CN111823876B

CN111823876B - Intelligent power supply control method and system

Info

Publication number: CN111823876B
Application number: CN202010776714.7A
Authority: CN
Inventors: 梅文庆; 李红波; 张超; 黄子昊; 罗文广; 张志学; 王雄
Original assignee: CRRC Zhuzhou Institute Co Ltd
Current assignee: CRRC Zhuzhou Institute Co Ltd
Priority date: 2020-08-05
Filing date: 2020-08-05
Publication date: 2022-03-08
Anticipated expiration: 2040-08-05
Also published as: CN111823876A

Abstract

The invention provides an intelligent power supply control system which is suitable for a rail transit system. The intelligent power supply control system comprises: the communication module is used for communicating with all bidirectional converter devices and all trains on a traction power grid of the rail transit system to acquire current operation parameters of all the bidirectional converter devices and all the trains; the input module is used for inputting a current power supply target of the traction power grid; and a processing module connected with the communication module, the processing module configured to: establishing a current circuit model of the traction power grid based on the current operating parameters; and determining the optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model.

Description

Intelligent power supply control method and system

Technical Field

The invention relates to the field of power supply control of rail transit systems, in particular to an intelligent power supply control method and an intelligent power supply control system.

Background

The power supply system of the existing urban rail transit system basically adopts a multi-pulse rectification technology to convert 35kV or 10kV medium-voltage alternating current into 1500V direct current so as to supply power to a train. However, the energy of the multi-pulse rectifier can only flow in a single direction, and the energy injected into the direct current power grid during braking of the rail transit system cannot be recovered, so that the braking energy is accumulated in the direct current power grid, the direct current voltage is increased, and the electric equipment is damaged.

For this reason, the prior art adopts a brake resistor to absorb the redundant braking energy, but this method is obviously a waste of the braking energy. In addition, the temperature in the track and the station is increased due to the heat generated when the braking resistor absorbs the braking energy, and the energy consumption of the environment control system is increased.

Meanwhile, the output characteristic of the multi-pulse rectifier is soft. For example, when a train starts or accelerates, the output voltage of the multi-pulse rectifier is reduced, and the system loss is increased; when the output voltage is greatly reduced, the over-modulation of the vehicle-mounted converter can be caused, and the harmonic current of the motor of the rail train is increased, so that the problems of temperature rise, vibration and the like of the motor of the rail train are caused.

Meanwhile, flexible energy scheduling cannot be achieved among all substations in the whole power supply network. Therefore, in order to meet the energy consumption requirement when the load is the maximum, the capacities of the substation devices in the power supply network are designed to be large, and waste of the utilization rate and the cost is caused.

In order to solve the above problems, the prior art proposes a scheme of a regenerative braking energy recovery device. This scheme has two directions:

1. the regenerative braking energy is returned to the 35kV power grid by adopting the energy feedback device and is used by other loads of the 35kV power grid;

2. regenerative braking energy is led into the energy storage element through the converter, and when the rail train is in a starting or accelerating state and the like, the converter releases the energy in the energy storage element to the rail train.

The energy feedback device can effectively feed the regenerative braking energy back to the 35kV power grid, but in the process of recovering the energy, when the braking energy is smaller than the capacity of the energy feedback device, the energy feedback device works in a voltage stabilizing mode, and at the moment, if the voltage instruction of the energy feedback device is not properly set, the transmission loss in the process of recovering the braking energy can be increased. Meanwhile, in the actual operation process, the situation that part of energy feedback devices feed back energy of a 35kV power grid to a 110kV public power grid for feedback occurs, so that the recovered energy cannot be utilized by a rail transit system.

The energy storage element recovers regenerative braking energy in a manner that is more energy efficient than the energy utilization of the energy feed device. However, the braking power or the overload power during starting of the rail train is pulse type, the peak value is high, but the average value is low, and in order to meet the energy storage requirement, a power type energy storage element, such as a super capacitor or a flywheel, needs to be adopted. However, the power type energy storage element has a large volume and high cost due to low energy density, and has limitations in practical application.

The Ningbo subway system adopts the combination use of multi-pulse wave rectification and energy feed device, designs the energy feed device into a model capable of flowing in two directions, and when the system is in a traction working condition, the energy feed device rectifies and injects energy into a direct current power grid. However, the direct current output voltage of the energy feeding device is clamped by the multi-pulse rectifier, the voltage is lower and does not exceed 1650V, the advantage that the energy feeding device can flexibly adjust the direct current voltage is lost, and the transmission loss is higher.

In addition, due to the characteristics of uncontrolled rectification, the use of switching conversion devices such as pulse energy consumption of rail trains, station frequency conversion and the like, and intermittent energy consumption during night outage, the power supply network of the urban rail transit system has the problems of a large amount of low-order harmonics, low power factor and high electric energy quality, and a harmonic wave treatment and reactive power compensation device needs to be additionally configured.

Meanwhile, the power data acquisition and monitoring system adopted by the urban rail transit power supply system only has the functions of monitoring partial state data of the power supply network and simply processing interface data, graphic display, report forms and other data, and does not have the function of carrying out load flow optimization scheduling on the whole power supply network by using data on line.

In order to solve the above problems, the present invention aims to provide an intelligent power supply method suitable for a rail transit system, which can achieve information communication among a rail vehicle, the rail system and a power supply network, and can achieve efficient control of the power supply network of the rail transit system with various control targets by using information of the rail vehicle, the rail system and the power supply network.

Disclosure of Invention

The following presents a simplified summary of one or more aspects in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements of all aspects nor delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more aspects in a simplified form as a prelude to the more detailed description that is presented later.

According to an aspect of the present invention, an intelligent power supply control method is provided, which is suitable for a rail transit system, and includes: acquiring a current power supply target of a traction power grid of the rail transit system, and current operation parameters of all bidirectional current transformers and all trains; establishing a current circuit model of the traction power grid based on the current operating parameters; and determining the optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model.

Still further, the establishing a current circuit model of the traction grid based on the current operating parameters includes: numbering the bidirectional converter devices and the trains on the traction power grid according to positions to serve as each node of the traction power grid; determining an admittance matrix of the traction grid based on all nodes of the traction grid; constructing a node voltage equation of the traction power grid by using the admittance matrix and the current operation parameters; and solving the node voltage equation to obtain a current circuit model of the traction power grid.

Still further, the current operating parameters include power parameters, and solving the node voltage equation to obtain the current circuit model of the traction grid includes: and solving the node voltage equation by adopting a power source node iterative method to obtain a current circuit model of the traction power grid.

Further, the solving the node voltage equation by using a power source node iterative method to obtain the current circuit model of the traction power grid comprises: initializing a node voltage of a power source node of the node voltage equation; iterating node voltages of the power source nodes until the node voltages of all the nodes meet a convergence condition; and taking the node voltages of all the nodes determined by the last iteration process as the node voltages of the corresponding nodes of the current circuit model to form the current circuit model.

Further, each iteration of the node voltage includes: taking the node voltage of the power source node determined in the previous iteration process as the initial node voltage of the power source node in the current iteration process to convert the power source node into a current source node; calculating branch current of the power source node according to the node voltage and the power of the power source node so as to convert the power source node into a current source node; solving the node voltage of each node by adopting a node voltage method or a modified node voltage method; calculating a calculated value of each node corresponding to the current operating parameter based on the node voltage of the node; and responding to the difference value between the calculated value of each node corresponding to the current operation parameter and the obtained current operation parameter being smaller than a set tolerance value, and judging that the node voltages of all the nodes meet the convergence condition.

Further, the obtaining of the current operation parameters of all bidirectional converters on the traction power grid and all trains includes: and responding to the situation that the node voltages of all the nodes meeting the convergence condition do not appear in the iteration process within the preset times, exiting the iteration, and re-obtaining all the bidirectional converter devices on the traction power grid and the current operation parameters of all the trains.

Still further, the intelligent power supply control method further includes: receiving life signals of all bidirectional variable flow devices; judging whether the bidirectional converter devices are in a normal state or not based on the life signals of each bidirectional converter device; and the acquiring all bidirectional converter devices on the traction power grid, the current operation parameters of all trains and the current power supply target comprises: and acquiring the bidirectional converter device in a normal state on the traction power grid, the current operation parameters of all trains and the current power supply target.

Still further, the determining the optimal control parameter of the rail transit system corresponding to the current power supply target based on the current circuit model comprises: and optimizing the optimal control parameters of the current circuit model corresponding to the current power supply target by using an intelligent algorithm.

Still further, the optimizing the optimal control parameters of the current circuit model corresponding to the current power supply target using an intelligent algorithm comprises: determining an objective function of the intelligent algorithm based on the current power supply objective; determining a fitness function of the intelligent algorithm based on the objective function; and solving an optimal solution of the current circuit model by using the intelligent algorithm based on the objective function and the fitness function to serve as the optimal control parameter.

Still further, the intelligent power supply control method further includes: judging the rationality of the optimal control parameters; in response to the optimal control parameter meeting a rationality requirement, sending the optimal control parameter to the bidirectional converter device to execute the optimal control parameter; and responding to the optimal control parameters not meeting the rationality requirement, and re-acquiring the current operation parameters of all bidirectional converter devices and all trains on the traction power grid.

According to another aspect of the present invention, there is also provided an intelligent power supply control system suitable for a rail transit system, the intelligent power supply control system including: the communication module is used for communicating with all bidirectional converter devices and all trains on a traction power grid of the rail transit system to acquire current operation parameters of all the bidirectional converter devices and all the trains; the input module is used for inputting a current power supply target of the traction power grid; and a processing module connected with the communication module, the processing module configured to: establishing a current circuit model of the traction power grid based on the current operating parameters; and determining the optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model.

Still further, the processing module is further configured to: numbering the bidirectional converter devices and the trains on the traction power grid according to positions to serve as each node of the traction power grid; determining an admittance matrix of the traction grid based on all nodes of the traction grid; constructing a node voltage equation of the traction power grid by using the admittance matrix and the current operation parameters; and solving the node voltage equation to obtain a current circuit model of the traction power grid.

Still further, the processing module is further configured to: and solving the node voltage equation by adopting a power source node iterative method to obtain a current circuit model of the traction power grid.

Still further, the processing module is further configured to: initializing a node voltage of a power source node of the node voltage equation; iterating node voltages of the power source nodes until the node voltages of all the nodes meet a convergence condition; and taking the node voltages of all the nodes determined by the last iteration process as the node voltages of the corresponding nodes of the current circuit model to form the current circuit model.

Further, in each iteration of the node voltage, the processing module is configured to: taking the node voltage of the power source node determined in the previous iteration process as the initial node voltage of the power source node in the current iteration process to convert the power source node into a current source node; calculating branch current of the power source node according to the node voltage and the power of the power source node so as to convert the power source node into a current source node; solving the node voltage of each node by adopting a node voltage method or a modified node voltage method; calculating a calculated value of each node corresponding to the current operating parameter based on the node voltage of the node; and responding to the difference value between the calculated value of each node corresponding to the current operation parameter and the obtained current operation parameter being smaller than a set tolerance value, and judging that the node voltages of all the nodes meet the convergence condition.

Still further, the processing module is further configured to: responding to the node voltages of all nodes meeting the convergence condition in the iteration process within the preset times, and exiting the iteration; and the communication module acquires all bidirectional converter devices on the traction power grid and the current operation parameters of all trains again.

Still further, the communication module further receives vital signals of all bidirectional current transformers, and the processing module is further configured to: judging whether the bidirectional converter devices are in a normal state or not based on the life signals of each bidirectional converter device; and the communication module is used for acquiring the bidirectional converter device in a normal state on the traction power grid and the current operation parameters of all trains.

Still further, the processing module is further configured to: and optimizing the optimal control parameters of the current circuit model corresponding to the current power supply target by using an intelligent algorithm.

Still further, the processing module is further configured to: determining an objective function of the intelligent algorithm based on the current power supply objective; determining a fitness function of the intelligent algorithm based on the objective function; and solving an optimal solution of the current circuit model by using the intelligent algorithm based on the objective function and the fitness function to serve as the optimal control parameter.

Still further, the processing module is further configured to: judging the rationality of the optimal control parameters; in response to the optimal control parameter meeting a rationality requirement, controlling the communication module to send the optimal control parameter to the bidirectional converter device to execute the optimal control parameter; and responding to the optimal control parameters not meeting the rationality requirement, and controlling the communication module to acquire all bidirectional converters on the traction power grid and the current operation parameters of all trains again.

According to another aspect of the present invention, there is also provided an intelligent power supply control device, comprising a memory, a processor and a computer program stored on the memory, wherein the processor is used for implementing the steps of the intelligent power supply control method as described in any one of the above embodiments when executing the computer program stored on the memory.

According to yet another aspect of the present invention, there is also provided a computer storage medium having a computer program stored thereon, the computer program when executed implementing the steps of the intelligent power supply control method as described in any one of the above embodiments.

Drawings

The above features and advantages of the present disclosure will be better understood upon reading the detailed description of embodiments of the disclosure in conjunction with the following drawings.

Fig. 1 is a schematic topology of a traction grid depiction according to a rail transit system in the prior art;

FIG. 2 is a block diagram of an intelligent power control system in one embodiment according to one aspect of the present invention;

FIG. 3 is a flow chart illustrating an intelligent power control method according to another aspect of the present invention;

FIG. 4 is a partial flow diagram of an intelligent power control method in an embodiment according to another aspect of the invention;

FIG. 5 is a partial flow diagram of an intelligent power control method in an embodiment according to another aspect of the invention;

FIG. 6 is a partial flow diagram of an intelligent power control method in an embodiment according to another aspect of the invention;

FIG. 7 is a partial flow diagram of an intelligent power control method in an embodiment according to another aspect of the invention;

fig. 8 is a schematic block diagram of an intelligent power supply control device in an embodiment according to yet another aspect of the present invention.

Detailed Description

The following description is presented to enable any person skilled in the art to make and use the invention and is incorporated in the context of a particular application. Various modifications, as well as various uses in different applications will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to a wide range of embodiments. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

In the following detailed description, numerous specific details are set forth in order to provide a more thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the practice of the invention may not necessarily be limited to these specific details. In other instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present invention.

The reader's attention is directed to all papers and documents which are filed concurrently with this specification and which are open to public inspection with this specification, and the contents of all such papers and documents are incorporated herein by reference. All the features disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise. Thus, unless expressly stated otherwise, each feature disclosed is one example only of a generic series of equivalent or similar features.

Note that where used, the designations left, right, front, back, top, bottom, positive, negative, clockwise, and counterclockwise are used for convenience only and do not imply any particular fixed orientation. In fact, they are used to reflect the relative position and/or orientation between the various parts of the object. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.

In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.

It is noted that, where used, further, preferably, still further and more preferably is a brief introduction to the exposition of the alternative embodiment on the basis of the preceding embodiment, the contents of the further, preferably, still further or more preferably back band being combined with the preceding embodiment as a complete constituent of the alternative embodiment. Several further, preferred, still further or more preferred arrangements of the belt after the same embodiment may be combined in any combination to form a further embodiment.

The invention is described in detail below with reference to the figures and specific embodiments. It is noted that the aspects described below in connection with the figures and the specific embodiments are only exemplary and should not be construed as imposing any limitation on the scope of the present invention.

According to one aspect of the invention, an intelligent power supply control system is provided and is suitable for controlling a traction power grid of a rail transit system.

The traction network of the rail transit system is shown in fig. 1, and a bidirectional converter 11 is adopted to replace a multi-pulse rectifier in the prior art. When the train accelerates or starts the east, the bidirectional converter device 11 converts 35kV alternating current into direct current (750V/1500V) to be supplied to a direct current bus so as to supply power to the train; when the train decelerates or brakes, the bidirectional converter 11 returns the direct current on the direct current bus to the 35kV alternating current power grid. The bidirectional converter device 11 has voltage conversion, harmonic suppression and reactive compensation functions, and therefore can be used as an execution mechanism of the intelligent power supply control system to execute a control command generated by the intelligent power supply control system.

In order to realize the real-time control of the traction power grid of the rail transit system, a communication module is configured for each train and each bidirectional converter device, so as to be used for receiving the operation parameters of each train and the operation parameters of each bidirectional converter device and sending the operation parameters to the intelligent power supply control system, and also be used for receiving the control parameters of each bidirectional converter device from the intelligent power supply control system and sending the control parameters to the bidirectional converter device so as to be convenient for the bidirectional converter device to execute. In fig. 1, the dashed line connected to the communication module and the bidirectional converter is a communication line on the traction power grid, and is used for transmitting the operating parameters of the bidirectional converter.

In one embodiment, as shown in fig. 2, the intelligent power control system 200 includes a communication module 210, an input module 220, and a processing module 230.

The communication module 210 is coupled to the bidirectional converter and the communication module on the train to obtain the current operating parameters of all bidirectional converters and all trains on the traction grid. The current operation parameters refer to operation parameters acquired in real time. The current operating parameters of the bidirectional converter device may include data of ac side voltage, dc side voltage, current, and position, and the current operating parameters of the train may include parameters of position and operating power of the train.

The communication module 210 may be a 4G communication module, or may be a 5G, 3G, 2G, WI-FI or any wireless or wired communication module capable of realizing data interaction between systems.

The input module 220 is used for receiving external input data, such as start and stop signals of the intelligent power supply control system, intrinsic electrical parameters of the traction power grid, and current power supply targets.

The start signal is a signal for starting the intelligent power supply control system or executing the optimal control parameter generated by the intelligent power supply control system, and the stop signal is a signal for closing the intelligent power supply control system or not executing the optimal control parameter generated by the intelligent power supply control system.

The intrinsic electrical parameters are constant parameters of the traction power grid, such as the impedance of the traction power grid and the impedance of the return rail, the ride-through impedance and the half-ride-through impedance of the transformer of each substation, the transformation ratio of the transformer, the power factor of the traction transformer, the power factor of the ac side of the auxiliary transformer, and the like. It can be understood that the inherent electrical parameters of the traction power grid of a rail transit system do not change frequently, so that the input of the inherent electrical parameters can be input only when the intelligent power supply control system is started for the first time to complete the configuration of the inherent electrical parameters of the traction power grid in the intelligent power supply control system. When in use, the input can be carried out again if the inherent electrical parameters of the traction power grid are changed.

The current power supply target refers to a control target of the intelligent power supply control system, such as that network loss reaches a minimum value when regenerative braking energy is recovered, energy rescue among different substations or power factor improvement of a traction power grid, and the like. The number of the current power supply targets may be one or more.

The input module 220 may be coupled with a superior system of the intelligent power supply control system to receive data input by the superior system. The input module 220 may also be a manual input device such as a keyboard or a touch screen to facilitate data input by a user.

The processing module 230 is connected to the communication module 210 and the input module 220 respectively to obtain the current operating parameters received by the communication module 210, the intrinsic electrical parameters received by the input module, and the current power supply target, so as to plan the control strategy of the traction power grid for the current power supply target.

The processing module 230 is configured to: establishing a current circuit model of a traction power grid of the rail transit system based on the current operation parameters; and determining the optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model.

It will be appreciated that the processing module 230 also requires initial configuration of the intrinsic electrical parameters of the traction grid when it is first run.

The current circuit model of the traction power grid refers to a circuit model corresponding to a current operating state of the traction power grid, and the current operating parameters are used for representing the current operating state of the traction power grid. Preferably, the acquired operating parameters may be time-stamped to characterize the time of acquisition of the data to determine that the operating parameters belong to the same batch, i.e., may be used to determine a circuit model at the same time.

On the basis of the current operation state of the traction power grid, if the requirement of the current power supply target is met, the optimal solution of the current circuit model corresponding to the current power supply target can be solved, and therefore the optimal control parameters can be determined.

In particular, to establish the current circuit model, the processing module 230 may be further configured to: numbering all bidirectional converter devices and all trains on a traction power grid according to positions to serve as each node of the traction power grid; determining an admittance matrix of the traction power grid based on all nodes of the traction power grid; constructing a node voltage equation of the traction power grid by using the admittance matrix of the traction power grid and the current operation parameters; and solving a node voltage equation of the traction power grid to obtain a current circuit model of the traction power grid.

It can be understood by those skilled in the art that the whole traction power grid can be regarded as a circuit network, all bidirectional converters and all trains on the traction power grid can be regarded as nodes on the circuit network, the inherent electrical parameters of each node can be regarded as fixed parameters of the circuit network, the current operation parameters of the bidirectional converters and the trains can be regarded as state parameters of each node, and the state parameters of each node are expressed by adopting an admittance matrix, so that a node voltage equation of the circuit network can be determined.

And solving the node voltage equation to obtain an equivalent model of the circuit network, namely a current circuit model of the traction power grid corresponding to the current operating parameters.

In general, the node voltage equation may include a voltage source node, a current source node, and a power source node. For a node voltage equation that includes only voltage source nodes and current source nodes, a conventional node voltage method or a modified node voltage method may be employed to solve. For a node voltage equation containing a power source node, a power source node iteration method is adopted for solving.

The power source node iteration method is a method for obtaining the output quantity of a power source node model meeting the error requirement in a circular iteration mode.

In the present invention, the operating parameters of the train include power parameter data, and therefore the node voltage equation must include the power source node, the processing module 230 is further configured to: and solving the node voltage equation by adopting a power source node iterative method to obtain a current circuit model of the traction power grid.

To implement the process of iteratively solving the node voltage equation for the power source node, the processing module 230 is further configured to: initializing a node voltage of a power source node of a node voltage equation; performing node voltage iteration on the power source node until the node voltages of all the nodes meet a convergence condition; and taking the node voltages of all the nodes determined by the last iteration process as the node voltages of the corresponding nodes of the current circuit model to form the current circuit model.

Initializing the node voltage of the power source node refers to giving an initial node voltage to a branch where the power source is located in the node voltage model. The initial node voltage may be a random value that conforms to the voltage-current operating characteristics of the traction grid.

In each iteration process, the power source node can be converted into a current source node through the initial node voltage, and then a conventional current source node model solving method is adopted to solve.

In one iteration, the processing module 230 is configured to: taking the node voltage of the power source node determined in the previous iteration process as the initial node voltage of the power source node in the current iteration process; calculating branch current of the power source node according to the initial node voltage and the power of the power source node so as to convert the power source node into a current source node; solving the node voltage (output quantity) of each node by adopting a node voltage method or a modified node voltage method; calculating a calculated value of each node corresponding to the current operating parameter based on the node voltage of the node; and responding to the difference value between the calculated value of each node corresponding to the current operation parameter and the obtained current operation parameter being smaller than a set tolerance value, and judging that the node voltages of all the nodes meet the convergence condition.

It is understood that during the first iteration, the node voltage of the initialization power source node can be regarded as the node voltage solved during the last iteration of the first iteration.

The node voltage of the power source node refers to the voltage difference across the power source. And dividing the power parameter of the power source node by the node voltage of the power source node to obtain the branch current of the branch in which the power source node is positioned. And regarding the branch current of the branch in which the power source node is positioned as a constant current source of the branch, so that the power source node is converted into a current source node.

When the power source node in the node voltage equation is converted into the current source node, the conventional node voltage method or the modified node voltage method can be adopted for solving.

The current circuit model is a model for simulating the current operation state of the traction power grid, and the current operation parameter of each node is data indicating the current operation state of the node, so that in an iteration process, a calculation value corresponding to the current operation parameter can be inversely calculated based on the solved voltage of each node so as to measure the conformity degree of the solved result and the actual operation state. For example, after the node voltage of a power source node is solved, the power calculation value of the power source node may be further back calculated based on the solved node voltage, and if the error between the power calculation value of the power source node and the obtained power parameter of the power source node is smaller, it indicates that the solved node voltage is closer to the actual voltage in the actual operation, and the corresponding circuit module is more accurate.

The set tolerance value is an error range value set based on the error tolerance of each node, and when the difference value between the calculated value corresponding to the current operation parameter of the node calculated based on the solved value of the node voltage of the node and the actually obtained current operation parameter is smaller than the set tolerance value, the calculated node voltage value can be considered to meet the error requirement. And when the node voltages of all nodes in the node voltage equation meet the error tolerance, the solution result of the node voltage equation can be considered to meet the convergence condition. And if not, performing next iteration, and taking the voltage value of each power source node calculated in the current iteration process as the assumed value of the node voltage of the corresponding power source node in the next iteration process.

When all the node voltages calculated in a certain iteration process meet the convergence condition, the iteration process is the last iteration process, and the next iteration is not carried out. And taking the calculated node voltage of each node in the last iteration process as the node voltage of the corresponding node in the current circuit model to form the current circuit model.

Further, it can be understood that if the calculated node voltage of the power source node cannot satisfy the convergence condition all the time, the process of solving the node voltage equation may be performed in an infinite loop, so that an iteration number may be set for the power source node iteration method, and correspondingly, the processing module 230 may be configured to: and responding to the situation that the node voltages of all nodes meeting the convergence condition do not appear in the iteration process within the preset times, exiting the iteration, and reacquiring the current operation parameters of all bidirectional converter devices and all trains on the traction power grid so as to carry out the next intelligent power supply process.

Furthermore, the obtained current power supply target is set as a target function of the current circuit model, and the optimal solution of the current circuit model corresponding to the target function is solved, so that the optimal control parameter corresponding to the current power supply target can be obtained.

The intelligent algorithm is a common algorithm for solving complex engineering problems and comprises a genetic algorithm, a particle swarm algorithm, a support vector machine algorithm, a neural network method and the like.

Preferably, to solve the current circuit model, the processing module 230 is further configured to: and optimizing the optimal control parameters of the current circuit model corresponding to the current power supply target by using an intelligent algorithm.

In particular, the processing module 230 may be configured to: determining an objective function of the intelligent algorithm based on a current power supply objective; determining a fitness function of an intelligent algorithm based on the objective function; and solving the optimal solution of the current circuit model by using an intelligent algorithm based on the objective function and the fitness function to serve as the optimal control parameter.

It will be appreciated that different optimal control parameters may be solved for different current power supply objectives. A common power supply target can be described in the following examples:

1. and setting the minimum network loss when the regenerative braking energy is recovered as the current power supply target, and solving the direct current output voltage instruction of each bidirectional converter. The minimum network loss target under different states can be realized by executing the solved instruction. For example, when a large impact power demand exists, the bidirectional converter devices in a plurality of substations are preferentially started to provide traction energy; when the braking energy is large, the bidirectional converter devices in the transformer substations are started preferentially to feed back energy, peak capacities of the transformer substations and the bidirectional converter devices are reduced, voltage fluctuation of a power grid is reduced, distribution of regenerated intelligent energy to a traction power grid is achieved, line loss is greatly reduced, and meanwhile energy fed into a 110kV public power grid by a 35kV ring network is reduced.

2. And setting the energy rescue of the adjacent station as the current power supply target, and solving the reactive power instruction of the alternating current side. The solved instruction is executed to realize the control of the working modes of different bidirectional converters, such as the working modes of rectification, inversion or active filtering, and the like, thereby realizing the flexible configuration of the power and the power flow direction output or recovered by each bidirectional converter from the direct current power supply network. When a certain transformer substation breaks down, the aim of emergency rescue of the adjacent transformer substation can be achieved, the power utilization continuity of the train is guaranteed, and the power quality of the whole power supply network is improved.

3. And setting the power factor for improving the traction power grid as a current power supply target, and solving a power factor angle instruction. The solved instruction is executed to realize the effect of starting the bidirectional converter to perform reactive compensation to charge the energy storage element at night or in a parking stage, so that the power factor of a power supply network is improved, the increase of the terminal voltage of a power supply line caused by the flange ladder effect is reduced, the system safety is improved, and the electric energy quality of the line is improved.

4. The minimum electricity consumption cost of the rail transit system is set as a current power supply target, so that a demand side response strategy for the public power grid, which is established based on real-time pricing of the power market, can be solved, and a corresponding effect is achieved: the power utilization curves of controllable loads such as an environment control system, a dynamic lighting system and an energy storage device in the urban rail transit station are adjusted, load translation is carried out, the maximum peak value in the system and the design capacity of equipment are reduced, the power supply pressure and the power supply cost of a public power grid are reduced, and the efficiency of energy transmission and utilization is improved.

Those skilled in the art can also set the current power supply target based on different requirements, and the present invention is not limited to the above embodiments.

Further, fitness is a scale for measuring survival and reproductive opportunities of individuals, and a fitness function quantifies a "natural choice" number corresponding to the fitness. Corresponding to practical engineering application, the fitness function value of each individual can be used for measuring the conformity between the individual and the target function, so that screening of individuals in a group is realized, and the optimal solution is finally determined. Generally, the fitness function is related to the objective function, and may be set to, for example, the inverse of the objective function.

After the objective function and the fitness function are determined, the current circuit model can be solved by using an intelligent algorithm to obtain an optimal solution corresponding to the current objective function, and the optimal solution is subjected to engineering disassembly to obtain optimal control parameters corresponding to the optimal solution.

Further, the optimal control parameters determined based on the current circuit model need to meet the actual engineering requirements, so the processing module 230 may be further configured to: judging the rationality of the optimal control parameters; responding to the optimal control parameter meeting the rationality requirement, and sending the optimal control parameter to a bidirectional converter device to execute the optimal control parameter; and responding to the situation that the optimal control parameters do not meet the rationality requirement, and re-acquiring the current operation parameters of all the bidirectional converter devices on the traction power grid and all the trains so as to carry out the next intelligent power supply process.

The rationality judgment refers to judgment of whether the optimal control parameters meet actual physical constraint conditions. The rationality requirement is then a quantitative relationship corresponding to the actual physical constraints.

Further, as shown in fig. 1, a plurality of traction substations are generally arranged on a traction power grid of a rail transit system, and each traction substation is provided with a bidirectional converter, so that a bidirectional converter with a fault cannot be avoided. Obviously, the failed bidirectional converter device cannot execute the corresponding control instruction, and therefore, the intelligent power supply control system needs to perform fault detection on each bidirectional converter device on the traction power grid, so as to perform intelligent power supply strategy planning based on the normal bidirectional converter devices.

Preferably, the communication module 210 can also receive vital signals of all two-wire variable current devices on the traction power grid and send the signals to the processing module 230.

Correspondingly, the processing module 230 may be configured to: and judging whether the bidirectional converter is in a normal state or not based on the vital signal of each bidirectional converter, and establishing a current circuit model based on the bidirectional converter in the normal state and the current operation parameters of the train.

It will be appreciated that correspondingly the determined optimal control parameters also only comprise the control parameters of the bidirectional converter device in the normal state.

Further, it can be understood by those skilled in the art that, to implement more functions of the intelligent power supply control system, the intelligent power supply control system may further include other adapted modules or devices, such as a server, a database, a display, or the like, and is not limited to the above embodiments.

Those skilled in the art will appreciate that the number and control areas of the intelligent power supply control systems of a rail transit system may be set correspondingly based on the line scale or the length of the line of the rail transit system. For example, for a rail transit system with multiple lines, an intelligent power supply control system may be provided for each line to generate a power supply strategy on the corresponding line; preferably, a total intelligent power supply control system can be arranged to comprehensively control the sub intelligent power supply control systems on the plurality of lines. For example, for a line with a long length, a traction substation on the line can be divided into a plurality of sub-areas, and an intelligent power supply control system is arranged in each sub-area to generate a power supply strategy in the corresponding area; preferably, a total intelligent power supply control system can be further arranged to comprehensively control the intelligent power supply control systems in the plurality of sub-areas.

According to another aspect of the invention, the invention further provides an intelligent power supply control method which is suitable for controlling the traction power grid of the rail transit system.

In one embodiment, as shown in FIG. 3, the intelligent power control method 300 includes steps S310-S330.

Wherein, step S310 is: and acquiring the current power supply target of the traction power grid of the rail transit system, the current operation parameters of all the bidirectional converter devices and all the trains.

The current operating parameter refers to an operating parameter obtained in real time. The current operating parameters of the bidirectional converter device may include data of ac side voltage, dc side voltage, current, and position, and the current operating parameters of the train may include parameters of position and operating power of the train.

Step S320 is: and establishing a current circuit model of the traction power grid based on the current operation parameters.

Specifically, as shown in FIG. 4, step S320 may include steps S321-S324.

Wherein, step S321 is: and numbering the bidirectional converter devices and the trains on the traction power grid according to positions to serve as each node of the traction power grid.

Step S322 is: an admittance matrix of a traction grid is determined based on all nodes of the traction grid.

Step S323 is: and constructing a node voltage equation of the traction power grid by using the admittance matrix and the current operation parameters.

The node voltage equations may also include intrinsic electrical parameters of the traction grid such as the impedance of the traction grid and the impedance of the return rails, the ride-through and half-ride-through impedances of the transformers of the various substations, the transformation ratio of the transformers, the power factor of the traction transformers, and the power factor of the ac side of the auxiliary transformers.

It can be understood that the inherent electrical parameters of the traction grid of a rail transit system do not change frequently, and therefore, the configuration of the inherent electrical parameters of the traction grid can be completed when the intelligent power supply control method is started for the first time without repeated configuration every time the intelligent power supply control method is started.

Step S324 is: and solving the node voltage equation to obtain a current circuit model of the traction power grid.

In the present invention, the operation parameters of the train include power parameter data, and therefore the node voltage equation necessarily includes a power source node, step S324 may be embodied as: and solving the node voltage equation by adopting a power source node iterative method to obtain a current circuit model of the traction power grid.

Specifically, as shown in FIG. 5, step S324 may include steps S510-S530.

Step S510 is: initializing a node voltage of a power source node of the node voltage equation.

Step S520 is: and iterating the node voltage of the power source node until the node voltages of all the nodes meet a convergence condition.

Specifically, as shown in fig. 6, step S520 may include steps S521 to S525.

Wherein, step S521 is: and taking the node voltage of the power source node determined in the previous iteration process as the initial node voltage of the power source node in the current iteration process.

Step S522 is: and calculating branch current of the power source node according to the node voltage and the power of the power source node so as to convert the power source node into a current source node.

The node voltage of the power source node refers to the voltage difference across the power source. And dividing the power parameter of the power source node by the node voltage of the power source node to obtain the branch current of the branch in which the power source node is positioned. And regarding the branch current of the branch in which the power source node is positioned as a constant current source of the branch, namely, converting the power source node into a current source node.

Step S523 is: and solving the node voltage of each node by adopting a node voltage method or a modified node voltage method.

It can be understood that the power source node in the node voltage equation can be solved by a conventional node voltage method or a modified node voltage method after being converted into a current source node.

Step S524 is: a calculated value for each node corresponding to the current operating parameter is calculated based on the node voltage of the node.

Step S525 is: and judging that the node voltages of all the nodes meet a convergence condition in response to the fact that the difference value between the calculated value of each node corresponding to the current operation parameter and the obtained current operation parameter is smaller than a set tolerance value.

The set tolerance value is an error range value set based on the error tolerance of each node, and when the difference value between the calculated value corresponding to the current operation parameter of the node calculated based on the solved value of the node voltage of the node and the actually obtained current operation parameter is smaller than the set tolerance value, the calculated node voltage value can be considered to meet the error requirement. And when the node voltages of all nodes in the node voltage equation meet the error tolerance, the solution result of the node voltage equation can be considered to meet the convergence condition. Otherwise, performing the next iteration, and taking the voltage value of each power source node calculated in the current iteration process as the assumed value of the node voltage of the corresponding power source node in the next iteration process, i.e., executing step S521.

Further, step S530 is: and taking the node voltages of all the nodes determined by the last iteration process as the node voltages of the corresponding nodes of the current circuit model to form the current circuit model.

Further, it can be understood that if the calculated node voltage of the power source node cannot meet the convergence condition all the time, the process of solving the node voltage equation may be performed in an infinite loop, so that an iteration number can be set for the power source node iteration method. And in response to that the node voltages of all the nodes meeting the convergence condition do not appear in the iteration process within the preset times, exiting the iteration, and re-acquiring the current operation parameters of all the bidirectional converters and all the trains on the traction power grid, namely executing the step S310 again.

On the basis of the current operation state of the traction power grid, if the requirement of the current power supply target is met, the optimal solution of the current circuit model corresponding to the current power supply target can be solved, and the optimal control parameters can be determined.

Further, step S330 is: and determining the optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model.

It can be understood that the optimal control parameter corresponding to the current power supply target can be obtained by setting the obtained current power supply target as the target function of the current circuit model and solving the optimal solution of the current circuit model corresponding to the target function.

The intelligent algorithm is a common algorithm for solving complex engineering problems and comprises a genetic algorithm, a particle swarm algorithm, a support vector machine algorithm, a neural network method and the like. Then, step S330 can be embodied as: and optimizing the optimal control parameters of the current circuit model corresponding to the current power supply target by using an intelligent algorithm.

More specifically, as shown in FIG. 7, step S330 may include steps S331-S333.

Wherein, step S331 is: an objective function of the intelligent algorithm is determined based on the current power supply objective.

Step S332 is: and determining a fitness function of the intelligent algorithm based on the objective function.

Fitness is a scale used for measuring survival and reproductive opportunities of individuals, and a fitness function quantifies a 'natural choice' number corresponding to the fitness. Corresponding to practical engineering application, the fitness function value of each individual can be used for measuring the conformity between the individual and the target function, so that screening of individuals in a group is realized, and the optimal solution is finally determined. Generally, the fitness function is related to the objective function, and may be set to, for example, the inverse of the objective function.

Step S333 is: and solving an optimal solution of the current circuit model by using the intelligent algorithm based on the target function and the fitness function to serve as the optimal control parameter.

Furthermore, the optimal control parameters calculated by adopting an intelligent algorithm need to meet the actual engineering requirements. As shown in fig. 3, the intelligent power supply control method further includes steps S340 to S350.

Wherein, step S340 is: and judging the rationality of the optimal control parameters.

The rationality judgment refers to judgment of whether the optimal control parameters meet actual physical constraint conditions.

Step S350 is: and responding to the optimal control parameters meeting the rationality requirement, and sending the optimal control parameters to the bidirectional converter device to execute the optimal control parameters.

The rationality requirement is then a quantitative relationship corresponding to the actual physical constraints. It is understood that when the solved optimal control parameter satisfies the rationality requirement, i.e. the optimal control parameter is specified to be executable, the corresponding current power supply target may be achievable.

Further, in response to the optimal control parameter not meeting the rationality requirement, the current operation parameters of all bidirectional converters and all trains on the traction power grid are re-acquired, that is, step S310 is repeatedly executed.

Further, the intelligent power supply control method may be turned on or off based on actual requirements, and whether to execute the solved optimal control parameter may be set correspondingly based on whether the intelligent power supply control method is in an on state. For example, if the running state word of the intelligent power supply control method is the starting state, the optimal control parameter is sent to the bidirectional converter device to execute the optimal control parameter, otherwise, the optimal control parameter is not sent.

Further, as shown in fig. 1, a plurality of traction substations are generally arranged on a traction power grid of a rail transit system, and each traction substation is provided with a bidirectional converter, so that a bidirectional converter with a fault cannot be avoided. Obviously, the failed bidirectional converter cannot execute the corresponding control instruction, and therefore, it is also necessary to perform fault detection on each bidirectional converter on the traction power grid, so as to perform intelligent power supply strategy planning based on the normal bidirectional converter.

Preferably, the intelligent power supply control method 300 may further include: and receiving the vital signals of all the two-wire converter devices on the traction power grid, and judging whether the two-way converter devices are in a normal state or not based on the vital signals of each two-way converter device.

Correspondingly, step S310 may be configured to: and acquiring the current operation parameters of the bidirectional converter device and the train in the normal state.

While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.

According to another aspect of the present invention, there is also provided an intelligent power supply control device, as shown in fig. 8, the intelligent power supply control device 800 includes a memory 810 and a processor 820.

The memory 810 has a computer program stored thereon.

The processor 820 is connected to the memory 820 and is used to execute a computer program stored on the memory 810, which when executed implements the steps of the intelligent power supply control method as described in any of the above embodiments.

Those of skill in the art would understand that information, signals, and data may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits (bits), symbols, and chips that may be referenced throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The various illustrative logical modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software as a computer program product, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk (disk) and disc (disc), as used herein, includes Compact Disc (CD), laser disc, optical disc, Digital Versatile Disc (DVD), floppy disk and blu-ray disc where disks (disks) usually reproduce data magnetically, while discs (discs) reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. It is to be understood that the scope of the invention is to be defined by the appended claims and not by the specific constructions and components of the embodiments illustrated above. Those skilled in the art can make various changes and modifications to the embodiments within the spirit and scope of the present invention, and these changes and modifications also fall within the scope of the present invention.

Claims

1. An intelligent power supply control method is suitable for a rail transit system and comprises the following steps:

acquiring a current power supply target of a traction power grid of the rail transit system, and current operation parameters of all bidirectional current transformers and all trains;

establishing a current circuit model of the traction power grid based on the current operating parameters, including: numbering the bidirectional converter devices and the trains on the traction power grid according to positions to serve as each node of the traction power grid; determining an admittance matrix of the traction grid based on all nodes of the traction grid; constructing a node voltage equation of the traction power grid by using the admittance matrix and the current operation parameters; solving the node voltage equation to obtain a current circuit model of the traction power grid;

the current operating parameters include power parameters, and solving a node voltage equation to obtain a current circuit model of the traction grid includes: solving the node voltage equation by adopting a power source node iterative method to obtain a current circuit model of the traction power grid, wherein the method comprises the following steps:

initializing a node voltage of a power source node of the node voltage equation;

iterating node voltages of the power source nodes until the node voltages of all the nodes meet a convergence condition; the iterative process of each node voltage comprises the following steps: taking the node voltage of the power source node determined in the previous iteration process as the initial node voltage of the power source node in the current iteration process to convert the power source node into a current source node; calculating branch current of the power source node according to the node voltage and the power of the power source node so as to convert the power source node into a current source node; solving the node voltage of each node by adopting a node voltage method or a modified node voltage method; calculating a calculated value of each node corresponding to the current operating parameter based on the node voltage of the node; responding to the difference value between the calculated value of each node corresponding to the current operation parameter and the obtained current operation parameter being smaller than a set tolerance value, and judging that the node voltages of all the nodes meet the convergence condition; and

taking the node voltages of all the nodes determined in the last iteration process as the node voltages of the corresponding nodes of the current circuit model to form the current circuit model; and

and determining the optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model.

2. The intelligent power supply control method according to claim 1, wherein the obtaining of the current operating parameters of all bidirectional converters on the traction power grid and all trains comprises:

and responding to the situation that the node voltages of all the nodes meeting the convergence condition do not appear in the iteration process within the preset times, exiting the iteration, and re-obtaining all the bidirectional converter devices on the traction power grid and the current operation parameters of all the trains.

3. The intelligent power supply control method of claim 1, further comprising:

receiving life signals of all bidirectional variable flow devices;

judging whether the bidirectional converter devices are in a normal state or not based on the life signals of each bidirectional converter device; and

the acquiring of all bidirectional converters on the traction power grid, the current operation parameters of all trains and the current power supply target comprises:

and acquiring the bidirectional converter device in a normal state on the traction power grid, the current operation parameters of all trains and the current power supply target.

4. The intelligent power supply control method of claim 1, wherein the determining optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model comprises:

and optimizing the optimal control parameters of the current circuit model corresponding to the current power supply target by using an intelligent algorithm.

5. The intelligent power supply control method of claim 4, wherein the optimizing optimal control parameters of the current circuit model corresponding to the current power supply target using an intelligent algorithm comprises:

determining an objective function of the intelligent algorithm based on the current power supply objective;

determining a fitness function of the intelligent algorithm based on the objective function; and

and solving an optimal solution of the current circuit model by using the intelligent algorithm based on the target function and the fitness function to serve as the optimal control parameter.

6. The intelligent power supply control method of claim 4, further comprising:

judging the rationality of the optimal control parameters;

in response to the optimal control parameter meeting a rationality requirement, sending the optimal control parameter to the bidirectional converter device to execute the optimal control parameter; and

and responding to the condition that the optimal control parameters do not meet the rationality requirement, and re-acquiring the current operation parameters of all the bidirectional converter devices and all the trains on the traction power grid.

7. An intelligent power supply control system suitable for a rail transit system, the intelligent power supply control system comprising:

the communication module is used for communicating with all bidirectional converter devices and all trains on a traction power grid of the rail transit system to acquire current operation parameters of all the bidirectional converter devices and all the trains;

the input module is used for inputting a current power supply target of the traction power grid; and

a processing module connected with the communication module, the processing module configured to:

establishing a current circuit model of the traction power grid based on the current operating parameters; and

determining optimal control parameters of the rail transit system corresponding to the current power supply target based on the current circuit model;

the processing module is further configured to:

numbering the bidirectional converter devices and the trains on the traction power grid according to positions to serve as each node of the traction power grid;

determining an admittance matrix of the traction grid based on all nodes of the traction grid;

constructing a node voltage equation of the traction power grid by using the admittance matrix and the current operation parameters; and

solving the node voltage equation to obtain a current circuit model of the traction power grid;

the processing module is further configured to:

solving the node voltage equation by adopting a power source node iterative method to obtain a current circuit model of the traction power grid;

the processing module is further configured to:

iterating node voltages of the power source nodes until the node voltages of all the nodes meet a convergence condition; and

taking the node voltages of all the nodes determined in the last iteration process as the node voltages of the corresponding nodes of the current circuit model to form the current circuit model;

in each iteration of the node voltage, the processing module is configured to:

taking the node voltage of the power source node determined in the previous iteration process as the initial node voltage of the power source node in the current iteration process to convert the power source node into a current source node;

calculating branch current of the power source node according to the node voltage and the power of the power source node so as to convert the power source node into a current source node;

solving the node voltage of each node by adopting a node voltage method or a modified node voltage method;

calculating a calculated value of each node corresponding to the current operating parameter based on the node voltage of the node; and

and judging that the node voltages of all the nodes meet a convergence condition in response to the fact that the difference value between the calculated value of each node corresponding to the current operation parameter and the obtained current operation parameter is smaller than a set tolerance value.

8. The intelligent power supply control system of claim 7,

the processing module is further configured to: responding to the node voltages of all nodes meeting the convergence condition in the iteration process within the preset times, and exiting the iteration; and

and the communication module acquires all bidirectional converter devices on the traction power grid and the current operation parameters of all trains again.

9. The intelligent power supply control system of claim 7 wherein the communication module further receives vital signs of all bidirectional current transformers,

the processing module is further configured to: judging whether the bidirectional converter devices are in a normal state or not based on the life signals of each bidirectional converter device; and

the communication module is used for acquiring the bidirectional converter device in a normal state on the traction power grid and the current operation parameters of all trains.

10. The intelligent power supply control system of claim 7, wherein the processing module is further configured to:

11. The intelligent power supply control system of claim 10, wherein the processing module is further configured to:

12. The intelligent power supply control system of claim 10, wherein the processing module is further configured to:

judging the rationality of the optimal control parameters;

in response to the optimal control parameter meeting a rationality requirement, controlling the communication module to send the optimal control parameter to the bidirectional converter device to execute the optimal control parameter; and

and responding to the condition that the optimal control parameters do not meet the rationality requirement, and controlling the communication module to reacquire the current operation parameters of all bidirectional converters and all trains on the traction power grid.

13. An intelligent power supply control device comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor is adapted to implement the steps of the intelligent power supply control method according to any one of claims 1 to 6 when executing the computer program stored on the memory.

14. A computer storage medium having a computer program stored thereon, wherein the computer program when executed implements the steps of the intelligent power supply control method of any one of claims 1-6.