Transformer area line loss calculation method based on intelligent electric energy meter data
By collecting multi-dimensional data from smart meters, a transformer substation topology model is constructed and weight coefficients are dynamically allocated, solving the problem of inaccurate line loss calculation and achieving accurate line loss calculation and power grid optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-27
- Publication Date
- 2026-03-13
Smart Images
Figure CN121659518A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power distribution network technology, and in particular to a method for calculating line loss in transformer substations based on data from smart meters. Background Technology
[0002] In power systems, distribution transformer line loss is a crucial indicator for measuring power supply efficiency and economic operation. Accurate line loss calculation helps power companies identify weak points in the power grid, optimize the grid structure, reduce power supply costs, and improve power quality. Traditional methods for calculating distribution transformer line loss mainly rely on manual meter reading data and theoretical formulas. These methods suffer from problems such as untimely and inaccurate data acquisition, and simplistic calculation models that cannot account for complex actual operating conditions. Consequently, the line loss calculation results have significant errors and fail to meet the needs of refined management in modern power systems.
[0003] With the development of smart grids, smart meters have been widely used. Smart meters can collect electrical energy data such as voltage, current, and power in real time and accurately, providing a rich data source for the accurate calculation of distribution area line loss. However, the current method for calculating distribution area line loss based on smart meter data is not perfect, fails to make full use of the multi-dimensional data of smart meters, and is insufficient in handling complex distribution area topology and dynamically changing power load.
[0004] Therefore, it is necessary to provide a new method for calculating line loss in transformer substations based on smart meter data to solve the above-mentioned technical problems. Summary of the Invention
[0005] To address the aforementioned technical problems, this invention provides a method for calculating line loss in transformer substations based on smart meter data.
[0006] The method for calculating line loss in transformer substations based on smart meter data provided by this invention includes the following steps:
[0007] Step 1, Data Acquisition Phase: Real-time collection of multi-dimensional energy data from each smart energy meter within the distribution area. The multi-dimensional energy data includes voltage, current, active power, reactive power, power factor, and energy consumption at different times. The acquisition frequency is set to at least once per minute.
[0008] Step 2: Data preprocessing: Preprocess the collected raw data;
[0009] Step 3: Topology Analysis: Using the installation location information of smart meters and the connection relationship of power lines, construct the topology model of the transformer area. Combine with the historical operation data of the transformer area, verify and optimize the topology model. The topology model of the transformer area uses nodes to represent the installation location of smart meters and edges to represent power line connections, clarifying the connection relationship and hierarchical structure between nodes.
[0010] Step 4: Construct a line loss calculation model: Based on the impact of different time periods, different line segments, and different electricity consumption types on line loss, a dynamic weight allocation mechanism is constructed. According to historical data and real-time operation, the weight coefficients of each influencing factor are determined. Based on the dynamic weight allocation mechanism, combined with the preprocessed smart meter data and the transformer area topology model, a line loss calculation model is constructed. By calculating the power loss of each line segment and accumulating it according to the topology, the line loss value of the entire transformer area is obtained.
[0011] Furthermore, in the constructed line loss calculation model, for each line segment, the resistance loss and reactance loss of the line segment are calculated based on the voltage and current data at both ends and the dynamic weighting coefficient. Then, the losses of all line segments are added together to obtain the total line loss of the transformer substation.
[0012] Furthermore, after obtaining the line loss value for the entire distribution area, the process also includes analyzing the calculated line loss results and calculating statistical indicators of the line loss rate, including the average, standard deviation, maximum, and minimum values, to assess the overall level of line loss in the distribution area.
[0013] Furthermore, in the construction of the line loss calculation model, the method for determining the weight coefficients of each influencing factor is as follows:
[0014] Collect historical operational data for the transformer substation, including line loss data under different time periods and load conditions, as well as corresponding line parameters and power consumption type information.
[0015] A judgment matrix is constructed using the analytic hierarchy process (AHP), and the relative importance of each influencing factor is determined through expert scoring or historical data analysis.
[0016] Perform a consistency check on the judgment matrix to ensure the rationality and accuracy of the weight allocation;
[0017] Based on the judgment matrix that passes the consistency test, calculate the weight coefficient of each influencing factor, and dynamically adjust the weight coefficient according to the real-time operation.
[0018] Furthermore, the calculation methods for resistive loss and reactive loss for each line segment are as follows:
[0019] Resistance loss calculation: based on the voltage drop across the two ends of the line segment. and current and line resistance Resistance loss The line resistance It is calculated based on the cross-sectional area, length, and resistivity of the conductor;
[0020] Reactance loss calculation: based on the current of the line segment Reactance and power factor Reactance loss Among them, line reactance Calculated based on conductor parameters and geometric distance;
[0021] The total loss of the line segment is obtained by adding the resistance loss and the reactance loss, and then the total loss of all line segments is obtained by summing up the losses of the entire transformer substation.
[0022] Furthermore, the influencing factors include line length, conductor cross-sectional area, load rate, and peak and off-peak electricity consumption periods.
[0023] Furthermore, the preprocessing includes the following steps:
[0024] Data cleaning removes erroneous data and outliers caused by communication failures, equipment malfunctions, and other reasons.
[0025] Data completion is performed; for missing data, interpolation is used to complete the data based on data from adjacent time periods and historical data from the same period.
[0026] Data standardization processes are performed to unify data of different dimensions into a specific range.
[0027] Furthermore, the interpolation method used in the data completion process is cubic spline interpolation. Based on the data points in adjacent time periods and the trend of historical data from the same period, a cubic spline function is constructed to perform data interpolation, thereby achieving smooth data completion.
[0028] Compared with related technologies, the method for calculating transformer line loss based on smart meter data provided by this invention has the following advantages:
[0029] 1. This invention not only collects traditional electricity consumption data, but also collects multi-dimensional data such as voltage, current, and power factor, and performs comprehensive data preprocessing to improve data quality. Based on the impact of different time periods, different line sections, and different electricity consumption types on line loss, it dynamically allocates weight coefficients to make the line loss calculation model more in line with the actual situation.
[0030] 2. This invention utilizes the installation location information of smart meters and the connection relationships of power lines to construct a topology model of a transformer substation. Combined with historical operating data of the substation, the topology model is verified and optimized. The substation's topology model uses nodes to represent the installation locations of smart meters and edges to represent power line connections, clearly defining the connection relationships and hierarchical structure between nodes. By combining the line loss calculation of the topology, the substation's topology model is constructed, clarifying the power transmission path. Line loss accumulation calculation is performed according to the topology, improving calculation accuracy. Attached Figure Description
[0031] Figure 1 A flowchart illustrating the method for calculating line loss in transformer substations based on smart meter data provided by this invention;
[0032] Figure 2 A flowchart for determining the weight coefficients of each influencing factor provided by the present invention;
[0033] Figure 3 A flowchart for calculating resistive loss and reactive loss provided by the present invention;
[0034] Figure 4 A flowchart of the preprocessing process provided by the present invention. Detailed Implementation
[0035] The present invention will be further described below with reference to the accompanying drawings and embodiments.
[0036] Please refer to the following: Figure 1 , Figure 2 , Figure 3 as well as Figure 4 ,in, Figure 1 A flowchart illustrating the method for calculating line loss in transformer substations based on smart meter data provided by this invention; Figure 2 A flowchart for determining the weight coefficients of each influencing factor provided by the present invention; Figure 3 A flowchart for calculating resistive loss and reactive loss provided by the present invention; Figure 4 A flowchart of the preprocessing process provided by the present invention.
[0037] In the specific implementation process, such as Figure 1 As shown, the method for calculating the line loss of a transformer area based on smart meter data includes the following steps:
[0038] Step 1: Data Acquisition Phase: Real-time collection of multi-dimensional energy data from each smart meter within the distribution area. This multi-dimensional energy data includes voltage... Current Active power reactive power Power factor and electricity consumption at different times The data acquisition frequency is set to at least once per minute, and the DL / T 645-2007 communication protocol is used to connect the smart energy meter and the data acquisition system to ensure the stability and reliability of data transmission.
[0039] Step 2: Data preprocessing: Preprocess the collected raw data;
[0040] Step 3: Topology Analysis: Using the installation location information of smart meters and the connection relationship of power lines, construct the topology model of the transformer area. Combine with the historical operation data of the transformer area, verify and optimize the topology model. The topology model of the transformer area uses nodes to represent the installation location of smart meters and edges to represent power line connections, clarifying the connection relationship and hierarchical structure between nodes.
[0041] Step 4: Construct a line loss calculation model: Based on the impact of different time periods, different line segments, and different electricity consumption types on line loss, a dynamic weight allocation mechanism is constructed. According to historical data and real-time operation, the weight coefficients of each influencing factor are determined. Based on the dynamic weight allocation mechanism, combined with the preprocessed smart meter data and the transformer area topology model, a line loss calculation model is constructed. By calculating the power loss of each line segment and accumulating it according to the topology, the line loss value of the entire transformer area is obtained.
[0042] Among the influencing factors are line length, conductor cross-sectional area, load rate, and peak and off-peak electricity consumption periods.
[0043] In some embodiments, the preprocessing includes the following steps:
[0044] Data cleaning removes erroneous data and outliers caused by communication failures, equipment malfunctions, and other reasons.
[0045] Data completion is performed, and for missing data, cubic spline interpolation is used for interpolation. Based on data points from adjacent time periods and the trend of historical data for the same period, a cubic spline function is constructed for data interpolation to achieve smooth data completion. For example, if data for minute n is missing, interpolation calculations can be performed based on data from minute n-1 and minute n+1, as well as the trend of historical data for minute n in the same period.
[0046] Data standardization is performed to unify data with different dimensions to a specific range for subsequent analysis and calculation. For data such as voltage and current, the maximum-minimum standardization method can be used to map the data to the [0, 1] interval. The calculation formula is as follows:
[0047]
[0048] in, The original data, and These are the minimum and maximum values of the data, respectively.
[0049] Data cleaning and judgment criteria: Voltage values exceeding ±20% of the rated voltage are considered abnormal. For example, for a transformer area with a rated voltage of 220V, voltage values below 176V or above 264V are considered abnormal data. Current values exceeding three times the normal load current range are considered abnormal. The normal load current range is determined based on the transformer area's historical power consumption data and the equipment's rated capacity. Active power and reactive power values deviating from the calculated values based on voltage and current by more than 15% are considered abnormal.
[0050] It should be noted that the interpolation method used in data completion is cubic spline interpolation. Based on the data points of adjacent time periods and the trend of historical data of the same period, a cubic spline function is constructed to perform data interpolation in order to achieve smooth data completion.
[0051] In some embodiments, the topology can be obtained by conducting on-site surveys, consulting power grid drawings, etc., to obtain the installation location and line connection information of smart meters, and then using graphical software to draw the topology diagram.
[0052] In some embodiments, after obtaining the line loss value of the entire transformer area, the method further includes analyzing the calculated line loss results of the transformer area and calculating statistical indicators of the line loss rate, including the average value, standard deviation, maximum value, minimum value, etc., to assess the overall level of line loss in the transformer area.
[0053] In some embodiments, in constructing the line loss calculation model, for each line segment, the resistance loss and reactance loss of the line segment are calculated based on the voltage and current data at both ends and the dynamic weighting coefficient. Then, the losses of all line segments are added together to obtain the total line loss of the transformer substation.
[0054] Establish a line loss anomaly detection mechanism. By comparing and analyzing the line loss data with historical data from the same period, line loss data from similar transformer areas, and theoretically calculated line loss values, the line loss is judged to be abnormal when the calculated line loss rate exceeds the preset normal range. At the same time, based on the topology model and the line loss calculation results of each line segment, the line segments or electrical equipment that may have anomalies are located.
[0055] It should be noted that the method for determining the weight coefficients of each influencing factor in constructing the line loss calculation model is as follows:
[0056] Collect historical operational data for the transformer substation, including line loss data for different time periods (such as peak, off-peak, and flat periods) and different load conditions, as well as corresponding line parameters (such as line length and conductor cross-sectional area) and electricity consumption type (such as residential electricity, industrial electricity, and commercial electricity).
[0057] A judgment matrix is constructed using the analytic hierarchy process (AHP). The relative importance of each influencing factor is determined by expert scoring or historical data analysis. For time-related factors, the impact of peak hours on line loss may be greater than that of trough hours, so peak hours can be assigned a higher relative importance score.
[0058] Perform a consistency check on the judgment matrix to ensure the rationality and accuracy of the weight allocation, and calculate the consistency index. and random consistency ratio ,when When the judgment matrix is considered to have satisfactory consistency, it is calculated as follows: , ,in To determine the largest eigenvalue of a matrix, Let be the order of the matrix. The average random consistency index;
[0059] Based on the judgment matrix that passes the consistency test, the weight coefficients of each influencing factor are calculated, and the weight coefficients are dynamically adjusted according to the real-time operation. During peak electricity consumption periods, the weight coefficients of time-related factors are appropriately increased.
[0060] It should be noted that the calculation methods for resistance loss and reactance loss for each line segment are as follows:
[0061] Resistance loss calculation: based on the voltage drop across the two ends of the line segment. and current and line resistance Resistance loss The line resistance The calculation formula is obtained based on the cross-sectional area, length, and resistivity of the conductor: ,in The resistivity of the conductor. For line length, The cross-sectional area of the conductor;
[0062] Reactance loss calculation: based on the current of the line segment Reactance and power factor Reactance loss Among them, line reactance Calculated based on conductor parameters and geometric distance;
[0063] The total loss of the line segment is obtained by adding the resistance loss and the reactance loss. Then, the total loss of the entire line segment is obtained by summing the losses of all line segments. The calculation formula is as follows: ,in, This refers to the number of line segments within the transformer substation area. and The first The resistive loss and reactive loss of each line segment;
[0064] The line loss rate can be calculated based on the line loss and power supply of the transformer substation. The calculation formula is as follows: ,in, The power supply to the transformer substation can be calculated using data from smart meters.
[0065] According to embodiments of the present invention, a computing device that can be used to implement the above method includes a processor and a memory;
[0066] The processor can be a multi-core processor or include multiple processors. In some embodiments, the processor may include a general-purpose main processor and one or more special coprocessors, such as a graphics processing unit (GPU), a digital signal processor (DSP), etc. In some embodiments, the processor may be implemented using custom circuitry, such as an application-specific integrated circuit (ASIC) or a field-programmable gate array (FPGA).
[0067] Memory can include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage devices. ROM can store static data or instructions required by the processor or other modules of the computer. Permanent storage devices can be read-write storage devices. Permanent storage devices can be non-volatile storage devices that retain stored instructions and data even when the computer is powered off. In some embodiments, permanent storage devices use mass storage devices (e.g., magnetic or optical disks, flash memory) as permanent storage devices. In other embodiments, permanent storage devices can be removable storage devices (e.g., floppy disks, optical drives). System memory can be a read-write storage device or a volatile read-write storage device, such as dynamic random access memory. System memory can store some or all of the instructions and data required by the processor during operation. Furthermore, memory can include any combination of computer-readable storage media, including various types of semiconductor memory chips (DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and disks and / or optical disks can also be used. In some implementations, the memory may include removable storage devices that are readable and / or writable, such as laser discs (CDs), read-only digital versatile optical discs (e.g., DVD-ROMs, dual-layer DVD-ROMs), read-only Blu-ray discs, ultra-high density optical discs, flash memory cards (e.g., SD cards, mini SD cards, Micro-SD cards, etc.), magnetic floppy disks, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or via wired connections.
[0068] It should be understood that, unless otherwise expressly stated herein, there is no strict order restriction on the execution of the above steps, and these steps may be executed in other orders. Moreover, at least some steps in the processes involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these steps or stages is not necessarily sequential, but may be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.
[0069] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.
[0070] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment includes only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.
Claims
1. A method for calculating line loss in transformer substations based on smart meter data, characterized in that, Includes the following steps: Step 1, Data Acquisition Phase: Real-time collection of multi-dimensional energy data from each smart energy meter within the distribution area. The multi-dimensional energy data includes voltage, current, active power, reactive power, power factor, and energy consumption at different times. Step 2: Data preprocessing: Preprocess the collected raw data; Step 3: Topology Analysis: Using the installation location information of smart meters and the connection relationship of power lines, construct the topology model of the transformer area. Combine with the historical operation data of the transformer area, verify and optimize the topology model. The topology model of the transformer area uses nodes to represent the installation location of smart meters and edges to represent power line connections, clarifying the connection relationship and hierarchical structure between nodes. Step 4: Construct a line loss calculation model: Based on the impact of different time periods, different line segments, and different electricity consumption types on line loss, a dynamic weight allocation mechanism is constructed. According to historical data and real-time operation, the weight coefficients of each influencing factor are determined. Based on the dynamic weight allocation mechanism, combined with the preprocessed smart meter data and the transformer area topology model, a line loss calculation model is constructed. By calculating the power loss of each line segment and accumulating it according to the topology, the line loss value of the entire transformer area is obtained.
2. The method for calculating transformer area line loss based on smart meter data according to claim 1, characterized in that, In the constructed line loss calculation model, for each line segment, the resistance loss and reactance loss of the line segment are calculated based on the voltage and current data at both ends and the dynamic weighting coefficient. Then, the losses of all line segments are added together to obtain the total line loss of the transformer substation.
3. The method for calculating transformer area line loss based on smart meter data according to claim 1, characterized in that, After obtaining the line loss value for the entire distribution area, the process also includes analyzing the calculated line loss results and calculating statistical indicators of the line loss rate, including the average, standard deviation, maximum, and minimum values, to assess the overall level of line loss in the distribution area.
4. The method for calculating transformer area line loss based on smart meter data according to claim 2, characterized in that, The method for determining the weight coefficients of each influencing factor in the construction of the line loss calculation model is as follows: Collect historical operational data for the transformer area, including line loss data under different time periods and load conditions, as well as corresponding line parameters and power consumption type information; A judgment matrix is constructed using the analytic hierarchy process (AHP), and the relative importance of each influencing factor is determined through expert scoring or historical data analysis. Perform a consistency check on the judgment matrix to ensure the rationality and accuracy of the weight allocation; Based on the judgment matrix that passes the consistency test, calculate the weight coefficient of each influencing factor, and dynamically adjust the weight coefficient according to the real-time operation.
5. The method for calculating transformer area line loss based on smart meter data according to claim 4, characterized in that, The calculation methods for resistance loss and reactance loss for each line segment are as follows: Resistance loss calculation: based on the voltage drop across the two ends of the line segment. and current and line resistance Resistance loss The line resistance It is calculated based on the cross-sectional area, length, and resistivity of the conductor; Reactance loss calculation: based on the current of the line segment Reactance and power factor Reactance loss Among them, line reactance Calculated based on conductor parameters and geometric distance; The total loss of the line segment is obtained by adding the resistance loss and the reactance loss, and then the total loss of all line segments is obtained by summing up the losses of the entire transformer substation.
6. The method for calculating transformer area line loss based on smart meter data according to claim 5, characterized in that, The influencing factors include line length, conductor cross-sectional area, load rate, and peak and off-peak electricity consumption periods.
7. The method for calculating transformer area line loss based on smart energy meter data according to claim 1, characterized in that, The preprocessing includes the following steps: Data cleaning removes erroneous data and outliers caused by communication failures, equipment malfunctions, and other reasons. Data completion is performed; for missing data, interpolation is used to complete the data based on data from adjacent time periods and historical data from the same period. Data standardization processes are performed to unify data of different dimensions into a specific range.
8. The method for calculating transformer area line loss based on smart energy meter data according to claim 7, characterized in that, The interpolation method used in the data completion process is cubic spline interpolation. Based on the data points of adjacent time periods and the trend of historical data from the same period, a cubic spline function is constructed to perform data interpolation in order to achieve smooth data completion.