Energy self-adaptive regulation method and system for railway power supply system

Abstract

The application discloses an energy self-adaptive regulation and control method and system of a railway power supply system; real-time monitoring of regenerative braking energy, traction load state, state of charge (SOC) of an energy storage device and power grid price state in a railway traction power supply system is performed; a cost function oriented to railway power supply safety is established; a three-state dynamic linearization data model is established, and a self-adaptive regulation and control algorithm is designed; based on the self-adaptive regulation and control algorithm, distribution weights among a bidirectional energy fusion device, the energy storage device and a grid-connected energy feeding device are rollingly corrected, an energy distribution path is selected, and self-adaptive regulation and control of regenerative braking energy in the railway traction power supply system is completed; the method and system provided by the application can self-adaptively regulate energy supply and demand relationship, and improve energy utilization efficiency of the power supply system.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of traction power supply, and particularly relates to an energy adaptive regulation method and system for a railway power supply system. BACKGROUND

[0002] With the rapid development of China's high-speed railway network and the continuous growth of electrified railway operation mileage, the railway traction power supply system is facing increasingly severe energy management challenges. Especially under the condition of frequent train starting and braking, the system needs to efficiently handle the dynamic allocation of regenerative braking energy. The traditional energy management method mainly adopts a fixed threshold control strategy, which is difficult to adapt to complex and variable operating conditions, and is prone to cause voltage fluctuations, equipment overload and other safety hazards.

[0003] The current energy management of the railway traction power supply system mainly has the following technical bottlenecks: first, the existing system lacks a multi-dimensional comprehensive consideration of power supply safety, especially lacks a safety risk assessment mechanism, and it is difficult to balance between key safety indicators such as voltage stability, current limitation and fault recovery; second, the traditional modeling method is difficult to accurately depict the dynamic characteristics of the multi-state coupling of traction load, energy storage state and grid price; more importantly, the existing energy distribution strategy lacks adaptive regulation capability, and it is difficult to provide weight distribution instructions for various energy conversion devices to adapt to different conditions, which restricts the response speed and energy utilization efficiency of the system.

[0004] In terms of safety control, the existing technology often uses static protection thresholds, which cannot adapt to real-time changes in system operating state. Especially in the face of sudden load fluctuations or power grid faults, the traditional method is difficult to adjust the energy distribution strategy in time, which may cause protection device misoperation or refusal to operate, seriously affecting power supply reliability. In addition, with the increase of new energy grid connection proportion and the deepening of power market reform, the power supply system also needs to consider economic operation requirements, which puts higher requirements on energy dynamic distribution algorithm.

[0005] In view of the above problems, it is urgent to develop an energy adaptive regulation method for a railway power supply system. The method establishes a multi-objective optimization safety cost function, constructs a dynamic linearization data model considering traction load, energy storage state and grid price, and designs an adaptive regulation algorithm to realize optimal allocation of regenerative braking energy and ensure safe, stable and economic operation of the power supply system. SUMMARY

[0006] In order to solve the problems in the background art, the purpose of the present application is to provide an energy adaptive regulation method for a railway power supply system. The energy adaptive regulation device includes a bidirectional energy integration device, an energy storage device and a grid-connected energy feeding device. The method comprises the following steps:

[0007] Step (1): Real-time monitoring of the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and grid electricity price status in the railway traction power supply system;

[0008] Step (2): establishing a cost function for railway power supply security, which is used as a constraint condition for determining the energy distribution weight among the bidirectional energy financing device, the energy storage device and the grid-connected energy feeding device;

[0009] Step (3): establishing a three-state dynamic linearized data model including a traction load state, a remaining charge state SOC of an energy storage device, and a grid electricity price state, wherein the dynamic linearized data model includes a pseudo-gradient matrix; designing an adaptive control algorithm based on the dynamic linearized data model, wherein the adaptive control algorithm includes an adaptive gain matrix; designing an estimation algorithm for the pseudo-gradient matrix and the adaptive gain matrix based on the dynamic linearized data model;

[0010] Among them, the adaptive control algorithm is:

[0011] ;

[0012] in, Indicated by The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; for Adaptive gain matrix at sampling time, Represented by vector The vector formed, express The first-order backward difference of express The error vector of the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state at the sampling time, , is an adjustable time window parameter; is the gain coefficient; is the energy allocation function vector;

[0013] Step (4): Based on the adaptive control algorithm, the energy distribution weights among the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device are revised in a rolling manner, and based on the distribution weights, an energy distribution path is selected; if Not 0, path 1 is executed first; if path 1 does not completely absorb energy and Not 0, execute path 2; if there is still remaining energy and If it is not 0, execute path three.

[0014] Specifically, the method for establishing a three-state dynamic linearized data model including the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state in step (3) is as follows:

[0015] ;

[0016] in, represents the sampling time; Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; for The pseudo gradient matrix at the sampling moment, Indicated by The vector formed, express The first-order backward difference of ,when hour, , , Respectively Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feedback device at the sampling time.

[0017] Furthermore, based on the dynamic linearized data model, the estimation algorithm of the adaptive gain matrix is ​​designed as follows:

[0018] ;

[0019] in, for Adaptive gain matrix at sampling time, , is a constant, is a one-step forward differential, and , for The pseudo gradient matrix at the sampling moment, is the one-step forward error, It is a three-state dynamic linearized data model; Indicated by , , The vector formed; , , respectively represent traction load state value at sampling time, residual state of charge SOC value of energy storage device, grid electricity price state value; represent a vector composed of , , ; , , respectively represent traction load state setting value at sampling time, residual state of charge SOC setting value of energy storage device, grid electricity price state setting value; represent a vector composed of vector , represent error vector of traction load state, residual state of charge SOC of energy storage device, grid electricity price state at sampling time, represent first-order backward difference of , is an adjustable time window parameter; represent a vector composed of , , ; , , respectively represent traction load state setting value at sampling time, residual state of charge SOC setting value of energy storage device, grid electricity price state setting value; represent a vector composed of , , ; , , respectively represent traction load state value at sampling time, residual state of charge SOC value of energy storage device, grid electricity price state value; represent first-order backward difference of represent a vector composed of , , , respectively represent energy distribution weight of bidirectional energy integration device, energy storage device, grid-connected energy feeding device at sampling time.

[0020] Further, based on the dynamic linearization data model, an estimation algorithm of the pseudo gradient matrix is designed, and the optimization target is , wherein the symbol minimizing function regarding variables taking values and the cost function is minimized as a constraint condition:

[0021] ;

[0022] wherein, is a pseudo-gradient matrix at a sampling time, denotes a first-order backward difference of denotes a vector composed of , , , , respectively represent a traction load state value, a state of charge (SOC) value of an energy storage device, and a grid price state value at a sampling time; denotes a first-order backward difference of denotes a vector composed of , , , , , respectively represent an energy distribution weight of a bidirectional energy integration device, an energy storage device, and a grid-connected energy feeding device at a sampling time; is a step factor, is a penalty factor, is a two-norm.

[0023] Further, the cost function for railway power supply safety in step (2) is:

[0024] ;

[0025] wherein, is a cost function weight, respectively represent a voltage stability cost item, a current limit cost item, and a fault recovery capability cost item.

[0026] Further, the energy distribution function vector is defined as wherein, is an energy distribution function of a bidirectional energy integration device, is an energy distribution function of an energy storage device, is an energy distribution function of a grid-connected energy feeding device; let , ,​​​ , represents the energy distribution function value of the bidirectional energy fusion device, represents the energy distribution function value of the energy storage device, represents the energy distribution function value of the grid-connected energy feedback device, is a normal number, which varies with the sampling time , and always satisfies .

[0027] Further, path one in step (4) represents that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy fusion device according to the traction load state; path two represents that the remaining regenerative braking energy is distributed to the energy storage device for storage according to the residual capacity state SOC adaptive algorithm of the energy storage device; and path three represents that the grid-connected energy feedback device is triggered to perform reverse power feeding.

[0028] The energy adaptive control system of the railway power supply system includes a bidirectional energy fusion device, an energy storage device, and a grid-connected energy feedback device, and comprises:

[0029] A data monitoring module is configured to monitor the regenerative braking energy, the traction load state, the residual capacity state SOC of the energy storage device, and the grid electricity price state in the railway traction power supply system in real time.

[0030] A safety optimization decision module is configured to establish a cost function for railway power supply safety, which contains voltage stability, current limitation, and fault recovery capability factors, and is used as a constraint condition for determining the energy distribution weight between the bidirectional energy fusion device, the energy storage device, and the grid-connected energy feedback device.

[0031] A control algorithm calculation module is configured to establish a three-state dynamic linearization data model containing the traction load state, the residual capacity state SOC of the energy storage device, and the grid electricity price state, and the dynamic linearization data model contains a pseudo-gradient matrix; based on the dynamic linearization data model, an adaptive control algorithm is designed, which contains an adaptive gain matrix; and based on the dynamic linearization data model, an estimation algorithm of the pseudo-gradient matrix and the adaptive gain matrix is designed.

[0032] The adaptive control algorithm is as follows:

[0033] ;

[0034] wherein, represents a vector composed of , , , , and Energy distribution weights of the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device at the sampling moment; For Adaptive gain matrix at the sampling moment, A vector composed of vectors , A first-order backward difference of , A first-order backward difference of Error vector of the traction load state, the state of charge SOC of the energy storage device and the grid electricity price state at the sampling moment, , Adjustable time window parameter; Gain coefficient; Energy distribution function vector;

[0035] The weight distribution strategy module is used for rolling correction of the distribution weights among the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device based on the adaptive regulation algorithm.

[0036] The weight distribution execution module is used for selecting an energy distribution path based on the distribution weights, if is not 0, path one is preferentially executed; if the energy is not completely consumed in path one and is not 0, path two is executed; if there is still remaining energy and is not 0, path three is executed.

[0037] Further, the present application adopts the following technical solutions:

[0038] A non-transitory computer readable storage medium, having stored thereon a computer program, the computer program being executed by a processor to implement the energy adaptive regulation method of the railway power supply system as described above.

[0039] Further, the present application adopts the following technical solutions:

[0040] An electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the energy adaptive regulation method of the railway power supply system as described above.

[0041] The present application has the following beneficial technical effects:

[0042] (1), the adaptive regulation mechanism is established, intelligent coordination two-way energy integration, energy storage device and grid connected energy conversion mode such as multiple energy conversion mode, make the regenerative braking energy get the optimal utilization, improve the energy utilization efficiency of power supply system, in this process, by establishing the cost function for railway power supply safety, voltage stability, current protection and fault recovery ability are included in the unified optimization framework, realize the dynamic prevention and control of power supply system safety risk, improve the reliability and safety of traction power supply system;

[0043] (2), the adaptive regulation algorithm proposed in the application does not depend on accurate mathematical model, the method has obvious advantages in dealing with strong time-varying, strong nonlinear and strong coupling traction power supply system, and is easy to be applied in practice. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The energy adaptive regulation method flow chart of the railway power supply system provided in embodiment 1 is given;

[0045] Figure 2 The energy adaptive regulation system module connection schematic diagram of the railway power supply system provided in embodiment 2 is given. DETAILED DESCRIPTION

[0046] The energy adaptive regulation method and system of railway power supply system are disclosed; the regenerative braking energy, traction load state, residual capacity state SOC of energy storage device and grid price state in the railway traction power supply system are monitored in real time; the cost function for railway power supply safety is established; the three-state dynamic linearization data model containing traction load, residual capacity state SOC of energy storage device and grid price is established, and the adaptive regulation algorithm is designed; the distribution weight between the two-way energy integration device, energy storage device and grid connected energy device is corrected based on the adaptive regulation algorithm; based on the distribution weight, the energy distribution path is selected, and the adaptive regulation of regenerative braking energy in the railway traction power supply system is completed; the method and system provided by the application can adaptively regulate the energy supply and demand relationship, and improve the energy utilization efficiency of power supply system.

[0047] The energy adaptive regulation method and system of railway power supply system provided by the application are further described clearly and completely in combination with the drawings: Embodiment 1

[0048] Figure 1 The energy adaptive regulation method flow chart of the railway power supply system provided in the embodiment is given; the energy adaptive regulation method of railway power supply system is provided, the energy adaptive regulation device includes two-way energy integration device, energy storage device, grid connected energy device, and the method comprises the following steps:

[0049] Step (1): Real-time monitoring of regenerative braking energy in the railway traction power supply system, traction load state, state of charge (SOC) of energy storage device, and grid electricity price state;

[0050] Step (2): Establishing a cost function for railway power supply safety, which contains voltage stability, current limitation, and fault recovery capability factors, and serving as a constraint condition for determining the energy distribution weight between the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device;

[0051] Step (3): Establishing a three-state dynamic linearization data model containing traction load state, state of charge (SOC) of energy storage device, and grid electricity price state, which contains a pseudo-gradient matrix; based on the dynamic linearization data model, designing an adaptive control algorithm containing an adaptive gain matrix; based on the dynamic linearization data model, designing an estimation algorithm for the pseudo-gradient matrix and adaptive gain matrix;

[0052] Step (4): Rolling correction of the energy distribution weight between the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device based on the adaptive control algorithm, and selection of energy distribution path based on the distribution weight; specifically, the energy distribution weight between the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device refers to the vector wherein , , are the distribution weights of the bidirectional energy fusion device, energy storage device, and grid-connected energy feeding device at the sampling time, and when selecting the energy distribution path, if is not 0, path one is preferentially executed; if path one does not completely consume energy and is not 0, path two is executed; if there is still remaining energy and is not 0, path three is executed; path one means that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy fusion device according to the traction load state; path two means that the remaining regenerative braking energy is adaptively allocated to the energy storage device for storage according to the state of charge (SOC) of the energy storage device; and path three means that the grid-connected energy feeding device is triggered to perform reverse power feeding;

[0053] Thus, the above steps (1)-(4) are repeated to complete the adaptive control and distribution of regenerative braking energy in the railway traction power supply system.

[0054] Specifically, the cost function for railway power supply safety established in step (2) is:

[0055] ;​

[0056] in, is the cost function weight, They represent the voltage stability cost, current limit cost, and fault recovery capability cost respectively;

[0057] The voltage stability cost term is used to penalize the traction network voltage from deviating from the target voltage value:

[0058] ;

[0059] in, Indicates the traction network i The actual voltage of each node, Indicates the target voltage value of the traction network. Indicates the number of traction network nodes;

[0060] The current limit penalty is used to penalize the traction network current exceeding the rated current value:

[0061] ;

[0062] in, Indicates the traction network i The actual current of each node, Indicates the rated current value;

[0063] The fault recovery capability cost item is:

[0064] ;

[0065] in, Indicates the traction power supply system l The recovery time at each fault, Indicates the l The influence coefficient of each fault location, Indicates the number of faults in the traction power supply system.

[0066] The method for establishing a three-state dynamic linearized data model including the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state in step (3) is as follows:

[0067] ;

[0068] in, represents the sampling time; Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; for The pseudo gradient matrix at the sampling moment, Indicated by The vector formed, express The first-order backward difference of ,when hour, , , Respectively Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feedback device at the sampling time.

[0069] Based on the dynamic linearized data model, a method for designing an adaptive control algorithm is as follows:

[0070] ;

[0071] in, Indicated by The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; for Adaptive gain matrix at sampling time, Represented by vector The vector formed, express The error vector of the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state at the sampling time, express The first-order backward difference of , It is an adjustable time window parameter, which is generally a positive integer within 5; express The set value vector of the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state at the sampling moment, , , Respectively The traction load state setting value, the remaining power state SOC setting value of the energy storage device, and the grid electricity price state setting value at the sampling moment; Indicated by , , The vector formed; , , respectively represent the traction load state value, the state of charge (SOC) value of the energy storage device, and the grid electricity price state value at the sampling time point; is a gain coefficient; is an energy distribution function vector.

[0072] Based on the dynamic linearization data model, an estimation algorithm of the pseudo-gradient matrix is designed, and the optimization objective is wherein the symbol represents a minimization function with respect to the variable , in the process of which the cost function is minimized as a constraint condition:

[0073] ;

[0074] wherein, is a pseudo-gradient matrix at the sampling time point, represents a first-order backward difference of , indicates a vector composed of , , , , , respectively represent the traction load state value, the state of charge (SOC) value of the energy storage device, and the grid electricity price state value at the sampling time point; represents a first-order backward difference of , indicates a vector composed of , , , , , respectively represent the energy distribution weights of the bidirectional energy exchange device, the energy storage device, and the grid-connected energy feeding device at the sampling time point; is a step factor, is a penalty factor, is a two-norm; Based on the dynamic linearization data model, an estimation algorithm of the adaptive gain matrix is designed as follows:

[0075]

[0076] ;

[0077] wherein, is ​​​​​Adaptive gain matrix at sampling time, , is constant, is one-step forward differentiation, and , is one-step forward error, is a three-state dynamic linearization data model; denotes a vector composed of , , ; , , denote traction load state value, remaining state of charge SOC value of energy storage device, grid electricity price state value at sampling time, respectively; denotes a vector composed of , , ; , , denote traction load state set value, remaining state of charge SOC set value of energy storage device, grid electricity price state set value at sampling time, respectively; denotes a vector composed of vector , denotes error vector of traction load state, remaining state of charge SOC of energy storage device, grid electricity price state at sampling time, denotes first-order backward difference of , is an adjustable time window parameter, generally a positive integer less than 5; denotes a vector composed of , , ; , , denote traction load state set value, remaining state of charge SOC set value of energy storage device, grid electricity price state set value at sampling time, respectively; denotes a vector composed of , , ; , , denote traction load state value, remaining state of charge SOC value of energy storage device, grid electricity price state value at sampling time, respectively; denotes The first-order backward difference of Indicated by The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feedback device at the sampling time.

[0078] The SOC adaptive algorithm for the energy storage device in the second energy distribution path includes the following steps:

[0079] When it is detected that the remaining power state SOC of the energy storage device is greater than or equal to the first threshold, the energy storage device allocation weight is reduced. The first threshold is used to prevent energy storage overcharge, and its value is based on the safety window parameter calibrated by the energy storage device;

[0080] When the remaining charge state SOC of the energy storage device is less than or equal to a second threshold, energy is prohibited from being released from the energy storage device; the second threshold is used to avoid over-discharge, and its value is based on a safety window parameter calibrated by the energy storage device.

[0081] The energy allocation function vector The definition of ,in, is the energy distribution function of the bidirectional energy fusion device, is the energy distribution function of the energy storage device, is the energy distribution function of the grid-connected energy feeding device; let , , , represents the energy distribution function value of the bidirectional energy fusion device, represents the energy allocation function value of the energy storage device, represents the energy distribution function value of the grid-connected energy feeding device, is a positive constant, with the sampling time Change, and always meet the needs during the change process ;For example, At the sampling moment, , , , and found that the bidirectional energy fusion device does not need too much energy at this time, so at the next moment, At the sampling moment, you can modify , , , so that more energy is distributed to the energy storage device.

[0082] The adaptive control algorithm in step (4) is used to rollingly correct the energy distribution weights among the bidirectional energy integration device, energy storage device and grid-connected energy feeding device. The rollingly correction refers to updating the adaptive control algorithm in real time according to the adaptive gain matrix , vector and energy distribution function vector in the adaptive control algorithm , , , , , , , . Embodiment 2

[0083] Figure 2 The energy adaptive control system module connection diagram of the railway power supply system in this embodiment is provided. The energy adaptive control device includes a bidirectional energy integration device, an energy storage device and a grid-connected energy feeding device, which includes:

[0084] A data monitoring module is used to monitor the regenerative braking energy, traction load state, residual capacity state SOC of the energy storage device and power grid price state in the railway traction power supply system in real time.

[0085] A safety optimization decision module is used to establish a cost function for railway power supply safety. The cost function contains voltage stability, current limitation and fault recovery capability factors, and is used as a constraint condition for determining the energy distribution weights among the bidirectional energy integration device, energy storage device and grid-connected energy feeding device.

[0086] A control algorithm calculation module is used to establish a three-state dynamic linearization data model containing the traction load state, residual capacity state SOC of the energy storage device and power grid price state. The dynamic linearization data model contains a pseudo-gradient matrix. Based on the dynamic linearization data model, an adaptive control algorithm is designed, which contains an adaptive gain matrix. Based on the dynamic linearization data model, an estimation algorithm of the pseudo-gradient matrix and adaptive gain matrix is designed.

[0087] A weight distribution strategy module is used to rollingly correct the distribution weights among the bidirectional energy integration device, energy storage device and grid-connected energy feeding device based on the adaptive control algorithm.

[0088] A weight distribution execution module is used to select an energy distribution path based on the distribution weights, wherein , , are respectively The distribution weight of the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device at the sampling moment is not 0, the path one is preferentially executed; if the path one does not completely consume the energy and the distribution weight of the bidirectional energy fusion device at the next sampling moment is not 0, the path two is executed; if there is still remaining energy and the distribution weight of the energy storage device at the next sampling moment is not 0, the path three is executed; the path one represents that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy fusion device according to the traction load state; the path two represents that the remaining regenerative braking energy is distributed to the energy storage device for storage according to the residual SOC state adaptive algorithm of the energy storage device; and the path three represents that the grid-connected energy feeding device is triggered to perform reverse power feeding. The path one represents that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy fusion device according to the traction load state; the path two represents that the remaining regenerative braking energy is distributed to the energy storage device for storage according to the residual SOC state adaptive algorithm of the energy storage device; and the path three represents that the grid-connected energy feeding device is triggered to perform reverse power feeding.

[0089] Further, the present application adopts the following technical solutions:

[0090] A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the energy adaptive regulation method of the railway power supply system as described above.

[0091] Further, the present application adopts the following technical solutions:

[0092] An electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the energy adaptive regulation method of the railway power supply system as described above when executing the program.

[0093] From the above description of the embodiments, those skilled in the art can clearly understand that the facilities of the present application can be implemented by means of software and the necessary general hardware platform. The embodiments of the present application can be implemented using existing processors, or by special-purpose processors used for this purpose or other purposes in appropriate systems, or by hardwired systems. The embodiments of the present application also include non-transitory computer readable storage media, which include machine-readable media for carrying or having stored thereon machine-executable instructions or data structures; such machine-readable media can be any available media that can be accessed by general or special-purpose computers or other machines with processors. For example, such machine-readable media can include RAM, ROM, EPROM, 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 machine-executable instructions or data structures and can be accessed by general or special-purpose computers or other machines with processors. When information is transmitted or provided to a machine over a network or other communication connection (hardwired, or wireless, or a combination of hardwired and wireless), the connection is also considered a machine-readable medium.

[0094] ​​The technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical schemes after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. An energy adaptive control method for a railway power supply system, wherein the energy adaptive control device includes a bidirectional energy fusion device, an energy storage device, and a grid-connected energy feeding device, and is characterized in that: The method comprises the following steps: Step (1): Real-time monitoring of the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and grid electricity price status in the railway traction power supply system; Step (2): establishing a cost function for railway power supply security, which is used as a constraint condition for determining the energy distribution weight among the bidirectional energy financing device, the energy storage device and the grid-connected energy feeding device; Step (3): establishing a three-state dynamic linearized data model including a traction load state, a remaining charge state SOC of an energy storage device, and a grid electricity price state, wherein the three-state dynamic linearized data model includes a pseudo-gradient matrix; designing an adaptive control algorithm based on the three-state dynamic linearized data model, wherein the adaptive control algorithm includes an adaptive gain matrix; designing an estimation algorithm for the pseudo-gradient matrix and the adaptive gain matrix based on the three-state dynamic linearized data model; Among them, the adaptive control algorithm is: ； in, Indicated by The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; for Adaptive gain matrix at sampling time, Represented by vector The vector formed, express The first-order backward difference of express The error vector of the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state at the sampling time, , is an adjustable time window parameter; is the gain coefficient; is the energy allocation function vector; Step (4): Based on the adaptive control algorithm, the energy distribution weights among the bidirectional energy fusion device, the energy storage device and the grid-connected energy feeding device are revised in a rolling manner, and based on the distribution weights, an energy distribution path is selected; if Not 0, path 1 is executed first; if path 1 does not completely absorb energy and Not 0, execute path 2; if there is still remaining energy and If it is not 0, execute path three; Based on the dynamic linearized data model, the estimation algorithm of the adaptive gain matrix is ​​designed as follows: ； in, for Adaptive gain matrix at sampling time, , is a constant, is a one-step forward differential, and , for The pseudo gradient matrix at the sampling moment, is the one-step forward error, It is a three-state dynamic linearized data model; Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; Indicated by , , The vector formed; , , Respectively The traction load state setting value at the sampling moment, the remaining power state SOC setting value of the energy storage device, and the grid electricity price state setting value; Represented by vector The vector formed, express The error vector of the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state at the sampling time, express The first-order backward difference of , is an adjustable time window parameter; Indicated by , , The vector formed; , , Respectively The traction load state setting value at the sampling moment, the remaining power state SOC setting value of the energy storage device, and the grid electricity price state setting value; Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; express The first-order backward difference of Indicated by The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; Based on the dynamic linearization data model, the estimation algorithm of the pseudo gradient matrix is ​​designed, and the optimization goal is , where the symbol Represents the minimization function About variables Take the value and minimize the cost function as a constraint: ； in, for The pseudo gradient matrix at the sampling moment, express The first-order backward difference of Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; express The first-order backward difference of Indicated by , , The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; is the step size factor, is the penalty factor, is the two-norm.

2. The method for adaptively controlling energy of a railway power supply system according to claim 1, characterized in that: The method for establishing a three-state dynamic linearized data model including the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state in step (3) is as follows: ； in, represents the sampling time; Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; for The pseudo gradient matrix at the sampling moment, Indicated by The vector formed, express The first-order backward difference of ,when hour, , , Respectively Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feedback device at the sampling time.

3. The method for adaptively controlling energy of a railway power supply system according to claim 1, characterized in that: In step (2), a cost function for railway power supply security is established, and the cost function is: ； in, is the cost function weight, They represent the voltage stability cost, current limit cost, and fault recovery capability cost respectively.

4. The method for adaptively controlling energy of a railway power supply system according to claim 1, characterized in that: The energy allocation function vector The definition of ,in, is the energy distribution function of the bidirectional energy fusion device, is the energy distribution function of the energy storage device, is the energy allocation function of the grid-connected energy feeding device; let , , , represents the energy distribution function value of the bidirectional energy fusion device, represents the energy allocation function value of the energy storage device, represents the energy distribution function value of the grid-connected energy feeding device, is a positive constant, with the sampling time Change, and always meet the needs during the change process .

5. The method for adaptively controlling energy of a railway power supply system according to claim 1, characterized in that: In step (4), path one indicates that the regenerative braking energy is preferentially transferred to the adjacent power supply arm through the bidirectional energy transfer device according to the traction load state; path two indicates that the remaining regenerative braking energy is allocated to the energy storage device for storage according to the remaining power state SOC adaptive algorithm of the energy storage device; path three indicates that the grid-connected energy feeding device is triggered to perform reverse feeding.

6. An energy adaptive control system for a railway power supply system, wherein the energy adaptive control device includes a bidirectional energy fusion device, an energy storage device, and a grid-connected energy feeding device, characterized in that: include: Data monitoring module, used to monitor the regenerative braking energy, traction load status, remaining charge state (SOC) of the energy storage device, and grid electricity price status in the railway traction power supply system in real time; A safety optimization decision module is used to establish a cost function for railway power supply safety, wherein the cost function includes voltage stability, current limit, and fault recovery capability factors, which are used as constraints to determine the energy distribution weights among the bidirectional energy financing device, the energy storage device, and the grid-connected energy feeding device; a control algorithm calculation module, configured to establish a three-state dynamic linearized data model comprising a traction load state, a remaining charge state (SOC) of an energy storage device, and a grid electricity price state, wherein the three-state dynamic linearized data model comprises a pseudo-gradient matrix; and design an adaptive control algorithm based on the three-state dynamic linearized data model, wherein the adaptive control algorithm comprises an adaptive gain matrix; Based on the three-state dynamic linearization data model, design an estimation algorithm for the pseudo gradient matrix and the adaptive gain matrix; Among them, the adaptive control algorithm is: ； in, Indicated by The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; for Adaptive gain matrix at sampling time, Represented by vector The vector formed, express The first-order backward difference of express The error vector of the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state at the sampling time, , is an adjustable time window parameter; is the gain coefficient; is the energy allocation function vector; A weight distribution strategy module, configured to roll-correct the distribution weights among the bidirectional energy fusion device, the energy storage device, and the grid-connected energy feeding device based on the adaptive control algorithm; The weight distribution execution module is used to select the energy distribution path based on the distribution weight. Not 0, path 1 is executed first; if path 1 does not completely absorb energy and Not 0, execute path 2; if there is still remaining energy and If it is not 0, execute path three; Based on the dynamic linearized data model, the estimation algorithm of the adaptive gain matrix is ​​designed as follows: ； in, for Adaptive gain matrix at sampling time, , is a constant, is a one-step forward differential, and , for The pseudo gradient matrix at the sampling moment, is the one-step forward error, It is a three-state dynamic linearized data model; Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; Indicated by , , The vector formed; , , Respectively The traction load state setting value at the sampling moment, the remaining power state SOC setting value of the energy storage device, and the grid electricity price state setting value; Represented by vector The vector formed, express The error vector of the traction load state, the remaining power state SOC of the energy storage device, and the grid electricity price state at the sampling time, express The first-order backward difference of , is an adjustable time window parameter; Indicated by , , The vector formed; , , Respectively The traction load state setting value at the sampling moment, the remaining power state SOC setting value of the energy storage device, and the grid electricity price state setting value; Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; express The first-order backward difference of Indicated by The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; Based on the dynamic linearization data model, the estimation algorithm of the pseudo gradient matrix is ​​designed, and the optimization goal is , where the symbol Represents the minimization function About variables Take the value and minimize the cost function as a constraint: ； in, for The pseudo gradient matrix at the sampling moment, express The first-order backward difference of Indicated by , , The vector formed; , , Respectively The traction load status value, the remaining power state (SOC) value of the energy storage device, and the grid electricity price status value at the sampling moment; express The first-order backward difference of Indicated by , , The vector formed, , , They are Energy allocation weights of the bidirectional energy integration device, energy storage device, and grid-connected energy feeding device at the sampling time; is the step size factor, is the penalty factor, is the two-norm.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the energy adaptive control method for a railway power supply system according to any one of claims 1 to 5 is implemented.

8. An electronic 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 program, the energy adaptive control method of the railway power supply system according to any one of claims 1 to 5 is implemented.

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