Railway traction power supply system control method and device

Abstract

The embodiment of the invention provides a railway traction power supply system control method and device, and the method comprises the steps: determining an electrical load prediction value of each road section outputted by an electrical load prediction model according to the electrical load parameters of each road section in a railway traction power supply system and a pre-constructed linear regression model; calculating a power distribution coefficient of each road section based on the electrical load parameter of each road section and the electrical load predicted value; adjusting power supply parameters of each road section according to the power distribution coefficient, so that the voltage fluctuation of each road section is kept within a preset threshold range; intelligent regulation and control of the railway traction power supply system can be achieved, voltage fluctuation is effectively reduced, the power supply stability is improved, and the energy utilization efficiency is optimized.

Description

Technical Field

[0001] The present application relates to the field of power supply, and in particular to a method and device for controlling a railway traction power supply system. Background Art

[0002] As the core infrastructure of high-speed and electrified railways, railway traction power supply systems shoulder the critical task of providing stable and reliable power to trains. With the rapid development of the railway transportation industry and the continuous increase in train operation density, higher requirements are being placed on the stability, energy efficiency management, and intelligent dispatching capabilities of the traction power supply system. Currently, the technology that integrates the operation and maintenance, operation, and dispatching interfaces of railway traction power supply systems, as a comprehensive solution integrating operation and maintenance management, operational scheduling, and intelligent optimization and control, plays a vital role in improving power supply efficiency, reducing energy consumption, and enhancing system reliability. However, in practical application, it still faces many technical challenges and urgently needs optimization and improvement.

[0003] First, in high-speed rail scenarios with dense traffic, the power supply system often struggles to accurately distribute power due to large fluctuations in train loads across different sections. This uneven power distribution can lead to high or low voltages in some sections, impacting stable train operation and potentially causing power supply equipment overload or failure. Therefore, an optimization method is needed that can monitor the load status of each section in real time and dynamically adjust power supply parameters based on the predicted results to ensure system voltage stability within a safe range.

[0004] Secondly, energy loss is particularly problematic when operating under high-power traction loads. Railway traction loads typically require high power for short periods of time, especially during train acceleration or when traveling on long slopes. The power supply system must provide additional power to meet this demand. However, the existing power supply system lacks sophisticated energy management, leading to increased energy loss during peak loads and impacting overall energy efficiency. Therefore, it is necessary to optimize power scheduling through power forecasting and intelligent allocation strategies, reduce unnecessary power losses, and improve the system's energy efficiency. Summary of the Invention

[0005] In response to the problems in the prior art, the present application provides a railway traction power supply system control method and device, which can realize intelligent regulation of the railway traction power supply system, effectively reduce voltage fluctuations, improve power supply stability, and optimize energy utilization efficiency.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions:

[0007] According to a first aspect of an embodiment of the present application, the present application provides a railway traction power supply system control method, comprising:

[0008] Determining the predicted electric load value of each section output by the electric load prediction model according to the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model;

[0009] Calculating the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value;

[0010] The power supply parameters of each road section are adjusted according to the power distribution coefficient so that the voltage fluctuation of each road section is kept within a preset threshold range.

[0011] According to any embodiment of the present application, the determining of the electric load prediction value of each section output by the electric load prediction model based on the electric load parameters of each section in the railway traction power supply system and the pre-built linear regression model includes:

[0012] Determining the average electric load of each road section within a preset time period based on the electric load parameters of each road section, and dynamically correcting the average electric load using environmental variables;

[0013] The average electric load is input into a pre-built linear regression model to obtain the electric load prediction value of each section, wherein the linear regression model is built based on the historical electric load data of the railway traction power supply system.

[0014] According to any embodiment of the present application, the dynamically correcting the average electric load by using environmental variables includes:

[0015] Input the temperature and humidity data of the current period into the pre-built load impact relationship model to obtain the impact weight of environmental factors on the electric load;

[0016] The average electric load is adjusted based on the impact weight.

[0017] According to any embodiment of the present application, after adjusting the average electric load based on the impact weight, the method further includes:

[0018] The revised electric load forecast value is stored in the system log;

[0019] When the revised electric load forecast value meets the requirements, the power supply adjustment instruction is executed; when the revised electric load forecast value does not meet the system operation requirements, an abnormal warning information is sent to the dispatching system.

[0020] According to any embodiment of the present application, adjusting the power supply parameters of each road section according to the power distribution coefficient includes:

[0021] In response to the power allocation coefficient of the road section being lower than a preset threshold, preferentially allocating power resources to the current road section;

[0022] In response to the power allocation coefficient of the road section being higher than a preset threshold, allocating power resources to the current road section is delayed.

[0023] According to any embodiment of the present application, it also includes:

[0024] Determine environmental impact factors based on temperature and humidity data for the current period;

[0025] In response to the environmental impact factor being greater than a preset environmental impact threshold, the frequency of monitoring the state of insulation materials in the current railway traction power supply system is increased.

[0026] According to any embodiment of the present application, it also includes:

[0027] Monitoring the communication signal strength between sections of the railway traction power supply system and determining the current signal interference parameter based on the communication signal strength;

[0028] In response to the signal interference parameter being greater than a preset interference threshold, the signal parameter of the current communication link is adjusted through a signal interference cancellation mechanism to reduce the interference signal strength.

[0029] According to any embodiment of the present application, it also includes:

[0030] In response to an average electric load parameter in a railway traction power supply system within a preset time period being lower than a preset low power consumption threshold, the output power of the main transformer is reduced.

[0031] According to a second aspect of the embodiments of the present application, the present application provides a railway traction power supply system control device, including:

[0032] A load prediction module is used to determine the predicted electric load value of each section output by the electric load prediction model based on the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model;

[0033] A coefficient determination module, configured to calculate the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value;

[0034] The power supply optimization module is used to adjust the power supply parameters of each section according to the power distribution coefficient so that the voltage fluctuation of each section is kept within a preset threshold range.

[0035] According to any embodiment of the present application, the load forecasting module includes:

[0036] An environmental correction unit, configured to determine an average electric load of each road section within a preset time period based on the electric load parameters of each road section, and dynamically correct the average electric load using environmental variables;

[0037] The load prediction unit is used to: input the average electric load into a pre-built linear regression model to obtain the electric load prediction value of each section, wherein the linear regression model is constructed based on the historical electric load data of the railway traction power supply system.

[0038] According to any embodiment of the present application, the environment correction unit includes:

[0039] The weight determination subunit is used to: input the temperature and humidity data of the current period into a pre-built load impact relationship model to obtain the impact weight of environmental factors on the electric load;

[0040] The compliance adjustment subunit is used to adjust the average electric load based on the impact weight.

[0041] According to any embodiment of the present application, an abnormality warning module is further included, which is used to:

[0042] The storage unit is used to store the corrected electric load forecast value in a system log;

[0043] The early warning unit is used to: execute the power supply adjustment instruction when the revised electric load forecast value meets the requirements; and send abnormal early warning information to the dispatching system when the revised electric load forecast value does not meet the system operation requirements.

[0044] According to any embodiment of the present application, the power supply optimization module is specifically configured to:

[0045] In response to the power allocation coefficient of the road section being lower than a preset threshold, preferentially allocating power resources to the current road section;

[0046] In response to the power allocation coefficient of the road section being higher than a preset threshold, allocating power resources to the current road section is delayed.

[0047] According to any embodiment of the present application, it further includes a material detection module for:

[0048] An environmental impact factor is determined based on temperature and humidity data of the current time period, and in response to the environmental impact factor being greater than a preset environmental impact threshold, a frequency of monitoring the insulation material status in the current railway traction power supply system is increased.

[0049] According to any embodiment of the present application, the system further includes an interference cancellation module, configured to:

[0050] Monitor the communication signal strength between each section in the railway traction power supply system, and determine the current signal interference parameter based on the communication signal strength. In response to the signal interference parameter being greater than a preset interference threshold, adjust the signal parameters of the current communication link through the signal interference elimination mechanism to reduce the interference signal strength.

[0051] According to any embodiment of the present application, a low power consumption module is further included, which is used to:

[0052] In response to an average electric load parameter in a railway traction power supply system within a preset time period being lower than a preset low power consumption threshold, the output power of the main substation is reduced.

[0053] According to the third aspect of the embodiment of the present application, the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor implements the steps of the railway traction power supply system control method when executing the program.

[0054] According to a fourth aspect of the embodiments of the present application, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the railway traction power supply system control method when executed by a processor.

[0055] According to the fifth aspect of the embodiment of the present application, the present application provides a computer program product, including a computer program / instruction, which implements the steps of the railway traction power supply system control method when executed by a processor.

[0056] It can be seen from the above technical solution that the present application provides a railway traction power supply system control method and device, which determines the electric load prediction value of each section output by the electric load prediction model based on the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model; calculates the power distribution coefficient of each section based on the electric load parameters of each section and the electric load prediction value; adjusts the power supply parameters of each section according to the power distribution coefficient, so that the voltage fluctuation of each section is kept within the preset threshold range, thereby realizing intelligent regulation of the railway traction power supply system, effectively reducing voltage fluctuations, improving power supply stability, and optimizing energy utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0058] Figure 1 This is one of the flow charts of the railway traction power supply system control method in the embodiment of the present application;

[0059] Figure 2 This is a second flow chart of a railway traction power supply system control method in an embodiment of the present application;

[0060] Figure 3 This is a third flow chart of the railway traction power supply system control method in an embodiment of the present application;

[0061] Figure 4 This is a fourth flow chart of a railway traction power supply system control method in an embodiment of the present application;

[0062] Figure 5 This is a fifth flow chart of the railway traction power supply system control method in an embodiment of the present application;

[0063] Figure 6 This is a sixth flow chart of the railway traction power supply system control method in an embodiment of the present application;

[0064] Figure 7 FIG7 is a flow chart of a railway traction power supply system control method according to an embodiment of the present application;

[0065] Figure 8 This is a structural diagram of a railway traction power supply system control device in an embodiment of the present application;

[0066] Figure 9 Schematic diagram of the structure of the electronic device in the embodiment of the present application. DETAILED DESCRIPTION

[0067] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0068] The acquisition, storage, use, and processing of data in the technical solution of this application comply with relevant laws and regulations.

[0069] The present application provides a railway traction power supply system control method and device to realize intelligent regulation of the railway traction power supply system, effectively reduce voltage fluctuations, improve power supply stability, and optimize energy utilization efficiency.

[0070] In order to realize intelligent control of railway traction power supply system, effectively reduce voltage fluctuation, improve power supply stability, and optimize energy utilization efficiency, the present application provides an embodiment of a railway traction power supply system control method, see Figure 1 The railway traction power supply system control method specifically includes the following contents:

[0071] Step S101: determining the electric load prediction value of each section output by the electric load prediction model according to the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model.

[0072] First, sensors and other monitoring equipment are used to collect current, voltage, and load data from each section of the railway in real time, ensuring high frequency and accuracy. This data provides a foundation for subsequent analysis. Load changes in railway traction systems are dynamic. For example, train starts and stops, accelerations, and decelerations all cause fluctuations in power demand. Therefore, collecting real-time data is crucial for accurately predicting future loads.

[0073] Subsequently, based on the collected data, the system uses a linear regression model to predict the future electrical load conditions of each road section. This model can be combined with historical load trends, current operating status, and environmental factors (such as temperature and humidity) to improve the accuracy of the prediction. For example, ARIMA (autoregressive integrated moving average model) can be used to analyze the time series changes of the load, combining short-term and long-term trends to predict future load demand. The goal of the prediction model is to maximize the prediction accuracy, and indicators such as mean square error (MSE) are usually used for optimization to ensure the accuracy of the prediction results.

[0074] Step S102: Calculating the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value.

[0075] In this step, the system calculates the power distribution coefficient for each road section based on real-time load, voltage fluctuations, and historical load change trends. This distribution coefficient reflects the power demand intensity of each road section and determines the subsequent power supply adjustment strategy. For example, if the voltage fluctuations on a certain road section are large, it may mean that the load demand in that area does not match the current power supply capacity, or there is a line overload problem. When calculating the distribution coefficient, multiple factors are taken into consideration, including:

[0076] Real-time load data: current and voltage levels at the current moment;

[0077] Historical load change trend: load fluctuation pattern over the past period of time (such as 24 hours or a week);

[0078] Voltage fluctuation: Is there an obvious voltage drop or rise trend?

[0079] The calculation of the power distribution coefficient provides a direct basis for subsequent power supply optimization, ensuring that the system can adjust the power supply parameters of each section in a targeted manner to avoid voltage instability.

[0080] Step S103: adjusting the power supply parameters of each road section according to the power distribution coefficient, so that the voltage fluctuation of each road section remains within a preset threshold range.

[0081] During this process, the power supply parameters adjusted include but are not limited to the transformer voltage setting value, feeder switch status, traction substation output power, etc., to ensure that the voltage fluctuations of each section remain within the preset threshold range. Specific measures for dynamic adjustment include:

[0082] Optimize transformer output voltage: For example, if the forecast load on a certain road section shows a significant increase within the next two hours, the system can increase the transformer output voltage in advance, reducing voltage fluctuations and improving power supply quality;

[0083] Dynamically adjust feeder switch status: When power distribution is uneven, timely adjust the power supply paths of different lines to ensure load balance;

[0084] Intelligently control the power supply strategy of traction substations: Combine power balance, equipment safety, and operating costs to dynamically adjust power supply capacity, improve energy efficiency, and avoid unnecessary energy loss.

[0085] Through the above dynamic adjustments, the system can respond to changes in power demand in real time, optimize the utilization efficiency of power resources, and ensure the economy, reliability and stability of the power supply system.

[0086] In one embodiment of the railway traction power supply system control method of the present application, see Figure 2 The method of determining the predicted electric load value of each section output by the electric load prediction model based on the electric load parameters of each section in the railway traction power supply system and the pre-built linear regression model includes:

[0087] Step S101A: determining the average electric load of each road section within a preset time period according to the electric load parameters of each road section, and dynamically correcting the average electric load according to environmental variables;

[0088] Step S101B: inputting the average electric load into a pre-built linear regression model to obtain a predicted electric load value for each section, wherein the linear regression model is constructed based on historical electric load data of the railway traction power supply system.

[0089] Among them, a linear regression model is constructed based on the historical electric load data of the railway traction power supply system. The historical electric load data includes current, voltage and load parameters in different time periods, and is normalized according to preset data processing rules to form a training sample data set.

[0090] Based on the electric load parameters of each section, the average electric load of each section within a preset time period is determined, and dynamically corrected in combination with environmental variables, wherein the electric load parameters are preset parameters related to the equipment body, and the electric load is data obtained through real-time monitoring.

[0091] The average electric load is input into a pre-built linear regression model to calculate the electric load prediction value of each section, and the power distribution coefficient of each section is adjusted accordingly to optimize the power distribution strategy of the railway traction power supply system.

[0092] The load demand of the railway traction power supply system changes over time and is affected by train operation mode, environmental factors, etc. Therefore, it is necessary to calculate the average power load based on historical data and make corrections based on environmental factors to improve the accuracy of the prediction.

[0093] First, the system obtains load data for each road section over a preset time period (e.g., the past 10 hours) from monitoring equipment (e.g., smart meters at substations). The system then records load changes at regular intervals (e.g., every 30 minutes). This value represents the overall load level for each road section within the set time window and mitigates errors caused by short-term fluctuations.

[0094] Then, to improve the accuracy of the prediction, the system will consider the impact of current environmental variables (such as weather factors) on the load and dynamically correct the average load data. In the railway traction power supply system, factors such as temperature and humidity have a significant impact on load demand. For example:

[0095] Hot weather will increase cooling demand and increase load;

[0096] Cold weather may reduce electricity use in unheated areas;

[0097] Rainy and snowy weather may reduce line resistance and affect load distribution.

[0098] Therefore, the system will introduce a weather factor (Wfactor), which usually ranges from 0.5 to 1.5, and the optimal value is set according to specific meteorological conditions. For example:

[0099] In summer, when the temperature is high, Wfactor = 1.2 (indicating that the load is 20% higher than normal);

[0100] In cold winter, Wfactor = 0.8 (indicating that the load is reduced by 20% compared to normal conditions).

[0101] Finally, the corrected average load value is calculated as:

[0102]

[0103] The above correction steps ensure that the load forecast can more accurately reflect the current environmental conditions and avoid forecast errors caused by sudden weather changes.

[0104] In one embodiment of the railway traction power supply system control method of the present application, see Figure 3 , the dynamically correcting the average electric load by using environmental variables includes:

[0105] Step S101A1: Input the temperature and humidity data of the current period into a pre-built load impact relationship model to obtain the impact weight of environmental factors on the electric load.

[0106] The load of railway traction power supply systems is significantly affected by environmental factors such as temperature and humidity. Therefore, the system requires real-time acquisition of temperature (T) and humidity (H) data for the current period and inputs this into a pre-trained load impact relationship model. This model, trained based on historical data, identifies the impact of weather factors on load changes and calculates the environmental impact factor (Winfluence).

[0107] Temperature impact factor (Tfactor): used to quantify the impact of temperature changes on load, usually ranging from 0 to 1, with larger values indicating greater impact of temperature on load.

[0108] Humidity impact factor (Hfactor): used to quantify the impact of humidity changes on load, usually ranging from 0 to 1. A larger value indicates a greater impact of humidity on load.

[0109] Weather impact factor (Winfluence): It is the final impact weight calculated by the model, indicating the increase or decrease of load under current climate conditions, usually positive or zero.

[0110] For example, in one embodiment:

[0111] Current environmental data: temperature T = 35°C, humidity H = 75%;

[0112] The load impact model calculates:

[0113] Tfactor = 0.6 (high temperature has a greater impact on load);

[0114] Hfactor = 0.5 (high humidity further increases the load);

[0115] Winfluence = 20MW (after comprehensive calculation of the impact of weather factors, the load is expected to increase by 20MW).

[0116] The system obtains the weather influence factor Winfluence through this step and uses it as a key parameter for adjusting the load forecast value to ensure that the forecast result can truly reflect the impact of environmental changes on power supply demand.

[0117] Step S101A2: adjusting the average electric load based on the impact weight.

[0118] After obtaining Winfluence, the system will dynamically adjust the preliminary load forecast results based on the influencing factor to ensure more accurate load forecasts under extreme weather conditions and avoid power shortages or oversupply due to forecast deviations. The calculation formula for the adjusted load forecast value is:

[0119] I adj =I pred +W influence

[0120] in:

[0121] Ipred: Preliminary load forecast value, calculated based on historical data;

[0122] Winfluence: weather impact factor, indicating the effect of weather factors on load increase or decrease;

[0123] Iadj: Adjusted load forecast value.

[0124] In addition, the system will determine whether the adjusted forecast load value exceeds the preset threshold:

[0125] I adj >I threshold

[0126] Ithreshold: Load adjustment threshold, set by system safety standards and energy efficiency requirements;

[0127] If Iadj>Ithreshold, the system will adjust the power supply strategy to increase power supply capacity or optimize scheduling;

[0128] If Iadj\leqIthreshold, there is no need to adjust the power supply, but the operator can pay attention to the load change trend to prevent sudden power supply problems.

[0129] For example, in one embodiment:

[0130] Preliminary load forecast value Ipred = 500MW;

[0131] Weather influence factor Winfluence = 20MW;

[0132] Load adjustment threshold Ithreshold = 550MW;

[0133] Calculate the adjusted load:

[0134] I adj =500MW+20MW=520MW

[0135] Judgment conditions: 520MW≤550MW;

[0136] Since the predicted load does not exceed the threshold, there is no need to adjust the power supply, but monitoring can be strengthened;

[0137] If Iadj=570MW, which exceeds the threshold of 550MW, the system will adjust the power supply plan in advance to ensure that the load demand is met.

[0138] In one embodiment of the railway traction power supply system control method of the present application, see Figure 4 , after adjusting the average electric load based on the impact weight, further comprising:

[0139] Step S101A3: Store the corrected electric load forecast value in the system log.

[0140] After adjusting the load data, the system stores the final, corrected load forecast in a log file for subsequent review, analysis, and optimization. This process ensures transparency and traceability of the power supply system, allowing technicians to review historical records at any time, analyze load trends, and optimize subsequent power supply strategies.

[0141] For example, in one embodiment, the original load forecast value Finit = 95,000 watts; after environmental variable correction, the final load forecast value Fpred = 120,000 watts; the system automatically records Fpred = 120,000 watts in the system log, including:

[0142] Calculation time, influencing factors (such as the contribution of temperature and humidity to load), load forecast values before and after adjustment, power supply adjustment status, etc.

[0143] Storing this data can not only be used for historical analysis of load management, but also optimize the power supply model in the future, improve the accuracy of load forecasting, and ensure the long-term stable operation of the system.

[0144] Step S101A4: Execute the power supply adjustment instruction when the revised electric load forecast value meets the requirements; send abnormal warning information to the dispatching system when the revised electric load forecast value does not meet the system operation requirements.

[0145] After storing the revised load forecast value, the system will make a conditional judgment to ensure that the power supply adjustment meets the operating requirements of the railway traction power supply system. The judgment formula is as follows:

[0146] F pred ≥R threshold in:

[0147] Fpred: Corrected electric load prediction value;

[0148] Rthreshold: The minimum safety load threshold for system operation, determined by the equipment configuration and safety standards of the railway traction power supply system.

[0149] The system will take the following two measures according to the judgment result:

[0150] 1. If the predicted value meets the requirements, execute the power supply adjustment instruction;

[0151] If Fpred ≥ Rthreshold, it means that the predicted load meets the operation requirements of the system. At this time, the system will automatically generate and execute the power supply adjustment instruction to ensure the stable operation of the power supply. For example:

[0152] The set threshold for system operation requirements Rthreshold = 100,000 watts;

[0153] The calculated corrected load prediction value Fpred = 120,000 F watts;

[0154] Since 120,000 ≥ 100,000, the system determines that the load prediction value meets the power supply demand and automatically executes the power supply adjustment instruction, such as:

[0155] Optimize the output power of the substation to ensure stable power supply;

[0156] Dynamically adjust the status of the feeder switch to optimize load distribution;

[0157] Control the operation mode of frequency conversion equipment to reduce energy consumption and improve system efficiency.

[0158] 2. If the predicted value does not meet the requirements, send an abnormal warning message;

[0159] If Fpred < Rthreshold, it means that the predicted load is lower than the system operation requirements, and there may be risks of abnormal power supply or equipment failure. At this time, the system will immediately trigger the alarm mechanism and send an abnormal warning message to the dispatching system to remind the operation and maintenance personnel to take emergency measures. For example:

[0160] The set threshold for system operation requirements Rthreshold = 100,000 watts;

[0161] The calculated corrected load prediction value Fpred = 80,000 watts;

[0162] Since 80,000 < 100, the system determines that the load prediction value is lower than the threshold and immediately triggers the alarm mechanism:

[0163] Send an abnormal warning message to the dispatching center;

[0164] Notify the operation and maintenance personnel to check the load situation of the power supply system and take necessary maintenance measures;

[0165] Automatically adjust backup power strategies to ensure temporary power availability to reduce possible power outages.

[0166] This abnormal warning mechanism ensures that the system can detect potential power supply problems in advance and take timely adjustment measures to avoid railway operation failures caused by insufficient power supply.

[0167] In one embodiment of the railway traction power supply system control method of the present application, see Figure 5 , the adjusting the power supply parameters of each road section according to the power distribution coefficient includes:

[0168] Step S103A: In response to the power allocation coefficient of the road section being lower than a preset threshold, preferentially allocating power resources to the current road section.

[0169] In the railway traction power supply system, the current, voltage, and load of each section are different. Therefore, it is necessary to calculate the power distribution coefficient to quantify the power supply and demand conditions of each section:

[0170]

[0171] in:

[0172] Ii: current in section i, in amperes (A);

[0173] Vi: voltage of section i, in kilovolts (kV);

[0174] Li: load of road section i, in megawatts (MW).

[0175] The system calculates the Ki value for each road section to determine whether it is in a low power supply state. When Ki falls below a preset power supply threshold, it means that the load on the road section is high relative to the power supply capacity and may face the risk of power shortage. Therefore, the system will prioritize the allocation of additional power resources to ensure normal operation of the road section.

[0176] For example, in one embodiment:

[0177] The set low power supply threshold Klow = 20;

[0178] The calculated power distribution coefficient Ki for a certain road section is 18, which is lower than the threshold;

[0179] System Response:

[0180] Prioritize increasing power supply to this section of road, such as increasing the output voltage of the substation;

[0181] Adjust the feeder switch status to direct more power supply to this section of road;

[0182] Optimize load scheduling to ensure the stability of train operation.

[0183] Through this mechanism, the system can prioritize power distribution to key sections with insufficient power supply, preventing railway transportation safety from being affected by insufficient power supply.

[0184] Step S103B: In response to the power allocation coefficient of the road section being higher than a preset threshold, delaying the allocation of power resources to the current road section.

[0185] After calculating the power allocation coefficient Ki, the system also needs to identify sections with sufficient or even excessive power to optimize energy utilization. When Ki exceeds a preset power supply threshold, the power supply to that section is sufficient relative to the load demand, and there may even be power waste. In this case, the system will delay or reduce power allocation to that section to free up resources for more constrained sections and improve the overall system power balance.

[0186] For example, in one embodiment:

[0187] The set high power supply threshold Khigh=30;

[0188] The calculated power distribution coefficient Ki for a certain road section is 32, which exceeds the threshold;

[0189] System Response:

[0190] Lower the power supply priority of this road section to avoid over-powering;

[0191] Reduce the output power of the substation to the road section to improve the energy efficiency of the system;

[0192] Dynamically adjust power distribution strategies to direct excess power to roads that need it more.

[0193] The core purpose of this strategy is to prioritize power supply to critical nodes during high-load conditions while reducing power waste at non-critical nodes. For example, if a sudden increase in load is detected on a critical road section while other sections have a higher power allocation coefficient, the system will prioritize increasing power supply to the critical section while reducing power supply to other sections, ensuring a balanced and efficient overall power supply.

[0194] In another alternative embodiment, the load demand of the railway traction power supply system fluctuates dynamically, especially during peak hours, when certain sections of the railway may experience power shortages due to increased power load. To optimize power resource allocation, the system uses a method based on a power supply parameter adjustment formula to gradually adjust power supply parameters to meet actual demand within a safe range while avoiding sudden system fluctuations.

[0195] The adjustment method is based on the following formula:

[0196] P 新 =P旧 +α×(P 总 -P 旧 )in:

[0197] Pnew: adjusted power supply parameters, i.e. new power supply;

[0198] POld: Current power supply parameters, i.e., the existing power supply;

[0199] Ptotal: the maximum allowable power supply parameter of the section, that is, the maximum power supply capacity of the section;

[0200] α: Adjustment factor, ranging from 0 to 1, used to control the adjustment range to ensure smooth power supply changes.

[0201] The core idea of this formula is to gradually adjust the power supply parameters so that they approach the maximum allowable power supply parameters based on the existing power supply.

[0202] If the power supply of the road section is insufficient, increase the power supply value;

[0203] If the power supply is sufficient, maintain it or make small adjustments to avoid overload or energy waste;

[0204] The power supply adjustment step size is controlled by adjusting the factor α to ensure that the system does not produce drastic fluctuations while optimizing power distribution.

[0205] The optimal value of α needs to be optimized based on historical data and actual load characteristics, and experimental calibration is usually used to determine the most appropriate value.

[0206] In one embodiment of the railway traction power supply system control method of the present application, see Figure 6 , also includes:

[0207] Step S104: determining the environmental impact factor according to the temperature and humidity data of the current period.

[0208] The insulation materials of railway traction power supply systems are affected by ambient temperature and humidity. High temperature and humidity can cause insulation aging, reduce dielectric strength, and even lead to safety hazards such as power leakage. Therefore, the system monitors temperature (T) and humidity (H) in real time and uses a computational model to determine the current environmental impact factor, which quantifies the degree of environmental impact on the insulation material.

[0209] For example, the system uses the following calculation method:

[0210] The temperature range is generally 0-50℃, and high temperature may accelerate insulation aging;

[0211] The humidity range is generally 0%-100%. High humidity may cause the insulation layer to become damp and increase dielectric loss.

[0212] The environmental impact factor is evaluated by judging the condition T+H>S (S is the comprehensive threshold). When the value exceeds the threshold, it means that the environmental conditions may have an adverse effect on the insulation material and the monitoring frequency needs to be increased.

[0213] In one embodiment, assuming that the ambient temperature T = 35°C, the relative humidity H = 70%, and the set environmental impact comprehensive threshold S = 60, the calculated value T + H = 105 > 60 indicates that the environmental conditions may affect the insulation performance, thereby triggering a subsequent adjustment mechanism.

[0214] Step S105: In response to the environmental impact factor being greater than a preset environmental impact threshold, increasing the frequency of monitoring the state of insulation materials in the current railway traction power supply system.

[0215] Under normal environmental conditions, insulation material monitoring is typically performed at a low frequency, such as once an hour, to reduce resource consumption. However, when environmental impact factors exceed a set threshold (e.g., T+H>S), the system automatically increases the monitoring frequency, for example, from once an hour to every 30 minutes, to more frequently check insulation status and identify potential problems promptly.

[0216] When the temperature or humidity approaches the material limit, the system automatically adjusts the monitoring frequency to improve safety;

[0217] According to different devices and application scenarios, the system can flexibly adjust thresholds and optimize monitoring strategies;

[0218] The system records monitoring data before and after adjustment, including temperature, humidity, monitoring frequency and insulation material status, for subsequent analysis and optimization.

[0219] For example, in the aforementioned embodiment, when the ambient temperature T = 35°C and the humidity H = 70%, the calculated environmental impact factor 105 is greater than the set threshold value 60. The system will automatically increase the monitoring frequency from once per hour to once every 30 minutes, and record the monitoring data before and after the adjustment to ensure that safety hazards can be discovered and handled in a timely manner, and prevent accidents such as power leakage.

[0220] In one embodiment of the railway traction power supply system control method of the present application, see Figure 7 , also includes:

[0221] Step S106: monitoring the communication signal strength between each section in the railway traction power supply system, and determining the current signal interference parameter according to the communication signal strength.

[0222] In railway traction power supply systems, communication signals along the line may be affected by factors such as power fluctuations and electromagnetic interference from equipment, resulting in a decrease in signal quality and even affecting the transmission of dispatch instructions. Therefore, the system collects communication signal strength data in real time through multiple sensors along the line and analyzes the possibility of signal interference based on changes in the line's power load. For example, if the voltage fluctuations on a certain section of the line are large, the system may identify it as a potential signal interference area and calculate the corresponding signal interference parameters. These parameters may include:

[0223] Signal strength attenuation value: indicates the attenuation of the communication signal;

[0224] Bit Error Rate (BER): measures the accuracy of communication data transmission;

[0225] Electromagnetic Interference Index: Evaluates possible interference sources based on the operation of power equipment.

[0226] Through this step, the system can grasp the communication signal status of each road section in real time, and predict possible interference areas in advance, providing data support for subsequent interference elimination strategies.

[0227] Step S107: In response to the signal interference parameter being greater than a preset interference threshold, the signal parameter of the current communication link is adjusted through a signal interference elimination mechanism to reduce the interference signal strength.

[0228] When the signal interference parameters are detected to exceed the preset threshold, the system will automatically trigger the signal interference elimination mechanism and dynamically adjust the signal parameters of the communication link to reduce interference. For example:

[0229] Adjusting communication frequencies: The system uses adaptive high-frequency communication technology. If the communication frequencies of two adjacent lines are too close, intermodulation interference may occur. In this case, the system calculates the frequency separation S = |F1-F2|. If this value is less than the preset minimum separation M, the system automatically adjusts the frequency of one line to move it away from the interference area. For example, if the communication frequencies of two trains are 10.5MHz and 10.6MHz, respectively, and the minimum separation is set to 0.5MHz, the system can adjust one of the frequencies to 11MHz to reduce the interference effect.

[0230] Optimizing communication signal parameters: In addition to adjusting frequency, the system can also dynamically adjust communication power, bandwidth allocation, and other parameters to enhance signal quality. For example, it can increase transmit power in areas of interference or optimize bandwidth allocation within a specific frequency band to reduce data transmission bit error rates.

[0231] Furthermore, after completing adjustments, the system records communication quality data and bit error rates before and after the adjustments to evaluate the optimization results and provide a basis for subsequent communication strategy adjustments. For example, by comparing bit error rate data before and after adjustments, the system can further optimize frequency allocation strategies, making future communication interference avoidance mechanisms more intelligent.

[0232] In one embodiment of the railway traction power supply system control method of the present application, the method further includes:

[0233] In response to an average electric load parameter in a railway traction power supply system within a preset time period being lower than a preset low power consumption threshold, the output power of the main transformer is reduced.

[0234] In the actual operation of the railway power supply system, the number of trains at night is usually greatly reduced, resulting in a reduction in the overall load of the system. If the power supply is still in the high-load mode during the day, it will not only cause a waste of electricity, but may also affect the long-term operating efficiency of the equipment. Therefore, this application calculates the current power consumption (P) of the system by real-time monitoring of current (I) and voltage (V), and determines whether to trigger the low-power mode based on the power value.

[0235] When the calculated power P = I × V is lower than the preset low power mode trigger threshold (Lmin), the system automatically adjusts the power supply strategy. For example:

[0236] The current range is usually 0-500A, and the optimal range is 0-300A;

[0237] The voltage range is usually 0-30kV, and the optimal range is 0-20kV;

[0238] When the calculated electric power PPP is lower than the set Lmin (such as 1200kW), the system will enter low power consumption mode.

[0239] When the system detects that the average power load parameter within a preset time period (such as nighttime) is lower than the preset threshold, it will automatically reduce the output power of the main substation. Specific adjustment strategies include:

[0240] Reduce the output power of the main substation to avoid energy waste caused by high power operation;

[0241] Adjust the operating frequency of the inverter to reduce the overall load of the substation and reduce unnecessary energy consumption;

[0242] Dynamically optimize the power supply strategy to ensure that the operating needs of a small number of trains can still be met in low-power mode while maintaining power supply stability.

[0243] For example, in one embodiment, if the real-time load of a certain section of railway line at night is as low as 100A and the voltage is 10kV, the calculated electric power is 1000kW, and the system's low-power mode trigger threshold is set to 1200kW. At this time, the system will enter low-power mode and further reduce the power supply power by adjusting the output power of the main substation, thereby achieving energy saving effects.

[0244] In addition to passively responding to low-load conditions, the system also utilizes intelligent algorithms, combined with historical data and real-time load feedback, to continuously optimize nighttime power supply strategies. For example, the system can analyze nighttime energy consumption trends over the past week or month and adjust the trigger threshold for low-power mode based on train operating patterns, enabling more refined energy management. This allows the system to intelligently adjust power supply even when occasional trains pass through at night, ensuring normal train operation while minimizing energy waste.

[0245] In order to realize intelligent control of railway traction power supply system, effectively reduce voltage fluctuation, improve power supply stability, and optimize energy utilization efficiency, the present application provides an embodiment of railway traction power supply system control device for realizing all or part of the contents of the railway traction power supply system control method, see Figure 8 The railway traction power supply system control device specifically includes the following contents:

[0246] The load prediction module 1101 is used to determine the predicted electric load value of each section output by the electric load prediction model based on the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model;

[0247] The coefficient determination module 1102 is configured to calculate the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value;

[0248] The power supply optimization module 1103 is configured to adjust the power supply parameters of each road section according to the power distribution coefficient so as to keep the voltage fluctuation of each road section within a preset threshold range.

[0249] According to any embodiment of the present application, the load forecasting module includes:

[0250] An environmental correction unit, configured to determine an average electric load of each road section within a preset time period based on the electric load parameters of each road section, and dynamically correct the average electric load using environmental variables;

[0251] The load prediction unit is used to: input the average electric load into a pre-built linear regression model to obtain the electric load prediction value of each section, wherein the linear regression model is constructed based on the historical electric load data of the railway traction power supply system.

[0252] According to any embodiment of the present application, the environment correction unit includes:

[0253] The weight determination subunit is used to: input the temperature and humidity data of the current period into a pre-built load impact relationship model to obtain the impact weight of environmental factors on the electric load;

[0254] The compliance adjustment subunit is used to adjust the average electric load based on the impact weight.

[0255] According to any embodiment of the present application, an abnormality warning module is further included, which is used to:

[0256] The storage unit is used to store the corrected electric load forecast value in a system log;

[0257] The early warning unit is used to: execute the power supply adjustment instruction when the revised electric load forecast value meets the requirements; and send abnormal early warning information to the dispatching system when the revised electric load forecast value does not meet the system operation requirements.

[0258] According to any embodiment of the present application, the power supply optimization module is specifically configured to:

[0259] In response to the power allocation coefficient of the road section being lower than a preset threshold, preferentially allocating power resources to the current road section;

[0260] In response to the power allocation coefficient of the road section being higher than a preset threshold, allocating power resources to the current road section is delayed.

[0261] According to any embodiment of the present application, it further includes a material detection module for:

[0262] An environmental impact factor is determined based on temperature and humidity data of the current time period, and in response to the environmental impact factor being greater than a preset environmental impact threshold, a frequency of monitoring the insulation material status in the current railway traction power supply system is increased.

[0263] According to any embodiment of the present application, the system further includes an interference cancellation module, configured to:

[0264] Monitor the communication signal strength between each section in the railway traction power supply system, and determine the current signal interference parameter based on the communication signal strength. In response to the signal interference parameter being greater than a preset interference threshold, adjust the signal parameters of the current communication link through the signal interference elimination mechanism to reduce the interference signal strength.

[0265] According to any embodiment of the present application, a low power consumption module is further included, which is used to:

[0266] In response to an average electric load parameter in a railway traction power supply system within a preset time period being lower than a preset low power consumption threshold, the output power of the main substation is reduced.

[0267] From the above description, it can be seen that the railway traction power supply system control device provided in the embodiment of the present application can realize intelligent regulation and control of the railway traction power supply system, effectively reduce voltage fluctuations, improve power supply stability, and optimize energy utilization efficiency.

[0268] From a hardware perspective, in order to achieve intelligent control of railway traction power supply systems, effectively reduce voltage fluctuations, improve power supply stability, and optimize energy utilization efficiency, the present application provides an embodiment of an electronic device for implementing all or part of the contents of the railway traction power supply system control method. The electronic device specifically includes the following contents:

[0269] A processor, a memory, a communications interface, and a bus; wherein the processor, memory, and communications interface communicate with each other via the bus; the communications interface is used to transmit information between the railway traction power supply system control device and related devices such as the core business system, user terminals, and related databases; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiments of the railway traction power supply system control method and the embodiments of the railway traction power supply system control device in the embodiments, the contents of which are incorporated herein and repeated parts are not repeated.

[0270] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0271] In practical applications, portions of the railway traction power supply system control method may be executed on the electronic device side as described above, or all operations may be performed on the client device. The specific selection may depend on the processing capabilities of the client device and the limitations of the user's usage scenario. This application does not impose any restrictions on this. If all operations are performed on the client device, the client device may also include a processor.

[0272] The client device may include a communication module (i.e., a communication unit) that can establish a communication connection with a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0273] Figure 9 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 9 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 9 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0274] In one embodiment, the railway traction power supply system control method function may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0275] Step S101: determining the electric load prediction value of each section output by the electric load prediction model according to the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model.

[0276] Step S102: Calculating the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value.

[0277] Step S103: adjusting the power supply parameters of each road section according to the power distribution coefficient, so that the voltage fluctuation of each road section remains within a preset threshold range.

[0278] From the above description, it can be seen that the electronic device provided in the embodiment of the present application realizes the intelligent control of the railway traction power supply system, effectively reduces voltage fluctuations, improves power supply stability, and optimizes energy utilization efficiency.

[0279] In another embodiment, the railway traction power supply system control device can be configured separately from the central processing unit 9100. For example, the railway traction power supply system control device can be configured as a chip connected to the central processing unit 9100, and the railway traction power supply system control method function can be implemented through the control of the central processing unit.

[0280] like Figure 9As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 9 In addition, the electronic device 9600 may also include all components shown in Figure 9 For components not shown, reference may be made to the prior art.

[0281] like Figure 9 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0282] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0283] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0284] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), or a SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is provided with more data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs or processes for executing the operation of the electronic device 9600 by the central processing unit 9100.

[0285] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0286] The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0287] Based on different communication technologies, multiple communication modules 9110 can be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing common telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is also coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0288] The embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the railway traction power supply system control method in the above-mentioned embodiments, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all steps of the railway traction power supply system control method in the above-mentioned embodiments, where the execution subject is a server or a client, are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0289] Step S101: determining the electric load prediction value of each section output by the electric load prediction model according to the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model.

[0290] Step S102: Calculating the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value.

[0291] Step S103: Adjust the power supply parameters of each section based on the power distribution coefficient to keep the voltage fluctuation of each section within a preset threshold range. As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application realizes intelligent control of the railway traction power supply system, effectively reduces voltage fluctuations, improves power supply stability, and optimizes energy utilization efficiency.

[0292] The embodiments of the present application also provide a computer program product capable of implementing all steps of the railway traction power supply system control method in the above-mentioned embodiment, where the execution subject is a server or a client. When the computer program / instructions are executed by a processor, the computer program / instructions implement the steps of the railway traction power supply system control method. For example, the computer program / instructions implement the following steps:

[0293] Step S101: determining the electric load prediction value of each section output by the electric load prediction model according to the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model.

[0294] Step S102: Calculating the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value.

[0295] Step S103: adjusting the power supply parameters of each road section according to the power distribution coefficient, so that the voltage fluctuation of each road section remains within a preset threshold range.

[0296] From the above description, it can be seen that the computer program product provided in the embodiment of the present application realizes the intelligent control of the railway traction power supply system, effectively reduces voltage fluctuations, improves power supply stability, and optimizes energy utilization efficiency.

[0297] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0298] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0299] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0300] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0301] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A railway traction power supply system control method, characterized in that: The method comprises: Determining the predicted electric load value of each section output by the electric load prediction model according to the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model; Calculating the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value; The power supply parameters of each road section are adjusted according to the power distribution coefficient so that the voltage fluctuation of each road section is kept within a preset threshold range.

2. The railway traction power supply system control method according to claim 1, characterized in that: The method of determining the electric load prediction value of each section output by the electric load prediction model based on the electric load parameters of each section in the railway traction power supply system and the pre-built linear regression model includes: Determining the average electric load of each road section within a preset time period based on the electric load parameters of each road section, and dynamically correcting the average electric load using environmental variables; The average electric load is input into a pre-built linear regression model to obtain the electric load prediction value of each section, wherein the linear regression model is built based on the historical electric load data of the railway traction power supply system.

3. The railway traction power supply system control method according to claim 2, characterized in that: The dynamically correcting the average electric load by using environmental variables includes: Input the temperature and humidity data of the current period into the pre-built load impact relationship model to obtain the impact weight of environmental factors on the electric load; The average electric load is adjusted based on the impact weight.

4. The railway traction power supply system control method according to claim 3, characterized in that: After adjusting the average electric load based on the impact weight, the method further includes: The revised electric load forecast value is stored in the system log; When the revised electric load forecast value meets the requirements, the power supply adjustment instruction is executed; when the revised electric load forecast value does not meet the system operation requirements, an abnormal warning information is sent to the dispatching system.

5. The railway traction power supply system control method according to claim 1, characterized in that: The adjusting the power supply parameters of each road section according to the power distribution coefficient includes: In response to the power allocation coefficient of the road section being lower than a preset threshold, preferentially allocating power resources to the current road section; In response to the power allocation coefficient of the road section being higher than a preset threshold, allocating power resources to the current road section is delayed.

6. The railway traction power supply system control method according to claim 1, characterized in that: Also includes: Determine environmental impact factors based on temperature and humidity data for the current period; In response to the environmental impact factor being greater than a preset environmental impact threshold, the frequency of monitoring the state of insulation materials in the current railway traction power supply system is increased.

7. The railway traction power supply system control method according to claim 1, characterized in that: Also includes: Monitoring the communication signal strength between sections of the railway traction power supply system and determining the current signal interference parameter based on the communication signal strength; In response to the signal interference parameter being greater than a preset interference threshold, the signal parameter of the current communication link is adjusted through a signal interference cancellation mechanism to reduce the interference signal strength.

8. The railway traction power supply system control method according to claim 1, characterized in that: Also includes: In response to an average electric load parameter in a railway traction power supply system within a preset time period being lower than a preset low power consumption threshold, the output power of the main transformer is reduced.

9. A railway traction power supply system control device, characterized in that: The device comprises: A load prediction module is used to determine the predicted electric load value of each section output by the electric load prediction model based on the electric load parameters of each section in the railway traction power supply system and a pre-built linear regression model; A coefficient determination module, configured to calculate the power distribution coefficient of each road section based on the power load parameters of each road section and the power load prediction value; The power supply optimization module is used to adjust the power supply parameters of each section according to the power distribution coefficient so that the voltage fluctuation of each section is kept within a preset threshold range.

10. The railway traction power supply system control device according to claim 9, characterized in that: The load forecasting module includes: An environmental correction unit, configured to determine an average electric load of each road section within a preset time period based on the electric load parameters of each road section, and dynamically correct the average electric load using environmental variables; The load prediction unit is used to: input the average electric load into a pre-built linear regression model to obtain the electric load prediction value of each section, wherein the linear regression model is constructed based on the historical electric load data of the railway traction power supply system.

11. The railway traction power supply system control device according to claim 10, characterized in that: The environmental correction unit comprises: The weight determination subunit is used to: input the temperature and humidity data of the current period into a pre-built load impact relationship model to obtain the impact weight of environmental factors on the electric load; The compliance adjustment subunit is used to adjust the average electric load based on the impact weight.

12. The railway traction power supply system control device according to claim 11, characterized in that: It also includes an abnormal warning module for: The storage unit is used to store the corrected electric load forecast value in a system log; The early warning unit is used to: execute the power supply adjustment instruction when the revised electric load forecast value meets the requirements; and send abnormal early warning information to the dispatching system when the revised electric load forecast value does not meet the system operation requirements.

13. The railway traction power supply system control device according to claim 9, characterized in that: The power supply optimization module is specifically used for: In response to the power allocation coefficient of the road section being lower than a preset threshold, preferentially allocating power resources to the current road section; In response to the power allocation coefficient of the road section being higher than a preset threshold, allocating power resources to the current road section is delayed.

14. The railway traction power supply system control device according to claim 9, characterized in that: Also includes material detection modules for: An environmental impact factor is determined based on temperature and humidity data of the current time period, and in response to the environmental impact factor being greater than a preset environmental impact threshold, a frequency of monitoring the insulation material status in the current railway traction power supply system is increased.

15. The railway traction power supply system control device according to claim 9, characterized in that: Also included is an interference cancellation module for: Monitor the communication signal strength between each section in the railway traction power supply system, and determine the current signal interference parameter based on the communication signal strength. In response to the signal interference parameter being greater than a preset interference threshold, adjust the signal parameters of the current communication link through the signal interference elimination mechanism to reduce the interference signal strength.

16. The railway traction power supply system control device according to claim 9, characterized in that: Also included are low-power modules for: In response to an average electric load parameter in a railway traction power supply system within a preset time period being lower than a preset low power consumption threshold, the output power of the main substation is reduced.

17. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the railway traction power supply system control method according to any one of claims 1 to 8 are implemented.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the railway traction power supply system control method according to any one of claims 1 to 8 are implemented.

19. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the railway traction power supply system control method according to any one of claims 1 to 8 are implemented.

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