Transformer winding temperature prediction method and system

Abstract

The invention discloses a transformer winding temperature prediction method and system, and the method comprises the steps: input data definition: extracting winding temperature historical data and load historical data, and converting the data into corresponding matrix forms; accumulation sequence and adjacent mean value sequence calculation: on the basis of the original historical data, respectively accumulating the winding temperature data and the load historical data to generate a corresponding accumulation sequence; summing adjacent elements in the winding temperature data accumulation sequence and averaging to generate a corresponding adjacent average sequence; prediction coefficient calculation: constructing a first coefficient matrix, a second coefficient matrix and a third coefficient matrix according to the winding temperature input data, the winding temperature adjacent mean value sequence and the load accumulation sequence, and performing matrix operation according to the three coefficient matrixes to obtain a corresponding first prediction coefficient and a corresponding second prediction coefficient; and performing matrix operation according to the first prediction coefficient, the second prediction coefficient, the winding temperature adjacent mean value sequence and the load accumulation sequence to obtain a corresponding winding temperature prediction value.

Description

Technical Field

[0001] The present invention relates to a temperature prediction method and system, and in particular to a transformer winding temperature prediction method and system. Background Art

[0002] In power systems, transformers are crucial devices responsible for stepping up and down the voltage of electrical energy. Transformer winding temperature is a key parameter for assessing its health and operational safety. Excessively high winding temperatures not only accelerate the aging of insulation materials and shorten the transformer's service life, but can also lead to equipment failure, resulting in serious economic losses and safety issues. Currently, transformer winding temperature monitoring primarily relies on temperature sensors and hotspot temperature calculation methods. While these methods provide effective temperature monitoring tools, they have limitations. For example, temperature sensors may be limited by their installation location and may not fully reflect the temperature distribution within the winding, while hotspot temperature calculation methods rely on complex mathematical models and numerous assumptions, which can reduce prediction accuracy.

[0003] Due to the ever-changing power load and volatile environmental conditions, transformer winding temperatures exhibit nonlinear and dynamic variations, placing higher demands on temperature prediction. Traditional methods often lack flexibility in handling these dynamic changes, and temperature trend predictions for small sample data may be inaccurate. Summary of the Invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a method and system for predicting transformer winding temperature to improve the prediction accuracy of transformer winding temperature.

[0005] Technical solution: The present invention comprises the following steps:

[0006] Input data definition: Extract winding temperature history data and load history data and convert them into corresponding matrix form;

[0007] Calculation of cumulative sequence and adjacent mean sequence: Based on the original historical data, the winding temperature data and load historical data are accumulated separately to generate the corresponding cumulative sequence; the adjacent elements in the winding temperature data cumulative sequence are summed and averaged to generate the corresponding adjacent mean sequence;

[0008] Prediction coefficient calculation: Based on the winding temperature input data, the winding temperature adjacent mean value sequence, and the load accumulation sequence, the first coefficient matrix, the second coefficient matrix, and the third coefficient matrix are constructed, and matrix operations are performed on the three coefficient matrices to obtain the corresponding first prediction coefficient and second prediction coefficient;

[0009] Matrix operations are performed according to the first prediction coefficient, the second prediction coefficient, the winding temperature adjacent mean value sequence, and the load accumulation sequence to obtain the corresponding winding temperature prediction value.

[0010] The matrix of the winding temperature history data is expressed as:

[0011]

[0012] in, is the winding temperature history data matrix, is the corresponding element, and n is the number of elements. The matrix of the load history data is represented as:

[0013]

[0014] in, is the winding temperature history data matrix, For the corresponding elements.

[0015] The cumulative series of the winding temperature is expressed as:

[0016]

[0017] Among them, X1 (1) is the winding temperature accumulation sequence matrix, x1 (1) (i) is the corresponding element.

[0018] The cumulative sequence of the load is expressed as:

[0019]

[0020] in, is the load accumulation sequence matrix, For the corresponding elements.

[0021] The matrix representation of the adjacent mean value sequence of the winding temperature can be expressed as:

[0022]

[0023] in, is the winding temperature adjacent mean sequence matrix, For the corresponding elements.

[0024] The first coefficient matrix, the second coefficient matrix, and the third coefficient matrix are:

[0025]

[0026] Among them, B1 is the first coefficient matrix, B2 is the second coefficient matrix, and B3 is the third coefficient matrix.

[0027] The calculation formula of the first prediction coefficient is expressed as:

[0028]

[0029] Among them, W1 is the first prediction coefficient;

[0030] The calculation formula of the second prediction coefficient is expressed as:

[0031]

[0032] Wherein, W2 is the second prediction coefficient.

[0033] The calculation formula of the predicted value is:

[0034]

[0035] in, is the predicted value of the winding temperature data.

[0036] A transformer winding temperature prediction system, comprising:

[0037] Input data definition module: used to define input data;

[0038] Cumulative sequence and adjacent mean sequence calculation module: used to calculate the cumulative sequence and adjacent mean sequence corresponding to the input data;

[0039] Prediction coefficient calculation module: used to calculate the prediction coefficient;

[0040] Prediction value calculation module: used to calculate the prediction value.

[0041] Beneficial effect: The present invention constructs winding temperature and load input data, calculates the cumulative sequence, calculates the prediction coefficient based on the above data, and finally calculates the predicted value, which effectively improves the prediction accuracy of transformer winding temperature. It has important theoretical and practical significance for guiding the predictive operation and maintenance of transformers and reducing the probability of failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flowchart of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be further described below with reference to the accompanying drawings.

[0044] like Figure 1 As shown, the transformer winding temperature prediction method of the present invention includes the following steps:

[0045] S1. Input data definition: Extract winding temperature history data and load history data and convert them into corresponding matrix forms to construct winding temperature input data and load input data.

[0046] Transformer winding temperature prediction requires two types of data: winding temperature history data and load history data. The matrix representation of winding temperature history data can be expressed as:

[0047]

[0048] in, is the winding temperature history data matrix, is the corresponding element, and n is the number of elements.

[0049] The matrix representation of load history data can be expressed as:

[0050]

[0051] in, is the winding temperature history data matrix, For the corresponding elements.

[0052] S2. Calculation of cumulative sequence and adjacent mean sequence: Based on the original historical data, the winding temperature data and load historical data are accumulated separately to generate corresponding cumulative sequences; the adjacent elements in the winding temperature data cumulative sequence are summed and averaged to generate the corresponding adjacent mean sequence to obtain a more stable temperature change trend and provide important basic data for subsequent predictions.

[0053] The cumulative series of winding temperature can be expressed as:

[0054]

[0055] in, is the winding temperature accumulation sequence matrix, For the corresponding elements.

[0056] The cumulative sequence of loads can be expressed as:

[0057]

[0058] in, is the load accumulation sequence matrix, For the corresponding elements.

[0059] The matrix representation of the adjacent mean value sequence of the winding temperature can be expressed as:

[0060]

[0061] in, is the winding temperature adjacent mean sequence matrix, For the corresponding elements.

[0062] S3. Calculation of prediction coefficients: Based on the winding temperature input data, the winding temperature adjacent mean sequence, and the load accumulation sequence, the first coefficient matrix, the second coefficient matrix, and the third coefficient matrix are constructed, and matrix operations are performed on the three coefficient matrices to obtain the corresponding first prediction coefficient and second prediction coefficient. The first prediction coefficient and the second prediction coefficient are key parameters for temperature prediction.

[0063] The first coefficient matrix, the second coefficient matrix, and the third coefficient matrix are:

[0064]

[0065] Among them, B1 is the first coefficient matrix, B2 is the second coefficient matrix, and B3 is the third coefficient matrix.

[0066] The calculation formula of the first prediction coefficient can be expressed as:

[0067]

[0068] Among them, W1 is the first prediction coefficient.

[0069] The calculation formula of the second prediction coefficient can be expressed as:

[0070]

[0071] Wherein, W2 is the second prediction coefficient.

[0072] S4. Prediction value calculation: Perform matrix operations based on the first prediction coefficient, the second prediction coefficient, the winding temperature adjacent mean value sequence, and the load accumulation sequence to obtain the corresponding winding temperature prediction value.

[0073] The calculation formula of the predicted value can be expressed as:

[0074]

[0075] in, is the predicted value of the winding temperature data.

[0076] The proposed method aims to predict winding temperature using advanced data analysis techniques, combining real-time monitoring data, historical transformer operating records, and external environmental factors that may affect winding temperature. By improving the accuracy and real-time nature of predictions, this method helps to promptly detect and prevent potential transformer failures, thereby ensuring reliable power system operation and the long-term stability of transformers.

[0077] The transformer winding temperature prediction system of the present invention comprises:

[0078] Input data definition module: used to define input data;

[0079] Cumulative sequence and adjacent mean sequence calculation module: used to calculate the cumulative sequence and adjacent mean sequence corresponding to the input data;

[0080] Prediction coefficient calculation module: used to calculate the prediction coefficient;

[0081] Prediction value calculation module: used to calculate the prediction value.

[0082] The present invention also discloses a computer-readable storage medium storing one or more programs, wherein the one or more programs include instructions, which, when executed by a computing device, enable the computing device to execute a transformer winding temperature prediction method.

[0083] Based on the same technical solution, the present invention also discloses a computing device, comprising one or more processors, one or more memories, and one or more programs, wherein the one or more programs are stored in the one or more memories and are configured to be executed by one or more processors, and the one or more programs include instructions for executing a transformer winding temperature prediction method.

[0084] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

Claims

1. A method for predicting transformer winding temperature, characterized in that: The following steps are involved: Input data definition: extract winding temperature history data and load history data, and convert them into corresponding matrix form; Cumulative sequence and adjacent mean sequence calculation: Based on the original historical data, the winding temperature data and load historical data are accumulated respectively to generate the corresponding cumulative sequence; The adjacent elements in the winding temperature data accumulation sequence are summed and averaged to generate the corresponding adjacent mean value sequence; Prediction coefficient calculation: According to the winding temperature input data, the winding temperature adjacent mean value sequence, and the load accumulation sequence, the first coefficient matrix, the second coefficient matrix, and the third coefficient matrix are constructed, and the corresponding first prediction coefficient and the second prediction coefficient are obtained by performing matrix operations on the three coefficient matrices; Matrix operations are performed according to the first prediction coefficient, the second prediction coefficient, the winding temperature adjacent mean value sequence, and the load accumulation sequence to obtain the corresponding winding temperature prediction value.

2. A transformer winding temperature prediction method according to claim 1, characterized in that: The matrix of the winding temperature history data is expressed as: in, is the winding temperature history data matrix, is the corresponding element, and n is the number of elements.

3. A transformer winding temperature prediction method according to claim 2, characterized in that: The matrix representation of the load history data is: in, is the winding temperature history data matrix, For the corresponding elements.

4. A transformer winding temperature prediction method according to claim 2 or 3, characterized in that: The cumulative series of the winding temperature is expressed as: in, is the winding temperature accumulation sequence matrix, For the corresponding elements.

5. A transformer winding temperature prediction method according to claim 4, characterized in that: The cumulative sequence of the load is expressed as: in, is the load accumulation sequence matrix, For the corresponding elements.

6. A transformer winding temperature prediction method according to claim 5, characterized in that: The matrix representation of the adjacent mean value sequence of the winding temperature is: in, is the winding temperature adjacent mean sequence matrix, For the corresponding elements.

7. A transformer winding temperature prediction method according to claim 6, characterized in that: The first coefficient matrix, the second coefficient matrix, and the third coefficient matrix are: Among them, B1 is the first coefficient matrix, B2 is the second coefficient matrix, and B3 is the third coefficient matrix.

8. A transformer winding temperature prediction method according to claim 7, characterized in that: The calculation formula of the first prediction coefficient is expressed as: Among them, W1 is the first prediction coefficient; The calculation formula of the second prediction coefficient is expressed as: Wherein, W2 is the second prediction coefficient.

9. A transformer winding temperature prediction method according to claim 8, characterized in that: The calculation formula of the predicted value is: in, is the predicted value of the winding temperature data.

10. A transformer winding temperature prediction system, characterized in that: include: Input data definition module: used to define input data; Cumulative sequence and adjacent mean sequence calculation module: used to calculate the cumulative sequence and adjacent mean sequence corresponding to the input data; Prediction coefficient calculation module: used to calculate the prediction coefficient; Prediction value calculation module: used to calculate the prediction value.

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