Measurement terminal optimal configuration method and device based on observability evaluation of power distribution network

Abstract

The invention discloses a measurement terminal optimal configuration method and device based on observability evaluation of a power distribution network, and the method comprises the steps: obtaining a topological graph of the power distribution network, and dividing the topological graph to obtain a plurality of observable regions; based on the initial configuration schemes of the measurement terminals in the plurality of observable areas, respectively carrying out observability analysis; on the basis of an observability analysis result, by taking the minimization of the arrangement number and cost of the measurement terminals and the maximization of observability as targets, constructing a target optimization model and giving out constraint conditions; and solving the target optimization model based on an optimization algorithm, and giving an optimal configuration scheme of the measurement terminals in the plurality of observable areas. According to the invention, while the investment cost of the measurement terminal is reduced, the situation awareness capability and the operation reliability of the power distribution network are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distribution network optimization configuration, and in particular relates to a measurement terminal optimization configuration method and device based on distribution network observability evaluation. Background Art

[0002] As an important part of the power grid, the distribution network is directly connected to users and is crucial to the reliability of power supply. In recent years, with the access of renewable energy, distributed energy storage, electric vehicles, etc., the traditional one-way flow pattern of the distribution network has been broken, the network structure has become more complex, and the randomness of the output of distributed power sources has led to frequent changes in the operating status. The real-time monitoring and control of the distribution network faces greater challenges.

[0003] Observability assessment is the process of determining whether the system operating state can be uniquely identified by state estimation (SE). Once the distribution network is observable, the voltage amplitude and phase angle of all nodes can be inferred using existing measurement data, thereby supporting situational awareness, fault diagnosis, etc. Although various measurement terminals have been deployed, such as feeder terminal units (FTUs) and intelligent fusion terminals (IFTs), there are usually unobservable areas in the distribution network. The distribution network is large in area and has many branches. On the one hand, if the measurement terminals are not properly deployed, state estimation cannot be achieved and the network is unobservable. On the other hand, the solution of equipping all nodes with measurement terminals is economically infeasible. Therefore, it is crucial to evaluate observability based on existing measurement terminals. In the case of insufficient measurement, key measurement locations that have a significant impact on network observability should be identified. By deploying the minimum necessary measurement terminals, the operating state of the distribution network is observable and describable. In this way, operators can reduce the investment cost of measurement terminals while enhancing the situational awareness of the distribution network.

[0004] Numerical methods are often used in the research of distribution network observability assessment. For example, patent CN107017631B discloses a three-phase distribution network observability analysis method based on linear circuits, which belongs to the field of power system dispatching automation and power grid simulation technology. The present invention first completes the modeling of various measurement of the three-phase distribution network, and then obtains the unobservable branches in the three-phase system based on the rank analysis of the measurement Jacobian matrix, and finally obtains the system observable island according to the branch observability analysis results. This method can handle the line voltage measurement and power measurement of triangle connection, can handle the influence of distributed power sources on observability, and can handle the observability analysis of distribution transformer gears. However, this numerical method relies on a large number of floating-point operations, which usually leads to high computational costs and cannot search for key measurement locations accordingly. In addition, when the distribution network terminal is configured, the configuration of the measurement terminal is realized only by considering the configuration quantity and cost. The final configuration scheme is only applicable to the case where part of the distribution network is operating normally. In the case of failure of the distribution network or data anomalies, its observability is poor.

[0005] Therefore, how to provide a measurement terminal configuration method with low cost, high observability and good robustness is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0006] In view of the defects in the above-mentioned prior art, the present invention provides a method and device for optimizing the configuration of measurement terminals based on observability evaluation of distribution network, which can reduce the investment cost of measurement terminals while improving the situation awareness capability and operation reliability of the distribution network.

[0007] In a first aspect, the present invention provides a method for optimizing configuration of measurement terminals based on distribution network observability evaluation, comprising:

[0008] Obtain the distribution network topology map and divide it into multiple observable areas;

[0009] Based on the initial configuration schemes of measurement terminals in multiple observable areas, observability analysis is performed separately;

[0010] Based on the observability analysis results, a target optimization model is constructed and constraints are given with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability.

[0011] The target optimization model is solved based on the optimization algorithm, and the optimal configuration scheme of measurement terminals in multiple observable areas is given.

[0012] Furthermore, the distribution network topology map is obtained and divided into multiple observable areas, including:

[0013] Based on the actual network structure of the distribution network, determine all branches and nodes of the distribution network topology diagram;

[0014] Obtain the initial node connected to the main substation through the branch in the distribution network topology diagram;

[0015] The nodes connected in sequence from each initial node through branches are divided into an observable area.

[0016] Furthermore, based on the initial configuration schemes of the measurement terminals in multiple observable areas, observability analysis is performed separately, including:

[0017] Obtain nodes configured with measurement terminals in each observable area and determine observable nodes;

[0018] Determine observability based on the determined observable nodes and all nodes in the observable area;

[0019] Compare the observability with the indicator threshold and present the observable areas that do not meet the indicator threshold.

[0020] Further, the measurement terminal includes an FTU measurement terminal and an IFT measurement terminal;

[0021] Determine observable nodes, including:

[0022] According to the nodes configured with FTU measurement terminals in the observable area, the nodes are determined as observable nodes;

[0023] When there is at most one node that is not configured with a measurement terminal among the neighboring nodes of the node configured with an IFT measurement terminal, the node is determined as an observable node.

[0024] Furthermore, based on the observability analysis results, a target optimization model is constructed with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability, including:

[0025] For observable areas that do not reach the indicator threshold, determine all nodes and branches in each observable area;

[0026] Based on the determined nodes, a target optimization model is constructed with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability;

[0027] Based on the determined nodes and branches, the constraints of the target optimization model are given.

[0028] Furthermore, the target optimization model is solved based on the optimization algorithm, including: solving the target optimization model through the NBI algorithm combined with the discrete binary particle swarm optimization algorithm.

[0029] Furthermore, the target optimization model is solved by combining the NBI algorithm with the discrete binary particle swarm optimization algorithm, including:

[0030] Based on the target optimization model, an observability reduction model is given;

[0031] Combining the observability reduction model and the target optimization model, a single-target optimization model among multiple targets is constructed through the NBI algorithm;

[0032] The discrete binary particle swarm optimization algorithm is used to update the speed and position of particles in the single-objective optimization model, and the key nodes with the largest contribution to observability are given.

[0033] Furthermore, an observability reduction model is given and combined with the target optimization model, and a single-target optimization model among multiple targets is constructed through the NBI algorithm, including:

[0034] According to the target optimization model, determining a first objective function that minimizes the number and cost of measurement terminal arrangements and a second objective function that maximizes observability;

[0035] Based on the situation that measurement terminals with different node configurations have failures and / or predetermined measurement errors, determining a third objective function that minimizes the degree of observability degradation;

[0036] The first objective function, the second objective function and the third objective function are optimized by the NBI algorithm, and a single objective optimization model is given;

[0037] Among them, the single-objective optimization model satisfies the following relationship:

[0038]

[0039] f3(x)=-R(x)

[0040]

[0041] Where f1(x) is the first objective function, f2(x) is the second objective function, f3(x) is the third objective function, and λ j is the weight of each objective function, v is the penalty parameter, d is the unit vector, as the direction of the objective function, c i is the cost of configuring the measurement terminal at the i-th node, x i is the decision variable of the ith node, x i =1 means the i-th node is equipped with a measurement terminal, x i =0 means that the i-th node is not equipped with a measurement terminal, φ i (x) is the observability of node i, R(x) is the average observability, and N is the number of nodes in the observable area.

[0042] Furthermore, the discrete binary particle swarm optimization algorithm is used to update the speed and position of particles in the single-objective optimization model, and the key nodes with the greatest contribution to observability are given, including:

[0043] The results of configuring the measurement terminal for each node in the observable area are integrated as particles of the discrete binary particle swarm optimization algorithm;

[0044] According to the number of nodes in the observable area and the results of configuring measurement terminals for all nodes, the number of particles, particle dimensions and maximum number of iterations are initialized, and the position and velocity of the particles are randomly initialized;

[0045] The constraints of the target optimization model and the single-objective optimization model are used as the constraints and objective function of the discrete binary particle swarm optimization algorithm, and the particle fitness is determined;

[0046] Based on the fitness of particles in the particle swarm, determine the individual optimal position and global optimal position of each particle, and repeatedly update the speed and position of the particle until the maximum number of iterations is reached;

[0047] Output the global optimal position and determine the nodes where the measurement terminals are configured within the observable area.

[0048] In a second aspect, the present invention further provides a device for optimizing configuration of measurement terminals based on distribution network observability evaluation, using the above-mentioned method for optimizing configuration of measurement terminals based on distribution network observability evaluation, the device for optimizing configuration of measurement terminals comprises:

[0049] A region division module is used to obtain a distribution network topology map and divide it into multiple observable regions;

[0050] An observation analysis module, which is used to perform observability analysis based on initial configuration schemes of measurement terminals in multiple observable areas;

[0051] A model building module is used to build a target optimization model and give constraints based on the observability analysis results, with the goal of minimizing the number and cost of measurement terminal arrangements and maximizing observability;

[0052] The scheme configuration module is used to solve the target optimization model based on the optimization algorithm and provide the optimal configuration scheme of the measurement terminals in multiple observable areas.

[0053] Furthermore, the region division module includes:

[0054] Based on the actual network structure of the distribution network, determine all branches and nodes of the distribution network topology diagram;

[0055] Obtain the initial node connected to the main substation through the branch in the distribution network topology diagram;

[0056] The nodes connected in sequence from each initial node through branches are divided into an observable area.

[0057] Furthermore, the observation and analysis module includes:

[0058] Obtain nodes configured with measurement terminals in each observable area and determine observable nodes;

[0059] Determine observability based on the determined observable nodes and all nodes in the observable area;

[0060] Compare the observability with the indicator threshold and present the observable areas that do not meet the indicator threshold.

[0061] Furthermore, the observation and analysis module also includes:

[0062] According to the nodes configured with FTU measurement terminals in the observable area, the nodes are determined as observable nodes;

[0063] When there is at most one node that is not configured with a measurement terminal among the neighboring nodes of the node configured with an IFT measurement terminal, the node is determined as an observable node.

[0064] Furthermore, the model building module includes:

[0065] For observable areas that do not reach the indicator threshold, determine all nodes and branches in each observable area;

[0066] Based on the determined nodes, a target optimization model is constructed with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability;

[0067] Based on the determined nodes and branches, the constraints of the target optimization model are given.

[0068] Furthermore, the solution configuration module includes:

[0069] Based on the target optimization model, an observability reduction model is given;

[0070] Combining the observability reduction model and the target optimization model, a single-target optimization model among multiple targets is constructed through the NBI algorithm;

[0071] The discrete binary particle swarm optimization algorithm is used to update the speed and position of particles in the single-objective optimization model, and the key nodes with the largest contribution to observability are given.

[0072] Furthermore, the solution configuration module also includes:

[0073] According to the target optimization model, determining a first objective function that minimizes the number and cost of measurement terminal arrangements and a second objective function that maximizes observability;

[0074] Based on the situation that measurement terminals with different node configurations have failures and / or predetermined measurement errors, determining a third objective function that minimizes the degree of observability degradation;

[0075] The first objective function, the second objective function and the third objective function are optimized by the NBI algorithm, and a single objective optimization model is given.

[0076] The present invention provides a method and device for optimizing the configuration of measurement terminals based on distribution network observability evaluation, which at least have the following beneficial effects:

[0077] (1) By analyzing and dividing the distribution network topology, the observability evaluation under the current measurement terminal layout can be realized. When the distribution network status is evaluated as unobservable, the key measurement locations are determined through the optimization algorithm, and the layout of the measurement terminals is optimized to ensure that the status of all nodes can be observed. Ultimately, the situational awareness and operational reliability of the distribution network can be improved while reducing the investment cost of the measurement terminals.

[0078] (2) The NBI algorithm combined with the discrete binary particle swarm optimization algorithm for auxiliary solution can be used to solve optimization problems with trade-offs between multiple objectives, such as: The layout cost of the measurement terminal: Minimize the number and cost of the measurement terminals while ensuring the observability of the distribution network. Network observability: Maximize the observability of the distribution network to ensure that the operating status of the system can be accurately monitored and estimated. Robustness: Optimize the layout of the measurement terminals so that the system still has a high degree of observability in the event of a fault or data anomaly. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 A flow chart of a method for optimizing configuration of measurement terminals based on distribution network observability evaluation provided by the present invention;

[0080] Figure 2 A flowchart of dividing an observable area provided in an embodiment of the present invention;

[0081] Figure 3 A flowchart for performing observability analysis provided for an embodiment of the present invention;

[0082] Figure 4 A flowchart of constructing a target optimization model provided by an embodiment of the present invention;

[0083] Figure 5 A topology diagram of an IEEE 123 node system provided in an embodiment of the present invention;

[0084] Figure 6 A schematic diagram of changes in the number of FTUs and the number of IFTs provided in an embodiment of the present invention;

[0085] Figure 7 A schematic diagram of a measurement terminal optimization configuration device based on distribution network observability evaluation provided by the present invention. DETAILED DESCRIPTION

[0086] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0087] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0088] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0089] like Figure 1 As shown, the present invention provides a method for optimizing configuration of measurement terminals based on distribution network observability evaluation, comprising:

[0090] Obtain the distribution network topology map and divide it into multiple observable areas;

[0091] Based on the initial configuration schemes of measurement terminals in multiple observable areas, observability analysis is performed separately;

[0092] Based on the observability analysis results, a target optimization model is constructed and constraints are given with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability.

[0093] The target optimization model is solved based on the optimization algorithm, and the optimal configuration scheme of measurement terminals in multiple observable areas is given.

[0094] The present invention can realize the observability evaluation under the current measurement terminal layout by analyzing and dividing the distribution network topology. When the state of the distribution network is evaluated as unobservable, the key measurement positions are determined by the optimization algorithm, the layout of the measurement terminal is optimized, and the state of all nodes can be observed. Ultimately, the situational awareness and operational reliability of the distribution network can be improved while reducing the investment cost of the measurement terminal.

[0095] Among them, Figure 2 As shown, the distribution network topology map is obtained and divided into multiple observable areas, including:

[0096] Based on the actual network structure of the distribution network, all branches and nodes of the distribution network topology are determined; specifically, the branches and nodes are obtained through a depth-first search (DFS) algorithm of the graph;

[0097] Obtain the initial node connected to the main substation through the branch in the distribution network topology diagram;

[0098] The nodes connected in sequence from each initial node through branches are divided into an observable area.

[0099] After dividing the topology of the distribution network, Figure 3 As shown, observability analysis can be performed based on the initial configuration schemes of measurement terminals in multiple observable areas, including:

[0100] Obtain nodes configured with measurement terminals in each observable area and determine observable nodes;

[0101] Determine observability based on the determined observable nodes and all nodes in the observable area;

[0102] Compare the observability with the indicator threshold and present the observable areas that do not meet the indicator threshold.

[0103] The measurement terminal may include an FTU measurement terminal and an IFT measurement terminal;

[0104] Determine observable nodes, including:

[0105] According to the nodes configured with FTU measurement terminals in the observable area, the nodes are determined as observable nodes;

[0106] When there is at most one node without a measurement terminal configured among the neighboring nodes of the node configured with an IFT measurement terminal, it is determined as an observable node. The neighboring node is a node connected to the node through a branch.

[0107] In practical application scenarios, for a distribution network with N nodes and b branches, the measurement equation can be expressed as

[0108] z=h(x)+v0

[0109] Among them, z is the measurement vector, h(x) is the nonlinear measurement function, x is the state vector, and v0 is the measurement error. The corresponding Jacobian matrix is ​​obtained by the following formula:

[0110]

[0111] If the Jacobian matrix is ​​of full rank, the state variables in x (i.e., the voltage magnitudes and angles at all nodes) can be derived through state estimation using the available measurements in z. In this case, the distribution network is observable.

[0112] Branch observability means that the active power and reactive power of all branches can be obtained through existing measurements. Assume that the voltage phasor Ui and the complex power S ij , S ik Available, then the voltage phase U j and U k It can be derived by the following formula:

[0113]

[0114] Where S ij =P ij +Q ij , S ik =P ik +Q ik , Z ij and Z ik are the impedances of branch ij and branch ik respectively.

[0115] The distribution network can be represented as a graph model with nodes and edges for analysis. For a network with N nodes and b branches, the graph model contains N nodes and b edges, and b = N-1. Assuming that a node is equipped with a medium-voltage side FTU measurement terminal, the complex power of the branch connected to the node can be obtained. The branch with the available complex power is defined as an observable branch. The original observable branch set Ω0 can be established using the medium-voltage measurement value.

[0116] Subsequently, the observable branch set is expanded based on the low-voltage measurement data collected by the IFT measurement terminal. The IFT measurement terminal measures the active power and reactive power on the low-voltage side of the distribution transformer. First, the node degree, that is, the number of branches directly connected to the node, is calculated. If the node is equipped with a distribution transformer, it is regarded as a load access node, and the corresponding degree is increased by 1. The degree list of all nodes is represented as D = {d1, d2, …, d N}.

[0117] Secondly, iteratively search for observable branches. Let the search degree d be the minimum degree in D, then for the node with degree d, find its observability. The observability is defined as the number of observable branches connected to the node. If the node is a load access node equipped with an IFT measurement terminal, the observability is increased by one. Specifically, when N-1 branches of a node are observable, another branch can also be observed, and its corresponding branch is added to the observable branch set. By iteratively accessing nodes with different observability degrees, the original observable branch set Ω0 is expanded, and the total observable branch set is recorded as Ω b .

[0118] The observability of the divided observable areas is analyzed one by one, based on the total observable branch set Ω b , use DFS to search for observable nodes. DFS is a traversal algorithm in graph theory. Let the head node be the initial visit node, and use DFS to visit all nodes in sequence. If all adjacent branches of a node are in the set Ω b If the node is within, then the node is observable. Let the observable node set be Ω N Then, observability islands are partitioned by aggregating adjacent observable nodes. Observability Φ is defined as:

[0119]

[0120] Where Ф0 is the number of observable nodes in the N-node distribution network, Ф∈[0,1], and the larger the Ф, the higher the observability. When the observability is less than 1, the distribution network is judged to be unobservable.

[0121] like Figure 4 As shown in the figure, after the observability analysis is performed on the observable area, a target optimization model can be constructed based on the observability analysis results with the goal of minimizing the number and cost of measurement terminal layouts and maximizing observability, including:

[0122] For observable areas that do not reach the indicator threshold, determine all nodes and branches in each observable area;

[0123] Based on the determined nodes, a target optimization model is constructed with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability;

[0124] Based on the determined nodes and branches, the constraints of the target optimization model are given.

[0125] Specifically, when the distribution network is unobservable, the key measurement locations that have a significant impact on the observability of the network are identified. Through the NBI algorithm, the optimal configuration solution for the smart terminal is provided. Based on the distribution network topology and the observable node set Ω N The constraints of the target optimization model are that the product of the adjacency matrix of the observable region and the measurement terminal configuration vector is not less than the unit vector; the connected nodes in the adjacency matrix are 1, otherwise they are 0, and the nodes corresponding to the measurement terminal configuration vector belong to the observable node set.

[0126] The above target optimization model expects that all branches can be observed when a minimum number of measurement terminals are deployed.

[0127] Solving the optimal configuration problem of the target optimization model, solving the target optimization model based on the optimization algorithm, can include: solving the target optimization model through the NBI algorithm in combination with the discrete binary particle swarm optimization algorithm. The NBI method is a technology for multi-objective optimization, which can be used to solve optimization problems that require trade-offs between multiple objectives. In the application scenario of the present invention, the NBI method can be used to optimize multiple objectives, such as: the layout cost of the measurement terminal: minimize the number and cost of the measurement terminal while ensuring the observability of the distribution network. Network observability: maximize the observability of the distribution network to ensure that the operating status of the system can be accurately monitored and estimated. Robustness: optimize the layout of the measurement terminal so that in the event of a fault or data anomaly, the system still has a high degree of observability.

[0128] Specifically, solving the target optimization model by using the NBI algorithm in combination with the discrete binary particle swarm optimization algorithm may include:

[0129] Based on the target optimization model, an observability reduction model is given;

[0130] Combining the observability reduction model and the target optimization model, a single-target optimization model among multiple targets is constructed through the NBI algorithm;

[0131] The single-objective optimization model is updated with the speed and position of particles through the discrete binary particle swarm optimization algorithm, and the key nodes with the greatest contribution to observability are given. Among them, particles refer to the configuration schemes of all nodes in the observable area configured with measurement terminals, and the configuration schemes of each node include configuring FTU measurement terminals or IFT measurement terminals, and not configuring measurement terminals.

[0132] Among them, the observability reduction model is given and combined with the target optimization model, and a single-objective optimization model between multiple objectives is constructed through the NBI algorithm, including:

[0133] According to the target optimization model, determining a first objective function that minimizes the number and cost of measurement terminal arrangements and a second objective function that maximizes observability;

[0134] Based on the situation that measurement terminals with different node configurations have failures and / or predetermined measurement errors, determining a third objective function that minimizes the degree of observability degradation;

[0135] The first objective function, the second objective function and the third objective function are optimized by the NBI algorithm, and a single objective optimization model is given.

[0136] Among them, if the measurement terminal fails or there is a predetermined measurement error, it means that the branch adjacent to the node is unobservable. When the other branches of another node connected to the unobservable branch are all observable, the other node is still observable, so that its observability does not decrease. When the other branches of another node connected to the unobservable branch still have an unobservable branch, the other node is unobservable.

[0137] The first objective function can be designed to reduce the number and layout cost of measurement terminals (such as FTU and IFT). The layout cost can be expressed as a linear or nonlinear function related to the number of measurement terminals, specifically satisfying the following relationship:

[0138]

[0139] The second objective function is designed to maximize network observability. The goal is to ensure high observability of the distribution network by arranging measurement terminals. Observability can also be defined as the proportion of observable nodes in the entire network. Specifically, the following relationship is satisfied:

[0140]

[0141] The third objective function is designed to improve the robustness of the system, which aims to ensure that the system can still maintain good observability when some terminals fail or the measurement error is large. Robustness can be achieved by modeling the degree of observability degradation under failure scenarios, satisfying the following relationship:

[0142] f3(x)=-R(x)

[0143]

[0144] In the formula, f1(x) is the first objective function, f2(x) is the second objective function, f3(x) is the third objective function, and c i is the cost of configuring the measurement terminal at the i-th node, x i is the decision variable of the ith node, x i =1 means the i-th node is equipped with a measurement terminal, x i =0 means that the i-th node is not equipped with a measurement terminal, φ i (x) is the observability of node i, R(x) is the average observability, and N is the number of nodes in the observable area.

[0145] After the objective function is determined, the NBI method is used to construct the Pareto frontier between the objective functions, and the best performing point in each objective function space is selected as the reference point (for example, the optimal solution obtained by independently optimizing each objective function). Several directions are defined from these reference points to other areas of the objective function space, each direction representing a different objective trade-off. Along each reference direction, a single-objective optimization problem is solved, that is, a single-objective optimization model is constructed. Through the solutions to these optimization problems, the entire Pareto frontier can be gradually constructed. Specifically, the single-objective optimization model satisfies the following relationship:

[0146]

[0147] In the formula, λ j is the weight of the jth objective function, v is the penalty parameter, and d is the unit vector, which is the direction of the objective function. j The focus of optimization is changed by adjusting the relative importance of the first objective function, the second objective function, and the third objective function, so that some objective functions can take precedence over other objective functions. The unit vector d is the search direction defined in the target space and is usually used to control how the solutions in the optimization process are distributed in the target space.

[0148] After constructing the single-objective optimization model, the above optimization problem is solved using DBPSO (discrete binary particle swarm optimization algorithm), which is a variant of the particle swarm optimization algorithm designed for binary space with variables 0 and 1. Particles represent binary solutions and update their positions based on the speed affected by personal and global best solutions. The advantages of DBPSO in solving binary optimization problems include simplicity, strong adaptability, large parallelism potential, and few adjustment parameters. Specifically, the speed and position of the particles in the single-objective optimization model are updated by the discrete binary particle swarm optimization algorithm, and the key nodes with the largest observability contribution are given, which can include:

[0149] The results of configuring the measurement terminal for each node in the observable area are integrated as particles of the discrete binary particle swarm optimization algorithm;

[0150] According to the number of nodes in the observable area and the results of configuring measurement terminals for all nodes, the number of particles, particle dimensions and maximum number of iterations are initialized, and the position and velocity of the particles are randomly initialized;

[0151] The constraints of the target optimization model and the single-objective optimization model are used as the constraints and objective function of the discrete binary particle swarm optimization algorithm, and the particle fitness is determined;

[0152] Based on the fitness of particles in the particle swarm, determine the individual optimal position and global optimal position of each particle, and repeatedly update the speed and position of the particle until the maximum number of iterations is reached;

[0153] Output the global optimal position and determine the node where the measurement terminal is configured in the observable area. The global optimal position is a vector with the same dimension as the number of nodes. The vector elements are 0 or 1. 1 indicates that the node is a key measurement position and has a significant impact on observability.

[0154] Among them, the number of particles is 2 N , N is the number of nodes in the observable area, and the particle dimension is the number of nodes in the observable area. The objective function of the discrete binary particle swarm optimization algorithm is defined as the particle fitness. The global optimal position of a particle is the best individual optimal position among the individual optimal positions of each particle. The functions used to update the particle velocity and position, as well as the key nodes that contribute the most to the output observability, are all conventional functions in the discrete binary particle swarm optimization algorithm.

[0155] The output of DBPSO gives the key measurement locations that contribute most to observability. By deploying a minimum number of measurement terminals at key measurement locations, this method ensures that the states of all nodes in the distribution network are observable. This can reduce the investment cost of the measurement terminal and improve the situational awareness of the distribution network. Among them, after determining the nodes to be configured with the measurement terminal, the nodes located at the upstream of the distribution network can be configured with FTU measurement terminals, and the other determined nodes can be configured with IFT measurement terminals. Furthermore, when the speed and position of the particles in the single-objective optimization model are updated by the discrete binary particle swarm optimization algorithm, the results of configuring the measurement terminal for each node can be set to three situations: no measurement terminal, FTU measurement terminal, and IFT measurement terminal, and the global optimal position can be updated.

[0156] Based on the measurement terminal optimization configuration method of the present invention, the network structure topology is built using the IEEE 123 node system, and the system observability under different schemes is compared and analyzed to verify the effectiveness of the present invention:

[0157] The measurement terminal optimization configuration method is verified by IEEE 123 node system and actual distribution network. Figure 5 As shown, the graph model of the IEEE 123-node system contains 123 nodes and 122 edges. Figure 5 The available measurement terminals on the medium voltage side (i.e., FTU) and the low voltage side (i.e., IFT) are marked in the figure. First, the original observable branch set Ω0 is established, and the FTU is deployed at the head node, i.e., Node 0, and Ω0 = Branch{0-1}. Then, the original observable branch set is expanded with the IFT measurement terminal to obtain the total observable branch set Ω b=Branch{0-1,3-4,3-5,5-6,1-3,11-14,10-14,9-14,27-33,26-27,26-31,31-32,25-26,30-120 ,29-30,51-122,50-51,65-66,64-65,15-16,15-17,15-34,82-83,81-82,81-84,84-85,80-81}.

[0158] Secondly, DFS is used to find the observable node set Ω in the distribution network. N Set the head node as the initial access node and visit some nodes in the network in sequence. As long as all the adjacent branches (branches) of the node are in the set Ω b If the node is within , the node is judged to be observable. Establish the observable node set Ω N Ω N ={0,3,4,5,6,10,11,14,15,16,17,26,27,31,32,33,30,120,51,122,65,66,81,82,83,84,85}. Then, by aggregating adjacent observable nodes, such as Figure 3 As shown in the figure, the number of observable nodes is 27, that is, N = 27, and the observability evaluation index is Ф = 0.22.

[0159] Finally, based on the distribution network topology and observable node set Ω N , establish the target optimization model, and use NBI combined with DBPSO to solve the target optimization model. In addition, under the condition of ensuring the observability of the IEEE 123 node system, the more IFTs there are, the fewer FTUs need to be deployed. Figure 6 The variation of the number of FTUs and their proportion to the total number of nodes with the increase of the number of IFTs is given while ensuring the observability of the network.

[0160] like Figure 7 As shown, the present invention also provides a device for optimizing configuration of measurement terminals based on observability evaluation of distribution network, adopting the above-mentioned method for optimizing configuration of measurement terminals based on observability evaluation of distribution network, the device for optimizing configuration of measurement terminals comprises:

[0161] A region division module is used to obtain a distribution network topology map and divide it into multiple observable regions;

[0162] An observation analysis module, which is used to perform observability analysis based on initial configuration schemes of measurement terminals in multiple observable areas;

[0163] A model building module is used to build a target optimization model and give constraints based on the observability analysis results, with the goal of minimizing the number and cost of measurement terminal arrangements and maximizing observability;

[0164] The scheme configuration module is used to solve the target optimization model based on the optimization algorithm and provide the optimal configuration scheme of the measurement terminals in multiple observable areas.

[0165] The regional division module includes:

[0166] Based on the actual network structure of the distribution network, determine all branches and nodes of the distribution network topology diagram;

[0167] Obtain the initial node connected to the main substation through the branch in the distribution network topology diagram;

[0168] The nodes connected in sequence from each initial node through branches are divided into an observable area.

[0169] The observation and analysis modules include:

[0170] Obtain nodes configured with measurement terminals in each observable area and determine observable nodes;

[0171] Determine observability based on the determined observable nodes and all nodes in the observable area;

[0172] Compare the observability with the indicator threshold and present the observable areas that do not meet the indicator threshold.

[0173] The observation analysis module also includes:

[0174] According to the nodes configured with FTU measurement terminals in the observable area, the nodes are determined as observable nodes;

[0175] When there is at most one node that is not configured with a measurement terminal among the neighboring nodes of the node configured with an IFT measurement terminal, the node is determined as an observable node.

[0176] Model building modules include:

[0177] For observable areas that do not reach the indicator threshold, determine all nodes and branches in each observable area;

[0178] Based on the determined nodes, a target optimization model is constructed with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability;

[0179] Based on the determined nodes and branches, the constraints of the target optimization model are given.

[0180] The solution configuration module includes:

[0181] An observability reduction model is given and combined with the target optimization model, and a single-target optimization model among multiple targets is constructed through the NBI algorithm;

[0182] The discrete binary particle swarm optimization algorithm is used to update the speed and position of particles in the single-objective optimization model, and the key nodes with the largest contribution to observability are given.

[0183] The solution configuration module also includes:

[0184] According to the target optimization model, determining a first objective function that minimizes the number and cost of measurement terminal arrangements and a second objective function that maximizes observability;

[0185] Based on the failure of measurement terminals of different node configurations and / or the existence of predetermined measurement errors, determining a third objective function that minimizes the degree of observability degradation;

[0186] The first objective function, the second objective function and the third objective function are optimized by the NBI algorithm, and a single objective optimization model is given.

[0187] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A measurement terminal optimization configuration method based on distribution network observability evaluation, characterized in that: include: Obtain the distribution network topology map and divide it into multiple observable areas; Based on the initial configuration schemes of measurement terminals in multiple observable areas, observability analysis is performed separately; Based on the observability analysis results, a target optimization model is constructed and constraints are given with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability. The target optimization model is solved based on the optimization algorithm, and the optimal configuration scheme of measurement terminals in multiple observable areas is given.

2. The measurement terminal optimization configuration method according to claim 1, characterized in that: Obtain the distribution network topology map and divide it into multiple observable areas, including: Based on the actual network structure of the distribution network, determine all branches and nodes of the distribution network topology diagram; Obtain the initial node connected to the main substation through the branch in the distribution network topology diagram; The nodes connected in sequence from each initial node through branches are divided into an observable area.

3. The measurement terminal optimization configuration method according to claim 1 or 2, characterized in that: Based on the initial configuration schemes of measurement terminals in multiple observable areas, observability analysis is performed separately, including: Obtain nodes configured with measurement terminals in each observable area and determine observable nodes; Determine observability based on the determined observable nodes and all nodes in the observable area; Compare the observability with the indicator threshold and present the observable areas that do not meet the indicator threshold.

4. The method for optimizing configuration of a measurement terminal according to claim 3, characterized in that: The measurement terminals include FTU measurement terminal and IFT measurement terminal; Determine observable nodes, including: According to the nodes configured with FTU measurement terminals in the observable area, the nodes are determined as observable nodes; When there is at most one node that is not configured with a measurement terminal among the neighboring nodes of the node configured with an IFT measurement terminal, the node is determined as an observable node.

5. The method for optimizing configuration of measurement terminals according to claim 3, characterized in that: Based on the observability analysis results, a target optimization model is constructed with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability, including: For observable areas that do not reach the indicator threshold, determine all nodes and branches in each observable area; Based on the determined nodes, a target optimization model is constructed with the goal of minimizing the number and cost of measurement terminal deployment and maximizing observability; Based on the determined nodes and branches, the constraints of the target optimization model are given.

6. The method for optimizing configuration of measurement terminals according to claim 1, characterized in that: The target optimization model is solved based on the optimization algorithm, including: solving the target optimization model through the NBI algorithm combined with the discrete binary particle swarm optimization algorithm.

7. The method for optimizing configuration of measurement terminals according to claim 6, characterized in that: The target optimization model is solved by using the NBI algorithm combined with the discrete binary particle swarm optimization algorithm, including: Based on the target optimization model, an observability reduction model is given; Combining the observability reduction model and the target optimization model, a single-target optimization model among multiple targets is constructed through the NBI algorithm; The discrete binary particle swarm optimization algorithm is used to update the speed and position of particles in the single-objective optimization model, and the key nodes with the largest contribution to observability are given.

8. The method for optimizing configuration of measurement terminals according to claim 7, characterized in that: The observability reduction model is given and combined with the target optimization model, and a single-target optimization model among multiple targets is constructed through the NBI algorithm, including: According to the target optimization model, determining a first objective function that minimizes the number and cost of measurement terminal arrangements and a second objective function that maximizes observability; Based on the situation that measurement terminals with different node configurations have failures and / or predetermined measurement errors, determining a third objective function that minimizes the degree of observability degradation; The first objective function, the second objective function and the third objective function are optimized by the NBI algorithm, and a single objective optimization model is given; Among them, the single-objective optimization model satisfies the following relationship: f3(x)=-R(x) Where f1(x) is the first objective function, f2(x) is the second objective function, f3(x) is the third objective function, and λ j is the weight of each objective function, v is the penalty parameter, d is the unit vector, as the direction of the objective function, c i is the cost of configuring the measurement terminal at the i-th node, x i is the decision variable of the ith node, x i =1 means the i-th node is equipped with a measurement terminal, x i =0 means that the i-th node is not equipped with a measurement terminal, φ i (x) is the observability of node i, R(x) is the average observability, and N is the number of nodes in the observable area.

9. The method for optimizing configuration of measurement terminals according to claim 7 or 8, characterized in that: The discrete binary particle swarm optimization algorithm is used to update the speed and position of particles in the single-objective optimization model, and the key nodes with the largest contribution to observability are given, including: The results of configuring the measurement terminal for each node in the observable area are integrated as particles of the discrete binary particle swarm optimization algorithm; According to the number of nodes in the observable area and the results of configuring measurement terminals for all nodes, the number of particles, particle dimensions and maximum number of iterations are initialized, and the position and velocity of the particles are randomly initialized; The constraints of the target optimization model and the single-objective optimization model are used as the constraints and objective function of the discrete binary particle swarm optimization algorithm, and the particle fitness is determined; Based on the fitness of particles in the particle swarm, determine the individual optimal position and global optimal position of each particle, and repeatedly update the speed and position of the particle until the maximum number of iterations is reached; Output the global optimal position and determine the nodes where the measurement terminals are configured within the observable area.

10. A device for optimizing configuration of measurement terminals based on observability evaluation of distribution network, characterized in that: The method for optimizing configuration of measurement terminals based on observability evaluation of distribution network according to any one of claims 1 to 9 is adopted, and the device for optimizing configuration of measurement terminals comprises: A region division module is used to obtain a distribution network topology map and divide it into multiple observable regions; An observation analysis module, which is used to perform observability analysis based on initial configuration schemes of measurement terminals in multiple observable areas; A model building module is used to build a target optimization model and give constraints based on the observability analysis results, with the goal of minimizing the number and cost of measurement terminal arrangements and maximizing observability; The scheme configuration module is used to solve the target optimization model based on the optimization algorithm and provide the optimal configuration scheme of the measurement terminals in multiple observable areas.

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