Train operation intelligent protection system based on data processing

Abstract

The present application relates to the technical field of data processing, and more particularly to a train operation intelligent protection system based on data processing, comprising a signal storage module, a data prediction module, a horizontal comparison module, a longitudinal comparison module and an intelligent protection module; wherein the horizontal comparison module determines a real-time temperature time domain graph and a historical temperature time domain graph to determine real-time temperature fluctuation values and historical temperature fluctuation values of each traction motor respectively, and determines the motor representation state accordingly; the longitudinal comparison module performs early warning on the real-time temperature data of each traction motor and determines the real-time representation state; the intelligent protection module acquires real-time network voltage data, real-time direct current data and real-time total traction energy consumption according to the real-time representation state of the traction motor to determine the fault level of the traction motor and formulate a protection strategy; the present application realizes the whole-chain intelligent protection from temperature fluctuation trend analysis to multi-level fault response, and improves the safety, reliability and operation efficiency of the traction system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a train operation intelligent protection system based on data processing. BACKGROUND

[0002] Current subway traction motor protection technology mainly focuses on ground protection, overvoltage and overcurrent protection, vibration protection, and daily maintenance: First, ground protection is an important measure to ensure the safe operation of the traction motor. By optimizing the design of the grounding system, such as setting a protective grounding resistor and grounding device, the risk of traction motor bearing corrosion can be effectively reduced to ensure the reliable operation of the equipment. Second, to address the overvoltage and overcurrent problems of the traction motor, the subway system can optimize the control strategy and improve the protection logic to trigger the protection mechanism in time when abnormal conditions are detected, avoiding motor damage. In addition, in order to reduce the impact of vibration on the traction motor during transportation, various protection measures are proposed, such as placing sensors at the motor shaft end to monitor and reduce vibration load, thereby protecting the motor bearing. Finally, daily maintenance is also an important part of traction motor protection, including regular cleaning, checking the motor ventilation filter, lubricating the bearing, and monitoring the voltage and current, etc. These measures help to timely detect and handle potential problems, extending the service life of the motor

[0003] Chinese Patent No. CN113687226B discloses a method for identifying asynchronous motor phase failure of urban rail vehicle control traction inverter, comprising the following steps: S1: sampling the three-phase current of the asynchronous motor stator within a sampling period T; S2: calculating the current effective value of the three-phase within the sampling period T; S3: comparing and judging the three-phase current effective value to determine the asynchronous motor stator road with phase failure; S4: extracting the first phase with a deviation exceeding the set threshold, which is the asynchronous motor stator road with phase failure. The invention proposes a high-reliability fault diagnosis method to accurately identify the problem of one-phase failure of a single motor of the urban rail asynchronous vehicle control traction inverter. A corresponding mathematical analysis model is established, the parameters and operating conditions of the motor are introduced, and the deviation between the fault occurrence road and the normal stator road current effective value is output to determine the final phase failure protection value. The algorithm is simple, the accuracy of phase failure identification is high, and it has a wide industrial prospect. As can be seen, the prior art has the following problems:

[0004] Traditional trains do not consider the consistency analysis of multiple traction motors and analyze the traction motor based on the consistency analysis results, resulting in the problem of rough fault classification and protection strategy. SUMMARY

[0005] To this end, the application provides a train operation intelligent protection system based on data processing, to overcome the problem that the conventional train does not consider the consistency analysis of multiple traction motors and separately analyzes the traction motor based on the consistency analysis result, thereby causing the problem of rough fault classification and protection strategy. The technical problems of the existing train traction motor monitoring system in real-time (short-time data window), multi-motor operation consistency (lateral comparison), single-motor temperature abnormality early warning (longitudinal comparison), fault classification protection (multi-dimensional analysis of power supply and energy consumption), and redundant system cooperative control (backup inverter switching) are solved, realizing the whole-chain intelligent protection from temperature fluctuation trend analysis to multi-level fault response, and improving the safety, reliability and operation efficiency of the traction system.

[0006] To achieve the above-mentioned purpose, the application provides a train operation intelligent protection system based on data processing, comprising:

[0007] A signal storage module, comprising a data storage unit for obtaining historical temperature data of each traction motor and a signal display unit for displaying a plurality of real-time data, the real-time data comprising real-time temperature data, real-time network voltage data, real-time direct current data and real-time total traction energy consumption;

[0008] A data prediction module connected with the signal storage module, for determining predicted temperature data according to the historical temperature data of each traction motor;

[0009] A lateral comparison module connected with the signal storage module, for determining corresponding temperature time domain graphs according to the historical temperature data of each traction motor to determine real-time temperature time domain graphs and historical temperature time domain graphs, and determining real-time temperature fluctuation values and historical temperature fluctuation values of each traction motor to determine motor representation states;

[0010] A longitudinal comparison module connected with the signal storage module, the data prediction module and the lateral comparison module, for obtaining motor representation states and determining whether to issue a warning to the corresponding traction motor according to the real-time temperature data and the predicted temperature data of each traction motor, to determine the real-time representation state of the corresponding traction motor in combination with the relationship between the real-time temperature data of each traction motor and the temperature threshold;

[0011] An intelligent protection module connected with the signal storage module, the lateral comparison module and the longitudinal comparison module, for obtaining real-time network voltage data, real-time direct current data and real-time total traction energy consumption according to the real-time representation state of the traction motor, to determine the fault level of the traction motor and formulate a protection strategy according to the fault level;

[0012] The protection strategy comprises reducing output power, switching a backup traction inverter and cutting off traction of the corresponding traction inverter.

[0013] Further, the data prediction module trains a neural network model according to historical temperature data of the single traction motor, and inputs real-time working condition data related to the traction motor into the trained neural network model to determine predicted temperature data of the corresponding traction motor.

[0014] Further, the transverse comparison module determines a corresponding real-time temperature time domain graph according to a current time point, and determines a historical temperature time domain graph according to the temperature time domain graph and the real-time temperature time domain graph, so as to determine a real-time temperature fluctuation value and a historical temperature fluctuation value according to the real-time temperature time domain graph and the historical temperature time domain graph respectively, and determine a motor characteristic state according to the real-time temperature fluctuation value and the historical temperature fluctuation value of each traction motor.

[0015] The motor characteristic state includes a consistent operation state and an abnormal operation state.

[0016] Further, the transverse comparison module determines an average temperature difference according to a temperature difference between any two adjacent extreme points of the temperature time domain graph, determines a maximum temperature difference according to a maximum temperature and a minimum temperature of the temperature time domain graph, and determines a temperature fluctuation value according to a ratio of the average temperature difference to the maximum temperature difference.

[0017] Further, the transverse comparison module determines a motor characteristic value of the corresponding traction motor based on a ratio of the real-time temperature fluctuation value to the historical temperature fluctuation value of the single traction motor, and determines the motor characteristic state of each traction motor according to the motor characteristic value of each traction motor.

[0018] The motor characteristic value of each traction motor in the consistent operation state is less than a preset characteristic value.

[0019] Further, the longitudinal comparison module determines to give a warning to the corresponding traction motor according to a size relationship that the real-time temperature data of any one traction motor is greater than the predicted temperature data based on a determination result of the consistent operation state.

[0020] Further, the longitudinal comparison module determines that the real-time characteristic state of the corresponding traction motor is a fault state according to a size relationship that the real-time temperature data of the corresponding traction motor is greater than a temperature threshold value based on a determination result of giving a warning to the traction motor.

[0021] The real-time characteristic state includes a fault state and a normal state.

[0022] Further, the intelligent protection module acquires real-time network voltage data, real-time direct current data and real-time total traction energy consumption according to a determination result that the traction motor is in the fault state.

[0023] Further, the intelligent protection module determines a corresponding fault level according to the real-time network voltage data, the real-time DC data and the real-time total traction energy consumption satisfying the first condition, the second condition or the third condition and formulates a protection strategy according to the fault level.

[0024] Further, the intelligent protection module comprises a condition comparison unit configured to determine whether the real-time network voltage data, the real-time DC data and the real-time total traction energy consumption satisfy any one of the first condition, the second condition or the third condition.

[0025] The first condition is that the total traction energy consumption is in a preset range and the real-time network voltage data or the real-time DC data has a deviation.

[0026] The second condition is that the total traction energy consumption is in a preset range and the real-time network voltage data and the real-time DC data both have deviations.

[0027] The third condition is that the total traction energy consumption exceeds the preset range.

[0028] Compared with the prior art, the train operation intelligent protection system provided by the present application realizes early warning, accurate positioning and hierarchical disposal of traction motor faults through multi-dimensional data monitoring (historical / real-time temperature, network voltage, DC, energy consumption), intelligent prediction (neural network temperature prediction), transverse (motor difference) and longitudinal (real-time / predicted comparison) state analysis, combined with hierarchical fault determination (first, second and third levels) and dynamic protection strategy, significantly improves the safety (avoids overheating / short circuit accidents), reliability (redundant switching ensures continuous operation) and economy (reduces unplanned downtime and maintenance costs) of train operation, and optimizes the energy efficiency of the traction system through data-driven intelligent decision-making, providing an efficient solution for intelligent operation and maintenance of rail transit.

[0029] Further, the transverse comparison module can accurately judge the running state of the motor by monitoring the temperature change of the traction motor in real time and combining with historical data for comparative analysis. It not only can quickly identify the motor running abnormally, but also can effectively distinguish the fault motor, providing a strong guarantee for the safe operation of the train. At the same time, by reasonably setting the preset time length and the range of the representation value, the module ensures the real-time and accuracy of the monitoring, and improves the reliability and maintenance efficiency of the train operation.

[0030] Further, the train longitudinal comparison module realizes accurate early warning and fault judgment of the single traction motor temperature state through dynamic comparison of real-time temperature data and predicted temperature data combined with the safety boundary design of the temperature threshold, can effectively identify temperature abnormalities beyond expectations, avoid system failure caused by single motor overheating, and at the same time, clearly reduce false positives in normal state, provide a hierarchical temperature safety protection for the train traction system, improve the safety, reliability and maintenance of the motor operation, ensure that early warning is triggered in the initial stage of temperature abnormalities, prevent fault escalation, and ensure the continuous and stable operation of the train;

[0031] Further, the intelligent protection module constructs a hierarchical response system of "fault level-protection strategy" through multi-dimensional analysis of real-time network voltage data, DC data and total traction energy consumption, realizes full-scene coverage from local power supply abnormalities to serious motor failure: first-level fault relieves short-time overload by reducing power, second-level fault uses redundant design to switch standby inverter to ensure continuous operation, and third-level fault decisively removes the fault unit to prevent disaster spread; This module quantifies fault hazards in two dimensions of power supply stability and load health, combined with progressive protection strategies, improves the fault tolerance, operation continuity and safety of the traction system, and through accurate fault positioning and hierarchical disposal, reduces maintenance costs and avoids operation interruption caused by excessive protection, providing an intelligent and multi-level active safety barrier for the train traction system. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 The connection diagram of the train operation intelligent protection system based on data processing of the embodiment of the application;

[0033] Figure 2 The working flowchart of the transverse comparison module of the embodiment of the application;

[0034] Figure 3 The working flowchart of the longitudinal comparison module of the embodiment of the application;

[0035] Figure 4 The working flowchart of the intelligent protection module of the embodiment of the application. DETAILED DESCRIPTION

[0036] In order to make the purpose and advantages of the application clearer and more apparent, the application will be further described below with reference to the embodiments; it should be understood that the specific embodiments described herein are only used to explain the application, and do not limit the protection scope of the application.

[0037] The preferred embodiments of the application will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the application, and are not intended to limit the protection scope of the application.

[0038] It should be noted that in the description of the present application, the terms of direction or position relationship indicated by "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or position relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0039] In addition, it should be noted that in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connecting" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0040] Please refer to Figure 1 As shown in the figure, it is a connection diagram of the train operation intelligent protection system based on data processing according to the embodiment of the present application. The embodiment of the present application provides a train operation intelligent protection system based on data processing, which comprises:

[0041] The signal storage module comprises a data storage unit for obtaining historical temperature data of each traction motor and a signal display unit for displaying a plurality of real-time data, wherein the real-time data comprises real-time temperature data, real-time network voltage data, real-time direct current data and real-time total traction energy consumption. It can be understood that the obtaining and storage of temperature data, network voltage data, direct current data and total traction energy consumption data in the subway train are all prior art and have been applied, so they will not be described again;

[0042] The data prediction module is connected with the signal storage module, and is used to determine the predicted temperature data according to the historical temperature data of each traction motor. It can be understood that the historical temperature data of each traction motor is used to train a neural network model, and then the trained neural network model is used to input the data of the current train operation to determine the predicted temperature data in real time;

[0043] a transverse comparison module connected with the signal storage module, configured to determine a corresponding temperature time-domain graph according to historical temperature data of each traction motor to determine a real-time temperature time-domain graph and a historical temperature time-domain graph, and to determine a real-time temperature fluctuation value and a historical temperature fluctuation value of each traction motor respectively to determine a motor characteristic state; it can be understood that the temperature time-domain graph is composed of the real-time temperature time-domain graph and the historical temperature time-domain graph; the transverse comparison module is configured to determine a difference degree between each traction motor to understand an overall state of each traction motor in the metro train (denoted as the motor characteristic state); if the difference degree between each traction motor is relatively small, the motor characteristic state is determined as a consistent operation state; if the difference degree between one or more traction motors is relatively large, the motor characteristic state is determined as an abnormal operation state; in the abnormal operation state, the abnormal traction motor can be directly locked and corresponding protection measures are taken; in implementation, the difference degree is determined by comparing a ratio of the real-time temperature fluctuation value and the corresponding historical temperature fluctuation value between each traction motor;

[0044] a longitudinal comparison module connected with the signal storage module, the data prediction module and the transverse comparison module respectively, configured to obtain the motor characteristic state and determine whether to give a warning to the corresponding traction motor according to real-time temperature data and predicted temperature data of each traction motor to determine a real-time characteristic state of the corresponding traction motor in combination with a relationship between the real-time temperature data of each traction motor and a temperature threshold; it can be understood that the longitudinal comparison module determines a real-time state of each traction motor (denoted as the real-time characteristic state) according to historical data of each traction motor;

[0045] in implementation, the state of each motor is determined respectively only when the transverse comparison module confirms that the difference degree between each motor is relatively small (i.e. the consistent operation state), so as to avoid the situation that all motors are abnormal at the same time or the situation that a single motor is different from its historical working condition;

[0046] An intelligent protection module is connected with the signal storage module, the transverse comparison module and the longitudinal comparison module respectively, to obtain real-time grid voltage data, real-time DC data and real-time total traction energy consumption according to the real-time characterization state of the traction motor, to determine the fault level of the traction motor and formulate a protection strategy according to the fault level; it can be understood that the traction motor and the traction inverter are core components of the rail transit traction drive system, and the relationship between the two is as follows: (1) energy conversion relationship: the traction inverter is responsible for converting the DC power of the intermediate DC circuit into three-phase AC power with controllable voltage and frequency to provide power for the traction motor; the traction motor converts electrical energy into mechanical energy (traction working condition) or mechanical energy into electrical energy (regenerative braking working condition), to drive the wheelset or realize energy recovery; (2) control relationship: the traction inverter adjusts the output current and frequency according to the control instruction to accurately control the operating state of the traction motor, and the real-time feedback (such as temperature, speed, current) of the traction motor is used for closed-loop control of the inverter to ensure stable operation of the system; therefore, when the traction motor is abnormal, the traction inverter needs to be adjusted to protect the operation of the subway train.

[0047] The protection strategy includes reducing output power, switching to a standby traction inverter and cutting off the traction of the corresponding traction inverter.

[0048] It can be understood that the train operation intelligent protection system provided by the embodiment of the application breaks through the single parameter threshold limit of the traditional train protection through the whole chain design of "data monitoring-intelligent prediction-multi-dimensional comparison-classified protection", realizes the active prevention, accurate disposal and energy efficiency optimization of the traction system fault: (1) dual state evaluation of fusion transverse (between motors) and longitudinal (real-time-history) comparison; (2) deep coupling of temperature prediction based on neural network and multi-parameter fault grading; (3) hierarchical protection strategy of dynamic adjustment of traction inverter, which finally improves the safety, reliability and economy in three dimensions, providing an integrated "measurement-judgment-control" solution for intelligent rail transit.

[0049] Specifically, the data prediction module trains a neural network model according to historical temperature data of a single traction motor, and inputs real-time working condition data related to the traction motor into the trained neural network model to determine the predicted temperature data of the corresponding traction motor.

[0050] It can be understood that the working condition data corresponding to each historical temperature data is also needed when training the neural network model to better train the neural network model. It can be understood that the historical temperature data contains the temperature change law of the motor under different working conditions, and the neural network has strong non-linear mapping ability and can learn these complex laws to establish a relationship model between the working condition and the temperature. In implementation, accurately predicting the temperature of the traction motor is of great significance to ensure the safe operation of the motor and optimize the maintenance plan. By inputting real-time working condition data, the predicted temperature of the motor can be obtained in time, and measures can be taken in advance to avoid overheating failure.

[0051] In implementation, the working condition data related to the traction motor usually includes the following categories: (1) electrical parameters: ① current: the current size of the traction motor directly reflects its load condition; the larger the current, the greater the heat generated by the motor, so the current is one of the important factors affecting the temperature of the motor; ② voltage: the voltage across the motor affects the operating efficiency and power consumption of the motor, and in turn affects the temperature, unstable voltage can cause motor overload or abnormal heating; ③ power: power is the product of current and voltage, which comprehensively reflects the working strength of the motor; under different power levels, the heating of the motor will also be different; (2) mechanical parameters: ① speed: the speed of the motor is closely related to the load and heat dissipation conditions; when running at high speed, the friction loss and ventilation loss of the motor will increase, causing the temperature to rise; while at low speed or no load, the temperature is relatively low; ② torque: torque is the rotational force output by the motor, which is proportional to the load size, and larger torque means that the motor needs to overcome greater resistance, thereby generating more heat; (3) environmental parameters: ① ambient temperature: the temperature of the surrounding environment affects the heat dissipation effect of the motor, in a high temperature environment, the heat dissipation speed of the motor will slow down, which is easy to cause temperature rise; while in a low temperature environment, the heat dissipation is relatively fast; ② humidity: humidity affects the insulation performance and heat dissipation efficiency of the motor, high humidity environment can cause the motor insulation to be damp, increasing the risk of electric leakage, and also reducing the heat dissipation efficiency; ③ ventilation conditions: good ventilation can carry away the heat generated by the motor, reducing the temperature, and ventilation parameters such as ventilation volume and air speed are crucial for accurately predicting the temperature of the motor; (4) running state parameters: ① running time: the longer the motor runs continuously, the more heat it accumulates, and the temperature will also rise accordingly; ② start / stop times: frequent start and stop will cause the motor to withstand a large current impact, resulting in large temperature fluctuations, and the statistical information of start / stop times can help predict the temperature change of the motor in different running stages; the above working condition data can be directly obtained in the signal display unit of the subway train.

[0052] Please refer to Figure 2As shown, it is a workflow diagram of the transverse comparison module of the embodiment of the application. Specifically, the transverse comparison module determines a corresponding real-time temperature time-domain graph according to a current time point, and determines a historical temperature time-domain graph according to the temperature time-domain graph and the real-time temperature time-domain graph, so as to determine a real-time temperature fluctuation value and a historical temperature fluctuation value according to the real-time temperature time-domain graph and the historical temperature time-domain graph respectively, and determine the motor characteristic state according to the real-time temperature fluctuation value and the historical temperature fluctuation value of each traction motor.

[0053] The motor characteristic state includes a consistent operation state and an abnormal operation state.

[0054] In implementation, the temperature time-domain graph within a preset time length (usually less than 10 min, and a too large preset time length will affect the real-time performance of the motor state, and preferably 6 min) before the current time point is determined as the real-time temperature time-domain graph, and the part of the temperature time-domain graph other than the real-time temperature time-domain graph is determined as the historical temperature time-domain graph. It can be understood that, as the current temperature continuously changes, the real-time temperature time-domain graph and the historical temperature time-domain graph also change.

[0055] Specifically, the transverse comparison module determines an average temperature difference according to the temperature difference between any two adjacent extreme points of the temperature time-domain graph, determines a maximum temperature difference according to the maximum temperature and the minimum temperature of the temperature time-domain graph, and determines a temperature fluctuation value according to the ratio of the average temperature difference to the maximum temperature difference.

[0056] It can be understood that the transverse comparison module determines an average real-time temperature difference according to the temperature difference between any two adjacent extreme points of the real-time temperature time-domain graph, determines a maximum real-time temperature difference according to the maximum temperature and the minimum temperature of the real-time temperature time-domain graph, and determines a real-time temperature fluctuation value according to the ratio of the average real-time temperature difference to the maximum real-time temperature difference; the transverse comparison module determines an average historical temperature difference according to the temperature difference between any two adjacent extreme points of the historical temperature time-domain graph, determines a maximum historical temperature difference according to the maximum temperature and the minimum temperature of the historical temperature time-domain graph, and determines a historical temperature fluctuation value according to the ratio of the average historical temperature difference to the maximum historical temperature difference.

[0057] Specifically, the transverse comparison module determines a motor characteristic value of the corresponding traction motor based on the ratio of the real-time temperature fluctuation value to the historical temperature fluctuation value of a single traction motor, and determines the motor characteristic state according to the motor characteristic values of the traction motors.

[0058] The motor characteristic value of each traction motor in the consistent operation state is less than or equal to a preset characteristic value.

[0059] It can be understood that the motor characteristic value = real-time temperature fluctuation value ÷ historical temperature fluctuation value, the closer the motor characteristic value to 1 represents the closer the real-time temperature fluctuation value and the historical temperature fluctuation value, that is, the closer the change state of the real-time temperature and the historical temperature; wherein, when the motor characteristic value is less than 1, it means that the temperature state of the motor is more stable compared with the historical state, and when the motor characteristic value is greater than 1, it means that the temperature state of the motor is more unstable compared with the historical state. Therefore, in the implementation, the preset characteristic value ∈ (1, 1.2], preferably the preset characteristic value is 1.1.

[0060] In the implementation, if the motor characteristic value of any one traction motor is greater than the preset characteristic value, it means that the current motor characteristic state is an abnormal running state, and the real-time characteristic state of the traction motor exceeding the preset characteristic value is determined as a fault state.

[0061] It can be understood that the transverse comparison module ensures real-time through short-period data updating (6 minutes) to adapt to the dynamic demand of high-speed train operation; the quantitative analysis based on extreme points and temperature difference avoids noise interference and captures real temperature fluctuation characteristics; the transverse comparison of multiple motor states ensures the consistency of the overall system operation and prevents local fault diffusion; preventive maintenance reduces downtime, prolongs motor life, and reduces operating costs.

[0062] Please refer to Figure 3 As shown in FIG. 6, which is a working flow chart of the longitudinal comparison module of the embodiment of the present application. Specifically, the longitudinal comparison module determines to prewarn the corresponding traction motor according to the size relationship that the real-time temperature data of any one traction motor is greater than the predicted temperature data based on the determination result of the running consistent state; it can be understood that the longitudinal comparison module determines not to prewarn the corresponding traction motor according to the size relationship that the real-time temperature data of any one traction motor is less than or equal to the predicted temperature data based on the determination result of the running consistent state; in the implementation, for the traction motor not prewarned, the real-time characteristic state thereof is a normal state.

[0063] Specifically, the longitudinal comparison module determines the real-time characteristic state of the corresponding traction motor as a fault state according to the determination result of prewarning the traction motor and the size relationship that the real-time temperature data of the corresponding traction motor is greater than the temperature threshold value;

[0064] The real-time characteristic state includes a fault state and a normal state.

[0065] In the implementation, the predicted temperature data should be less than the temperature threshold value, the predicted temperature data should be within the normal temperature range (which should be indicated in the safety manual of the traction motor when the traction motor is tested out of the factory), and the temperature threshold value is usually less than the maximum temperature of the traction motor in the design stage; in the implementation, it is usually set to 80% of the maximum design temperature ~ 90% of the maximum design temperature.

[0066] Referring to Figure 4 As shown in the figure, it is a workflow diagram of the intelligent protection module of the embodiment of the application. Specifically, the intelligent protection module obtains real-time grid voltage data, real-time DC data and real-time total traction energy consumption according to the determination result of the traction motor being in a fault state.

[0067] It can be understood that the traction motor being in a fault state can be determined when the motor representation state in the horizontal comparison module is an abnormal running state, or can be determined through the longitudinal comparison module when the motor representation state in the horizontal comparison module is a consistent running state.

[0068] Specifically, the intelligent protection module determines the corresponding fault level according to the fact that the real-time grid voltage data, the real-time DC data and the real-time total traction energy consumption satisfy the first condition, the second condition or the third condition, and formulates a protection strategy according to the fault level, including,

[0069] According to the determination result that the real-time grid voltage data, the real-time DC data and the real-time total traction energy consumption satisfy the first condition, it is determined that the fault level is a first-level fault, and the protection strategy is to reduce the output power; it can be understood that the total traction energy consumption is in a preset range (normal load), but there is a single deviation in the grid voltage or DC data (such as grid voltage fluctuation exceeding the threshold or DC current zero drift), which may cause the motor to be overloaded or abnormally cooled for a short time, but does not affect the overall energy consumption; at this time, only the output power needs to be reduced to reduce the motor load and heat, so as to avoid further deterioration of the power supply system (such as preventing overcurrent caused by voltage deviation);

[0070] According to the determination result that the real-time grid voltage data, the real-time DC data and the real-time total traction energy consumption satisfy the second condition, it is determined that the fault level is a second-level fault, and the protection strategy is to switch to a standby traction inverter; it can be understood that the total traction energy consumption is in a preset range (normal load), but both the grid voltage and the DC data are deviated (the grid voltage fluctuates continuously and the DC current is abnormal), which indicates that the grid voltage is unstable and the DC link is faulty, which may be due to the unstable input of the current traction inverter, threatening the reliable operation of the motor; at this time, the standby inverter is switched to avoid the power supply risk of the faulty inverter, that is, the redundancy design is used to ensure the continuous operation of the motor (the standby system needs to be ensured to be normal);

[0071] According to the determination result that the real-time grid voltage data, the real-time DC data and the real-time total traction energy consumption satisfy the third condition, it is determined that the fault level is a third-level fault, and the protection strategy is to cut off the traction of the traction inverter at the corresponding position; it can be understood that the total traction energy consumption exceeds the preset range, that is, the energy consumption is out of limit, indicating that the motor load is too high (such as mechanical jamming, winding short circuit) or the efficiency is sharply decreased (such as cooling system failure), and continuous operation will cause serious overheating or device damage; at this time, the traction of the faulty inverter should be cut off to the warehouse for processing to avoid the spread of the fault (such as preventing fire or damage to other equipment caused by short circuit).

[0072] It can be understood that the grid voltage data reflects the stability of the external power supply system (such as grid voltage fluctuation, deviation); the DC data reflects the state of the intermediate DC link in the traction system (such as current deviation, zero drift); the total traction energy consumption reflects the load intensity and energy consumption efficiency of the traction motor (directly related to motor heating and torque output); therefore, the fault level of the fault state traction motor can be determined through the three real-time data;

[0073] Specifically, the intelligent protection module includes a condition comparison unit to determine whether the real-time grid voltage data, real-time DC data and real-time total traction energy consumption satisfy any one of a first condition, a second condition or a third condition.

[0074] The first condition is that the total traction energy consumption is in a preset range, and the real-time grid voltage data or the real-time DC data has a deviation.

[0075] The second condition is that the total traction energy consumption is in a preset range, and the real-time grid voltage data and the real-time DC data both have deviations.

[0076] The third condition is that the total traction energy consumption exceeds the preset range.

[0077] In implementation: the first level fault is a single power supply parameter (grid voltage or DC) anomaly, with normal energy consumption, i.e. local slight anomaly; the second level fault is double power supply parameter (grid voltage and DC) anomaly, with normal energy consumption, i.e. multiple anomalies of the power supply system; the third level fault is energy consumption exceeding the limit (whether the power supply parameter is abnormal or not), representing serious deterioration of the motor load or efficiency.

[0078] It can be understood that the power supply stability (grid voltage / DC deviation) reflects the reliability of the external input and the intermediate link; the load rationality (energy consumption) directly reflects the health degree of the motor operating state; the fault conditions in the present application gradually escalate from "local anomaly - system anomaly - serious failure" according to the fault hazard.

[0079] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after these changes or replacements will all fall within the protection scope of the present application.

[0080] The above description is only the preferred embodiments of the present application and is not used to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A train operation intelligent protection system based on data processing, characterized in that, The application relates to a traction motor temperature prediction and protection system. The system comprises: a signal storage module, which comprises a data storage unit for obtaining historical temperature data of each traction motor and a signal display unit for displaying real-time data, the real-time data including real-time temperature data, real-time network voltage data, real-time direct current data and real-time total traction energy consumption; a data prediction module connected to the signal storage module, which is used to determine predicted temperature data according to the historical temperature data of each traction motor; a transverse comparison module connected to the signal storage module, which is used to determine corresponding temperature time-domain graphs according to the historical temperature data of each traction motor to determine real-time temperature time-domain graphs and historical temperature time-domain graphs, and to determine real-time temperature fluctuation values and historical temperature fluctuation values of each traction motor to determine motor characteristic states; a longitudinal comparison module connected to the signal storage module, the data prediction module and the transverse comparison module, which is used to obtain motor characteristic states and determine whether to issue a warning for a corresponding traction motor according to real-time temperature data and predicted temperature data of each traction motor, and to determine real-time characteristic states of the corresponding traction motor in combination with a relationship between the real-time temperature data of each traction motor and a temperature threshold; an intelligent protection module connected to the signal storage module, the transverse comparison module and the longitudinal comparison module, which is used to obtain real-time network voltage data, real-time direct current data and real-time total traction energy consumption according to real-time characteristic states of the traction motor, to determine a fault level of the traction motor and to formulate a protection strategy according to the fault level; 2. The data processing based train operation intelligent protection system according to claim 1, characterized in that, wherein the protection strategy comprises reducing output power, switching a standby traction inverter and cutting off traction of a corresponding traction inverter.

3. The data processing based train operation intelligent protection system according to claim 1, characterized in that, The data prediction module trains a neural network model according to historical temperature data of a single traction motor, and inputs real-time working condition data related to the traction motor into the trained neural network model to determine predicted temperature data of the corresponding traction motor. The transverse comparison module determines a corresponding real-time temperature time-domain graph according to a current time point, determines a historical temperature time-domain graph according to the temperature time-domain graph and the real-time temperature time-domain graph, determines real-time temperature fluctuation values and historical temperature fluctuation values according to the real-time temperature time-domain graph and the historical temperature time-domain graph, and determines motor characteristic states according to the real-time temperature fluctuation values and the historical temperature fluctuation values of each traction motor.

4. The data processing based train operation intelligent protection system according to claim 3, characterized in that, The motor characteristic states include a consistent operation state and an abnormal operation state.

5. The data processing based train operation intelligent protection system according to claim 3, characterized in that, The transverse comparison module determines an average temperature difference according to a temperature difference between any two adjacent extreme points of the temperature time-domain graph, determines a maximum temperature difference according to a maximum temperature and a minimum temperature of the temperature time-domain graph, and determines a temperature fluctuation value according to a ratio of the average temperature difference to the maximum temperature difference. The transverse comparison module determines motor characteristic values of the corresponding traction motor based on a ratio of the real-time temperature fluctuation value to the historical temperature fluctuation value of a single traction motor, and determines motor characteristic states according to the motor characteristic values of each traction motor. The motor characteristic values of the traction motors in the consistent operation state are all less than a preset characteristic value.

6. The data processing based train operation intelligent protection system according to claim 1, wherein, The longitudinal comparison module determines, based on the determination result of the running consistent state, a real-time temperature data of any one traction motor is greater than a predicted temperature data to determine a corresponding traction motor to be pre-warned.

7. The data processing based train operation intelligent protection system according to claim 1, wherein, The longitudinal comparison module determines, based on the determination result of the traction motor to be pre-warned, a real-time temperature data of the corresponding traction motor is greater than a temperature threshold to determine a real-time representation state of the corresponding traction motor to be a fault state. The real-time representation state includes a fault state and a normal state.

8. The data processing based train operation intelligent protection system according to claim 1, wherein, The intelligent protection module acquires real-time grid voltage data, real-time DC data and real-time total traction energy consumption based on the determination result of the traction motor being in the fault state.

9. The data processing based train operation intelligent protection system according to claim 1, wherein, The intelligent protection module determines a corresponding fault level based on the real-time grid voltage data, the real-time DC data and the real-time total traction energy consumption satisfying a first condition, a second condition or a third condition and formulates a protection strategy according to the fault level.

10. The data processing based train operation intelligent protection system according to claim 9, characterized in that, The intelligent protection module includes a condition comparison unit to determine whether the real-time grid voltage data, the real-time DC data and the real-time total traction energy consumption satisfy any one of the first condition, the second condition or the third condition. The first condition is that the total traction energy consumption is in a preset range and the real-time grid voltage data or the real-time DC data has a deviation. The second condition is that the total traction energy consumption is in the preset range and the real-time grid voltage data and the real-time DC data both have deviations. The third condition is that the total traction energy consumption exceeds the preset range.

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