Intelligent control method and system for flexible power supply of rail transit

Abstract

The invention discloses an intelligent control method and system for rail transit flexible power supply, and relates to the technical field of rail transit intelligent control, and the method comprises the steps: collecting train operation data in real time through an integrated sensor, carrying out the preprocessing of the train operation data, and storing the data in a time sequence database InfluxDB; and calculating a frequency disturbance prediction index Prd, calculating a composite phase difference ratio phi cp when the Prd exceeds a safety range, and determining a smooth control power compensation parameter PLBC and a frequency deviation correction coefficient PLXZ according to the composite phase difference ratio phi cp so as to realize advanced compensation of frequency fluctuation and stability after frequency switching. The above parameters are summarized and calculated to obtain a comprehensive frequency regulation coefficient ZTK, and secondary evaluation is carried out to ensure that the frequency is in a stable operation interval, so that the flexibility and reliability of a power supply system are further improved, and the operation efficiency of rail transit is optimized. The method has important application value and innovativeness, and has obvious influence on the safety and economy of the rail transit system.

Description

Technical Field

[0001] The present invention relates to the technical field of rail transit intelligent control, and in particular to an intelligent control method and system for flexible power supply of rail transit. Background Art

[0002] In the development of modern cities, rail transit, as an important mode of public transportation, has received increasing attention. With the acceleration of urbanization, the demand for efficient and environmentally friendly transportation solutions in densely populated areas is increasing. The rail transit system can not only alleviate urban traffic congestion, but also effectively reduce automobile emissions, improve air quality, and promote sustainable development. However, the rail transit system faces problems of frequency fluctuation and power supply stability during operation, which puts higher requirements on the safety and comfort of trains. At the same time, with the advancement of science and technology, the development of intelligent control and data acquisition technology has provided new opportunities for the optimization of rail transit systems, which can help operators to monitor and adjust the power supply system in a timely manner and ensure the stability of the frequency of trains during operation. By studying and implementing more flexible power supply control methods, it is possible to better meet the growing urban transportation needs and provide strong support for future urban transportation construction.

[0003] Traditional control systems usually rely on fixed parameters and static models, and lack real-time monitoring and response to dynamic operating conditions. This makes it difficult to correct frequency disturbances in a timely manner when the train approaches the phase-splitting area, which may cause unstable power supply or safety hazards. Although the existing power supply system has certain flexibility and energy-saving in theory, in actual operation, especially when the train passes through the phase-splitting area, frequency fluctuations are still a problem that needs to be solved urgently. Existing methods mostly rely on traditional control algorithms, which are not easy to fully consider the complex "vehicle-grid" coupling relationship, resulting in inaccurate frequency disturbance prediction, which in turn affects the stable operation of the train. This frequency fluctuation will not only reduce the power supply efficiency, but may also cause equipment overload and even threaten the safe operation of the train. Therefore, there is an urgent need for an intelligent and real-time responsive control method to effectively meet this challenge. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent control method and system for flexible power supply for rail transit, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent control method for flexible power supply of rail transit, comprising the following steps:

[0006] S1. When the train is about to enter the phase separation area, the integrated sensors installed inside the train collect the train operation data in real time, perform preprocessing, obtain the operation characteristic data group, and then store the operation characteristic data group in the time series database InfluxDB;

[0007] S2, extracting the train travel data group and the inertia response data group from the operation characteristic data group, performing summary calculation, and obtaining the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl;

[0008] S3. The obtained operating frequency disturbance coefficient Lrd and inertia response relative increment Gzl are combined with the state data group for summary calculation to obtain the frequency disturbance prediction index Prd, and the frequency change when the train passes through the phase separation area is predicted. Then, the lower limit stability threshold xxT and the upper limit stability threshold sxT are obtained by analyzing the historical and real-time data through the quartile method, and the current frequency disturbance situation is preliminarily evaluated with the frequency disturbance prediction index Prd;

[0009] S4. When the fluctuation amplitude of the frequency disturbance prediction index Prd is not within the safe fluctuation range, extract the power supply phase data group in the operation characteristic data group, and then perform summary calculation to obtain the composite phase difference ratio φ cp , and associate it with the frequency disturbance prediction index Prd to obtain the smoothing control power compensation parameter PLBC, and then build the frequency deviation correction coefficient PLXZ formula by monitoring the deviation between the actual train running frequency pl and the standard frequency Jcp;

[0010] S5. Based on the obtained frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ, a summary calculation is performed to obtain the comprehensive frequency control coefficient ZTK, and a secondary evaluation is performed with the lower stability threshold xxT and the upper stability threshold sxT of the train operating frequency.

[0011] Preferably, said S1 includes S11, S12 and S13;

[0012] S11. When the train is about to enter the phase separation area, the integrated sensors installed inside the train are used to monitor the train operation data in real time;

[0013] The integrated sensors include a speed sensor, an acceleration sensor, a current sensor, a weight sensor, a brake sensor, a frequency sensor, an angle sensor, a phase sensor and a phase change sensor;

[0014] S12, performing denoising, smoothing, data correction, data interpolation and normalization processing on the acquired train operation data to obtain an operation characteristic data group;

[0015] The operation characteristic data group includes a train travel data group, an inertia response data group, a state data group and a power supply phase data group;

[0016] The train travel data group includes track impedance zk, train speed v, train acceleration js and train running current I;

[0017] The inertia response data group includes the train mass zl, the real-time monitored actual train operation frequency pl and the train braking time zs;

[0018] The state data group includes the standard frequency Jcp and the running angle jd when the train passes through the phase separation point;

[0019] The power supply phase data group includes power supply phase ps, phase power supply frequency fs, and phase change rate rp;

[0020] S13, annotate the acquired operation characteristic data group with timestamp, field, sensor ID and independent tag according to the data type and acquisition path, and transmit the annotated operation characteristic data group to the time series database InfluxDB using a wireless network.

[0021] Preferably, S2 includes S21 and S22;

[0022] S21, extracting the real-time train travel data group in the time series database InfluxDB, performing summary calculation, and obtaining the running frequency disturbance coefficient Lrd;

[0023] The operating frequency disturbance coefficient Lrd is calculated by the following formula:

[0024]

[0025] In the formula, t represents the time variable;

[0026] S22, extracting the real-time inertia response data group in the time series database InfluxDB, performing summary calculation, and obtaining the inertia response relative increment Gzl;

[0027] The inertia response relative increment Gzl is calculated and obtained by the following formula:

[0028]

[0029] In the formula, ∈ represents the minimum value, t represents the time variable, and e represents the exponential function.

[0030] Preferably, said S3 includes S31 and S32;

[0031] S31, based on the obtained operating frequency disturbance coefficient Lrd and the relative increment of inertia response Gzl, extract the real-time status data group in the time series database InfluxDB for summary calculation to obtain the frequency disturbance prediction index Prd;

[0032] The frequency disturbance prediction index Prd is calculated by the following formula:

[0033]

[0034] Wherein, k1, k2 and k3 represent the adjustment coefficients of the train disturbance coefficient Lrd, the relative increment of inertia response Gzl and the running angle jd when the train passes the phase separation point, t represents the time variable, c represents the first time constant, and e represents the exponential function.

[0035] Preferably, the S32 includes S321 and S322;

[0036] S321. By collecting the historical operation characteristic data group of the train operation in the time series database InfluxDB, including the frequency fluctuation range under normal operation and the fluctuation data under abnormal conditions, the frequency disturbance amount within a certain period of time is accumulated and analyzed, the maximum value, minimum value and common range of the frequency disturbance are calculated, the fluctuation amplitude and change trend are counted, and the lower limit stability threshold xxT and the upper limit stability threshold sxT of the train operation frequency are obtained by using the quartile method;

[0037] S322, preliminarily compare the obtained frequency disturbance prediction index Prd with the lower stability threshold xxT and the upper stability threshold sxT to evaluate the current frequency disturbance situation. The specific evaluation scheme is as follows;

[0038] When the frequency disturbance prediction index Prd is less than the lower stability threshold xxT, it means that the predicted frequency fluctuation amplitude is not within the safe fluctuation range. At this time, the first adjustment instruction is generated, and power compensation and adjustment are automatically performed;

[0039] When the lower stability threshold xxT≤frequency disturbance prediction index Prd≤upper stability threshold sxT, it means that the predicted frequency fluctuation range is within the safe fluctuation range;

[0040] When the frequency disturbance prediction index Prd> the upper limit stability threshold sxT, it means that the predicted frequency fluctuation amplitude is not in the safe fluctuation range. At this time, the second adjustment instruction is generated, and power compensation and adjustment are automatically performed.

[0041] Preferably, S4 includes S41, S42 and S43;

[0042] S41. When the fluctuation amplitude of the predicted frequency disturbance prediction index Prd is not within the safe fluctuation range, extract the real-time power supply phase data group in the time series database InfluxDB for summary calculation to obtain the composite phase difference ratio φ cp ;

[0043] The composite phase difference ratio φ cp Calculated by the following formula;

[0044]

[0045] In the formula, ∈ represents the minimum value, t represents the time variable, c1 represents the second time constant, and e represents the exponential function;

[0046] S42, combined with the composite phase difference ratio φ cp The smooth control power compensation parameter PLBC required for the train to pass through the phase separation area is calculated by the frequency disturbance prediction index Prd to achieve advance compensation for frequency fluctuations. The specific formula for obtaining the smooth control power compensation parameter PLBC is as follows;

[0047]

[0048] Wherein, t represents the time variable, c2 represents the third time constant, and e represents the exponential function.

[0049] Preferably, S43, by real-time monitoring the deviation between the actual train running frequency pl and the standard frequency Jcp, and according to the deviation and the composite phase difference ratio φ cp Construct a frequency correction adjustment formula to obtain the frequency deviation correction coefficient PLXZ to ensure that the frequency after switching remains within the target range. The specific frequency deviation correction coefficient PLXZ formula is as follows;

[0050] PLXZ=(pl-Jcp) 2 +φ cp *sin(t);

[0051] Where sin represents the sine function and t represents the time variable.

[0052] Preferably, said S5 includes S51 and S52;

[0053] S51, summarizing and calculating the obtained frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ to obtain a comprehensive frequency regulation coefficient ZTK;

[0054] The comprehensive frequency control coefficient ZTK is calculated by the following formula:

[0055]

[0056] In the formula, cos represents cosine function, sin represents sine function, π represents pi, e represents exponential function, c3 represents the fourth time constant, and t represents time variable.

[0057] Preferably, S52, the obtained comprehensive frequency control coefficient ZTK is secondarily evaluated with the lower stability threshold xxT and the upper stability threshold sxT of the train running frequency, the frequency stability after adjustment is deeply evaluated, and relevant instructions are generated according to the evaluation results. The specific evaluation scheme is as follows;

[0058] When the comprehensive frequency control coefficient ZTK is less than the lower limit stability threshold xxT, it means that the frequency has not been adjusted to the stable operation range. At this time, a third adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operation range.

[0059] When the lower stability threshold xxT≤the comprehensive frequency control coefficient ZTK≤the upper stability threshold sxT, it means that the frequency is adjusted to the stable operation range;

[0060] When the comprehensive frequency control coefficient ZTK>the upper limit stability threshold sxT, it means that the frequency is not adjusted to the stable operation range. At this time, a fourth adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operation range.

[0061] An intelligent control system for rail transit flexible power supply, comprising an operation data acquisition module, a frequency fluctuation prediction module, a disturbance frequency evaluation module, a compensation correction module and a comprehensive evaluation module;

[0062] The operation data acquisition module collects train operation data in real time through integrated sensors installed inside the train, performs preprocessing, obtains operation characteristic data groups, and then stores the operation characteristic data groups in the time series database InfluxDB;

[0063] The frequency fluctuation prediction module is used to extract the train travel data group and the inertia response data group in the operation characteristic data group, perform summary calculation, and obtain the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl;

[0064] The disturbance frequency evaluation module is used to obtain the operating frequency disturbance coefficient Lrd and the inertia response relative increment Gzl, and then combine the state data group to perform summary calculation, obtain the frequency disturbance prediction index Prd, predict the frequency change when the train passes through the phase separation area, and then use the quartile method to analyze the historical and real-time data to obtain the lower limit stability threshold xxT and the upper limit stability threshold sxT, and perform a preliminary evaluation of the current frequency disturbance with the frequency disturbance prediction index Prd;

[0065] The compensation correction module is used to extract the power supply phase data group in the operation characteristic data group when the fluctuation amplitude of the frequency disturbance prediction index Prd is not within the safe fluctuation range, and then perform summary calculation to obtain the composite phase difference ratio φ cp , and associate it with the frequency disturbance prediction index Prd to obtain the smoothing control power compensation parameter PLBC, and then build the frequency deviation correction coefficient PLXZ formula by monitoring the deviation between the actual train running frequency pl and the standard frequency Jcp;

[0066] The comprehensive evaluation module is used to perform summary calculations based on the acquired frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ, obtain the comprehensive frequency control coefficient ZTK, and perform secondary evaluation with the lower stability threshold xxT and upper stability threshold sxT of the train operating frequency.

[0067] The present invention provides an intelligent control method and system for flexible power supply for rail transit, which has the following beneficial effects:

[0068] (1) When the train is about to enter the phase separation zone, this method collects train operation data in real time through integrated sensors. After denoising, smoothing, data correction, data interpolation and normalization, the data obtains the operation characteristic data set and stores it in the time series database InfluxDB for subsequent analysis. This process ensures that the system can quickly and accurately grasp the running status of the train before the train enters the phase separation zone, providing a solid data foundation for subsequent frequency disturbance prediction. This not only improves the data integrity of the system, but also lays a good foundation for accurate decision-making, thereby enhancing the safety and reliability of rail transit as a whole.

[0069] (2) This method can accurately predict the frequency change when the train passes through the phase separation area by extracting the operating frequency disturbance coefficient Lrd and the relative increment of inertia response Gzl. The frequency disturbance prediction index Prd calculated based on the real-time monitoring data can reflect the current frequency fluctuation. In addition, the quartile method is used to obtain the lower stability threshold xxT and the upper stability threshold sxT. The evaluation of the frequency disturbance prediction index Prd has a more scientific quantitative standard, which further improves the response efficiency of the system in dealing with frequency anomalies. If the frequency disturbance prediction index Prd exceeds the safe fluctuation range, an adjustment instruction will be generated, and the composite phase difference ratio φ is calculated based on the power supply phase data group. cp , and then associated with the frequency disturbance prediction index Prd to obtain the smooth control power compensation parameter PLBC to offset potential frequency fluctuations in advance. Through this dynamic adjustment mechanism, the system effectively realizes real-time management of frequency fluctuations and ensures the safety and stability of rail transit power supply.

[0070] (3) This method combines the calculation and evaluation of the frequency regulation coefficient ZTK, providing in-depth analysis and feedback of frequency regulation. The calculation of this coefficient takes into account the frequency disturbance prediction index Prd, the smoothing control power compensation parameter PLBC and the frequency deviation correction coefficient PLXZ, ensuring the accuracy of frequency regulation. After adjustment, the system will re-evaluate the comprehensive frequency regulation coefficient ZTK and the lower stability threshold xxT and upper stability threshold sxT of the train operating frequency to ensure that the train operating frequency can be stabilized within the safe operating range. When the train operating frequency does not reach the stable range, further adjustment instructions will be generated for self-iterative adjustment. This intelligent and adaptive control mechanism not only improves the overall efficiency of the rail transit power supply system, but also provides a flexible solution for responding to emergencies and ensures the sustainable development of rail transit. This intelligent control method provides strong technical support for the frequency stability research of rail transit flexible power supply systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 A schematic diagram of the steps of an intelligent control method for flexible power supply for rail transit according to the present invention;

[0072] Figure 2 The present invention is a schematic diagram of the flow of an intelligent control system for flexible power supply for rail transit. DETAILED DESCRIPTION

[0073] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Example 1

[0075] See also Figure 1 The present invention provides an intelligent control method for flexible power supply of rail transit. To achieve the above purpose, the present invention is implemented by the following technical scheme: comprising the following steps:

[0076] S1. When the train is about to enter the phase separation area, the integrated sensors installed inside the train collect the train operation data in real time, perform preprocessing, obtain the operation characteristic data group, and then store the operation characteristic data group in the time series database InfluxDB;

[0077] S2, extracting the train travel data group and the inertia response data group from the operation characteristic data group, performing summary calculation, and obtaining the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl;

[0078] S3. The obtained operating frequency disturbance coefficient Lrd and inertia response relative increment Gzl are combined with the state data group for summary calculation to obtain the frequency disturbance prediction index Prd, and the frequency change when the train passes through the phase separation area is predicted. Then, the lower limit stability threshold xxT and the upper limit stability threshold sxT are obtained by the quartile method, and the current frequency disturbance situation is preliminarily evaluated with the frequency disturbance prediction index Prd;

[0079] S4. When the fluctuation amplitude of the predicted frequency disturbance prediction index Prd is not within the safe fluctuation range, extract the power supply phase data group in the operation characteristic data group, and then perform summary calculation to obtain the composite phase difference ratio φ cp , and associate it with the frequency disturbance prediction index Prd to obtain the smoothing control power compensation parameter PLBC, and then build the frequency deviation correction coefficient PLXZ formula by monitoring the deviation between the actual train running frequency pl and the standard frequency Jcp;

[0080] S5. Based on the obtained frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ, a summary calculation is performed to obtain the comprehensive frequency control coefficient ZTK, and a secondary evaluation is performed with the lower stability threshold xxT and the upper stability threshold sxT of the train operating frequency.

[0081] In this embodiment, in S1, the integrated sensor collects the train operation data in real time and performs preprocessing, generates the operation characteristic data group and stores it in the time series database InfluxDB. This process ensures the real-time and accuracy of the data, and provides a strong basic support for subsequent analysis. Through scientific data management and real-time monitoring, the operation status of the train can be reflected in time, thereby improving the responsiveness and safety of the rail transit system. In S2 and S3, the operating frequency disturbance coefficient Lrd and the inertia response relative increment Gzl are obtained by summarizing and calculating the train travel data group and the inertia response data group. These data provide the necessary information basis for the prediction of frequency changes. The lower limit stability threshold xxT and the upper limit stability threshold sxT are obtained by analyzing the historical and real-time data by the quartile method, effectively identifying the risk of frequency disturbances, and then responding in time. This dynamic monitoring and prediction capability significantly enhances the system's response efficiency in the face of frequency fluctuations, and has higher accuracy and flexibility than traditional methods. In S4 and S5, the frequency disturbance prediction index Prd, the smoothing control power compensation parameter PLBC and the frequency deviation correction coefficient PLXZ are comprehensively calculated to generate the comprehensive frequency control coefficient ZTK, and then evaluated with the lower stability threshold xxT and the upper stability threshold sxT. This comprehensive control method can automatically adjust the power compensation to achieve more accurate frequency control. Compared with previous static or periodic control methods, this intelligent control method has the characteristics of real-time feedback and adaptive adjustment, and can more effectively respond to emergencies and frequency fluctuations. This series of innovations and improvements ultimately improves the stability, safety and overall operating efficiency of the rail transit power supply system, providing a strong guarantee for the sustainable development of rail transit.

[0082] Example 2

[0083] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the S1 includes S11, S12 and S13;

[0084] S11. When the train is about to enter the phase separation area, the integrated sensors installed inside the train are used to monitor the train operation data in real time;

[0085] The integrated sensors include a speed sensor, an acceleration sensor, a current sensor, a weight sensor, a brake sensor, a frequency sensor, an angle sensor, a phase sensor and a phase change sensor;

[0086] S12, performing denoising, smoothing, data correction, data interpolation and normalization processing on the acquired train operation data to obtain an operation characteristic data group;

[0087] The operation characteristic data group includes a train travel data group, an inertia response data group, a state data group and a power supply phase data group;

[0088] The train travel data group includes track impedance zk, train speed v, train acceleration js and train running current I;

[0089] The inertia response data group includes the train mass zl, the real-time monitored actual train operation frequency pl and the train braking time zs;

[0090] The state data group includes the standard frequency Jcp and the running angle jd when the train passes through the phase separation point;

[0091] The power supply phase data group includes power supply phase ps, phase power supply frequency fs, and phase change rate rp;

[0092] S13, annotate the acquired operation characteristic data group with timestamp, field, sensor ID and independent tag according to the data type and acquisition path, and transmit the annotated operation characteristic data group to the time series database InfluxDB using a wireless network.

[0093] In this embodiment, the diversified configuration of integrated sensors ensures comprehensive monitoring of the train's operating status. This all-round data collection not only improves the accuracy of the data, but also provides rich basic information for subsequent analysis. In the data processing stage, through technical means such as denoising, smoothing, correction, interpolation and normalization, it is possible to effectively filter noise and improve data quality, so that the final generated operating characteristic data set has higher reliability and availability. In addition, the use of wireless networks to annotate and transmit the processed data to the time series database InfluxDB not only facilitates real-time access and analysis, but also lays the foundation for subsequent decision support and system optimization. In summary, the implementation of this step effectively improves the real-time, accuracy and availability of the data, provides strong support for the intelligent control and optimization of rail transit power supply, and thus ensures the safe and stable operation of the system.

[0094] Example 3

[0095] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S22;

[0096] S21, extracting the real-time train travel data group in the time series database InfluxDB, performing summary calculation, and obtaining the running frequency disturbance coefficient Lrd;

[0097] The operating frequency disturbance coefficient Lrd is calculated by the following formula:

[0098]

[0099] In the formula, t represents the time variable;

[0100] S22, extracting the real-time inertia response data group in the time series database InfluxDB, performing summary calculation, and obtaining the inertia response relative increment Gzl;

[0101] The inertia response relative increment Gzl is calculated and obtained by the following formula:

[0102]

[0103] In the formula, ∈ represents the minimum value, t represents the time variable, and e represents the exponential function.

[0104] In this embodiment, the operating frequency disturbance coefficient Lrd and the relative increment of inertia response Gzl are successfully obtained by summarizing and calculating the train travel data group and the inertia response data group. The key benefit of this process is that it realizes an in-depth analysis of the dynamic operation state of the train, so that the system can grasp the frequency disturbance characteristics of the train under different operating conditions in real time. By accurately calculating Lrd, the system can quantify the frequency fluctuations in the operation of the train, providing a scientific basis for subsequent frequency prediction and regulation; and the acquisition of the relative increment of inertia response Gzl provides important parameters for analyzing the dynamic response capability of the train when the load changes. This systematic calculation not only improves the sensitivity to frequency disturbances, but also enhances the adaptive ability of the rail transit power supply system in a complex operating environment, and ultimately lays a solid foundation for optimizing power supply stability and improving operational safety.

[0105] Example 4

[0106] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31 and S32;

[0107] S31, based on the obtained operating frequency disturbance coefficient Lrd and the relative increment of inertia response Gzl, extract the real-time status data group in the time series database InfluxDB for summary calculation to obtain the frequency disturbance prediction index Prd;

[0108] The frequency disturbance prediction index Prd is calculated by the following formula:

[0109]

[0110] Wherein, k1, k2 and k3 represent the adjustment coefficients of the train disturbance coefficient Lrd, the relative increment of inertia response Gzl and the running angle jd when the train passes the phase separation point, t represents the time variable, c represents the first time constant, and e represents the exponential function.

[0111] The S32 includes S321 and S322;

[0112] S321. By collecting the historical operation characteristic data group of the train operation in the time series database InfluxDB, including the frequency fluctuation range under normal operation and the fluctuation data under abnormal conditions, the frequency disturbance amount within a certain period of time is accumulated and analyzed, the maximum value, minimum value and common range of the frequency disturbance are calculated, the fluctuation amplitude and change trend are counted, and the lower limit stability threshold xxT and the upper limit stability threshold sxT of the train operation frequency are obtained by using the quartile method;

[0113] S322, preliminarily compare the obtained frequency disturbance prediction index Prd with the lower stability threshold xxT and the upper stability threshold sxT to evaluate the current frequency disturbance situation. The specific evaluation scheme is as follows;

[0114] When the frequency disturbance prediction index Prd is less than the lower stability threshold xxT, it means that the predicted frequency fluctuation amplitude is not within the safe fluctuation range. At this time, the first adjustment instruction is generated, and power compensation and adjustment are automatically performed;

[0115] When the lower stability threshold xxT≤frequency disturbance prediction index Prd≤upper stability threshold sxT, it means that the predicted frequency fluctuation range is within the safe fluctuation range;

[0116] When the frequency disturbance prediction index Prd> the upper limit stability threshold sxT, it means that the predicted frequency fluctuation amplitude is not in the safe fluctuation range. At this time, the second adjustment instruction is generated, and power compensation and adjustment are automatically performed.

[0117] In this embodiment, based on the comprehensive calculation of the state data group, the operating frequency disturbance coefficient Lrd and the relative increment of the inertia response Gzl, the frequency disturbance prediction index Prd is obtained, which can accurately predict the frequency change, thereby improving the ability to predict potential risks. The fluctuation range is analyzed using historical operating characteristic data, and the lower limit stability threshold xxT and the upper limit stability threshold sxT are obtained by the quartile method, which provides a scientific basis for the safe monitoring of frequency disturbances. When the frequency disturbance prediction index Prd is not in the safe fluctuation range, the system can automatically generate adjustment instructions and adjust the power compensation in time, realizing adaptive dynamic control. This real-time monitoring and feedback mechanism not only improves the response speed and safety of the power supply system, but also significantly reduces the impact of frequency fluctuations on train operation, ensuring the safe and stable operation of rail transit, and thus laying a solid foundation for the efficient management and sustainable development of the system.

[0118] Example 5

[0119] This embodiment is explained in Example 4. Please refer to Figure 1 , specifically: the S4 includes S41, S42 and S43;

[0120] S41. When the fluctuation amplitude of the predicted frequency disturbance prediction index Prd is not within the safe fluctuation range, extract the real-time power supply phase data group in the time series database InfluxDB for summary calculation to obtain the composite phase difference ratio φ cp ;

[0121] The composite phase difference ratio φ cp Calculated by the following formula;

[0122]

[0123] In the formula, ∈ represents the minimum value, t represents the time variable, c1 represents the second time constant, and e represents the exponential function;

[0124] S42, combined with the composite phase difference ratio φ cp The smooth control power compensation parameter PLBC required for the train to pass through the phase separation area is calculated by the frequency disturbance prediction index Prd to achieve advance compensation for frequency fluctuations. The specific formula for obtaining the smooth control power compensation parameter PLBC is as follows;

[0125]

[0126] Wherein, t represents the time variable, c2 represents the third time constant, and e represents the exponential function.

[0127] S43, by real-time monitoring of the deviation between the actual train running frequency pl and the standard frequency Jcp, and according to the deviation and the composite phase difference ratio φ cp Construct a frequency correction adjustment formula to obtain the frequency deviation correction coefficient PLXZ to ensure that the frequency after switching remains within the target range. The specific frequency deviation correction coefficient PLXZ formula is as follows;

[0128] PLXZ=(pl-Jcp) 2 +φ cp *sin(t);

[0129] Where sin represents the sine function and t represents the time variable.

[0130] In this embodiment, when the predicted frequency disturbance prediction index Prd is not within the safe fluctuation range, the system can quickly calculate the composite phase difference ratio φ cp This indicator provides the system with a precise basis for power supply phase regulation. Next, combined with the composite phase difference ratio φ cpThe system calculates the smoothing control power compensation parameter PLBC based on the frequency disturbance prediction index Prd, thereby realizing advance compensation for frequency fluctuations. This predictive and forward-looking control method effectively reduces the impact of frequency fluctuations on the safe operation of rail transit and ensures the stability of the power supply system. In addition, by real-time monitoring of the deviation between the actual frequency pl and the standard frequency Jcp of the train, and based on the composite phase difference ratio φ cp Adjusting the frequency deviation correction coefficient PLXZ can quickly restore to the target range after the frequency switch. This series of innovative measures not only improves the accuracy and response speed of frequency control, but also provides a strong technical guarantee for the safe operation of rail transit, further enhances the system's anti-interference ability and adaptability, and lays a solid foundation for efficient and safe rail transit operations.

[0131] Example 6

[0132] This embodiment is explained in Example 5. Please refer to Figure 1 , specifically: the S5 includes S51 and S52;

[0133] S51, summarizing and calculating the obtained frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ to obtain a comprehensive frequency regulation coefficient ZTK;

[0134] The comprehensive frequency control coefficient ZTK is calculated by the following formula:

[0135]

[0136] In the formula, cos represents cosine function, sin represents sine function, π represents pi, e represents exponential function, c3 represents the fourth time constant, and t represents time variable.

[0137] S52, perform a secondary evaluation on the obtained comprehensive frequency control coefficient ZTK and the lower stability threshold xxT and upper stability threshold sxT of the train running frequency, deeply evaluate the frequency stability after adjustment, and generate relevant instructions according to the evaluation results. The specific evaluation plan is as follows;

[0138] When the comprehensive frequency control coefficient ZTK is less than the lower limit stability threshold xxT, it means that the frequency has not been adjusted to the stable operation range. At this time, a third adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operation range.

[0139] When the lower stability threshold xxT≤the comprehensive frequency control coefficient ZTK≤the upper stability threshold sxT, it means that the frequency is adjusted to the stable operation range;

[0140] When the comprehensive frequency control coefficient ZTK>the upper limit stability threshold sxT, it means that the frequency is not adjusted to the stable operation range. At this time, a fourth adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operation range.

[0141] In this embodiment, by performing a secondary evaluation of the comprehensive frequency control coefficient ZTK with the lower stability threshold xxT and the upper stability threshold sxT of the train operating frequency, the intelligent control method significantly improves the frequency management capability of the rail transit flexible power supply system. This method can not only monitor and adjust power compensation in real time, but also ensure that the frequency always remains within a safe operating range by dynamically generating adjustment instructions. This mechanism achieves rapid response and precise control of frequency fluctuations, effectively reducing the risk of equipment damage and operational failures that may be caused by frequency instability. In addition, the system's adaptive adjustment capability enables it to flexibly respond to various emergencies in a complex operating environment, ensuring the efficiency and reliability of power supply. This innovative control strategy not only optimizes the operational efficiency of rail transit, but also lays a solid foundation for improving the safety and stability of the entire system, demonstrating the huge potential for the application of modern intelligent control technology in the transportation field.

[0142] Example 7

[0143] See also Figure 1 and Figure 2 , an intelligent control system for rail transit flexible power supply, including an operation data acquisition module, a frequency fluctuation prediction module, a disturbance frequency evaluation module, a compensation correction module and a comprehensive evaluation module;

[0144] The operation data acquisition module collects train operation data in real time through integrated sensors installed inside the train, performs preprocessing, obtains operation characteristic data groups, and then stores the operation characteristic data groups in the time series database InfluxDB;

[0145] The frequency fluctuation prediction module is used to extract the train travel data group and the inertia response data group in the operation characteristic data group, perform summary calculation, and obtain the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl;

[0146] The disturbance frequency evaluation module is used to obtain the operating frequency disturbance coefficient Lrd and the inertia response relative increment Gzl, and then combine the state data group to perform summary calculation, obtain the frequency disturbance prediction index Prd, predict the frequency change when the train passes through the phase separation area, and then use the quartile method to analyze the historical and real-time data to obtain the lower limit stability threshold xxT and the upper limit stability threshold sxT, and perform a preliminary evaluation of the current frequency disturbance with the frequency disturbance prediction index Prd;

[0147] The compensation correction module is used to extract the power supply phase data group in the operation characteristic data group when the fluctuation amplitude of the frequency disturbance prediction index Prd is not within the safe fluctuation range, and then perform summary calculation to obtain the composite phase difference ratio φ cp , and associate it with the frequency disturbance prediction index Prd to obtain the smoothing control power compensation parameter PLBC, and then build the frequency deviation correction coefficient PLXZ formula by monitoring the deviation between the actual train running frequency pl and the standard frequency Jcp;

[0148] The comprehensive evaluation module is used to perform summary calculations based on the acquired frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ, obtain the comprehensive frequency control coefficient ZTK, and perform secondary evaluation with the lower stability threshold xxT and upper stability threshold sxT of the train operating frequency.

[0149] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for flexible power supply for rail transit, characterized in that: The following steps are involved: S1. When the train is about to enter the phase separation area, the integrated sensors installed inside the train collect the train operation data in real time, perform preprocessing, obtain the operation characteristic data group, and then store the operation characteristic data group in the time series database InfluxDB; S2, extracting the train travel data group and the inertia response data group from the operation characteristic data group, performing summary calculation, and obtaining the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl; S3. The obtained operating frequency disturbance coefficient Lrd and inertia response relative increment Gzl are combined with the state data group for summary calculation to obtain the frequency disturbance prediction index Prd, and the frequency change when the train passes through the phase separation area is predicted. Then, the lower limit stability threshold xxT and the upper limit stability threshold sxT are obtained by analyzing the historical and real-time data through the quartile method, and the current frequency disturbance situation is preliminarily evaluated with the frequency disturbance prediction index Prd; S4. When the fluctuation amplitude of the frequency disturbance prediction index Prd is not within the safe fluctuation range, extract the power supply phase data group in the operation characteristic data group, and then perform summary calculation to obtain the composite phase difference ratio φ cp , and associate it with the frequency disturbance prediction index Prd to obtain the smoothing control power compensation parameter PLBC, and then build the frequency deviation correction coefficient PLXZ formula by monitoring the deviation between the actual train running frequency pl and the standard frequency Jcp; S5. Based on the obtained frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ, a summary calculation is performed to obtain the comprehensive frequency control coefficient ZTK, and a secondary evaluation is performed with the lower stability threshold xxT and the upper stability threshold sxT of the train operating frequency.

2. According to claim 1, an intelligent control method for flexible power supply for rail transit is characterized in that: Said S1 includes S11, S12 and S13; S11. When the train is about to enter the phase separation area, the integrated sensors installed inside the train are used to monitor the train operation data in real time; The integrated sensors include a speed sensor, an acceleration sensor, a current sensor, a weight sensor, a brake sensor, a frequency sensor, an angle sensor, a phase sensor and a phase change sensor; S12, performing denoising, smoothing, data correction, data interpolation and normalization processing on the acquired train operation data to obtain an operation characteristic data group; The operation characteristic data group includes a train travel data group, an inertia response data group, a state data group and a power supply phase data group; The train travel data group includes track impedance zk, train speed v, train acceleration js and train running current I; The inertia response data group includes the train mass zl, the real-time monitored actual train operation frequency pl and the train braking time zs; The state data group includes the standard frequency Jcp and the running angle jd when the train passes through the phase separation point; The power supply phase data group includes power supply phase ps, phase power supply frequency fs, and phase change rate rp; S13, annotate the acquired operation characteristic data group with timestamp, field, sensor ID and independent tag according to the data type and acquisition path, and transmit the annotated operation characteristic data group to the time series database InfluxDB using a wireless network.

3. The intelligent control method for flexible power supply for rail transit according to claim 2 is characterized in that: The S2 includes S21 and S22; S21, extracting the real-time train travel data group in the time series database InfluxDB, performing summary calculation, and obtaining the running frequency disturbance coefficient Lrd; The operating frequency disturbance coefficient Lrd is calculated by the following formula: In the formula, t represents the time variable; S22, extracting the real-time inertia response data group in the time series database InfluxDB, performing summary calculation, and obtaining the inertia response relative increment Gzl; The inertia response relative increment Gzl is calculated and obtained by the following formula: In the formula, ∈ represents the minimum value, t represents the time variable, and e represents the exponential function.

4. The intelligent control method for flexible power supply for rail transit according to claim 3 is characterized in that: The S3 includes S31 and S32; S31, based on the obtained operating frequency disturbance coefficient Lrd and the relative increment of inertia response Gzl, extract the real-time status data group in the time series database InfluxDB for summary calculation to obtain the frequency disturbance prediction index Prd; The frequency disturbance prediction index Prd is calculated by the following formula: Wherein, k1, k2 and k3 represent the adjustment coefficients of the train disturbance coefficient Lrd, the relative increment of inertia response Gzl and the running angle jd when the train passes the phase separation point, t represents the time variable, c represents the first time constant, and e represents the exponential function.

5. The intelligent control method for flexible power supply for rail transit according to claim 4 is characterized in that: The S32 includes S321 and S322; S321. By collecting the historical operation characteristic data group of the train operation in the time series database InfluxDB, including the frequency fluctuation range under normal operation and the fluctuation data under abnormal conditions, the frequency disturbance amount within a certain period of time is accumulated and analyzed, the maximum value, minimum value and common range of the frequency disturbance are calculated, the fluctuation amplitude and change trend are counted, and the lower limit stability threshold xxT and the upper limit stability threshold sxT of the train operation frequency are obtained by using the quartile method; S322, preliminarily compare the obtained frequency disturbance prediction index Prd with the lower stability threshold xxT and the upper stability threshold sxT to evaluate the current frequency disturbance situation. The specific evaluation scheme is as follows; When the frequency disturbance prediction index Prd is less than the lower stability threshold xxT, it means that the predicted frequency fluctuation amplitude is not within the safe fluctuation range. At this time, the first adjustment instruction is generated, and power compensation and adjustment are automatically performed; When the lower stability threshold xxT≤frequency disturbance prediction index Prd≤upper stability threshold sxT, it means that the predicted frequency fluctuation range is within the safe fluctuation range; When the frequency disturbance prediction index Prd> the upper limit stability threshold sxT, it means that the predicted frequency fluctuation amplitude is not in the safe fluctuation range. At this time, the second adjustment instruction is generated, and power compensation and adjustment are automatically performed.

6. The intelligent control method for flexible power supply for rail transit according to claim 5 is characterized in that: The S4 includes S41, S42 and S43; S41. When the fluctuation amplitude of the predicted frequency disturbance prediction index Prd is not within the safe fluctuation range, extract the real-time power supply phase data group in the time series database InfluxDB for summary calculation to obtain the composite phase difference ratio φ cp ; The composite phase difference ratio φ cp Calculated by the following formula; In the formula, ∈ represents the minimum value, t represents the time variable, c1 represents the second time constant, and e represents the exponential function; S42, combined with the composite phase difference ratio φ cp The smooth control power compensation parameter PLBC required for the train to pass through the phase separation area is calculated by the frequency disturbance prediction index Prd to achieve advance compensation for frequency fluctuations. The specific formula for obtaining the smooth control power compensation parameter PLBC is as follows; Wherein, t represents the time variable, c2 represents the third time constant, and e represents the exponential function.

7. The intelligent control method for flexible power supply for rail transit according to claim 6 is characterized in that: S43, by real-time monitoring of the deviation between the actual train running frequency pl and the standard frequency Jcp, and according to the deviation and the composite phase difference ratio φ cp Construct a frequency correction adjustment formula to obtain the frequency deviation correction coefficient PLXZ to ensure that the frequency after switching remains within the target range. The specific frequency deviation correction coefficient PLXZ formula is as follows; PLXZ=(pl-Jcp) 2 +φ cp *sin(t); Where sin represents the sine function and t represents the time variable.

8. The intelligent control method for flexible power supply for rail transit according to claim 7 is characterized in that: The S5 includes S51 and S52; S51, summarizing and calculating the obtained frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ to obtain a comprehensive frequency regulation coefficient ZTK; The comprehensive frequency control coefficient ZTK is calculated by the following formula: In the formula, cos represents cosine function, sin represents sine function, π represents pi, e represents exponential function, c3 represents the fourth time constant, and t represents time variable.

9. The intelligent control method for flexible power supply for rail transit according to claim 8, characterized in that: S52, perform a secondary evaluation on the obtained comprehensive frequency control coefficient ZTK and the lower stability threshold xxT and upper stability threshold sxT of the train running frequency, deeply evaluate the frequency stability after adjustment, and generate relevant instructions according to the evaluation results. The specific evaluation plan is as follows; When the comprehensive frequency control coefficient ZTK is less than the lower limit stability threshold xxT, it means that the frequency has not been adjusted to the stable operation range. At this time, a third adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operation range. When the lower stability threshold xxT≤the comprehensive frequency control coefficient ZTK≤the upper stability threshold sxT, it means that the frequency is adjusted to the stable operation range; When the comprehensive frequency control coefficient ZTK>the upper limit stability threshold sxT, it means that the frequency is not adjusted to the stable operation range. At this time, a fourth adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operation range.

10. An intelligent control system for rail transit flexible power supply, comprising an intelligent control method for rail transit flexible power supply according to any one of claims 1 to 9, characterized in that: It includes operation data acquisition module, frequency fluctuation prediction module, disturbance frequency assessment module, compensation correction module and comprehensive assessment module; The operation data acquisition module collects train operation data in real time through integrated sensors installed inside the train, performs preprocessing, obtains operation characteristic data groups, and then stores the operation characteristic data groups in the time series database InfluxDB; The frequency fluctuation prediction module is used to extract the train travel data group and the inertia response data group in the operation characteristic data group, perform summary calculation, and obtain the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl; The disturbance frequency evaluation module is used to obtain the operating frequency disturbance coefficient Lrd and the inertia response relative increment Gzl, and then combine the state data group to perform summary calculation, obtain the frequency disturbance prediction index Prd, predict the frequency change when the train passes through the phase separation area, and then use the quartile method to analyze the historical and real-time data to obtain the lower limit stability threshold xxT and the upper limit stability threshold sxT, and perform a preliminary evaluation of the current frequency disturbance with the frequency disturbance prediction index Prd; The compensation correction module is used to extract the power supply phase data group in the operation characteristic data group when the fluctuation amplitude of the frequency disturbance prediction index Prd is not within the safe fluctuation range, and then perform summary calculation to obtain the composite phase difference ratio φ cp , and associate it with the frequency disturbance prediction index Prd to obtain the smoothing control power compensation parameter PLBC, and then build the frequency deviation correction coefficient PLXZ formula by monitoring the deviation between the actual train running frequency pl and the standard frequency Jcp; The comprehensive evaluation module is used to perform summary calculations based on the acquired frequency disturbance prediction index Prd, smoothing control power compensation parameter PLBC and frequency deviation correction coefficient PLXZ, obtain the comprehensive frequency control coefficient ZTK, and perform secondary evaluation with the lower stability threshold xxT and upper stability threshold sxT of the train operating frequency.

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