A method for optimizing arrangement of optical fiber temperature sensors of power equipment based on temperature field analysis

Abstract

The application relates to a power equipment optical fiber temperature sensor optimization arrangement method based on temperature field analysis, which comprises the following steps: constructing a three-dimensional digital model according to a power equipment structure; based on the three-dimensional digital model, establishing a power equipment component thermal loss calculation model based on thermal-flow coupling method according to thermal load characteristics under a power equipment operation state, and solving thermal loss characteristics; acquiring power equipment temperature rise data, simulating power equipment temperature field distribution conditions; constructing a three-dimensional power equipment temperature sensor optimization arrangement model, setting sensor arrangement constraint conditions based on the thermal loss characteristics and the temperature field distribution conditions, and simplifying the model; and solving an optimal arrangement scheme of the optical fiber temperature sensor based on the simplified temperature sensor optimization arrangement model. Compared with the prior art, the application solves the problems of inaccurate temperature rise data under the power equipment operation state and low sensor arrangement benefits, guarantees high reliability, and reduces arrangement costs.

Description

Technical Field

[0001] This invention relates to the field of sensor optimization arrangement, and in particular to a method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis. Background Technology

[0002] In power systems, electrical equipment is crucial for ensuring the safe and reliable operation of the system. Overheating faults are the most frequent type of fault in electrical equipment, and temperature rise is the most direct indicator for detecting these faults. The optimal placement of sensors is critical for the health monitoring, condition assessment, and subsequent maintenance of electrical equipment. Therefore, the optimized placement of temperature sensors in electrical equipment is of great practical significance for intelligent sensing and ensuring the safe and stable operation of power equipment.

[0003] The optimal arrangement of sensors has a crucial impact on the effectiveness of data acquisition. Considering factors such as actual site conditions and economic conditions, only a relatively small number of sensors can be arranged in limited locations in the spatial structure. Therefore, the arrangement of sensors should meet the following two objectives: (1) to reflect the characteristics of the detected information in the spatial structure to the greatest extent; (2) to be sufficiently sensitive to changes in the detected information. In traditional power equipment temperature monitoring experiments, the selection and arrangement of sensors are often judged by operators with long-term accumulated experience. The determination of the optimal number and location lacks reasonable and effective theoretical methods, which inevitably leads to inaccurate modal data measured in the experiment, and cannot better reflect the temperature rise characteristics during the operation of power equipment. At present, there are many methods for optimizing sensor arrangement, mainly including: (1) Modal kinetic energy method. Calculate the contribution of each candidate position of the sensor to the modal, and arrange the sensor in the position with larger modal kinetic energy and modal strain energy; (2) Classical optimization algorithm. Transform the sensor optimization arrangement problem into a typical constrained or unconstrained optimization problem through some algorithms; (3) Information theory method. Use the recognition error to construct a performance function to maximize the benefits of sensor arrangement; (4) Heuristic algorithm. The problem of sensor optimization can be likened to the biological evolution process in nature, where optimization is achieved through processes similar to genetic evolution, such as evolution, selection, crossover, and mutation. However, most existing methods for sensor optimization calculate the optimal sensor positions using two-dimensional theoretical models, without considering the three-dimensional structural characteristics of the sensor objects themselves or the influence of the field distribution. This inevitably leads to problems such as inaccurate monitoring data and low sensor placement benefits. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized arrangement method for fiber optic temperature sensors in power equipment based on temperature field analysis, thereby improving the sensing efficiency of temperature sensors in power equipment.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for optimizing the placement of fiber optic temperature sensors in power equipment based on temperature field analysis includes the following steps:

[0007] S1. Construct a three-dimensional digital model based on the structure of the power equipment;

[0008] S2. Based on the three-dimensional digital model, and according to the heat load characteristics of the power equipment under the operating state, a heat loss calculation model for the power equipment components is established based on the heat-flow coupling method, and the heat loss characteristics are solved.

[0009] S3. Obtain temperature rise data of power equipment and simulate the temperature field distribution of power equipment based on a three-dimensional digital model;

[0010] S4. Construct a three-dimensional optimized layout model of temperature sensors for power equipment, and set sensor layout constraints based on heat loss characteristics and temperature field distribution to simplify the optimized layout model of temperature sensors.

[0011] S5. Based on the simplified temperature sensor optimization layout model, a multi-objective genetic algorithm is used to solve for the optimal layout scheme of the fiber optic temperature sensors.

[0012] Step S1 specifically involves: creating a three-dimensional model of the basic structure of the power equipment based on the physical operating status information of the power equipment; using a lightweight processing method to remove interference and simplify the three-dimensional digital model, omitting parts in the power equipment that do not affect heat generation and heat dissipation or the normal flow of internal gas, and establishing a three-dimensional digital model of the power equipment.

[0013] Step S2 specifically involves: based on a three-dimensional digital model, according to the heat load characteristics of the power equipment under operating conditions, and based on the heat-fluid coupling method, establishing a heat loss calculation model for the power equipment components, setting convection heat transfer boundary conditions for each power equipment component and thermal radiation boundary conditions between each power equipment component and the environment, calculating the three-dimensional heat loss of the power equipment components, and obtaining the heat loss characteristics.

[0014] The convective heat transfer boundary conditions for each component of the power equipment are as follows:

[0015]

[0016] in, ε i The thermal conductivity of the outer surface of each power equipment component; ω Equivalent emissivity; σ The overall heat transfer coefficient; T 2. T 3 represents the external surface temperature of the power equipment components and the internal temperature of the cabinet, respectively. It is the ratio of the temperature difference between adjacent isothermal surfaces to the normal distance; i , k These represent the component numbers of the power equipment and the total number of components in the power equipment, respectively.

[0017] The boundary conditions between the various components of the power equipment and the ambient thermal radiation are as follows:

[0018]

[0019] in, ε j The thermal conductivity of the conductors inside the components of each power equipment; φ The convective heat transfer coefficient between the outer surface of the component and the environment; T 1. T 4 represents the internal conductor temperature of power equipment components and the ambient temperature, respectively. It is the ratio of the temperature difference between adjacent isothermal surfaces to the normal distance; j , k These represent the component numbers of the power equipment and the total number of components in the power equipment, respectively.

[0020] The method for calculating the three-dimensional thermal loss of power equipment components is as follows:

[0021]

[0022] in, λ The thermal conductivity of the medium; t Temperature at the center of electrical equipment components; qv This refers to the heat generated per unit volume per unit time.

[0023] The temperature sensor optimization layout model uses graph theory to convert the distribution of temperature field information of power equipment into a three-dimensional distribution function, and converts the constraints of fiber optic temperature sensors into temperature field coverage detection, replacing the optimal sensor layout solution with the sensor temperature field coverage problem.

[0024] The constraints of the fiber optic temperature sensor are set according to the basic parameters of the fiber optic temperature sensor and the basic structural characteristics of the power equipment. The basic parameters of the fiber optic temperature sensor include field of view, size, and sensing distance.

[0025] The sensor's coverage of the temperature field is represented as follows:

[0026]

[0027] in η (x，y，z) for( x , y , z Location coverage probability; e (x，y，z)for( x , y , z Temperature field value at location ) e th This is the temperature detection threshold.

[0028] The solution method for the optimized arrangement model of the temperature sensor is as follows: establish the benefit function for the optimized arrangement of optical fiber temperature sensors in power equipment, and based on the constraints of the optical fiber temperature sensors, use a multi-objective genetic algorithm to solve for the installation position and number of optical fiber temperature sensors when the benefit function reaches its maximum value, thereby obtaining the optimal arrangement scheme of the optical fiber temperature sensors.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] (1) The present invention constructs a three-dimensional digital model of power equipment based on the physical entity operation status information of power equipment, which can more comprehensively represent the three-dimensional features of power equipment.

[0031] (2) This invention simulates the operating status of power equipment by establishing a thermal loss calculation model for each component, which can more accurately reflect the temperature field distribution of power equipment.

[0032] (3) Based on graph theory, this invention converts the temperature field information distribution of power equipment into a three-dimensional distribution function and the constraint conditions of fiber optic temperature sensors into temperature field coverage, which can effectively simplify the problem of three-dimensional sensor optimization and layout and shorten the algorithm time.

[0033] (4) The sensor optimization arrangement method of the present invention has a clear process and simple calculation, and can be widely used in the intelligent sensing of various electrical equipment. It has strong applicability and high practical value. Attached Figure Description

[0034] Figure 1 This is a flowchart of the method of the present invention;

[0035] Figure 2 Sensor layout diagram for a 3D digital model of a switchgear;

[0036] Figure 3 This is a temperature field distribution diagram of the switchgear.

[0037] Figure 4 This is a schematic diagram of the profit function;

[0038] Figure 5 This is a diagram showing the optimal arrangement of fiber optic temperature sensors in a switchgear cabinet. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0040] This embodiment provides a method for optimizing the placement of fiber optic temperature sensors in power equipment based on temperature field analysis, such as... Figure 1 As shown, it includes the following steps:

[0041] S1. Construct a three-dimensional digital model based on the structure of the power equipment.

[0042] Based on the physical operating status information of the power equipment, a three-dimensional model of the basic structure of the power equipment is created. A lightweight processing method is used to remove interference and simplify the three-dimensional digital model, omitting parts of the power equipment that do not affect heat generation and heat dissipation or the normal flow of internal gas, and thus establishing a three-dimensional digital model of the power equipment.

[0043] This embodiment utilizes a 3D modeling software platform to establish a digital model of the power equipment. The digital twin model of the power equipment is created using the structural features of a common 10kV switchgear in power equipment. Lightweight processing methods are employed to remove interference and simplify the 3D model, and the main fiber optic temperature sensor measurement components are marked within the 3D digital model, resulting in the following... Figure 2 The diagram shows the sensor layout of the 3D digital model of the switchgear.

[0044] S2. Based on the three-dimensional digital model, and according to the heat load characteristics of the power equipment under the operating state, a heat loss calculation model for the power equipment components is established based on the heat-flow coupling method to solve the heat loss characteristics.

[0045] Based on a three-dimensional digital model and the thermal load characteristics of power equipment under operating conditions, a thermal loss calculation model for power equipment components is established using the heat-fluid coupling method. Convective heat transfer boundary conditions and thermal radiation boundary conditions between power equipment components and the environment are set, and the three-dimensional thermal loss of power equipment components is calculated to obtain thermal loss characteristics.

[0046] The convective heat transfer boundary conditions for each component of the power equipment are as follows:

[0047]

[0048] in, ε i The thermal conductivity of the outer surface of each power equipment component; ω Equivalent emissivity; σ The overall heat transfer coefficient; T 2. T3 represents the external surface temperature of the power equipment components and the internal temperature of the cabinet, respectively. It is the ratio of the temperature difference between adjacent isothermal surfaces to the normal distance; i , k These represent the component numbers of the power equipment and the total number of components in the power equipment, respectively.

[0049] The boundary conditions between the various components of the power equipment and the ambient thermal radiation are as follows:

[0050]

[0051] in, ε j The thermal conductivity of the conductors inside the components of each power equipment; φ The convective heat transfer coefficient between the outer surface of the component and the environment; T 1. T 4 represents the internal conductor temperature of power equipment components and the ambient temperature, respectively. It is the ratio of the temperature difference between adjacent isothermal surfaces to the normal distance; j , k These represent the component numbers of the power equipment and the total number of components in the power equipment, respectively.

[0052] The method for calculating the three-dimensional thermal loss of power equipment components is as follows:

[0053]

[0054] in, λ The thermal conductivity of the medium; t Temperature at the center of electrical equipment components; qv This refers to the heat generated per unit volume per unit time.

[0055] S3. Obtain temperature rise data of power equipment. Based on the three-dimensional digital model and according to the temperature field simulation requirements, use the finite element method to simulate and analyze the internal temperature field of the power equipment, and simulate the temperature field distribution of the power equipment.

[0056] This embodiment uses a high-voltage laboratory platform to build a switchgear temperature rise simulation experiment, employing a PT100 fiber optic temperature sensor to acquire switchgear temperature data. The acquired data is then used to simulate the temperature field distribution using the finite element method, yielding the following results: Figure 3 The temperature field distribution of the switch cabinet is shown.

[0057] S4. Construct a three-dimensional optimized layout model of temperature sensors for power equipment, and set sensor layout constraints based on heat loss characteristics and temperature field distribution to simplify the optimized layout model of temperature sensors.

[0058] The temperature sensor optimization layout model uses graph theory to convert the distribution of temperature field information of power equipment into a three-dimensional distribution function, and converts the constraints of fiber optic temperature sensors into temperature field coverage detection, replacing the optimal sensor layout solution with the sensor temperature field coverage problem.

[0059] The constraints of the fiber optic temperature sensor are set according to the basic parameters of the fiber optic temperature sensor and the basic structural characteristics of the power equipment. The basic parameters of the fiber optic temperature sensor include field of view, size, and sensing distance.

[0060] The sensor's coverage of the temperature field is represented as follows:

[0061]

[0062] in η (x，y，z) for( x , y , z Location coverage probability; e (x，y，z) for( x , y , z Temperature field value at location ) e th This is the temperature detection threshold.

[0063] S5. Based on the simplified temperature sensor optimization layout model, a multi-objective genetic algorithm is used to solve for the optimal layout scheme of the fiber optic temperature sensors.

[0064] The solution method for the optimized arrangement model of the temperature sensor is as follows: establish the benefit function for the optimized arrangement of optical fiber temperature sensors in power equipment, and based on the constraints of the optical fiber temperature sensors, use a multi-objective genetic algorithm to solve for the installation position and number of optical fiber temperature sensors when the benefit function reaches its maximum value, thereby obtaining the optimal arrangement scheme of the optical fiber temperature sensors.

[0065] Based on the sensing characteristics of fiber optic temperature sensors, multiple constraints are defined in the digital model, including sensor sensing volume and installation coordinate range. A multi-objective genetic algorithm is then used to solve for effective coverage. x Placed in a high temperature rise change zone y The revenue function of each sensor W xy ,when W xy The sensor layout is optimal when the maximum value is reached, i.e., it satisfies a series of conditions:

[0066]

[0067] in, m This represents the maximum value of the sensor's temperature rise variation region (with the finest 3D mesh division).n This represents the maximum number of fiber optic temperature sensors.

[0068] This example uses a multi-objective genetic algorithm to solve the payoff function. W xy The profit function primarily targets minimizing the number of fiber optic temperature sensors and minimizing temperature monitoring error. A schematic diagram illustrating the relationship between the profit function value and high-profit monitoring points for fiber optic temperature sensors is shown below. Figure 4 As shown; based on the relationship between the sensor placement positions in the switchgear and the revenue function, the optimal arrangement scheme for the fiber optic temperature sensors that maximizes the revenue function value can be obtained. According to engineering design requirements, high-revenue monitoring points of the fiber optic temperature sensors can be classified and merged according to the sensing range of the fiber optic temperature sensors. The final three-dimensional coverage effect and the optimal arrangement scheme are shown in the figure. Figure 5 As shown.

[0069] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis, characterized in that, Includes the following steps: S1. Construct a three-dimensional digital model based on the structure of the power equipment; S2. Based on the three-dimensional digital model, and according to the heat load characteristics of the power equipment under the operating state, a heat loss calculation model for the power equipment components is established based on the heat-flow coupling method, and the heat loss characteristics are solved. S3. Obtain temperature rise data of power equipment and simulate the temperature field distribution of power equipment based on a three-dimensional digital model; S4. Construct a three-dimensional optimized layout model of temperature sensors for power equipment, and set sensor layout constraints based on heat loss characteristics and temperature field distribution to simplify the optimized layout model of temperature sensors. The temperature sensor optimization layout model uses graph theory to convert the distribution of temperature field information of power equipment into a three-dimensional distribution function, and converts the constraints of fiber optic temperature sensors into temperature field coverage detection, replacing the optimal sensor layout solution with the sensor temperature field coverage problem. The constraints of the fiber optic temperature sensor are set according to the basic parameters of the fiber optic temperature sensor and the basic structural characteristics of the power equipment. The basic parameters of the fiber optic temperature sensor include field of view, size, and sensing distance. The sensor's coverage of the temperature field is represented as follows: in η (x，y，z) for( x , y , z Location coverage probability; e (x，y，z) for( x , y , z Temperature field value at location ) e th This is the temperature detection threshold; S5. Based on the simplified temperature sensor optimization layout model, a multi-objective genetic algorithm is used to solve for the optimal layout scheme of the fiber optic temperature sensors.

2. The method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis according to claim 1, characterized in that, Step S1 specifically involves: creating a three-dimensional model of the basic structure of the power equipment based on the physical operating status information of the power equipment; using a lightweight processing method to remove interference and simplify the three-dimensional digital model, omitting parts in the power equipment that do not affect heat generation and heat dissipation or the normal flow of internal gas, and establishing a three-dimensional digital model of the power equipment.

3. The method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis according to claim 1, characterized in that, Step S2 specifically involves: based on a three-dimensional digital model, according to the heat load characteristics of the power equipment under operating conditions, and based on the heat-fluid coupling method, establishing a heat loss calculation model for the power equipment components, setting convection heat transfer boundary conditions for each power equipment component and thermal radiation boundary conditions between each power equipment component and the environment, calculating the three-dimensional heat loss of the power equipment components, and obtaining the heat loss characteristics.

4. The method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis according to claim 3, characterized in that, The convective heat transfer boundary conditions for each component of the power equipment are as follows: in, ε i The thermal conductivity of the outer surface of each power equipment component; φ The convective heat transfer coefficient between the outer surface of the component and the environment; T 2. T 3 represents the external surface temperature of the power equipment components and the internal temperature of the cabinet, respectively. It is the ratio of the temperature difference between adjacent isothermal surfaces to the normal distance; i , k These represent the component numbers of the power equipment and the total number of components in the power equipment, respectively.

5. The method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis according to claim 3, characterized in that, The boundary conditions between the various components of the power equipment and the ambient thermal radiation are as follows: in, ε j The thermal conductivity of the conductors inside the components of each power equipment; φ The convective heat transfer coefficient between the outer surface of the component and the environment; T 1. T 4 represents the internal conductor temperature of power equipment components and the ambient temperature, respectively. ω Equivalent emissivity; σ The overall heat transfer coefficient; It is the ratio of the temperature difference between adjacent isothermal surfaces to the normal distance; j , k These represent the component numbers of the power equipment and the total number of components in the power equipment, respectively.

6. The method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis according to claim 3, characterized in that, The method for calculating the three-dimensional thermal loss of power equipment components is as follows: in, λ The thermal conductivity of the medium; t Temperature at the center of electrical equipment components; qv This refers to the heat generated per unit volume per unit time.

7. The method for optimizing the arrangement of fiber optic temperature sensors in power equipment based on temperature field analysis according to claim 1, characterized in that, The solution method for the optimized arrangement model of the temperature sensor is as follows: establish the benefit function for the optimized arrangement of optical fiber temperature sensors in power equipment, and based on the constraints of the optical fiber temperature sensors, use a multi-objective genetic algorithm to solve for the installation location and number of optical fiber temperature sensors when the benefit function reaches its maximum value, thereby obtaining the optimal arrangement scheme of the optical fiber temperature sensors.

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