normalized vector [0.825, 0.588, 0] is obtained. The cosine similarity of the normalized eigenvectors between each pair of units is calculated, constructing a cosine similarity matrix. The cosine similarity between the normalized eigenvectors of distributed power supply access units #5 and #6 is 0.91. Hierarchical clustering analysis is performed on the cosine similarity matrix, and based on the distribution network topology information of the transformer substation, the number of clusters is set to 3, resulting in three feeder branch sets: feeder branches... : (4 distributed power supply access units); feeder branches : (6 distributed power supply access units); feeder branches : (5 distributed power supply access units). It should be noted that access units on the same feeder branch, due to their similar electrical paths, exhibit high similarity in their carrier communication signal characteristics (received signal strength, signal-to-noise ratio, and repeater hop count). Cosine similarity measures the directional consistency of feature vectors and is unaffected by vector magnitude. Clustering algorithms are used to group the access units with the highest similarity into the same feeder branch. In practical applications, the number of feeder branches can be obtained from the distribution network topology diagram, or the optimal number of clusters can be automatically determined using clustering evaluation indicators such as the profile coefficient and the Davidson-Bolding index.

[0050] The method for obtaining the line loss sensitivity coefficient by performing line loss analysis based on the feeder branch of the distributed power access unit, the power timing data, and the bus loss timing data includes: Acquire power timing data and transformer bus loss timing data of the distributed power access unit within the feeder branch; perform data preprocessing on the power timing data and transformer bus loss timing data to obtain denoised power timing data and transformer bus loss timing data; calculate the line loss sensitivity coefficient based on the denoised power timing data and transformer bus loss timing data; the line loss sensitivity coefficient... The calculation formula is: .

[0051] in, Distributed power access unit In time sequence The power; For the distributed power access unit in Average power of a continuous time series; For time sequence The bus loss in the station area; For the aforementioned transformer area Average bus loss over a continuous timing sequence; This represents the number of valid time-series data points after data preprocessing.

[0052] For example, the power data and corresponding transformer bus loss data of distributed power access unit #5 at 12 time sequences are obtained. After outlier removal, missing value completion, and time alignment, all 12 time sequence data are valid. The denoised power data and transformer bus loss data of distributed power access unit #5 at 12 time sequences are as follows: Time Sequence 1: Power is -1.2 The bus loss in the distribution area is 0.85. Timing 2: Power is -1.5 The bus loss in the distribution area is 0.78. Timing 3: Power is -1.3 The bus loss in the distribution area is 0.82. Timing 4: Power is -0.8 The bus loss in the distribution area is 0.95. Timing 5: Power is 2.5 The bus loss in the distribution area is 2.20kW; timing 6: power is 5.8kW. The bus loss in the distribution area is 4.15. Timing 7: Power is 7.2. The bus loss in the distribution area is 5.10. Timing 8: Power is 6.5 The bus loss in the distribution area is 4.68. Timing 9: Power is 4.3. The bus loss in the distribution area is 3.35. Timing 10: Power is 1.5 The bus loss in the distribution area is 1.85. Timing 11: Power is -0.5 The bus loss in the distribution area is 1.15. Timing 12: Power is -1.0 The bus loss in the distribution area is 0.92. The above data presents a two-way power flow characteristic: time series 1-4 and 11-12 represent nighttime periods with negative power values, indicating power consumption; time series 5-10 represent daytime periods with positive power values, indicating power generation. The corresponding transformer substation bus losses are higher during daytime power generation periods (2.20-5.10). Electricity consumption is lower during nighttime hours (0.78-1.15kW). Based on the power data for the above 12 time periods, the average power consumption is calculated to be 1.79 kW. The average bus loss is 2.23. Based on the line loss sensitivity coefficient The line loss sensitivity coefficient is calculated using the following formula: It is 0.489.

[0053] It should be noted that, to facilitate verification of the accuracy of the calculation method, this embodiment selects 12 representative time series for detailed explanation. In practical applications, the more time series data collected, the more stable the statistical characteristics, and the higher the calculation accuracy of the line loss sensitivity coefficient. The optimal number of time series data collected... One time series (at least one complete daily cycle). Line loss sensitivity coefficient. This reflects the sensitivity of the power changes of the distributed power access unit to the changes in bus losses in the distribution area. The larger the value, the farther the distributed power supply unit is from the transformer, and the greater the impact of power changes on line losses. Line loss sensitivity coefficient. The calculation formula uses the ratio of covariance to variance to eliminate the influence of power fluctuation amplitude and more accurately reflect the correlation between power changes and line loss changes. For distributed photovoltaic access units, their power exhibits obvious bidirectional power flow characteristics: during daytime power generation, power is injected into the grid ( ), power is absorbed from the grid when using electricity at night or on rainy days ( The line loss fluctuations caused by this bidirectional power flow provide richer feature information for location analysis. Compared to traditional unidirectional loads, distributed power sources exhibit more severe power fluctuations, larger covariance values, and higher distinguishability in line loss sensitivity coefficients, thereby improving the accuracy of line distance calculations.

[0054] The method for preprocessing the power timing data and transformer bus loss timing data to obtain denoised power timing data and transformer bus loss timing data includes: Calculate the standard deviation of power time series data When power deviation When the time is abnormal, the power timing data is marked as an outlier and removed to obtain normal power timing data.

[0055] When the normal power timing data is missing, power timing data before and after the normal power timing data are selected, and linear interpolation is performed to complete the data to obtain denoised power timing data.

[0056] The timestamps of the denoised power timing data and the transformer bus loss timing data are obtained; the timing deviation is calculated by the difference between the timestamps of the denoised power timing data and the transformer bus loss timing data; the transformer bus loss timing data whose timing deviation exceeds a preset time window are removed to obtain the denoised transformer bus loss timing data.

[0057] For example, taking the power timing data of distributed power access unit #5 as an example, the calculated average power is 2.8. The standard deviation is 3.5. .when At any given time, the data points were marked as outliers and removed. The total number of power time-series data points was 672; after removing outliers, 656 normal power time-series data points were obtained. Power time-series data points were missing at a certain time; these were filled in using linear interpolation. The power in the previous time-series data was 5.2. The power time series data for the next moment is 6.8. Linear interpolation of the power time-series data at the specified time point yields a power time-series data of 6.0 at that time point. After completion, 672 denoised power timing data points were obtained. The timestamps of the denoised power timing data and the transformer substation bus loss timing data were obtained, and the timing deviation was calculated to be 28. Set the preset time window to 60. , due to 28 <60 The timing data is retained. After time alignment, 638 denoised bus loss timing data points for the substation area are obtained.

[0058] It should be noted that outliers are usually caused by measurement malfunctions, communication errors, or extreme operating conditions, and can severely distort statistical properties. Using the 3-standard-deviation criterion (3σ criterion), under the assumption of a normal distribution, normal data has a 99.7% probability of falling within the normal range. Data outside the specified range is considered outlier. Missing value completion uses linear interpolation, suitable for scenarios with short-term missing values ​​and gradual power changes. Time alignment ensures strict temporal correspondence between power timing data and bus loss timing data. The preset time window is preferably 1 minute, which tolerates clock errors in the measurement equipment while ensuring data time consistency. After preprocessing, the number of valid data points in the power timing data must be no less than 0.8 * the original number of data points (in this embodiment, 0.8 * 672 = 537.6, but in reality, 638 > 537.6) to ensure the reliability of statistical analysis.

[0059] The method for obtaining the line distance of the distributed power generation access unit within the feeder branch through power flow analysis based on the line loss sensitivity coefficient includes: A relationship model between the line loss sensitivity coefficient and the line distance is established based on the power flow characteristics of the distribution network; the relationship model is as follows: .

[0060] in, Distributed power access unit The feeder branch to which it belongs The transformer area constant; Distributed power access unit The distance of the route.

[0061] Obtain the bus loss rate of the transformer area The target line loss rate of the feeder branch is calculated proportionally based on the power ratio of the feeder branch; the target line loss rate of the feeder branch The calculation formula is: .

[0062] in, The bus loss rate of the distribution area; feeder branch A collection of distributed power supply access units within the system; Distributed power access unit The average power.

[0063] In the feeder branch Within, according to the distributed power access unit within the feeder branch. average power and line loss sensitivity coefficient Establish theoretical line loss rate Regarding the transformer area constant The functional relationship is: .

[0064] in, This is the preset line loss rate scaling factor.

[0065] Based on the theoretical line loss rate and the target line loss rate, establish a target transformer area constant identification function: .

[0066] The line distance of the distributed power supply access unit is obtained based on the target transformer area constant identification function and the relationship model between the line loss sensitivity coefficient and the line distance.

[0067] For example, a model is established to show the relationship between the line loss sensitivity coefficient and the line distance. Obtain the bus loss rate of the transformer area. It is 5.8%. It should be noted that the bus loss rate for the distribution area is... By collecting data from the continuous transformer area Months The total monthly power supply and total electricity sales data were calculated. The specific method is as follows: The monthly line loss rate for each month was calculated, and outliers were removed (a month's line loss rate deviating from the average by more than two standard deviations was considered an outlier and removed). Then, the arithmetic mean of the remaining valid monthly line loss rates was taken. In this embodiment, data for three consecutive months was collected, with monthly line loss rates of 4.84%, 5.00%, and 7.24%, respectively. All three months were within two standard deviations and did not require removal; the average was 5.69%, approximated as 5.8%. The load capacity of each branch was then calculated. The feeder branch is 10.3. , The feeder branch is 12.3. , The feeder branch is 14.6. The total load capacity of the transformer area is 37.2. Based on the target line loss rate The calculation formula yields branches The target line loss rate is 1.92%. Establish the theoretical line loss rate. Regarding the transformer area constant The functional relationship. Based on... Average power of branch distributed power access units (3.5 for distributed power access unit #5). Distributed power supply access unit #6 is 4.0. Distributed power supply access unit #7 is version 3.0. Distributed power supply access unit #8 is 2.5. Distributed power supply access unit #9 is version 2.0. The distributed power supply access unit #10 is 1.5. ).

[0068] Preset line loss rate scaling factor The line loss rate scaling factor α is determined based on typical parameters of the distribution network in the transformer area (such as conductor resistivity, voltage level, conductor cross-sectional area, line topology characteristics, etc.) and with reference to typical parameters of distribution networks in similar transformer areas. The method for determining the line loss rate scaling factor α is as follows: At least one distributed power supply access unit with a known line distance is selected in the transformer area as a reference unit. The line loss sensitivity coefficient of this reference unit and its known line distance are substituted into the relational model to obtain an initial estimate of the transformer area constant. Then, constraints are established based on the measured theoretical line loss rate and the target line loss rate of the transformer area, and the result is calculated in reverse. When there is no known distance reference unit within the transformer area, The line loss rate scaling factor can be selected by referring to historical calibration values ​​of similar transformer substations (same voltage level, similar conductor cross-sectional area). In this embodiment, the line loss rate scaling factor corresponds to the transformer substation (low voltage 0.22kV, aluminum core conductor, cross-sectional area 240mm²). Pick .

[0069] The theoretical line loss rate function is calculated based on the above data. Establish the target transformer area constant identification function: .

[0070] It should be noted that the relational model It is derived from the physical laws governing power flow characteristics and line loss calculations in distribution networks. When the power of a distributed generation unit changes, the resulting change in line loss is directly proportional to the line distance from the distributed generation unit to the transformer. According to Joule's law, conductor power loss is proportional to the square of the current and the conductor resistance, and conductor resistance is proportional to distance. Therefore, the line loss sensitivity coefficient... Distance from the line Proportional relationship. Distribution area constant. This comprehensively reflects the combined influence of physical parameters such as conductor resistivity, cross-sectional area, rated voltage, and average current, as well as operating status parameters, and is expressed as... ;in, The average current of the transformer area. The resistivity of the conductor in the transformer area. This is the rated voltage of the transformer substation. This represents the cross-sectional area of ​​the conductor in the distribution transformer area. The power flow of the distributed generation unit exhibits bidirectional characteristics (power generation during the day, power absorption at night), and the average current is a combined reflection of the forward and reverse currents. Since line loss calculation uses the square of the current, the line loss is always positive regardless of the current direction and is proportional to the distance; therefore, the relationship model... This also applies to bidirectional power flow. The feeder branch line loss rate is theoretically calculated by substituting the line distance calculated from the relationship model into Joule's law. The line loss rate, rather than the line loss value, is used for matching because it is more stable and reliable as it is unaffected by power fluctuations. Target line loss rate. The bus loss rate of the transformer area is obtained by allocating it according to the power ratio, which reflects the actual operating status of the feeder branch.

[0071] because For about The monotonic function, through the... Perform a golden section search, retaining each time. The half-interval containing interior points with smaller values ​​can be efficiently approximated. The root of this equation is the actual transformer constant. The objective function has a unique zero, ensuring the algorithm converges to a unique global solution. The actual transformer area constant not only considers the physical parameters of the conductors but also implicitly incorporates the pre-processed actual operating data and line loss sensitivity coefficient through the target line loss rate, achieving optimal matching between the theoretical model and actual operating data. Optimization using the golden section method can efficiently and accurately solve for the actual transformer area constant, thereby obtaining the line distance of the distributed power supply access unit.

[0072] The method for obtaining the line distance of the distributed power supply access unit based on the target transformer area constant identification function and the relationship model between the line loss sensitivity coefficient and the line distance includes: The target transformer area constant identification function is processed using the golden section method to obtain the actual transformer area constant; the steps of the golden section method include: The initial search interval is set based on the historical parameter data of the distribution network in the aforementioned area. .

[0073] Within the initial search interval, two interior points are selected according to the golden ratio. and And calculate the interior points of the two interior points. and Target area constant identification function value and .

[0074] when If so, the initial search interval is modified to .

[0075] when If so, the initial search interval is modified to .

[0076] Repeat the above steps until the length of the final search interval is less than the preset accuracy threshold; obtain the actual station area constant based on the final search interval.

[0077] For example, the target distribution area constant identification function is processed using the golden section method to obtain the actual distribution area constant. The initial search interval is set based on the historical parameter data of the distribution network in the distribution area. In the first iteration, two interior points are selected within the initial search interval according to the golden ratio. , Calculate the absolute value of the target transformer area constant identification function. It is 0.0152. Approximately 0.0167. Because... Modify the search range to Repeat the above steps, and after 31 iterations, the search interval is obtained. The search interval length is Less than the preset accuracy threshold The convergence condition is met. Taking the midpoint of the final search interval as the actual transformer area constant, the actual transformer area constant is obtained as follows: According to the relational model The line distance of the distributed power supply access unit is calculated: It is 21.1 , It is 22.7 , It is 17.0 , It is 14.8 , It is 10.1 , It is 8.3 It should be noted that the golden section method approximates the optimal solution by continuously narrowing the search interval. Each iteration can reuse the function value of an interior point, improving computational efficiency. The golden ratios of 0.618 (the golden ratio) and 0.382 (1-0.618) have the best search efficiency. The advantages of the golden section method are that it does not require calculating the derivative of the objective function, making it suitable for non-analytic functions; it has good convergence and is not sensitive to the choice of the initial interval; typically, 20-40 iterations are sufficient to achieve the required engineering accuracy. The preset accuracy threshold is preferably... This ensures computational accuracy without generating excessive iterations.

[0078] The line distance of the distributed power supply access unit is calculated based on the relationship model between the actual transformer area constant and the line loss sensitivity coefficient and the line distance.

[0079] The initial search interval is set based on the historical parameter data of the distribution network in the transformer area. The methods include: Obtain historical parameter data of the distribution network in the transformer substation; calculate the theoretical transformer substation constant based on the historical parameter data of the distribution network in the transformer substation; the theoretical transformer substation constant... The calculation formula is: .

[0080] in, The average power of the distribution network in the transformer area; The resistivity of the conductors in the distribution network of the transformer substation; This refers to the cross-sectional area of ​​the conductors in the distribution network of the transformer substation. This refers to the rated voltage of the distribution network in the transformer substation.

[0081] An initial search interval is generated based on the maximum and minimum values ​​of the theoretical plateau constant, combined with a preset search margin. .

[0082] For example, typical parameters are obtained from the historical operation records of the distribution network in the area: based on the typical installed capacity of distributed photovoltaic power in residential communities, the average power of a single access unit ranges from 1 to 10. For example, the minimum power is 1. The maximum power is 10 The resistivity of the wire is According to the distribution network design specifications for the transformer substation, the cross-sectional area of ​​low-voltage distribution line conductors ranges from 50 to 240 mm². The main trunk line uses a thicker conductor, 240. The branch lines use thinner conductors, 50 mm². The rated voltage U is 220. According to the theoretical plateau constant... The calculation formula yields a theoretical minimum value for the plateau constant of approximately [value missing]. (Thickest wire, minimum power), maximum value is approximately (Thinnest wire, maximum power). Considering parameter deviation and measurement error, a preset search margin of 1000 times is set. Based on this preset search margin, an initial search interval is generated as follows: .

[0083] It should be noted that the calculation of the theoretical transformer constant provides the physical basis for the initial search interval. This is derived from the calculation of distribution network line losses. The preset search margin of 1000 times considers the parameter deviations caused by factors such as conductor aging, joint contact resistance, and temperature effects in actual distribution networks. Setting a reasonable initial search interval is crucial for the convergence speed and accuracy of the golden section method; an interval that is too narrow may miss the optimal solution, while an interval that is too wide will increase the number of iterations. Generating the initial search interval based on the maximum and minimum values ​​of the theoretical transformer constant provides both a physical basis and adaptability to parameter differences in different distribution networks.

[0084] Example 2: Based on the same inventive concept, such as Figure 2 As shown, this embodiment also provides a remote precise positioning system for distributed power supply access units, including: The data acquisition and identification module is used to collect power timing data and bus loss timing data of the distributed power supply access units in the transformer area; and to identify the feeder branches of the distributed power supply access units through signal feature similarity analysis.

[0085] The line loss sensitivity analysis module is used to perform line loss analysis based on the feeder branches of the distributed power access unit, the power timing data, and the bus loss timing data to obtain the line loss sensitivity coefficient.

[0086] The line distance calculation module is used to obtain the line distance of the distributed power supply access unit within the feeder branch through power flow analysis based on the line loss sensitivity coefficient.

[0087] The positioning module is used to sort the distributed power access units within the feeder branch according to the line distance to obtain the complete topological location information of the distributed power access units.

[0088] It should be noted that the specific ways in which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0089] Finally, it should be noted that although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for remote and precise positioning of a distributed power supply access unit, characterized in that, The method includes: Collect power timing data and bus loss timing data of distributed power supply access units within the transformer area; identify feeder branches of distributed power supply access units through signal feature similarity analysis; The line loss sensitivity coefficient is obtained by performing line loss analysis based on the feeder branch of the distributed power access unit, the power timing data, and the bus loss timing data; The line distance of the distributed power source access unit within the feeder branch is obtained through power flow analysis based on the line loss sensitivity coefficient. Within the feeder branch, the distributed power access units are sorted according to the line distance to obtain the complete topology location information of the distributed power access units.

2. The method for remote and precise positioning of a distributed power supply access unit according to claim 1, characterized in that, The method for collecting power timing data and bus loss timing data of distributed power supply access units within the transformer area includes: The power timing data and bus loss timing data of the distributed power access units in the transformer area are collected continuously. The bus loss timing data is calculated by the difference between the total power supply of the transformer area and the sum of the power of the distributed power access units in the transformer area.

3. The method for remote and precise positioning of a distributed power supply access unit according to claim 2, characterized in that, The method for identifying feeder branches of a distributed power supply access unit through signal feature similarity analysis includes: The distribution area concentrator sends positioning detection frames to the distributed power supply access unit via power line carrier communication; it collects the received signal strength, signal-to-noise ratio, and relay hop count of the distributed power supply access unit; it constructs a signal feature vector based on the received signal strength, signal-to-noise ratio, and relay hop count of the distributed power supply access unit; it normalizes the signal feature vector; it calculates the cosine similarity between each pair of signal feature vectors of the distributed power supply access units to construct a cosine similarity matrix; and it clusters the cosine similarity matrix to obtain the feeder branches of the distributed power supply access unit.

4. The method for remote and precise positioning of a distributed power supply access unit according to claim 3, characterized in that, The method for obtaining the line loss sensitivity coefficient by performing line loss analysis based on the feeder branch of the distributed power access unit, the power timing data, and the bus loss timing data includes: Acquire power timing data and transformer bus loss timing data of the distributed power access unit within the feeder branch; perform data preprocessing on the power timing data and transformer bus loss timing data to obtain denoised power timing data and transformer bus loss timing data; calculate the line loss sensitivity coefficient based on the denoised power timing data and transformer bus loss timing data; the line loss sensitivity coefficient... The calculation formula is: ； in, Distributed power access unit In time sequence The power; For the distributed power access unit in Average power of a continuous time series; For time sequence The bus loss in the station area; For the aforementioned transformer area Average bus loss over a continuous timing sequence; This represents the number of valid time-series data points after data preprocessing.

5. The method for remote and precise positioning of a distributed power supply access unit according to claim 4, characterized in that, The method for preprocessing the power timing data and transformer bus loss timing data to obtain denoised power timing data and transformer bus loss timing data includes: Calculate the standard deviation of power time series data When power deviation When an outlier occurs, the power timing data is marked as an anomaly and removed to obtain normal power timing data. When the normal power timing data is missing, power timing data before and after the normal power timing data are selected and linear interpolation is performed to complete the denoised power timing data. The timestamps of the denoised power timing data and the transformer bus loss timing data are obtained; the timing deviation is calculated by the difference between the timestamps of the denoised power timing data and the transformer bus loss timing data; the transformer bus loss timing data whose timing deviation exceeds a preset time window are removed to obtain the denoised transformer bus loss timing data.

6. The method for remote and precise positioning of a distributed power supply access unit according to claim 5, characterized in that, The method for obtaining the line distance of the distributed power generation access unit within the feeder branch through power flow analysis based on the line loss sensitivity coefficient includes: A relationship model between the line loss sensitivity coefficient and the line distance is established based on the power flow characteristics of the distribution network; the relationship model is as follows: ； in, Distributed power access unit The feeder branch to which it belongs The transformer area constant; Distributed power access unit The distance of the route; Obtain the bus loss rate of the transformer area The target line loss rate of the feeder branch is calculated proportionally based on the power ratio of the feeder branch; the target line loss rate of the feeder branch The calculation formula is: ； in, The bus loss rate of the distribution area; feeder branch A collection of distributed power supply access units within the system; Distributed power access unit Average power; In the feeder branch Within, according to the distributed power access unit within the feeder branch. average power and line loss sensitivity coefficient Establish theoretical line loss rate Regarding the transformer area constant The functional relationship is: ； in, This is the preset line loss rate scaling factor; Based on the theoretical line loss rate and the target line loss rate, establish a target transformer area constant identification function: ； The line distance of the distributed power supply access unit is obtained based on the target transformer area constant identification function and the relationship model between the line loss sensitivity coefficient and the line distance.

7. The method for remote and precise positioning of a distributed power supply access unit according to claim 6, characterized in that, The method for obtaining the line distance of the distributed power supply access unit based on the target transformer area constant identification function and the relationship model between the line loss sensitivity coefficient and the line distance includes: The target transformer area constant identification function is processed using the golden section method to obtain the actual transformer area constant; the steps of the golden section method include: The initial search interval is set based on the historical parameter data of the distribution network in the aforementioned area. ; Within the initial search interval, two interior points are selected according to the golden ratio. and And calculate the interior points of the two interior points. and Target area constant identification function value and ; when If so, the initial search interval is modified to ; when If so, the initial search interval is modified to ; Repeat the above steps until the length of the final search interval is less than the preset accuracy threshold; obtain the actual station area constant based on the final search interval; The line distance of the distributed power supply access unit is calculated based on the relationship model between the actual transformer area constant and the line loss sensitivity coefficient and the line distance.

8. The method for remote and precise positioning of a distributed power supply access unit according to claim 7, characterized in that, The initial search interval is set based on the historical parameter data of the distribution network in the transformer area. The methods include: Obtain historical parameter data of the distribution network in the transformer substation; calculate the theoretical transformer substation constant based on the historical parameter data; the theoretical transformer substation constant... The calculation formula is: ； in, The average power of the distribution network in the transformer area; The resistivity of the conductors in the distribution network of the transformer substation; This refers to the cross-sectional area of ​​the conductors in the distribution network of the transformer substation. The rated voltage of the distribution network in the transformer substation; An initial search interval is generated based on the maximum and minimum values ​​of the theoretical plateau constant, combined with a preset search margin. .

9. A remote precision positioning system for distributed power supply access units, characterized in that, The system includes: The data acquisition and identification module is used to collect power timing data and bus loss timing data of the distributed power supply access units in the transformer area; and to identify the feeder branches of the distributed power supply access units through signal feature similarity analysis. The line loss sensitivity analysis module is used to perform line loss analysis based on the feeder branch of the distributed power access unit, the power timing data, and the bus loss timing data to obtain the line loss sensitivity coefficient. The line distance calculation module is used to obtain the line distance of the distributed power access unit in the feeder branch through power flow analysis based on the line loss sensitivity coefficient. The positioning module is used to sort the distributed power access units within the feeder branch according to the line distance to obtain the complete topological location information of the distributed power access units.

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