An intelligent control method and system for flexible power supply of rail transit

Abstract

The present invention discloses an intelligent control method and system for flexible power supply of rail transit, which relates to the field of intelligent control technology of rail transit. The method uses integrated sensors to collect train operation data in real time, and stores it in the time series database InfluxDB after preprocessing. The frequency disturbance prediction index Prd is calculated. When Prd exceeds the safety range, the composite phase difference ratio φ is calculated. cp Based on this, the smoothing control power compensation parameter PLBC and the frequency deviation correction coefficient PLXZ are determined to achieve early compensation for frequency fluctuations and stability after frequency switching. These parameters are aggregated and calculated to obtain the comprehensive frequency control coefficient ZTK, which is then re-evaluated to ensure that the frequency remains within the stable operating range, further improving the flexibility and reliability of the power supply system and optimizing the operational efficiency of rail transit. This method has important application value and innovation, significantly impacting the safety and economic efficiency of rail transit systems.

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 modern urban development, rail transit, as a key public transportation mode, is gaining increasing attention. With accelerating urbanization, densely populated areas are increasingly demanding efficient and environmentally friendly transportation solutions. Rail transit systems not only alleviate urban traffic congestion but also effectively reduce vehicle emissions, improve air quality, and promote sustainable development. However, rail transit systems face challenges in frequency fluctuation and power supply stability during operation, placing higher demands on train safety and comfort. Simultaneously, technological advancements, including the development of intelligent control and data acquisition technologies, are providing new opportunities for optimizing rail transit systems. These technologies can help operators monitor and adjust power supply systems in a timely manner, ensuring frequency stability during train operation. By researching and implementing more flexible power supply control methods, we can better meet the growing demands of urban transportation and provide strong support for future urban transportation development.

[0003] Traditional control systems typically rely on fixed parameters and static models, lacking real-time monitoring and response to dynamic operating conditions. This makes it difficult to promptly correct frequency disturbances when a train approaches a split-phase zone, potentially leading to unstable power supply or safety hazards. Although existing power supply systems are theoretically flexible and energy-efficient, frequency fluctuations remain a pressing issue in actual operation, especially when trains pass through split-phase zones. Existing methods often rely on traditional control algorithms, which do not readily take into account the complex "vehicle-grid" coupling relationship, resulting in inaccurate frequency disturbance predictions and, in turn, affecting the stable operation of the train. Such frequency fluctuations not only reduce power supply efficiency but can also cause equipment overloads and even threaten the safe operation of the train. Therefore, an intelligent and real-time responsive control method is urgently needed to effectively address 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 a train is about to enter the phase separation zone, the integrated sensors installed inside the train collect and pre-process the train operation data in real time to obtain the operation characteristic data set, which is then stored in the time series database InfluxDB.

[0007] S2. Extract the train travel data group and the inertia response data group from the operation characteristic data group, perform summary calculations, and obtain 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. The frequency change when the train passes through the phase separation zone is predicted. The lower limit stability threshold xxT and the upper limit stability threshold sxT are then obtained by analyzing the historical and real-time data using the quartile method. The frequency disturbance prediction index Prd is then used to preliminarily assess the current frequency disturbance.

[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 The smoothing control power compensation parameter PLBC is obtained by correlating it with the frequency disturbance prediction index Prd. Then, by monitoring the deviation between the actual train frequency pl and the standard frequency Jcp, the frequency deviation correction coefficient PLXZ formula is constructed.

[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 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 zone, 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 set;

[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 set includes track impedance zk, train speed v, train acceleration js and train running current I;

[0017] The inertia response data set 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 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. Label the acquired operating characteristic data group with a timestamp, field, sensor ID, and independent tag according to the data type and acquisition path, and transmit the labeled operating characteristic data group to the time series database InfluxDB using a wireless network.

[0021] Preferably, said S2 includes S21 and S22;

[0022] S21. Extract the real-time train travel data group from the time series database InfluxDB, perform summary calculation, and obtain the running frequency disturbance coefficient Lrd;

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

[0024]

[0025] Where, t represents the time variable;

[0026] S22. Extract the real-time inertia response data group from the time series database InfluxDB, perform summary calculations, and obtain the inertia response relative increment Gzl;

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

[0028]

[0029] Where ∈ 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 from the time series database InfluxDB, perform summary calculation, and obtain the frequency disturbance prediction index Prd;

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

[0033]

[0034] Where 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, respectively; 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. Collect historical train operation characteristic data from the time series database InfluxDB, including the frequency fluctuation range under normal operation and fluctuation data under abnormal conditions. Accumulate and analyze the frequency disturbance within a certain period of time. Calculate the maximum, minimum, and common range of the frequency disturbance. Statistically calculate the fluctuation amplitude and change trend. Use the quartile method to obtain the lower and upper stability thresholds xxT and sxT of the train operating frequency.

[0037] S322: Preliminary comparison is performed between the obtained frequency disturbance prediction index Prd and the lower stability threshold xxT and the upper stability threshold sxT to evaluate the current frequency disturbance. 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 ≤ the frequency disturbance prediction index Prd ≤ the upper stability threshold sxT, it means that the predicted frequency fluctuation amplitude 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, said 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] Where, ∈ represents the minimum value, t represents the time variable, c1 represents the second time constant, and e represents the exponential function;

[0046] S42, combined 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 based on the frequency disturbance prediction index Prd to achieve early compensation for frequency fluctuations. The specific formula for obtaining the smooth control power compensation parameter PLBC is as follows:

[0047]

[0048] Where 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 frequency pl of the train 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 control coefficient ZTK;

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

[0055]

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

[0057] Preferably, S52, a secondary evaluation is performed on the obtained comprehensive frequency control coefficient ZTK and the lower stability threshold xxT and upper stability threshold sxT of the train operating frequency, an in-depth evaluation of the frequency stability after adjustment is conducted, 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 stability threshold xxT, it indicates that the frequency has not been adjusted to the stable operating range. At this time, a third adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operating 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 operating range;

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

[0061] An intelligent control system for flexible power supply of rail transit, comprising an operation data acquisition module, a frequency fluctuation prediction module, a disturbance frequency assessment module, a compensation correction module and a comprehensive assessment 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 from the operation characteristic data group, perform summary calculations, and obtain the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl;

[0064] The disturbance frequency assessment module is used to obtain the operating frequency disturbance coefficient Lrd and the inertia response relative increment Gzl, and then perform summary calculations in combination with the state data group to 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 stability threshold xxT and the upper stability threshold sxT, and perform a preliminary assessment of the current frequency disturbance situation 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 The smoothing control power compensation parameter PLBC is obtained by correlating it with the frequency disturbance prediction index Prd. Then, by monitoring the deviation between the actual train frequency pl and the standard frequency Jcp, the frequency deviation correction coefficient PLXZ formula is constructed.

[0066] The comprehensive evaluation module is used to perform summary calculations based on the obtained 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 a secondary evaluation with the lower limit stability threshold xxT and upper limit stability threshold sxT of the train operating frequency.

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

[0068] (1) This method uses integrated sensors to collect train operation data in real time when the train is about to enter the phase separation zone. After denoising, smoothing, data correction, data interpolation, and normalization, the data is processed to obtain the operation characteristic data set and stored in the time series database InfluxDB for subsequent analysis. This process ensures that the system can quickly and accurately grasp the train's operating status before the train enters the phase separation zone, providing a solid data foundation for subsequent frequency disturbance prediction. This not only improves the system's data integrity, 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 will be calculated based on the power supply phase data group. cp , then correlated with the frequency disturbance prediction index Prd to obtain the smoothing control power compensation parameter PLBC, which can offset potential frequency fluctuations in advance. Through this dynamic adjustment mechanism, the system effectively manages frequency fluctuations in real time, ensuring the safety and stability of rail transit power supply.

[0070] (3) This method integrates the calculation and evaluation of the frequency control 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 control 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 This is 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 figure is a flow chart of an intelligent control system for flexible power supply of rail transit according to the present invention. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts 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 solution: comprising the following steps:

[0076] S1. When a train is about to enter the phase separation zone, the integrated sensors installed inside the train collect and pre-process the train operation data in real time to obtain the operation characteristic data set, which is then stored in the time series database InfluxDB.

[0077] S2. Extract the train travel data group and the inertia response data group from the operation characteristic data group, perform summary calculations, and obtain 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. The frequency change when the train passes through the phase separation area is predicted. The lower limit stability threshold xxT and the upper limit stability threshold sxT are then obtained using the quartile method. The current frequency disturbance is preliminarily assessed using 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 The smoothing control power compensation parameter PLBC is obtained by correlating it with the frequency disturbance prediction index Prd. Then, by monitoring the deviation between the actual train frequency pl and the standard frequency Jcp, the frequency deviation correction coefficient PLXZ formula is constructed.

[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 upper stability threshold sxT of the train operating frequency.

[0081] In this embodiment, in S1, integrated sensors collect and preprocess train operation data in real time, generate an operation characteristic data set, and store it in the time series database InfluxDB. This process ensures the real-time and accuracy of the data, providing a strong foundation for subsequent analysis. Through scientific data management and real-time monitoring, the operating status of the train can be reflected in a timely manner, 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 set and the inertia response data set. These data provide the necessary information basis for the prediction of frequency changes. By analyzing historical and real-time data using the quartile method, the lower stability threshold xxT and the upper stability threshold sxT are obtained, effectively identifying the risk of frequency disturbances and responding in a timely manner. 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, a comprehensive calculation is performed based on the frequency disturbance prediction index Prd, the smoothing control power compensation parameter PLBC, and the frequency deviation correction factor PLXZ to generate the comprehensive frequency control coefficient ZTK. This coefficient is then evaluated against the lower stability threshold xxT and the upper stability threshold sxT. This comprehensive control method automatically adjusts power compensation, achieving more precise frequency control. Compared with previous static or periodic control methods, this intelligent control method features real-time feedback and adaptive adjustment, enabling more effective response to emergencies and frequency fluctuations. This series of innovations and improvements has ultimately enhanced the stability, safety, and overall operational 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: said S1 includes S11, S12 and S13;

[0084] S11. When the train is about to enter the phase separation zone, 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 set;

[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 set includes track impedance zk, train speed v, train acceleration js and train running current I;

[0089] The inertia response data set 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 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. Label the acquired operating characteristic data group with a timestamp, field, sensor ID, and independent tag according to the data type and acquisition path, and transmit the labeled operating 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 out noise and improve data quality, so that the final generated operating characteristic data set has higher reliability and availability. In addition, the use of a wireless network 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. Extract the real-time train travel data group from the time series database InfluxDB, perform summary calculation, and obtain the running frequency disturbance coefficient Lrd;

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

[0098]

[0099] Where, t represents the time variable;

[0100] S22. Extract the real-time inertia response data group from the time series database InfluxDB, perform summary calculations, and obtain the inertia response relative increment Gzl;

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

[0102]

[0103] Where ∈ 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 inertia response relative increment 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 operating state of the train, enabling the system to 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 during train operation, providing a scientific basis for subsequent frequency prediction and regulation; and the acquisition of the inertia response relative increment Gzl provides an important parameter for analyzing the dynamic response capability of the train under load changes. This systematic calculation not only improves the sensitivity to frequency disturbances, but also enhances the adaptability of the rail transit power supply system in a complex operating environment, ultimately laying 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: said 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 from the time series database InfluxDB, perform summary calculation, and obtain the frequency disturbance prediction index Prd;

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

[0109]

[0110] Where 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, respectively; 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. Collect historical train operation characteristic data from the time series database InfluxDB, including the frequency fluctuation range under normal operation and fluctuation data under abnormal conditions. Accumulate and analyze the frequency disturbance within a certain period of time. Calculate the maximum, minimum, and common range of the frequency disturbance. Statistically calculate the fluctuation amplitude and change trend. Use the quartile method to obtain the lower and upper stability thresholds xxT and sxT of the train operating frequency.

[0113] S322: Preliminary comparison is performed between the obtained frequency disturbance prediction index Prd and the lower stability threshold xxT and the upper stability threshold sxT to evaluate the current frequency disturbance. 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 ≤ the frequency disturbance prediction index Prd ≤ the upper stability threshold sxT, it means that the predicted frequency fluctuation amplitude 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 frequency changes, thereby improving the ability to foresee 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, providing a scientific basis for the safe monitoring of frequency disturbances. When the frequency disturbance prediction index Prd is not within 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: 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] Where, ∈ represents the minimum value, t represents the time variable, c1 represents the second time constant, and e represents the exponential function;

[0124] S42, combined 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 based on the frequency disturbance prediction index Prd to achieve early compensation for frequency fluctuations. The specific formula for obtaining the smooth control power compensation parameter PLBC is as follows:

[0125]

[0126] Where 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 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 control. Next, combined with the composite phase difference ratio φ cpThe system calculates the smooth control power compensation parameter PLBC based on the frequency disturbance prediction index Prd, thereby achieving early compensation for frequency fluctuations. This predictive and forward-looking control method effectively reduces the impact of frequency fluctuations on the safe operation of rail transportation and ensures the stability of the power supply system. In addition, by monitoring the deviation between the actual train frequency pl and the standard frequency Jcp in real time, and based on the composite phase difference ratio φ cp Adjusting the frequency deviation correction factor PLXZ allows for a rapid return to the target range after a frequency switch. This series of innovative measures not only improves the accuracy and responsiveness of frequency control, but also provides strong technical support for the safe operation of rail transit, further enhancing the system's anti-interference capabilities and adaptability, and laying 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: 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 control coefficient ZTK;

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

[0135]

[0136] Wherein, cos represents a cosine function, sin represents a sine function, π represents pi, e represents an exponential function, c3 represents the fourth time constant, and t represents a 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 operating frequency, deeply evaluate the frequency stability after adjustment, and generate relevant instructions based on the evaluation results. The specific evaluation plan is as follows;

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

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

[0141] In this embodiment, by performing a secondary evaluation of the comprehensive frequency control coefficient ZTK and the lower stability threshold xxT and 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 assessment module, a compensation correction module and a comprehensive assessment 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 from the operation characteristic data group, perform summary calculations, and obtain the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl;

[0146] The disturbance frequency assessment module is used to obtain the operating frequency disturbance coefficient Lrd and the inertia response relative increment Gzl, and then perform summary calculations in combination with the state data group to 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 stability threshold xxT and the upper stability threshold sxT, and perform a preliminary assessment of the current frequency disturbance situation 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 The smoothing control power compensation parameter PLBC is obtained by correlating it with the frequency disturbance prediction index Prd. Then, by monitoring the deviation between the actual train frequency pl and the standard frequency Jcp, the frequency deviation correction coefficient PLXZ formula is constructed.

[0148] The comprehensive evaluation module is used to perform summary calculations based on the obtained 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 a secondary evaluation with the lower limit stability threshold xxT and upper limit stability threshold sxT of the train operating frequency.

[0149] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for flexible power supply for rail transit, characterized by: The following steps are involved: S1. When a train is about to enter the phase separation zone, the integrated sensors installed inside the train collect and pre-process the train operation data in real time to obtain the operation characteristic data set, which is then stored in the time series database InfluxDB. S2. Extract the train travel data group and the inertia response data group from the operation characteristic data group, perform summary calculations, and obtain 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. The frequency change when the train passes through the phase separation zone is predicted. The lower limit stability threshold xxT and the upper limit stability threshold sxT are then obtained by analyzing the historical and real-time data using the quartile method. The frequency disturbance prediction index Prd is then used to preliminarily assess the current frequency disturbance. 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 The smoothing control power compensation parameter PLBC is obtained by correlating it with the frequency disturbance prediction index Prd. Then, by monitoring the deviation between the actual train frequency pl and the standard frequency Jcp, the frequency deviation correction coefficient PLXZ formula is constructed. 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 upper stability threshold sxT of the train operating frequency.

2. The intelligent control method for flexible power supply for rail transit according to claim 1, characterized in that: Said S1 includes S11, S12 and S13; S11. When the train is about to enter the phase separation zone, 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 set; 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 set includes track impedance zk, train speed v, train acceleration js and train running current I; The inertia response data set 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 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. Label the acquired operating characteristic data group with a timestamp, field, sensor ID, and independent tag according to the data type and acquisition path, and transmit the labeled operating 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, characterized in that: Said S2 includes S21 and S22; S21. Extract the real-time train travel data group from the time series database InfluxDB, perform summary calculation, and obtain the running frequency disturbance coefficient Lrd; The operating frequency disturbance coefficient Lrd is calculated by the following formula: Where, t represents the time variable; S22. Extract the real-time inertia response data group from the time series database InfluxDB, perform summary calculations, and obtain the inertia response relative increment Gzl; The inertia response relative increment Gzl is calculated and obtained by the following formula: Where ∈ 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, characterized in that: Said 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 from the time series database InfluxDB, perform summary calculation, and obtain the frequency disturbance prediction index Prd; The frequency disturbance prediction index Prd is calculated by the following formula: Where 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, respectively; 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, characterized in that: The S32 includes S321 and S322; S321. Collect historical train operation characteristic data from the time series database InfluxDB, including the frequency fluctuation range under normal operation and fluctuation data under abnormal conditions. Accumulate and analyze the frequency disturbance within a certain period of time. Calculate the maximum, minimum, and common range of the frequency disturbance. Statistically calculate the fluctuation amplitude and change trend. Use the quartile method to obtain the lower and upper stability thresholds xxT and sxT of the train operating frequency. S322: Preliminary comparison is performed between the obtained frequency disturbance prediction index Prd and the lower stability threshold xxT and the upper stability threshold sxT to evaluate the current frequency disturbance. 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 ≤ the frequency disturbance prediction index Prd ≤ the upper stability threshold sxT, it means that the predicted frequency fluctuation amplitude 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, characterized in that: Said 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; Where, ∈ represents the minimum value, t represents the time variable, c1 represents the second time constant, and e represents the exponential function; S42, combined 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 based on the frequency disturbance prediction index Prd to achieve early compensation for frequency fluctuations. The specific formula for obtaining the smooth control power compensation parameter PLBC is as follows: Where 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, characterized in that: S43, by real-time monitoring of the deviation between the actual train 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, characterized in that: Said 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 control coefficient ZTK; The comprehensive frequency control coefficient ZTK is calculated by the following formula: Wherein, cos represents a cosine function, sin represents a sine function, π represents pi, e represents an exponential function, c3 represents the fourth time constant, and t represents a 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 operating frequency, deeply evaluate the frequency stability after adjustment, and generate relevant instructions based on the evaluation results. The specific evaluation plan is as follows; When the comprehensive frequency control coefficient ZTK is less than the lower stability threshold xxT, it indicates that the frequency has not been adjusted to the stable operating range. At this time, a third adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operating 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 operating range; When the comprehensive frequency control coefficient ZTK>the upper limit stability threshold sxT, it means that the frequency has not been adjusted to the stable operating range. At this time, a fourth adjustment instruction is generated and transmitted to S4 for iterative adjustment to the stable operating range.

10. An intelligent control system for flexible power supply for rail transit, comprising the intelligent control method for flexible power supply for rail transit 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 from the operation characteristic data group, perform summary calculations, and obtain the operation frequency disturbance coefficient Lrd and the inertia response relative increment Gzl; The disturbance frequency assessment module is used to obtain the operating frequency disturbance coefficient Lrd and the inertia response relative increment Gzl, and then perform summary calculations in combination with the state data group to 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 stability threshold xxT and the upper stability threshold sxT, and perform a preliminary assessment of the current frequency disturbance situation 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 The smoothing control power compensation parameter PLBC is obtained by correlating it with the frequency disturbance prediction index Prd. Then, by monitoring the deviation between the actual train frequency pl and the standard frequency Jcp, the frequency deviation correction coefficient PLXZ formula is constructed. The comprehensive evaluation module is used to perform summary calculations based on the obtained 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 a secondary evaluation with the lower limit stability threshold xxT and upper limit stability threshold sxT of the train operating frequency.

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