Rural power distribution network line loss and power supply reliability collaborative optimization analysis method and system

Abstract

The invention discloses a rural power distribution network line loss and power supply reliability collaborative optimization analysis method and system, and relates to the technical field of power distribution network optimization, and the method comprises the steps: obtaining time-division power utilization data of a user, carrying out the time-frequency decomposition, and extracting a load fluctuation period to generate a time-frequency characteristic spectrum; constructing a load change track spectrum based on the time-frequency characteristic spectrum to identify a load transmission channel; acquiring meteorological and geographical environment parameters to calculate the bearing pressure and stress loss of the equipment; constructing a load coupling degree matrix to evaluate power quality and power supply continuity; dividing power supply grids to execute dynamic load distribution; constructing a load flow potential energy diagram to calculate equipment distribution coordinates; and adjusting the distribution equipment layout. According to the invention, matching optimization of distribution equipment layout and load transfer characteristics is realized, and the operation efficiency and power supply reliability of the power distribution network are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network optimization technology, specifically to a method and system for the collaborative optimization analysis of line loss and power supply reliability in rural power distribution networks. Background Technology

[0002] With the continuous growth of rural electricity load, the reliability of power distribution networks and line loss control face severe challenges. Rural power distribution network line loss analysis methods are mainly based on fixed-time window statistical electricity consumption data, which cannot reflect the dynamic fluctuations of agricultural production load and domestic load, leading to discrepancies between line loss assessment results and actual operating conditions. Traditional power supply reliability evaluation methods ignore the influence of geographical environment during load transmission and fail to fully consider changes in the load-bearing capacity of power distribution equipment under adverse weather conditions, making it difficult to accurately predict power supply failure risks.

[0003] Current power distribution equipment layout optimization methods often treat line loss control and power supply reliability as independent optimization objectives, lacking in-depth analysis of the dynamic distribution characteristics of loads and failing to achieve coordinated matching between power distribution equipment layout and load transfer channels. Existing load allocation schemes lack comprehensive consideration of power quality degradation and power supply continuity, resulting in insufficient rationality in power grid division and load transfer decisions.

[0004] Therefore, there is an urgent need for a distribution network collaborative optimization method that can integrate load dynamic characteristic analysis, geographical environmental impact assessment and equipment layout optimization, so as to achieve a balance between line loss control and power supply reliability. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0006] The technical solution of this invention is: a method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability, comprising the following steps: S1. Obtain electricity consumption data of rural users in different time periods, perform time-frequency decomposition on the electricity consumption data, extract the fluctuation period of agricultural production load and domestic load, and generate load time-frequency characteristic spectrum; S2. Construct a load change trajectory map based on the load time-frequency characteristic spectrum, and identify the load transfer channels between power distribution equipment from the load change trajectory map; S3. Obtain meteorological data and geographical environmental parameters along the load transmission channel, and calculate the load-bearing pressure and stress loss values ​​of the power distribution equipment; S4. Construct a load coupling degree matrix based on the bearing pressure and stress loss values, and calculate the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel based on the load coupling degree matrix; S5. Based on the power quality attenuation coefficient and power supply continuity evaluation value, divide the power supply grid into zones, calculate the load transfer threshold of each zone, generate feeder switch switching instructions, and perform dynamic load allocation; S6. Construct a load flow potential energy diagram based on the topology of the load transmission channel, and calculate the coordinates of the power distribution equipment based on the load flow potential energy diagram and the load dynamic allocation results; S7. Adjust the layout of power distribution equipment according to the coordinates of the power distribution equipment locations and the load coupling matrix.

[0007] In step S1, electricity consumption data of rural users at different times is obtained, and the electricity consumption data is decomposed into time-frequency components to extract the fluctuation periods of agricultural production load and domestic load, generating a load time-frequency characteristic spectrum, including: S1.1. Obtain the time-segmented electricity consumption data waveform curves of rural users, calculate the electricity consumption data change rate, determine the segment position, divide the electricity consumption data waveform curves into multiple time segments, perform orthogonal transformation on each time segment, and generate an electricity consumption data frequency feature sequence. S1.2. Calculate the power factors of agricultural production load and domestic load based on the frequency characteristic sequence of electricity consumption data, and generate the load time distribution matrix; S1.3. Extract the temporal variation patterns of agricultural production load and domestic load from the load time distribution matrix, obtain fluctuation period data based on the time variation patterns, and perform frequency domain transformation on the fluctuation period data to obtain frequency domain component data; S1.4. The time variation pattern is fused with the frequency domain component data to generate the load time-frequency characteristic spectrum.

[0008] In step S2, a load change trajectory map is constructed based on the load time-frequency characteristic spectrum, and load transfer channels between power distribution equipment are identified from the load change trajectory map, including: S2.1. Perform wavelet decomposition on the load time-frequency characteristic spectrum, extract the instantaneous amplitude and phase angle of load change, calculate the power factor at different times based on the instantaneous amplitude and phase angle, and generate a load change characteristic sequence; S2.2. Perform time series analysis on the load change characteristic sequence, calculate the phase angle difference between adjacent sampling times to construct a phase difference matrix, calculate the time correlation degree of load change based on the phase difference matrix and the power factor, and construct the time correlation degree as a time series feature matrix; S2.3. Identify load transmission nodes based on the time-series feature matrix, calculate the transmission direction and transmission intensity between load transmission nodes, and generate a load change trajectory map based on the transmission direction and transmission intensity; S2.4. Calculate the power loss between nodes based on the transmission direction and power factor of the load transmission nodes in the load change trajectory diagram, and use the power loss and transmission strength to calculate the impedance characteristics between nodes, and construct the topology connection matrix; S2.5. Recursively decompose the topology connection matrix to obtain the connection relationship characteristics of the load transmission nodes, and combine the connection relationship characteristics with the transmission strength to construct the load transmission characteristic matrix; S2.6. Identify load transfer channels between power distribution equipment based on the connection strength between power distribution equipment in the load transfer feature matrix.

[0009] In step S3, meteorological data and geographical environmental parameters are acquired along the load transfer channel, and the load-bearing pressure and stress loss values ​​of the power distribution equipment are calculated, including: S3.1. Collect meteorological data along the load transfer channel, and generate a meteorological impact index based on the changing trend of the meteorological data; S3.2. Collect the geographical environmental parameters of the load transfer channel, and generate the geographical impact index based on the coupling relationship between the geographical environmental parameters and the meteorological impact index; S3.3. Calculate the environmental constraint coefficient based on the changing patterns of the meteorological impact index and the geographical impact index; S3.4. The load-bearing pressure of the power distribution equipment is obtained by weighting the environmental constraint coefficient with the load transfer amount on the load transfer channel; S3.5. Calculate the stress loss value of the power distribution equipment based on the bearing pressure and environmental constraint coefficient.

[0010] In step S4, a load coupling degree matrix is ​​constructed based on the bearing pressure and stress loss values. Based on this load coupling degree matrix, the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel are calculated, including: S4.1. Decompose the bearing pressure into static bearing base value and dynamic bearing change; S4.2. Construct a node bearing capacity matrix based on the static bearing base value, construct a node response matrix based on the dynamic bearing change, and combine the node bearing capacity matrix and the node response matrix to obtain a bearing pressure mapping matrix; S4.3. Decompose the stress loss value according to the transmission relationship between adjacent nodes to obtain the stress loss mapping matrix. After normalizing the bearing pressure mapping matrix and the stress loss mapping matrix, combine them to construct the load coupling degree matrix. S4.4. Calculate the voltage fluctuation transmission coefficient and power loss attenuation coefficient based on the load coupling degree matrix, and combine the voltage fluctuation transmission coefficient and power loss attenuation coefficient to obtain the power quality attenuation coefficient. S5.5. Substitute the power quality attenuation coefficient into the load coupling degree matrix for iterative calculation. Update the load coupling degree matrix according to the results of each iteration. When the difference between the load coupling degree matrices of two adjacent iterations is less than the preset convergence threshold, output the power supply continuity evaluation value.

[0011] In step S5, based on the power quality attenuation coefficient and the power supply continuity evaluation value, the power supply grid is divided into zones, the load transfer threshold for each zone is calculated, and feeder switch switching instructions are generated to perform dynamic load allocation, including: S5.1. Map the power quality attenuation coefficient to the power transmission index, map the power supply continuity evaluation value to the power supply reliability index, construct a node evaluation matrix based on the power transmission index and the power supply reliability index, and divide the power supply grid into partitions according to the node evaluation matrix. S5.2. Obtain the power supply capacity value and load demand value of each node in the power supply grid partition according to the node evaluation matrix, and calculate the load transfer threshold between adjacent partitions based on the matching relationship between the power supply capacity value and the load demand value; S5.3. Substitute the load transfer threshold into the power grid partition to calculate the load distribution coefficient between each partition, construct the feeder switch state combination based on the load distribution coefficient, and generate the feeder switch switching command; S5.4. Using the feeder switch switching command and load allocation coefficient as constraints, adjust the load distribution ratio within the power grid zone and perform dynamic load allocation.

[0012] In step S6, a load flow potential energy diagram is constructed based on the topology of the load transfer channel. The coordinates of the power distribution equipment locations are calculated based on the load flow potential energy diagram and the load dynamic allocation results, including: S6.1. Calculate the power supply node distribution and inter-node transmission impedance based on the topology of the load transmission channel, construct the power supply node distribution as a node adjacency matrix, construct the inter-node transmission impedance as a transmission impedance matrix, and combine the node adjacency matrix and the transmission impedance matrix to construct a load flow potential energy diagram. S6.2. Obtain the node power distribution and node voltage distribution based on the load dynamic allocation results, calculate the transmission loss between nodes, and construct the node power supply weight matrix; S6.3. Substitute the node power supply weight matrix into the load flow potential energy diagram to calculate the voltage distribution curve and power distribution curve. Determine the load centroid point based on the intersection of the voltage distribution curve and the power distribution curve, and map the load centroid point to the coordinates of the power distribution equipment layout.

[0013] In step S7, the layout of the power distribution equipment is adjusted according to the coordinates of the power distribution equipment locations and the load coupling matrix, including: S7.1. Calculate the distance matrix between equipment and the power supply coverage area based on the coordinates of the power distribution equipment. Combine the distance matrix between equipment and the power supply coverage area to obtain the power supply overlap area. Construct an equipment layout optimization matrix based on the power supply overlap area and the load coupling degree matrix. S7.2. Obtain the equipment power supply capacity and load distribution capacity according to the equipment layout optimization matrix, and perform matching calculations on the equipment power supply capacity and load distribution capacity to obtain the equipment supply and demand balance value; S7.3. Calculate the equipment position offset based on the equipment supply and demand balance value and the equipment layout optimization matrix, correct the power distribution equipment layout coordinates based on the equipment position offset, generate a power distribution equipment layout scheme, and adjust the power distribution equipment layout based on the power distribution equipment layout scheme.

[0014] A collaborative optimization analysis system for rural power distribution network line loss and power supply reliability includes: The extraction module is used to acquire electricity consumption data of rural users in different time periods, perform time-frequency decomposition on the electricity consumption data, extract the fluctuation period of agricultural production load and domestic load, and generate a load time-frequency characteristic spectrum. The identification module is used to construct a load change trajectory map based on the load time-frequency characteristic spectrum, and to identify the load transfer channels between power distribution equipment from the load change trajectory map; The calculation module is used to acquire meteorological data and geographical environmental parameters along the load transmission channel, and to calculate the load-bearing pressure and stress loss values ​​of the power distribution equipment. A calculation module is constructed to build a load coupling degree matrix based on the bearing pressure and stress loss values, and to calculate the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel based on the load coupling degree matrix. The partitioning calculation module is used to divide the power grid into zones based on the power quality attenuation coefficient and the power supply continuity evaluation value, calculate the load transfer threshold of each zone, generate feeder switch switching instructions, and perform dynamic load allocation. The point layout module is used to construct a load flow potential energy diagram based on the topology of the load transmission channel, and to calculate the layout coordinates of the power distribution equipment based on the load flow potential energy diagram and the load dynamic allocation results. The layout module is used to adjust the layout of power distribution equipment based on the coordinates of the power distribution equipment locations and the load coupling matrix.

[0015] This invention accurately identifies the dynamic changes in agricultural and residential loads by performing time-frequency decomposition on rural user electricity consumption data; it precisely depicts load flow patterns by analyzing load transfer relationships between power distribution equipment based on load change trajectory maps; it improves the accuracy of equipment reliability assessment by combining meteorological data and geographical environmental parameters to evaluate the operating status of power distribution equipment; it provides a scientific basis for power grid division by analyzing power quality and power supply continuity through load coupling degree matrix analysis; and it optimizes the layout of power distribution equipment based on load flow potential energy diagrams, achieving dynamic matching between equipment location and load distribution. This method effectively reduces power distribution network line losses, improves power supply reliability, enhances power distribution network operating efficiency, and facilitates the planning and renovation of rural power distribution networks. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart of a method for collaborative optimization analysis of rural power distribution network line loss and power supply reliability provided in an embodiment of the present invention. Detailed Implementation

[0018] like Figure 1 As shown in the figure, the method for collaborative optimization analysis of rural power distribution network line loss and power supply reliability provided in this embodiment of the invention includes the following steps: S1. Obtain electricity consumption data of rural users in different time periods, perform time-frequency decomposition on the electricity consumption data, extract the fluctuation period of agricultural production load and domestic load, and generate load time-frequency characteristic spectrum; S2. Construct a load change trajectory map based on the load time-frequency characteristic spectrum, and identify the load transfer channels between power distribution equipment from the load change trajectory map; S3. Obtain meteorological data and geographical environmental parameters along the load transmission channel, and calculate the load-bearing pressure and stress loss values ​​of the power distribution equipment; S4. Construct a load coupling degree matrix based on the bearing pressure and stress loss values, and calculate the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel based on the load coupling degree matrix; S5. Based on the power quality attenuation coefficient and power supply continuity evaluation value, divide the power supply grid into zones, calculate the load transfer threshold of each zone, generate feeder switch switching instructions, and perform dynamic load allocation; S6. Construct a load flow potential energy diagram based on the topology of the load transmission channel, and calculate the coordinates of the power distribution equipment based on the load flow potential energy diagram and the load dynamic allocation results; S7. Adjust the layout of power distribution equipment according to the coordinates of the power distribution equipment locations and the load coupling matrix.

[0019] In step S1, electricity consumption data of rural users at different times is obtained, and the electricity consumption data is decomposed into time-frequency components to extract the fluctuation periods of agricultural production load and domestic load, generating a load time-frequency characteristic spectrum, including: S1.1. Obtain the time-segmented electricity consumption data waveform curves of rural users, calculate the electricity consumption data change rate, determine the segment position, divide the electricity consumption data waveform curves into multiple time segments, perform orthogonal transformation on each time segment, and generate an electricity consumption data frequency feature sequence. S1.2. Calculate the power factors of agricultural production load and domestic load based on the frequency characteristic sequence of electricity consumption data, and generate the load time distribution matrix; S1.3. Extract the temporal variation patterns of agricultural production load and domestic load from the load time distribution matrix, obtain fluctuation period data based on the time variation patterns, and perform frequency domain transformation on the fluctuation period data to obtain frequency domain component data; S1.4. The time variation pattern is fused with the frequency domain component data to generate the load time-frequency characteristic spectrum.

[0020] The process involves acquiring time-of-use electricity consumption data waveforms for rural users, reflecting the changes in electricity consumption over a 24-hour period. The rate of change in electricity consumption at each time point is calculated, calculated as the difference in electricity consumption between two adjacent time points divided by the time interval. Segmentation points are determined based on significant changes in the rate of change; when the rate of change exceeds a preset threshold, it is marked as a segmentation point. Taking a rural area as an example, the rate of change in electricity consumption is relatively large during the time periods of 5:00-8:00 AM, 11:00-2:00 PM, and 5:00-9:00 PM, which can be used as segmentation points. The electricity consumption data waveform curve is divided into multiple time segments. For this region, it can be divided into four time segments: nighttime off-peak period, morning peak period, midday stable period, and evening peak period. An orthogonal transformation is performed on each time segment, using the discrete cosine transform (DCT) method to convert the time-domain signal into a frequency-domain signal, generating a frequency characteristic sequence of electricity consumption data. For the morning peak period time segment, the frequency characteristic sequence obtained after DCT contains multiple frequency components, where the low-frequency component reflects the overall trend of electricity consumption, and the high-frequency component reflects the fluctuations in electricity consumption.

[0021] Power factors for agricultural production load and domestic load were calculated based on the frequency characteristic sequence of electricity consumption data. The power factor calculation considers the ratio of active power to reactive power. In rural electricity environments, agricultural production load mainly consists of electric motors, while domestic load is mostly lighting and household appliances. The power characteristics of the two types of loads differ significantly. By analyzing the energy distribution characteristics of the frequency characteristic sequence, the low-frequency and high-frequency components are distinguished; the low-frequency component mainly corresponds to agricultural production load, and the high-frequency component mainly corresponds to domestic load. Data analysis for this region shows that the power factor for agricultural production load is approximately 0.85, and the power factor for domestic load is approximately 0.92. Based on these two load power factors and the division into time segments, a load time distribution matrix is ​​generated. The rows of this matrix represent different time segments, the columns represent different types of loads, and the matrix element values ​​represent the proportion of a specific load type within the corresponding time segment.

[0022] The temporal variation patterns of agricultural production load and residential load were extracted from the load time distribution matrix. Time-series analysis was performed on the agricultural production load and residential load columns in the matrix to identify periodic patterns. Agricultural production load is mainly concentrated during daytime working hours, exhibiting a clear diurnal periodicity, and showing seasonal variations during the busy farming season. Residential load, on the other hand, shows a clear "morning and evening peak" characteristic, with differences between weekends and weekdays. Based on these temporal variation patterns, fluctuation cycle data were obtained. The main fluctuation cycles of agricultural production load are 24-hour and seasonal cycles, while the main fluctuation cycles of residential load are 12-hour, 24-hour, and weekly fluctuations. The fluctuation cycle data were then transformed into frequency domain data using Fourier transform. The frequency domain component data shows that the spectrum of agricultural production load is mainly concentrated in the low-frequency band, while residential load has a clear energy distribution in the mid-frequency band.

[0023] By fusing temporal variation patterns with frequency domain component data, a load time-frequency characteristic spectrum is generated, where the horizontal axis represents time and the vertical axis represents frequency. Using a sliding window Fourier transform method, the time domain data is segmented, and each segment undergoes a frequency domain transformation. The results are then arranged in chronological order to form the time-frequency characteristic spectrum. This spectrum clearly shows the low-frequency energy concentration of agricultural production load during working hours and the mid-frequency energy distribution of residential load during morning and evening hours. The time-frequency characteristic spectrum visually demonstrates the variation patterns of rural electricity load with time and frequency, providing a foundation for subsequent load decomposition and forecasting.

[0024] This invention extracts the fluctuation cycles and time-frequency characteristic spectra of agricultural production load and domestic load using the above-mentioned time-frequency decomposition method. This allows for precise characterization of rural electricity consumption behavior and accurate identification and separation of load types. It solves the identification difficulties caused by the complexity of rural electricity consumption in traditional load analysis, improving the accuracy and reliability of load analysis. The obtained load characteristic spectra can be used to guide distribution network operation optimization, line loss analysis, and reliability assessment, providing data support for the coordinated optimization of line loss and power supply reliability in rural distribution networks.

[0025] In step S2, a load change trajectory map is constructed based on the load time-frequency characteristic spectrum, and load transfer channels between power distribution equipment are identified from the load change trajectory map, including: S2.1. Perform wavelet decomposition on the load time-frequency characteristic spectrum, extract the instantaneous amplitude and phase angle of load change, calculate the power factor at different times based on the instantaneous amplitude and phase angle, and generate a load change characteristic sequence; S2.2. Perform time series analysis on the load change characteristic sequence, calculate the phase angle difference between adjacent sampling times to construct a phase difference matrix, calculate the time correlation degree of load change based on the phase difference matrix and the power factor, and construct the time correlation degree as a time series feature matrix; S2.3. Identify load transmission nodes based on the time-series feature matrix, calculate the transmission direction and transmission intensity between load transmission nodes, and generate a load change trajectory map based on the transmission direction and transmission intensity; S2.4. Calculate the power loss between nodes based on the transmission direction and power factor of the load transmission nodes in the load change trajectory diagram, and use the power loss and transmission strength to calculate the impedance characteristics between nodes, and construct the topology connection matrix; S2.5. Recursively decompose the topology connection matrix to obtain the connection relationship characteristics of the load transmission nodes, and combine the connection relationship characteristics with the transmission strength to construct the load transmission characteristic matrix; S2.6. Identify load transfer channels between power distribution equipment based on the connection strength between power distribution equipment in the load transfer feature matrix.

[0026] The obtained load time-frequency characteristic spectrum is decomposed using wavelet decomposition, and the characteristic spectrum signal is analyzed at multiple scales using wavelet transform. A wavelet basis function suitable for the power signal characteristics is selected to decompose the load time-frequency characteristic spectrum, obtaining wavelet coefficients at different scales. Instantaneous amplitude and phase angle information of load changes are extracted from the wavelet coefficients. For a rural distribution line, the instantaneous amplitude obtained after wavelet decomposition reaches 3.5 kW during peak electricity consumption periods, while it is only 0.8 kW during off-peak periods; the phase angle is close to 0 degrees in the purely resistive load region, while it reaches 25 degrees in the motor starting region. The power factor at different times is calculated based on the instantaneous amplitude and phase angle. The power factor is equal to the ratio of active power to apparent power, which can be obtained from the cosine value of the phase angle. A load change characteristic sequence containing time, instantaneous amplitude, phase angle, and power factor is generated. This characteristic sequence reflects the dynamic characteristics of load changes over time, providing basic data for identifying load transmission channels in rural distribution networks.

[0027] A time-series analysis was performed on the load change characteristic sequence to calculate the phase angle difference between adjacent sampling times. Using a 10-minute sampling interval, the phase angle difference between every two adjacent times was calculated for 24 consecutive hours of data, constructing a phase difference matrix. This matrix is ​​a 144×144 square matrix, with diagonal elements set to 0 and off-diagonal elements representing the phase difference between different times. The temporal correlation of load changes was calculated based on the phase difference matrix and the power factor. The temporal correlation represents the correlation between load changes at different times. The calculation of the temporal correlation considers the product of the cosine of the phase difference and the power factor; a higher temporal correlation is indicated when the load change trends at two times are consistent and the power factor is high. The temporal correlation was then constructed as a time-series characteristic matrix, also a 144×144 square matrix, where element values ​​range from -1 to 1. Values ​​closer to 1 indicate a stronger correlation, and values ​​closer to -1 indicate a stronger negative correlation.

[0028] Load transmission nodes were identified based on the time-series feature matrix. Cluster analysis was applied to group highly correlated moments in the matrix into clusters, each cluster representing a load transmission node. For this rural distribution network, 12 typical load transmission nodes were identified, including substation outgoing lines, key branch points of main lines, and terminal load aggregation points. The transmission direction and intensity between load transmission nodes were calculated. The transmission direction was determined by the phase angle change trend; when the phase change of node A leads that of node B, the load transmission direction is from A to B. The transmission intensity was determined by the absolute value of the time correlation; the greater the correlation, the greater the transmission intensity. A load change trajectory map was generated based on the transmission direction and intensity. This map visually displays the flow path and intensity distribution of loads in the distribution network, providing a spatial reference for subsequent line loss analysis.

[0029] Power loss between nodes is calculated based on the transmission direction and power factor of load transmission nodes in the load change trajectory diagram. This calculation considers both the amount of power transmitted and the transmission distance. The amount of power transmitted is determined by both the transmission intensity and the load amplitude, while the transmission distance is determined by the physical layout of the distribution network. For two adjacent load transmission nodes, the power loss equals the transmitted power multiplied by the transmission distance and then multiplied by the unit distance loss coefficient. The impedance characteristics between nodes are calculated using power loss and transmission intensity. Impedance characteristics reflect the electrical characteristics of the line, including resistive and inductive reactance components. According to Ohm's law, the impedance value equals the ratio of voltage drop to current. Given the power loss and transmission intensity, the equivalent impedance between nodes can be estimated. A topology connection matrix is ​​constructed, which describes the connection relationships and impedance characteristics between each load transmission node, providing a data foundation for distribution network topology analysis.

[0030] The topology connectivity matrix is ​​recursively decomposed using spectral decomposition to obtain eigenvalues ​​and eigenvectors. Eigenvalues ​​represent the magnitude of the connection strength, while eigenvectors represent the directional characteristics of the connections. Eigenvalue analysis identifies key connectivity features, discarding noisy and weak connections. This yields the connectivity characteristics of load transmission nodes, encompassing connection strength, directionality, and impedance characteristics. These connectivity characteristics are then combined with transmission strength to construct a load transmission feature matrix. This matrix comprehensively reflects the spatial transmission characteristics of the load, providing complete information for identifying load transmission channels.

[0031] Load transfer channels between power distribution equipment are identified based on the connection strength between the equipment in the load transfer feature matrix. A connection strength threshold is set; when the connection strength between nodes exceeds the threshold, a load transfer channel is identified. For this rural power distribution network, the connection strength threshold is set to 0.75, and a total of 9 main load transfer channels are identified, including 3 main channels and 6 branch channels. These channels constitute the load transfer backbone of the rural power distribution network and are the key targets for line loss analysis and reliability assessment. The identification results of load transfer channels intuitively show the main paths of energy flow in the power distribution network, providing targeted targets for subsequent line loss optimization and reliability improvement.

[0032] This invention constructs a load change trajectory map based on the load's time-frequency characteristic spectrum and identifies load transmission channels between distribution equipment, achieving accurate characterization of complex load characteristics and accurate identification of transmission paths in rural distribution networks. It overcomes the technical bottlenecks of fuzzy load transmission paths and unclear line loss distribution in traditional distribution network analysis, effectively capturing the spatiotemporal variation characteristics and transmission patterns of loads in rural distribution networks. Through the identified load transmission channels, areas with concentrated line losses can be precisely located, allowing for targeted line modifications and equipment upgrades, thereby reducing the overall line loss rate of the distribution network.

[0033] In step S3, meteorological data and geographical environmental parameters are acquired along the load transfer channel, and the load-bearing pressure and stress loss values ​​of the power distribution equipment are calculated, including: S3.1. Collect meteorological data along the load transfer channel, and generate a meteorological impact index based on the changing trend of the meteorological data; S3.2. Collect the geographical environmental parameters of the load transfer channel, and generate the geographical impact index based on the coupling relationship between the geographical environmental parameters and the meteorological impact index; S3.3. Calculate the environmental constraint coefficient based on the changing patterns of the meteorological impact index and the geographical impact index; S3.4. The load-bearing pressure of the power distribution equipment is obtained by weighting the environmental constraint coefficient with the load transfer amount on the load transfer channel; S3.5. Calculate the stress loss value of the power distribution equipment based on the bearing pressure and environmental constraint coefficient.

[0034] Meteorological data, including key meteorological parameters such as temperature, humidity, wind speed, precipitation, and lightning activity, are collected along the load transfer channels. Real-time data acquisition is achieved using automatic weather stations and intelligent sensor networks. The data collection points are set up according to the principle of "full coverage of key nodes and sampling collection of general areas," ensuring that data collection points are located at important locations along the load transfer channels. Taking a rural power distribution network as an example, 15 meteorological data collection points are set up along the main load transfer channels, with a collection frequency of once per hour. The collected meteorological data undergoes preprocessing, including outlier detection, missing value completion, and data smoothing. Outlier detection uses the three-standard-deviation method; when data deviates from the average by more than three standard deviations, it is identified as an outlier and replaced with the average of nearby time points. Missing values ​​are completed using linear interpolation. Data smoothing uses the moving average method with a window width of 5 time points. Based on the preprocessed meteorological data, its changing trends are analyzed. Time series analysis methods are used to extract the seasonal variations, periodic fluctuations, and abrupt changes of meteorological parameters. The changing trends of each meteorological parameter are normalized, converted into normalized values ​​between 0 and 1. Weighting coefficients are assigned based on the importance of meteorological parameters to power distribution equipment. For example, temperature has a weight of 0.3, humidity 0.15, wind speed 0.2, precipitation 0.2, and lightning activity 0.15. The normalized meteorological parameters are multiplied by their corresponding weighting coefficients and summed to obtain the meteorological impact index. This index reflects the comprehensive influence of meteorological factors on the operating status of power distribution equipment.

[0035] Geographic environmental parameters of the load transfer corridor were collected, including altitude, topographic relief, vegetation cover, soil type, and corrosivity. The collection of these parameters combined field surveys with geographic information data to ensure accuracy and completeness. Digital elevation model (DEM) data was used for altitude and topographic relief; vegetation cover was obtained through remote sensing image analysis; and soil type and corrosivity were determined through sampling analysis. Taking this rural power distribution network as an example, the altitude of the load transfer corridor is between 500 and 800 meters, the topographic relief index is 0.35, the vegetation cover is 65%, the soil type is mainly red soil, and the corrosivity index is 0.28. The geographic environmental parameters were standardized, converting parameters of different dimensions into dimensionless index values. A geographic impact index was generated based on the coupling relationship between the standardized geographic environmental parameters and the meteorological impact index. The establishment of the coupling relationship was based on expert experience and historical data analysis, using a weighted superposition method. Weighting coefficients were set for the geographical environmental parameters: altitude (0.2), topographic relief (0.25), vegetation cover (0.2), soil type (0.15), and corrosivity (0.2). The standardized geographical environmental parameters were multiplied by their corresponding weighting coefficients and coupled with the meteorological impact index to obtain the geographical impact index. This index reflects the degree of influence of geographical environmental factors on the operating status of power distribution equipment.

[0036] The environmental constraint coefficient was calculated based on the variation patterns of the meteorological and geographical impact indices. The analysis of these patterns was based on spatiotemporal correlation and interaction, employing correlation and regression analysis methods. The correlation coefficient between the meteorological and geographical impact indices was calculated to analyze their coordinated changes and interactions. For this rural power distribution network, the correlation coefficient between the meteorological and geographical impact indices was 0.72, indicating a strong correlation. A calculation model for the environmental constraint coefficient was established, which is a weighted combination of the meteorological and geographical impact indices. The weighting coefficients were determined based on the correlation analysis results. The weight of the meteorological impact index was 0.65, and the weight of the geographical impact index was 0.35. The calculated environmental constraint coefficient reflects the comprehensive constraint degree of environmental factors on the operation of power distribution equipment, ranging from 0 to 1, with larger values ​​indicating stricter constraints.

[0037] The load-bearing pressure of the power distribution equipment is obtained by weighting the environmental constraint coefficient with the load transfer volume on the load transfer channel. The load transfer volume is extracted from the load change trajectory map, representing the power transmission task that the power distribution equipment needs to undertake. The weighted calculation adopts a nonlinear mapping method to consider the interaction between environmental constraints and load transfer. The environmental constraint coefficient is converted into an adjustment factor, which is equal to 1 plus the environmental constraint coefficient. The product of the load transfer volume and the adjustment factor is calculated to obtain the load-bearing pressure after considering the environmental impact. For the main line of this rural power distribution network, the environmental constraint coefficient is 0.42, the load transfer volume is 350 kW, and the calculated load-bearing pressure is 497 kW. The load-bearing pressure reflects the actual load pressure that the power distribution equipment needs to undertake under actual environmental conditions and is an important indicator for evaluating the operating status of the equipment.

[0038] The stress loss value of power distribution equipment is calculated based on the bearing pressure and environmental constraint coefficient. The stress loss value represents the degree of loss experienced by the equipment under bearing pressure and is a key parameter in line loss analysis. The calculation of the stress loss value considers the magnitude of the bearing pressure, its duration, and the severity of the environmental constraints. The calculation method is: the stress loss value equals the bearing pressure multiplied by the environmental constraint coefficient, and then multiplied by the time accumulation coefficient. The time accumulation coefficient is determined based on the duration of the bearing pressure; the longer the duration, the larger the accumulation coefficient. For the transformer equipment in this rural power distribution network, the bearing pressure is 230 kW, the environmental constraint coefficient is 0.38, the duration is 12 hours, and the corresponding time accumulation coefficient is 1.25. The calculated stress loss value is 109.25 kW. The stress loss value directly reflects the degree of loss and potential risk of the power distribution equipment and is an important basis for line loss management and equipment maintenance.

[0039] This invention acquires meteorological data and geographical environmental parameters along the load transmission channel to calculate the load-bearing pressure and stress loss values ​​of power distribution equipment, enabling accurate assessment of the operating status of rural power distribution network equipment and in-depth analysis of the causes of line losses. By introducing meteorological and geographical influence indices, complex and variable environmental factors are transformed into quantifiable constraints, improving the accuracy and relevance of line loss analysis. The calculation of load-bearing pressure and stress loss values ​​provides a scientific basis for equipment maintenance and upgrades, contributing to the optimization of power distribution network operation strategies and resource allocation.

[0040] In step S4, a load coupling degree matrix is ​​constructed based on the bearing pressure and stress loss values. Based on this load coupling degree matrix, the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel are calculated, including: S4.1. Decompose the bearing pressure into static bearing base value and dynamic bearing change; S4.2. Construct a node bearing capacity matrix based on the static bearing base value, construct a node response matrix based on the dynamic bearing change, and combine the node bearing capacity matrix and the node response matrix to obtain a bearing pressure mapping matrix; S4.3. Decompose the stress loss value according to the transmission relationship between adjacent nodes to obtain the stress loss mapping matrix. After normalizing the bearing pressure mapping matrix and the stress loss mapping matrix, combine them to construct the load coupling degree matrix. S4.4. Calculate the voltage fluctuation transmission coefficient and power loss attenuation coefficient based on the load coupling degree matrix, and combine the voltage fluctuation transmission coefficient and power loss attenuation coefficient to obtain the power quality attenuation coefficient. S5.5. Substitute the power quality attenuation coefficient into the load coupling degree matrix for iterative calculation. Update the load coupling degree matrix according to the results of each iteration. When the difference between the load coupling degree matrices of two adjacent iterations is less than the preset convergence threshold, output the power supply continuity evaluation value.

[0041] The load-bearing capacity is decomposed into a static load-bearing baseline value and a dynamic load-bearing variation value. The static load-bearing baseline value reflects the basic load level of the power distribution equipment under normal operating conditions, while the dynamic load-bearing variation value reflects the fluctuation characteristics of the load over time. Using time series decomposition technology, the long-term trend of the load-bearing capacity is extracted as the static load-bearing baseline value using a moving average method. The dynamic load-bearing variation value is obtained by subtracting the static load-bearing baseline value from the original load-bearing capacity. For the main line of a rural power distribution network, the load-bearing capacity data are continuous 24-hour monitoring values. Using a 12-hour window moving average, the static load-bearing baseline value is 380 kW, and the dynamic load-bearing variation value fluctuates between -120 kW and +150 kW. The static load-bearing baseline value reflects the long-term load level of the power distribution equipment and is an important reference for assessing the basic operating status of the equipment; the dynamic load-bearing variation value reflects the fluctuation characteristics of the load and is a key factor in analyzing the stability and reliability of the power grid.

[0042] A node load-bearing capacity matrix is ​​constructed based on static load-bearing baseline values. This matrix reflects the basic load-bearing capacity of each node in the distribution network. After normalizing the static load-bearing baseline values ​​of each node, a diagonal matrix is ​​formed, where the diagonal elements are the normalized load-bearing baseline values ​​of the nodes, and the off-diagonal elements are 0. For this rural distribution network, there are 12 key nodes, forming a 12×12 node load-bearing capacity matrix. A node response matrix is ​​constructed based on dynamic load-bearing changes, reflecting the load response relationship between nodes. The correlation coefficient between the dynamic load-bearing changes of different nodes is calculated; a higher correlation coefficient indicates a closer relationship between node responses. The node load-bearing capacity matrix and the node response matrix are combined to obtain a load-bearing pressure mapping matrix, which is obtained by multiplying corresponding elements of the two matrices. The load-bearing pressure mapping matrix comprehensively reflects the load-bearing capacity of nodes and the response relationship between nodes, providing basic data for load coupling analysis.

[0043] The stress loss value is decomposed according to the transmission relationship between adjacent nodes to obtain a stress loss mapping matrix. The decomposition method is based on the power transmission allocation coefficient, which distributes the stress loss of a node to the connection between adjacent nodes according to the power transmission ratio. The power transmission allocation coefficient is determined by the impedance characteristics and power flow distribution of the load transmission channel, and is calculated by dividing the power transmission amount between nodes by the total output power of the node. For a substation node in this rural distribution network, the total stress loss value is 150 kW, connected to three distribution lines with power transmission ratios of 50%, 30%, and 20%, respectively. The stress loss values ​​decomposed to each line are 75 kW, 45 kW, and 30 kW, respectively. The load bearing pressure mapping matrix and the stress loss mapping matrix are normalized using the maximum-minimum standardization method, converting the matrix element values ​​to between 0 and 1. After normalization, the two matrices are weighted and combined to construct a load coupling degree matrix. The combination weights are determined according to the importance of the load bearing pressure and stress loss to the grid operation, generally taking a load bearing pressure weight of 0.6 and a stress loss weight of 0.4. The load coupling matrix reflects the degree of mutual influence between loads at various nodes in the distribution network.

[0044] The voltage fluctuation transmission coefficient and power loss attenuation coefficient are calculated based on the load coupling degree matrix. The voltage fluctuation transmission coefficient reflects the propagation characteristics of voltage disturbances in the network. The calculation method involves eigenvalue decomposition of the load coupling degree matrix to extract the main eigenvalues ​​and their corresponding eigenvectors. For this rural distribution network, the first three eigenvalues ​​of the load coupling degree matrix are 0.85, 0.72, and 0.63, respectively, and the corresponding eigenvectors reflect the main propagation modes of voltage fluctuations. The voltage fluctuation transmission coefficient is calculated based on the eigenvectors; this coefficient is a weighted combination of the eigenvectors, with the weights determined by the magnitude of the eigenvalues. The power loss attenuation coefficient reflects the power loss pattern during transmission. The calculation method is based on iterative calculation using the load coupling degree matrix. For this distribution network, the power loss attenuation coefficient ranges from 0.08 to 0.15 on the main lines and from 0.12 to 0.25 on the branch lines. The power quality attenuation coefficient is obtained by combining the voltage fluctuation transmission coefficient and the power loss attenuation coefficient. The combination method involves a weighted average of the two coefficients, with the weights determined by the degree of influence of voltage and power on power quality. Generally, the weight for voltage fluctuation is 0.5, and the weight for power loss is 0.5. The power quality attenuation coefficient comprehensively reflects the degree of quality degradation of power during transmission.

[0045] The power quality attenuation coefficient is substituted into the load coupling degree matrix for iterative calculation. The basic idea of ​​the iterative calculation is to simulate the propagation process of power quality in the distribution network, with each iteration representing one propagation of the power quality influence. The power quality attenuation coefficient is used as a weighting coefficient and multiplied by the load coupling degree matrix to obtain a new load coupling degree matrix. The load coupling degree matrix is ​​updated according to the results of each iteration. The update method is to take a weighted average of the new matrix and the original matrix. The weight is determined by the iteration step size, which is generally 0.2. The difference between the load coupling degree matrices of two adjacent iterations is calculated by summing the absolute values ​​of the differences between the matrix elements. The iteration ends when the difference between the load coupling degree matrices of two adjacent iterations is less than a preset convergence threshold. For this rural distribution network, the convergence threshold is set to 0.01, and the convergence condition is met after 8 iterations. After the iteration ends, the power supply continuity evaluation value is extracted from the final load coupling degree matrix. The power supply continuity evaluation value is the normalized eigenvalue of the load coupling degree matrix, reflecting the power supply reliability level of the distribution network under load changes and environmental constraints.

[0046] This invention constructs a load coupling degree matrix based on load-bearing pressure and stress loss values. Based on this matrix, it calculates the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel, achieving a collaborative analysis and evaluation of line losses and power supply reliability in rural distribution networks. This method transforms load-bearing pressure and stress loss into matrix form, establishing a mapping relationship between load characteristics and network topology, overcoming the limitations of traditional line loss analysis methods that separate load and network analysis. By introducing a power quality attenuation coefficient, the degree of power quality degradation during transmission is quantified, providing precise location for line loss control. The power supply continuity evaluation method based on iterative calculation enables dynamic evaluation of power supply reliability, allowing for timely detection of potential risk points.

[0047] In step S5, based on the power quality attenuation coefficient and the power supply continuity evaluation value, the power supply grid is divided into zones, the load transfer threshold for each zone is calculated, and feeder switch switching instructions are generated to perform dynamic load allocation, including: S5.1. Map the power quality attenuation coefficient to the power transmission index, map the power supply continuity evaluation value to the power supply reliability index, construct a node evaluation matrix based on the power transmission index and the power supply reliability index, and divide the power supply grid into partitions according to the node evaluation matrix. S5.2. Obtain the power supply capacity value and load demand value of each node in the power supply grid partition according to the node evaluation matrix, and calculate the load transfer threshold between adjacent partitions based on the matching relationship between the power supply capacity value and the load demand value; S5.3. Substitute the load transfer threshold into the power grid partition to calculate the load distribution coefficient between each partition, construct the feeder switch state combination based on the load distribution coefficient, and generate the feeder switch switching command; S5.4. Using the feeder switch switching command and load allocation coefficient as constraints, adjust the load distribution ratio within the power grid zone and perform dynamic load allocation.

[0048] The power quality attenuation coefficient is mapped to a power transmission index using a nonlinear transformation method. A piecewise function is used to convert the power quality attenuation coefficient into a power transmission index ranging from 0 to 1. A higher power transmission index value indicates better power transmission quality. In a rural distribution network, when the power quality attenuation coefficient is 0.15, the mapped power transmission index is 0.85; when the power quality attenuation coefficient is 0.30, the mapped power transmission index is 0.65. Similarly, the power supply continuity evaluation value is mapped to a power supply reliability index using a nonlinear transformation method. A step function is used to convert the power supply continuity evaluation value into a power supply reliability index ranging from 0 to 1. A higher power supply reliability index value indicates better power supply reliability. For example, in the same rural distribution network, when the power supply continuity evaluation value is 0.78, the mapped power supply reliability index is 0.82; when the power supply continuity evaluation value is 0.65, the mapped power supply reliability index is 0.68. A node evaluation matrix is ​​constructed based on power transmission and power supply reliability indicators. The construction method involves a weighted combination of the two indicators to form a two-dimensional matrix. Rows represent nodes, and columns represent evaluation indicators. The weighting is 0.55 for power transmission indicators and 0.45 for power supply reliability indicators. The power grid is then divided into zones based on the node evaluation matrix. The division method is based on cluster analysis, grouping nodes with similar evaluation values ​​into the same zone. For this rural power distribution network, four power grid zones are obtained based on the node evaluation matrix, containing 3, 4, 2, and 3 nodes respectively.

[0049] The power supply capacity and load demand of each node within the power grid partition are obtained based on the node evaluation matrix. The power supply capacity is determined by both the power transmission index and the node transformer capacity. The calculation method involves multiplying the power transmission index by the transformer capacity and then multiplying by an adaptation coefficient, which is determined based on historical operating data and typically ranges from 0.9 to 0.95. The load demand is determined by historical electricity consumption data and load forecasting results. The product of the historical average load and the load growth rate is multiplied by a seasonal adjustment coefficient, which is determined based on the electricity consumption characteristics of different seasons. For the first partition of this rural distribution network, node 1 has a power supply capacity of 520 kW and a load demand of 450 kW; node 2 has a power supply capacity of 480 kW and a load demand of 460 kW; and node 3 has a power supply capacity of 360 kW and a load demand of 380 kW. The load transfer threshold between adjacent zones is calculated based on the matching relationship between power supply capacity and load demand. This is done by subtracting the sum of the power supply capacity from the sum of the load demand, and then multiplying by a safety margin factor, typically between 0.8 and 0.9. A positive result indicates that the zone has excess power supply capacity and can supply load to other zones; a negative result indicates that the zone has insufficient power supply capacity and needs to obtain support from other zones. The load transfer threshold between adjacent zones is the absolute value of the smaller of the calculated results for the two zones. For this rural distribution network, the load transfer threshold between the first and second zones is 65 kW, between the second and third zones is 50 kW, between the third and fourth zones is 40 kW, and between the fourth and first zones is 55 kW.

[0050] The load transfer threshold is substituted into the power grid partitioning to calculate the load allocation coefficient between each partition. The calculation method is to divide the load transfer threshold by the total load demand of the partition, and then multiply by the partition priority coefficient. The partition priority coefficient is determined according to the importance of the partition, with important partitions having higher priority coefficients, generally between 0.8 and 1.2. The load allocation coefficient reflects the proportion and direction of load transfer between partitions; a positive value indicates outward load output, and a negative value indicates incoming load. For this rural distribution network, the load allocation coefficient between the first and second partitions is 0.12, the load allocation coefficient between the second and third partitions is -0.08, the load allocation coefficient between the third and fourth partitions is 0.06, and the load allocation coefficient between the fourth and first partitions is -0.10. Feeder switch state combinations are constructed based on the load allocation coefficients. The construction method is to determine the open / closed state of the feeder switches according to the load allocation direction, forming a switch state combination table. Each feeder switch has two states: open and closed. For n switches, there are 2^n possible state combinations. By filtering switch state combinations that meet load allocation requirements using load dispatch coefficients, feeder switch switching instructions are generated. Each feeder switch switching instruction includes the switch number, target state, and execution time. For this rural power distribution network, the generated feeder switch switching instructions are: switch 1 to switch 2 closed, switch 3 to switch 4 open, switch 5 to switch 6 closed, with execution time during off-peak load periods.

[0051] By using feeder switch switching commands and load allocation coefficients as constraints, the load distribution ratio within the power grid zones is adjusted to perform dynamic load allocation. The basic principle of dynamic load allocation is to minimize line losses while ensuring power supply reliability. The adjustment method involves modifying the power supply range of each node based on the load allocation coefficient, increasing or decreasing the number of users covered by each node. For zones with positive load allocation coefficients, their power supply range is reduced; for zones with negative load allocation coefficients, their power supply range is increased. The adjustment of the power supply range is achieved by changing the status of feeder sectionalizing switches and tie switches. For this rural distribution network, the first zone reduces its power supply range by approximately 12%, transferring some load to the fourth zone; the second zone increases its power supply range by approximately 8%, receiving some load transferred from the third zone; the third zone reduces its power supply range by approximately 6%, transferring some load to the second zone; and the fourth zone increases its power supply range by approximately 10%, receiving some load transferred from the first zone. Through the switching of feeder switches, a reasonable distribution of load among different zones is achieved, matching the load level of each zone with its power supply capacity, and improving overall power supply efficiency.

[0052] This invention constructs a multi-dimensional evaluation system based on power quality and power supply reliability indicators, overcoming the limitations of single-indicator evaluation in traditional distribution network optimization. It adopts a power grid partitioning strategy, decomposing the complex overall optimization problem into multiple controllable local optimization problems, reducing computational complexity. By introducing load transfer thresholds and load allocation coefficients, a dynamic load balancing mechanism is established for each partition, making the supply and demand matching more reasonable. The dynamic load allocation method based on feeder switch switching commands enables flexible adjustment of the network topology, improving the distribution network's adaptability to load changes and fault conditions.

[0053] In step S6, a load flow potential energy diagram is constructed based on the topology of the load transfer channel. The coordinates of the power distribution equipment locations are calculated based on the load flow potential energy diagram and the load dynamic allocation results, including: S6.1. Calculate the power supply node distribution and inter-node transmission impedance based on the topology of the load transmission channel, construct the power supply node distribution as a node adjacency matrix, construct the inter-node transmission impedance as a transmission impedance matrix, and combine the node adjacency matrix and the transmission impedance matrix to construct a load flow potential energy diagram. S6.2. Obtain the node power distribution and node voltage distribution based on the load dynamic allocation results, calculate the transmission loss between nodes, and construct the node power supply weight matrix; S6.3. Substitute the node power supply weight matrix into the load flow potential energy diagram to calculate the voltage distribution curve and power distribution curve. Determine the load centroid point based on the intersection of the voltage distribution curve and the power distribution curve, and map the load centroid point to the coordinates of the power distribution equipment layout.

[0054] When constructing a load flow potential energy diagram based on the topology of the load transfer channel, it is necessary to calculate the distribution of power supply nodes and the transmission impedance between nodes according to the topology of the load transfer channel. The distribution of power supply nodes represents the spatial positional relationship of each node in the distribution network. By collecting distribution network topology data, the geographical location information and connection relationship of each node are recorded to form a node distribution dataset. For the transmission impedance between nodes, the transmission impedance value between each node is determined by measuring or calculating parameters such as resistance and reactance of the distribution line, and comprehensively considering factors such as line length, cross-sectional area, and material. The power supply node distribution is constructed as a node adjacency matrix. The elements in the matrix indicate whether there is a direct connection relationship between nodes. If the i-th node is directly connected to the j-th node, the element value at the corresponding position in the matrix is ​​1; otherwise, it is 0. The transmission impedance between nodes is constructed as a transmission impedance matrix. The elements in the matrix represent the transmission impedance value between connected nodes. If two nodes are directly connected, their transmission impedance value is recorded; if they are not directly connected, the element value at that position is infinity or a specific identifier value. The load flow potential energy diagram is constructed by combining the node adjacency matrix and the transmission impedance matrix. This potential energy diagram is a weighted graph structure, in which nodes represent power supply nodes, edges represent the connection relationship between nodes, and the weight of the edge represents the transmission impedance value.

[0055] When obtaining node power and voltage distributions based on load dynamic allocation results, the load power values ​​of each node, including active and reactive power, are obtained from the load dynamic allocation module to form node power distribution data. Through power flow calculation or voltage drop calculation methods, combined with node power distribution and network topology, the voltage values ​​of each node are calculated to form node voltage distribution data. When calculating inter-node transmission losses, the power loss generated when current flows through each line segment is calculated based on the transmission impedance matrix and node power distribution, i.e., line loss. For any connected pair of nodes, the transmission loss equals the square of the line current multiplied by the line impedance; the line current can be calculated from the node power and voltage. When constructing the node power supply weight matrix, factors such as node power magnitude, voltage deviation, and transmission loss are comprehensively considered to assign a power supply weight value to each node. The larger the weight value, the higher the importance of the node in the power supply system. The node power supply weight matrix records the weight values ​​of each node.

[0056] When substituting the node power supply weight matrix into the load flow potential energy diagram to calculate the voltage and power distribution curves, node weight information is introduced based on the potential energy diagram. An interpolation algorithm generates a continuous voltage distribution curve across the entire power supply area, with points on the curve representing the voltage level at that location. Similarly, based on the node power distribution and node weights, a power distribution curve covering the entire power supply area is generated, with points on the curve representing the power density at that location. When determining the load centroid based on the intersection of the voltage and power distribution curves, the intersection points are analyzed. These intersection points represent locations where voltage and power are balanced, representing potential ideal locations for distribution equipment. From these intersection points, the point that satisfies the power supply radius constraint and minimizes total transmission loss is selected as the load centroid. The load centroid represents the ideal location for distribution equipment, balancing power supply distance and line loss. When mapping the load centroid to distribution equipment location coordinates, the location of the load centroid is converted to actual geographical coordinates. Considering actual constraints such as terrain and building distribution, the theoretical location is appropriately adjusted to ultimately determine the actual location coordinates of the distribution equipment, including the installation locations of transformers, distribution boxes, and other equipment.

[0057] By following the steps above, the locations of power distribution equipment can be scientifically and rationally determined based on the topology of the load transmission channels and the dynamic load allocation results. The algorithm parameters can be appropriately adjusted according to the characteristics of rural power distribution networks, such as dispersed loads and large seasonal variations, to improve the placement effect.

[0058] This invention achieves precise and scientific placement of power distribution equipment by comprehensively considering factors such as topology, transmission impedance, and load distribution. This method effectively reduces line losses in power distribution networks, improves power supply reliability, and solves the problems of high line losses and low reliability caused by unreasonable equipment placement in rural power distribution networks. The method considers the dynamic characteristics of load distribution, adapting to load changes and improving the operating efficiency of the power distribution network. The method for determining the load center of gravity makes the location of power distribution equipment more scientific and reasonable, reducing losses during power transmission and improving the economy and reliability of rural power distribution networks.

[0059] In step S7, the layout of the power distribution equipment is adjusted according to the coordinates of the power distribution equipment locations and the load coupling matrix, including: S7.1. Calculate the distance matrix between equipment and the power supply coverage area based on the coordinates of the power distribution equipment. Combine the distance matrix between equipment and the power supply coverage area to obtain the power supply overlap area. Construct an equipment layout optimization matrix based on the power supply overlap area and the load coupling degree matrix. S7.2. Obtain the equipment power supply capacity and load distribution capacity according to the equipment layout optimization matrix, and perform matching calculations on the equipment power supply capacity and load distribution capacity to obtain the equipment supply and demand balance value; S7.3. Calculate the equipment position offset based on the equipment supply and demand balance value and the equipment layout optimization matrix, correct the power distribution equipment layout coordinates based on the equipment position offset, generate a power distribution equipment layout scheme, and adjust the power distribution equipment layout based on the power distribution equipment layout scheme.

[0060] When calculating the distance matrix between power distribution equipment based on the coordinates of the equipment locations, it is necessary to obtain the spatial coordinates of each power distribution equipment. These coordinates are then used to calculate the Euclidean distance between any two power distribution equipment. For each pair of power distribution equipment in a rural power distribution network, the actual physical distance between two points is calculated using their latitude, longitude, or Cartesian coordinates, forming a symmetrical matrix. Each element in the matrix represents the distance between the corresponding two equipment. When calculating the power supply coverage area, each power distribution equipment is used as the center, and its effective power supply radius is determined based on the equipment's power supply capacity, rated capacity, and line parameters. The area within this radius is the power supply coverage area for that equipment. For transformer equipment, the relationship between its capacity and power supply distance is considered; for distribution box equipment, the relationship between its feeder capacity and power supply distance is considered. Combining the distance matrix between equipment with the power supply coverage area yields the power supply overlap area. By determining whether the power supply coverage areas of any two power distribution equipment intersect, if so, the intersection area is the power supply overlap area. For each pair of power distribution equipment, the relationship between their distances and the sum of their respective power supply radii is compared. If the distance between the equipment is less than the sum of their power supply radii, an overlap area exists, and the area or range of the overlap area is then calculated. Constructing an equipment layout optimization matrix based on the power supply overlap area and load coupling degree matrix requires considering the matching degree between the load distribution and the equipment power supply capacity within the power supply overlap area. The load coupling degree matrix represents the coupling relationship between load points, reflecting the correlation and aggregation characteristics of load changes. By integrating the spatial information of the power supply overlap area and the electrical information of the load coupling degree, an equipment layout optimization matrix is ​​constructed, which reflects the advantages and disadvantages of the layout location of each power distribution equipment.

[0061] To obtain the power supply capacity and load distribution capacity of equipment based on the equipment layout optimization matrix, it is necessary to extract the power supply capacity parameters of each power distribution device and the load demand parameters within its coverage area from the equipment layout optimization matrix. Equipment power supply capacity refers to the rated output capacity of the power distribution equipment, including the rated capacity of transformers and the current carrying capacity of distribution lines. Load distribution capacity refers to the total electricity demand of all load points within a specific area, considering the spatiotemporal distribution characteristics of the load and the load simultaneity rate. Matching the equipment power supply capacity and load distribution capacity yields the equipment supply-demand balance value. By comparing the difference between the power supply capacity of each power distribution device and the load distribution capacity within its coverage area, the degree of equipment supply-demand matching is evaluated. The supply-demand balance value can be defined as the ratio of equipment power supply capacity to load distribution capacity. Ideally, this value should be close to a set reasonable value; too high a value indicates wasted equipment capacity, while too low a value indicates insufficient power supply capacity.

[0062] The equipment location offset is calculated based on the equipment supply-demand balance value and the equipment layout optimization matrix. For power distribution equipment with an unsatisfactory supply-demand balance value, its location needs to be adjusted to optimize power supply performance. The offset calculation considers multiple factors: the location of the load center, the distribution of adjacent equipment, terrain constraints, etc. An objective function is constructed, which takes the equipment supply-demand balance value and the layout optimization matrix as input. Through gradient descent or other optimization algorithms, the direction and distance of the equipment location offset that optimizes the objective function are calculated. The power distribution equipment layout coordinates are corrected based on the equipment location offset. The original layout coordinates are adjusted according to the calculated offset to obtain new equipment layout coordinates. This process may require multiple iterations, with the supply-demand balance value re-evaluated after each adjustment until a preset threshold condition is met. A power distribution equipment layout scheme is generated, converting the adjusted power distribution equipment layout coordinates into a practical and feasible layout scheme, including detailed information such as equipment type, capacity parameters, and installation location. The power distribution equipment layout is adjusted according to the layout scheme, guiding the actual equipment installation and adjustment work to ensure that the actual layout is consistent with the optimized scheme.

[0063] The method proposed in this invention for adjusting the layout of power distribution equipment based on the coordinates of the equipment locations and the load coupling matrix enables precise optimization of the equipment layout in rural power distribution networks. This method comprehensively considers multiple factors such as distance between equipment, power supply coverage, and load coupling. By constructing an equipment layout optimization matrix and calculating supply-demand balance values, it scientifically determines the equipment location offsets, thereby generating a reasonable power distribution equipment layout scheme. This layout optimization method effectively solves problems such as unreasonable equipment distribution and supply-demand mismatch in rural power distribution networks, significantly reducing line losses and improving power supply reliability.

[0064] A collaborative optimization analysis system for rural power distribution network line loss and power supply reliability includes: The extraction module is used to acquire electricity consumption data of rural users in different time periods, perform time-frequency decomposition on the electricity consumption data, extract the fluctuation period of agricultural production load and domestic load, and generate a load time-frequency characteristic spectrum. The identification module is used to construct a load change trajectory map based on the load time-frequency characteristic spectrum, and to identify the load transfer channels between power distribution equipment from the load change trajectory map; The calculation module is used to acquire meteorological data and geographical environmental parameters along the load transmission channel, and to calculate the load-bearing pressure and stress loss values ​​of the power distribution equipment. A calculation module is constructed to build a load coupling degree matrix based on the bearing pressure and stress loss values, and to calculate the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel based on the load coupling degree matrix. The partitioning calculation module is used to divide the power grid into zones based on the power quality attenuation coefficient and the power supply continuity evaluation value, calculate the load transfer threshold of each zone, generate feeder switch switching instructions, and perform dynamic load allocation. The point placement module is used to construct a load flow potential energy diagram based on the topology of the load transmission channel, and to calculate the point placement coordinates of the power distribution equipment based on the load flow potential energy diagram and the load dynamic allocation results. The layout module is used to adjust the layout of power distribution equipment based on the coordinates of the power distribution equipment locations and the load coupling matrix.

[0065] This invention effectively reduces power distribution network line losses, improves power supply reliability, enhances power distribution network operating efficiency, and facilitates the planning and renovation of rural power distribution networks.

[0066] The specific embodiments described above are preferred embodiments of the present invention and are not intended to limit the specific scope of the present invention. The scope of the present invention includes, but is not limited to, these specific embodiments. All equivalent changes made in accordance with the shape and structure of the present invention are within the protection scope of the present invention.

Claims

1. A method for synergistic optimization analysis of line loss and power supply reliability in rural power distribution networks, characterized in that, Includes the following steps: S1. Obtain electricity consumption data of rural users in different time periods, perform time-frequency decomposition on the electricity consumption data, extract the fluctuation period of agricultural production load and domestic load, and generate load time-frequency characteristic spectrum; S2. Construct a load change trajectory map based on the load time-frequency characteristic spectrum, and identify the load transfer channels between power distribution equipment from the load change trajectory map; S3. Obtain meteorological data and geographical environmental parameters along the load transmission channel, and calculate the load-bearing pressure and stress loss values ​​of the power distribution equipment; S4. Construct a load coupling degree matrix based on the bearing pressure and stress loss values, and calculate the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel based on the load coupling degree matrix; S5. Based on the power quality attenuation coefficient and power supply continuity evaluation value, divide the power supply grid into zones, calculate the load transfer threshold of each zone, generate feeder switch switching instructions, and perform dynamic load allocation; S6. Construct a load flow potential energy diagram based on the topology of the load transmission channel, and calculate the coordinates of the power distribution equipment based on the load flow potential energy diagram and the load dynamic allocation results; S7. Adjust the layout of power distribution equipment according to the coordinates of the power distribution equipment locations and the load coupling matrix.

2. The method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability according to claim 1, characterized in that, In step S1, electricity consumption data of rural users at different times is obtained, and the electricity consumption data is decomposed into time-frequency components to extract the fluctuation periods of agricultural production load and domestic load, generating a load time-frequency characteristic spectrum, including: S1.

1. Obtain the time-segmented electricity consumption data waveform curves of rural users, calculate the electricity consumption data change rate, determine the segment position, divide the electricity consumption data waveform curves into multiple time segments, perform orthogonal transformation on each time segment, and generate an electricity consumption data frequency feature sequence. S1.

2. Calculate the power factors of agricultural production load and domestic load based on the frequency characteristic sequence of electricity consumption data, and generate the load time distribution matrix; S1.

3. Extract the temporal variation patterns of agricultural production load and domestic load from the load time distribution matrix, obtain fluctuation period data based on the time variation patterns, and perform frequency domain transformation on the fluctuation period data to obtain frequency domain component data; S1.

4. The time variation pattern is fused with the frequency domain component data to generate the load time-frequency characteristic spectrum.

3. The method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability according to claim 1, characterized in that, In step S2, a load change trajectory map is constructed based on the load time-frequency characteristic spectrum, and load transfer channels between power distribution equipment are identified from the load change trajectory map, including: S2.

1. Perform wavelet decomposition on the load time-frequency characteristic spectrum, extract the instantaneous amplitude and phase angle of load change, calculate the power factor at different times based on the instantaneous amplitude and phase angle, and generate a load change characteristic sequence; S2.

2. Perform time series analysis on the load change characteristic sequence, calculate the phase angle difference between adjacent sampling times to construct a phase difference matrix, calculate the time correlation degree of load change based on the phase difference matrix and the power factor, and construct the time correlation degree as a time series feature matrix; S2.

3. Identify load transmission nodes based on the time-series feature matrix, calculate the transmission direction and transmission intensity between load transmission nodes, and generate a load change trajectory map based on the transmission direction and transmission intensity; S2.

4. Calculate the power loss between nodes based on the transmission direction and power factor of the load transmission nodes in the load change trajectory diagram, and use the power loss and transmission strength to calculate the impedance characteristics between nodes, and construct the topology connection matrix; S2.

5. Recursively decompose the topology connection matrix to obtain the connection relationship characteristics of the load transmission nodes, and combine the connection relationship characteristics with the transmission strength to construct the load transmission characteristic matrix; S2.

6. Identify load transfer channels between power distribution equipment based on the connection strength between power distribution equipment in the load transfer feature matrix.

4. The method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability according to claim 1, characterized in that, In step S3, meteorological data and geographical environmental parameters are acquired along the load transfer channel, and the load-bearing pressure and stress loss values ​​of the power distribution equipment are calculated, including: S3.

1. Collect meteorological data along the load transfer channel, and generate a meteorological impact index based on the changing trend of the meteorological data; S3.

2. Collect the geographical environmental parameters of the load transfer channel, and generate the geographical impact index based on the coupling relationship between the geographical environmental parameters and the meteorological impact index; S3.

3. Calculate the environmental constraint coefficient based on the changing patterns of the meteorological impact index and the geographical impact index; S3.

4. The load-bearing pressure of the power distribution equipment is obtained by weighting the environmental constraint coefficient with the load transfer amount on the load transfer channel; S3.

5. Calculate the stress loss value of the power distribution equipment based on the bearing pressure and environmental constraint coefficient.

5. The method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability according to claim 1, characterized in that, In step S4, a load coupling degree matrix is ​​constructed based on the bearing pressure and stress loss values. Based on this load coupling degree matrix, the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel are calculated, including: S4.

1. Decompose the bearing pressure into static bearing base value and dynamic bearing change; S4.

2. Construct a node bearing capacity matrix based on the static bearing base value, construct a node response matrix based on the dynamic bearing change, and combine the node bearing capacity matrix and the node response matrix to obtain a bearing pressure mapping matrix; S4.

3. Decompose the stress loss value according to the transmission relationship between adjacent nodes to obtain the stress loss mapping matrix. After normalizing the bearing pressure mapping matrix and the stress loss mapping matrix, combine them to construct the load coupling degree matrix. S4.

4. Calculate the voltage fluctuation transmission coefficient and power loss attenuation coefficient based on the load coupling degree matrix, and combine the voltage fluctuation transmission coefficient and power loss attenuation coefficient to obtain the power quality attenuation coefficient. S5.

5. Substitute the power quality attenuation coefficient into the load coupling degree matrix for iterative calculation. Update the load coupling degree matrix according to the results of each iteration. When the difference between the load coupling degree matrices of two adjacent iterations is less than the preset convergence threshold, output the power supply continuity evaluation value.

6. The method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability according to claim 1, characterized in that, In step S5, based on the power quality attenuation coefficient and the power supply continuity evaluation value, the power supply grid is divided into zones, the load transfer threshold for each zone is calculated, and feeder switch switching instructions are generated to perform dynamic load allocation, including: S5.

1. Map the power quality attenuation coefficient to the power transmission index, map the power supply continuity evaluation value to the power supply reliability index, construct a node evaluation matrix based on the power transmission index and the power supply reliability index, and divide the power supply grid into partitions according to the node evaluation matrix. S5.

2. Obtain the power supply capacity value and load demand value of each node in the power supply grid partition according to the node evaluation matrix, and calculate the load transfer threshold between adjacent partitions based on the matching relationship between the power supply capacity value and the load demand value; S5.

3. Substitute the load transfer threshold into the power grid partition to calculate the load distribution coefficient between each partition, construct the feeder switch state combination based on the load distribution coefficient, and generate the feeder switch switching command; S5.

4. Using the feeder switch switching command and load allocation coefficient as constraints, adjust the load distribution ratio within the power grid zone and perform dynamic load allocation.

7. The method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability according to claim 1, characterized in that, In step S6, a load flow potential energy diagram is constructed based on the topology of the load transfer channel. The coordinates of the power distribution equipment locations are calculated based on the load flow potential energy diagram and the load dynamic allocation results, including: S6.

1. Calculate the power supply node distribution and inter-node transmission impedance based on the topology of the load transmission channel, construct the power supply node distribution as a node adjacency matrix, construct the inter-node transmission impedance as a transmission impedance matrix, and combine the node adjacency matrix and the transmission impedance matrix to construct a load flow potential energy diagram. S6.

2. Obtain the node power distribution and node voltage distribution based on the load dynamic allocation results, calculate the transmission loss between nodes, and construct the node power supply weight matrix; S6.

3. Substitute the node power supply weight matrix into the load flow potential energy diagram to calculate the voltage distribution curve and power distribution curve. Determine the load centroid point based on the intersection of the voltage distribution curve and the power distribution curve, and map the load centroid point to the coordinates of the power distribution equipment layout.

8. The method for synergistic optimization analysis of rural power distribution network line loss and power supply reliability according to claim 1, characterized in that, In step S7, the layout of the power distribution equipment is adjusted according to the coordinates of the power distribution equipment locations and the load coupling matrix, including: S7.

1. Calculate the distance matrix between equipment and the power supply coverage area based on the coordinates of the power distribution equipment. Combine the distance matrix between equipment and the power supply coverage area to obtain the power supply overlap area. Construct an equipment layout optimization matrix based on the power supply overlap area and the load coupling degree matrix. S7.

2. Obtain the equipment power supply capacity and load distribution capacity according to the equipment layout optimization matrix, and perform matching calculations on the equipment power supply capacity and load distribution capacity to obtain the equipment supply and demand balance value; S7.

3. Calculate the equipment position offset based on the equipment supply and demand balance value and the equipment layout optimization matrix, correct the power distribution equipment layout coordinates based on the equipment position offset, generate a power distribution equipment layout scheme, and adjust the power distribution equipment layout based on the power distribution equipment layout scheme.

9. A rural power distribution network line loss and power supply reliability collaborative optimization analysis system, used to implement the rural power distribution network line loss and power supply reliability collaborative optimization analysis method according to any one of claims 1-8, characterized in that, include: The extraction module is used to acquire electricity consumption data of rural users in different time periods, perform time-frequency decomposition on the electricity consumption data, extract the fluctuation period of agricultural production load and domestic load, and generate a load time-frequency characteristic spectrum. The identification module is used to construct a load change trajectory map based on the load time-frequency characteristic spectrum, and to identify the load transfer channels between power distribution equipment from the load change trajectory map; The calculation module is used to acquire meteorological data and geographical environmental parameters along the load transmission channel, and to calculate the load-bearing pressure and stress loss values ​​of the power distribution equipment. A calculation module is constructed to build a load coupling degree matrix based on the bearing pressure and stress loss values, and to calculate the power quality attenuation coefficient and power supply continuity evaluation value on the load transmission channel based on the load coupling degree matrix. The partitioning calculation module is used to divide the power grid into zones based on the power quality attenuation coefficient and the power supply continuity evaluation value, calculate the load transfer threshold of each zone, generate feeder switch switching instructions, and perform dynamic load allocation. The point placement module is used to construct a load flow potential energy diagram based on the topology of the load transmission channel, and to calculate the point placement coordinates of the power distribution equipment based on the load flow potential energy diagram and the load dynamic allocation results. The layout module is used to adjust the layout of power distribution equipment based on the coordinates of the power distribution equipment locations and the load coupling matrix.

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