Contact line icing operation evaluation method considering influence of multiple environmental factors

Abstract

The invention provides a contact line icing operation evaluation method considering influence of multiple environmental factors. A contact line icing operation evaluation platform considering multiple environmental factors is established. The method comprises the following steps: sending a control signal to a corresponding device through an upper computer, recording environmental condition data in an experimental incubator to obtain a contact line icing thickness reference value calculation formula, further obtaining a contact line maximum icing thickness calculation formula through genetic algorithm optimization, and calculating a contact line icing operation state comprehensive evaluation factor lambda, and evaluating the icing operation state of the contact line. According to the method, the contact line icing thickness under different environmental conditions can be effectively calculated, the contact line operation state is evaluated according to the contact line icing operation state comprehensive evaluation factor lambda, and an important guarantee is provided for safe and stable operation of a railway contact line system.

Description

Technical Field

[0001] The invention belongs to the field of contact network safe operation assessment, and in particular relates to a contact line icing operation assessment method taking into account the influence of multiple environmental factors. Background Art

[0002] In the railway electrification system, the contact network plays an important role in directly transmitting electricity to the train. Its stable and reliable operation is crucial to ensure the safe operation of the entire railway system. However, in some cold and rainy areas, icing of the contact line poses a serious threat to the safe and reliable operation of the train. Contact line icing is a phenomenon of ice on the surface of the conductor caused by specific meteorological conditions. Its formation is related to many factors, including ambient temperature, moisture content in the air, wind speed, rainfall, etc. Severe icing of the contact line will cause a significant decrease in the electrical contact performance of the bow-net, triggering an arc on the bow-net and even burning and breaking the contact line, ablating the carbon slide plate of the pantograph, changing the original mechanical and motion characteristics of the contact line, and greatly affecting the current collection stability of the train. Therefore, it is extremely important to evaluate the safety and reliability of the contact line under the operating state of icing.

[0003] At present, there is a relative lack of evaluation methods for contact line iced operation. There is an urgent need for an evaluation method for contact line iced operation that takes into account the influence of multiple environmental factors to evaluate the operating status of the contact line under iced conditions, provide a basis for relevant personnel in the railway industry to evaluate the operating status of the contact line under iced conditions, and make and implement relevant response strategies in a timely manner, thereby greatly reducing the hazards caused by contact line iced conditions. Summary of the invention

[0004] The object of the present invention is to provide a contact line icing operation assessment method taking into account the influence of multiple environmental factors.

[0005] The technical solution for achieving the purpose of the present invention is as follows:

[0006] The first step is to build a contact line icing operation evaluation platform that takes into account the influence of multiple environmental factors. The platform includes: a temperature and water content dual-factor environmental monitoring device (1), an intelligent rainfall simulation device (2), a simulated rainfall control and adjustment device (3), an adjustable speed air outlet device (4), a wind speed control and adjustment device (5), a host computer (6), a wall fixing device (7), a supporting silicon frame (8), a wall fixing device (9), a supporting silicon frame (10), a contact line (11), a temperature control and adjustment device (12), a high-definition anti-fog and icing intelligent monitoring device (13), a large-capacity image storage device (14), and an experimental constant temperature box (15);

[0007] The data output end of the temperature and water content dual-factor environmental monitoring device (1) is electrically connected to a host computer (6);

[0008] The signal input end of the intelligent simulated rainfall device (2) is electrically connected to the control signal output end of the simulated rainfall control and adjustment device (3), and the signal input end of the simulated rainfall control and adjustment device (3) is electrically connected to the host computer (6);

[0009] The adjustable speed air outlet device (4) is electrically connected to the control signal output end of the wind speed control and adjustment device (5), and the signal input end of the wind speed control and adjustment device (5) is electrically connected to the host computer (6);

[0010] The wall fixing device 1 (7) is mechanically connected to the wall of the experimental constant temperature box (15);

[0011] The supporting silicon frame (8) is mechanically connected to the wall fixing device (7);

[0012] The second wall fixing device (9) is mechanically connected to the wall of the experimental constant temperature box (15);

[0013] The second supporting silicon frame (10) is mechanically connected to the first wall fixing device (9);

[0014] The contact wire (11) is stably placed on a support consisting of a first supporting silicon frame (8) and a second supporting silicon frame (10);

[0015] The control signal input end of the temperature control and adjustment device (12) is electrically connected to the host computer (6);

[0016] The data output end of the high-definition anti-fog and ice-covering intelligent monitoring device (13) is electrically connected to the data input end of the large-capacity image storage device (14), and the data output end of the large-capacity image storage device (14) is electrically connected to the host computer (6);

[0017] Step 2: Based on a contact line icing operation assessment platform that takes into account the influence of multiple environmental factors, a contact line icing operation assessment method that takes into account the influence of multiple environmental factors is proposed, including the following steps:

[0018] S1: Turn on the temperature and water content dual-factor environmental monitoring device (1) to monitor the ambient temperature and liquid water content in the air in real time in the experimental thermostatic box (15); send a control signal to the simulated rainfall control and adjustment device (3) through the host computer (6); the intelligent simulated rainfall device (2) receives the control signal sent by the simulated rainfall control and adjustment device (3) and starts to simulate rainfall; send a control signal to the wind speed control and adjustment device (5) through the host computer (6); the adjustable speed air outlet device (4) receives the control signal sent by the wind speed control and adjustment device (5) and starts to work; send a control signal to the temperature control and adjustment device (12) through the host computer (6) to adjust the internal ambient temperature of the experimental thermostatic box (15); turn on the high-definition anti-fog and ice intelligent monitoring device (13); and transmit the monitoring image to the host computer (6) through the large-capacity image storage device (14);

[0019] S2: After the whole device has been running for 60 minutes, the ambient temperature T of the experimental constant temperature box (15) and the liquid water content Q in the air are collected and recorded by the host computer (6); the diameter D of the raindrops simulated by the intelligent simulated rainfall device (2) is recorded by the host computer (6); the wind speed v of the adjustable speed air outlet device (4) is recorded by the host computer (6); the monitoring image of the high-definition anti-fog and icing intelligent monitoring device (13) is analyzed by the host computer (6) to obtain the actual measured value W of the maximum ice thickness of the contact line (11) t ; Carry out m experiments and record m sets of data through the host computer (6);

[0020] S3: The reference value W of ice thickness on the contact line under different ambient temperatures, liquid water content in the air, raindrop diameter and wind speed is calculated by the following formula:

[0021]

[0022] In formula (1), T is the ambient temperature, Q is the liquid water content in the air, D is the raindrop diameter, v is the experimental wind speed, p is the error coefficient, and t is the integral variable;

[0023] S4: Use genetic algorithm to optimize formula (1) and obtain the p0 value that minimizes the error. The specific steps are:

[0024] 1) Randomly generate an initial solution p and calculate the objective function f(p):

[0025]

[0026] In formula (2), f(p) is the objective function, W j is the reference value of ice thickness on the contact line in the jth experiment, W tj is the measured value of ice thickness on the contact line in the jth experiment, m is the number of experiments, and t is the integral variable;

[0027] 2) Generate a new perturbed solution p' and calculate the objective function Δf = f(p) - f(p'); if Δf ≥ 0, accept the new solution; otherwise, obtain the new solution according to the probability acceptance criterion;

[0028] 3) Determine whether the number of iterations has been reached. If so, proceed to step 4). If not, proceed to step 2).

[0029] 4) Determine whether the termination condition is met. If the termination condition is met, end the operation and return the optimal solution p0. If the termination condition is not met, reset the number of iterations and go to step 2);

[0030] S5: Substitute the optimal solution p0 returned in S4 into formula (1) to obtain the optimized contact line ice thickness calculation formula:

[0031]

[0032] S6: Calculate the comprehensive evaluation factor λ of the contact line icing operation status by the following formula:

[0033] λ=5W0 (4)

[0034] When λ∈(0,1], it indicates that the catenary ice thickness is within the safe driving range of the train. When λ∈(1,2.5], the relevant responsible personnel can decide whether to de-ice the catenary according to the number of trains running and the running time. When λ∈(2.5,+∞], the relevant responsible personnel should be notified to carry out de-icing operations in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the overall structure of the present invention. DETAILED DESCRIPTION

[0036] The specific embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0037] A contact line icing operation assessment method taking into account the influence of multiple environmental factors includes the following steps:

[0038] The first step is to build a contact line icing operation evaluation platform that takes into account the influence of multiple environmental factors, the platform comprising: a temperature and water content dual-factor environmental monitoring device (1), an intelligent rainfall simulation device (2), a rainfall simulation control and adjustment device (3), an adjustable speed air outlet device (4), a wind speed control and adjustment device (5), a host computer (6), a wall fixing device (7), a supporting silicon frame (8), a wall fixing device (9), a supporting silicon frame (10), a contact line (11), a temperature control and adjustment device (12), a high-definition anti-fog and icing intelligent monitoring device (13), a large-capacity image storage device (14), and an experimental constant temperature box (15);

[0039] The data output end of the temperature and water content dual-factor environmental monitoring device (1) is electrically connected to a host computer (6);

[0040] The signal input end of the intelligent simulated rainfall device (2) is electrically connected to the control signal output end of the simulated rainfall control and adjustment device (3), and the signal input end of the simulated rainfall control and adjustment device (3) is electrically connected to the host computer (6);

[0041] The adjustable speed air outlet device (4) is electrically connected to the control signal output end of the wind speed control and adjustment device (5), and the signal input end of the wind speed control and adjustment device (5) is electrically connected to the host computer (6);

[0042] The wall fixing device 1 (7) is mechanically connected to the wall of the experimental constant temperature box (15);

[0043] The supporting silicon frame (8) is mechanically connected to the wall fixing device (7);

[0044] The second wall fixing device (9) is mechanically connected to the wall of the experimental constant temperature box (15);

[0045] The second supporting silicon frame (10) is mechanically connected to the first wall fixing device (9);

[0046] The contact wire (11) is stably placed on a support consisting of a first supporting silicon frame (8) and a second supporting silicon frame (10);

[0047] The control signal input end of the temperature control and adjustment device (12) is electrically connected to the host computer (6);

[0048] The data output end of the high-definition anti-fog and ice-covering intelligent monitoring device (13) is electrically connected to the data input end of the large-capacity image storage device (14), and the data output end of the large-capacity image storage device (14) is electrically connected to the host computer (6);

[0049] Step 2: Turn on the temperature and water content dual-factor environmental monitoring device (1) to monitor the ambient temperature and liquid water content in the experimental constant temperature box (15) in real time; send a control signal to the simulated rainfall control and adjustment device (3) through the host computer (6), and the intelligent simulated rainfall device (2) receives the control signal sent by the simulated rainfall control and adjustment device (3) to start simulating rainfall; send a control signal to the wind speed control and adjustment device (5) through the host computer (6), and the adjustable speed air outlet device (4) receives the control signal sent by the wind speed control and adjustment device (5) to start working; send a control signal to the temperature control and adjustment device (12) through the host computer (6) to adjust the internal ambient temperature of the experimental constant temperature box (15); turn on the high-definition anti-fog and ice intelligent monitoring device (13), and transmit the monitoring image to the host computer (6) through the large-capacity image storage device (14);

[0050] Step 3: After the whole device has been running for 60 minutes, the ambient temperature T of the experimental constant temperature box (15) and the liquid water content Q in the air are collected and recorded by the host computer (6); the diameter D of the raindrops simulated by the intelligent simulated rainfall device (2) is recorded by the host computer (6); the wind speed v of the adjustable speed air outlet device (4) is recorded by the host computer (6); the monitoring image of the high-definition anti-fog and ice-covering intelligent monitoring device (13) is analyzed by the host computer (6) to obtain the actual measured value W of the maximum ice-covering thickness of the contact line (11) t ; Carry out m experiments and record m sets of data through the host computer (6);

[0051] Step 4: Calculate the reference value W of ice thickness on the contact line under different ambient temperatures, liquid water content in the air, raindrop diameters, and wind speeds by the following formula:

[0052]

[0053] In formula (1), T is the ambient temperature, Q is the liquid water content in the air, D is the raindrop diameter, v is the experimental wind speed, p is the error coefficient, and t is the integral variable;

[0054] Step 5: Use genetic algorithm to optimize formula (1) and obtain the p0 value that minimizes the error. The specific steps are:

[0055] 1) Randomly generate an initial solution p and calculate the objective function f(p):

[0056]

[0057] In formula (2), f(p) is the objective function, W j is the reference value of ice thickness on the contact line in the jth experiment, W tj is the measured value of ice thickness on the contact line in the jth experiment, m is the number of experiments, and t is the integral variable;

[0058] 2) Generate a new perturbed solution p' and calculate the objective function Δf = f(p) - f(p'); if Δf ≥ 0, accept the new solution; otherwise, obtain the new solution according to the probability acceptance criterion;

[0059] 3) Determine whether the number of iterations has been reached. If so, proceed to step 4). If not, proceed to step 2).

[0060] 4) Determine whether the termination condition is met. If the termination condition is met, end the operation and return the optimal solution p0. If the termination condition is not met, reset the number of iterations and go to step 2);

[0061] Step 6: Substitute the optimal solution p0 returned in step 5 into formula (1) to obtain the optimized contact line ice thickness calculation formula:

[0062]

[0063] Step 7: Calculate the comprehensive evaluation factor λ of the contact line icing operation status by the following formula:

[0064] λ=5W0 (4)

[0065] When λ∈(0,1], it indicates that the catenary ice thickness is within the safe driving range of the train. When λ∈(1,2.5], the relevant responsible personnel can decide whether to de-ice the catenary according to the number of trains running and the running time. When λ∈(2.5,+∞], the relevant responsible personnel should be notified to carry out de-icing operations in time.

Claims

1. A contact line icing operation evaluation method taking into account the influence of multiple environmental factors, and a contact line icing operation evaluation platform taking into account the influence of multiple environmental factors is built, and the platform includes: Temperature and water content dual-factor environmental monitoring device (1), intelligent simulated rainfall device (2), simulated rainfall control and adjustment device (3), adjustable speed air outlet device (4), wind speed control and adjustment device (5), host computer (6), wall fixing device 1 (7), supporting silicon frame 1 (8), wall fixing device 2 (9), supporting silicon frame 2 (10), contact line (11), temperature control and adjustment device (12), high-definition anti-fog and ice-covering intelligent monitoring device (13), large-capacity image storage device (14), experimental constant temperature box (15); The data output end of the temperature and water content dual-factor environmental monitoring device (1) is electrically connected to a host computer (6); The signal input end of the intelligent simulated rainfall device (2) is electrically connected to the control signal output end of the simulated rainfall control and adjustment device (3), and the signal input end of the simulated rainfall control and adjustment device (3) is electrically connected to the host computer (6); The adjustable speed air outlet device (4) is electrically connected to the control signal output end of the wind speed control and adjustment device (5), and the signal input end of the wind speed control and adjustment device (5) is electrically connected to the host computer (6); The wall fixing device 1 (7) is mechanically connected to the wall of the experimental constant temperature box (15); The supporting silicon frame (8) is mechanically connected to the wall fixing device (7); The second wall fixing device (9) is mechanically connected to the wall of the experimental constant temperature box (15); The second supporting silicon frame (10) is mechanically connected to the first wall fixing device (9); The contact wire (11) is stably placed on a support consisting of a first supporting silicon frame (8) and a second supporting silicon frame (10); The control signal input end of the temperature control and adjustment device (12) is electrically connected to the host computer (6); The data output end of the high-definition anti-fog and ice-covering intelligent monitoring device (13) is electrically connected to the data input end of the large-capacity image storage device (14), and the data output end of the large-capacity image storage device (14) is electrically connected to the host computer (6); A contact line icing operation assessment method taking into account the influence of multiple environmental factors comprises the following steps: S1: Turn on the temperature and water content dual-factor environmental monitoring device (1) to monitor the ambient temperature and liquid water content in the air in real time in the experimental thermostatic box (15); send a control signal to the simulated rainfall control and adjustment device (3) through the host computer (6); the intelligent simulated rainfall device (2) receives the control signal sent by the simulated rainfall control and adjustment device (3) and starts to simulate rainfall; send a control signal to the wind speed control and adjustment device (5) through the host computer (6); the adjustable speed air outlet device (4) receives the control signal sent by the wind speed control and adjustment device (5) and starts to work; send a control signal to the temperature control and adjustment device (12) through the host computer (6) to adjust the internal ambient temperature of the experimental thermostatic box (15); turn on the high-definition anti-fog and ice intelligent monitoring device (13); and transmit the monitoring image to the host computer (6) through the large-capacity image storage device (14); S2: After the whole device has been running for 60 minutes, the ambient temperature T of the experimental constant temperature box (15) and the liquid water content Q in the air are collected and recorded by the host computer (6); the diameter D of the raindrops simulated by the intelligent simulated rainfall device (2) is recorded by the host computer (6); the wind speed v of the adjustable speed air outlet device (4) is recorded by the host computer (6); the monitoring image of the high-definition anti-fog and icing intelligent monitoring device (13) is analyzed by the host computer (6) to obtain the actual measured value W of the maximum ice thickness of the contact line (11) t ; Carry out m experiments and record m sets of data through the host computer (6); S3: The reference value W of ice thickness on the contact line under different ambient temperatures, liquid water content in the air, raindrop diameter and wind speed is calculated by the following formula: In formula (1), T is the ambient temperature, Q is the liquid water content in the air, D is the raindrop diameter, v is the experimental wind speed, p is the error coefficient, and t is the integral variable; S4: Use genetic algorithm to optimize formula (1) and obtain the p0 value that minimizes the error. The specific steps are: 1) Randomly generate an initial solution p and calculate the objective function f(p): In formula (2), f(p) is the objective function, W j is the reference value of ice thickness on the contact line in the jth experiment, W tj is the measured value of ice thickness on the contact line in the jth experiment, m is the number of experiments, and t is the integral variable; 2) Generate a new perturbed solution p' and calculate the objective function Δf = f(p) - f(p'); if Δf ≥ 0, accept the new solution; otherwise, obtain the new solution according to the probability acceptance criterion; 3) Determine whether the number of iterations has been reached. If so, proceed to step 4). If not, proceed to step 2). 4) Determine whether the termination condition is met. If the termination condition is met, end the operation and return the optimal solution p0. If the termination condition is not met, reset the number of iterations and go to step 2); S5: Substitute the optimal solution p0 returned in S4 into formula (1) to obtain the optimized contact line ice thickness calculation formula: S6: Calculate the comprehensive evaluation factor λ of the contact line icing operation status by the following formula: λ=5W0 (4) When λ∈(0,1], it indicates that the catenary ice thickness is within the safe driving range of the train. When λ∈(1,2.5], the relevant responsible personnel can decide whether to de-ice the catenary according to the number of trains running and the running time. When λ∈(2.5,+∞], the relevant responsible personnel should be notified to carry out de-icing operations in time.

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