Method and device for optimizing configuration of measurement terminal of power distribution network

Abstract

The invention discloses a power distribution network measurement terminal configuration optimization method and apparatus. The method comprises the steps of obtaining a topological structure diagram of a power distribution network; according to the topological structure diagram, giving an observable branch set and an observable node set, and dividing observable islands; analyzing the feasibility of each observable island, and determining the observability of the power distribution network; and according to the observability of the power distribution network, an optimization model is used for optimizing the positions and the number of the measurement terminals in the power distribution network to obtain an optimization result of the measurement terminal configuration, and the optimization model is used for optimizing the measurement terminal configuration based on the corrected particle swarm optimization and is used for balancing the cost of the measurement terminals and the observability of the power distribution network. According to the method, the observability of the power distribution network is obtained by analyzing the nodes and the branches in the power distribution network, configuration optimization is performed on the measurement terminals in the power distribution network by using the optimization model, the optimization model balances the number and the cost of the measurement terminals, and the observability of the power distribution network is ensured to a certain extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution network configuration optimization, and in particular relates to a method and device for optimizing the configuration of a power distribution network measurement terminal. 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] Distribution network observability assessment is the process of determining whether known measurement data can uniquely determine the state of the distribution network. If the distribution network is observable, the voltage amplitude and phase angle of all nodes in the distribution network can be inferred using the existing measurement data, thereby supporting situational awareness, fault diagnosis, etc.

[0004] Although various measurement terminals, such as feeder terminal units (FTUs) and intelligent fusion terminals (IFTs), have been deployed in distribution networks, it is not feasible to equip all nodes of the distribution network with measurement terminals based on economic considerations. How to properly deploy them in all nodes of the distribution network to make the distribution network observable is a problem that needs to be solved at present. Therefore, it is crucial to evaluate observability based on existing measurement terminals. In the case where the distribution network is unobservable, key measurement locations that have a significant impact on the observability of the distribution network should be identified. By deploying the minimum necessary measurement terminals, the operating status of the distribution network can be observable and describable, the investment cost of the measurement terminals can be reduced, and the situational awareness capability of the distribution network can be enhanced.

[0005] Patent CN117595385A discloses a method for distribution network observability assessment and key measurement location mining, which constructs a distribution network topology model, constructs and obtains an initial observable line set and expands it according to the location of the existing medium-voltage side measurement terminal of the distribution network to obtain the entire observable line set; in the distribution network topology model, all nodes are visited in turn and it is determined whether the node is observable to construct an observable node set; based on the distribution network observability assessment index, the distribution network observability is evaluated. If the distribution network is not observable, an optimization problem for mining key measurement locations is constructed and solved to obtain the key measurement locations of the distribution network. The technical solution realizes the observability island search and observability assessment of the distribution network; when the distribution network is not observable, a distribution network key measurement location mining solution is provided. However, when optimizing the key measurement locations, the technical solution does not adaptively adjust the optimization model, resulting in poor economic feasibility of the key measurement locations and quantities. Summary of the invention

[0006] In view of the defects existing in the above-mentioned prior art, the present invention provides a method for optimizing the configuration of a distribution network measurement terminal, comprising:

[0007] Obtain a topological structure diagram of the distribution network, where the topological structure diagram includes N nodes and b branches;

[0008] According to the topological structure diagram, the observable branch set and observable node set are given and the observable islands are divided;

[0009] Analyze the feasibility of each observable island to determine the observability of the distribution network;

[0010] According to the observability of the distribution network, the optimization model is used to optimize the location and number of measurement terminals in the distribution network, and the optimization result of the measurement terminal configuration is obtained;

[0011] Among them, the optimization model is based on the modified particle swarm algorithm to optimize the configuration of the measurement terminal, which is used to balance the cost of the measurement terminal and the observability of the distribution network.

[0012] Through the above technical scheme, the branches and nodes in the topological structure diagram of the distribution diagram are analyzed respectively, and the observable branch set and the observable node set are obtained and the observable islands are divided. According to the feasibility of the observable islands, the observability of the distribution network is determined; according to the observability of the distribution network, the configuration of the measurement terminals in the distribution network is optimized using the optimization model. Under the premise of ensuring the observability of the distribution network, the configuration cost of the measurement terminals is reduced, and the economic feasibility of the configuration of the distribution network measurement terminals is improved.

[0013] In a possible implementation, providing an observable branch set and an observable node set and dividing observable islands according to the topological structure diagram includes:

[0014] According to the topological structure diagram, an initial observable branch set and a load access node are determined, wherein the observable branches in the initial observable branch set are branches connected to nodes equipped with measurement terminals in the topological structure diagram, and the load access node is a node equipped with a distribution transformer in the topological structure diagram;

[0015] Given the degree of each node in the topological structure graph, we can get the node degree sequence D = {d1, d2, d3, ..., d N}, the degree of a node is the number of branches connected to the node, and if the node is a load access node, the degree of the node is increased by 1;

[0016] According to the sequence of node degrees, the observable degree of each node is given, and the initial observable branch set is expanded to obtain the observable branch set;

[0017] Based on the observable branch set, a depth-first search algorithm is used to give the observable node set and determine the observable island. The observable node is a node whose adjacent branches are in the observable branch set. The observable island is obtained by aggregating adjacent observable nodes.

[0018] In a possible implementation, the observable degree of each node is given according to the sequence of the node degrees, and the initial observable branch set is expanded to obtain the observable branch set, including:

[0019] Set the current search depth a =min{d1,d2,...,d N};

[0020] Get a node whose search degree is the current search degree, and calculate the observability of the node, where the observability of the node is the number of observable branches adjacent to the node, and if the node is equipped with a fusion terminal, the observability of the node is increased by 1;

[0021] Determine whether the observability of the node is greater than or equal to d a -1;

[0022] If so, add all adjacent lines of the node to the observable branch set;

[0023] When a =max{d1,d2,...,d N} and the nodes under the current search degree are traversed, and the observable branch set of the distribution network is output.

[0024] In a possible implementation manner, analyzing the feasibility of each observable island to determine the observability of the distribution network includes:

[0025]

[0026] Among them, Φ is the observability of the distribution network, Φ0 is the number of observable nodes among the N nodes in the distribution network, Φ∈[0,1], when the observability of the distribution network is less than 1, the distribution network is unobservable.

[0027] In a possible implementation, the optimizing the positions and quantities of the measuring terminals in the distribution network using the optimization model according to the observability of the distribution network to obtain the optimization result of the configuration of the measuring terminals includes:

[0028] When the observability of the distribution network is unobservable;

[0029] Initialize the parameters of the optimization model, which include the number of particles, particle dimension and maximum number of iterations. The number of particles is the total number of nodes in the distribution network. Each node corresponds to each dimension. Whether each node is equipped with a terminal corresponds to the particle dimension value of each dimension, which is 0 or 1.

[0030] Set the objective function and constraints to evaluate the fitness of particles in the particle swarm;

[0031] Record the individual optimal position and global optimal position of each particle;

[0032] Update the particle dimension value by updating the particle's speed and position;

[0033] Determine whether the maximum number of iterations has been reached;

[0034] If yes, the iteration is stopped, and the optimization result of the measurement terminal is output based on the particle dimension value. The optimization result of the measurement terminal is a vector with the same dimension as the number of nodes in the power distribution diagram, and the vector elements are 0 or 1;

[0035] If not, continue to evaluate the fitness of the particles in the particle swarm, record the individual optimal position and global optimal position of each particle, update the particle speed and position, and update the particle dimension value.

[0036] In one possible implementation, the constraints are determined in the following manner:

[0037]

[0038] sA B×N x N×1 ≥ b B×1

[0039] x k =1,k∈Ω N

[0040] x i ∈{0,1}

[0041] Among them, A B×Nis the adjacency matrix established using the topological structure graph, the vector x N×1 =[x1,x2,…,x N ] is the required measurement placement vector, the elements are x i ∈{0,1},x i =1 means that a measurement terminal should be deployed on node i, Φ y is the observability of the observable island y, b B×1 is a vector whose elements are all 1, Ω N is the observable node set, and k is the observable node number.

[0042] In one possible implementation, the objective function is determined in the following manner:

[0043]

[0044] Among them, c i is the cost of equipping the i-th node with a measurement terminal, z i is a binary variable indicating whether the node is equipped with a terminal, φ i is the observability of node i, α and β are weight coefficients, α is used to adjust the importance of cost, and β is used to adjust the importance of observability.

[0045] In a possible implementation, updating the speed and position of the particle includes:

[0046] The velocity update expression and position update expression of each particle:

[0047] v[i][j]=w·v[i][j]+c1·rand()·(pbest[i][j]-x[i][j])+c2·rand()·(gbest[j]-x[i][j])

[0048]

[0049] Among them, particle x=[x1,x2,…,x N ] is a binary vector with a length equal to the number of nodes in the distribution network, N. Each bit x in the vector i Corresponding to the i-th node of the distribution network, the value is 0 or 1, v[i][j] is the speed of particle i in dimension j, x[i][j] is the position of particle i in dimension j, pbest[i][j] is the historical optimal position of particle i, gbest[j] is the global optimal position, σ(v[i][j]) is the sigmoid function of the speed, w is the chaos factor, c1 is the learning factor of the individual optimal position, c2 is the learning factor of the global optimal position, and rand() is a random number between [0,1).

[0050] In a possible implementation manner, the observability of the power distribution network is determined in the following manner:

[0051] For a distribution network with N nodes and b branches, the measurement equation can be expressed as

[0052] z=h(x)+v;

[0053] Where z is the measurement vector, h(x) is the nonlinear measurement function, x is the state vector, and v is the measurement error;

[0054] Get the Jacobian matrix

[0055] Observability of the distribution network according to the rank of the Jacobian matrix.

[0056] The present invention also discloses a device for optimizing the configuration of a distribution network measurement terminal, comprising:

[0057] A data acquisition unit, used to acquire a topological structure diagram of the distribution network;

[0058] A set determination unit, used for providing an observable branch set and an observable node set and dividing observable islands according to the topological structure diagram;

[0059] An observation and analysis unit, used to analyze the feasibility of each observable island and determine the observability of the distribution network;

[0060] The configuration optimization unit is used to optimize the location and number of measurement terminals in the distribution network using an optimization model according to the observability of the distribution network, and obtain the optimization result of the measurement terminal configuration; wherein the optimization model is based on the modified particle swarm algorithm to optimize the measurement terminal configuration, which is used to balance the cost of the measurement terminal and the observability of the distribution network.

[0061] The method for optimizing the configuration of distribution network measurement terminals provided by the present invention has at least the following beneficial effects:

[0062] By analyzing the branches and nodes in the topological structure diagram of the distribution diagram, the observable branch set and the observable node set are obtained and the observable island is divided. According to the feasibility of the observable island, the observability of the distribution network is determined. According to the observability of the distribution network, the configuration of the measurement terminals in the distribution network is optimized using an optimization model. Under the premise of ensuring the observability of the distribution network, the configuration cost of the measurement terminals is reduced and the economic feasibility of the configuration of the distribution network measurement terminals is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flowchart of a method for optimizing the configuration of a distribution network measurement terminal provided by an embodiment of the present invention;

[0064] Figure 2A partial structural diagram of a distribution network provided by an embodiment of the present invention;

[0065] Figure 3 A schematic diagram of a process for obtaining an observable branch set provided by an embodiment of the present invention;

[0066] Figure 4 A schematic diagram of a depth-first search method according to an embodiment of the present invention;

[0067] Figure 5 A schematic diagram of an optimization process of an optimization model provided in an embodiment of the present invention;

[0068] Figure 6 A local topological structure diagram of a distribution network provided in an embodiment of the present invention;

[0069] Figure 7 A schematic diagram of a device for optimizing distribution network measurement terminal configuration provided by an embodiment of the present invention.

[0070] In the figure, 201 is a data acquisition unit; 202 is a set determination unit; 203 is an observation and analysis unit; 204 is a configuration optimization unit. DETAILED DESCRIPTION

[0071] 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 making creative work are within the scope of protection of the present invention.

[0072] 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.

[0073] 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.

[0074] like Figure 1As shown, the present application provides a method for optimizing the configuration of a distribution network measurement terminal, which specifically includes the following steps:

[0075] S101: Obtain a topological structure diagram of a distribution network.

[0076] Specifically, the topology diagram includes N nodes and b branches, and b=N-1. After obtaining the topology diagram of the distribution network, it is necessary to first determine the observability of the distribution network. The methods for determining whether the distribution network is observable mainly include the following:

[0077] The first is an evaluation method based on the distribution network calculation and analysis algorithm: through the distribution network calculation and analysis algorithm that affects the distribution network observability index, the distribution network calculation and analysis algorithm set is determined. Then, the calculation and analysis result matrix, the collection result matrix and the error value matrix are constructed for each algorithm. Finally, the distribution network observability index is determined based on these matrices. The index is used to indicate the observability status of the distribution network system, realize the integration of the distribution network and the digital system, and effectively monitor the observability level of the distribution network.

[0078] The second method is based on meter configuration analysis: the observability of the system is evaluated by properly configuring meters in the distribution network. By analyzing the configuration of meters, the observability of the distribution network under different states can be determined. This method is suitable for scenarios where the distribution network state needs to be accurately monitored and controlled.

[0079] The third method is based on linear circuit analysis: it is necessary to model various measurements of the three-phase distribution network and determine the unobservable branches in the system through the rank analysis of the measurement Jacobian matrix. According to the results of the branch observability analysis, the observable island of the system can be obtained. This method can handle the influence of complex circuit structures and distributed power sources on observability.

[0080] Fourth, the observability of state estimation based on topological methods: By dividing the large-scale distribution network into connected sub-networks, the computational burden is reduced, and the state estimation is performed using the measurement island rule. By merging the available injected measurements, the maximum observable island can be obtained, thus providing support for system observability analysis in distributed state computing.

[0081] The above methods have their own advantages and disadvantages and are suitable for different scenarios and needs. For example, the method based on meter configuration is suitable for scenarios that require accurate monitoring and control of the distribution network status, while the state estimation method based on topology is suitable for distributed computing of large-scale distribution networks. Choosing the right method depends on the specific application scenario and needs.

[0082] In one implementation of the present application, the observability of the distribution network can be determined by the rank of the Jacobian matrix, and the specific steps are as follows:

[0083] For a distribution network with N nodes and b branches, the measurement equation can be expressed as

[0084] z=h(x)+v;

[0085] Where z is the measurement vector, h(x) is the nonlinear measurement function, x is the state vector, and v is the measurement error. Then, the Jacobian matrix is ​​obtained by

[0086]

[0087] If the Jacobian matrix is ​​of full rank, the state variables in x (i.e., the voltage magnitudes and angles at all nodes in the distribution network) can be derived through state estimation (SE) using the available measurements in z. In this case, the distribution network is observable.

[0088] In another implementation of the present application, the fourth method mentioned above is used to determine the observability of the distribution network, and the specific process is shown in steps S102 to S103.

[0089] S102: According to the topological structure diagram, an observable branch set and an observable node set are given and observable islands are divided.

[0090] Specifically, according to the topological structure diagram, the initial observable branch set and the load access node are determined. The observable branches in the initial observable branch set are the branches connected to the nodes equipped with the measurement terminals in the topological structure diagram, and the load access node is the node equipped with the distribution transformer in the topological structure diagram.

[0091] The initial observable branch set is obtained in the following way:

[0092] Based on the foregoing, it can be understood that the observable branches in the initial observable branch set are branches connected to nodes equipped with measurement terminals in the topology diagram, and the observable branches in the initial observable branch set are branches that can be detected in the topology diagram. If a node is equipped with a measurement terminal such as a medium-voltage side FTU, the complex power of the branch connected to the node can be obtained, that is, the branch can be detected. In the embodiment provided in the present application, the branch with the obtainable complex power is used as the observable branch in the initial observable branch set Ω0.

[0093] Reference Figure 2 In a specific example, assuming that the voltage phasor U i and complex power S ij , S ik Available, then the voltage phase U j and U k It can be obtained by the following formula:

[0094]

[0095] Among them, 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.

[0096] After determining the initial observable branch set, the initial observable branch set can be expanded according to the low voltage measurement data collected by IFT. IFT is used to measure the active power and reactive power on the low voltage side of the distribution transformer.

[0097] It should be understood that active power refers to the average value of the integration of the instantaneous power emitted or consumed by the load in one cycle in the AC circuit, also known as average power. In a sinusoidal circuit, the active power P can be calculated by the formula P = UI cosθ, where U is the effective value of the voltage, I is the effective value of the current, and θ is the phase difference between the voltage and the current. Reactive power refers to the power that converts energy between components such as inductance or capacitance and the power supply in the AC circuit. In a sinusoidal circuit, the reactive power Q can be calculated by the formula Q = UI sinθ, where U is the effective value of the voltage, I is the effective value of the current, and θ is the phase difference between the voltage and the current. Complex power is a calculation quantity defined for the convenience of circuit calculation. In a sinusoidal circuit, the complex power S is defined as S = UI*, where U is the effective value of the voltage, I is the effective value of the current, and I* is the conjugate complex number of I. Complex power can be expanded as S = UI cosθ + jUIsinθ, where the real part UI cosθ is the active power P, and the imaginary part UI sinθ is the reactive power Q. Therefore, when IFT measures the active power and reactive power on the low-voltage side of the distribution transformer, the complex power of the corresponding branch can also be obtained.

[0098] First, the degree of each node in the topological structure graph is given, and the sequence of node degrees D = {d1, d2, d3, ..., d N}, the degree of a node is the number of branches connected to the node, and if the node is a load access node, the degree of the node is increased by 1. According to the sequence of node degrees, the observable degree of each node is given, the initial observable branch set is expanded, and the observable branch set is obtained.

[0099] Further, refer to Figure 3 , the above sequence of node degrees gives the observable degree of each node, expands the initial observable branch set, and obtains the observable branch set, including:

[0100] Set the current search depth a =min{d1,d2,...,dN};

[0101] Get a node whose search degree is the current search degree, and calculate the observability of the node, where the observability of the node is the number of observable branches adjacent to the node, and if the node is equipped with a fusion terminal, the observability of the node is increased by 1;

[0102] Determine whether the observability of the node is greater than or equal to d a -1;

[0103] If so, add all adjacent lines of the node to the observable branch set;

[0104] When a =max{d1,d2,...,d N} and the nodes under the current search degree are traversed, and the observable branch set of the distribution network is output.

[0105] It is understandable that when N-1 degrees of a node are observable, another degree is also observable, and its corresponding branch is added to the initial observable branch set. By iteratively accessing nodes of different degrees, the initial observable analysis set is expanded to obtain the expanded observable branch set.

[0106] Based on the observable branch set, the depth-first search algorithm (DFS) is used to give the observable node set and determine the observable island. The observable node is a node whose adjacent branches are in the observable branch set. The observable island is obtained by aggregating adjacent observable nodes.

[0107] Reference Figure 4 , the specific steps of using the DFS algorithm are as follows:

[0108] Let the head node be the initial visited node, and visit all nodes in sequence.

[0109] Step 1: Get the observable branch set;

[0110] Step 2: Visit the initial visited node and mark it as a "visited node";

[0111] Step 3: Determine whether the initial access node is observable;

[0112] Step 4: Determine whether all N nodes in the distribution network have been visited. If so, jump to step 7; otherwise, go to step 5;

[0113] Step 5: Visit all adjacent nodes of the initially visited node and mark them as "visited nodes"; determine whether the adjacent nodes are observable;

[0114] Step 6: If yes, add the observable node to the observable node set; otherwise, update the neighboring nodes that have not been visited to the initial visited nodes and return to step 2;

[0115] Step 7: Output the observable node set.

[0116] S103: Analyze the feasibility of each observable island and determine the observability of the distribution network.

[0117] Specifically, Among them, Φ is the observability of the distribution network, Φ0 is the number of observable nodes among the N nodes in the distribution network, Φ∈[0,1], when the observability of the distribution network is less than 1, the observability of the distribution network is unobservable.

[0118] S104: According to the observability of the distribution network, the location and number of measurement terminals in the distribution network are optimized using an optimization model to obtain an optimization result of the measurement terminal configuration.

[0119] Specifically, the optimization model is based on the modified particle swarm algorithm to optimize the configuration of the measurement terminal, which is used to balance the cost of the measurement terminal and the observability of the distribution network.

[0120] Reference Figure 5 , when the observability of the distribution network is unobservable;

[0121] Initialize the parameters of the optimization model, which include the number of particles, particle dimension and maximum number of iterations. The number of particles is the total number of nodes in the distribution network. Each node corresponds to each dimension. Whether each node is equipped with a terminal corresponds to the particle dimension value of each dimension, which is 0 or 1.

[0122] Set the objective function and constraints to evaluate the fitness of particles in the particle swarm;

[0123] Record the individual optimal position and global optimal position of each particle;

[0124] Update the particle dimension value by updating the particle's speed and position;

[0125] Determine whether the maximum number of iterations has been reached;

[0126] If yes, the iteration is stopped, and the optimization result of the measurement terminal is output based on the particle dimension value. The optimization result of the measurement terminal is a vector with the same dimension as the number of nodes in the power distribution diagram, and the vector elements are 0 or 1;

[0127] If not, continue to evaluate the fitness of the particles in the particle swarm, record the individual optimal position and global optimal position of each particle, update the particle speed and position, and update the particle dimension value.

[0128] When the observability of the distribution network is unobservable, it is necessary to identify the key measurement locations in the distribution network that have a significant impact on the observability. This application uses the normal boundary intersection (NBI) algorithm and combines the discrete binary particle swarm optimization (DBPSO) algorithm for auxiliary solution. NBI 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, NBI can be used to optimize multiple objectives, such as: the layout cost of the measurement terminal, network observability and robustness. Optimizing the layout cost of the measurement terminal is to minimize the number and cost of the measurement terminal under the premise of ensuring the observability of the distribution network. Optimizing network observability is to maximize the observability of the distribution network and ensure that the operating status of the system can be accurately monitored and estimated. Optimizing robustness is to optimize the layout of the measurement terminal so that in the event of a fault or data anomaly, the system still has a high observability.

[0129] According to the foregoing, the 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. The specific contents are as follows:

[0130] Set constraints:

[0131]

[0132] sA B×N x N×1 ≥ b B×1

[0133] x k =1,k∈Ω N

[0134] x i ∈{0,1}

[0135] Among them, A B×N The adjacency matrix is ​​established using the topological structure graph. When branch m and node n are directly connected, the element in the adjacency matrix is ​​a mn =1, otherwise the element is a mn = 0, vector x N×1 =[x1,x2,…,x N ] is the required measurement placement vector, the elements are x i ∈{0,1},x i =1 means that a measurement terminal should be deployed on node i, Φ y is the observability of the observable island y, bB×1 is a vector whose elements are all 1, Ω N is the observable node set, and k is the observable node number.

[0136] In one embodiment, the objective function is defined as:

[0137]

[0138] Among them, c i is the cost of equipping the i-th node with a measurement terminal, z i is a binary variable indicating whether the node is equipped with a terminal, φ i is the observability of node i, α and β are weight coefficients, α is used to adjust the importance of cost, and β is used to adjust the importance of observability.

[0139] After the objective function is determined, DBPSO is used to solve the above optimization problem. According to the rules of the DBPSO algorithm, the speed and position of each particle are updated:

[0140] The velocity update expression and position update expression of each particle are as follows:

[0141] v[i][j]=w·v[i][j]+c1·rand()·(pbest[i][j]-x[i][j])+c2·rand()·(gbest[j]-x[i][j])

[0142]

[0143] Among them, particle x=[x1,x2,…,x N ] is a binary vector with a length equal to the number of nodes in the distribution network, N. Each bit x in the vector i Corresponding to the i-th node of the distribution network, the value is 0 or 1, v[i][j] is the speed of particle i in dimension j, x[i][j] is the position of particle i in dimension j, pbest[i][j] is the historical optimal position of particle i, gbest[j] is the global optimal position, σ(v[i][j]) is the sigmoid function of the speed, w is the chaos factor, c1 is the learning factor of the individual optimal position, c2 is the learning factor of the global optimal position, and rand() is a random number between [0,1).

[0144] By updating the velocity and position of each particle, we adjust the z in the objective function i When z i When the value of makes the result of the objective function close to the optimal or reaches the optimal value, the optimization result of the measurement terminal is obtained.

[0145] In another embodiment, sub-objective functions of different objectives are constructed respectively, and then a plurality of sub-objective functions are comprehensively considered to form an objective function. The specific steps are as follows:

[0146]

[0147] f3(z)=-R(z)

[0148]

[0149] Among them, c i is the cost of placing the measurement terminal at node i, z i is the decision variable, z i Indicates whether the measurement terminal is arranged at node i; f2(z) is used to maximize the observability of the distribution network, f3(z) is used to improve the robustness of the system, and R(z) is the average observability under various fault conditions;

[0150] After the objective function is determined, the NBI method is used to construct the Pareto frontier between the sub-objective functions, and the best performing point in each sub-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. Through the solutions to these optimization problems, the entire Pareto frontier can be gradually constructed. The specific contents are as follows:

[0151] Construct the Pareto frontier between each sub-objective function and obtain the objective function Among them, λ i is the weight of each sub-objective function, ν is the penalty parameter used to control the deviation of the solution from the reference direction, d is the unit vector, d is the objective function direction, and f i (z) represents each sub-objective function. In this example, f i (z) is f1(z), f2(z), f3(z).

[0152] The Pareto front is a key concept in multi-objective optimization, which refers to a set of solutions that achieve the best trade-off between multiple objectives. In this set, no solution can improve one objective without harming other objectives.

[0153] In multi-objective programming, due to conflicts and incomparable phenomena between objectives, a solution is the best in one objective, but may be relatively poor in other objectives. The collection of these non-dominated solutions is the so-called Pareto frontier. All solutions located in the Pareto frontier are not dominated by solutions outside the Pareto frontier (and other solutions within the Pareto frontier curve), so these non-dominated solutions have the least objective conflicts compared to other solutions, and can provide decision makers with a better choice space. When improving any objective function based on a non-dominated solution, at least one other objective function will inevitably be weakened.

[0154] It can be understood that according to the aforementioned optimization model, the configuration results of the measurement terminal are continuously adjusted within the scope of the constraints to find a configuration result that is closest to the objective function, that is, the optimization result of the distribution network measurement terminal. In order to verify the effectiveness of the above-mentioned optimization method, the embodiment of the present application uses the IEEE 15-node system to build a topological structure diagram of the distribution network, and compares and analyzes the observability of the distribution network under different schemes. The specific contents are as follows:

[0155] like Figure 6 As shown, the graph model of the IEEE 15-node system contains 15 nodes and 14 edges. Figure 6 The available measurement terminals on the medium voltage side (FTU) and the low voltage side (IFT) are marked in Figure 1. Among them, 15 nodes are load access nodes.

[0156] First, the initial observable branch set Ω0 is established, and the FTU is deployed on the head node, Node 0, and Ω0 = Branch{0-1}. Then, the initial observable branch set is expanded using IFT to obtain the observable branch set Ω b =Branch{0-1,3-4,3-5,5-6,1-3,11-14,10-14,9-14,}.

[0157] Secondly, DFS is used to find the observable node set Ω in the distribution network. N According to the DFS algorithm, the head node is set as the initial visit node, and some nodes in the network are visited in sequence, as long as all adjacent branches of the node are in the observable branch set Ω. b If the node is within , the node is judged to be observable. Establish the observable node set Ω N ={0, 3, 4, 5, 6, 10, 11, 14}. Then, the observable islands are divided by aggregating adjacent observable nodes, such as Figure 6 As shown in the figure, the number of observable nodes is 7, that is, N = 7, and the observability evaluation index is Φ = 7 / 15 = 0.47.

[0158] Finally, based on the topological structure diagram of the distribution network and the observable node set ΩN , establish the OPMT problem model, and use NBI combined with DBPSO to solve the optimization model (OPMT) problem. In addition, under the condition of ensuring the observability of the IEEE 15-node system, the more IFTs there are, the fewer FTUs need to be deployed.

[0159] Reference Figure 7 , the embodiment of the present application provides a device for optimizing the configuration of a distribution network measurement terminal, including:

[0160] The data acquisition unit 201 is used to acquire a topological structure diagram of the distribution network;

[0161] A set determination unit 202, configured to provide an observable branch set and an observable node set and divide observable islands according to the topological structure diagram;

[0162] An observation analysis unit 203, used to analyze the feasibility of each observable island and determine the observability of the distribution network;

[0163] The configuration optimization unit 204 is used to optimize the location and number of measurement terminals in the distribution network using an optimization model according to the observability of the distribution network, and obtain an optimization result of the measurement terminal configuration; wherein the optimization model is based on the modified particle swarm algorithm to optimize the measurement terminal configuration, which is used to balance the cost of the measurement terminal and the observability of the distribution network.

[0164] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0165] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0166] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0167] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0168] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0169] 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 method for optimizing the configuration of distribution network measurement terminals, characterized in that: include: Obtain a topological structure diagram of the distribution network, where the topological structure diagram includes N nodes and b branches; According to the topological structure diagram, the observable branch set and observable node set are given and the observable islands are divided; Analyze the feasibility of each observable island to determine the observability of the distribution network; According to the observability of the distribution network, the optimization model is used to optimize the location and number of measurement terminals in the distribution network, and the optimization result of the measurement terminal configuration is obtained; Among them, the optimization model is based on the modified particle swarm algorithm to optimize the configuration of the measurement terminal, which is used to balance the cost of the measurement terminal and the observability of the distribution network.

2. The method for optimizing the configuration of the distribution network measurement terminal according to claim 1, characterized in that: According to the topological structure diagram, the observable branch set and the observable node set are given and the observable island is divided, including: According to the topological structure diagram, an initial observable branch set and a load access node are determined, wherein the observable branches in the initial observable branch set are branches connected to nodes equipped with measurement terminals in the topological structure diagram, and the load access node is a node equipped with a distribution transformer in the topological structure diagram; Given the degree of each node in the topological structure graph, we can get the node degree sequence D = {d1, d2, d3, ..., d N }, the degree of a node is the number of branches connected to the node, and if the node is a load access node, the degree of the node is increased by 1; According to the sequence of node degrees, the observable degree of each node is given, and the initial observable branch set is expanded to obtain the observable branch set; Based on the observable branch set, a depth-first search algorithm is used to give the observable node set and determine the observable island. The observable node is a node whose adjacent branches are in the observable branch set. The observable island is obtained by aggregating adjacent observable nodes.

3. The method for optimizing the configuration of the distribution network measurement terminal according to claim 2, characterized in that: According to the sequence of node degrees, the observable degree of each node is given, and the initial observable branch set is expanded to obtain the observable branch set, including: Set the current search depth a =min{d1,d2,...,d N }; Get a node whose search degree is the current search degree, and calculate the observability of the node, where the observability of the node is the number of observable branches adjacent to the node, and if the node is equipped with a fusion terminal, the observability of the node is increased by 1; Determine whether the observability of the node is greater than or equal to d a -1; If so, add all adjacent lines of the node to the observable branch set; When a =max{d1,d2,...,d N } and the nodes under the current search degree are traversed, and the observable branch set of the distribution network is output.

4. The method for optimizing the configuration of distribution network measurement terminals according to claim 1, characterized in that: The feasibility of each observable island is analyzed to determine the observability of the distribution network, including: Among them, Φ is the observability of the distribution network, Φ0 is the number of observable nodes among the N nodes in the distribution network, Φ∈[0,1], when the observability of the distribution network is less than 1, the distribution network is unobservable.

5. The method for optimizing the configuration of distribution network measurement terminals according to claim 1, characterized in that: According to the observability of the distribution network, the optimization model is used to optimize the location and number of measurement terminals in the distribution network to obtain the optimization result of the measurement terminal configuration, including: When the observability of the distribution network is unobservable; Initialize the parameters of the optimization model, which include the number of particles, particle dimension and maximum number of iterations. The number of particles is the total number of nodes in the distribution network. Each node corresponds to each dimension. Whether each node is equipped with a terminal corresponds to the particle dimension value of each dimension, which is 0 or 1. Set the objective function and constraints to evaluate the fitness of particles in the particle swarm; Record the individual optimal position and global optimal position of each particle; Update the particle dimension value by updating the particle's speed and position; Determine whether the maximum number of iterations has been reached; If yes, the iteration is stopped, and the optimization result of the measurement terminal is output based on the particle dimension value. The optimization result of the measurement terminal is a vector with the same dimension as the number of nodes in the power distribution diagram, and the vector elements are 0 or 1; If not, continue to evaluate the fitness of the particles in the particle swarm, record the individual optimal position and global optimal position of each particle, update the particle speed and position, and update the particle dimension value.

6. The method for optimizing the configuration of distribution network measurement terminals according to claim 5, characterized in that: The constraints are determined in the following way: s.t.A B×N x N×1 ≥b B×1 x k =1,k∈Ω N x i ∈{0,1} Among them, A B×N is the adjacency matrix established using the topological structure graph, the vector x N×1 =[x1,x2,…,x N ] is the required measurement placement vector, the elements are x i ∈{0,1},x i =1 means that a measurement terminal should be deployed on node i, Φ y is the observability of the observable island y, b B×1 is a vector whose elements are all 1, Ω N is the observable node set, and k is the observable node number.

7. The method for optimizing the configuration of distribution network measurement terminals according to claim 5, characterized in that: The objective function is determined in the following way: Among them, c i is the cost of equipping the i-th node with a measurement terminal, z i is a binary variable indicating whether the node is equipped with a terminal, φ i is the observability of node i, α and β are weight coefficients, α is used to adjust the importance of cost, and β is used to adjust the importance of observability.

8. The method for optimizing the configuration of distribution network measurement terminals according to claim 5, characterized in that: The updating of the particle speed and position includes: The velocity update expression and position update expression of each particle: v[i][j]=w·v[i][j]+c1·rand()·(pbest[i][j]-x[i][j])+c2·rand()·(gbest[j]-x[i][j]) Among them, particle x=[x1,x2,…,x N ] is a binary vector with a length equal to the number of nodes in the distribution network, N. Each bit x in the vector i Corresponding to the i-th node of the distribution network, the value is 0 or 1, v[i][j] is the speed of particle i in dimension j, x[i][j] is the position of particle i in dimension j, pbest[i][j] is the historical optimal position of particle i, gbest[j] is the global optimal position, σ(v[i][j]) is the sigmoid function of the speed, w is the chaos factor, c1 is the learning factor of the individual optimal position, c2 is the learning factor of the global optimal position, and rand() is a random number between [0,1).

9. The method for optimizing the configuration of distribution network measurement terminals according to claim 1, characterized in that: The observability of the distribution network is determined in the following way: For a distribution network with N nodes and b branches, the measurement equation can be expressed as z=h(x)+v; Where z is the measurement vector, h(x) is the nonlinear measurement function, x is the state vector, and v is the measurement error; Get the Jacobian matrix Observability of the distribution network according to the rank of the Jacobian matrix.

10. A device for optimizing the configuration of distribution network measurement terminals, characterized in that: A method for optimizing the configuration of a distribution network measurement terminal according to any one of claims 1 to 9, comprising: A data acquisition unit, used to acquire a topological structure diagram of the distribution network; A set determination unit, used for providing an observable branch set and an observable node set and dividing observable islands according to the topological structure diagram; An observation and analysis unit, used to analyze the feasibility of each observable island and determine the observability of the distribution network; The configuration optimization unit is used to optimize the location and number of measurement terminals in the distribution network using an optimization model according to the observability of the distribution network, and obtain the optimization result of the measurement terminal configuration; wherein the optimization model is based on the modified particle swarm algorithm to optimize the measurement terminal configuration, which is used to balance the cost of the measurement terminal and the observability of the distribution network.

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