A multi-source measurement configuration method for improving the comprehensive observability of an ac-dc distribution network
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTHWEST JIAOTONG UNIV
- Filing Date
- 2026-03-25
- Publication Date
- 2026-06-19
Smart Images
Figure CN122241941A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of distribution network measurement configuration technology, specifically to a multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks. Background Technology
[0002] With the significant increase in the penetration rate of new energy sources in AC / DC distribution networks, the uncertainty of distribution network operation has increased dramatically, placing stringent demands on the real-time performance, comprehensiveness, and accuracy of measurement data. Existing traditional measurement systems, including SCADA (Supervisory Control and Data Acquisition) and AMI (Advanced Measurement Instrument) systems, suffer from deficiencies such as incomplete data types, low timeliness, and lack of time stamps, failing to provide reliable data support for real-time optimization of distribution network operation. While micro-synchronous phasor measurement devices (μPMUs) possess advantages such as high-frequency sampling and good synchronization, which can compensate for the shortcomings of traditional measurements, their measurement performance cannot be fully utilized according to the traditional distribution network's "first and last-end configuration" principle. This results in the absence of measurements at some important points in the distribution network topology, thereby affecting the overall observability of the AC / DC distribution network.
[0003] Current measurement configuration methods fail to effectively integrate multi-source measurement data, and cannot simultaneously ensure the performance, observability improvement, and configuration economy of the micro-synchronous phasor measurement unit (μPMU), resulting in wasted measurement resources or insufficient observability. Therefore, there is an urgent need for an optimized configuration method that can integrate multi-source measurement data, quantify dual observability indicators, and balance cost and efficiency to solve the aforementioned technical problems. Summary of the Invention To address the aforementioned shortcomings in existing technologies, this invention provides a multi-source measurement configuration method to improve the overall observability of AC / DC distribution networks, thereby solving the problems of insufficient μPMU performance, inadequate observability, and waste of measurement resources in existing technologies.
[0004] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: A method for configuring multi-source measurements to improve the overall observability of AC / DC distribution networks includes the following steps: S1. After time-scale alignment of the multi-source measurement data of the multi-source measurement device of the AC / DC distribution network at different sampling frequencies using the Pearson correlation coefficient, voltage state estimation is performed to obtain the state estimation function of the multi-source measurement data and voltage state. Combined with the actual power flow state, time observability is calculated. S2. Based on the three-phase node admittance range on the AC side, after calculating the equivalent electrical distance between each node, perform topology analysis on each node and line. Calculate the evaluation index of each node and line, sum the evaluation index of each node and line, and import it into the network topology matrix to calculate topology observability. S3. Calculate the configuration cost of the multi-source measurement device, combine time observability and topological observability to construct an optimal configuration model for the multi-source measurement device, and use the simulated annealing method to solve it to obtain the optimal configuration scheme of the multi-source measurement device in the AC / DC distribution network.
[0005] The present invention has the following beneficial effects: This invention proposes a multi-source measurement configuration method to improve the overall observability of AC / DC distribution networks. By integrating the high-frequency synchronization characteristics of μPMU with the wide coverage characteristics of SCADA and AMI, it overcomes the shortcomings of a single measurement system and improves the comprehensiveness and reliability of measurement data. Simultaneously, by quantifying the temporal and topological observability of state variables, it optimizes the measurement configuration, solves the problems of missing and wasted measurements at important locations, and improves the accuracy of state estimation. Finally, the configuration optimization model also considers configuration cost and observability, and obtains the optimal configuration scheme through simulated annealing, maximizing overall benefits while controlling economic input. Attached Figure Description
[0006] Figure 1 This is a flowchart illustrating a multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks proposed in this invention. Figure 2 This is a schematic diagram of the AC / DC distribution network system structure in the embodiment; Figure 3 This is a schematic diagram of the data error range after SCADA and AMI fusion in the embodiment; Figure 4 This is a schematic diagram of the fitness convergence curve of the measurement configuration model during the iterative process of the simulated annealing method in the embodiment. Figure 5 This is a schematic diagram illustrating the time observability verification of the voltage state in the embodiment. Detailed Implementation
[0007] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.
[0008] The specific embodiments of this invention are as follows: like Figure 1 As shown, a multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks includes the following steps: S1. After time-scale alignment of the multi-source measurement data of the multi-source measurement device in the AC / DC distribution network at different sampling frequencies using the Pearson correlation coefficient, voltage state estimation is performed to obtain the state estimation function of the multi-source measurement data and voltage state. Combined with the actual power flow state, the time observability is calculated.
[0009] The principle behind this step is as follows: time-scale alignment of multi-source measurement data is performed based on the Pearson correlation coefficient. Then, interval analysis is used to affine the state estimation results (the output of the state estimation function) of the multi-source measurement data to an interval, further quantifying the time observability characterizing the accuracy of the state estimation results. The multi-source measurement devices include a micro synchronous phasor measurement unit (μPMU), an advanced measurement system (AMI), and a data acquisition and monitoring system (SCADA), with both the AMI and SCADA being traditional measurement systems. The multi-source measurement data includes voltage, current, and power data.
[0010] Therefore, after time-scale alignment of the multi-source measurement data from multi-source measurement devices in AC / DC distribution networks at different sampling frequencies using the Pearson correlation coefficient, voltage state estimation is performed to obtain the state estimation function of the multi-source measurement data and the voltage state. Combined with the actual power flow state, the specific process for calculating time observability is as follows: The system collects μPMU measurement data of AC / DC distribution networks at high sampling frequencies, and simultaneously collects AMI and SCADA measurement data of AC / DC distribution networks at low sampling frequencies.
[0011] Calculate AMI measurement data and μPMU measurement data from the first The Pearson correlation coefficients of equal-length time series starting from each sampling point are used to select the sampling points corresponding to the largest Pearson correlation coefficients. This is used as the optimal time offset between AMI measurement data and μPMU measurement data.
[0012] Specifically, the AMI measurement data and μPMU measurement data are calculated from the first... The formula for the Pearson correlation coefficient of an isochronous time series starting from sampling points is:
[0013] In the formula, For AMI measurement data and μPMU measurement data from the first Pearson correlation coefficients for isochronous time sequences starting from sampling points; To perform operations to obtain covariance; This is a time series of AMI measurement data; For μPMU measurement data from the first Starting from each sampling point, and the time series Equal-length time segments; Time series Standard deviation; For time sequence segments The standard deviation.
[0014] Based on the optimal time offset between AMI measurement data and μPMU measurement data, the time of AMI measurement data is aligned to the corresponding time of μPMU measurement data, i.e.:
[0015] In the formula, The time stamp after AMI measurement data alignment; This is the timestamp for receiving AMI measurement data uploaded to the information center; The optimal time offset between AMI measurement data and μPMU measurement data; The sampling period for μPMU measurement data.
[0016] Calculate SCADA measurement data and μPMU measurement data from the first The Pearson correlation coefficients of equal-length time series starting from each sampling point are used to select the sampling points corresponding to the largest Pearson correlation coefficients. This is used as the optimal time offset between SCADA measurement data and μPMU measurement data; Specifically, the SCADA measurement data and μPMU measurement data are calculated from the first... The formula for the Pearson correlation coefficient of an isochronous time series starting from sampling points is:
[0017] In the formula, For SCADA measurement data and μPMU measurement data from the first Pearson correlation coefficients for isochronous time sequences starting from sampling points; To perform operations to obtain covariance; Time series of SCADA measurement data; For μPMU measurement data from the first Starting from each sampling point, and the time series Equal-length time segments; Time series Standard deviation; For time sequence segments The standard deviation.
[0018] Based on the optimal time offset between SCADA measurement data and μPMU measurement data, the time of the SCADA measurement data is aligned to the corresponding time of the μPMU measurement data, i.e.:
[0019] In the formula, This serves as the time identifier after SCADA measurement data alignment. This is the timestamp for receiving SCADA measurement data uploaded to the information center; The optimal time offset between SCADA measurement data and μPMU measurement data; The sampling period for μPMU measurement data.
[0020] After shifting and filling the missing values of the aligned multi-source measurement data, voltage state estimation of the AC / DC distribution network is performed to obtain the state estimation function of the multi-source measurement data and voltage state, and at the same time, the true power flow state of the AC / DC distribution network is obtained.
[0021] Calculate the absolute value of the deviation between the state estimation function and the actual power flow state, i.e.:
[0022] In the formula, The absolute value of the deviation between the state estimation function and the actual power flow state; , , The first μPMU measurement data, SCADA measurement data, and AMI measurement data at specific times; This is a state estimation function for multi-source measurement data and voltage state, used to represent the mapping relationship between interval measurement data and voltage state; For the first Real-time power flow status of AC / DC distribution networks.
[0023] Determine whether the absolute value of the deviation between the state estimation function and the actual power flow state is less than the observability threshold of the state variables. If so, the state variables are observable within the observation period, and time observability is calculated. Otherwise, the state variables are not observable within the observation period, and time observability is calculated.
[0024] This step is the observability evaluation process for state variables. Unobservability indicates the performance of the configuration scheme during the subsequent optimization algorithm solution process. The configuration scheme will be automatically corrected during the iteration process. Therefore, even if the state variables are found to be unobservable after evaluation, time observability still needs to be calculated so that the optimization algorithm can be used to optimize the configuration scheme and make the state variables observable.
[0025] Specifically, the formula for calculating time observability is:
[0026] In the formula, For time observability; The range of fluctuation of the state variable within the observation period; For the observable interval of the state variable; This refers to the total length of the observation period for the AC / DC distribution network; This is for the operation of retrieving the maximum value; This is an operation to retrieve the minimum value; For the first The state estimation function at time t.
[0027] In this step, the fluctuations of the state variable within the observation period are mapped to an interval, denoted as . and the observable interval of the state variable By comparison, the proportion of the overlapping portion within the fluctuation range is obtained. This refers to time observability.
[0028] In summary, this step enables refined modeling of errors in real-time applications of time-scale-free measurement data after alignment. Furthermore, by constructing temporal observability measures of the numerical accuracy of the estimation results through state interval results, it improves the numerical accuracy of the state estimation results corresponding to the measurement device configuration scheme throughout the overall observation period. Simultaneously, this invention fully leverages the synergistic and complementary advantages of μPMU high-frequency synchronous measurement, SCADA wide-coverage measurement, and AMI fine-grained load measurement, overcoming the shortcomings of traditional single-measurement data types such as incomplete data types, insufficient timeliness, and poor synchronization, thus constructing a comprehensive and reliable measurement data support system.
[0029] S2. Based on the three-phase node admittance range on the AC side, after calculating the equivalent electrical distance between each node, perform topology analysis on each node and line. Calculate the evaluation index of each node and line, sum the evaluation index of each node and line, and import it into the network topology matrix to calculate topology observability.
[0030] The principle behind this step is to use the node admittance matrix to quantify the electrical distance between nodes in the AC / DC distribution network, and then couple it to the node and line evaluation module to quantify the topological observability that characterizes the measurement performance of key network points.
[0031] Based on the above principles, and using the three-phase node admittance range on the AC side, after calculating the equivalent electrical distance between each node, a topology analysis is performed on each node and line. Evaluation indices for each node and line are calculated, and the summation of these indices is then imported into the network topology matrix. The specific process for calculating topology observability is as follows: Based on the three-phase node admittance range on the AC side, the equivalent electrical distance between each node is calculated and used as the topology weight of the line, i.e.:
[0032]
[0033] In the formula, For nodes With nodes Inter-line in phase and The equivalent electrical distance between phases; For nodes With nodes Inter-line phase and Alternating, by Phase-dominant sensitivity; For nodes With nodes Inter-line phase and Alternating, by Phase-dominant sensitivity; For nodes With nodes The equivalent electrical distance between lines, i.e., nodes With nodes Topological weights of inter-line routes; , , These refer to phases A, B, and C on the AC side of an AC / DC distribution network. For nodes With nodes Inter-line phase and Interphase mutual admittance; For nodes of Self-admittance; For nodes of Self-admittance.
[0034] The principle used in this step is as follows: based on the equivalent electrical distance of the AC / DC distribution network topology within the node admittance interval, the calculation results of the AC / DC lines are normalized and assigned as the weights of the lines between nodes in the topology.
[0035] A topology analysis is performed on each node and line to calculate evaluation metrics for each node and line, including node connectivity, power betweenness, and density, as well as line vulnerability and connectivity, expressed as follows:
[0036]
[0037]
[0038] In the formula, For nodes The degree of connectivity; The total number of nodes; These are elements of the adjacency matrix; For nodes The power betweenness; For nodes With nodes The shortest path between; For nodes With nodes Find the number of all shortest paths between them, where the shortest path is the number of edges. The number of shortest paths is the number of nodes. To the node The path is The number of connection methods; For nodes With nodes The total number of paths between nodes To the node All connection methods (including those with path greater than) ) number; For nodes The tightness; For nodes With nodes The shortest electrical distance between lines; For nodes With nodes The vulnerability of the inter-line; For nodes Local centrality; For nodes Local centrality; , These are first-order and second-order adjacent nodes, respectively; For nodes The set of adjacent nodes; First-order adjacent nodes The set of adjacent nodes; For nodes With nodes Inter-line connectivity; For nodes The degree of connectivity; The average connectivity of all nodes; For the set of all nodes; This represents the total number of lines.
[0039] In this step, topology analysis is performed on each node and line based on complex network theory, thereby calculating the evaluation index of each node and line in the topology so that subsequent steps can calculate topology observability.
[0040] S23. Sum the evaluation metrics of each node to obtain the node evaluation result, and sum the evaluation metrics of the line to obtain the line evaluation result. Import the node evaluation results and line evaluation results into the network topology matrix to transform the topology observation into the sum of the measured node and line evaluation results, thus obtaining the topology observability, i.e.:
[0041] In the formula, For topological observability; For the set of node measurements; Number the nodes to be measured; The node evaluation results; For line measurement sets; The line number to be measured; This is the result of the route evaluation.
[0042] In this step, the evaluation indicators of each node and the evaluation indicators of the line calculated in the above steps are summed and then imported into the network topology matrix. Thus, the observation of the topology by the measurement unit is transformed into the sum of the evaluation results of the measured nodes and lines, which is the topology observability.
[0043] In summary, this step enables the equivalent calculation of electrical distances in AC / DC distribution networks, and coupling it to the topology matrix enhances the physical connection between the topology and electrical components. Based on this, the key locations selected using graph theory are more closely linked to electrical connections, improving the monitoring capability of the measurement device configuration for AC / DC distribution network topology.
[0044] S3. Calculate the configuration cost of the multi-source measurement device, combine time observability and topological observability to construct an optimal configuration model for the multi-source measurement device, and use the simulated annealing method to solve it to obtain the optimal configuration scheme of the multi-source measurement device in the AC / DC distribution network.
[0045] The principle adopted in this step is to take into account the time observability of step S1, the topological observability of step S2, and the configuration cost of multi-source measurement devices, to construct an optimal configuration model of multi-source measurement devices, and then solve it through simulated annealing to obtain the optimal configuration scheme (i.e., configuration sites and quantity) of each measurement configuration device.
[0046] Based on the above principles, the configuration cost of multi-source measurement devices is calculated. Combining time observability and topological observability, an optimal configuration model for multi-source measurement devices is constructed, and the simulated annealing method is used to solve it. The specific process for obtaining the optimal configuration scheme of multi-source measurement devices in AC / DC distribution networks is as follows: Calculate the configuration cost of the multi-source measurement device, i.e.:
[0047] In the formula, The configuration cost of multi-source measurement devices; , , The configuration costs for μPMU, SCADA, and AMI are respectively. , , These represent the number of configurations for μPMU, SCADA, and AMI, respectively.
[0048] Based on total configuration cost, time observability, and topological observability, an optimal configuration model for multi-source measurement devices is constructed, namely:
[0049] In the formula, This is an operation to retrieve the minimum value; To optimize the objective function value of the configuration model; , , The weights for time observability, topology observability, and total configuration cost are respectively determined, and the values of each weight can be determined according to the needs of the distribution network. For areas that require important monitoring, the weights of time observability and topology observability can be increased to obtain configuration schemes that improve data accuracy. For areas that require economical operation, the weight of total configuration cost can be increased to obtain configuration schemes with lower costs. , These are temporal observability and topological observability, respectively.
[0050] In this step, before constructing the optimal configuration model, the total configuration cost, time observability, and topological observability need to be reduced to a uniform order of magnitude to achieve synergistic optimization of the three objectives. Simultaneously, the constraints of the optimal configuration model for the multi-source measurement device are nonlinear power flow constraints in state estimation.
[0051] The simulated annealing method is used to solve the optimal configuration model, and the configuration scheme of the multi-source measurement device is updated using a perturbation range reversal strategy that adapts to temperature changes.
[0052] In the formula, For the first The configuration scheme of the multi-source measurement device in the next iteration is a 0-1 variable, where 0 indicates that there is no measurement configuration at the site or line, and 1 indicates that there is a measurement configuration at the site or line. This is the disturbance factor, and its value is generally determined by the configuration scheme. 15% of the length and rounded down; For the first Configuration scheme for multi-source measurement devices in the next iteration.
[0053] Calculate the acceptance probability of the updated multi-source measurement device configuration scheme, i.e.:
[0054] In the formula, For the probability of acceptance; For the first Optimize the objective function value of the configuration model in the next iteration; For the first Optimize the objective function value of the configuration model in the next iteration; It is an exponential function; This represents the temperature in the current iteration.
[0055] In this step, the Metropolis criterion is used to calculate the acceptance probability of the result after each update, ensuring the global search capability of the optimization algorithm in the initial stage and the reliability of the optimal solution in the final stage.
[0056] Determine if the temperature in the current iteration is lower than the set value. If so, take the configuration of the multi-source measurement device in the current iteration as the optimal configuration. Otherwise, continue the iteration.
[0057] In this step, the final optimal configuration scheme takes into account both the observability of the AC / DC distribution network and the overall benefits of the configuration cost of multi-source measurement devices.
[0058] In summary, this step achieves a comprehensive benefit by considering both the configuration cost of measurement devices and the improvement of the overall observability level of the AC / DC distribution network. While improving the utilization efficiency of measurement resources, it ensures high-precision status perception of the AC / DC distribution network, which is sufficient to provide reliable data support for the refined perception and real-time optimized operation of the AC / DC distribution network.
[0059] Furthermore, to verify that the method proposed in this invention can improve network topology observability and temporal observability of operating status by integrating μPMUs in AC / DC distribution networks, in situations such as... Figure 2 The simulation is performed on the AC / DC distribution network system framework shown. Figure 2 Nodes 1-33 and their lines form a three-phase AC system, while nodes 34-44 and their lines form a DC system. The voltage source converter (VSC) is connected to the following locations as shown in Table 1: photovoltaic power (PV) is connected to nodes 36, 39, and 42, and wind turbine generators (WT) are connected to nodes 17 and 32. The state estimation is set to 5 seconds per iteration, and the total simulation time is set to 1 hour.
[0060] Table 1. VSC Connection Location Table for Voltage Source Converter
[0061] Based on the above simulation parameters, experiments were conducted using the method proposed in this invention: like Figure 3As shown, it illustrates the error range obtained after fusing SCADA measurement data and AMI measurement data with low sampling frequency.
[0062] Meanwhile, when determining observability, the observability threshold for state variables is the observability threshold for voltage amplitude, which is set to 1.5% of the observable interval length of the actual power flow index, and the observable interval length for voltage phase angle is set to ±0.3°.
[0063] like Figure 4 As shown in Table 2, the iterative convergence process of the simulated annealing method is demonstrated. When the annealing temperature is below 0.01 degrees Celsius, the final configuration scheme output is the optimal configuration scheme that takes into account both the observability of the AC / DC distribution network and the comprehensive benefits of measurement configuration costs. Table 2 Optimal Configuration Scheme for Measurement Devices
[0064] Table 2 shows that the optimal configuration scheme achieves 82.5% topological observability, basically covering key nodes and important feeders in the network. Temporal observability reaches 92.25%. To verify the observability performance, 1000 random measurements with error boundary conditions equal to the interval length are set, and then state estimation based on the least squares method is used. Figure 5 It can be seen that the measurement data collected by the obtained scheme can ensure that the state estimation results are all within an observable range. The time observability is not 100% due to the conservative nature of the interval algorithm, which compresses the intersection of interval boundaries during the optimization process, resulting in a calculated observability value lower than the actual value. Therefore, the above verification process demonstrates that the method proposed in this invention fully leverages the advantages of multi-source measurements to improve the comprehensiveness and reliability of the measurement data. Simultaneously, this invention also solves the problem of missing measurements at important locations and improves the accuracy of state estimation, providing a practical technical means for μPMU access to AC / DC distribution networks.
[0065] In summary, the multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks proposed in this invention achieves the following technical effects: 1. It can fully leverage the advantages of multi-source measurement: By integrating the high-frequency synchronization characteristics of μPMU with the wide coverage characteristics of SCADA and AMI, it makes up for the shortcomings of a single measurement system and improves the comprehensiveness and reliability of measurement data. 2. Improved observability accuracy: By quantifying temporal and topological observability, the measurement configuration was optimized, the problem of missing measurements at important sites was solved, and the accuracy of state estimation was improved; 3. Achieving optimal overall benefits: The configuration optimization model takes into account both configuration cost and observability, and obtains the optimal configuration scheme through simulated annealing, thereby maximizing overall benefits while controlling economic input.
[0066] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.
[0067] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.
Claims
1. A multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks, characterized in that, Includes the following steps: S1. After time-scale alignment of the multi-source measurement data of the multi-source measurement device of the AC / DC distribution network at different sampling frequencies using the Pearson correlation coefficient, voltage state estimation is performed to obtain the state estimation function of the multi-source measurement data and voltage state. Combined with the actual power flow state, time observability is calculated. S2. Based on the three-phase node admittance range on the AC side, after calculating the equivalent electrical distance between each node, perform topology analysis on each node and line. Calculate the evaluation index of each node and line, sum the evaluation index of each node and line, and import it into the network topology matrix to calculate topology observability. S3. Calculate the configuration cost of the multi-source measurement device, combine time observability and topological observability to construct an optimal configuration model for the multi-source measurement device, and use the simulated annealing method to solve it to obtain the optimal configuration scheme of the multi-source measurement device in the AC / DC distribution network.
2. The multi-source measurement configuration method for improving the comprehensive observability of AC / DC distribution networks according to claim 1, characterized in that, The measurement data includes voltage, current, and power data.
3. The multi-source measurement configuration method for improving the comprehensive observability of AC / DC distribution networks according to claim 1, characterized in that, Step S1 specifically includes: S11. Collect μPMU measurement data of AC / DC distribution network at high sampling frequency, and simultaneously collect AMI measurement data and SCADA measurement data of AC / DC distribution network at low sampling frequency. S12, Calculate AMI measurement data and μPMU measurement data from the... The Pearson correlation coefficients of equal-length time series starting from each sampling point are used to select the sampling points corresponding to the largest Pearson correlation coefficients. This is used as the optimal time offset between AMI measurement data and μPMU measurement data; S13. Based on the optimal time offset between AMI measurement data and μPMU measurement data, align the time of the AMI measurement data to the corresponding time of the μPMU measurement data, i.e.: in, This is the time stamp after AMI measurement data alignment. This is the timestamp for receiving AMI measurement data uploaded to the information center. The optimal time offset between AMI measurement data and μPMU measurement data. The sampling period for μPMU measurement data; S14, Calculate SCADA measurement data and μPMU measurement data from the... The Pearson correlation coefficients of equal-length time series starting from each sampling point are used to select the sampling points corresponding to the largest Pearson correlation coefficients. This is used as the optimal time offset between SCADA measurement data and μPMU measurement data; S15. Based on the optimal time offset between SCADA measurement data and μPMU measurement data, align the time of the SCADA measurement data to the corresponding time of the μPMU measurement data, i.e.: in, This is the time stamp after SCADA measurement data alignment. This is the timestamp for receiving SCADA measurement data uploaded to the information center. The optimal time offset between SCADA measurement data and μPMU measurement data. The sampling period for μPMU measurement data; S16. After shifting and filling the missing values of the aligned multi-source measurement data, the voltage state of the AC / DC distribution network is estimated to obtain the state estimation function of the multi-source measurement data and the voltage state, and at the same time, the true power flow state of the AC / DC distribution network is obtained. S17. Calculate the absolute value of the deviation between the state estimation function and the actual power flow state, i.e.: in, Let be the absolute value of the deviation between the state estimation function and the actual power flow state. , , The first μPMU measurement data, SCADA measurement data, and AMI measurement data at specific times. The state estimation function is derived from multi-source measurement data and voltage state. For the first Real-time power flow status of AC / DC distribution networks; S18. Determine whether the absolute value of the deviation between the state estimation function and the actual power flow state is less than the observability threshold of the state variables. If so, each state variable is observable within the observation period, and time observability is calculated. Otherwise, each state variable is not observable within the observation period, and time observability is calculated.
4. The multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks according to claim 3, characterized in that, Calculate AMI measurement data and μPMU measurement data from the first The formula for the Pearson correlation coefficient of an isochronous time series starting from sampling points is: in, For AMI measurement data and μPMU measurement data from the first Pearson correlation coefficients for isochronous time series starting from sampling points. To perform covariance operations, For AMI measurement data, time series For μPMU measurement data from the first Starting from each sampling point, and the time series Equal-length time segments, Time series standard deviation For time sequence segments The standard deviation.
5. The multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks according to claim 3, characterized in that, Calculate SCADA measurement data and μPMU measurement data from the first The formula for the Pearson correlation coefficient of an isochronous time series starting from sampling points is: in, For SCADA measurement data and μPMU measurement data from the first Pearson correlation coefficients for isochronous time series starting from sampling points. To perform covariance operations, For SCADA measurement data time series, For μPMU measurement data from the first Starting from each sampling point, and the time series Equal-length time segments, Time series standard deviation For time sequence segments The standard deviation.
6. The multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks according to claim 3, characterized in that, The formula for calculating time observability is: in, For time observability, The range of fluctuation of the state variable during the observation period. Let be the observable interval of the state variable. The total length of the observation period for AC / DC distribution networks. To perform the maximum value operation, To perform the minimum value operation, For the first The state estimation function at time t.
7. The multi-source measurement configuration method for improving the comprehensive observability of AC / DC distribution networks according to claim 1, characterized in that, Step S2 specifically includes: S21. Based on the three-phase node admittance range on the AC side, calculate the equivalent electrical distance between each node, and use it as the topology weight of the line, i.e.: in, For nodes With nodes Inter-line in phase and The equivalent electrical distance between phases, For nodes With nodes Inter-line phase and Alternately, by Phase-dominant sensitivity, For nodes With nodes Inter-line phase and Alternately, by Phase-dominant sensitivity, For nodes With nodes The equivalent electrical distance between lines, i.e., nodes With nodes Topology weights of inter-line lines, , , These refer to phases A, B, and C on the AC side of an AC / DC distribution network. For nodes With nodes Inter-line phase and Interphase mutual admittance, For nodes of Self-admittance, For nodes of Self-admittance; S22. Perform topology analysis on each node and line, and calculate the evaluation indicators for each node and line, including the connectivity, power betweenness, and density of the nodes, as well as the vulnerability and connectivity of the lines, which are expressed as follows: in, For nodes connectivity, The total number of nodes. For adjacency matrix elements, For nodes The power betweenness, For nodes i With nodes j The shortest path between, For nodes With nodes The number of all shortest paths between them. For nodes With nodes The total number of paths between them. For nodes The density, For nodes With nodes The shortest electrical distance between lines, For nodes With nodes The vulnerability of the inter-line, For nodes Local centrality, For nodes Local centrality, , These are first-order and second-order adjacent nodes, respectively. For nodes The set of adjacent nodes, First-order adjacent nodes The set of adjacent nodes, For nodes With nodes Inter-line connectivity For nodes connectivity, The average connectivity of all nodes. For the set of all nodes, Total number of lines; S23. Add the evaluation metrics of each node to obtain the node evaluation result. At the same time, add the evaluation metrics of the line to obtain the line evaluation result. Import the node evaluation result and the line evaluation result into the network topology matrix to convert the topology observation into the sum of the evaluation results of the measured nodes and lines, and obtain the topology observability.
8. The multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks according to claim 7, characterized in that, The formula for calculating topological observability is: in, For topological observability, For the set of node measurements, Number the nodes to be measured. For node evaluation results, For line measurement sets, The line number to be measured. This is the result of the route evaluation.
9. The multi-source measurement configuration method for improving the overall observability of AC / DC distribution networks according to claim 1, characterized in that, Step S3 specifically includes: Calculate the configuration cost of the multi-source measurement device, i.e.: in, To account for the configuration cost of multi-source measurement devices, , , The configuration costs for μPMU, SCADA, and AMI are respectively. , , These represent the number of configurations for μPMU, SCADA, and AMI, respectively. Based on total configuration cost, time observability, and topological observability, an optimal configuration model for multi-source measurement devices is constructed, namely: in, To perform the minimum value operation, To optimize the objective function value of the configuration model, , , The weights for time observability, topological observability, and total configuration cost are respectively. , These are temporal observability and topological observability, respectively. The simulated annealing method is used to solve the optimal configuration model, and the configuration scheme of the multi-source measurement device is updated using a perturbation range reversal strategy that adapts to temperature changes. in, For the first Configuration scheme of multi-source measurement device in the next iteration The disturbance coefficient is... For the first Configuration scheme of multi-source measurement device in the next iteration; Calculate the acceptance probability of the updated configuration scheme for the multi-source measurement device; Determine if the temperature in the current iteration is lower than the set value. If so, take the configuration of the multi-source measurement device in the current iteration as the optimal configuration. Otherwise, continue the iteration.
10. The multi-source measurement configuration method for improving the comprehensive observability of AC / DC distribution networks according to claim 9, characterized in that, The formula for calculating the acceptance probability of the updated multi-source measurement device configuration is as follows: in, To accept probability, For the first Optimize the objective function value of the configuration model in the next iteration. For the first Optimize the objective function value of the configuration model in the next iteration. It is an exponential function. This represents the temperature in the current iteration.