An electrical equipment internal environment prediction analysis method, system, terminal and medium

Abstract

The application discloses an electrical equipment internal environment prediction analysis method and system, a terminal and a medium, relates to the technical field of data analysis, and has the technical scheme as follows: the application comprehensively analyzes the influence of the current cumulative effect, the external temperature and the internal humidity change on the internal temperature change of the electrical equipment, can accurately locate the extreme point of the temperature fluctuation in the internal temperature fluctuation process of the electrical equipment, and realizes the internal temperature prediction of the electrical equipment by predicting the internal humidity, the internal current and the external temperature, so that compared with directly predicting the internal temperature of the electrical equipment, the error amplitude can be effectively reduced, and the accuracy and reliability of the internal temperature prediction of the electrical equipment are improved.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and more specifically, to a method, system, terminal, and medium for predicting and analyzing the internal environment of electrical equipment. Background Technology

[0002] The main function of electrical equipment is to transmit, distribute, and convert electrical energy, which is accomplished through the flow of electric current. Many faults and accidents in electrical equipment are caused by overheating. Early detection and elimination of overheating can greatly reduce power system faults and accidents, and improve power supply reliability.

[0003] Currently, the prediction of internal temperature in electrical equipment is mainly achieved using machine learning and data fitting methods, such as deep learning prediction models and PSO-LSSVM prediction models. However, the internal temperature changes of electrical equipment are not only affected by the magnitude of the current flowing through it, but also by factors such as the external temperature of the equipment, the heat dissipation performance of the equipment's casing, and the internal humidity. When using machine learning models for prediction, a large number of samples are required to train the model to ensure accuracy and reliability. Furthermore, different electrical equipment has different casing heat dissipation performance and different environmental conditions, meaning that different electrical equipment requires training a separate machine learning model. For power systems containing a large number of electrical devices, using machine learning models to predict the temperature of each device would require significant resources, hindering widespread application. On the other hand, using data fitting methods to predict temperature changes influenced by multiple factors suffers from large errors and poor reliability.

[0004] Therefore, how to research and design a method, system, terminal, and medium for predicting and analyzing the internal environment of electrical equipment that can overcome the above-mentioned defects is an urgent problem that we need to solve. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method, system, terminal, and medium for predicting and analyzing the internal environment of electrical equipment. This method can accurately locate the extreme points of temperature fluctuations during the internal temperature fluctuation process of electrical equipment, effectively reduce the error amplitude, and improve the accuracy and reliability of internal temperature prediction for electrical equipment.

[0006] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0007] Firstly, a method for predicting and analyzing the internal environment of electrical equipment is provided, including the following steps:

[0008] Real-time acquisition of internal temperature data, internal humidity data, internal current data, and external temperature data of electrical equipment;

[0009] Based on the internal temperature data, internal humidity data, and external temperature data, determine the rate of change of internal temperature, internal humidity, and external temperature over time within the fluctuation period.

[0010] Determine the average rate of change of the average current over time within the fluctuation period based on the internal current data.

[0011] A rate influence function within the fluctuation period is established with the average current change rate, internal humidity change rate, and external temperature change rate as independent variables and the internal temperature change rate as the dependent variable.

[0012] Analyze the predicted internal humidity, predicted internal current, and predicted external temperature of electrical equipment at the next moment or in the next time period;

[0013] The predicted rates of change for internal humidity, internal current, and external temperature are determined based on the predicted values ​​for internal humidity, internal current, and external temperature, respectively.

[0014] The predicted rate of change of internal humidity, internal current, and external temperature are input into the rate influence function to calculate the predicted rate of change of internal temperature. Combined with the real-time internal temperature of the electrical equipment, the predicted internal temperature value of the electrical equipment at the next moment or the next time period is calculated.

[0015] Furthermore, the process of determining the fluctuation period specifically involves:

[0016] Select continuously distributed temperature trough poles from the internal temperature data;

[0017] The internal temperature data is divided into a continuously distributed dynamic period, starting from the m-th temperature trough pole and ending at the (m+1)-th temperature trough pole.

[0018] The dynamic cycle closest to the current time point is selected as the fluctuation cycle.

[0019] Furthermore, if there is interference power in the fluctuation period, the corresponding dynamic period is filtered and a new fluctuation period is selected.

[0020] The interference power can be any one of the following: ventilation and heat dissipation control power, air-cooled circulation control power, and humidity control power.

[0021] Furthermore, the specific formula for calculating the average current change rate is as follows:

[0022]

[0023]

[0024] in, This represents the rate of change of the average current at time j within the fluctuation period. This represents the average current value at time j within the fluctuation period. The value of I(t) represents the average current at time j-Δn within the fluctuation period; Δn represents the time interval for calculating the rate of change of the average current; T represents the fluctuation period; and I(t) represents the current function that changes with time in the internal current data.

[0025] Furthermore, the rate influence function was obtained by fitting a multivariable function using MATLAB software.

[0026] Furthermore, the expression for the rate influence function is specifically as follows:

[0027]

[0028] in, This represents the rate of change of the internal temperature at time i; The value represents the average current change rate at time i, and the + indicates that the internal temperature change rate is positively correlated with the average current change rate. The value represents the rate of change of the external temperature at time i, and the + indicates that the rate of change of the internal temperature is positively correlated with the rate of change of the external temperature. The value represents the rate of change of internal humidity at time i, and the corresponding - indicates that the rate of change of internal temperature is negatively correlated with the rate of change of internal humidity.

[0029] Furthermore, the specific formula for calculating the rate influence function is as follows:

[0030]

[0031] Where a represents the correlation coefficient of the rate of change of average current; b represents the correlation coefficient of the rate of change of external temperature; and c represents the correlation coefficient of the rate of change of internal humidity.

[0032] Secondly, a predictive analysis system for the internal environment of electrical equipment is provided, including:

[0033] The real-time data acquisition module is used to collect internal temperature data, internal humidity data, internal current data, and external temperature data of electrical equipment in real time.

[0034] The first rate determination module is used to determine the rate of change of internal temperature, internal humidity and external temperature over time within the fluctuation period based on internal temperature data, internal humidity data and external temperature data, respectively.

[0035] The second rate determination module is used to determine the average current change rate of the average current value over time within the fluctuation period based on the internal current data.

[0036] The rate function fitting module is used to establish a rate influence function within the fluctuation period with the average current change rate, internal humidity change rate, and external temperature change rate as independent variables and the internal temperature change rate as the dependent variable.

[0037] The independent variable value prediction module is used to analyze the predicted values ​​of internal humidity, internal current, and external temperature of electrical equipment at the next moment or in the next period.

[0038] The rate of change conversion module is used to determine the predicted rate of change of internal humidity, the predicted rate of change of internal current, and the predicted rate of change of external temperature based on the predicted values ​​of internal humidity, internal current, and external temperature, respectively.

[0039] The equipment temperature prediction module is used to input the predicted rate of change of internal humidity, the predicted rate of change of internal current, and the predicted rate of change of external temperature into the rate influence function to calculate the predicted rate of change of internal temperature. Combined with the real-time internal temperature of the electrical equipment, it calculates the predicted internal temperature value of the electrical equipment at the next moment or the next period.

[0040] Thirdly, a computer terminal is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements a method for predicting and analyzing the internal environment of electrical equipment as described in any one of the first aspects.

[0041] Fourthly, a computer-readable medium is provided having a computer program stored thereon, the computer program being executed by a processor to implement a method for predictive analysis of the internal environment of electrical equipment as described in any one of the first aspects.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] 1. The present invention provides a method for predicting and analyzing the internal environment of electrical equipment, which comprehensively analyzes the influence of current accumulation effect, external temperature and internal humidity changes on the internal temperature change of electrical equipment. It can accurately locate the extreme point of temperature fluctuation during the internal temperature fluctuation process of electrical equipment. Moreover, by predicting the internal humidity, internal current and external temperature, the internal temperature of electrical equipment can be predicted. Compared with directly predicting the internal temperature of electrical equipment, it can effectively reduce the error range and improve the accuracy and reliability of the internal temperature prediction of electrical equipment.

[0044] 2. In the process of predicting temperature changes, the present invention has a temperature rise segment and a temperature fall segment in a complete fluctuation cycle. This not only covers the promoting effect of influencing factors on temperature changes, but also covers the inhibiting effect of influencing factors on temperature changes, thus ensuring the rationality when analyzing the influence of multiple factors on the internal temperature changes of electrical equipment.

[0045] 3. This invention takes into account the different heat dissipation performance of the housings of different electrical equipment, and uses the average current change rate as the basis for determining the change of current accumulation effect, which can reduce the impact of time factors on the prediction of the internal temperature of electrical equipment.

[0046] 4. When fitting and constructing the rate influence function, this invention clarifies the correlation between different influencing factors and the internal temperature of electrical equipment, and can fit rate influence functions of various types, including linear functions and exponential functions, according to the actual situation. Attached Figure Description

[0047] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0048] Figure 1 This is a flowchart from Embodiment 1 of the present invention;

[0049] Figure 2 This is a system block diagram in Embodiment 2 of the present invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0051] Example 1: A method for predicting and analyzing the internal environment of electrical equipment, such as... Figure 1 As shown, it includes the following steps:

[0052] S1: Real-time acquisition of internal temperature data, internal humidity data, internal current data, and external temperature data of electrical equipment;

[0053] S2: Based on the internal temperature data, internal humidity data, and external temperature data, determine the rate of change of internal temperature, internal humidity, and external temperature over time within the fluctuation period.

[0054] S3: Determine the average current change rate over time within the fluctuation period based on the internal current data.

[0055] S4: Establish a rate influence function within the fluctuation period with the average current change rate, internal humidity change rate, and external temperature change rate as independent variables and the internal temperature change rate as dependent variable.

[0056] S5: Analyze the predicted internal humidity, predicted internal current, and predicted external temperature of electrical equipment at the next moment or in the next time period;

[0057] S6: Determine the predicted rate of change of internal humidity, internal current, and external temperature based on the predicted values ​​of internal humidity, internal current, and external temperature, respectively.

[0058] S7: Input the predicted rate of change of internal humidity, the predicted rate of change of internal current, and the predicted rate of change of external temperature into the rate influence function to calculate the predicted rate of change of internal temperature. Combine this with the real-time internal temperature of the electrical equipment to calculate the predicted internal temperature value of the electrical equipment at the next moment or the next time period.

[0059] It should be noted that, since the time interval between adjacent fluctuation cycles is relatively short, the changes in the heat dissipation performance of the casing and the insulation performance of the insulating material are relatively small. Therefore, when predicting the internal temperature, the rate influence function of the previous fluctuation cycle can be used.

[0060] In this embodiment, the process of determining the fluctuation period is as follows: continuously distributed temperature trough poles are selected from the internal temperature data; the internal temperature data is divided into continuously distributed dynamic periods with the m-th temperature trough pole as the starting point and the (m+1)-th temperature trough pole as the ending point; and the dynamic period closest to the current time node is selected as the fluctuation period.

[0061] To avoid the impact of additional power interference on the accuracy of temperature prediction, if interference power exists in the fluctuation period, the corresponding dynamic period will be filtered and a new fluctuation period will be selected. Interference power includes, but is not limited to, ventilation and heat dissipation control power, air-cooled circulation control power, and humidity control power.

[0062] This invention takes into account the different heat dissipation performance of the casings of various electrical devices, and uses the average current change rate to determine the change in current accumulation effect, which can reduce the influence of time factors on the prediction of the internal temperature of electrical devices. Therefore, the specific formula for calculating the average current change rate is as follows:

[0063]

[0064]

[0065] in, This represents the rate of change of the average current at time j within the fluctuation period. This represents the average current value at time j within the fluctuation period. The value of I(t) represents the average current at time j-Δn within the fluctuation period; Δn represents the time interval for calculating the rate of change of the average current; T represents the fluctuation period; and I(t) represents the current function that changes with time in the internal current data.

[0066] In this embodiment, the rate influence function is obtained by fitting a multi-variable function using MATLAB software. However, other software or algorithm models with multi-variable function fitting capabilities can also be used, and there are no restrictions on this.

[0067] In fitting and constructing the rate influence function, this invention clarifies the correlation between different influencing factors and the internal temperature of electrical equipment. The specific expression of the rate influence function is as follows:

[0068]

[0069] in, This represents the rate of change of the internal temperature at time i; The value represents the average current change rate at time i, and the + indicates that the internal temperature change rate is positively correlated with the average current change rate. The value represents the rate of change of the external temperature at time i, and the + indicates that the rate of change of the internal temperature is positively correlated with the rate of change of the external temperature. The value represents the rate of change of internal humidity at time i, and the corresponding - indicates that the rate of change of internal temperature is negatively correlated with the rate of change of internal humidity.

[0070] It should be noted that the rate influence function can be fitted into linear functions, exponential functions, etc., depending on the actual situation. Taking a multivariate linear function as an example, the specific formula for calculating the rate influence function is as follows:

[0071]

[0072] Where a represents the correlation coefficient of the rate of change of average current; b represents the correlation coefficient of the rate of change of external temperature; and c represents the correlation coefficient of the rate of change of internal humidity.

[0073] Example 2: An internal environment prediction and analysis system for electrical equipment, which implements the internal environment prediction and analysis method for electrical equipment described in Example 1, such as... Figure 2 As shown, it includes a real-time data acquisition module, a first rate determination module, a second rate determination module, a rate function fitting module, an independent variable value prediction module, a rate of change conversion module, and an equipment temperature prediction module.

[0074] The system includes: a real-time data acquisition module for acquiring real-time internal temperature, internal humidity, internal current, and external temperature data of the electrical equipment; a first rate determination module for determining the rates of change of internal temperature, internal humidity, and external temperature over time within the fluctuation period, based on the internal temperature, internal humidity, and external temperature data; a second rate determination module for determining the average current rate of change over time within the fluctuation period, based on the internal current data; and a rate function fitting module for establishing a rate function within the fluctuation period, using the average current rate of change, internal humidity rate of change, and external temperature rate of change as independent variables and the internal temperature rate of change as the dependent variable. The system includes: a response function; an independent variable value prediction module, used to analyze the predicted values ​​of internal humidity, internal current, and external temperature of electrical equipment at the next moment or time period; a rate of change conversion module, used to determine the predicted rate of change of internal humidity, internal current, and external temperature based on the predicted values ​​of internal humidity, internal current, and external temperature, respectively; and an equipment temperature prediction module, used to input the predicted rates of change of internal humidity, internal current, and external temperature into the rate influence function to calculate the predicted rate of change of internal temperature, and combine this with the real-time internal temperature of the electrical equipment to calculate the predicted value of the internal temperature of the electrical equipment at the next moment or time period.

[0075] Working principle: This invention comprehensively analyzes the impact of current accumulation effect, external temperature, and internal humidity changes on the internal temperature changes of electrical equipment. It can accurately locate the extreme point of temperature fluctuation during the internal temperature fluctuation process of electrical equipment. Furthermore, by predicting internal humidity, internal current, and external temperature, the internal temperature of electrical equipment can be predicted. Compared with directly predicting the internal temperature of electrical equipment, this invention can effectively reduce the error range and improve the accuracy and reliability of the internal temperature prediction of electrical equipment.

[0076] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0078] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0080] The above specific embodiments further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting and analyzing the internal environment of electrical equipment, characterized in that, Includes the following steps: Real-time acquisition of internal temperature data, internal humidity data, internal current data, and external temperature data of electrical equipment; Based on the internal temperature data, internal humidity data, and external temperature data, determine the rate of change of internal temperature, internal humidity, and external temperature over time within the fluctuation period. Determine the average rate of change of the average current over time within the fluctuation period based on the internal current data. A rate influence function within the fluctuation period is established with the average current change rate, internal humidity change rate, and external temperature change rate as independent variables and the internal temperature change rate as the dependent variable. Analyze the predicted internal humidity, predicted internal current, and predicted external temperature of electrical equipment at the next moment or in the next time period; The predicted rates of change for internal humidity, internal current, and external temperature are determined based on the predicted values ​​for internal humidity, internal current, and external temperature, respectively. The predicted rate of change of internal humidity, internal current and external temperature are input into the rate influence function to calculate the predicted rate of change of internal temperature. Combined with the real-time internal temperature of the electrical equipment, the predicted internal temperature value of the electrical equipment at the next moment or the next period is calculated. The process of determining the fluctuation period is as follows: Select continuously distributed temperature trough poles from the internal temperature data; The internal temperature data is divided into a continuously distributed dynamic period, starting from the m-th temperature trough pole and ending at the (m+1)-th temperature trough pole. Select the dynamic period closest to the current time point as the fluctuation period; The specific expression for the rate influence function is as follows: ； in, express The rate of change of internal temperature at any given moment; express The average rate of change of current at time t, corresponding to This indicates that the rate of change of internal temperature is positively correlated with the rate of change of average current. express The rate of change of external temperature at time t, corresponding to This indicates that the rate of change of internal temperature is positively correlated with the rate of change of external temperature. express The rate of change of internal humidity at any given time, corresponding to This indicates that the rate of change of internal temperature is negatively correlated with the rate of change of internal humidity.

2. The method for predicting and analyzing the internal environment of electrical equipment according to claim 1, characterized in that, If there is interference power in the fluctuation period, the corresponding dynamic period will be filtered and a new fluctuation period will be selected. The interference power can be any one of the following: ventilation and heat dissipation control power, air-cooled circulation control power, and humidity control power.

3. The method for predicting and analyzing the internal environment of electrical equipment according to claim 1, characterized in that, The formula for calculating the average current change rate is as follows: ； in, Indicates the period of fluctuation The average rate of change of current at time t; Indicates the period of fluctuation The average current at any given time; Indicates the period of fluctuation The average current at any given time; This represents the time interval for calculating the rate of change of the average current. Indicates the fluctuation cycle; This represents the current function that describes how the current changes over time in the internal current data.

4. The method for predicting and analyzing the internal environment of electrical equipment according to claim 1, characterized in that, The rate influence function was obtained by fitting a multivariable function using MATLAB software.

5. The method for predicting and analyzing the internal environment of electrical equipment according to claim 1, characterized in that, The specific formula for calculating the rate influence function is as follows: ； in, The correlation coefficient representing the rate of change of average current; The correlation coefficient representing the rate of change of external temperature; The correlation coefficient represents the rate of change of internal humidity.

6. A predictive analysis system for the internal environment of electrical equipment, characterized in that, include: The real-time data acquisition module is used to collect internal temperature data, internal humidity data, internal current data, and external temperature data of electrical equipment in real time. The first rate determination module is used to determine the rate of change of internal temperature, internal humidity and external temperature over time within the fluctuation period based on internal temperature data, internal humidity data and external temperature data, respectively. The second rate determination module is used to determine the average current change rate of the average current value over time within the fluctuation period based on the internal current data. The rate function fitting module is used to establish a rate influence function within the fluctuation period with the average current change rate, internal humidity change rate, and external temperature change rate as independent variables and the internal temperature change rate as the dependent variable. The independent variable value prediction module is used to analyze the predicted values ​​of internal humidity, internal current, and external temperature of electrical equipment at the next moment or in the next period. The rate of change conversion module is used to determine the predicted rate of change of internal humidity, the predicted rate of change of internal current, and the predicted rate of change of external temperature based on the predicted values ​​of internal humidity, internal current, and external temperature, respectively. The equipment temperature prediction module is used to input the predicted rate of change of internal humidity, the predicted rate of change of internal current and the predicted rate of change of external temperature into the rate influence function to calculate the predicted rate of change of internal temperature, and combine it with the real-time internal temperature of the electrical equipment to calculate the predicted value of the internal temperature of the electrical equipment at the next moment or the next period. The process of determining the fluctuation period is as follows: Select continuously distributed temperature trough poles from the internal temperature data; The internal temperature data is divided into a continuously distributed dynamic period, starting from the m-th temperature trough pole and ending at the (m+1)-th temperature trough pole. Select the dynamic period closest to the current time point as the fluctuation period; The specific expression for the rate influence function is as follows: ； in, express The rate of change of internal temperature at any given moment; express The average rate of change of current at time t, corresponding to This indicates that the rate of change of internal temperature is positively correlated with the rate of change of average current. express The rate of change of external temperature at time t, corresponding to This indicates that the rate of change of internal temperature is positively correlated with the rate of change of external temperature. express The rate of change of internal humidity at any given time, corresponding to This indicates that the rate of change of internal temperature is negatively correlated with the rate of change of internal humidity.

7. A computer terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements a method for predicting and analyzing the internal environment of electrical equipment as described in any one of claims 1-5.

8. A computer-readable medium having a computer program stored thereon, characterized in that, The computer program, when executed by a processor, can implement a method for predicting and analyzing the internal environment of electrical equipment as described in any one of claims 1-5.

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