A method and control device for optimizing starting voltage of an inverter feedback device

Abstract

The present invention provides a method for optimizing the starting voltage of an inverter feedback device, comprising: S1. performing a correlation analysis on the total feedback energy of all up and down departure time differences and the system-level traction energy consumption at each departure interval, and obtaining the correlation coefficients γ1 to γN corresponding to the departure intervals T1 to TN; S2. judging whether the absolute value of γn is greater than the threshold value γT, if not, setting the initial value U0 of the starting voltage corresponding to the nth departure interval to the default value UM, and entering S4; if so, entering S3; S3. judging whether γn is positive, if so, setting U0 to increase to UH, UH>UM; if not, setting U0 to decrease to UL, UL<UM; S4. optimizing the starting voltage of the nth departure interval according to the initial value U0 of the starting voltage corresponding to the nth departure interval, and obtaining the optimal starting voltage of all departure intervals. The present invention optimizes the starting voltage in different time periods, and takes into account the energy flow relationship of the traction power supply system.

Description

Technical Field

[0001] The present invention relates to the technical field of inverter feedback devices for urban rail transit, and in particular to a method and a control device for optimizing the starting voltage of an inverter feedback device. Background Art

[0002] The structure of urban rail transit traction power supply system is as follows: Figure 2 As shown in the figure, the main substation is connected to the traction step-down hybrid substation and the step-down substation through medium voltage cables. The traction step-down hybrid substation contains a rectifier unit, a step-down load, and an inverter feedback device. The inverter feedback device inverts the regenerative braking energy fed back by the train to the traction network to the AC side for use by the step-down load to improve the energy utilization efficiency of the system.

[0003] The starting voltage of the inverter feedback device is closely related to its energy-saving effect. However, the starting voltage of the inverter feedback device is currently determined based on human experience, which may lead to poor energy-saving effect. The optimization of the starting voltage of the inverter feedback device generally relies on artificial intelligence algorithms, but the selection of the initial value of the starting voltage is relatively arbitrary, and the energy flow relationship of the traction power supply system in multiple time periods, that is, different departure intervals, is not taken into account, resulting in poor optimization effect. Summary of the invention

[0004] The present invention provides an inverter feedback device starting voltage optimization method and control device, which takes into account the optimization of the starting voltage of different inverter feedback devices in different time periods, and takes into account the energy flow relationship of the traction power supply system, and limits the initial value of the inverter feedback device starting voltage optimization, which can improve the optimization efficiency.

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

[0006] One aspect of an embodiment of this specification discloses a method for optimizing the starting voltage of an inverter feedback device, comprising:

[0007] S1. Perform a correlation analysis on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval to obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN;

[0008] S2. Determine whether the absolute value of γn is greater than the threshold value γT. If not, set the initial value U0 of the starting voltage corresponding to the nth departure interval to the default value UM and enter S4; if yes, enter S3; n = 1, 2...N;

[0009] S3. Determine whether γn is positive. If so, increase U0 to UH, where UH>UM; if not, decrease U0 to UL, where UL<UM;

[0010] S4. According to the initial value U0 of the starting voltage corresponding to the n-th departure interval, the starting voltage of the n-th departure interval is optimized. After the starting voltage optimization of all departure intervals is completed, the optimal starting voltage of all departure intervals is obtained.

[0011] In the embodiment of this specification, S1 includes:

[0012] S11. Simulate the power supply system for all uplink and downlink departure time differences of each departure interval;

[0013] S12. Based on the simulation results, a correlation analysis is performed on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval to obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN.

[0014] In the embodiment of this specification, S11 includes:

[0015] S111. Determine the power supply system simulation software, which may be PSCAD, SIMULINK or DIgSILENT;

[0016] S112. Establish a simulation model of the power supply system in the simulation software, the simulation model includes a main substation, a traction step-down hybrid station, a step-down substation, cable lines, a traction network and a train;

[0017] S113. Set simulation parameters, including train traction calculation results, AC / DC system parameters, rectifier units, inverter feedback device parameters and control strategies, and set the train operation plan for all uplink and downlink departure time differences for each departure interval;

[0018] S114. Perform simulation calculations, simulate all train operation plans respectively, to obtain the operation results of the power supply system, and calculate the total feedback energy and system-level traction energy consumption under each train operation plan.

[0019] In the examples of this specification, in S1, the Pearson correlation coefficient is used for correlation analysis.

[0020] In the embodiment of this specification, S4 includes:

[0021] S41. According to the initial value U0 of the starting voltage corresponding to the nth departure interval, the starting voltage is optimized using an artificial intelligence algorithm for the nth departure interval;

[0022] S42. After the optimization is completed, determine whether n is equal to N. If so, it means that the starting voltage of all departure intervals has been optimized, and the optimal starting voltage of all departure intervals and the corresponding system-level traction energy consumption are obtained; if not, n is increased by 1, and S41 is continued.

[0023] In the embodiment of this specification, S41 includes:

[0024] S411. Perform the i-th power supply calculation simulation, the input parameters include the starting voltage U(i-1) and the train operation plan corresponding to the n-th departure interval, and the output results are the total feedback energy and system-level traction energy consumption; wherein, i≥1; when i=1, the starting voltage U(i-1) is the initial value U0 of the starting voltage corresponding to the n-th departure interval; when i>1, the starting voltage U(i-1) is the i-1th iterative calculation voltage output when S412 was last performed;

[0025] S412. Perform artificial intelligence algorithm calculation based on the output result of S411 and the starting voltage U(i-1), and the output result is the i-th iteration calculation voltage Ui;

[0026] S413. Perform the i-th iteration convergence judgment. When the convergence condition is met, the optimization is terminated. The optimal starting voltage corresponding to the n-th departure interval is Ui, and the system-level traction energy consumption corresponding to the n-th departure interval is Eni. When the convergence condition is not met, the number of simulations is increased by 1 and enters S411.

[0027] In the embodiments of this specification, the artificial intelligence algorithm is a genetic algorithm or a swarm intelligence algorithm.

[0028] In the embodiment of this specification, the inverter feedback device is used to be arranged in a traction power supply system, and the traction power supply system is provided with a plurality of the inverter feedback devices;

[0029] The method for optimizing the starting voltage of the inverter feedback device comprises:

[0030] According to S1 to S3, the initial value U0 of the starting voltage at each departure interval after optimization is obtained; wherein, at the same departure interval, the initial value U0 of the starting voltage of each inverter feedback device is the same;

[0031] According to S4, the starting voltage of each inverter feedback device is optimized respectively.

[0032] Another aspect of the embodiments of this specification discloses a control device, including:

[0033] The correlation analysis module is used to perform correlation analysis on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval, and obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN;

[0034] The starting voltage optimization initial value adjustment module is used to determine whether the absolute value of the corresponding γn is greater than the threshold γT for each departure interval. If not, the initial value U0 of the starting voltage is set to the default value UM and enter the starting voltage optimization module; if so, further determine whether γn is positive. If so, U0 is set to UH, UH>UM; if not, U0 is set to UL, UL<UM;

[0035] The starting voltage optimization module is used to optimize the starting voltage of each departure interval respectively with the starting voltage initial value set by the starting voltage optimization initial value adjustment module as the input condition, and output the optimal starting voltage of all departure intervals.

[0036] In the embodiment of this specification, the control device also includes:

[0037] A processor, connected to the correlation analysis module, the starting voltage optimization initial value adjustment module and the starting voltage optimization module respectively;

[0038] a memory connected to the processor and storing a computer program executable on the processor;

[0039] When the processor executes the computer program, the processor controls the correlation analysis module, the starting voltage optimization initial value adjustment module and the starting voltage optimization module to operate so as to implement the above-mentioned starting voltage optimization method of the inverter feedback device.

[0040] In summary, the present invention has at least the following beneficial effects:

[0041] The present invention takes into account the optimization of the starting voltage of different inverter feedback devices in different time periods, and takes into account the energy flow relationship of the traction power supply system, and limits the initial value of the inverter feedback device starting voltage optimization, which can improve the optimization efficiency. Moreover, when there are multiple inverter feedback devices, the starting voltage of each inverter feedback device is optimized separately rather than optimized as a whole, so as to avoid uneven distribution of regenerative braking energy and achieve better energy-saving effect of the inverter feedback device. The present invention also optimizes the starting voltage of the inverter feedback device in different time periods to further improve the energy-saving effect of the system. The present invention limits the initial value of the starting voltage optimization based on energy correlation analysis, which improves the optimization efficiency and saves time. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0043] Figure 1It is a schematic diagram of the steps of the startup voltage optimization method of the inverter feedback device involved in the present invention.

[0044] Figure 2 It is a schematic diagram of the structure of the urban rail transit traction power supply system involved in the present invention.

[0045] Figure 3 It is a flow chart of the startup voltage optimization method of the inverter feedback device involved in the present invention.

[0046] Figure 4 It is a schematic diagram of the specific steps of S4 involved in the present invention.

[0047] Figure 5 It is a schematic diagram of the specific steps of S41 involved in the present invention.

[0048] Figure 6 The figure is a schematic diagram of the steps of the method for starting voltage optimization of the inverter feedback device when there are multiple inverter feedback devices involved in the present invention.

[0049] Figure 7 It is a module schematic diagram of the optimization system of the starting voltage of the inverter feedback device involved in the present invention. DETAILED DESCRIPTION

[0050] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0051] The disclosure below provides many different embodiments or examples to implement different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the embodiments of the present invention. In addition, the embodiments of the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed.

[0052] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0053] like Figure 1 As shown, one aspect of the embodiments of this specification discloses a method for optimizing the starting voltage of an inverter feedback device, comprising:

[0054] S1. Perform a correlation analysis on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval to obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN.

[0055] In some embodiments, S1 includes:

[0056] S11. Simulate the power supply system for all uplink and downlink departure time differences of each departure interval;

[0057] S12. Based on the simulation results, a correlation analysis is performed on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval to obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN.

[0058] In some embodiments, S11 includes:

[0059] S111. Determine the power supply system simulation software, which may be PSCAD, SIMULINK or DIgSILENT;

[0060] S112. Establish a simulation model of the power supply system in the simulation software, the simulation model includes a main substation, a traction step-down hybrid station, a step-down substation, cable lines, a traction network and a train;

[0061] S113. Set simulation parameters, including train traction calculation results, AC / DC system parameters, rectifier units, inverter feedback device parameters and control strategies, and set the train operation plan for all uplink and downlink departure time differences for each departure interval;

[0062] S114. Perform simulation calculations, simulate all train operation plans respectively, to obtain the operation results of the power supply system, and calculate the total feedback energy and system-level traction energy consumption under each train operation plan.

[0063] In some embodiments, in S1, Pearson correlation coefficient is used for correlation analysis.

[0064] S2. Determine whether the absolute value of γn is greater than the threshold value γT. If not, set the initial value U0 of the starting voltage corresponding to the nth departure interval to the default value UM and enter S4; if yes, enter S3; n = 1, 2...N;

[0065] S3. Determine whether γn is positive. If so, increase U0 to UH, UH>UM; if not, decrease U0 to UL, UL<UM.

[0066] S4. According to the initial value U0 of the starting voltage corresponding to the n-th departure interval, the starting voltage of the n-th departure interval is optimized. After the starting voltage optimization of all departure intervals is completed, the optimal starting voltage of all departure intervals is obtained.

[0067] In some embodiments, Figure 4 As shown, S4 includes:

[0068] S41. According to the initial value U0 of the starting voltage corresponding to the nth departure interval, the starting voltage is optimized using an artificial intelligence algorithm for the nth departure interval;

[0069] S42. After the optimization is completed, determine whether n is equal to N. If so, it means that the starting voltage of all departure intervals has been optimized, and the optimal starting voltage of all departure intervals and the corresponding system-level traction energy consumption are obtained; if not, n is increased by 1, and S41 is continued.

[0070] In some embodiments, Figure 5 As shown, S41 includes:

[0071] S411. Perform the i-th power supply calculation simulation, the input parameters include the starting voltage U(i-1) and the train operation plan corresponding to the n-th departure interval, and the output results are the total feedback energy and system-level traction energy consumption; wherein, i≥1; when i=1, the starting voltage U(i-1) is the initial value U0 of the starting voltage corresponding to the n-th departure interval; when i>1, the starting voltage U(i-1) is the i-1th iterative calculation voltage output when S412 was last performed;

[0072] S412. Perform artificial intelligence algorithm calculation based on the output result of S411 and the starting voltage U(i-1), and the output result is the i-th iteration calculation voltage Ui;

[0073] S413. Perform the i-th iteration convergence judgment. When the convergence condition is met, the optimization is terminated. The optimal starting voltage corresponding to the n-th departure interval is Ui, and the system-level traction energy consumption corresponding to the n-th departure interval is Eni. When the convergence condition is not met, the number of simulations is increased by 1 and enters S411.

[0074] In some embodiments, the artificial intelligence algorithm is a genetic algorithm or a swarm intelligence algorithm.

[0075] It should be understood that one or more traction step-down hybrids can be considered as a whole, and the initial value U0 of all inverter feedback devices can be set to UM, and m uplink and downlink time differences under n departure intervals can be simulated (implemented using simulation software).

[0076] Based on n×m simulation results, the total feedback energy and system-level traction energy consumption of all uplink and downlink time difference simulation results of each departure interval are analyzed for correlation coefficients γ1~γN under n departure intervals, and each correlation coefficient is analyzed to determine the initial value U0 corresponding to each correlation coefficient, that is, the initial value U0 of the starting voltage of all inverter feedback devices under each departure interval is obtained (it can be calculated manually or by software such as Matlab);

[0077] Taking the initial value of the starting voltage U0 of the inverter feedback device as the input condition, the starting voltage of each inverter feedback device is the optimization object (the starting voltage of each inverter feedback device is independent of each other and is not necessarily the same as the starting voltage of other inverter feedback devices) (not every traction step-down hybrid station has an inverter feedback device), and using artificial intelligence algorithm for optimization, the optimization goal is to minimize the system-level traction energy consumption. The optimal U corresponding to each inverter feedback device at each departure interval is obtained. In actual engineering, it is realized through the inverter feedback device controller (control device).

[0078] In some embodiments, the inverter feedback device is used to be arranged in a traction power supply system, and the traction power supply system is provided with a plurality of inverter feedback devices;

[0079] like Figure 6 As shown, the method for optimizing the starting voltage of the inverter feedback device includes:

[0080] According to S1 to S3, the initial value U0 of the starting voltage at each departure interval after optimization is obtained; wherein, at the same departure interval, the initial value U0 of the starting voltage of each inverter feedback device is the same;

[0081] According to S4, the starting voltage of each inverter feedback device is optimized respectively.

[0082] In this embodiment, there are multiple inverter feedback devices in the traction power supply system, and the starting voltage of each inverter feedback device is optimized individually rather than being optimized as a whole with a unified value, so as to avoid uneven distribution of regenerative braking energy and achieve better energy-saving effect of the inverter feedback device.

[0083] like Figure 7 As shown, another aspect of the embodiment of this specification discloses a control device, including:

[0084] The correlation analysis module is used to perform correlation analysis on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval, and obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN;

[0085] The starting voltage optimization initial value adjustment module is used to determine whether the absolute value of the corresponding γn is greater than the threshold γT for each departure interval. If not, the initial value U0 of the starting voltage is set to the default value UM and enter the starting voltage optimization module; if so, further determine whether γn is positive. If so, U0 is set to UH, UH>UM; if not, U0 is set to UL, UL<UM;

[0086] The starting voltage optimization module is used to optimize the starting voltage of each departure interval respectively with the starting voltage initial value set by the starting voltage optimization initial value adjustment module as the input condition, and output the optimal starting voltage of all departure intervals.

[0087] In some embodiments, the control device further comprises:

[0088] The processor is respectively connected with the correlation analysis module, the starting voltage optimization initial value adjustment module and the starting voltage optimization module;

[0089] a memory connected to the processor and storing a computer program executable on the processor;

[0090] When the processor executes the computer program, the processor controls the correlation analysis module, the starting voltage optimization initial value adjustment module and the starting voltage optimization module to work, so as to implement the above-mentioned inverter feedback device starting voltage optimization method.

[0091] The technical concept of the present invention is as follows:

[0092] First, the initial value of the starting voltage U is graded. Here, three grades are used: high voltage UH, medium voltage UM, and low voltage UL (such as 1740V, 1720V, and 1700V). This value can also be divided into more levels. UM is the default value. The initial value of the optimized U is U0. There are N types of departure intervals, corresponding to intervals T1, T2, ..., TN. For each departure interval, a total of m uplink and downlink departure time differences are set. The equal distribution method can be used to determine the uplink and downlink departure time differences as 0, 1*T1 / m, 2*T1 / m, ..., (m-1)*T1 / m. The method for determining the departure time difference can be adjusted. Select a certain uplink and downlink departure time difference as the default value, which represents the departure time difference closest to the actual situation. The total feedback energy of the inverter feedback device refers to the sum of the feedback power of all inverter feedback devices. The system-level traction energy consumption can measure the traction energy consumption level of the system, and the calculation method is the difference between the total energy consumption of the main station and the total energy consumption of the step-down load. This indicator can also be replaced by other indicators. Preset the Pearson correlation coefficient threshold γT. Preset the upper limit of the number of iterations (such as 100) and the convergence condition (such as the starting voltage of this optimization is exactly the same as the last optimization) of the artificial intelligence algorithm optimization.

[0093] like Figure 3 As shown, the starting voltage U of all inverter feedback devices is set to UM, and all uplink and downlink departure time differences under all departure intervals are simulated.

[0094] Based on the simulation results, the total feedback energy and system-level traction energy consumption of all uplink and downlink departure time differences at each departure interval are analyzed for correlation, and the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN are obtained. The correlation analysis can be calculated using the Pearson correlation coefficient γ. The closer γ is to 1, the stronger the positive correlation between the two variables; the closer γ is to -1, the stronger the negative correlation between the two variables; the closer γ is to 0, the weaker the correlation between the two variables.

[0095] The overall goal of optimizing the starting voltage U is to minimize the exhaustion of system-level traction energy at each departure interval. The optimization algorithm converges under the premise of minimizing the number of iterations. The optimization output results are: U of all inverter feedback devices at each departure interval, and the corresponding system-level traction energy consumption.

[0096] First, optimize U for the case of n=1, that is, the first departure interval. Determine whether the absolute value of γn is greater than the threshold γT (such as 0.5). If it is greater than γT, it means that the total feedback energy and the system-level traction energy consumption are highly correlated. At this time, by adjusting U to change the total feedback energy, the system-level traction energy consumption can be more effectively affected. If not, it is not very effective to change the total feedback energy by adjusting U to affect the system-level traction energy consumption. Therefore, let U0 be the default value UM, and then optimize all U of all inverter feedback devices. The U of each inverter feedback device does not have to be the same as the U of other inverter feedback devices. The optimization algorithm can be an artificial intelligence algorithm. If the absolute value of γn is greater than γT, further determine whether γn is positive. If it is positive, increase U0 to UH. This indicates that the feedback energy and system-level traction energy consumption are positively correlated. At this time, the departure interval is generally small, that is, there are more trains on the line and the distance between trains is short, which means that the proportion of the regenerative braking energy in the regenerative braking energy transfer path of the adjacent vehicle is large. At this time, the efficiency of the adjacent vehicle energy transfer is higher than that of the feedback through the inverter feedback device. Therefore, in this case, the more energy the inverter feedback device feedbacks, the lower the system efficiency, that is, the higher the system-level traction energy consumption. At this time, U should be increased, that is, to increase the startup threshold of the inverter feedback device, the number of inverter feedback device startups decreases, and the feedback power of the inverter feedback device decreases. Then, take U0 as the initial value and optimize all U of all inverter feedback devices. If γn is not positive, reduce U0 to UL. Then, take U0 as the initial value and optimize all U of all inverter feedback devices. When the upper limit of the number of iterations of the artificial intelligence algorithm or the convergence condition is reached, the optimization ends, otherwise the optimization continues. After the optimization is completed, determine whether n is equal to N. If so, it means that all departure intervals have been optimized, and the optimal U of all departure intervals and the corresponding system-level traction energy consumption can be obtained. Otherwise, n is increased by 1, and the process returns to the step of determining whether the absolute value of γn is greater than γT.

[0097] The above-described embodiments are used to illustrate the present invention, not to limit the present invention, so changes in the exemplified values ​​or replacement of equivalent elements should still fall within the scope of the present invention.

[0098] From the above detailed description, it can be understood by those skilled in the art that the present invention can indeed achieve the aforementioned objectives and is in compliance with the provisions of the Patent Law.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. It should be pointed out that any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0100] It should be noted that the above description of the relevant process is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to the process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification.

[0101] The basic concepts have been described above. Obviously, for those of ordinary skill in the art who have read this application, the above invention disclosure is only for example and does not constitute a limitation of this application. Although not explicitly stated here, those of ordinary skill in the art may make various modifications, improvements and amendments to this application. Such modifications, improvements and amendments are suggested in this application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of this application.

[0102] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0103] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful combination of processes, machines, products or substances, or any new and useful improvements thereto. Therefore, various aspects of the present application may be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "units", "modules" or "systems". In addition, various aspects of the present application may take the form of a computer program product embodied in one or more computer-readable media, wherein computer-readable program code is contained therein.

[0104] The computer program code required for the operation of each part of the application can be written in any one or more programming languages, including object-oriented programming languages ​​such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., such as C programming language, VisualBasic, Fortran2103, Perl, COBOL2102, PHP, ABAP, such as Python, Ruby and Groovy dynamic programming language or other programming languages. The program code can be run completely on the user's computer, or run on the user's computer as an independent software package, or run partly on the user's computer and partly on the remote computer, or run completely on the remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0105] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this application, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this application. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this application. For example, although the implementation of the various components described above can be embodied in a hardware device, it can also be implemented as a pure software solution, for example, installation on an existing server or mobile device.

[0106] Similarly, it should be noted that in order to simplify the description disclosed in this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of the application, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this method of the application should not be interpreted as reflecting the intention that the claimed object requires more features than those explicitly stated in each claim. On the contrary, the subject of the invention should have fewer features than the above single embodiment.

Claims

1. A method for optimizing the starting voltage of an inverter feedback device, characterized in that: include: S1. Perform a correlation analysis on the total feedback energy and system-level traction energy consumption of all uplink and downlink departure time differences at each departure interval, and obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN; S2. Determine whether the absolute value of γn is greater than the threshold value γT. If not, set the initial value U0 of the starting voltage corresponding to the nth departure interval to the default value UM and enter S4; if yes, enter S3; n=1, 2…N; S3. Determine whether γn is positive. If so, increase U0 to UH, where UH>UM; if not, decrease U0 to UL, where UL<UM; S4. According to the initial value U0 of the starting voltage corresponding to the nth departure interval, the starting voltage of the nth departure interval is optimized, and after the starting voltage optimization of all departure intervals is completed, the optimal starting voltage of all departure intervals is obtained; S4 includes: S41. According to the initial value U0 of the starting voltage corresponding to the nth departure interval, the starting voltage is optimized using an artificial intelligence algorithm for the nth departure interval; S42. After the optimization is completed, it is determined whether n is equal to N. If so, it means that the starting voltage of all departure intervals has been optimized, and the optimal starting voltage of all departure intervals and the corresponding system-level traction energy consumption are obtained: if not, n is increased by 1, and S41 is continued; S41 includes: S411. Perform the i-th power supply calculation simulation, the input parameters include the starting voltage U(i-1), the train operation plan corresponding to the n-th departure interval, and the output results are the total feedback energy and system-level traction energy consumption; where i≥1; when i=1, the starting voltage U(i-1) is the initial value U0 of the starting voltage corresponding to the n-th departure interval; when i>1, the starting voltage U(i-1) is the i-1th iteration calculation voltage output when S412 was last performed; S412. According to the output result of S411 and the starting voltage U(i-1), an artificial intelligence algorithm is calculated, and the output result is the i-th iteration calculation voltage Ui; S413. Perform the i-th iteration convergence judgment. When the convergence condition is met, the optimization is terminated. The optimal starting voltage corresponding to the n-th departure interval is Ui, and the system-level traction energy consumption corresponding to the n-th departure interval is Eni. When the convergence condition is not met, the number of simulations is increased by 1 and enters S411.

2. The method for optimizing the starting voltage of an inverter feedback device according to claim 1, characterized in that: S1 includes: S11. Simulate the power supply system for all uplink and downlink departure time differences of each departure interval; S12. Based on the simulation results, a correlation analysis is performed on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval to obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN.

3. The method for optimizing the starting voltage of an inverter feedback device according to claim 2, characterized in that: S11 includes: S111. Determine the power supply system simulation software; S112. Establish a simulation model of the power supply system in the simulation software, the simulation model includes a main substation, a traction step-down hybrid station, a step-down substation, cable lines, a traction network and a train; S113. Set simulation parameters, including train traction calculation results, AC / DC system parameters, rectifier units, inverter feedback device parameters and control strategies, and set the train operation plan for all uplink and downlink departure time differences for each departure interval; S114. Perform simulation calculations, simulate all train operation plans respectively, to obtain the operation results of the power supply system, and calculate the total feedback energy and system-level traction energy consumption under each train operation plan.

4. The method for optimizing the starting voltage of an inverter feedback device according to claim 1, characterized in that: In S1, Pearson's correlation coefficient was used for correlation analysis.

5. The method for optimizing the starting voltage of an inverter feedback device according to claim 1, characterized in that: The artificial intelligence algorithm is a genetic algorithm or a swarm intelligence algorithm.

6. The method for optimizing the startup voltage of an inverter feedback device according to claim 1, characterized in that: The inverter feedback device is used to be arranged in a traction power supply system, and the traction power supply system is provided with a plurality of the inverter feedback devices; The method for optimizing the starting voltage of the inverter feedback device comprises: According to S1 to S3, the initial value U0 of the starting voltage at each departure interval after optimization is obtained; wherein, at the same departure interval, the initial value U0 of the starting voltage of each inverter feedback device is the same; According to S4, the starting voltage of each inverter feedback device is optimized respectively.

7. A control device, characterized in that: include: The correlation analysis module is used to perform correlation analysis on the total feedback energy of all uplink and downlink departure time differences and the system-level traction energy consumption at each departure interval, and obtain the correlation coefficients γ1~γN corresponding to the departure intervals T1~TN; The starting voltage optimization initial value adjustment module determines whether the absolute value of γn is greater than the threshold value γT. If not, the initial value U0 of the starting voltage corresponding to the nth departure interval is set to the default value UM, and the starting voltage optimization module is entered; if so, it is further determined whether γn is positive. If so, U0 is set to UH, UH>UM; if not, U0 is set to UL, UL<UM; A starting voltage optimization module is used to optimize the starting voltage of each departure interval respectively with the starting voltage initial value set by the starting voltage optimization initial value adjustment module as an input condition, and output the optimal starting voltage of all departure intervals; A processor, connected to the correlation analysis module, the starting voltage optimization initial value adjustment module and the starting voltage optimization module respectively; a memory connected to the processor and storing a computer program executable on the processor; Wherein, when the processor executes the computer program, the processor controls the correlation analysis module, the starting voltage optimization initial value adjustment module and the starting voltage optimization module to work, so as to implement the starting voltage optimization method of the inverter feedback device as described in any one of claims 1 to 6.

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