A day-ahead reactive power optimization method for traction power supply system and related device

Abstract

The application discloses a kind of traction power supply system day-to-day reactive power optimization method and related device, belong to rail transit and power system dispatching automation technical field;The method is according to the topological structure of rail transit traction power supply network, distribution network and the number of OLTC and CB actually deployed, clear decision variable;Construct the reactive power optimization model of traction power supply system based on particle swarm algorithm;Collect day-to-day prediction data of load and multiple distributed photovoltaic systems as model input;The population initialization and inertia weight of particle swarm algorithm are improved, and the reactive power optimization model of traction power supply system based on particle swarm algorithm is solved, to obtain the action strategy of each reactive power compensation equipment;The action time of each reactive power compensation equipment is determined using Fisher optimal segmentation method, and finally the reactive power optimization result is obtained.The application divides period scientifically and enhances the search efficiency of algorithm, achieves efficient reactive power optimization in day-to-day time dimension under the premise of ensuring stable operation of equipment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of rail transit and power system dispatching automation, and relates to a traction power supply system day-ahead reactive power optimization method and related device. BACKGROUND

[0002] In the traction power supply system of rail transit, reactive power optimization is a key link to ensure stable operation of trains and improve power supply efficiency. With the rapid development of urban rail transit systems and the widespread application of power electronic devices, the demand for reactive power in the traction power supply system is increasing. Reactive power is not only crucial for maintaining the voltage stability of the power supply network, but also directly affects the energy efficiency and service life of traction equipment. An efficient reactive power optimization strategy can significantly reduce power loss and improve the economy and reliability of the power supply system, thereby better meeting the growing operational needs of rail transit. Therefore, developing advanced reactive power optimization technology suitable for the traction power supply system of rail transit is of great significance to improving the overall system performance.

[0003] Traditional reactive power optimization methods, such as linear programming and nonlinear programming, have obvious limitations in the application of the traction power supply system of rail transit. These methods often fail to fully consider the special operating constraints of traction power supply equipment, especially the use restrictions of OLTC (On-Load Tap Changer) and CB (Capacitor Bank) under frequent adjustment, which can easily lead to frequent operation of equipment, exacerbate equipment wear and tear, and increase the risk of failure. At the same time, the high computational complexity of traditional methods makes it difficult to adapt to rapid changes in rail transit loads and complex operating scenarios, limiting their application effect in real-time control and dynamic optimization.

[0004] Therefore, the current reactive power optimization strategy still faces significant challenges in the practical application of the traction power supply system of rail transit, not only making it difficult to effectively control the action frequency of OLTC and CB, but also easily falling into a local optimal solution, resulting in poor optimization results. At the same time, traditional strategies have obvious shortcomings in calculation speed and global optimization ability, making it difficult to achieve efficient and accurate reactive power regulation in the complex traction power supply network. SUMMARY

[0005] The present application aims to provide a traction power supply system day-ahead reactive power optimization method and related device to solve the technical problems of high computational complexity and low accuracy of existing reactive power optimization strategies, which are difficult to adapt to rapid changes in rail transit loads and complex operating scenarios.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a day-ahead reactive power optimization method for a traction power supply system, comprising the following steps:

[0008] According to the topological structure of the traction power supply network and the distribution network, and the number of OLTCs and CBs actually deployed, the decision variables are determined;

[0009] Based on the decision variables, a particle swarm optimization model for the traction power supply system is constructed;

[0010] Collecting day-ahead prediction data of the load and multiple distributed photovoltaic systems, and inputting the particle swarm optimization model for the traction power supply system;

[0011] The population initialization and inertia weight of the particle swarm optimization algorithm are improved, and the particle swarm optimization model for the traction power supply system is solved to obtain the action strategy of each reactive power compensation device;

[0012] The action strategy of each reactive power compensation device is regarded as a sample sequence of ordered clustering, and the Fisher optimal segmentation method is used to determine the action time of each reactive power compensation device, and finally the reactive power optimization result is obtained.

[0013] Further, the step of determining the decision variables according to the topological structure of the traction power supply network and the distribution network, and the number of OLTCs and CBs actually deployed, specifically comprises:

[0014] According to the topological structure of the traction power supply network and the distribution network, the connection mode of nodes, lines and devices in the topological structure is analyzed, and the number of OLTCs and CBs actually deployed in the distribution network is determined;

[0015] Based on the number of OLTCs and CBs, the decision variables are defined, and the total number of decision variables is determined;

[0016] The value range and the limitation condition of each decision variable are set.

[0017] Further, the step of constructing the particle swarm optimization model for the traction power supply system based on the decision variables, specifically comprises:

[0018] The power flow constraints of the distribution network, the operation safety constraints of the distribution network, and the operation constraints of the OLTCs and CBs are established;

[0019] Based on the operation reliability of the distribution network, a voltage deviation index is defined; based on the operation economy of the distribution network, an expected network loss index and a reactive power compensation device switching cost index are defined; according to the voltage deviation index, the expected network loss index and the reactive power compensation device switching cost index, a target function is constructed, and the particle swarm optimization model for the traction power supply system is obtained.

[0020] Furthermore, the expression of the distribution network flow constraint is:

[0021]

[0022] Where, For nodes Injected active power; For nodes The injected reactive power, For nodes voltage; For nodes and nodes The conductance between For nodes and nodes The susceptance between For nodes and nodes The phase angle difference between is the number of branches;

[0023] The expression of the distribution network operation safety constraint is:

[0024]

[0025] Where, is the maximum allowable voltage; is the minimum allowable voltage; is the upper limit of branch current; is the branch current; For nodes voltage;

[0026] The expressions of the OLTC and CB operation constraints are:

[0027]

[0028]

[0029] Where, is the node at time t Reactive power of CB; is the compensation power of each group of CB; is the number of CB switching groups at time t; is the upper limit value of CB at time t; is the lower limit value of CB at time t; is the branch at time t The actual transformation ratio of the OLTC; is the branch at time t The upper limit of OLTC; is the branch at time t lower limit value of the OLTC; tap position of the OLTC at time t; adjustment step of the OLTC transformation ratio.

[0030] Further, the voltage deviation variance index is:

[0031]

[0032] wherein, total number of nodes, total number of time periods, voltage of the node at time t, rated voltage of the node; maximum allowable voltage; minimum allowable voltage;

[0033] the expected network loss index is:

[0034]

[0035] wherein, number of branches; active power of the branch in which the node at time t is located; reactive power of the branch in which the node at time t is located, resistance of the branch in which the node at time t is located; total number of time periods;

[0036] the reactive compensation device switching cost index is:

[0037]

[0038] wherein, single action cost of the OLTC; single action cost of the CB, number of actions of the OLTC; number of actions of the CB.

[0039] Further, the step of improving the population initialization and inertia weight of the particle swarm algorithm specifically comprises:​​​​​​​​​​​​

[0040] The population of the particle swarm algorithm is initialized by Bernoulli shift chaos equation, and the Bernoulli shift chaos equation is as follows:

[0041]

[0042] In the formula, x is a chaotic sequence between 0 and 1 generated by Bernoulli shift chaos equation; x is a chaotic sequence between 0 and 1 generated by Bernoulli shift chaos equation; x is a chaotic sequence between 0 and 1 generated by Bernoulli shift chaos equation; x is a chaotic sequence between 0 and 1 generated by Bernoulli shift chaos equation; x is a chaotic sequence between 0 and 1 generated by Bernoulli shift chaos equation;

[0043] If the value range of a variable is [a, b], the corresponding initial value is:

[0044]

[0045] In the formula, x is an initial value of the variable; x is a lower limit of the value of the variable; x is an upper limit of the value of the variable; The inertia weight of the particle swarm algorithm is dynamically adjusted based on a symmetric sigmoid curve, and the inertia weight of the first iteration is:

[0046]

[0047]

[0048] In the formula, x is a minimum weight; x is a maximum weight; x is a current iteration number; x is a maximum iteration number. Further, the step of determining the action time of each reactive compensation device by using the Fisher optimal segmentation method specifically includes the following steps.

[0049] An optimal segmentation scheme is defined, and an ordered sample sequence of n clusters is segmented into m parts, and the segmentation point is represented by x, so that the segmentation result is represented as:

[0050]

[0051]

[0052] All segmentation schemes exist in an optimal segmentation scheme that minimizes the objective function value. ​​​​​​​​​​

[0053] Define the objective function The objective of optimal partition is to minimize the sum of squared deviations of each part, any partition scheme The objective function of the partition scheme is expressed as:

[0054]

[0055] In the formula, The sum of squared deviations of the cth part, The calculation formula of is:

[0056]

[0057] In the formula, The value of the qth sample is represented; The partition point of the c+1th part is represented; The partition point of the cth part is represented; The sample mean of the cth part is represented, The calculation formula of is:

[0058]

[0059] In the formula, The partition point of the c+1th part is represented;

[0060] The Fisher optimal partition method is used to solve the objective function combined with the recursive method, and the recursive formula used by the recursive method is:

[0061]

[0062] In the formula, The partition point The objective function value of the optimal m-1 partition of all previous samples is represented; The partition point The sum of squared deviations of the samples after the partition point to the nth sample is represented; The objective function value of the m-1 partition of the first q-1 samples is represented; The sum of squared deviations of the qth sample to the nth sample is represented; The mth optimal partition point is represented;

[0063] According to the recursive formula, the optimal bisection of the first b samples is calculated first The specific calculation formula is:

[0064]

[0065] In the formula, the value range of b is ;

[0066] The optimal trisection of b samples is sequentially solved optimal 4-division optimal m-1-division ;

[0067] On the basis of the optimal m-1-division scheme, the optimal m-division scheme of the first n sample sequences is solved according to a recursive formula , and the corresponding mth optimal division point is determined ;

[0068] Based on the mth optimal division point , the (m-1)th optimal division point is solved , so as to meet:

[0069]

[0070] All optimal division points are finally solved, and the action time of each reactive power compensation device is obtained.

[0071] In a second aspect, the present application provides a traction power supply system day-ahead reactive power optimization system, comprising:

[0072] A decision variable determination module is configured to determine decision variables according to the topological structure of the rail transit traction power supply network and the distribution network, and the number of OLTCs and CBs actually deployed.

[0073] A model construction module is configured to construct a traction power supply system reactive power optimization model based on a particle swarm algorithm based on the decision variables.

[0074] A data acquisition module is configured to collect day-ahead prediction data of loads and a plurality of distributed photovoltaic systems, and input the traction power supply system reactive power optimization model based on the particle swarm algorithm.

[0075] A model solving module is configured to improve the population initialization and inertia weight of the particle swarm algorithm, and solve the traction power supply system reactive power optimization model based on the particle swarm algorithm to obtain the action strategy of each reactive power compensation device.

[0076] A reactive power optimization module is configured to regard the action strategy of each reactive power compensation device as a sample sequence of ordered clustering, determine the action time of each reactive power compensation device by using Fisher optimal division method, and finally obtain the reactive power optimization result.

[0077] In a third aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the traction power supply system day-ahead reactive power optimization method when executing the computer program.

[0078] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the traction power supply system day-ahead reactive power optimization method.

[0079] Compared with the prior art, the present application has the following beneficial effects:

[0080] The present application discloses a traction power supply system day-ahead reactive power optimization method and related device, which effectively reduces the frequent adjustment of equipment, prolongs the service life of equipment, and improves the optimization accuracy and calculation efficiency by reasonably dividing the time period and limiting the operation frequency of OLTC and CB. The improved particle swarm optimization algorithm introduces chaos population initialization and inertia weight adjustment method, which not only improves the global search ability, but also speeds up the convergence speed and avoids the local optimal problem. Combined with the Fisher optimal segmentation method for accurate scheduling of reactive power compensation device action time, the reliability and safety of the traction power supply system are further improved. In the practical application of the rail transit traction power supply system, the present application can effectively improve the real-time and flexibility of reactive power optimization, reduce the equipment action frequency by adjusting the optimization parameters, improve the voltage stability and economy, and has important significance for promoting the industry technology progress and sustainable development. BRIEF DESCRIPTION OF DRAWINGS

[0081] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0082] Figure 1 The flow chart of the method of the present application;

[0083] Figure 2 The principle diagram of the system of the present application;

[0084] Figure 3 The overall flow chart of the traction power supply system day-ahead reactive power optimization method of the embodiment of the present application;

[0085] Figure 4 The model network topology diagram of the embodiment of the present application;

[0086] Figure 5 The structure schematic diagram of the computer equipment of the present application. DETAILED DESCRIPTION

[0087] The present application will be described in detail below with reference to the drawings and in combination with the embodiments. It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0088] The following detailed description is merely exemplary in nature and is intended to provide further detail regarding the present application. Unless otherwise defined, all technical terms used herein are to be interpreted according to their ordinary meaning in the field of the present application. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the example embodiments according to the present application.

[0089] Referring to Figure 1 and Figure 3 , the embodiments of the present application disclose a traction power supply system day-ahead reactive power optimization method, comprising the following steps:

[0090] S1, according to the topological structure of the rail transit traction power supply network and the distribution network, and the number of OLTCs and CBs actually deployed, the decision variables are determined;

[0091] S101, according to the topological structure of the rail transit traction power supply network and the distribution network, the connection mode of the nodes, lines and equipment in the topological structure is analyzed; the number of OLTCs and CBs actually deployed in the distribution network is determined.

[0092] S102, based on the number and type of equipment, the decision variables are defined, such as the adjustment gear of each OLTC, the switching group number of CB, etc., and the total number of decision variables is determined.

[0093] S103, a reasonable value range and restriction condition are set for each decision variable, to ensure the effectiveness of the subsequent optimization process.

[0094] S2, referring to Figure 4 , based on the decision variables, a traction power supply system reactive power optimization model based on particle swarm algorithm is constructed;

[0095] S201, distribution network power flow constraints, distribution network operation safety constraints, and OLTC and CB operation constraints are established;

[0096] (1) Distribution network power flow constraints:

[0097]

[0098] In the formula, is the active power injected by node ; is the reactive power injected by node , is the voltage of node ; is the conductance between node and node ; is the conductance between node and node susceptance between the nodes; the phase angle difference between the nodes and the node ; the number of branches.

[0099] (2) Power distribution network operation safety constraints:

[0100]

[0101] wherein, is the maximum allowable voltage; is the minimum allowable voltage; is the upper limit of branch current; is the branch current; is the voltage of the node .

[0102] (3) OLTC, CB operation constraints:

[0103]

[0104]

[0105] wherein, is the reactive power of the CB in the node at time t; is the compensation power of each CB group; is the number of switched CB groups at time t; is the upper limit of the CB at time t; is the lower limit of the CB at time t; is the actual ratio of the OLTC in the branch at time t; is the upper limit of the OLTC in the branch at time t; is the lower limit of the OLTC in the branch at time t; is the tap position of the OLTC at time t; is the adjustment step of the OLTC ratio.

[0106] S202, constructing a target function according to the voltage deviation index, the expected network loss index and the reactive power compensation device switching cost index, to obtain a traction power supply system reactive power optimization model based on a particle swarm algorithm.

[0107] (1) Based on the reliability of the power distribution network operation, the voltage deviation index is defined, and the formula is:

[0108]

[0109] wherein, is the total number of nodes, is the total number of time periods, is the voltage of node at time , is the rated voltage of node ; is the maximum allowed voltage; is the minimum allowed voltage.

[0110] (2) Based on the economic operation of the distribution network, define the expected network loss index , the switching cost index of reactive power compensation equipment , the formulas are respectively:

[0111]

[0112] In the formula, is the number of branches, is the active power of the branch where node is located at time ; is the reactive power of the branch where node is located at time , is the resistance of the branch where node is located at time ; is the voltage of the first end of the branch where node is located at time ; is the total number of time periods.

[0113]

[0114] In the formula, is the single action cost of OLTC; is the single action cost of CB, is the number of actions of OLTC; is the number of actions of CB.

[0115] S3, collect the day-ahead prediction data of the load and multiple distributed photovoltaic systems, and input the traction power supply system reactive power optimization model based on the particle swarm algorithm;

[0116] The collected data is cleaned and preprocessed to ensure the accuracy and consistency of the data. The data is arranged in time series format to facilitate subsequent model input.

[0117] S4, improve the population initialization and inertia weight of the particle swarm algorithm, and solve the traction power supply system reactive power optimization model based on the particle swarm algorithm to obtain the action strategy of each reactive power compensation equipment;

[0118] The current reactive power / voltage optimization control model belongs to a typical mixed integer nonlinear programming (MINLP) problem, which has high complexity. The traditional particle swarm optimization algorithm is prone to fall into local optimal solution in the solving process, and has high computational complexity, which is difficult to meet the accuracy requirements of actual power grid. Therefore, the population initialization and inertia weight of the particle swarm optimization algorithm are improved.

[0119] (1) The population of the particle swarm optimization algorithm is initialized by Bernoulli shift chaos equation. For the nonlinear programming problem with multiple constraints, multiple dimensions and multiple peaks, the low convergence accuracy, the randomly generated non-uniform initial population may make the particle swarm unable to search the solution space comprehensively, and may fall into local optimum and appear "premature" phenomenon. Compared with the typical Logistic chaotic system, the Bernoulli shift chaotic system is more uniform in [0, 1]. The Bernoulli shift chaotic equation is:

[0120]

[0121] In the formula, is the chaotic sequence generated between 0 and 1; is the next chaotic sequence generated according to is a constant.

[0122] Suppose the value range of a variable is , then the corresponding initial value is

[0123]

[0124] In the formula, is the initial value of the variable; is the lower limit of the variable value; is the upper limit of the variable value.

[0125] (2) Dynamic adjustment of inertia weight based on symmetric Sigmoid curve. When solving actual optimization problems, it is often desired to first use global search to quickly converge the search space to a certain region, and then use local fine search to obtain high-precision solution. Therefore, an adaptive adjustment strategy is proposed, that is, the value of is reduced accordingly as the iteration proceeds. The inertia weight is dynamically adjusted by using a symmetric Sigmoid curve. The inertia weight of the first iteration is

[0126] ​​

[0127] wherein, is the minimum weight; is the maximum weight; is the current iteration number; is the maximum iteration number.

[0128] S5, the action strategy of each reactive power compensation device is regarded as a sample sequence of ordered clustering, the Fisher optimal segmentation method is used to determine the action time of each reactive power compensation device, and finally the reactive power optimization result is obtained.

[0129] S501, defining an optimal segmentation scheme. The sample sequence of n ordered clusters is segmented into m parts, and the segmentation point is represented by , then the segmentation result can be represented as:

[0130]

[0131] All segmentation schemes must have a segmentation scheme that minimizes the objective function value .

[0132] S502, defining the objective function .

[0133] The objective of optimal segmentation is to minimize the sum of the squared deviations of each part. The objective function of any segmentation scheme can be represented as:

[0134]

[0135] wherein, represents the squared deviation of the cth part, and the calculation formula is:

[0136]

[0137] wherein, represents the value of the qth sample; represents the segmentation point of the c+1th part; represents the segmentation point of the cth part; represents the sample mean of the cth part, and the calculation formula is:

[0138]

[0139] wherein, represents the segmentation point of the cth part; represents the segmentation point of the c+1th part; represents the value of the qth sample; ​

[0140] S503, the Fisher optimal segmentation method is used to solve the objective function by combining the recursive method.

[0141] The Fisher optimal segmentation method uses the sum of squared deviations to represent the difference between samples of the same class. The optimal segmentation point is determined by recursive calculation to minimize the difference between samples of the same class and maximize the difference between samples of different classes. Therefore, the key of the recursive method lies in the solution of the segmentation point.

[0142] As can be seen from the definition of the objective function, the optimal m-1 segmentation scheme of the sample sequence can be obtained by adding the last segment to the optimal m-1 segmentation scheme , and the recursive formula is as follows:

[0143]

[0144] In the formula, represents the objective function value of the optimal m-1 segmentation of all samples before the segmentation point ; represents the sum of squared deviations of the samples after the segmentation point to the nth sample; represents the objective function value of the first q-1 samples divided into m-1 segments; represents the sum of squared deviations of the qth sample to the nth sample; represents the mth optimal segmentation point.

[0145] According to the recursive formula, the optimal bisection of the first b samples is calculated first, and the calculation formula is as follows:

[0146]

[0147] In the formula, the value range of b is .

[0148] Similarly, the optimal trisection of the b samples, the optimal four-segmentation of the b samples, …, and the optimal m-1 segmentation of the b samples are sequentially solved. On the basis of the optimal m-1 segmentation scheme, the optimal m segmentation scheme of the first n sample sequence is solved according to the recursive formula

[0149] , and the corresponding mth optimal segmentation point is determined.

[0150] ​​​​​​ Based on step , the m-1th optimal segmentation point is solved , so as to satisfy:

[0151]

[0152] By analogy, all optimal segmentation points can be solved, and the action time of each reactive power compensation device is obtained.

[0153] In dynamic reactive power optimization, the action sequence of OLTC and capacitor bank can be regarded as a sample sequence of ordered clustering, the maximum action number of discrete reactive power compensation device allowed by the power grid is the optimal segmentation number, and the action time is the optimal segmentation point. The mean value of each classification is taken as the action value of all time periods in the class. In addition, since the action value of the reactive power compensation device is a discrete integer value, the integer value closest to the mean value is taken as the actual action scheme, and finally the reactive power optimization result is obtained.

[0154] Referring to Figure 2 , the embodiment of the present application discloses a traction power supply system day-ahead reactive power optimization system, comprising a decision variable determination module, a model construction module, a data acquisition module, a model solving module and a reactive power optimization module.

[0155] The decision variable determination module is used to determine the decision variable according to the topological structure of the rail transit traction power supply network and the distribution network and the number of OLTC and CB actually deployed. The model construction module is used to construct a traction power supply system reactive power optimization model based on a particle swarm algorithm based on the decision variable. The data acquisition module is used to collect day-ahead prediction data of loads and multiple distributed photovoltaic systems and input the traction power supply system reactive power optimization model based on the particle swarm algorithm. The model solving module is used to improve the population initialization and inertia weight of the particle swarm algorithm and solve the traction power supply system reactive power optimization model based on the particle swarm algorithm to obtain the action strategy of each reactive power compensation device. The reactive power optimization module is used to regard the action strategy of each reactive power compensation device as a sample sequence of ordered clustering, determine the action time of each reactive power compensation device by using the Fisher optimal segmentation method, and finally obtain the reactive power optimization result.

[0156] In an embodiment of the present application, referring to Figure 5The application provides a computer device, which comprises a processor and a memory for storing a computer program, wherein the computer program comprises program instructions, and the processor is used for executing the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function. The processor in the embodiment of the application can be used for the operation of the traction power supply system day-ahead reactive power optimization method.

[0157] The application further provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in the computer device and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include the built-in storage medium in the computer device, and of course can also include the expansion storage medium supported by the computer device. The computer readable storage medium provides a storage space, and the storage space stores the operating system of the terminal. Furthermore, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM (Random Access Memory, random access memory), and can also be a non-volatile memory (non-volatile memory), for example, at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the traction power supply system day-ahead reactive power optimization method in the above embodiment.

[0158] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM (Compact Disc Read-Only Memory), optical storage, etc.) containing computer-usable program code.

[0159] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0160] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0161] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0162] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing reactive power of a traction power supply system, characterized in that: The following steps are involved: Clarify the decision variables based on the topology of the rail transit traction power supply network, distribution network, and the number of OLTCs and CBs actually deployed; Based on the decision variables, a reactive power optimization model for the traction power supply system is constructed using the particle swarm algorithm; Collecting day-ahead forecast data of loads and multiple distributed photovoltaic systems and inputting the data into the reactive power optimization model of the traction power supply system based on the particle swarm algorithm; Improve the population initialization and inertia weight of the particle swarm algorithm, solve the reactive power optimization model of the traction power supply system based on the particle swarm algorithm, and obtain the action strategy of each reactive compensation device; The action strategy of each reactive compensation device is regarded as an ordered clustered sample sequence, and the Fisher optimal partitioning method is used to determine the action time of each reactive compensation device, and finally the reactive power optimization result is obtained. The step of using the Fisher optimal partitioning method to determine the action time of each reactive compensation device specifically includes: Define the optimal segmentation scheme; cluster n ordered sample sequences Divide into m parts, and use the division points Represents that the segmentation result Expressed as: All partitioning schemes There exists an optimal partitioning scheme that minimizes the objective function value. ; Define the objective function ; The goal of optimal segmentation is to minimize the sum of the squares of the deviations of each part. Any segmentation scheme The objective function is expressed as: Where, represents the sum of squares of deviations of part c, The calculation formula is: Where, represents the value of the qth sample; Indicates the split point of the c+1th part; Indicates the split point of part c; represents the sample mean of part c, The calculation formula is: Where, Indicates the split point of the c+1th part; The Fisher optimal segmentation method is used in combination with a recursive method to solve the objective function. The recursive formula used in the recursive method is: Where, Indicates the split point The objective function value of the optimal m-1 segmentation of all previous samples; Indicates the split point The sum of squared deviations from the last sample to the nth sample; Indicates the objective function value of the first q-1 samples divided into m-1 segments; Represents the sum of squares of deviations from the qth sample to the nth sample; represents the mth optimal segmentation point; According to the recursive formula, the optimal binary split of the first b samples is first calculated. , the specific calculation formula is: In the formula, the value range of b is ; Solve the optimal three-part split of b samples in sequence , optimal four-division , ..., optimal m-1 split ; Based on the optimal m-1 segmentation scheme, the optimal m segmentation scheme of the first n sample sequences is solved according to the recursive formula , and determine the corresponding mth optimal segmentation point ; Based on the mth optimal segmentation point , solve the m-1th optimal split point , so that it satisfies: Finally, all the optimal split points are solved and the action time of each reactive compensation device is obtained.

2. The method for optimizing reactive power of a traction power supply system according to claim 1, characterized in that: The steps for clarifying decision variables based on the topology of the rail transit traction power supply network, distribution network, and the number of OLTCs and CBs actually deployed specifically include: Based on the topology of the rail transit traction power supply network and distribution network, analyze the connection methods of nodes, lines and equipment in the topology; determine the number of OLTCs and CBs actually deployed in the distribution network; Based on the number of OLTCs and CBs, define the decision variables and clarify the total number of decision variables; Set value ranges and restrictions for each decision variable.

3. The method for optimizing reactive power of a traction power supply system according to claim 1, characterized in that: The step of constructing a reactive power optimization model for a traction power supply system based on a particle swarm algorithm based on decision variables specifically includes: Establish distribution network power flow constraints, distribution network operation safety constraints, and OLTC and CB operation constraints; Based on the reliability of distribution network operation, the voltage deviation index is defined; based on the economic efficiency of distribution network operation, the expected network loss index and the switching cost index of reactive compensation equipment are defined; according to the voltage deviation index, the expected network loss index and the switching cost index of reactive compensation equipment, the objective function is constructed to obtain the reactive power optimization model of the traction power supply system based on particle swarm algorithm.

4. The method for optimizing reactive power of a traction power supply system according to claim 3, characterized in that: The expression of the distribution network flow constraint is: Where, For nodes Injected active power; For nodes The injected reactive power, For nodes voltage; For nodes and nodes The conductance between For nodes and nodes The susceptance between For nodes and nodes The phase angle difference between is the number of branches; The expression of the distribution network operation safety constraint is: Where, is the maximum allowable voltage; is the minimum allowable voltage; is the upper limit of branch current; is the branch current; For nodes voltage; The expressions of the OLTC and CB operation constraints are: Where, is the node at time t Reactive power of CB; is the compensation power of each group of CB; is the number of CB switching groups at time t; is the upper limit value of CB at time t; is the lower limit value of CB at time t; is the branch at time t The actual transformation ratio of the OLTC; is the branch at time t The upper limit of OLTC; is the branch at time t The lower limit of OLTC; is the tap position of OLTC at time t; is the adjustment step of OLTC transformation ratio.

5. The method for optimizing reactive power of a traction power supply system according to claim 3, characterized in that: The voltage bias variance indicator for: Where, is the total number of nodes, is the total number of time periods, for Time Node The voltage, For nodes Rated voltage; is the maximum allowable voltage; is the minimum allowable voltage; The expected network loss index for: Where, is the number of branches; for Time Node Active power of the branch; for Time Node The reactive power of the branch, for Time Node The resistance of the branch; for Time Node The voltage at the head end of the branch; is the total number of time periods; The switching cost index of the reactive compensation equipment for: Where, is the single action cost of OLTC; is the cost of a single action of CB, is the number of OLTC actions; is the number of actions of CB.

6. The method for optimizing reactive power of a traction power supply system according to claim 1, characterized in that: The steps of improving the population initialization and inertia weight of the particle swarm algorithm specifically include: The population of the particle swarm algorithm is initialized by Bernoulli shift chaos equation; the Bernoulli shift chaos equation is: Where, is the chaotic sequence between 0 and 1; Based on The next chaotic sequence generated; is a constant; Suppose the value range of a variable is , then the corresponding initial value is: Where, is the initial value of the variable; is the lower limit of the variable value; The upper limit of the variable value; Dynamic adjustment of the inertia weight of the particle swarm algorithm based on the symmetric Sigmoid curve, Iteration inertia weight for: Where, is the minimum weight; is the maximum weight; is the current iteration number; is the maximum number of iterations.

7. A day-ahead reactive power optimization system for a traction power supply system, characterized in that: A method for optimizing reactive power of a traction power supply system according to claim 1, comprising: The decision variable determination module is used to determine the decision variables based on the topology of the rail transit traction power supply network, distribution network, and the number of OLTCs and CBs actually deployed; A model building module is used to build a reactive power optimization model of the traction power supply system based on the particle swarm algorithm based on decision variables; a data acquisition module, configured to collect day-ahead forecast data of loads and multiple distributed photovoltaic systems, and input the data into the reactive power optimization model of the traction power supply system based on the particle swarm algorithm; A model solving module is used to improve the population initialization and inertia weight of the particle swarm algorithm, and solve the reactive power optimization model of the traction power supply system based on the particle swarm algorithm to obtain the action strategy of each reactive compensation device; The reactive power optimization module is used to regard the action strategy of each reactive compensation device as an ordered clustered sample sequence, and use the Fisher optimal partitioning method to determine the action time of each reactive compensation device, and finally obtain the reactive power optimization result.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for optimizing the day-ahead reactive power of a traction power supply system as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the day-ahead reactive power of a traction power supply system as described in any one of claims 1 to 6 are implemented.

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