High-speed rail traction transformer capacity optimization design

Abstract

The invention discloses a high-speed rail traction transformer capacity optimization design which comprises the following steps: collecting and analyzing actually measured load data of a high-speed rail traction substation, and constructing a typical load curve; and based on the typical load curve, establishing a transformer temperature rise model by using an exponential equation, and calculating the traction transformer winding hot-spot temperature and life loss. And then, optimizing the capacity of the traction transformer according to a calculation result so as to improve the capacity utilization rate and the economical efficiency of the transformer. The optimized transformer is subjected to field test to verify the performance of the transformer in actual operation, including but not limited to capacity utilization rate, temperature rise condition, energy efficiency ratio and the like. And according to a field test result, necessary adjustment and improvement are carried out on an optimization scheme. According to the method, the accuracy of the temperature rise model is ensured by determining the temperature rise relation between the winding hot-spot temperature and the top-layer oil temperature and solving the steady-state temperature rise by using the index equation. Meanwhile, the influence of the environment temperature change on the temperature rise is considered, and the applicability and accuracy of the model are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system and high-speed railway traction power supply, and in particular to the capacity optimization design of high-speed railway traction transformer. Background Art

[0002] With the rapid development of high-speed railways, higher requirements are placed on the reliability and economy of traction power supply systems. As the core equipment of the high-speed railway traction power supply system, the capacity design of the traction transformer directly affects the stability, energy efficiency and operating cost of the power supply system. Reasonable traction transformer capacity design can not only ensure the power supply needs of the train during high-speed operation, but also effectively reduce energy consumption and extend the service life of the equipment, thereby achieving the economy and sustainability of high-speed railway operations.

[0003] Traditional methods often use simplified temperature rise models to calculate transformer winding hot spot temperatures and life loss, without fully considering the impact of load changes and ambient temperature on transformer temperature rise. This results in a large deviation between the calculation results and the actual situation, making it impossible to accurately evaluate the thermal performance and life of the transformer.

[0004] When optimizing transformer capacity, traditional designs often only consider the performance and economy of a single transformer, but lack comprehensive analysis and optimization of the entire power supply system. This results in the designed transformer capacity not matching the operating requirements of the entire system, affecting the overall performance of the system. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the above technical defects and provide a capacity optimization design for high-speed railway traction transformers.

[0006] In order to solve the above problems, the technical solution of the present invention comprises the following steps:

[0007] S1. Collect and analyze measured load data of high-speed railway traction substations, and construct a typical load curve based on the data;

[0008] S2. Based on the typical load curve, a transformer temperature rise model is established using an exponential equation to calculate the hot spot temperature and life loss of the traction transformer winding;

[0009] S3, optimizing the traction transformer capacity according to the calculation results to improve the transformer capacity utilization and economy;

[0010] S4. Conduct field tests on the optimized traction transformer to verify its performance in actual operation, including but not limited to capacity utilization, temperature rise and energy efficiency ratio;

[0011] S5. According to the field test results, make necessary adjustments and improvements to the optimization plan to ensure that the optimized traction transformer can meet the actual operation requirements of the high-speed railway traction power supply system.

[0012] Further, the step of establishing the transformer temperature rise model in step S2 includes:

[0013] S2.1. Determine the temperature rise relationship between the hot spot temperature of the transformer winding and the top oil temperature;

[0014] S2.2. Calculate the dynamic change of hot spot temperature of transformer winding according to load change;

[0015] S2.3. Use the exponential equation to solve the steady-state temperature rise to accurately calculate the winding hot spot temperature of the traction transformer under the typical load curve.

[0016] Furthermore, the step of identifying and removing abnormal data in step S1.2 includes:

[0017] S1.2.1. Apply statistical methods or machine learning algorithms to identify data points outside the normal range;

[0018] S1.2.1. For the identified abnormal data points, use interpolation or other appropriate methods to correct or delete them to ensure the consistency and reliability of the load data.

[0019] Furthermore, the formulation and implementation of the optimization plan for the traction transformer capacity in step S3.3 includes:

[0020] S3.3.1. Analyze the initial investment cost and long-term operating cost of transformers of different capacity levels;

[0021] S3.3.2. Consider the performance indicators, service life and maintenance cost of the transformer and select the transformer capacity with the best cost performance;

[0022] S3.3.3. Regularly evaluate the actual operating status of the transformer and adjust the optimization plan as needed to ensure efficient operation of the transformer.

[0023] Furthermore, the step of constructing the typical load curve in step S1 includes:

[0024] S1.1. Classify and count the measured load data of existing high-speed railway traction substations;

[0025] S1.2. Identify and eliminate abnormal data to ensure the accuracy and representativeness of the data;

[0026] S1.3. Based on the statistical results, draw a typical load curve of the high-speed rail traction load.

[0027] Furthermore, the step of optimizing the traction transformer capacity in step S3 includes:

[0028] S3.1. Determine the reasonable range of traction transformer capacity based on the calculation results;

[0029] S3.2. Consider the overload capacity and economy of the transformer and select the optimal capacity level;

[0030] S3.3. Develop and implement a plan to optimize the capacity of traction transformers.

[0031] Furthermore, the step of solving the steady-state temperature rise by using the exponential equation in step S2.3 also includes:

[0032] S2.3.1. Consider the impact of ambient temperature changes on transformer temperature rise and adjust the relevant parameters in the exponential equation;

[0033] S2.3.2. Verify the accuracy and applicability of the temperature rise model by simulating the temperature rise under different ambient temperature conditions.

[0034] Furthermore, the process of adjusting and improving the optimization scheme in step S5 includes the following steps:

[0035] S5.1. Analyze the field test data in detail to identify the problems or deficiencies that may exist in the actual operation of the optimization scheme;

[0036] S5.2. Based on the identified problems or deficiencies and the actual operation requirements of the high-speed railway traction power supply system, make targeted adjustments and improvements to the optimization plan;

[0037] S5.3. The adjusted optimization plan shall be tested and verified again in the field to ensure that its performance in actual operation meets the expected requirements.

[0038] The advantages of the present invention compared with the prior art are:

[0039] 1. The present invention provides a capacity optimization design for a high-speed railway traction transformer. By determining the temperature rise relationship between the hot spot temperature of the winding and the top oil temperature and solving the steady-state temperature rise using an exponential equation, the present invention ensures the accuracy of the temperature rise model. In addition, considering the influence of ambient temperature changes on the temperature rise, the applicability and accuracy of the model are further improved.

[0040] 2. The present invention provides a capacity optimization design for high-speed rail traction transformers. According to the calculation results of the temperature rise model, the capacity of the traction transformer is optimized to improve the capacity utilization and economy of the transformer. By comprehensively considering the overload capacity and economy of the transformer, the optimal capacity level is selected and a detailed optimization plan is formulated. This method not only improves the use efficiency of the transformer, but also reduces the investment cost and operating cost. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] The capacity optimization design of high-speed railway traction transformer includes the following steps:

[0043] S1. Collect and analyze the measured load data of high-speed railway traction substations, and construct a typical load curve based on the data;

[0044] S2. Based on the typical load curve, the transformer temperature rise model is established using the exponential equation to calculate the hot spot temperature and life loss of the traction transformer winding;

[0045] S3. According to the calculation results, optimize the traction transformer capacity to improve the transformer capacity utilization and economy;

[0046] S4. Conduct field tests on the optimized traction transformer to verify its performance in actual operation, including but not limited to capacity utilization, temperature rise and energy efficiency ratio;

[0047] S5. According to the field test results, make necessary adjustments and improvements to the optimization plan to ensure that the optimized traction transformer can meet the actual operation requirements of the high-speed railway traction power supply system.

[0048] Furthermore, the steps of constructing the typical load curve in step S1 include:

[0049] S1.1. Classify and count the measured load data of existing high-speed railway traction substations;

[0050] S1.2. Identify and eliminate abnormal data to ensure the accuracy and representativeness of the data;

[0051] S1.3. Based on the statistical results, draw a typical load curve of the high-speed rail traction load.

[0052] Furthermore, the step of establishing the transformer temperature rise model in step S2 includes:

[0053] S2.1. Determine the temperature rise relationship between the hot spot temperature of the transformer winding and the top oil temperature;

[0054] S2.2. Calculate the dynamic change of hot spot temperature of transformer winding according to load change;

[0055] S2.3. Use the exponential equation to solve the steady-state temperature rise to accurately calculate the winding hot spot temperature of the traction transformer under the typical load curve.

[0056] Furthermore, the step of optimizing the traction transformer capacity in step S3 includes:

[0057] S3.1. Determine the reasonable range of traction transformer capacity based on the calculation results;

[0058] S3.2. Consider the overload capacity and economy of the transformer and select the optimal capacity level;

[0059] S3.3. Develop and implement a plan to optimize the capacity of traction transformers.

[0060] Furthermore, the step of identifying and removing abnormal data in step S1.2 includes:

[0061] S1.2.1. Apply statistical methods or machine learning algorithms to identify data points outside the normal range;

[0062] S1.2.1. For the identified abnormal data points, use interpolation or other appropriate methods to correct or delete them to ensure the consistency and reliability of the load data.

[0063] Furthermore, the step of solving the steady-state temperature rise using the exponential equation in step S2.3 also includes:

[0064] S2.3.1. Consider the impact of ambient temperature changes on transformer temperature rise and adjust the relevant parameters in the exponential equation;

[0065] S2.3.2. Verify the accuracy and applicability of the temperature rise model by simulating the temperature rise under different ambient temperature conditions.

[0066] Furthermore, the optimization plan for the traction transformer capacity is formulated and implemented in step S3.3, including:

[0067] S3.3.1. Analyze the initial investment cost and long-term operating cost of transformers of different capacity levels;

[0068] S3.3.2. Consider the performance indicators, service life and maintenance cost of the transformer and select the transformer capacity with the best cost performance;

[0069] S3.3.3. Regularly evaluate the actual operating status of the transformer and adjust the optimization plan as needed to ensure efficient operation of the transformer.

[0070] Furthermore, the process of adjusting and improving the optimization scheme in step S5 includes the following steps:

[0071] S5.1. Analyze the field test data in detail to identify the problems or deficiencies that may exist in the actual operation of the optimization scheme;

[0072] S5.2. Based on the identified problems or deficiencies and the actual operation requirements of the high-speed railway traction power supply system, make targeted adjustments and improvements to the optimization plan;

[0073] S5.3. The adjusted optimization plan shall be tested and verified again in the field to ensure that its performance in actual operation meets the expected requirements.

[0074] In specific use, measured load data is collected from multiple high-speed rail traction substations, covering different time periods (such as weekdays, weekends, holidays) and different seasons (spring, summer, autumn, and winter). The data is classified by time period and season to analyze the load characteristics under different conditions. Statistical software (such as Excel, SPSS) is used to summarize the data and calculate the average load, maximum load, and minimum load for each time period and season.

[0075] Statistical methods (such as standard deviation, box plot) or machine learning algorithms (such as isolation forest) are used to identify data points that are beyond the normal range. For the identified abnormal data points, interpolation methods (such as linear interpolation, spline interpolation) or other appropriate methods are used to correct or delete them to ensure the consistency and reliability of the load data.

[0076] Use tools such as Matplotlib or Excel to draw typical load curves for different time periods and seasons based on statistical results, including daily load curves and annual load curves. Determine the temperature rise relationship between the hot spot temperature of the transformer winding and the top oil temperature through experiments or by consulting relevant literature, for example: 热点 =T 油项 +ΔΤ, where ΔΤ is the temperature rise coefficient. Using the typical load curve and temperature rise relationship, the dynamic change of the hot spot temperature of the transformer winding under different load conditions is calculated. Assuming that the change of temperature rise over time conforms to the exponential law, the exponential equation is established: Τ(t)=Τ 环境 +(Τ0-Τ 环境 ) -kt , where Τ(t) is the temperature rise at time t, Τ 环境 is the ambient temperature, Τ0 is the initial temperature rise, and k is the thermal conductivity. Considering the impact of ambient temperature changes on the temperature rise of the transformer, the relevant parameters in the exponential equation are adjusted. The accuracy and applicability of the temperature rise model are verified by simulating the temperature rise under different ambient temperature conditions.

[0077] According to the temperature rise calculation results, determine the reasonable range of traction transformer capacity to ensure that the temperature rise of the transformer does not exceed the allowable value under various load conditions. Analyze the initial investment cost and long-term operating cost of transformers of different capacity levels, including electricity costs, maintenance costs, etc. Comprehensively consider the performance indicators of the transformer (such as energy efficiency ratio, overload capacity), service life and maintenance costs, and select the transformer capacity with the highest cost performance.

[0078] Formulate specific optimization plans, including the selection of transformer capacity, installation location, design of cooling system, etc., implement the optimization plans in actual projects, and carry out on-site installation and commissioning. Test items include but are not limited to capacity utilization, temperature rise, and energy efficiency ratio, etc. Use portable temperature rise testers, power analyzers and other equipment to record the operating data of transformers under different load conditions.

[0079] A detailed analysis of the field test data was conducted to identify possible problems or deficiencies in the optimization scheme in actual operation. Combined with the actual operation requirements of the high-speed rail traction power supply system, specific problems (such as excessive temperature rise, low energy efficiency ratio, etc.) were identified. Based on the identified problems, targeted adjustments and improvements were made to the optimization scheme, such as adjusting the cooling system design, optimizing the operation strategy, etc. The adjusted optimization scheme was again field tested and verified to ensure that its performance in actual operation met the expected requirements.

[0080] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0081] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0082] The present invention and its implementation methods are described above, and such description is not restrictive, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design structures and embodiments similar to the technical solution without creativity, which should all fall within the protection scope of the present invention.

Claims

1. High-speed railway traction transformer capacity optimization design, characterized by: The following steps are involved: S1. Collect and analyze measured load data of high-speed railway traction substations, and construct a typical load curve based on the data; S2. Based on the typical load curve, a transformer temperature rise model is established using an exponential equation to calculate the hot spot temperature and life loss of the traction transformer winding; S3, optimizing the traction transformer capacity according to the calculation results to improve the transformer capacity utilization and economy; S4. Conduct field tests on the optimized traction transformer to verify its performance in actual operation, including but not limited to capacity utilization, temperature rise and energy efficiency ratio; S5. According to the field test results, make necessary adjustments and improvements to the optimization plan to ensure that the optimized traction transformer can meet the actual operation requirements of the high-speed railway traction power supply system.

2. The high-speed railway traction transformer capacity optimization design according to claim 1 is characterized in that: The steps of constructing the typical load curve in step S1 include: S1.

1. Classify and count the measured load data of existing high-speed railway traction substations; S1.

2. Identify and eliminate abnormal data to ensure the accuracy and representativeness of the data; S1.

3. Based on the statistical results, draw a typical load curve of the high-speed rail traction load.

3. The high-speed railway traction transformer capacity optimization design according to claim 1 is characterized in that: The step of establishing the transformer temperature rise model in step S2 includes: S2.

1. Determine the temperature rise relationship between the hot spot temperature of the transformer winding and the top oil temperature; S2.

2. Calculate the dynamic change of hot spot temperature of transformer winding according to load change; S2.

3. Use the exponential equation to solve the steady-state temperature rise to accurately calculate the winding hot spot temperature of the traction transformer under the typical load curve.

4. The high-speed railway traction transformer capacity optimization design according to claim 1 is characterized in that: The step of optimizing the traction transformer capacity in step S3 includes: S3.

1. Determine the reasonable range of traction transformer capacity based on the calculation results; S3.

2. Consider the overload capacity and economy of the transformer and select the optimal capacity level; S3.

3. Develop and implement a plan to optimize the capacity of traction transformers.

5. The high-speed railway traction transformer capacity optimization design according to claim 2 is characterized in that: The step of identifying and removing abnormal data in step S1.2 includes: S1.2.

1. Apply statistical methods or machine learning algorithms to identify data points outside the normal range; S1.2.

1. For the identified abnormal data points, use interpolation or other appropriate methods to correct or delete them to ensure the consistency and reliability of the load data.

6. The high-speed railway traction transformer capacity optimization design according to claim 3 is characterized in that: The step of solving the steady-state temperature rise by using the exponential equation in step S2.3 also includes: S2.3.

1. Consider the impact of ambient temperature changes on transformer temperature rise and adjust the relevant parameters in the exponential equation; S2.3.

2. Verify the accuracy and applicability of the temperature rise model by simulating the temperature rise under different ambient temperature conditions.

7. The high-speed railway traction transformer capacity optimization design according to claim 4 is characterized in that: The optimization plan for the traction transformer capacity is formulated and implemented in step S3.3, including: S3.3.

1. Analyze the initial investment cost and long-term operating cost of transformers of different capacity levels; S3.3.

2. Consider the performance indicators, service life and maintenance cost of the transformer and select the transformer capacity with the best cost performance; S3.3.

3. Regularly evaluate the actual operating status of the transformer and adjust the optimization plan as needed to ensure efficient operation of the transformer.

8. The high-speed railway traction transformer capacity optimization design according to claim 1 is characterized in that: The process of adjusting and improving the optimization scheme in step S5 includes the following steps: S5.

1. Analyze the field test data in detail to identify the problems or deficiencies that may exist in the actual operation of the optimization scheme; S5.

2. Based on the identified problems or deficiencies and the actual operation requirements of the high-speed railway traction power supply system, make targeted adjustments and improvements to the optimization plan; S5.

3. The adjusted optimization plan shall be tested and verified again in the field to ensure that its performance in actual operation meets the expected requirements.