A kind of SF 6 Insulation fault assessment method for opening and closing coils of circuit breakers
By establishing a high-frequency equivalent circuit model and a BP neural network evaluation model, the problem of insulation fault evaluation of the SF6 circuit breaker split-closing coil is solved, and effective evaluation and fault prevention of circuit breaker reliability are achieved.
Patent Information
- Application Number
- CN202111412302.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-11-25
- Publication Date
- 2025-06-03
- Estimated Expiration
- 2041-11-25
AI Technical Summary
The insulation failure of the SF6 circuit breaker open and close coil will cause the coil to overheat and burn, affecting the reliability of the circuit breaker and the stable operation of the power grid. It is difficult for the existing technology to effectively evaluate and prevent such faults.
By establishing a high-frequency equivalent circuit model of the split-closing coil, calculating the inductance and capacitance matrix, and combining the BP neural network evaluation model, the evaluation and diagnosis of coil insulation faults can be achieved.
This method can effectively evaluate the insulation failure of the opening and closing coil, improve the reliability of the circuit breaker, and reduce production losses and safety accidents.
Smart Images
Figure CN114239340B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of insulation performance evaluation of SF 6 circuit breaker closing and opening coil, and particularly relates to a method for evaluating insulation faults of SF 6 circuit breaker closing and opening coil. Background Art
[0002] A circuit breaker is a key switching device in the power grid. It needs to break and close normal working current, as well as break overload current and short - circuit current. When a fault occurs in the power system, the circuit breaker is required to operate reliably to cut off the faulty part to ensure the stable operation of the non - faulty part. During actual operation, the circuit breaker may fail to operate due to faults, often causing serious production losses and safety accidents. The failure of the closing and opening coil is a major factor leading to the circuit breaker's failure to operate. And the insulation fault of the closing and opening coil will cause the coil current to increase and overheat and burn out. Therefore, studying the insulation faults of the closing and opening coil winding and proposing an effective fault evaluation scheme is of great significance for improving the reliability of the power system. As an important component of the circuit breaker operating mechanism, the closing and opening coil greatly affects the reliability of the circuit breaker's operation. For an operating circuit breaker, due to the influence of various factors such as electricity, heat, and environment, the insulating paint of its closing and opening coil gradually ages, resulting in a decline in insulation performance, which may lead to inter - turn short - circuit faults and inter - layer short - circuit faults. When a slight short - circuit fault occurs, even if the circuit breaker can still operate normally, the decrease in the coil resistance will cause the current passing through the coil during operation to increase, and the coil heats up severely. In severe cases, the coil burns out, affecting the closing and opening function of the circuit breaker and leading to a failure to operate, thus affecting the stable operation of the power grid. Therefore, timely detection of the insulation faults of the closing and opening coil and taking corresponding maintenance measures are of great significance for improving the reliability of the circuit breaker. Summary of the Invention
[0003] The purpose of the present invention is to propose a method for evaluating insulation faults of SF 6 circuit breaker closing and opening coil, which establishes a high - frequency equivalent circuit model of the coil, has a complete structure, a step - by - step, systematic and comprehensive test scheme, and a high feasibility of the results.
[0004] To achieve the above purpose, the present invention provides a method for evaluating insulation faults of SF 6 circuit breaker closing and opening coil, including the following steps: S1: Calculation of the inductance - capacitance matrix of the closing and opening coil;
[0005] S2: Establishing a high - frequency equivalent circuit model of the coil;
[0006] S3: Conducting an algorithm for evaluating insulation faults of the circuit breaker closing and opening coil.
[0007] As a further technical improvement, step S1 specifically includes the following steps:
[0008] S1-1. Establish a finite element simulation calculation model according to the actual coil model;
[0009] S1-2. Calculate the capacitance matrix. Calculate the capacitance matrix through the electrostatic field module, and its calculation formula is
[0010]
[0011]
[0012] where C ii is the self-capacitance of the conductor, i.e., the capacitance to the ground, and C ij is the mutual capacitance between conductor i and conductor j, V i , V j are the electric potentials of conductors i and j, and W e is the energy of the entire system;
[0013] S1-3. Calculate the inductance matrix. Calculate the inductance matrix through the magnetic field module, and its calculation formula is
[0014]
[0015]
[0016] where L ii is the self-inductance of the conductor, L ij is the mutual inductance between conductor i and conductor j, I i , I j are the currents passing through the conductor, and W m is the energy of the entire conductor.
[0017] As a further technical improvement, step S2 specifically includes the following steps:
[0018] S2-1. Based on ATP-EMTP, establish an equivalent wave impedance circuit model considering the inter-turn capacitance and self-inductance of the coil. Each layer of 7 turns is equivalent to 7 wave impedances, with a total of 21 layers; each turn includes self-inductance, self-impedance, the inter-turn capacitance with adjacent turns, and the inter-layer capacitance with adjacent layers. For the first layer close to the iron core and the first and last turns of each layer, consider the capacitance to the ground.
[0019] S2-2. Apply a high-frequency low-voltage square wave pulse signal at the head of the circuit, with a pulse voltage amplitude of 10 V and a frequency of 2 MHz, and obtain the response curve at the end;
[0020] S2-3. Set short-circuit faults at different positions and with different degrees by directly short-circuiting the wire at both ends of the wave impedance, and obtain the response curve at the end under fault conditions;
[0021] S2-4. Subtract the end response curve under fault conditions from the end response curve under fault-free conditions to obtain the characteristic curve.
[0022] As a further technical improvement, step S3 specifically includes the following steps:
[0023] S3-1. Divide the curve into 21 equal parts at every 100 sampling points, and calculate the area enclosed by each part of the curve and the horizontal axis;
[0024] S3-2. Establish a BP neural network with 21 input neurons and 21 output neurons;
[0025] S3-3. Use the area data calculated in step S3-1 as the input parameter and the short-circuit degree of each layer of the coil as the output parameter to train the BP neural network, and obtain a BP neural network evaluation model that can evaluate the short-circuit condition of the closing and opening coils.
[0026] As a further technical improvement, the finite element simulation calculation model includes an iron core, a winding, and a casing. For simplicity of calculation, a two-dimensional axisymmetric model is adopted.
[0027] As a further technical improvement, in step S3-3, use the short-circuit degree of each layer of the coil as the output parameter to train the BP neural network, and use the data of the validation set to verify the accuracy of the BP neural network evaluation. If the accuracy meets the requirements, an evaluation model for evaluating the insulation fault of the closing and opening coils is obtained. If the accuracy does not meet the requirements, return and continue the training of the BP neural network.
[0028] Based on the idea of field-circuit coupling, the present invention establishes a two-dimensional axisymmetric model of the closing and opening coil windings, calculates the capacitance and inductance matrix of the windings through the energy of the electrostatic field and magnetic field. Then, based on the single-winding equivalent model of the transformer, considering the resistance, self-inductance, inter-turn capacitance, inter-layer capacitance, and capacitance to ground of the turns, a high-frequency equivalent circuit model of the closing and opening coil windings is established. Apply an excitation at the head end of the circuit model, and its response curve can be measured at the end. Set short-circuit faults at different positions and different degrees to obtain corresponding characteristic curves, calculate the area value of every one hundred points as the input parameter of the BP neural network evaluation model, and establish a BP neural network-based evaluation model for the closing and opening coils.
[0029] Compared with the prior art, the present invention has the following beneficial technical effects:
[0030] The evaluation method adopted by the present invention effectively introduces the field-circuit coupling method into the insulation fault evaluation of the closing and opening coils, establishes a high-frequency equivalent circuit model of the coils, with a complete structure, a step-by-step, systematic and comprehensive test scheme, and a high feasibility of the results. Description of the Drawings
[0031] To more clearly illustrate the embodiments of the present invention, the accompanying drawings required for the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.
[0032] Figure 1 is the finite element simulation model diagram of the present invention.
[0033] Figure 2 is the simulation circuit model diagram of the present invention.
[0034] Figure 3 is the neural network topology diagram of the present invention.
[0035] Figure 4 is the flowchart of the evaluation process of the present invention. Detailed Embodiments
[0036] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
[0037] The following specific examples illustrate the implementation manners of the present disclosure. Those skilled in the art can easily understand other advantages and effects of the present disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of them. The present disclosure can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts belong to the scope of protection of the present disclosure.
[0038] Embodiment:
[0039] As shown in the Figures 1-4 accompanying drawings, this embodiment provides a method for evaluating the insulation fault of the closing and opening coils of an SF 6 circuit breaker, which is characterized by including the following steps:
[0040] S1. Calculation of the inductance-capacitance matrix of the closing and opening coils; specifically including the following steps:
[0041] S1-1. According to the actual coil model, establish a finite element simulation calculation model, including the iron core, winding, and outer shell. For simplicity of calculation, a two-dimensional axisymmetric model is adopted.
[0042] S1-2. Capacitance matrix calculation. Calculate the capacitance matrix through the electrostatic field module, and its calculation formula is
[0043]
[0044]
[0045] Among them, C ii is the self-capacitance of the conductor, i.e., the capacitance to the ground, and C ij is the mutual capacitance between conductor i and conductor j, and V i and V j are the electric potentials of conductors i and j, and W e is the energy of the entire system;
[0046] S1-3, Inductance matrix calculation. Calculate the inductance matrix through the magnetic field module, and its calculation formula is
[0047]
[0048]
[0049] Among them, L ii is the self-inductance of the conductor, L ij is the mutual inductance between conductor i and conductor j, I i and I j are the currents passing through the conductor, and W m is the energy of the entire conductor.
[0050] S2. Establish a high-frequency equivalent circuit model of the coil; specifically including the following steps:
[0051] S2-1. Based on ATP-EMTP, establish an equivalent wave impedance circuit model considering the inter-turn capacitance and self-inductance of the coil. Each layer of 7 turns is equivalent to 7 wave impedances, with a total of 21 layers;
[0052] S2-2. Apply a high-frequency low-voltage square wave pulse signal at the head of the circuit, with a pulse voltage amplitude of 10 V and a frequency of 2 MHz, and obtain the response curve at the end;
[0053] S2-3. Set short-circuit faults at different positions and with different degrees by directly short-circuiting the wave impedance with a wire, and obtain the response curve at the end under fault conditions;
[0054] S2-4. Subtract the response curve at the end under fault conditions from the response curve at the end without faults to obtain the characteristic curve.
[0055] S3. Conduct an insulation fault assessment algorithm for the circuit breaker closing and opening coils, specifically including the following steps:
[0056] S3-1. Divide the curve into 21 equal parts according to every 100 sampling points, and calculate the area enclosed by each part of the curve and the horizontal axis;
[0057] S3-2. Establish a BP neural network with 21 input neurons and 21 output neurons;
[0058] S3-3. Use the area data calculated in step S3-1 as the input parameter and the short-circuit degree of each layer of the coil as the output parameter to train the BP neural network, and obtain a BP neural network evaluation model that can evaluate the short-circuit condition of the closing and opening coils. Use the short-circuit degree of each layer of the coil as the output parameter to train the BP neural network, and use the data of the validation set to verify the accuracy of the BP neural network evaluation. If the accuracy meets the requirements, an evaluation model for evaluating the insulation fault of the closing and opening coils is obtained. If the accuracy does not meet the requirements, return and continue the training of the BP neural network.
[0059] As shown in the appendix Figure 1 , considering the axisymmetric structural characteristics of the closing and opening coils, establish a two-dimensional axisymmetric model to reduce the calculation amount, including structures such as the iron core, the outer shell, and the coil. Each layer of the coil has 7 turns, and there are 21 layers in total.
[0060] Appendix Figure 2 shows the high-frequency equivalent model, which considers the self-inductance, resistance of each turn of the coil, and the inter-turn capacitance with adjacent turns. For the winding of the first layer of the coil close to the iron core and the first and last turns of each layer, the capacitance to the ground is considered. When a high-frequency excitation is applied to the circuit head end, the potentials at different positions of the circuit are different, and a response to the high-frequency excitation is generated at the end. When a short-circuit fault occurs, the structure of the circuit model changes, and the response at the end also changes. For different positions and degrees of short-circuit faults, their effects on the response at the end to the ground are also different, and this information is contained in the characteristic curve. Therefore, by calculating the areas of various parts of the characteristic curve, the mapping relationship information contained therein is mined.
[0061] Appendix Figure 3 is the neural network topology diagram. An artificial neural network can find the mathematical relationship between the model input parameters and output parameters based on its own characteristics, without having to provide an accurate mathematical expression like other methods, and at the same time ensure good accuracy. The neural network has good processing capabilities for complex non-linear problems with multiple inputs and outputs, can build a relationship model between the dynamic capacitance supply parameters and the contact state indication parameters, and achieve the goal of evaluating the contact state of the circuit breaker.
[0062] Figure 4It is a flowchart of the evaluation process. In actual application, first, through simulation calculation, the inductance-capacitance matrix of the closing and opening coils is obtained. Then, based on the inductance-capacitance matrix, a high-frequency equivalent model of the circuit is established. By setting short-circuit faults at different positions and with different degrees, the end response curve is obtained. Then, the difference between the response curve with faults and the response curve without faults is calculated to obtain the characteristic curve. Next, the area enclosed by the curve and the horizontal axis is calculated as the input parameter of the BP neural network, and the degree and position of the short-circuit fault are used as the output parameters of the BP neural network. A BP neural network model is established, and then sample data is used to train the network. After the training is completed, the data in the validation set is used to verify the accuracy of the BP neural network evaluation.
[0063] The above are only the preferred and feasible embodiments of the present invention, and do not limit the scope of rights of the present invention. For those of ordinary skill in the art of this technology, without departing from the principle described in the present invention, several improvements and refinements can still be made. Any equivalent changes made by using the content of the specification and drawings of the present invention are included within the scope of rights of the present invention.
Claims
1. A kind of SF 6 Insulation fault assessment method for opening and closing coils of circuit breakers It is characterized in that it includes the following steps: S1. Calculation of the inductance-capacitance matrix of the closing and opening coils; S2. Establishment of the high-frequency equivalent circuit model of the coil; S3. Implementation of the insulation fault assessment algorithm for the closing and opening coils of the circuit breaker; Among them, in step S1, the calculation of the capacitance-inductance matrix is obtained by establishing a two-dimensional axisymmetric model of the closing and opening coil windings, and then calculating the electrostatic field module and the magnetic field module respectively; Among them, step S2 specifically includes the following steps: S2-1. Based on ATP-EMTP, an equivalent wave impedance circuit model is established considering the inter-turn capacitance and self-inductance of the coil; S2-2. Apply a high-frequency low-voltage square wave pulse signal at the head of the circuit to obtain the terminal response curve; S2-3. Set short-circuit faults at different positions and with different degrees by directly short-circuiting the wire at both ends of the wave impedance, and obtain the terminal response curve under the fault condition; S2-4. Subtract the terminal response curve under the fault condition from the terminal response curve under the non-fault condition to obtain the characteristic curve; And step S3 specifically includes the following steps: S3-1. Divide the curve into 21 equal parts with 100 sampling points each, and calculate the area enclosed by each part of the curve and the horizontal axis; S3-2. Establish a BP neural network with 21 input neurons and 21 output neurons; S3-3. Use the area data calculated in step S3-1 as the input parameter and the short-circuit degree of each layer of the coil as the output parameter to train the BP neural network, and obtain an evaluation model that can evaluate the insulation fault of the closing and opening coils.
2. The SF according to claim 1 6 Method for evaluating insulation fault of breaker closing and opening coils It is characterized in that step S1 specifically includes the following steps: S1-1. Establish a finite element simulation calculation model according to the actual coil model; S1-2. Capacitance matrix calculation, calculate the capacitance matrix through the electrostatic field module, and its calculation formula is Among them, C ii is the self-capacitance of the conductor, i.e., the capacitance to the ground, and C ij is the mutual capacitance between conductor i and conductor j, and V i , V j are the electric potentials of conductors i and j, and W e is the energy of the entire system; S1-3. Inductance matrix calculation, calculate the inductance matrix through the magnetic field module, and its calculation formula is Among them, L ii is the self-inductance of the conductor, and L ij is the mutual inductance between conductor i and conductor j, and I i , I j is the current passing through the conductor, and W m is the energy of the entire conductor.
3. The SF according to claim 2 6 Method for evaluating insulation fault of breaker closing and opening coils It is characterized in that the finite element simulation calculation model includes an iron core, a winding, and a casing. To simplify the calculation, a two-dimensional axisymmetric model is adopted.
4. The SF according to claim 2 6 Method for evaluating insulation fault of breaker closing and opening coils It is characterized in that In step S3-3, the short-circuit degree of each layer of the coil is used as the output parameter to train the BP neural network. Use the data of the validation set to verify the accuracy of the BP neural network evaluation. If the accuracy meets the requirements, an evaluation model for evaluating the insulation fault of the closing and opening coils is obtained. If the accuracy does not meet the requirements, return and continue the training of the BP neural network.