Topological structure rapid identification method for real-time operation of power distribution network

Abstract

The invention discloses a topological structure rapid identification method for real-time operation of a power distribution network, and the method comprises the steps: obtaining multi-source measurement data of a plurality of data sources in the power distribution network, and carrying out the preprocessing of the multi-source measurement data, and obtaining effective measurement data; generating a candidate topology set based on the on-off state of the power distribution network switch equipment; performing state estimation on each candidate topology in the candidate topology set to obtain a state estimation value; calculating a topology error between the state estimation value of each candidate topology and the corresponding measurement value in the effective measurement data; and screening out an effective topology from the candidate topology set based on the topology error, and taking the effective topology as the operation topology of the power distribution network at the current moment. According to the scheme of the invention, the accuracy and efficiency of topology identification of the power distribution network are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution network topology identification technology, specifically a method for rapid topology identification in real-time operation of power distribution networks. Background Technology

[0002] As a crucial link connecting the transmission network and users in the power system, the distribution network's topology directly impacts the accuracy of core control operations such as fault location, load transfer, and power flow calculation. With the large-scale integration of distributed generation and the rapid growth of new loads, the distribution network topology is becoming increasingly complex, exhibiting frequent switching and diverse states. Accurately and in real-time understanding of the actual operating topology of the distribution network is fundamental to achieving advanced applications such as power flow calculation, fault location, and load transfer. Traditional topology identification methods relying on manual experience or offline modeling are no longer sufficient to meet real-time control requirements.

[0003] The existing power distribution network faces the following challenges: 1. Insufficient data sparsity and real-time performance: Compared to transmission networks, distribution networks have fewer measurement points and rely somewhat on high-precision equipment such as synchronous phasor measurement units (PMUs). However, the deployment density of PMUs in distribution networks is low, making it difficult to fully cover complex feeders. Measurement data collected by systems such as SCADA / AMI have large granularity or poor real-time performance, making it difficult for traditional state estimation methods to accurately identify the topology in distribution networks without redundant measurements.

[0004] 2. Dynamic changes in topology: The switching of distributed power sources, load fluctuations, and fault isolation and reconfiguration operations cause frequent dynamic changes in the distribution network topology, making traditional topology identification methods that rely on static switch information prone to failure or delay.

[0005] 3. Algorithm Efficiency: For complex feeders, the number of possible switch combinations is enormous, leading to a "combinatorial explosion" problem in topology reconstruction and identification. Traditional exhaustive methods or optimization algorithms are insufficient to meet the timeliness requirements of distribution networks for minute-level rapid response. When using traditional weighted least squares (WLS) for state estimation, the measurement weights are fixed, which cannot adapt to the reliability differences of SCADA / AMI multi-source data, and it is not optimized for the topological characteristics of complex feeders, making it difficult to balance identification accuracy and speed.

[0006] Therefore, there is an urgent need for a method that can solve the problems of poor accuracy and low efficiency in the identification of existing power distribution network topologies. Summary of the Invention

[0007] To address the aforementioned technical problems of poor accuracy and low efficiency in existing distribution network topology identification, this invention provides a rapid topology identification method for real-time operation of distribution networks, thereby effectively improving the accuracy and efficiency of distribution network topology identification.

[0008] To address this, the present invention employs the following technical solution: a method for rapid topology identification in real-time operation of a distribution network, comprising: acquiring multi-source measurement data from multiple data sources in the distribution network, and preprocessing the multi-source measurement data to obtain effective measurement data; generating a candidate topology set based on the on / off states of distribution network switching equipment, the candidate topology set containing multiple candidate topologies; performing state estimation on each candidate topology in the candidate topology set to obtain a state estimate value; the state estimation includes a calculation process based on weighted least squares, the weighting coefficients of which are determined according to the variance of the corresponding measurement data; calculating the topology error between the state estimate value of each candidate topology and the corresponding measurement value in the effective measurement data; and selecting effective topologies from the candidate topology set based on the topology error as the operating topology of the distribution network at the current moment.

[0009] Preferably, the preprocessing of the multi-source measurement data includes: removing outlier data using the 3σ criterion; normalizing data of different dimensions; and completing missing data using linear interpolation.

[0010] Preferably, generating a candidate topology set includes: dividing the complex feeder scenario into multiple partitions according to the region, and generating local candidate topologies for each partition; combining the local candidate topologies by switching on and off the inter-regional interconnection switches to form a global candidate topology set.

[0011] Preferably, the state estimation further includes: constructing a state estimation model, which includes the following elements: state variables: node voltage magnitude and phase angle; constraints: power flow equations, branch power constraints, and equipment capacity limits.

[0012] Preferably, the weighting coefficients are calculated as follows:

[0013] In the formula, The weighting coefficient for the i-th measurement data is... Let be the variance of the i-th measurement data. Dynamic weighting is used to improve the accuracy of state estimation.

[0014] Preferably, the topology error is calculated as follows:

[0015] In the formula, For topological error, The weight of the i-th measurement data. For the i-th actual measurement value, This is the i-th measurement value calculated based on the state estimation results.

[0016] Preferably, the method further includes: performing power flow calculation verification on the selected valid topology; in response to successful verification, confirming that the valid topology is the running topology at the current moment; in response to unsuccessful verification, selecting the topology with the second smallest topology error from the remaining candidate topologies as a new valid topology and performing power flow calculation verification until successful verification.

[0017] Preferably, the method further includes: continuously collecting the multi-source measurement data and repeatedly executing all the steps in one or more of the aforementioned technical solutions to achieve dynamic updating of the distribution network operation topology.

[0018] Preferably, the determination of the weighting coefficients further includes: assigning a weighting coefficient with a value range of 0.8 to 1.0 to the SCADA system data; and assigning a weighting coefficient with a value range of 0.6 to 0.9 to the AMI system data.

[0019] Preferably, selecting a valid topology from the candidate topology set includes: determining the candidate topology with the smallest topology error as the valid topology.

[0020] The beneficial effects of this invention are as follows: Based on multi-source system data, the solution can be directly applied to complex feeder scenarios without the need for additional high-precision monitoring equipment. It solves the combinatorial explosion problem by generating candidate topologies through partitioning, narrows the search range by eliminating invalid topology combinations, and improves state estimation efficiency by combining an improved WLS algorithm, thus achieving rapid identification of the topology structure and meeting real-time control requirements. Furthermore, this invention optimizes the WLS algorithm using dynamic weight allocation to adapt to the differences in the reliability of multi-source data, and combines this with a validity verification step to ensure the accuracy and security of the identification results.

[0021] Furthermore, this invention supports continuous updates based on real-time data, enabling rapid adaptation to dynamic changes in the distribution network topology and providing real-time and reliable topology support for services such as fault handling and load transfer.

[0022] Furthermore, this invention utilizes the principle of error minimization to quickly lock the true topology from the candidate set. Its computational efficiency is higher than that of traditional topology identification models based on complex optimization or deep learning, and it can meet the needs of distribution networks for real-time updates and rapid response. Attached Figure Description

[0023] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1This is a flowchart illustrating a method for rapid topology identification in real-time operation of a power distribution network according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the process of identifying a power grid topology using the method of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] Figure 1 This is a flowchart illustrating a method 100 for rapid topology identification in real-time operation of a power distribution network according to an embodiment of the present invention.

[0027] like Figure 1 As shown, in step S101, multi-source measurement data from multiple data sources in the distribution network are acquired, and the multi-source measurement data is preprocessed to obtain valid measurement data. In some embodiments, real-time measurement data such as branch current and key node voltage in the distribution network are collected through a SCADA system, and user-side voltage and current data are collected through an AMI system, which are then integrated to form a multi-source measurement dataset. Preprocessing of the multi-source measurement data includes: using the 3σ criterion to remove abnormal data to avoid interference with the identification results; normalizing data of different dimensions, for example, voltage and current data of different dimensions can be normalized to unify the data range; and using linear interpolation to complete missing data to ensure the integrity of the input data.

[0028] In step S102, a candidate topology set is generated based on the on / off status of the distribution network switchgear. This candidate topology set contains multiple candidate topologies. In some embodiments, generating the candidate topology set includes: dividing the complex feeder scenario into multiple partitions by region and generating local candidate topologies for each partition; combining the local candidate topologies by the on / off combinations of inter-regional tie switches to form a global candidate topology set. Specifically, based on the physical characteristics and on / off constraints of the distribution network switchgear (including sectionalizing switches and tie switches), all possible on / off combinations of switches are traversed. Combining the core requirements of radial operation of the distribution network, invalid combinations that do not comply with safety rules, such as those involving closed-loop operation or isolated nodes, are eliminated to generate the candidate topology set. For complex feeder scenarios, candidate topologies for each partition are generated by feeder partition, and then a global candidate topology set is formed by the on / off combinations of inter-regional tie switches, narrowing the search range and improving generation efficiency.

[0029] In one application scenario, a candidate topology set that satisfies radial operation constraints can be generated by combining all operable switches (including sectionalizing switches and tie switches) in the distribution network based on all possible "on" or "off" combinations. Constraint filtering can then be applied to the candidate topologies in this set; for example, preliminary filtering can be performed to exclude topologies that clearly do not meet grid operation safety constraints (such as voltage overruns or line overloads).

[0030] In step S103, state estimation is performed on each candidate topology in the candidate topology set to obtain a state estimate value. The state estimation includes a calculation process based on the weighted least squares method, and the weight coefficients are determined according to the variance of the corresponding measurement data.

[0031] In some embodiments, state estimation further includes constructing a state estimation model. For each topology in the candidate topology set, a corresponding state estimation model is constructed using its network structure and known line parameters. Specifically, the state estimation model includes the following elements: State variables: node voltage magnitude and phase angle; Constraints: power flow equations, branch power constraints, and equipment capacity limitations, etc.

[0032] This invention employs an improved weighted least squares (WLS) method. The improvement lies in the dynamic weighting of SCADA / AMI data based on measurement accuracy and reliability. SCADA system data, due to its high measurement frequency and accuracy, receives a higher initial weight, while AMI system data has its weights adaptively adjusted based on transmission stability. Real-time acquired SCADA / AMI measurement data is used as input to perform topology state estimation, yielding estimated state vectors (such as node voltage phasors). This improved WLS aims to optimize the convergence and accuracy of traditional WLS in distribution networks facing sparse measurements and low redundancy.

[0033] The weighting coefficients are calculated as follows:

[0034] In the formula, The weighting coefficient for the i-th measurement data is... Let be the variance of the i-th measurement data. Dynamic weighting is used to improve the accuracy of state estimation.

[0035] In one application scenario, determining the weight coefficients also includes setting them to a corresponding range. Specifically, a weight coefficient ranging from 0.8 to 1.0 is assigned to SCADA system data, and a weight coefficient ranging from 0.6 to 0.9 is assigned to AMI system data.

[0036] In one application scenario, another improvement of the weighted least squares method in this invention lies in analyzing historical data and weather factors through short-term load forecasting or generative adversarial networks (GANs) to generate power data for areas without installed measurement facilities as "pseudo-measurements," filling data gaps and thus solving the problem that traditional WLS state estimation highly relies on redundant real-time measurement data. Data augmentation can also utilize data augmentation techniques (such as GANs) to generate sufficient, reasonable, and evenly distributed augmented data based on limited samples, enabling the WLS algorithm to perform effective state estimation even when faced with scarce measurement data, significantly improving the observability of the distribution network.

[0037] The improved WLS in this invention employs a dynamic weight adjustment strategy. It assigns different weight values ​​based on the reliability of the data source (e.g., real-time SCADA data is more reliable than pseudo-measurements). This ensures that highly reliable data plays a dominant role in the estimation calculation, preventing low-quality data (e.g., pseudo-measurements with large errors) from interfering with the extreme value estimation of the objective function through the same weights. Through dynamic weighting, the linear power flow equations (physical constraints) and pseudo-measurement results are fused in a multimodal manner, further optimizing the accuracy and robustness of state estimation.

[0038] In step S104, the topology error between the state estimate of each candidate topology and the corresponding measurement value in the valid measurement data is calculated. In some embodiments, the topology error is calculated as follows:

[0039] In the formula, For topological error, The weight of the i-th measurement data. For the i-th actual measurement value, This is the i-th measurement value calculated based on the state estimation results.

[0040] In step S105, a valid topology is selected from the candidate topology set based on the topology error, and this valid topology is used as the operating topology of the distribution network at the current moment. In one application scenario, the candidate topology with the smallest topology error can be determined as the valid topology.

[0041] Furthermore, the method of the present invention further includes: performing power flow calculation verification on the selected effective topology; in response to passing the verification, confirming that the effective topology is the running topology at the current moment; in response to failing the verification, selecting the topology with the second smallest topology error from the remaining candidate topologies as a new effective topology and performing power flow calculation verification until the verification passes.

[0042] In addition, the multi-source measurement data can be continuously collected, and the above steps S101 to S105 can be repeatedly executed to achieve dynamic updates of the distribution network operation topology.

[0043] This invention transforms the objective function of WLS (i.e., the weighted sum of squared residuals) into a criterion for topology identification. It does not assume a fixed topology, but instead performs state estimation for each candidate topology in the candidate topology set. By calculating and comparing the measurement residuals (estimation errors) under different topology structures, the topology with the smallest residual is selected as the current actual operating topology. This improved application method based on residuals allows the algorithm to quickly verify the consistency of the topology using real-time cross-sectional voltage and current data, thereby adapting to dynamic changes in the distribution network topology.

[0044] The invention will now be described in detail in conjunction with specific application scenarios. Figure 2 This is a schematic diagram illustrating the process 200 of identifying a power grid topology using the method of the present invention.

[0045] like Figure 2 As shown, in step S201, data acquisition and preprocessing take place.

[0046] Real-time measurement data such as branch currents and key node voltages in the distribution network are collected through a SCADA system, while user-side voltage and current data are collected through an AMI system, integrating them to form a multi-source measurement dataset. The 3σ criterion is used to identify and remove outliers from the data to avoid interference with the identification results. Voltage and current data of different dimensions are normalized to unify the data range. To address the issue of missing data, linear interpolation is used to complete the missing data, ensuring the integrity of the input data.

[0047] Furthermore, this invention addresses the challenges of time alignment, high sparsity, and significant noise interference in multi-source heterogeneous data, which hinders the convergence of traditional deterministic equation-based methods (such as network-wide state estimation). By treating the voltage measurement data of each node as a time-series vector, the linear correlation between two node voltage vectors is quantified by calculating the Pearson correlation coefficient. Based on the correlation level and a set threshold, the voltage fluctuation trend of nodes within a certain time window is recorded. This approach, by setting a threshold, identifies highly correlated nodes as physically connected, thereby rapidly constructing the topology and effectively handling noise and latency issues in multi-source heterogeneous data.

[0048] In step S202, a candidate topology set is generated.

[0049] Based on the physical characteristics and on / off constraints of distribution network switchgear (including sectionalizing switches and tie switches), all possible on / off combinations are traversed. Considering the core requirements of radial operation of the distribution network, invalid combinations that do not comply with safety rules, such as those involving closed-loop operation or isolated nodes, are eliminated, generating a candidate topology set.

[0050] For complex feeder scenarios, candidate topologies for each feeder partition are generated, and then a global candidate topology set is formed by combining the on and off states of inter-regional interconnection switches, which narrows the search range and improves generation efficiency.

[0051] Candidate topologies are initially filtered to exclude those that clearly do not meet the safety constraints of power grid operation, such as those with voltage exceeding limits or line overload.

[0052] In step S203, the WLS state estimation is improved.

[0053] For each candidate topology, a corresponding distribution network state estimation model is constructed. State variables are selected as node voltage magnitude and phase angle, and constraints include power flow equations, branch power constraints, and equipment capacity limitations. An improved weighted least squares method is used to solve the model. The improvement lies in: dynamically assigning weights based on the measurement accuracy and reliability of SCADA / AMI data. SCADA system data receives a higher initial weight due to its high measurement frequency and accuracy, while AMI system data has its weights adaptively adjusted based on transmission stability. The weight coefficients are calculated in real-time using the variance of the measurement data, as shown in the following formula:

[0054] In the formula, The weight of the i-th measurement data. Let be the variance of the i-th measurement data. Dynamic weighting is used to improve the accuracy of state estimation.

[0055] In step S204, topology error is calculated and filtered.

[0056] Calculate the error index between the state estimate of each candidate topology and the preprocessed valid input data. This error index can be expressed as a weighted sum of squares, i.e., the topology error, and its formula is as follows:

[0057] In the formula, For topological error, The weight of the i-th measurement data. For the i-th actual measurement value, This is the i-th measurement value calculated based on the state estimation results.

[0058] In step S205, topology validity is verified and updated in real time.

[0059] Power flow calculations are performed on the selected effective topologies to verify whether they meet the radial operation requirements, whether the branch current exceeds the limit, and whether the node voltage is within the allowable range. If they do not meet the requirements, the candidate topology with the second smallest error is re-selected and verified until an effective topology that meets the constraints is obtained.

[0060] In some embodiments, the optimal topology can also be selected by error comparison. Specifically, the state estimation errors of all topologies in the candidate topology set are compared. The topology with the smallest error is selected as the effective operating topology of the distribution network at the current time.

[0061] The method of this invention is suitable for real-time updates of SCADA / AMI real-time measurement data. By continuously collecting SCADA / AMI real-time data and repeating the above steps, the topology structure is dynamically updated in real time, adapting to dynamic changes in distribution network switching operations, equipment commissioning and decommissioning, and other scenarios. In one application scenario, at the next sampling time or when a potential change occurs in the distribution network switching status, steps S101 to S105 are automatically triggered to quickly re-identify the current topology. This method can be used to solve the dynamic topology identification problem of feeders with complex topologies, providing accurate network structure information for the real-time operation, fault handling, and load transfer of the distribution network.

[0062] Taking a complex feeder system of a 10kV distribution network as an example, the system includes 3 feeders, 20 nodes, 15 sectional switches, and 3 tie switches. The SCADA system has a sampling frequency of 5 minutes / time, and the AMI system is connected to 12 user-side measurement nodes.

[0063] The method of this invention for topology identification includes the following steps: Data acquisition and preprocessing: Collect SCADA / AMI voltage and current data within 1 hour, remove 2 outlier data points using the 3σ criterion, normalize the voltage (kV level) and current (A level) data, and use linear interpolation to complete 3 missing data points; Candidate topology set generation: Based on the on / off constraints, an initial set of 86 candidate topologies is generated through traversal. 32 invalid topologies, such as those with closed-loop operation and isolated nodes, are removed, resulting in 54 valid candidate topologies. Improved WLS state estimation: Construct a state estimation model for each candidate topology, and assign weight coefficients based on the variance of the measurement data. The weight coefficients for SCADA data range from 0.8 to 1.0, and the weight coefficients for AMI data range from 0.6 to 0.9. Topology error calculation and screening: Calculate the error index of each candidate topology, and screen the topology with the smallest error as the effective topology. The error index is 0.032. Validity verification and real-time updates: Power flow calculations are performed to verify the valid topology. Node voltage deviations are all within ±5%, and branch currents do not exceed limits. Real-time data is continuously collected, and the topology identification results are updated every 5 minutes to dynamically track topology changes caused by switching operations.

[0064] Tests showed that the topology identification method of this invention takes an average of 8 seconds and has an accuracy rate of 98.5%, which can meet the timeliness and accuracy requirements of real-time control of power distribution networks.

[0065] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for rapid topology identification in real-time operation of distribution networks, characterized in that, include: Acquire multi-source measurement data from multiple data sources in the power distribution network, and preprocess the multi-source measurement data to obtain effective measurement data; A candidate topology set is generated based on the on / off status of the distribution network switching equipment, and the candidate topology set contains multiple candidate topologies; State estimation is performed on each candidate topology in the candidate topology set to obtain a state estimate value; the state estimation includes a calculation process based on weighted least squares, and the weight coefficients are determined according to the variance of the corresponding measurement data; Calculate the topology error between the state estimate of each candidate topology and the corresponding measurement value in the valid measurement data; Based on the topology error, a valid topology is selected from the candidate topology set and used as the operating topology of the distribution network at the current moment.

2. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, Preprocessing of the multi-source measurement data includes: The 3σ criterion was used to remove outlier data. Normalize data of different dimensions; and Missing data were filled in using linear interpolation.

3. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, The generation of candidate topology sets includes: The complex feeder scenario is divided into multiple partitions according to the region, and local candidate topologies for each partition are generated. The local candidate topologies are combined by switching the inter-regional communication switches on and off to form a global candidate topology set.

4. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, The state estimation also includes: Construct a state estimation model, which includes the following elements: State variables: node voltage magnitude and phase angle; constraints: power flow equations, branch power constraints, and equipment capacity limits.

5. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, The weighting coefficients are calculated as follows: In the formula, The weighting coefficient for the i-th measurement data is... Let be the variance of the i-th measurement data.

6. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, The topology error is calculated as follows: In the formula, For topological error, The weight of the i-th measurement data. For the i-th actual measurement value, This is the i-th measurement value calculated based on the state estimation results.

7. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, The method further includes: Power flow calculations were performed to verify the selected valid topologies; Upon successful verification, the valid topology is confirmed as the running topology at the current moment; In response to a failed verification, the topology with the second smallest topology error is selected from the remaining candidate topologies as a new valid topology, and power flow calculation is performed for verification until the verification passes.

8. The method for rapid topology identification for real-time operation of distribution networks according to any one of claims 1 to 7, characterized in that, The method further includes: The multi-source measurement data is continuously collected, and all steps of the rapid identification method for real-time operation of distribution network described in any one of claims 1 to 7 are repeatedly executed to achieve dynamic updating of the operation topology of distribution network.

9. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, The determination of the weighting coefficients also includes: The SCADA system data is assigned a weight coefficient ranging from 0.8 to 1.0; the AMI system data is assigned a weight coefficient ranging from 0.6 to 0.

9.

10. The method for rapid topology identification for real-time operation of distribution networks according to claim 1, characterized in that, Selecting valid topologies from the candidate topology set includes: The candidate topology with the smallest topology error is determined as the effective topology.

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