Distribution automation switch encryption stationing optimization method and system

Abstract

The invention provides a distribution automation switch encryption stationing optimization method and system. The method comprises the following steps: acquiring basic data of a distribution automation switch from a distribution network; the basic data comprises position, quantity, real-time state, current, voltage and fault information; constructing a power distribution network optimal encryption stationing generation model which combines a micro-grid operation optimization algorithm and a binary particle swarm optimization algorithm and takes minimization of line power loss, voltage fluctuation and / or voltage deviation of the power distribution network as a target function; and inputting the basic data into the distribution network optimal encryption stationing generation model, and outputting a target distribution network encryption stationing scheme. According to the invention, the problems of unreasonable distribution of distribution automation switches, large line power loss, voltage fluctuation and voltage deviation in a traditional distribution network are solved, and the operation efficiency and stability of the distribution network are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution automation, and in particular to a method and system for optimizing the encrypted distribution of power distribution automation switches. Background Art

[0002] In the power system, the encryption deployment technology of distribution automation switches is based on advanced communication technology and control strategies, aiming to improve the operating efficiency, safety and reliability of the distribution network. With the introduction of quantum encryption technology, the communication security of distribution automation switches has been significantly improved. This technology uses the non-replicability and anti-interference of quantum states to achieve the secure distribution and transmission of keys and information through the quantum key distribution protocol QKD and the quantum secret sharing protocol QSNet. In the distribution automation system, intelligent switches using quantum encryption technology can ensure the super security of key data transmission in the power grid in a wireless access environment, while realizing remote control, telemetry and telesignaling functions. This not only improves the efficiency of fault handling, but also reduces the safety hazards and power outage duration caused by manual operation.

[0003] However, in traditional distribution networks, the layout of distribution automation switches is often unreasonable. Due to historical reasons or technical limitations, the layout of switches may not fully consider the network structure, load distribution and operation and maintenance requirements, resulting in insufficient number of switches or improper locations in some areas. This unreasonable layout method not only increases the difficulty of operation and maintenance, but also affects the reliability and safety of the distribution network. In addition, due to the unreasonable layout of switches, the problem of line power loss has become increasingly prominent. During the transmission process, electric energy is lost due to factors such as resistance and inductance, resulting in increased line power loss, affecting the economy and efficiency of power supply. At the same time, voltage fluctuations and voltage deviations also occur from time to time. Due to factors such as load changes and line impedance, the voltage may fluctuate or deviate in different time periods and locations, which not only affects the normal operation of power equipment, but also may cause damage to sensitive equipment such as household appliances. Therefore, optimizing the layout of distribution automation switches, reducing line power loss, and stabilizing voltage levels are issues that need to be urgently addressed in the current distribution network. Summary of the invention

[0004] To this end, the present invention provides a method and system for optimizing the encrypted distribution of distribution automation switches, aiming to solve the technical problems of unreasonable distribution automation switch distribution, large line power loss, voltage fluctuation and voltage deviation in traditional distribution networks.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] According to a first aspect of the present invention, the present invention provides a method for optimizing the encrypted distribution of distribution automation switches, the method comprising:

[0007] Obtain basic data of distribution automation switches from the distribution network; the basic data includes location, quantity, real-time status, current, voltage and fault information;

[0008] Constructing a distribution network optimal density point generation model combining a microgrid operation optimization algorithm and a binary particle swarm algorithm with minimizing line power loss, voltage fluctuation and / or voltage deviation of the distribution network as an objective function;

[0009] The basic data is input into the optimal distribution network density point generation model, and the target distribution network density point plan is output.

[0010] Furthermore, the construction combines the microgrid operation optimization algorithm and the binary particle swarm algorithm to minimize the line power loss, voltage fluctuation and / or voltage deviation of the distribution network as the objective function to generate the optimal encrypted point distribution model of the distribution network, including:

[0011] Acquire historical basic data of the distribution point automation switch, and construct a network topology map using the historical basic data;

[0012] Using a binary particle swarm algorithm to search and optimize the state combination of the distribution automation switches in the network topology diagram, to obtain a target state combination where the line power loss value is less than a preset loss threshold;

[0013] The target state combination is screened using a microgrid operation optimization algorithm to obtain an optimal distribution network density layout plan that minimizes voltage fluctuations and voltage deviations.

[0014] Furthermore, the obtaining of historical basic data of the distribution point automation switch and constructing a network topology map using the historical basic data includes:

[0015] Acquire historical basic data of distribution automation switches within a historical time period from the distribution network; the historical basic data includes location, quantity, real-time status, current, voltage and fault information;

[0016] The positions of the distribution automation switches are used as nodes, and the lines between the distribution automation switches are used as edges to construct a network topology graph;

[0017] The nodes include the number, real-time status, current, voltage and fault information of distribution automation switches.

[0018] Furthermore, the binary particle swarm algorithm is used to search and optimize the state combination of the distribution automation switch in the network topology diagram to obtain a target state combination in which the line power loss value is less than a preset loss threshold, including:

[0019] Step S1: using the on state and the off state of the distribution automation switch as binary decision variables, and using the binary particle swarm algorithm to search and optimize the state combination of the distribution automation switch in the network topology diagram to obtain an optimized network topology diagram;

[0020] Step S2: performing power flow calculation on the optimized network topology diagram to obtain a line power loss value;

[0021] Step S3: determining whether the line power loss value is less than the preset loss threshold;

[0022] Step S4: If the line power loss value is less than the preset loss threshold, the state combination of the distribution automation switch is used as the target state combination; if the line power loss value is greater than / equal to the preset loss threshold, the above steps S1 to S4 are re-executed until the line power loss value is less than the preset loss threshold.

[0023] Furthermore, the binary particle swarm algorithm is used to search and optimize the state combination of the distribution automation switches in the network topology diagram to obtain an optimized network topology diagram, including:

[0024] With the goal of minimizing the line power loss of the distribution network, a line power loss function is defined;

[0025] Randomly initializing a particle swarm to generate a state combination of distribution automation switches in the network topology diagram;

[0026] Repeat the following steps S11 to S14 until the change in the fitness value of the state combination of the distribution automation switch reaches a preset change threshold:

[0027] Step S11: calculating the fitness value of the state combination of the distribution automation switch in the network topology diagram according to the line power loss function;

[0028] Step S12, increasing the number of the distribution automation switches or changing the positions of the distribution automation switches to update the state combination of the distribution automation switches in the network topology diagram;

[0029] Step S13: determining a state combination of distribution automation switches with minimum line power loss in the distribution network, and a corresponding line power loss value;

[0030] Step S14, generating an optimized network topology diagram according to the state combination of the distribution automation switches with the smallest line power loss value.

[0031] Furthermore, the target state combination is screened by using the microgrid operation optimization algorithm to obtain the optimal distribution network density point layout scheme that minimizes voltage fluctuation and voltage deviation, including:

[0032] Inputting the target state combination into the microgrid operation optimization algorithm;

[0033] With the goal of minimizing voltage fluctuation and voltage deviation, the state of the distribution automation switch and the power / load distribution are adjusted to obtain a distribution automation switch state combination, and voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination;

[0034] When the voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination meet the preset constraint conditions, the distribution automation switch state combination is used as the optimal distribution network encryption point layout plan.

[0035] Furthermore, the preset constraints include:

[0036] Node voltage constraint, the formula is as follows:

[0037] 0.95V max,i ≤V i ≤1.05V max,i

[0038] Among them, V i represents the actual voltage of the ith node in the power distribution network; V max,i represents the rated voltage of the ith node in the power distribution network;

[0039] and / or,

[0040] Line current constraint, the formula is as follows:

[0041] I j ≤0.8I max,j

[0042] Among them, I j I represents the actual current of the jth line in the power distribution network; max,j represents the rated current of the jth circuit;

[0043] and / or,

[0044] The total input active power of the power distribution network = the total output active power; the total input reactive power of the power distribution network = the total output reactive power;

[0045] and / or,

[0046] Within any preset time period, the number of state changes of a single distribution automation switch does not exceed the change number threshold.

[0047] Furthermore, the method further comprises:

[0048] Taking installation cost, distribution network balance reliability index and load reduction into consideration, the optimal encrypted point generation model of the distribution network is trained using a loss function; the formula of the loss function is as follows:

[0049]

[0050] Among them, L represents the loss function value; P loss represents the line power loss function; α represents the weight influence factor of the line power loss function; σ 2 v represents the voltage fluctuation loss function; v represents the distribution network voltage; σ 2 represents the variance of the voltage fluctuation of the distribution network; β represents the weight influence factor of the voltage fluctuation loss function; γ represents the weight influence factor of the voltage deviation loss function; the formula of the voltage deviation loss function is as follows:

[0051]

[0052] Where N represents the total number of nodes in the distribution network; V i represents the actual voltage of the ith node in the power distribution network, V max,i represents the rated voltage of the ith node in the distribution network; Cost installation represents the installation cost; δ represents the weighted influencing factor of the installation cost; Reliability index represents the distribution network balance reliability index of the index-th distribution automation switch state combination; ε represents the weight influencing factor of the distribution network balance reliability index; the formula for the sum of the squares of the load reduction of all lines in the distribution network under overload or fault conditions is as follows:

[0053] Wherein, ξ represents the weighted influencing factor of the sum of squares of the load reduction amount; represents the sum of squares of load reduction of the jth line in the distribution network under overload or fault conditions, and M represents the total number of lines in the distribution network.

[0054] Furthermore, before searching and optimizing the state combination of the distribution automation switches in the network topology diagram using the binary particle swarm algorithm, the method further includes:

[0055] A depth-first search method is used to remove redundant nodes in the network topology graph.

[0056] According to a second aspect of the present invention, the present invention provides a distribution automation switch encryption point optimization system, the system comprising:

[0057] A data acquisition module is used to acquire basic data of distribution automation switches from the distribution network; the basic data includes location, quantity, real-time status, current, voltage and fault information;

[0058] A model building module, used to build a distribution network optimal encrypted point generation model combining a microgrid operation optimization algorithm and a binary particle swarm algorithm to minimize line power loss, voltage fluctuation and / or voltage deviation of the distribution network as an objective function;

[0059] The point distribution optimization module is used to input the basic data into the optimal encrypted point distribution generation model of the distribution network and output the target distribution network encrypted point distribution plan.

[0060] The present invention adopts the above technical solution and has at least the following beneficial effects:

[0061] Through the scheme of the present invention, the basic data of the distribution automation switch is obtained from the distribution network; the basic data includes location, quantity, real-time status, current, voltage and fault information; a distribution network optimal encrypted point generation model is constructed by combining the microgrid operation optimization algorithm and the binary particle swarm algorithm to minimize the line power loss, voltage fluctuation and / or voltage deviation of the distribution network as the objective function; the basic data is input into the distribution network optimal encrypted point generation model, and the target distribution network encrypted point generation plan is output. In this way, the problems of unreasonable distribution automation switch point distribution, large line power loss, voltage fluctuation and voltage deviation in the traditional distribution network are solved, and the operation efficiency and stability of the distribution network are greatly improved.

[0062] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A schematic diagram of a flow chart of a method for optimizing the encrypted layout of switches for power distribution automation provided by an embodiment of the present invention is shown;

[0065] Figure 2 A schematic diagram of the structure of a distribution automation switch encryption point optimization system provided by an embodiment of the present invention is shown;

[0066] Figure 3A schematic diagram of the structure of a distribution automation switch encryption point optimization system provided by another embodiment of the present invention is shown;

[0067] Figure 4 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0068] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0069] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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 process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0070] The embodiment of the present invention provides a method for optimizing the encrypted layout of distribution automation switches, such as Figure 1 As shown, at least the following steps S101 to S103 may be included:

[0071] Step S101, obtaining basic data of distribution automation switches from the distribution network.

[0072] In the embodiment of the present invention, the basic data of the distribution automation switch is obtained from the distribution network, which may include location, quantity, status, current, voltage and fault information, etc. Thus, it is ensured that the acquired data covers all relevant automation switches in the distribution network, and the provided data is real-time or recent, and can reflect the current status of the distribution network.

[0073] Step S102, constructing a distribution network optimal density point generation model combining a microgrid operation optimization algorithm and a binary particle swarm algorithm with minimizing line power loss, voltage fluctuation and / or voltage deviation of the distribution network as an objective function.

[0074] The embodiment of the present invention provides a distribution network optimal encrypted point generation model that outputs a distribution network encrypted point plan based on basic data. Through model construction and model training, a distribution network encrypted point plan with the goal of minimizing the line power loss, voltage fluctuation and / or voltage deviation of the distribution network is obtained. In addition, by combining the microgrid operation optimization algorithm and the binary particle swarm algorithm, the solution efficiency and accuracy of the distribution network optimal encrypted point generation model are improved, and a variety of factors are comprehensively considered to ensure that the generated point plan is optimal in multiple dimensions. At the same time, the operation efficiency and stability of the distribution network are improved with the goal of minimizing line power loss and reducing voltage fluctuations and deviations.

[0075] Specifically, the historical basic data of distribution point automation switches can be obtained, and the network topology diagram can be constructed using the historical basic data; the binary particle swarm algorithm can be used to search and optimize the state combination of distribution automation switches in the network topology diagram, and the target state combination in which the line power loss value is less than the preset loss threshold can be obtained; the microgrid operation optimization algorithm can be used to screen the target state combination to obtain the optimal distribution network encryption point layout plan that minimizes voltage fluctuations and voltage deviations.

[0076] The optimal encrypted point generation model for the power distribution network in the embodiment of the present invention uses historical basic data as input data for subsequent processing and training verification. First, a network topology diagram is constructed, which specifically includes obtaining historical basic data of the distribution automation switches in the historical time period from the distribution network. The historical basic data may include location, quantity, real-time status, current, voltage, and fault information. Then, the location of the distribution automation switch is used as a node, and the lines between the distribution automation switches are used as edges to construct a network topology diagram; it can be understood that the nodes may include the number, real-time status, current, voltage, and fault information of the distribution automation switches.

[0077] It should be noted that in order to improve data processing efficiency, after constructing the network topology graph, the redundant nodes in the network topology graph can be removed by depth-first search, and the network topology graph after removing the redundant nodes can be used for subsequent calculations.

[0078] Furthermore, the binary particle swarm algorithm is used to search and optimize the state combination of the distribution automation switch in the network topology diagram to obtain the target state combination in which the line power loss value is less than the preset loss threshold, which may specifically include the following steps S1 to S4:

[0079] Step S1: Taking the on state and off state of the distribution automation switch as binary decision variables, the binary particle swarm algorithm is used to search and optimize the state combination of the distribution automation switch in the network topology diagram to obtain the optimized network topology diagram.

[0080] First, the state of the distribution automation switch is used as a binary decision variable, where the state of the automation switch includes on and off. With the goal of minimizing the line power loss of the distribution network, a line power loss loss function is defined; the particle swarm is randomly initialized to generate the state combination of the distribution automation switch in the network topology diagram. Then, the following steps S11 to S14 are repeated until the change in the fitness value of the state combination of the distribution automation switch reaches the preset change threshold:

[0081] Step S11: calculating the fitness value of the state combination of the distribution automation switch in the network topology diagram according to the line power loss function;

[0082] Step S12, increasing the number of distribution automation switches or changing the positions of distribution automation switches to update the state combination of distribution automation switches in the network topology diagram;

[0083] Step S13: determining a state combination of distribution automation switches with minimum line power loss in the distribution network, and a corresponding line power loss value;

[0084] Step S14, generating an optimized network topology diagram according to the state combination of the distribution automation switch with the smallest line power loss value.

[0085] Thus, a network topology diagram in which the fitness value of the state combination of the distribution automation switch reaches the preset change threshold is obtained as the final optimized network topology diagram. It should be noted that in practical applications, the preset change threshold can be set according to actual needs, and the present invention does not limit this.

[0086] Step S2: Calculate the power flow of the optimized network topology to obtain the line power loss value;

[0087] Step S3: Determine whether the line power loss value is less than a preset loss threshold;

[0088] Step S4: If the line power loss value is less than the preset loss threshold, the state combination of the distribution automation switch is used as the target state combination; if the line power loss value is greater than / equal to the preset loss threshold, the above steps S1 to S4 are re-executed until the line power loss value is less than the preset loss threshold.

[0089] Thus, a state combination in which the line power loss value is less than the preset loss threshold is obtained as the target state combination. It should be noted that in practical applications, the preset loss threshold can be set according to actual needs, and the present invention does not limit this.

[0090] Furthermore, the target state combination is screened by using the microgrid operation optimization algorithm to obtain the optimal distribution network density layout plan that minimizes voltage fluctuations and voltage deviations. Specifically, the target state combination can be input into the microgrid operation optimization algorithm; with the goal of minimizing voltage fluctuations and voltage deviations, the state of the distribution automation switch and the power / load distribution are adjusted to obtain the distribution automation switch state combination, and the voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination; when the voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination meet the preset constraints, the distribution automation switch state combination is used as the optimal distribution network density layout plan.

[0091] The constraints in the embodiment of the present invention may include the following four items: node voltage constraint, line current constraint, power constraint and state change constraint. Among them, the node voltage constraint means that the voltage of all nodes in the distribution network must fluctuate within the rated voltage range of ±5%, and the formula is as follows:

[0092] 0.95V max,i ≤V i ≤1.05V max,i

[0093] Among them, V i represents the actual voltage of the ith node in the power distribution network; V max,i Represents the rated voltage of the ith node in the distribution network.

[0094] Line current constraint means that the current of all lines in the distribution network shall not exceed 80% of the rated current, which is expressed as:

[0095] I j ≤0.8I max,j

[0096] Among them, I j represents the actual current of the jth line in the distribution network; I max,j Represents the rated current of the jth circuit.

[0097] The power constraint means that the total input active power of the distribution network must be equal to the total output active power; the total input reactive power of the distribution network must be equal to the total output reactive power.

[0098] The state change constraint means that the number of state changes of a single distribution automation switch shall not exceed the change number threshold within any preset time period. For example, the number of state changes of a single distribution automation switch shall not exceed 5 times within any 24 hours.

[0099] Furthermore, the embodiment of the present invention can also comprehensively consider the installation cost, the distribution network balance reliability index and the load reduction, and use the loss function to train the distribution network optimal density distribution generation model to obtain the distribution network optimal density distribution generation model that can be used to generate the target distribution network density distribution plan according to the current basic data. The formula of the loss function is as follows:

[0100]

[0101] Among them, L represents the loss function value; P loss represents the line power loss function; α represents the weight influence factor of the line power loss function; σ 2 v represents the voltage fluctuation loss function; v represents the distribution network voltage; σ 2 represents the variance of the voltage fluctuation in the distribution network; β represents the weighted influence factor of the voltage fluctuation loss function; γ represents the weighted influence factor of the voltage deviation loss function; the formula of the voltage deviation loss function is as follows:

[0102]

[0103] Where N represents the total number of nodes in the distribution network; V i represents the actual voltage of the ith node in the power distribution network, V max,i represents the rated voltage of the ith node in the distribution network; Cost installation represents the installation cost; δ represents the weighted influencing factor of the installation cost; Reliability index represents the distribution network balance reliability index of the index-th distribution automation switch state combination; ε represents the weight influencing factor of the distribution network balance reliability index; the formula for the sum of the squares of the load reduction of all lines in the distribution network under overload or fault conditions is as follows:

[0104] Among them, ξ represents the weighted influencing factor of the sum of squares of load reduction; represents the sum of squares of load reduction of the jth line in the distribution network under overload or fault conditions, and M represents the total number of lines in the distribution network.

[0105] Step S103, inputting basic data into the optimal distribution network density point generation model, and outputting the target distribution network density point plan.

[0106] Based on the model construction and model training in step S102, an optimal distribution network density distribution generation model is obtained for generating a target distribution network density distribution plan according to current basic data. By inputting the basic data of the distribution automation switches in the current distribution network, the optimal target distribution network density distribution plan can be output intelligently.

[0107] The embodiment of the present invention provides a method for optimizing the encrypted distribution of distribution automation switches, which obtains the basic data of the distribution automation switches from the distribution network; the basic data includes the location, quantity, real-time status, current, voltage and fault information; constructs a distribution network optimal encrypted distribution generation model that combines the microgrid operation optimization algorithm and the binary particle swarm algorithm to minimize the line power loss, voltage fluctuation and / or voltage deviation of the distribution network as the objective function; inputs the basic data into the distribution network optimal encrypted distribution generation model, and outputs the target distribution network encrypted distribution solution. Based on this, the present invention has at least the following beneficial effects:

[0108] 1) It can make full use of the detailed basic data obtained from the distribution network, including but not limited to the location, quantity, real-time status, current, voltage and fault information of switches, providing a solid foundation for accurate analysis of the current status of the distribution network. Through the pre-trained distribution network optimal encryption point generation model, the optimal distribution automation switch encryption point solution is intelligently output;

[0109] 2) By constructing a network topology diagram, the binary particle swarm algorithm is used to search and optimize the state combination of distribution automation switches in the distribution network, ensuring that the optimal distribution automation switch state combination is screened out under the premise of minimizing line power loss, effectively reducing the line power loss of the distribution network and improving energy utilization efficiency;

[0110] 3) Introducing microgrid operation optimization algorithm, in view of voltage fluctuation and voltage deviation problems, adjusting and optimizing the state combination of distribution automation switches and the distribution of power supply and load, ensuring that the voltage fluctuation of all nodes is within the rated voltage range of ±5%, and the current of all lines does not exceed 80% of the rated current, which not only significantly reduces voltage fluctuation and voltage deviation, improves voltage quality, but also ensures the safe and stable operation of the distribution network;

[0111] 4) In-depth consideration of the installation cost, distribution network balance reliability index and load reduction will not only help reduce the operating cost of the distribution network and improve energy efficiency, but also enhance the adaptability and resilience of the distribution network, ensuring stable and reliable power supply under various working conditions, providing users with more stable and reliable power supply services.

[0112] Further, as Figure 1 The specific implementation of the present invention provides a distribution automation switch encryption point optimization system, such as Figure 2 As shown, the system may include: a data acquisition module 210 , a model building module 220 and a point layout optimization module 230 .

[0113] The data acquisition module 210 can be used to obtain basic data of the distribution automation switch from the distribution network; the basic data includes location, quantity, real-time status, current, voltage and fault information;

[0114] The model building module 220 can be used to build a distribution network optimal density point generation model that combines a microgrid operation optimization algorithm and a binary particle swarm algorithm to minimize line power loss, voltage fluctuation and / or voltage deviation of the distribution network as an objective function;

[0115] The point distribution optimization module 230 can be used to input basic data into the optimal encrypted point distribution generation model of the distribution network and output the target distribution network encrypted point distribution plan.

[0116] Alternatively, if Figure 3 As shown, another embodiment of the present invention provides a distribution automation switch encryption optimization system, which also includes: a model training module 240.

[0117] The model training module 240 can be used to comprehensively consider the installation cost, the distribution network balance reliability index and the load reduction, and use the loss function to train the distribution network optimal encryption point generation model; the formula of the loss function is as follows:

[0118]

[0119] Among them, L represents the loss function value; P loss represents the line power loss function; α represents the weight influence factor of the line power loss function; σ 2 v represents the voltage fluctuation loss function; v represents the distribution network voltage; σ 2 represents the variance of the voltage fluctuation in the distribution network; β represents the weighted influence factor of the voltage fluctuation loss function; γ represents the weighted influence factor of the voltage deviation loss function; the formula of the voltage deviation loss function is as follows:

[0120]

[0121] Where N represents the total number of nodes in the distribution network; V i represents the actual voltage of the ith node in the power distribution network, V max,i represents the rated voltage of the ith node in the power distribution network; Cost installation represents the installation cost; δ represents the weighted influencing factor of the installation cost; Reliability index represents the distribution network balance reliability index of the index-th distribution automation switch state combination; ε represents the weight influencing factor of the distribution network balance reliability index; the formula for the sum of the squares of the load reduction of all lines in the distribution network under overload or fault conditions is as follows:

[0122] Among them, ξ represents the weighted influencing factor of the sum of squares of load reduction; represents the sum of squares of load reduction of the jth line in the distribution network under overload or fault conditions, and M represents the total number of lines in the distribution network.

[0123] Optionally, the model building module 220 may also be used to obtain historical basic data of the distribution point automation switch and use the historical basic data to build a network topology map;

[0124] The binary particle swarm algorithm is used to search and optimize the state combination of the distribution automation switch in the network topology diagram, and the target state combination with the line power loss value less than the preset loss threshold is obtained;

[0125] The microgrid operation optimization algorithm is used to screen the target state combination and obtain the optimal distribution network density layout plan that minimizes voltage fluctuation and voltage deviation.

[0126] Optionally, the model building module 220 may also be used to obtain historical basic data of distribution automation switches within a historical time period from the distribution network; the historical basic data includes location, quantity, real-time status, current, voltage and fault information;

[0127] The locations of distribution automation switches are taken as nodes, and the lines between distribution automation switches are taken as edges to construct a network topology graph;

[0128] The nodes include the number, real-time status, current, voltage and fault information of distribution automation switches.

[0129] Optionally, the model building module 220 may also be used to perform the following steps:

[0130] Step S1: Taking the on state and off state of the distribution automation switch as binary decision variables, using the binary particle swarm algorithm to search and optimize the state combination of the distribution automation switch in the network topology diagram to obtain the optimized network topology diagram;

[0131] Step S2: Calculate the power flow of the optimized network topology to obtain the line power loss value;

[0132] Step S3: Determine whether the line power loss value is less than a preset loss threshold;

[0133] Step S4: If the line power loss value is less than the preset loss threshold, the state combination of the distribution automation switch is used as the target state combination; if the line power loss value is greater than / equal to the preset loss threshold, the above steps S1 to S4 are re-executed until the line power loss value is less than the preset loss threshold.

[0134] Optionally, the model building module 220 may also be used to define a line power loss loss function with the goal of minimizing the line power loss of the power distribution network;

[0135] Randomly initialize the particle swarm to generate the state combination of the distribution automation switches in the network topology diagram;

[0136] Repeat the following steps S11 to S14 until the change in the fitness value of the state combination of the distribution automation switch reaches a preset change threshold:

[0137] Step S11: calculating the fitness value of the state combination of the distribution automation switch in the network topology diagram according to the line power loss function;

[0138] Step S12, increasing the number of distribution automation switches or changing the positions of distribution automation switches to update the state combination of distribution automation switches in the network topology diagram;

[0139] Step S13: determining a state combination of distribution automation switches with minimum line power loss in the distribution network, and a corresponding line power loss value;

[0140] Step S14, generating an optimized network topology diagram according to the state combination of the distribution automation switch with the smallest line power loss value.

[0141] Optionally, the model building module 220 may also be used to input the target state combination into the microgrid operation optimization algorithm;

[0142] With the goal of minimizing voltage fluctuation and voltage deviation, the state of the distribution automation switch and the power / load distribution are adjusted to obtain the distribution automation switch state combination and the voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination;

[0143] When the voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination meet the preset constraints, the distribution automation switch state combination is used as the optimal distribution network encryption layout plan.

[0144] Optionally, the model building module 220 may also be used to remove redundant nodes in the network topology diagram using a depth-first search method before searching and optimizing the state combinations of distribution automation switches in the network topology diagram using a binary particle swarm algorithm.

[0145] It should be noted that for other corresponding descriptions of the functional modules involved in the power distribution automation switch encryption point optimization system provided by the embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0146] Based on the above Figure 1The method shown, accordingly, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the distribution automation switch encryption point optimization method of any of the above embodiments are implemented.

[0147] Based on the above Figure 1 The method shown and Figure 3 The embodiment of the system shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 4 As shown, the computer device may include a communication bus, a processor, a memory and a communication interface, and may also include an input and output interface and a display device, wherein each functional unit may communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the steps of the distribution automation switch encryption point optimization method of the above embodiment.

[0148] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they are not further described here.

[0149] In addition, the functional units in various embodiments of the present invention may be physically independent of each other, or two or more functional units may be integrated together, or all functional units may be integrated into one processing unit. The above integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.

[0150] Those skilled in the art can understand that if the integrated functional unit is implemented in the form of software and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can essentially or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, which includes a number of instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention when running the instructions. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

[0151] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware associated with program instructions (such as a computing device such as a personal computer, a server, or a network device), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of a computing device, the computing device executes all or part of the steps of the methods described in the embodiments of the present invention.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate from the protection scope of the present invention.

Claims

1. A method for optimizing the encrypted distribution of distribution automation switches, characterized in that: The method comprises: Obtain basic data of distribution automation switches from the distribution network; the basic data includes location, quantity, real-time status, current, voltage and fault information; Constructing a distribution network optimal density point generation model combining a microgrid operation optimization algorithm and a binary particle swarm algorithm with minimizing line power loss, voltage fluctuation and / or voltage deviation of the distribution network as an objective function; The basic data is input into the optimal distribution network density point generation model, and the target distribution network density point plan is output.

2. The method according to claim 1, characterized in that The construction combines the microgrid operation optimization algorithm and the binary particle swarm algorithm to minimize the line power loss, voltage fluctuation and / or voltage deviation of the distribution network as the objective function to generate the optimal encrypted point distribution model of the distribution network, including: Acquire historical basic data of the distribution point automation switch, and construct a network topology map using the historical basic data; Using a binary particle swarm algorithm to search and optimize the state combination of the distribution automation switches in the network topology diagram, to obtain a target state combination where the line power loss value is less than a preset loss threshold; The target state combination is screened using a microgrid operation optimization algorithm to obtain an optimal distribution network density layout plan that minimizes voltage fluctuations and voltage deviations.

3. The method according to claim 2, characterized in that The step of obtaining historical basic data of the distribution point automation switch and constructing a network topology map using the historical basic data includes: Acquire historical basic data of distribution automation switches within a historical time period from the distribution network; the historical basic data includes location, quantity, real-time status, current, voltage and fault information; The positions of the distribution automation switches are used as nodes, and the lines between the distribution automation switches are used as edges to construct a network topology graph; The nodes include the number, real-time status, current, voltage and fault information of distribution automation switches.

4. The method according to claim 2, characterized in that: The binary particle swarm algorithm is used to search and optimize the state combination of the distribution automation switch in the network topology diagram to obtain a target state combination in which the line power loss value is less than a preset loss threshold, including: Step S1: using the on state and the off state of the distribution automation switch as binary decision variables, and using the binary particle swarm algorithm to search and optimize the state combination of the distribution automation switch in the network topology diagram to obtain an optimized network topology diagram; Step S2: performing power flow calculation on the optimized network topology diagram to obtain a line power loss value; Step S3: determining whether the line power loss value is less than the preset loss threshold; Step S4: If the line power loss value is less than the preset loss threshold, the state combination of the distribution automation switch is used as the target state combination; if the line power loss value is greater than / equal to the preset loss threshold, the above steps S1 to S4 are re-executed until the line power loss value is less than the preset loss threshold.

5. The method according to claim 4, characterized in that The method of using the binary particle swarm algorithm to search and optimize the state combination of the distribution automation switches in the network topology diagram to obtain an optimized network topology diagram includes: With the goal of minimizing the line power loss of the distribution network, a line power loss function is defined; Randomly initializing a particle swarm to generate a state combination of distribution automation switches in the network topology diagram; Repeat the following steps S11 to S14 until the change in the fitness value of the state combination of the distribution automation switch reaches a preset change threshold: Step S11: calculating the fitness value of the state combination of the distribution automation switch in the network topology diagram according to the line power loss function; Step S12, increasing the number of the distribution automation switches or changing the positions of the distribution automation switches to update the state combination of the distribution automation switches in the network topology diagram; Step S13: determining a state combination of distribution automation switches with minimum line power loss in the distribution network, and a corresponding line power loss value; Step S14, generating an optimized network topology diagram according to the state combination of the distribution automation switches with the smallest line power loss value.

6. The method according to claim 2, characterized in that The method of using the microgrid operation optimization algorithm to screen the target state combination to obtain the optimal distribution network encryption point layout plan that minimizes voltage fluctuations and voltage deviations includes: Inputting the target state combination into the microgrid operation optimization algorithm; With the goal of minimizing voltage fluctuation and voltage deviation, the state of the distribution automation switch and the power / load distribution are adjusted to obtain a distribution automation switch state combination, and voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination; When the voltage fluctuation and voltage deviation values ​​corresponding to the distribution automation switch state combination meet the preset constraint conditions, the distribution automation switch state combination is used as the optimal distribution network encryption point layout plan.

7. The method according to claim 6, characterized in that The preset constraints include: Node voltage constraint, the formula is as follows: 0.95V max,i ≤V i ≤1.05V max,i Among them, V i represents the actual voltage of the ith node in the power distribution network; V max,i represents the rated voltage of the ith node in the power distribution network; and / or, Line current constraint, the formula is as follows: IN j ≤0.8I max,j Among them, I j I represents the actual current of the jth line in the power distribution network; max,j represents the rated current of the jth circuit; and / or, The total input active power of the power distribution network = the total output active power; the total input reactive power of the power distribution network = the total output reactive power; and / or, Within any preset time period, the number of state changes of a single distribution automation switch does not exceed the change number threshold.

8. The method according to claim 1, characterized in that The method further comprises: Taking installation cost, distribution network balance reliability index and load reduction into consideration, the optimal encrypted point generation model of the distribution network is trained using a loss function; the formula of the loss function is as follows: Among them, L represents the loss function value; P loss represents the line power loss function; α represents the weight influence factor of the line power loss function; σ 2 v represents the voltage fluctuation loss function; v represents the distribution network voltage; σ 2 represents the variance of the voltage fluctuation of the distribution network; β represents the weight influence factor of the voltage fluctuation loss function; γ represents the weight influence factor of the voltage deviation loss function; the formula of the voltage deviation loss function is as follows: Where N represents the total number of nodes in the distribution network; V i Represents the actual voltage of the ith node in the power distribution network, V max,i represents the rated voltage of the ith node in the power distribution network; Cost installation represents the installation cost; δ represents the weighted influencing factor of the installation cost; Reliability index represents the distribution network balance reliability index of the index-th distribution automation switch state combination; ε represents the weight influencing factor of the distribution network balance reliability index; the formula for the sum of the squares of the load reduction of all lines in the distribution network under overload or fault conditions is as follows: Wherein, ξ represents the weighted influencing factor of the sum of squares of the load reduction amount; represents the sum of squares of load reduction of the jth line in the distribution network under overload or fault conditions, and M represents the total number of lines in the distribution network.

9. The method according to any one of claims 2 to 8, characterized in that: Before searching and optimizing the state combination of the distribution automation switches in the network topology diagram using the binary particle swarm algorithm, the method further includes: A depth-first search method is used to remove redundant nodes in the network topology graph.

10. A distribution automation switch encryption and point optimization system, characterized in that: The system comprises: A data acquisition module is used to acquire basic data of distribution automation switches from the distribution network; the basic data includes location, quantity, real-time status, current, voltage and fault information; A model building module, used to build a distribution network optimal encrypted point generation model combining a microgrid operation optimization algorithm and a binary particle swarm algorithm to minimize line power loss, voltage fluctuation and / or voltage deviation of the distribution network as an objective function; The point distribution optimization module is used to input the basic data into the optimal encrypted point distribution generation model of the distribution network and output the target distribution network encrypted point distribution plan.

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