State prediction method and system for power terminal equipment

Abstract

The invention relates to the technical field of data processing, in particular to a state prediction method and system for power terminal equipment. The method comprises the steps of collecting output current data and output voltage data, and obtaining a first weight coefficient of each output current data according to an inverse proportion relation between the output current data and the output voltage data; acquiring a second weight coefficient of each piece of output current data according to the surrounding data segment of each piece of output voltage data and the first weight coefficient, and acquiring a prediction error of the current output current data according to the second weight coefficient of each piece of output current data; according to the method, the prediction value of the current output current data is obtained according to the prediction error of the current output current data, the abnormal condition of the current output current data is judged according to the prediction value of the current output current data, and then the abnormity of the transformer is judged, so that the accuracy of abnormity detection is improved, and the influence of noise data is avoided.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a state prediction method and system for power terminal equipment. Background Art

[0002] By predicting the status of power terminal equipment, potential faults can be discovered in advance and the risk of sudden equipment failure can be reduced. As one of the terminal equipment in the power system, the transformer is mainly used for voltage conversion and adjustment, and its health status directly affects the stability and safety of power supply. The output current of the transformer is an important indicator for monitoring its health status. The output current reflects the load condition of the transformer. By monitoring the change of current, the potential faults of the transformer can be predicted. However, when the output current data of the transformer is collected, the transformer itself will generate an electromagnetic field when working. These electromagnetic fields can interfere with the sensor through wires and other channels, resulting in noise in the output current data. Therefore, the accuracy of the transformer fault warning is reduced by analyzing the directly collected output current data. Therefore, the present invention predicts the output current data at each sampling moment, and comprehensively judges whether a transformer has a fault based on the predicted value and actual value of the output current data.

[0003] Zheng Junbao and Zhang Xu, Research on time series prediction methods based on historical trends and prediction errors, Computer Age, Issue 9, 2023. In this paper, the principle and process of the HTFE algorithm are proposed.

[0004] When using the HTFE algorithm to predict the current output current data, it is first necessary to multiply the error between the predicted value and the true value of the output current data at the previous sampling moment by an error factor to obtain the predicted error of the current output current data, and then obtain the predicted value of the current output current data based on the predicted error of the current output current data in combination with the HTFE algorithm to judge the current transformer fault. However, due to the existence of electromagnetic interference, some noise data may exist in the historical data. Then, when there is potential noise interference in the historical data, this method of relying solely on the traditional HTFE algorithm to obtain the prediction error of the current output current data and then obtaining the predicted value of the current output current data is not accurate enough, thereby affecting the accuracy of the transformer fault judgment. Summary of the invention

[0005] In order to solve the technical problem that electromagnetic interference causes noise data to exist in historical data, resulting in prediction errors of the traditional HTFE algorithm for obtaining the current output current data, and the method for obtaining the predicted value of the current output current data is not accurate enough, the present invention provides a state prediction method and system for power terminal equipment.

[0006] In a first aspect, the present invention provides a method for predicting the state of a power terminal device, which adopts the following technical solution: A method for predicting the state of a power terminal device comprises the following steps: Collect output current data and output voltage data; obtain the second weight coefficient of each output current data , A second weight coefficient representing the nth output current data; represents the first weight coefficient of the nth output current data; exp() represents an exponential function with a natural constant as the base; Represents the number of extreme value points in the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data; represents the range in the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data; norm() represents the normalization function; Get the prediction error of the current output current data , M represents the number of historical data of the current output current data; Represents the error value of the mth historical data of the current output current data; A second weight coefficient representing the mth historical data of the current output current data; The sum of the second weight coefficients of all historical data representing the current output current data; The HTFE algorithm is used in combination with the prediction error of the current output current data to obtain the prediction value of the current output current data; based on the prediction value, the abnormality of the current output current data is determined, and then whether the transformer is abnormal is determined.

[0007] The innovation of the present invention lies in obtaining the second weight coefficient of each output current data according to the numerical characteristics of the output current data and the output voltage data, and correcting the error value of each output current data according to the second weight coefficient of each output current data, so as to obtain the prediction error of the current output current data. This step takes into account the errors of more output current data, avoids the susceptibility to noise interference when relying on the error of a single output current data, and makes the prediction result more stable and has higher robustness; and this step can automatically give more reliable output current data a greater influence during calculation by dynamically adjusting the second weight coefficient of each output current data, while the noise data point will reduce its contribution to the final prediction due to its greater unreality, thereby reducing the influence of noise on the prediction result, enhancing the accuracy and stability of the fault diagnosis system, and being able to identify potential transformer faults earlier and reduce the risk of misjudgment and missed judgment.

[0008] Preferably, the acquisition of the first weight coefficient of the output current data includes: ; In the formula, represents the first weight coefficient of the nth output current data; R represents the number of reference data; represents the absolute value of the difference between the value of the nth output current data and the value of the rth reference data; The number of data whose values ​​of all reference data representing the n-th output current data are greater than the value of the n-th output current data; represents the number of data whose values ​​of all reference data of the output voltage data corresponding to the sampling moment of the nth output current data are greater than the value of the output voltage data; exp() represents an exponential function with a natural constant as the base; represents a hyperparameter; || represents an absolute value symbol.

[0009] The smaller the first weight coefficient of the output current data is, the greater the probability that the output current data is more noise data, and the error thereof will be less referenced subsequently.

[0010] Preferably, obtaining the reference data includes: The number of reference data R is preset, and the R output current data before each output current data are used as reference data for each output current data; and the R output current data before each output voltage data are used as reference data for each output voltage data.

[0011] Preferably, the acquisition of the surrounding data segments of the output voltage data includes: The data segment formed by the reference data of each output voltage data and each output voltage data is recorded as the surrounding data segment of each output voltage data.

[0012] Preferably, the acquisition of historical data of the current output current data includes: The M output current data before the sampling moment of the current output current data are recorded as the historical data of the current output current data.

[0013] Preferably, obtaining the error value of the historical data includes: The predicted value of each historical data of the current output current data is obtained according to the HTFE algorithm, and the absolute value of the difference between the predicted value of each historical data of the current output current data and the actual value is used as the error value of each historical data of the current output current data.

[0014] This facilitates obtaining the prediction error of the current output current data based on the error value of each historical data of the current output current data.

[0015] Preferably, the obtaining of the predicted value of the current output current data includes: The error factor of the preset prediction interval is T. According to the prediction error of the current output current data, the HTFE algorithm is used to calculate the predicted value of the current output current data to obtain the predicted value of the current output current data.

[0016] The predicted value of the current output current data obtained is more accurate.

[0017] Preferably, judging the abnormality of the current output current data according to the predicted value, and then judging whether the transformer is abnormal, includes: Presets , if the absolute value of the difference between the predicted value and the true value of the current output current data is greater than the absolute value of the predicted value and the true value of the current output current data When the product of is obtained, the current output current data is considered to be abnormal data. If the number of abnormal data exceeds 3 within one minute after the sampling time corresponding to the current output current data, the transformer issues an early warning.

[0018] Improved anomaly detection results.

[0019] In a second aspect, the present invention provides a state prediction system for power terminal equipment, which adopts the following technical solution: A state prediction system for electric power terminal equipment comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the state prediction method for electric power terminal equipment is implemented.

[0020] By adopting the above technical solution, the above-mentioned state prediction method of a power terminal device is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby manufacturing a terminal device based on the memory and the processor for easy use.

[0021] The present invention has the following technical effects: the purpose of the present invention is to obtain the second weight coefficient of each output current data according to the numerical characteristics of the output current data and the output voltage data, and to correct the error value of each output current data according to the second weight coefficient of each output current data, so as to obtain the prediction error of the current output current data. This step takes into account the errors of more output current data, avoids the susceptibility to noise interference when relying on the error of a single output current data, makes the prediction result more stable and has higher robustness, and by dynamically adjusting the second weight coefficient of each output current data, more reliable output current data can be automatically given a greater influence during calculation, while noise data points will reduce their contribution to the final prediction due to their greater unreality, thereby reducing the impact of noise on the prediction results, enhancing the accuracy and stability of the fault diagnosis system, and being able to identify potential transformer faults earlier and reduce the risk of misjudgment and missed judgment. BRIEF DESCRIPTION OF THE DRAWINGS By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 It is a flow chart of a method for predicting the state of a power terminal device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The embodiment of the present invention discloses a state prediction method of a power terminal device, referring to Figure 1 , comprising steps S1 to S3: S1: Collect output current data and output voltage data.

[0026] In an embodiment of the present invention, a sampling time is preset at 2 seconds / time, a current sensor and a voltage sensor are installed on the transformer, and output current data and output voltage data are collected simultaneously for a total of one hour to obtain output current data and output voltage data at each sampling time, and an analog-to-digital conversion device is used to perform analog-to-digital conversion on the output current data and output voltage data at each sampling time to obtain digital representations of the two data.

[0027] S2: Obtain a first weight coefficient for each output current data according to the inverse proportional relationship between the output current data and the output voltage data; obtain a second weight coefficient for each output current data according to the surrounding data segments of each output voltage data and the first weight coefficient for each output current data.

[0028] It should be noted that when using the HTFE algorithm to predict the current output current data, it is first necessary to multiply the error between the predicted value and the true value of the output current data at the previous sampling moment by an error factor to obtain the predicted error of the current output current data, and then obtain the predicted value of the current output current data based on the predicted error of the current output current data in combination with the HTFE algorithm to judge the current transformer fault. However, due to the existence of electromagnetic interference, some noise data will exist in the historical data. Then, when there is potential noise interference in the historical data, this method of relying solely on the traditional HTFE algorithm to obtain the prediction error of the current output current data and then obtaining the predicted value of the current output current data becomes inaccurate, thereby affecting the accuracy of the transformer fault judgment. Therefore, the present invention proposes a state prediction method and system for power terminal equipment. In order to avoid the influence of noise data, the numerical change characteristics of the output current data before the current output current data are first analyzed to obtain the second weight coefficient of each output current data. Then, the error between the predicted value and the true value of each output current data is corrected according to the second weight coefficient of each output current data to obtain the prediction error of the current output current data. Then, based on the prediction error of the current output current data and combined with the HTFE algorithm, the prediction value of the current output current data is obtained to judge the fault of the current transformer, thereby avoiding the influence of noise data on the prediction result.

[0029] Step S2 includes steps S20 and S21, which are specifically as follows: S20: Obtaining a first weight coefficient for each output current data according to an inverse proportional relationship between the output current data and the output voltage data.

[0030] It should be noted that the first weight coefficient of each output current data is obtained by analyzing the numerical change characteristics of the output current data and the output voltage data. In the transformer working scenario, if the power at the input end is equal to the power at the output end, it can be seen that if there is a change between the input current data and the output voltage data, the two can only be in an inverse proportional relationship. This is because power = voltage multiplied by current. Therefore, when analyzing the first weight coefficient of the output current data, the more prominent the value of the output current data and the looser the inverse proportional relationship between the output current data and the output voltage data, the greater the probability that the output current data belongs to noise data, and the lower the reference value of the output current data error, the smaller the corresponding first weight coefficient.

[0031] In an embodiment of the present invention, the number of reference data R is preset, and the R output current data before each output current data are used as reference data for each output current data; the R output current data before each output voltage data are used as reference data for each output voltage data; in an embodiment of the present invention, the number of reference data R is preset to 100. In other embodiments, implementers may preset the value of the number of reference data R according to the specific implementation method.

[0032] Get the first weight coefficient of each output current data: ; In the formula, represents the first weight coefficient of the nth output current data; R represents the number of reference data; represents the absolute value of the difference between the value of the nth output current data and the value of the rth reference data; The number of data whose values ​​of all reference data representing the n-th output current data are greater than the value of the n-th output current data; represents the number of data whose values ​​of all reference data of the output voltage data corresponding to the sampling moment of the nth output current data are greater than the value of the output voltage data; exp() represents an exponential function with a natural constant as the base; represents a hyperparameter. In order to avoid The value of is equal to 0; Represents the sum of the differences between the nth output current data and its reference output current data. The larger the value, the more prominent the numerical performance of the nth output current data. The greater the probability that the output current data belongs to noise data, the lower the reference value of the error value at the output current data, and the smaller the corresponding first weight coefficient. It reflects the closeness of the inverse proportional relationship between the nth output current data and its corresponding output voltage data. The smaller the value, the closer the inverse proportional relationship between the nth output current data and the output voltage data. The larger the value, the greater the probability that the nth output current data belongs to noise data. The reference value of the error value at the output current data will be lower, and the corresponding first weight coefficient will be smaller.

[0033] S21: Acquire a second weight coefficient of each output current data according to surrounding data segments of each output voltage data and a first weight coefficient of each output current data.

[0034] It should be noted that it is known that the first weight coefficient of each output current data is obtained. The analysis of this indicator is based on the numerical change characteristics of the output current data and the output voltage data at the same sampling time. However, in actual scenarios, the output voltage data will also generate noise data due to electromagnetic interference. Then, when any output voltage data belongs to noise data, the first weight coefficient of the output current data corresponding to the sampling time of the output voltage data will become inaccurate, and it cannot accurately reflect the reference value corresponding to the error value of the output current data; therefore, it is necessary to obtain the surrounding data segments of each output voltage data, analyze the data change characteristics in the surrounding data segments of each output voltage data, and optimize the first weight coefficient of each output current data to obtain the second weight coefficient of each output current data, wherein, if the data change characteristics in the surrounding data segments of any output voltage data are more stable, it can be shown that the output voltage data belongs to normal data rather than noise data. The greater the credibility, the greater the credibility of the first weight coefficient of the output current data corresponding to the sampling time of the output voltage data will be, and the corresponding second weight coefficient will also be greater.

[0035] In the embodiment of the present invention, the data segment formed by the reference data of each output voltage data and each output voltage data is recorded as the surrounding data segment of each output voltage data.

[0036] Get the second weight coefficient for each output current data: ; In the formula, A second weight coefficient representing the nth output current data; represents the first weight coefficient of the nth output current data; exp() represents an exponential function with a natural constant as the base; norm() represents a normalization function; Represents the number of extreme value points in the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data; represents the range in the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data; The larger the value of is, the greater the reference value of the error value of the nth output current data is, and the larger the corresponding second weight coefficient is; The smaller the value of is, the smaller the value change in the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data is, and the greater the stability of the surrounding data segment of the output voltage data is; The smaller the value of is, the greater the stability of the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data is. The smaller the value is, the more reliable it is that the corresponding output voltage data is normal data rather than noise data. The higher the reliability of the first weight coefficient of the output current data will be, and the larger the corresponding second weight coefficient will be.

[0037] S3: According to the second weight coefficient of each output current data, obtain the prediction error of the current output current data; according to the prediction error of the current output current data, obtain the prediction value of the current output current data, and according to the prediction value of the current output current data, judge the abnormality of the current output current data, and then judge the abnormality of the transformer.

[0038] Step S3 includes steps S30 and S31, which are specifically as follows: S30: Obtaining a prediction error of current output current data according to a second weight coefficient of each output current data.

[0039] It should be noted that the second weight coefficient of each output current data is obtained. Therefore, the predicted error of the current output current data can be obtained based on the second weight coefficient of each output current data and the error value of each output current data. If the second weight coefficient of any output current data is larger, it means that the error value of the output current data should be referred to.

[0040] In an embodiment of the present invention, M output current data before the sampling moment of the current output current data are recorded as the historical data of the current output current data; the predicted value of each historical data of the current output current data is obtained according to the HTFE algorithm, and the absolute value of the difference between the predicted value and the actual value of each historical data of the current output current data is used as the error value of each historical data of the current output current data; in an embodiment of the present invention, the preset number of historical data M=100, and in other embodiments, the implementer can preset the value of the number of historical data M according to the specific implementation method.

[0041] Get the prediction error of the current output current data: ; In the formula, represents the prediction error of the current output current data; M represents the number of historical data of the current output current data; Represents the error value of the mth historical data of the current output current data; A second weight coefficient representing the mth historical data of the current output current data; The sum of the second weight coefficients of all historical data representing the current output current data.

[0042] S31: obtaining a predicted value of the current output current data according to a predicted error of the current output current data, judging an abnormality of the current output current data according to the predicted value of the current output current data, and further judging an abnormality of the transformer.

[0043] It should be noted that after obtaining the prediction error of the current output current data, it is necessary to obtain the prediction value of the current output current data based on the prediction error of the current output current data in combination with the HTFE algorithm.

[0044] In an embodiment of the present invention, the error factor of the preset prediction interval is T. According to the prediction error of the current output current data, the HTFE algorithm is used to calculate the predicted value of the current output current data to obtain the predicted value of the current output current data; in an embodiment of the present invention, the error factor of the preset prediction interval is T=0.4. In other embodiments, the implementer may preset the value of T according to the specific implementation situation.

[0045] Presets =0.05. In other embodiments, the implementer may preset the value according to the specific implementation situation. If the absolute value of the difference between the predicted value and the true value of the current output current data is greater than the absolute value of the difference between the predicted value and the true value of the current output current data When the product of is obtained, the current output current data is considered to be abnormal data. If the number of abnormal data exceeds 3 within one minute after the sampling time corresponding to the current output current data, the transformer will issue an early warning.

[0046] An embodiment of the present invention further discloses a state prediction system for power terminal equipment, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a state prediction method for power terminal equipment according to the present invention is implemented.

[0047] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0048] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory, a dynamic random access memory, a static random access memory, an enhanced dynamic random access memory, a high bandwidth memory, a hybrid storage cube, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of the device or accessible or connectable to the device.

[0049] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0050] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A state prediction method for power terminal equipment, characterized in that: Includes steps: Collect output current data and output voltage data; obtain the second weight coefficient of each output current data , A second weight coefficient representing the nth output current data; represents the first weight coefficient of the nth output current data; exp() represents an exponential function with a natural constant as the base; Represents the number of extreme value points in the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data; represents the range in the surrounding data segment of the output voltage data corresponding to the sampling moment of the nth output current data; norm() represents the normalization function; Get the prediction error of the current output current data , M represents the number of historical data of the current output current data; Represents the error value of the mth historical data of the current output current data; A second weight coefficient representing the mth historical data of the current output current data; The sum of the second weight coefficients of all historical data representing the current output current data; The HTFE algorithm is used in combination with the prediction error of the current output current data to obtain the prediction value of the current output current data; based on the prediction value, the abnormality of the current output current data is determined, and then whether the transformer is abnormal is determined.

2. A state prediction method for power terminal equipment according to claim 1, characterized in that: The obtaining of the first weight coefficient of the output current data includes: ； In the formula, represents the first weight coefficient of the nth output current data; R represents the number of reference data; represents the absolute value of the difference between the value of the nth output current data and the value of the rth reference data; The number of data whose values ​​of all reference data representing the n-th output current data are greater than the value of the n-th output current data; represents the number of data whose values ​​of all reference data of the output voltage data corresponding to the sampling moment of the nth output current data are greater than the value of the output voltage data; exp() represents an exponential function with a natural constant as the base; represents a hyperparameter; || represents an absolute value symbol.

3. A state prediction method for power terminal equipment according to claim 2, characterized in that: The acquisition of the reference data includes: The number of reference data R is preset, and the R output current data before each output current data are used as reference data for each output current data; and the R output current data before each output voltage data are used as reference data for each output voltage data.

4. A method for predicting the state of a power terminal device according to claim 1, characterized in that: The acquisition of the surrounding data segments of the output voltage data includes: The data segment formed by the reference data of each output voltage data and each output voltage data is recorded as the surrounding data segment of each output voltage data.

5. A method for predicting the state of a power terminal device according to claim 1, characterized in that: The acquisition of historical data of the current output current data includes: The M output current data before the sampling moment of the current output current data are recorded as the historical data of the current output current data.

6. A method for predicting the state of a power terminal device according to claim 1, characterized in that: The acquisition of the error value of the historical data includes: The predicted value of each historical data of the current output current data is obtained according to the HTFE algorithm, and the absolute value of the difference between the predicted value of each historical data of the current output current data and the actual value is used as the error value of each historical data of the current output current data.

7. A method for predicting the state of a power terminal device according to claim 1, characterized in that: The obtaining of the predicted value of the current output current data includes: The error factor of the preset prediction interval is T. According to the prediction error of the current output current data, the HTFE algorithm is used to calculate the predicted value of the current output current data to obtain the predicted value of the current output current data.

8. A method for predicting the state of a power terminal device according to claim 1, characterized in that: The step of judging the abnormality of the current output current data according to the predicted value, and then judging whether the transformer is abnormal, includes: Presets , if the absolute value of the difference between the predicted value and the true value of the current output current data is greater than the absolute value of the predicted value and the true value of the current output current data When the product of is obtained, the current output current data is considered to be abnormal data. If the number of abnormal data exceeds 3 within one minute after the sampling time corresponding to the current output current data, the transformer issues an early warning.

9. A state prediction system for power terminal equipment, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a state prediction method for a power terminal device according to any one of claims 1 to 8 is implemented.

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