Power distribution network observability analysis method and device

Abstract

The invention provides a power distribution network observability analysis method and device, and relates to the field of power system maintenance, and the method comprises the steps: constructing a topological structure of a power distribution network according to the node operation electrical parameters of the power distribution network; wherein the topological structure comprises a bus table; traversing the bus table according to a pre-constructed three-dimensional label column vector to obtain an initial observable island set; performing optimization adjustment on the initial observable island set by using predetermined observability analysis complexity and observability analysis imbalance factors to obtain an optimized and adjusted observable island set; and carrying out observability analysis on the power distribution network based on the optimized and adjusted observable island set. According to the method, the observability analysis speed of the power distribution network can be remarkably improved under the condition that complete topological information is lost.

Description

Technical Field

[0001] This application relates to the field of power system maintenance, specifically a method and apparatus for observability analysis of distribution networks. Background Technology

[0002] In the context of new power system construction, the transparency and digital sensing of low-voltage distribution substations have become a crucial foundation for the coordinated operation of the power generation, grid, and load. With the continuous integration of distributed photovoltaic systems, electric vehicles, and energy storage devices, user-side behavior is becoming increasingly complex, characterized by a large number of nodes, sparse and uneven branch distribution, and strong asymmetry and distributed network features. These factors significantly increase the modeling difficulty and computational burden of system-level observability analysis.

[0003] Unlike the synchronous measurement systems in transmission networks that rely on synchronous phasor measurement units (PMUs), low-voltage distribution areas are limited by cost, space, and communication conditions, making it impossible to deploy high-precision synchronous devices. Instead, they primarily rely on asynchronous, low-precision devices such as smart meters and low-voltage acquisition terminals to acquire terminal data. These measurements have significant limitations in terms of accuracy, coverage, and real-time performance, making it difficult to directly support topology identification and state analysis of complex networks. Furthermore, low-voltage distribution areas have irregular structures, frequent topology changes, and diverse branching levels, making traditional observability analysis methods developed for transmission networks poorly adaptable to these scenarios. Especially when facing scenarios with a large number of nodes, uneven measurement configurations, and frequent topology changes, they are prone to algorithmic computational bottlenecks. Particularly in ultra-large-scale distribution networks, the matrix dimension expands dramatically, leading to severe analysis time consumption and even memory overflow.

[0004] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section. Summary of the Invention

[0005] To address the problems in the prior art, this application provides a method and apparatus for distribution network observability analysis, which can significantly improve the speed of distribution network observability analysis even when complete topology information is missing.

[0006] To solve the above-mentioned technical problems, this application provides the following technical solution:

[0007] Firstly, this application provides a method for observability analysis of a power distribution network, including:

[0008] The topology of the distribution network is constructed based on the operating electrical parameters of the nodes; wherein, the topology includes a bus table;

[0009] The initial set of observable islands is obtained by traversing the busbar table based on the pre-constructed three-dimensional label column vector;

[0010] The initial set of observable islands is optimized and adjusted using a predetermined observability analysis complexity and observability analysis imbalance factor to obtain an optimized set of observable islands.

[0011] The observability analysis of the distribution network is performed based on the optimized and adjusted set of observable islands.

[0012] Furthermore, the node operating electrical parameters include node power time series; the topology also includes a grid adjacency matrix; the construction of the distribution network topology based on the node operating electrical parameters of the distribution network includes:

[0013] Discrete wavelet decomposition is performed on the acquired node power time series to obtain the node power feature series;

[0014] The relationships between nodes in the distribution network are determined based on the node power characteristic sequence.

[0015] The power grid adjacency matrix and the bus table are constructed based on the relationships between the nodes.

[0016] Further, the step of traversing the busbar table according to the pre-constructed three-dimensional label column vector to obtain the initial observable island set includes:

[0017] The three-dimensional label column vector is constructed based on the power grid adjacency matrix; wherein, the three-dimensional label column vector includes nodes and corresponding initial observable islands;

[0018] The three-dimensional label column vector, the preset node priority, and the bus table are input into the depth-first search model to obtain the initial set of observable islands.

[0019] Furthermore, the step of determining the observability analysis complexity includes:

[0020] The first sum number is determined based on the number of observable islands and the first fitting coefficient;

[0021] The second sum is determined based on the number of observable islands measured and the second fitting coefficient;

[0022] The third sum is determined based on the sparsity of the observable islands and the third fitting coefficient;

[0023] The fourth sum number is determined based on the map diameter of the observable island and the fourth fitting coefficient;

[0024] The first sum, the second sum, the third sum, and the fourth sum are determined as the observability analysis complexity.

[0025] Further, the step of determining the imbalance factor in the observability analysis includes:

[0026] Determine the maximum value of the observability analysis complexity;

[0027] The observability analysis imbalance factor is determined based on the number of observable islands and the maximum value.

[0028] Furthermore, the optimization and adjustment of the initial observable island set using a predetermined observability analysis complexity and observability analysis imbalance factor to obtain an optimized observable island set includes:

[0029] Based on the observability analysis complexity and the observability analysis imbalance factor, generate the objective function and constraints;

[0030] Under the constraints, the objective function is locally optimized to obtain the observable island adjustment strategy;

[0031] The initial set of observable islands is optimized and adjusted according to the observable island adjustment strategy to obtain the observable island set.

[0032] Secondly, this application provides a power distribution network observability analysis device, comprising:

[0033] A topology construction unit is used to construct the topology of the distribution network based on the operating electrical parameters of the nodes of the distribution network; wherein, the topology includes a bus table;

[0034] An observable island determination unit is used to traverse the busbar table according to a pre-constructed three-dimensional label column vector to obtain an initial set of observable islands;

[0035] An observable island optimization unit is used to optimize and adjust the initial observable island set using a predetermined observability analysis complexity and observability analysis imbalance factor, so as to obtain an optimized observable island set.

[0036] An observability analysis unit is used to perform observability analysis on the distribution network based on the optimized and adjusted set of observable islands.

[0037] Furthermore, the node operating electrical parameters include node power time series; the topology also includes a grid adjacency matrix; the topology construction unit includes:

[0038] The feature sequence generation module is used to perform discrete wavelet decomposition on the acquired node power time series to obtain the node power feature sequence;

[0039] A node relationship determination module is used to determine the relationship between nodes in the distribution network based on the node power characteristic sequence.

[0040] The matrix bus generation module is used to construct the power grid adjacency matrix and the bus table based on the relationships between the nodes.

[0041] Furthermore, the observable island determination unit includes:

[0042] A label vector construction module is used to construct the three-dimensional label column vector based on the power grid adjacency matrix; wherein the three-dimensional label column vector includes nodes and corresponding initial observable islands;

[0043] The observation set generation module is used to input the three-dimensional label column vector, the preset node priority, and the bus table into the depth-first search model to obtain the initial observable island set.

[0044] Furthermore, the observable island optimization unit includes:

[0045] The first sum determination module is used to determine the first sum based on the number of busbars of the observable islands and the first fitting coefficient;

[0046] The second sum determination module is used to determine the second sum based on the number of measurements of the observable islands and the second fitting coefficient;

[0047] The third sum determination module is used to determine the third sum based on the sparsity of the observable islands and the third fitting coefficient.

[0048] The fourth sum determination module is used to determine the fourth sum based on the map diameter of the observable island and the fourth fitting coefficient;

[0049] A complexity analysis module is used to determine the first sum, the second sum, the third sum, and the fourth sum as the observability analysis complexity.

[0050] Furthermore, the observable island optimization unit includes:

[0051] The maximum complexity determination module is used to determine the maximum value of the observability analysis complexity;

[0052] The unevenness factor determination module is used to determine the unevenness factor of the observability analysis based on the number of observable islands and the maximum value.

[0053] Furthermore, the observable island optimization unit includes:

[0054] The function constraint determination module is used to generate the objective function and constraints based on the observability analysis complexity and the observability analysis imbalance factor.

[0055] The adjustment strategy generation module is used to perform local optimization on the objective function under the constraints to obtain an observable island adjustment strategy;

[0056] The observation set optimization module is used to optimize and adjust the initial observable island set according to the observable island adjustment strategy to obtain the observable island set.

[0057] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the power distribution network observability analysis method.

[0058] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the power distribution network observability analysis method.

[0059] Fifthly, this application provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the power distribution network observability analysis method.

[0060] To address the problems in existing technologies, the distribution network observability analysis method and apparatus provided in this application can transform raw time-series data into structure-aware vectors using wavelet transform and characteristic power extraction. This process compresses data redundancy while preserving important information, and then measures the connection strength between nodes through complexity-invariant distance, adapting to the actual characteristics of incomplete measurements and rapid frequency changes in real distribution networks. Furthermore, an improved depth-first search strategy is introduced during the construction of observable islands, overcoming the stack overflow problem in traditional large-scale graph structures. Finally, by introducing an objective function optimization model and a hybrid genetic local search algorithm, the initial observable islands are aggregated and reconstructed, further reducing the computational complexity of numerical observability analysis. In summary, this method responds to the urgent need for efficient observability assessment methods in new power systems, possesses good theoretical innovation, engineering adaptability, and industrial applicability, and is of great significance for improving the sensing capabilities of distribution networks and supporting state estimation and dispatching decisions. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a flowchart of the distribution network observability analysis method in the embodiments of this application;

[0063] Figure 2 This is a flowchart illustrating the construction of the power distribution network topology in the embodiments of this application;

[0064] Figure 3 This is a flowchart illustrating the process of obtaining the initial observable island set in this application embodiment;

[0065] Figure 4 This is a flowchart illustrating the determination of observability analysis complexity in the embodiments of this application;

[0066] Figure 5 This is a flowchart illustrating the determination of the imbalance factor in observability analysis in an embodiment of this application;

[0067] Figure 6 This is a flowchart of the optimized and adjusted set of observable islands obtained in the embodiments of this application;

[0068] Figure 7 This is a structural diagram of the power distribution network observability analysis device in the embodiments of this application;

[0069] Figure 8 This is a structural diagram of the topology building unit in the embodiments of this application;

[0070] Figure 9 This is a structural diagram of the observable island determination unit in the embodiments of this application;

[0071] Figure 10 This is one of the structural diagrams of the observable island optimization unit in the embodiments of this application;

[0072] Figure 11 This is the second structural diagram of the observable island optimization unit in the embodiments of this application;

[0073] Figure 12 This is the third structural diagram of the observable island optimization unit in the embodiments of this application;

[0074] Figure 13 This is a schematic diagram of the structure of the electronic device in the embodiments of this application;

[0075] Figure 14 This is the overall flowchart of the embodiments of this application;

[0076] Figure 15 This is a schematic diagram illustrating the working principle and key points of each step in the embodiments of this application;

[0077] Figure 16 This is an example diagram illustrating the construction and merging process of observable islands in an embodiment of this application. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0079] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties. The collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data all comply with the relevant laws, regulations and standards of the relevant countries and regions, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation portals are provided for users to choose to authorize or refuse.

[0080] Provide users with corresponding operation entry points, allowing them to choose to agree to or reject the automated decision results; if the user chooses to reject, the process will proceed to the expert decision-making process.

[0081] In one embodiment, see Figure 1 To significantly improve the speed of distribution network observability analysis when complete topology information is missing, this application provides a distribution network observability analysis method, including:

[0082] S101: Construct the topology of the distribution network based on the operating electrical parameters of the nodes; wherein, the topology includes a bus table;

[0083] S102: Traverse the busbar table according to the pre-constructed three-dimensional label column vector to obtain the initial set of observable islands;

[0084] S103: The initial set of observable islands is optimized and adjusted using a predetermined observability analysis complexity and observability analysis imbalance factor to obtain an optimized set of observable islands.

[0085] S104: Perform observability analysis on the distribution network based on the optimized and adjusted set of observable islands.

[0086] Understandably, this application proposes an accelerated observability analysis method for large-scale distribution networks, innovatively integrating node power time-series feature mining, complexity-invariant distance calculation, an improved depth-first search mapping strategy, and an observable island structured merging optimization mechanism. This method not only rapidly reconstructs the network structure even in the absence of complete topology information, but also obtains observability region partitioning in a low-complexity and high-efficiency manner. Furthermore, by introducing computational complexity indicators and load balancing factors, it rationally schedules analysis resources, significantly improving the overall analysis speed.

[0087] Specifically, this method employs wavelet transform and feature power extraction to convert the original time-series data into structure-aware vectors. This preserves important information while compressing data redundancy. Furthermore, it measures the connection strength between nodes using complexity-invariant distance, adapting to the real-world characteristics of distribution networks, such as incomplete measurements and rapid frequency changes. Simultaneously, an improved depth-first search strategy is introduced during the construction of observable islands, overcoming the stack overflow problem of traditional Dynamic Frequency Selection (DFS) in large-scale graph structures. Finally, by introducing an objective function optimization model and a hybrid genetic-local search algorithm, the initial observable islands are aggregated and reconstructed, further reducing the computational complexity of numerical observability analysis.

[0088] The optimization method for improving the speed of observability analysis in large-scale distribution networks provided in this application can be found in [reference needed]. Figure 14 ,include:

[0089] 1) Step 1: Based on the electricity consumption data of different nodes, obtain the topology of the large-scale power distribution network through feature extraction algorithm.

[0090] 2) Step two: Construct the initial observable island by searching the measurement spanning tree in the network using an improved depth-first search algorithm.

[0091] 3) Step three: Propose the numerical observability analysis complexity index C for observable islands. i And observability analysis of the imbalance factor LIF.

[0092] 4) Step four: merge different observable islands to reduce the computational load of the overall distribution network observability analysis.

[0093] As described above, the distribution network observability analysis method provided in this application can transform the original time-series data into structure-aware vectors using wavelet transform and characteristic power extraction. This process compresses data redundancy while preserving important information, and then measures the connection strength between nodes through complexity-invariant distance, adapting to the actual characteristics of incomplete measurements and rapid frequency changes in real distribution networks. Furthermore, an improved depth-first search strategy is introduced during the construction of observable islands, overcoming the stack overflow problem in traditional large-scale graph structures. Finally, by introducing an objective function optimization model and a hybrid genetic local search algorithm, the initial observable islands are aggregated and reconstructed, further reducing the computational complexity of numerical observability analysis. In summary, this method responds to the urgent need for efficient observability assessment methods in new power systems, possesses good theoretical innovation, engineering adaptability, and industrial applicability, and is of great significance for improving the sensing capabilities of distribution networks and supporting state estimation and dispatching decisions.

[0094] In one embodiment, see Figure 2The node operating electrical parameters include node power time series; the topology also includes a grid adjacency matrix; constructing the distribution network topology based on the node operating electrical parameters includes:

[0095] S201: Perform discrete wavelet decomposition on the acquired node power time series to obtain the node power feature series;

[0096] S202: Determine the relationship between nodes in the distribution network based on the node power characteristic sequence;

[0097] S203: Construct the power grid adjacency matrix and the bus table based on the relationships between the nodes.

[0098] It is understood that the embodiments of this application are based on electricity consumption data x from different nodes. u (t) generates the topology of a large-scale power distribution network through a feature extraction algorithm and models it as an unweighted directed graph, represented by an adjacency matrix T and a bus set B. The specific method is as follows:

[0099] For a power distribution network with n nodes, the time series of electricity consumption of the u-th node is x. u (t), for x u (t) Performing a first-order forward difference yields the power time series P of the u-th node. u (t):

[0100]

[0101] For the power time series P u (t) Perform discrete wavelet decomposition with a level of 3, and express the decomposition result as P. AD :

[0102]

[0103] Among them, A3, D1, D2, and D3 are all row vectors, and their elements are wavelet coefficients obtained after wavelet decomposition; A3 is the low-frequency component, reflecting the overall trend of power consumption; D1, D2, and D3 are the high-frequency components, reflecting the detailed characteristics of power consumption.

[0104] Since the low-frequency component A3 only reflects the basic trend of the power sequence, all wavelet coefficients in A3 are set to 0 to obtain A0; wavelet coefficients greater than λ in D1, D2, and D3 are retained, and the remaining coefficients are all set to 0. This completes the analysis of the power P... AD Feature extraction yields the feature power sequence P. T , means as follows:

[0105]

[0106] Among them, A 3T D 1T D 2T D 3T All are row vectors.

[0107] Given that the characteristic power sequences of two nodes u and v are P Tu and P Tv Then the complexity-invariant distance between these two sequences is:

[0108]

[0109] Among them, P Tu (k) and P Tv (k) represents the k-th element in the sequence.

[0110] It is evident that the greater the complexity-invariant distance between two nodes, the greater the likelihood of the two nodes being connected. For any node F1, its power value at any time t will not be greater than the power value of its parent node F2 at the corresponding time, which can be expressed as:

[0111]

[0112] If node u is identified as the parent node of node v (i.e., the energy consumed by node v comes from node u), this relationship is recorded in the adjacency matrix T and the bus list B.

[0113]

[0114] Where T(u,v) represents the element located in the u-th row and v-th column of the neighbor matrix T; each element (u,v) in set B represents the parent node of node u as v, and there is a parent line between the two.

[0115] As can be seen from the above description, the distribution network observability analysis method provided in this application can construct the topology of the distribution network based on the operating electrical parameters of the nodes.

[0116] In one embodiment, see Figure 3 The step of traversing the busbar table according to the pre-constructed three-dimensional label column vector to obtain the initial observable island set includes:

[0117] S301: Construct the three-dimensional label column vector based on the power grid adjacency matrix; wherein, the three-dimensional label column vector includes nodes and corresponding initial observable islands;

[0118] S302: Input the three-dimensional label column vector, the preset node priority, and the bus table into the depth-first search model to obtain the initial observable island set.

[0119] Understandably, by using an improved depth-first search algorithm, an initial observable island is constructed by searching for a measurement spanning tree in the network and stored in a three-dimensional label column vector ξ. The specific algorithm is as follows:

[0120] 1) All buses are initialized to an unvisited state, the three-dimensional label column vector ξ is initialized to a zero vector, and the currently observable island code i is initialized to 1;

[0121]

[0122] 2) Starting from the unvisited initial bus s, mark it as visited and set it as the current bus t. Add bus t to the set of observed buses and add bus t to the currently observable island i.

[0123]

[0124] Where u and v are the parent and child nodes on both sides of the bus t, respectively.

[0125] 3) If the current bus is the initial bus s, then the search for the current observable island i is complete, and a new unvisited initial bus s is searched; if all buses are marked as visited, the search algorithm is terminated; otherwise, a new initial observable island is generated and the process is switched to (4).

[0126] The operation to generate a new initial observable island is as follows:

[0127]

[0128] 4) Propose a priority index ρ(r): Traverse the buses connected to the current bus t, update the bus r with the highest priority ρ(r) to the current bus t, and add bus r and node u in the current observable island i, where u is the connection node between the two buses.

[0129]

[0130] Where node v is the node on the other side of node u where the bus r is located; the calculation process of ρ(r) is as follows:

[0131]

[0132] Where v is the node of the bus r on the other side of u; the function d + (v) represents the out-degree of node v, which can be solved based on the adjacency matrix T. The specific solution process is described in existing techniques and will not be repeated here. Using a priority index maximizes the information coverage of the initially observable islands generated, reducing the complexity of subsequent merging strategies.

[0133] 5) Return to the previous bus and jump to (3).

[0134] As can be seen from the above description, the distribution network observability analysis method provided in this application can traverse the bus table according to the pre-constructed three-dimensional label column vector to obtain the initial observable island set.

[0135] In one embodiment, see Figure 4 The steps for determining the observability analysis complexity include:

[0136] S401: Determine the first sum number based on the number of observable islands and the first fitting coefficient;

[0137] S402: Determine the second sum number based on the number of observable islands and the second fitting coefficient;

[0138] S403: Determine the third sum number based on the sparsity of the observable islands and the third fitting coefficient;

[0139] S404: Determine the fourth sum number based on the map diameter of the observable island and the fourth fitting coefficient;

[0140] S405: The first sum, the second sum, the third sum, and the fourth sum are determined as the observability analysis complexity.

[0141] Understandably, the complexity index C of the numerical observability analysis of observable islands is... i The calculation process is as follows:

[0142]

[0143] Among them, C i The numerical observability analysis complexity index of observable island i, n i Let m be the number of busbars for the i-th observable island. i Let s be the number of measurements taken for the i-th observable island. i Let α be the sparsity of the i-th observed island, and Topo(i) be the graph diameter of the i-th observable island; α, β, γ, δ are empirical fitting coefficients.

[0144] sparsity s i Defined by the following formula:

[0145]

[0146] The graph diameter Topo(i) of the i-th observable island is defined by the following formula:

[0147]

[0148] In this context, nodes u and v both belong to observable island i, and dis(u,v) represents the shortest path between nodes u and v. Algorithms suitable for finding the shortest path in directed unweighted graphs, such as Dijkstra's algorithm or Bellman-Ford's algorithm, can be used, which will not be elaborated here.

[0149] The edge weights are calculated as follows:

[0150] If nodes F1 and F2 are adjacent nodes, then the weight of the edge connecting them is:

[0151]

[0152] Where W(F1,F2) is the edge weight; T(F1,F2) is the element in the aforementioned adjacency matrix T corresponding to node F1 and node F2.

[0153] As can be seen from the above description, the distribution network observability analysis method provided in this application can determine the observability analysis complexity.

[0154] In one embodiment, see Figure 5 The step of determining the imbalance factor in the observability analysis includes:

[0155] S501: Determine the maximum value of the observability analysis complexity;

[0156] S502: Determine the observability analysis imbalance factor based on the number of observable islands and the maximum value.

[0157] Understandably, if a network contains L observable islands, then the observability analysis imbalance factor (LIF) of that network is:

[0158]

[0159] Among them, C i This represents the observability analysis complexity of the i-th observable island in the network.

[0160] As can be seen from the above description, the distribution network observability analysis method provided in this application can determine the unbalance factors in observability analysis.

[0161] In one embodiment, see Figure 6 The optimization and adjustment of the initial observable island set using a predetermined observability analysis complexity and observability analysis imbalance factor to obtain an optimized observable island set includes:

[0162] S601: Generate the objective function and constraints based on the observability analysis complexity and the observability analysis imbalance factor;

[0163] S602: Under the constraints, perform local optimization on the objective function to obtain the observable island adjustment strategy;

[0164] S603: Optimize and adjust the initial set of observable islands according to the observable island adjustment strategy to obtain the observable island set.

[0165] Understandably, merging different observable islands reduces the computational burden of observability analysis of the overall distribution network.

[0166] The merging of different observable islands should follow the following principle: if L observable islands are connected by the same network, and at least L-1 of the islands have injected measurements on their boundary busbars, then these L observable islands can be merged.

[0167] Considering that the total time for parallel computation of observable islands to analyze observability depends on the time required for the analysis of the most complex observable island, and that a more balanced complexity among the analyses of each observable island can fully utilize idle computing resources and improve the overall computation speed, a method is proposed that comprehensively considers the numerical analysis complexity C of the merged observable islands. j The objective function of the imbalance factor LIF in the maximum value and network observability analysis should be minimized by the merging operation.

[0168] Using L-dimensional decision variables z to determine how the L initial observable islands in the original network should be merged into L' observable islands, this decision problem is transformed into a combinatorial optimization problem:

[0169]

[0170]

[0171] Where z is an L-dimensional decision variable, and each element in z is an integer between 1 and L'. i The numerical value of the element represents the ith observable island before the merge being assigned to the zth island after the merge. i There are three observable islands; a and b are weighting coefficients, respectively. Empirically, setting a between 0.1 and 0.5 for a and between 0.6 and 0.8 for b yields better optimization results. C j To determine the complexity of the j-th observable island after merging decision variables z, n j m is the number of generatrices for the observable island. j The number of measurements for the observable island: n γ,(1) ,n γ,(2) , n γ,(3) ...represent the number of parent lines of several initial observable islands merged into observable island j, m γ,(1), m γ,(2) m γ,(3)……These represent the number of measurements from several initial observable islands merged into observable island j; It represents the set of positive integers.

[0172] The genetic algorithm is improved using a local search method to solve for the minimum value of the objective function J. The crossover operation of the genetic algorithm is as follows: From two parent generations z... (a) and z (b) New solutions are generated from individuals, inheriting local best structures while ensuring the diversity of the group.

[0173]

[0174] Where, r i Let be a random number at the i-th position in the decision variables, which determines which parent the current position inherits the gene from, and follows a 0-1 uniform distribution.

[0175]

[0176] Where U(0,1) represents a uniform distribution of 0-1.

[0177] Local search optimization is updated as follows: the optimal neighbor is found in the neighborhood of the current solution z to fine-tune the merging structure and improve the convergence quality.

[0178]

[0179] Here, z' represents the element in the neighborhood N(z) of the current solution z.

[0180] Based on the optimal decision variables obtained from solving the combinatorial optimization problem, the original L initial observable islands can be reconstructed into L' new observable islands. Subsequently, numerical observability analysis is performed on each merged observable island. This method can improve the speed of observability analysis for large-scale distribution networks. Since related numerical observability analyses, such as the Jacobian matrix rank determination method, are relatively mature, they will not be elaborated upon in this paper.

[0181] As can be seen from the above description, the distribution network observability analysis method provided in this application can optimize and adjust the initial observable island set by using a predetermined observability analysis complexity and observability analysis imbalance factor, so as to obtain an optimized observable island set.

[0182] See Figure 15 and Figure 16 Using the IEEE 33-node system as an example, the process of constructing and merging observable islands is demonstrated. The green dashed line represents the initial observable island division generated based on the improved algorithm, while the red solid line represents the observable island structure merged according to the optimization results.

[0183] Table 1 shows a comparison of the observability analysis speed in different distribution network cases before and after applying the method proposed in this paper.

[0184] Table 1

[0185]

[0186] In summary, this invention proposes a method to improve the speed of observability analysis in large-scale distribution networks. By integrating data-driven topology identification, graph theory analysis, and combinatorial optimization algorithms, it effectively alleviates the problems of heavy computational burden and poor real-time performance of traditional observability analysis methods when dealing with large-scale complex distribution networks. First, addressing the characteristics of sparse measurement information and frequent dynamic changes in the structure of distribution networks, a feature power extraction method based on wavelet decomposition and complexity-invariant distance is introduced to accurately extract the implicit connectivity relationships in the electricity consumption behavior of different nodes, constructing an approximate topology and laying the foundation for observability analysis. Second, an improved depth-first search algorithm is proposed, avoiding the stack overflow and low traversal efficiency problems faced by traditional DFS when processing large-scale network graphs, achieving efficient partitioning of initial observable islands. Furthermore, an observability analysis complexity index and imbalance factor are innovatively designed to quantitatively evaluate the computational load and structural complexity of each observable region, providing a theoretical basis for subsequent parallel analysis and merge optimization. Finally, during the optimization process, a merged optimization model was constructed with the goal of reducing overall computational complexity and improving parallel efficiency. An improved genetic algorithm with integrated local search mechanism was introduced, which significantly accelerated the convergence speed while ensuring solution accuracy. By flexibly setting observable island partitioning and merging strategies, the adaptability of the algorithm in ultra-large-scale distribution networks was improved, and reasonable scheduling and load balancing of computing resources were achieved.

[0187] The method proposed in this invention outperforms existing mainstream methods in terms of observability analysis speed, resource utilization efficiency, and algorithm stability. While ensuring analysis accuracy, it significantly reduces the overall computation time, with its advantages being particularly pronounced in network structures with hundreds or thousands of nodes. By comparing performance indicators under different observable island configurations, the method's good engineering practicality in sensing region partitioning, topology evolution adaptation, and parallel deployment feasibility is verified. In summary, the method proposed in this invention for improving the speed of observability analysis in large-scale distribution networks is innovative in theoretical modeling, algorithm design, and engineering implementation. It significantly improves the efficiency and robustness of distribution network state perception, providing important technical support for real-time perception and efficient dispatching of new power systems, and has good practical application value and promising prospects for promotion.

[0188] Based on the same inventive concept, this application also provides a distribution network observability analysis device, which can be used to implement the method described in the above embodiments, as described in the following embodiments. Since the principle of the distribution network observability analysis device in solving the problem is similar to that of the distribution network observability analysis method, the implementation of the distribution network observability analysis device can refer to the implementation of the software performance benchmark determination method, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the system described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0189] In one embodiment, see Figure 7 To significantly improve the speed of distribution network observability analysis when complete topology information is missing, this application provides a distribution network observability analysis device, comprising:

[0190] The topology construction unit 701 is used to construct the topology of the distribution network based on the node operating electrical parameters of the distribution network; wherein, the topology includes a bus table;

[0191] The observable island determination unit 702 is used to traverse the bus table according to the pre-constructed three-dimensional label column vector to obtain an initial observable island set;

[0192] The observable island optimization unit 703 is used to optimize and adjust the initial observable island set using a predetermined observability analysis complexity and observability analysis imbalance factor, so as to obtain an optimized observable island set.

[0193] The observability analysis unit 704 is used to perform observability analysis on the distribution network based on the optimized and adjusted set of observable islands.

[0194] In one embodiment, see Figure 8 The node operating electrical parameters include node power time series; the topology also includes a grid adjacency matrix; the topology construction unit 701 includes:

[0195] The feature sequence generation module 801 is used to perform discrete wavelet decomposition on the acquired node power time series to obtain the node power feature sequence;

[0196] The node relationship determination module 802 is used to determine the relationship between nodes in the distribution network based on the node power characteristic sequence.

[0197] The matrix bus generation module 803 is used to construct the power grid adjacency matrix and the bus table based on the relationship between each node.

[0198] In one embodiment, see Figure 9 The observable island determination unit 702 includes: a label vector construction module 901 and an observation set generation module 902.

[0199] The label vector construction module 901 is used to construct the three-dimensional label column vector based on the power grid adjacency matrix; wherein the three-dimensional label column vector includes nodes and corresponding initial observable islands;

[0200] The observation set generation module 902 is used to input the three-dimensional label column vector, the preset node priority and the bus table into the depth-first search model to obtain the initial observable island set.

[0201] In one embodiment, see Figure 10 The observable island optimization unit 703 includes:

[0202] The first sum determination module 1001 is used to determine the first sum based on the number of busbars of the observable islands and the first fitting coefficients;

[0203] The second sum determination module 1002 is used to determine the second sum based on the number of measurements of the observable islands and the second fitting coefficient;

[0204] The third sum determination module 1003 is used to determine the third sum based on the sparsity of the observable islands and the third fitting coefficient.

[0205] The fourth sum determination module 1004 is used to determine the fourth sum based on the map diameter of the observable island and the fourth fitting coefficient;

[0206] The complexity analysis module 1005 is used to determine the first sum, the second sum, the third sum, and the fourth sum as the observability analysis complexity.

[0207] In one embodiment, see Figure 11 The observable island optimization unit 703 includes:

[0208] The maximum complexity determination module 1101 is used to determine the maximum value of the observability analysis complexity;

[0209] The unevenness factor determination module 1102 is used to determine the observability analysis unevenness factor based on the number of observable islands and the maximum value.

[0210] In one embodiment, see Figure 12 The observable island optimization unit 703 includes:

[0211] The function constraint determination module 1201 is used to generate an objective function and constraint conditions based on the observability analysis complexity and the observability analysis imbalance factor.

[0212] The adjustment strategy generation module 1202 is used to perform local optimization on the objective function under the constraints to obtain an observable island adjustment strategy;

[0213] The observation set optimization module 1203 is used to optimize and adjust the initial observable island set according to the observable island adjustment strategy to obtain the observable island set.

[0214] From a hardware perspective, in order to significantly improve the speed of distribution network observability analysis when complete topology information is missing, this application provides an embodiment of an electronic device for implementing all or part of the aforementioned distribution network observability analysis method. The electronic device specifically includes the following components:

[0215] The system comprises a processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to realize information transmission between the distribution network observability analysis device and core business systems, user terminals, and related databases and other related equipment; the logic controller can be a desktop computer, tablet computer, or mobile terminal, etc., and this embodiment is not limited to these. In this embodiment, the logic controller can be implemented with reference to the embodiments of the distribution network observability analysis method and the distribution network observability analysis device in the embodiments, the content of which is incorporated herein, and repeated details will not be described again.

[0216] It is understood that the user terminal may include smartphones, tablet computers, network set-top boxes, portable computers, desktop computers, personal digital assistants (PDAs), in-vehicle devices, smart wearable devices, etc. Among these, the smart wearable devices may include smart glasses, smartwatches, smart bracelets, etc.

[0217] In practical applications, some aspects of the distribution network observability analysis method can be executed on the electronic equipment side as described above, or all operations can be completed in the client device. The choice can be made based on the processing power of the client device and the limitations of the user's usage scenario. This application does not impose any limitations on this. If all operations are completed in the client device, the client device may further include a processor.

[0218] The aforementioned client device may have a communication module (i.e., a communication unit) that can communicate with a remote server to achieve data transmission. The server may include a server on the task scheduling center side; in other implementation scenarios, it may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a distributed server structure.

[0219] Figure 13 This is a schematic block diagram illustrating the system configuration of the electronic device 9600 according to an embodiment of this application. Figure 13 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that... Figure 13 This is an example; other types of structures can also be used to supplement or replace this structure to achieve telecommunications functions or other functions.

[0220] In one embodiment, the distribution network observability analysis method functionality can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control:

[0221] S101: Construct the topology of the distribution network based on the operating electrical parameters of the nodes; wherein, the topology includes a bus table;

[0222] S102: Traverse the busbar table according to the pre-constructed three-dimensional label column vector to obtain the initial set of observable islands;

[0223] S103: The initial set of observable islands is optimized and adjusted using a predetermined observability analysis complexity and observability analysis imbalance factor to obtain an optimized set of observable islands.

[0224] S104: Perform observability analysis on the distribution network based on the optimized and adjusted set of observable islands.

[0225] As described above, the distribution network observability analysis method provided in this application can transform the original time-series data into structure-aware vectors using wavelet transform and characteristic power extraction. This process compresses data redundancy while preserving important information, and then measures the connection strength between nodes through complexity-invariant distance, adapting to the actual characteristics of incomplete measurements and rapid frequency changes in real distribution networks. Furthermore, an improved depth-first search strategy is introduced during the construction of observable islands, overcoming the stack overflow problem in traditional large-scale graph structures. Finally, by introducing an objective function optimization model and a hybrid genetic local search algorithm, the initial observable islands are aggregated and reconstructed, further reducing the computational complexity of numerical observability analysis. In summary, this method responds to the urgent need for efficient observability assessment methods in new power systems, possesses good theoretical innovation, engineering adaptability, and industrial applicability, and is of great significance for improving the sensing capabilities of distribution networks and supporting state estimation and dispatching decisions.

[0226] In another embodiment, the distribution network observability analysis device can be configured separately from the central processing unit 9100. For example, the data composite transmission device distribution network observability analysis device can be configured as a chip connected to the central processing unit 9100, and the function of the distribution network observability analysis method can be realized through the control of the central processing unit.

[0227] like Figure 13 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily need to include these components. Figure 13 All components shown; in addition, the electronic device 9600 may also include Figure 13 For components not shown, please refer to existing technologies.

[0228] like Figure 13 As shown, the central processing unit 9100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device, which receives inputs and controls the operation of various components of the electronic device 9600.

[0229] The memory 9140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 9100 may execute the program stored in the memory 9140 to perform information storage or processing, etc.

[0230] Input unit 9120 provides input to central processing unit 9100. Input unit 9120 may be, for example, a keypad or touch input device. Power supply 9170 provides power to electronic device 9600. Display 9160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0231] The memory 9140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 9140 can also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 via the central processing unit 9100.

[0232] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0233] The communication module 9110 is a transmitter / receiver that sends and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processing unit 9100 to provide input signals and receive output signals, which is the same as in a conventional mobile communication terminal.

[0234] Based on different communication technologies, multiple communication modules 9110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby realizing typical telecommunications functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 9130 is also coupled to a central processing unit 9100, enabling on-device recording via the microphone 9132 and on-device playback of stored sound via the speaker 9131.

[0235] Embodiments of this application also provide a computer-readable storage medium capable of implementing all steps of the distribution network observability analysis method with a server or client execution subject in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the distribution network observability analysis method with a server or client execution subject in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0236] S101: Construct the topology of the distribution network based on the operating electrical parameters of the nodes; wherein, the topology includes a bus table;

[0237] S102: Traverse the busbar table according to the pre-constructed three-dimensional label column vector to obtain the initial set of observable islands;

[0238] S103: The initial set of observable islands is optimized and adjusted using a predetermined observability analysis complexity and observability analysis imbalance factor to obtain an optimized set of observable islands.

[0239] S104: Perform observability analysis on the distribution network based on the optimized and adjusted set of observable islands.

[0240] As described above, the distribution network observability analysis method provided in this application can transform the original time-series data into structure-aware vectors using wavelet transform and characteristic power extraction. This process compresses data redundancy while preserving important information, and then measures the connection strength between nodes through complexity-invariant distance, adapting to the actual characteristics of incomplete measurements and rapid frequency changes in real distribution networks. Furthermore, an improved depth-first search strategy is introduced during the construction of observable islands, overcoming the stack overflow problem in traditional large-scale graph structures. Finally, by introducing an objective function optimization model and a hybrid genetic local search algorithm, the initial observable islands are aggregated and reconstructed, further reducing the computational complexity of numerical observability analysis. In summary, this method responds to the urgent need for efficient observability assessment methods in new power systems, possesses good theoretical innovation, engineering adaptability, and industrial applicability, and is of great significance for improving the sensing capabilities of distribution networks and supporting state estimation and dispatching decisions.

[0241] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0242] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0243] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0244] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0245] 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.

Claims

1. A method for observability analysis of a power distribution network, characterized in that, include: The topology of the distribution network is constructed based on the operating electrical parameters of the nodes; wherein, the topology includes a bus table; The initial set of observable islands is obtained by traversing the busbar table based on the pre-constructed three-dimensional label column vector; The initial set of observable islands is optimized and adjusted using a predetermined observability analysis complexity and observability analysis imbalance factor to obtain an optimized set of observable islands. The observability analysis of the distribution network is performed based on the optimized and adjusted set of observable islands.

2. The distribution network observability analysis method according to claim 1, characterized in that, The node operating electrical parameters include node power time series; the topology also includes a grid adjacency matrix; constructing the distribution network topology based on the node operating electrical parameters includes: Discrete wavelet decomposition is performed on the acquired node power time series to obtain the node power feature series; The relationships between nodes in the distribution network are determined based on the node power characteristic sequence. The power grid adjacency matrix and the bus table are constructed based on the relationships between the nodes.

3. The distribution network observability analysis method according to claim 2, characterized in that, The process of traversing the busbar table based on a pre-constructed 3D label column vector to obtain an initial set of observable islands includes: The three-dimensional label column vector is constructed based on the power grid adjacency matrix; wherein, the three-dimensional label column vector includes nodes and corresponding initial observable islands; The three-dimensional label column vector, the preset node priority, and the bus table are input into the depth-first search model to obtain the initial set of observable islands.

4. The distribution network observability analysis method according to claim 1, characterized in that, The steps for determining the observability analysis complexity include: The first sum number is determined based on the number of observable islands and the first fitting coefficient; The second sum is determined based on the number of observable islands measured and the second fitting coefficient; The third sum is determined based on the sparsity of the observable islands and the third fitting coefficient; The fourth sum number is determined based on the map diameter of the observable island and the fourth fitting coefficient; The first sum, the second sum, the third sum, and the fourth sum are determined as the observability analysis complexity.

5. The distribution network observability analysis method according to claim 4, characterized in that, The steps for determining the disequilibrium factor in the observability analysis include: Determine the maximum value of the observability analysis complexity; The observability analysis imbalance factor is determined based on the number of observable islands and the maximum value.

6. The distribution network observability analysis method according to claim 1, characterized in that, The optimization and adjustment of the initial observable island set using a pre-determined observability analysis complexity and observability analysis imbalance factor to obtain an optimized observable island set includes: Based on the observability analysis complexity and the observability analysis imbalance factor, generate the objective function and constraints; Under the constraints, the objective function is locally optimized to obtain the observable island adjustment strategy; The initial set of observable islands is optimized and adjusted according to the observable island adjustment strategy to obtain the observable island set.

7. A power distribution network observability analysis device, characterized in that, include: A topology construction unit is used to construct the topology of the distribution network based on the operating electrical parameters of the nodes of the distribution network; wherein, the topology includes a bus table; An observable island determination unit is used to traverse the busbar table according to a pre-constructed three-dimensional label column vector to obtain an initial set of observable islands; An observable island optimization unit is used to optimize and adjust the initial observable island set using a predetermined observability analysis complexity and observability analysis imbalance factor, so as to obtain an optimized observable island set. An observability analysis unit is used to perform observability analysis on the distribution network based on the optimized and adjusted set of observable islands.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the distribution network observability analysis method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the distribution network observability analysis method according to any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the distribution network observability analysis method according to any one of claims 1 to 6.

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