Electrical automation power supply system detection method

Abstract

The invention discloses an electrical automatic power supply system detection method, and relates to the technical field of electrical automatic power supply system detection, and the method comprises the steps of transformer winding detection, transformer winding data acquisition, transformer winding analysis and early warning prompt. And analyzing to obtain a corresponding winding data set, judging whether the winding data of the winding in the transformer corresponding to each detection time period is normal or not, analyzing to obtain an abnormal reason corresponding to the winding in the transformer, and judging to obtain whether the transformer has a fault or not, thereby realizing deep analysis of the transformer in the electrical automatic power supply system. The power supply risk and failure rate of the electrical automatic power supply system are reduced, the safety problem of the winding in the transformer is found in time, the stability of the electrical automatic power supply system is improved, the power supply quality of the electrical automatic power supply system is guaranteed, and safe and efficient power supply of the electrical automatic power supply system is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical automation power supply system detection, and particularly relates to a detection method for an electrical automation power supply system. Background Art

[0002] With the wide application of the electrical automation power supply system, its influence is profound. Therefore, it is necessary to perform corresponding operation detections on the electrical automation power supply system to ensure the safe and stable operation of the electrical automation system. By detecting the corresponding winding data of the transformer, the basic state of the windings in the transformer can be understood in real time, the abnormalities of the windings in the transformer can be responded to in a timely manner, the operation risks and the impacts of power supply-related abnormalities can be reduced, and the safe operation of the electrical automation power supply system can be achieved.

[0003] A prior art detection method for an electrical automation power supply system disclosed in the invention patent application with publication number CN118606874B includes: obtaining the current data of each carbon brush in the generator of the electrical automation power supply system at each acquisition moment; obtaining each current subsequence of each carbon brush; combining the change trend and random fluctuation degree of the current data in each current subsequence to determine the carbon brush current trend fluctuation amount of each current subsequence; combining the abnormal degree of the peak value in each current subsequence and the carbon brush current trend fluctuation amount to determine the current peak abnormal coefficient of each current subsequence; based on the similarity degree between the current data of any two carbon brushes and the corresponding current peak abnormal coefficients, determining the current balance coefficient between the any two carbon brushes; and further judging the fault condition of the carbon brushes in the generator. This application aims to reduce the detection error of the operating state of the electrical automation power supply system.

[0004] For the above solution, there are the following technical problems: The above invention mainly obtains the current data of each carbon brush in the generator of the electrical automation power supply system at each acquisition moment, judges the fault condition of the carbon brushes in the generator, and reduces the detection error of the operating state of the electrical automation power supply system. It does not start from the perspective of the windings in the transformer of the electrical automation power supply system, fails to obtain the winding data of the windings in the corresponding transformer of the electrical automation power supply system at each detection time point, cannot know whether the winding data of the windings in the transformer in each detection time period is normal, cannot analyze the abnormal reasons corresponding to the windings in the transformer based on the obtained winding data results, thereby judging whether the transformer is faulty, and cannot timely understand the operation state of the transformer in the electrical automation power supply system, increasing the operation risk of the power supply system. Summary of the Invention

[0005] Aiming at the above-mentioned existing technical deficiencies, the purpose of the present invention is to provide a detection method for an electrical automation power supply system, mainly for the real-time detection of the corresponding windings of a transformer, so as to timely understand the operating state of the corresponding transformer and effectively prevent transformer failures.

[0006] To solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a detection method for an electrical automation power supply system, including: Step 1, Transformer winding detection: Obtain the winding data of the windings in the transformer at each detection time point.

[0007] Step 2, Transformer winding data acquisition: Based on the winding data of the windings in the transformer at each detection time period, further analyze to obtain the winding data set corresponding to each detection time period of the windings in the transformer, and determine whether the winding data corresponding to each detection time period of the windings in the transformer is normal.

[0008] Step 3, Transformer winding analysis: Based on the winding data results judged for each detection time period of the windings in the transformer, obtain the winding information when the winding data of the windings in the transformer is abnormal for each detection time period, further analyze to obtain the corresponding abnormal reasons of the windings in the transformer, and obtain the transformer information corresponding to the abnormal reasons of the windings in the transformer, and judge whether the transformer is faulty.

[0009] Step 4, Early warning prompt: Give an early warning prompt when the winding data of a certain detection time period of the windings in the transformer is abnormal or the transformer is faulty.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0011] 1. A detection method for an electrical automation power supply system provided by the present application, by obtaining the winding data of the windings in the transformer at each detection time point, further analyzing to obtain the corresponding winding data set, and determining whether the winding data corresponding to each detection time period of the windings in the transformer is normal, analyzing to obtain the corresponding abnormal reasons of the windings in the transformer, and judging whether the transformer is faulty, realizes in-depth analysis of the transformer in the electrical automation power supply system, efficiently guarantees the operation safety and operation efficiency of the transformer in the electrical automation power supply system, reduces the power supply risk and failure rate of the electrical automation power supply system, timely discovers the safety problems of the windings in the transformer, improves the stability of the electrical automation power supply system, guarantees the power supply quality of the electrical automation power supply system, and realizes safe and efficient power supply of the electrical automation power supply system.

[0012] 2. Based on the winding data of the windings in the transformer during each detection time period, analyze to obtain the winding data set corresponding to each detection time period of the windings in the transformer, and determine whether the winding data corresponding to each detection time period of the windings in the transformer is normal, so as to quickly understand the basic working conditions corresponding to the windings in the current transformer, thereby providing effective data support for subsequent transformer fault judgment, facilitating obtaining a more accurate transformer judgment result, reducing the transformer fault risk, and ensuring the stability of the electrical automation power supply system.

[0013] 3. Based on the winding data results corresponding to each detection time period of the windings in the transformer, obtain the winding information when the winding data of the windings in the transformer is abnormal during each detection time period, and then analyze the corresponding abnormal reasons of the windings in the transformer, and determine whether the transformer is faulty, so as to realize the efficient and accurate determination of the transformer state, thereby effectively ensuring the power supply of the electrical automation power supply system and realizing the safe and efficient power supply of the electrical automation power supply system. Brief Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0015] Figure 1 It is a schematic flow chart of the implementation steps of the method of the present invention. Detailed Embodiments

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0017] Embodiment 1

[0018] Please refer to Figure 1 As shown, a detection method for an electrical automation power supply system includes: Step 1. Transformer winding detection: Obtain the winding data of the windings in the transformer at each detection time point.

[0019] It should be noted that, by using a multimeter to perform inspections on the transformer of the electrical automation power supply system during operation at various time periods, the DC resistance of the windings in the transformer for each inspection time period is obtained. By using a megohmmeter to perform inspections on the transformer of the electrical automation power supply system during operation at various time periods, the insulation resistance of the windings in the transformer for each inspection time period is obtained. Based on the measurements by the multimeter, the input voltage and output voltage of the windings in the transformer for each inspection time period are obtained, and the input voltage is divided by the output voltage to obtain the voltage ratio of the windings in the transformer for each inspection time period. By using a transformer winding deformation tester to obtain the response frequency signals of the windings in the transformer for each inspection time period, the frequency of the windings in the transformer for each inspection time period is obtained.

[0020] In a specific embodiment of the present invention, the winding data includes DC resistance, insulation resistance, voltage ratio, and frequency.

[0021] Step 2: Obtaining transformer winding data: Based on the winding data of the windings in the transformer for each inspection time period, further analyze to obtain the winding data set of the windings in the transformer corresponding to each inspection time period, and determine whether the winding data of the windings in the transformer corresponding to each inspection time period is normal.

[0022] In a specific embodiment of the present invention, for an electrical automation power supply system detection method as described in claim 2, it is characterized in that the process of specifically analyzing to obtain the winding data set of the windings in the transformer corresponding to each inspection time period is as follows: Based on the winding data of the windings in the transformer for each inspection time period, analyze to obtain the DC resistance characteristic value and insulation resistance characteristic value of the windings in the transformer corresponding to each inspection time period, respectively denoted as β u and δ u , where u is the number of each inspection time period, u = 1, 2,..., n, and n is any integer greater than 2.

[0023] Import the DC resistance characteristic value and insulation resistance characteristic value of the windings in the transformer corresponding to each inspection time period into the transformer winding resistance analysis model where η u is the resistance characteristic value of the windings in the transformer corresponding to the u-th inspection time period, p is the set lower limit value of the resistance corresponding to the windings in the transformer, and p' is the set upper limit value of the resistance corresponding to the windings in the transformer.

[0024] Based on the winding data of the windings in the transformer for each inspection time period, analyze to obtain the voltage ratio characteristic value and frequency response characteristic value of the windings in the transformer corresponding to each inspection time period, respectively denoted as M u and N u , and import them into the transformer winding state analysis model where θ uis the winding change characteristic value of the winding in the transformer corresponding to the \(u\)th detection time period, \(L\) is the set upper limit value of the change of the winding in the transformer, and \(L'\) is the set lower limit value of the change of the winding in the transformer.

[0025] Based on the DC resistance characteristic value, insulation resistance characteristic value, voltage ratio characteristic value, and frequency response characteristic value of the winding in the transformer in each detection time period, a winding data set of the winding in the transformer in each detection time period is constructed.

[0026] It should be noted that professional power personnel set the upper resistance value, lower resistance value, upper change value, and lower change value corresponding to the winding in the transformer in the electrical automation power supply system; the set upper resistance value corresponding to the winding in the transformer is a reference value for judging whether the winding in the transformer is normal, and the lower resistance value, upper change value, and lower change value corresponding to the winding in the transformer have the same function, so they will not be elaborated here.

[0027] In a specific embodiment of the present invention, the DC resistance characteristic value and insulation resistance characteristic value of the winding in the transformer corresponding to each detection time period are analyzed. The specific analysis process is as follows: Compare the DC resistance data of the winding in the current transformer corresponding to each detection time period with the DC resistance threshold of the winding in the historical transformer stored in the database. If the DC data of the winding in the transformer corresponding to a certain detection time period is less than the DC resistance threshold of the winding in the historical transformer stored in the database, then record the DC resistance characteristic value of the winding in the transformer corresponding to this detection time period as -1, otherwise record it as 1, and so on to obtain the DC resistance characteristic values of the winding in the transformer corresponding to each detection time period. Similarly, the DC insulation resistance characteristic values of the winding in the transformer corresponding to each detection time period can be obtained.

[0028] In a specific embodiment of the present invention, the voltage ratio characteristic value and frequency response characteristic value of the winding in the transformer corresponding to each detection time period are analyzed. The specific analysis process is as follows: Compare the voltage ratio data of the winding in the current transformer corresponding to each detection time period with the voltage ratio threshold of the winding in the historical transformer stored in the database. If the voltage ratio data of the winding in the current transformer corresponding to a certain detection time period is less than the voltage ratio threshold of the winding in the historical transformer stored in the database, then record the voltage meter characteristic value of the winding in the transformer corresponding to this detection time period as -1, otherwise record it as 1, and so on to obtain the voltage ratio characteristic values of the winding in the transformer corresponding to each detection time period. Similarly, the frequency response characteristic values of the winding in the transformer corresponding to each detection time period can be obtained.

[0029] In a specific embodiment of the present invention, to judge whether the winding data of the winding in the transformer corresponding to each detection time period is normal, the specific judgment process is as follows: Based on the resistance characteristic value \(\eta\) of the winding in the transformer corresponding to each detection time period u and the winding change characteristic value \(\theta\) u, if η u = 1 ∨ θ u = 0 or η u = 0 ∨ θ u = 1 or η u = 0 ∨ θ u = 0, it is determined that the winding data of the winding corresponding to the u-th detection time period in the transformer is abnormal, and the winding data of the detection time period numbered u is recorded as a type of abnormal data.

[0030] If η u = -1 ∧ θ u = -1, it is determined that the winding data of the winding corresponding to the u-th detection time period in the transformer is abnormal, and the winding data of the detection time period numbered u is recorded as a type of abnormal data.

[0031] If η u = 1 ∧ θ u = 1, it is determined that the winding data of the winding corresponding to the u-th detection time period in the transformer is normal, and the winding data of the detection time period numbered u is recorded as normal data. By analogy, it can be obtained whether the winding data of the winding corresponding to each detection time period in the transformer is normal.

[0032] Based on the winding data of the winding in the transformer in each detection time period, the winding data set corresponding to the winding in the transformer in each detection time period is analyzed, and it is judged whether the winding data of the winding corresponding to each detection time period in the transformer is normal, so as to quickly understand the basic working conditions of the winding corresponding to the current transformer, thereby providing effective data support for subsequent transformer fault judgment, facilitating obtaining a more accurate transformer judgment result, reducing the transformer fault risk, and ensuring the stability of the electrical automation power supply system.

[0033] Step 3. Transformer winding analysis: Based on the winding data results judged for each detection time period of the winding in the transformer, obtain the winding information when the winding data of the winding in each detection time period of the transformer is abnormal, and then analyze to obtain the abnormal cause corresponding to the winding in the transformer, and obtain the transformer information corresponding to the abnormal cause of the winding in the transformer, and judge whether the transformer is faulty.

[0034] It should be noted that the winding information includes the oil leakage amount, harmonics and temperature.

[0035] In a specific embodiment of the present invention, the process of specifically obtaining the winding information when the winding data of the winding in each detection time period of the transformer is abnormal is as follows: Collect the transformer data corresponding to the winding in the transformer. By detecting the corresponding main body of the transformer, use an ultrasonic leak detector to detect whether there is oil in the transformer. If not, exclude the abnormality of the transformer main body. If it is detected that there is oil in the transformer, perform oil leakage data recording to obtain the oil leakage amount.

[0036] Obtain the association information of the transformer corresponding to each associated device, and use a harmonic analyzer to obtain the harmonics of the transformer corresponding to each associated device. If the harmonics of the transformer corresponding to each associated device do not exceed the reference harmonic values stored in the database, then exclude the abnormality of the associated device corresponding to the transformer. If the harmonics of a certain associated device corresponding to the transformer exceed the reference harmonic values existing in the database, then determine that the harmonic data is abnormal, and record the harmonics of this associated device. Use this record to obtain the harmonics of the transformer corresponding to each associated device.

[0037] Obtain the environmental data when the transformer is working, and use a temperature sensor to obtain the corresponding temperature when the transformer is working. If the temperature corresponding to the transformer does not exceed the reference temperature stored in the database, then exclude environmental abnormalities. If the temperature corresponding to the transformer exceeds the reference temperature stored in the database, then determine that the temperature data is abnormal, and execute the record of the temperature corresponding to the transformer.

[0038] Based on the above, obtain the winding information when the winding data of each detection time period in the transformer is abnormal.

[0039] In a specific embodiment of the present invention, the abnormal cause corresponding to the winding in the transformer is analyzed as follows: Import the winding information when the winding data of each detection time period in the transformer is abnormal into the transformer state analysis model: Where K is the abnormal value corresponding to the winding in the transformer, B is the oil leakage amount corresponding to the transformer, B' is the set reference oil leakage amount, M' is the set reference harmonic, M i is the harmonic of the i-th associated device corresponding to the transformer, G' is the set reference temperature, G u is the temperature corresponding to the transformer, and τ is the set reference abnormal value of the transformer.

[0040] When the abnormal value corresponding to the winding in the transformer is 1, it is determined that the abnormal cause corresponding to the winding in the transformer is not a combined abnormality. When the abnormal value corresponding to the winding in the transformer is 0, it is determined that the abnormal cause corresponding to the winding in the transformer is a combined abnormality.

[0041] It should be noted again that the combined abnormality is a comprehensive manifestation of the transformer's own abnormality and the abnormality caused by the associated device or the transformer's own abnormality and the abnormality caused by the environment or the abnormality caused by the associated device and the environment or the transformer's own abnormality, the abnormality caused by the associated device and the abnormality caused by the environment.

[0042] In a specific embodiment of the present invention, the determination of whether the transformer is faulty is as follows: Obtain the transformer information corresponding to the abnormal cause of the winding in the transformer, where the transformer information includes short-circuit impedance, winding temperature, DC resistance, and dissolved gas content, and import the transformer information corresponding to the abnormal cause of the winding in the transformer into the transformer winding fault assessment model, and then analyze to obtain the fault result value corresponding to the winding in the transformer. If the fault result value corresponding to the winding in the transformer is 1, it is determined that the transformer is not faulty and is a transient abnormal fluctuation of the winding in the transformer. If the fault result value corresponding to the winding in the transformer is 0, it is determined that the transformer is faulty, and an alarm is issued to notify the staff, so as to determine whether the transformer is faulty.

[0043] It should be noted that the short-circuit impedance of the transformer corresponding to the abnormal cause of the winding in the transformer is detected by using a short-circuit impedance tester; the temperature of the transformer corresponding to the abnormal cause of the winding in the transformer is detected by using a thermal resistance temperature sensor; the DC resistance of the transformer corresponding to the abnormal cause of the winding in the transformer is detected based on a DC resistance tester; the dissolved gas content of the transformer corresponding to the abnormal cause of the winding in the transformer is detected by using an oil chromatograph analyzer.

[0044] In a specific embodiment of the present invention, the analysis to obtain the fault result value corresponding to the winding in the transformer is as follows: Through the transformer winding fault assessment model:

[0045] Analyze to obtain the fault result value ψ corresponding to the winding in the transformer. v′ is the set reference short-circuit impedance, x′ is the set reference winding temperature, g′ is the set reference DC resistance, j′ is the set reference dissolved gas content, v is the short-circuit impedance corresponding to the winding in the transformer, x is the winding temperature corresponding to the winding in the transformer, g is the DC resistance corresponding to the winding in the transformer, j is the dissolved gas content corresponding to the winding in the transformer, and c is the set reference fault value of the winding in the transformer.

[0046] It should be noted that the dissolved gas includes but is not limited to hydrogen, acetylene, or methane, etc.

[0047] It should be noted that the reference short-circuit impedance, reference winding temperature, reference DC resistance, reference dissolved gas content, and reference fault value of the winding in the transformer are set by professional power personnel; the reference short-circuit impedance is a reference value for judging whether the transformer is faulty, and its reference winding temperature, reference DC resistance, and reference dissolved gas content have the same function, so they will not be elaborated here; the reference fault value of the winding in the transformer is a reference value for judging whether the transformer is faulty.

[0048] Based on the winding data results corresponding to each detection time period in the transformer, obtain the winding information when the winding data of the transformer winding is abnormal in each detection time period, and then analyze the corresponding abnormal reasons of the winding in the transformer, and judge whether the transformer is faulty, so as to realize the efficient and accurate determination of the transformer state, thus effectively guaranteeing the power supply of the electrical automation power supply system and realizing the safe and efficient power supply of the electrical automation power supply system.

[0049] Step 4, warning prompt: When the winding data of a certain detection time period of the winding in the transformer is abnormal or the transformer is faulty, a warning prompt is given.

[0050] The database is used to store winding data, direct current resistance data set, voltage ratio data set, transformer data and transformer information.

[0051] In the embodiment of the present invention, by obtaining the winding data of the winding in the transformer at each detection time point, and then analyzing to obtain the corresponding winding data set, and judging whether the winding data of the winding in the transformer corresponding to each detection time period is normal, analyzing the corresponding abnormal reasons of the winding in the transformer, and judging whether the transformer is faulty, so as to realize the in-depth analysis of the transformer in the electrical automation power supply system, efficiently guarantee the operation safety and operation efficiency of the transformer in the electrical automation power supply system, reduce the power supply risk and failure rate of the electrical automation power supply system, timely discover the safety problems of the winding in the transformer, improve the stability of the electrical automation power supply system, guarantee the power supply quality of the electrical automation power supply system, and realize the safe and efficient power supply of the electrical automation power supply system.

[0052] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology make various modifications or supplements or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all belong to the protection scope of the present invention.

Claims

1. A detection method for an electrical automation power supply system, characterized in that, It includes the following steps: Step 1, Transformer winding detection: Obtain the winding data of the windings in the transformer at each detection time point; Step 2, Transformer winding data acquisition: Based on the winding data of the windings in the transformer during each detection time period, further analyze to obtain the winding data set corresponding to each detection time period of the windings in the transformer, and determine whether the winding data corresponding to each detection time period of the windings in the transformer is normal; Step 3, Transformer winding analysis: Based on the judged winding data results corresponding to each detection time period of the windings in the transformer, obtain the winding information when the winding data of the windings in the transformer is abnormal during each detection time period, further analyze to obtain the corresponding abnormal reasons of the windings in the transformer, and obtain the transformer information corresponding to the abnormal reasons of the windings in the transformer, and judge whether the transformer is faulty; Step 4, Early warning prompt: Give an early warning prompt when the winding data of a certain detection time period of the windings in the transformer is abnormal or the transformer is faulty.

2. The detection method of an electrical automation power supply system according to claim 1, characterized in that, In the said Step 1, the winding data includes direct current resistance, insulation resistance, voltage ratio and frequency.

3. The detection method of an electrical automation power supply system according to claim 2, characterized in that, In the said Step 2, to analyze and obtain the winding data set corresponding to each detection time period of the windings in the transformer, the specific analysis process is as follows: Based on the analysis of the winding data of the transformer in each detection time period, the DC resistance characteristic value and the insulation resistance characteristic value of the winding in the transformer corresponding to each detection time period are obtained, and are respectively denoted as β u and δ u , where u is the number of each detection time period, u = 1, 2,..., n, and n is any integer greater than 2; Import the DC resistance characteristic values and insulation resistance characteristic values of the windings in the transformer corresponding to each detection time period into the transformer winding resistance analysis model where η u is the resistance characteristic value of the winding in the transformer corresponding to the u-th detection time period, p is the set lower limit value of the resistance corresponding to the winding in the transformer, and p' is the set upper limit value of the resistance corresponding to the winding in the transformer; Based on the analysis of the winding data of the transformer in each detection time period, the voltage ratio characteristic value and the frequency response characteristic value of the winding in the transformer corresponding to each detection time period are obtained, denoted as M u and N u , and imported into the transformer winding state analysis model where θ u is the winding change characteristic value of the winding in the transformer corresponding to the u-th detection time period, L is the set upper limit value of the change of the winding in the transformer, and L′ is the set lower limit value of the change of the winding in the transformer; Construct the winding data set corresponding to each detection time period of the windings in the transformer based on the direct current resistance characteristic values, insulation resistance characteristic values, voltage ratio characteristic values and frequency response characteristic values of the windings in the transformer during each detection time period.

4. A method for detecting an electrical automation power supply system according to claim 3, characterized in that The analysis of obtaining the direct current resistance characteristic values and insulation resistance characteristic values corresponding to each detection time period of the windings in the transformer is as follows: Compare the direct current resistance data corresponding to each detection time period of the windings in the current transformer with the direct current resistance threshold values corresponding to the windings in the historical transformers stored in the database. If the direct current data corresponding to a certain detection time period of the windings in the transformer is less than the direct current resistance threshold value corresponding to the windings in the historical transformers stored in the database, then record the direct current resistance characteristic value of the windings in the transformer corresponding to this detection time period as -1, otherwise record it as 1, and so on to obtain the direct current resistance characteristic values corresponding to each detection time period of the windings in the transformer. Similarly, obtain the direct current insulation resistance characteristic values corresponding to each detection time period of the windings in the transformer.

5. A method for detecting an electrical automation power supply system according to claim 3, characterized in that, The analysis of obtaining the voltage ratio characteristic values and frequency response characteristic values corresponding to each detection time period of the windings in the transformer is as follows: Compare the voltage ratio data corresponding to each detection time period of the windings in the current transformer with the voltage ratio threshold values corresponding to the windings in the historical transformers stored in the database. If the voltage ratio data corresponding to a certain detection time period of the windings in the current transformer is less than the voltage ratio threshold value corresponding to the windings in the historical transformers stored in the database, then record the voltage meter characteristic value of the windings in the transformer corresponding to this detection time period as -1, otherwise record it as 1, and so on to obtain the voltage ratio characteristic values corresponding to each detection time period of the windings in the transformer. Similarly, the frequency response characteristic values corresponding to each detection time period of the windings in the transformer can be obtained.

6. The detection method of an electrical automation power supply system according to claim 3, wherein In the said Step 2, to judge whether the winding data corresponding to each detection time period of the windings in the transformer is normal, the judgment process is as follows: Based on the resistance characteristic value η of the winding in the transformer corresponding to each detection time period u and the winding change characteristic value θ u , if η u = 1 ∨ θ u = 0 or η u = 0 ∨ θ u = 1 or η u = 0 ∨ θ u = 0, it is determined that the winding data of the winding in the transformer corresponding to the u-th detection time period is abnormal, and the winding data of the detection time period numbered u is recorded as a type of abnormal data; If η u = -1 ∧ θ u = -1, it is determined that the winding data corresponding to the u-th detection time period in the transformer is abnormal, and the winding data of the detection time period numbered u is recorded as secondary abnormal data; If η u = 1 ∧ θ u = 1, it is determined that the winding data corresponding to the u-th detection time period in the transformer is normal, and the winding data of the detection time period numbered u is recorded as normal data. By analogy, it can be obtained whether the winding data corresponding to each detection time period in the transformer is normal.

7. A method for detecting an electrical automation power supply system according to claim 1, characterized in that, In the said Step 3, to obtain the winding information when the winding data of the windings in the transformer is abnormal during each detection time period, the specific obtaining process is as follows: Collect the transformer data corresponding to the windings in the transformer. By detecting the transformer body, use an ultrasonic leak detector to check whether there is oil in the transformer. If there is no oil, exclude the abnormality of the transformer body. If oil is detected in the transformer, record the oil leakage data to obtain the amount of oil leakage. Obtain the associated information of each associated device corresponding to the transformer. Use a harmonic analyzer to obtain the harmonics of each associated device corresponding to the transformer. If the harmonics of each associated device corresponding to the transformer do not exceed the reference harmonic values stored in the database, exclude the abnormality of the associated device corresponding to the transformer. If the harmonics of a certain associated device corresponding to the transformer exceed the reference harmonic values existing in the database, determine that the harmonic data is abnormal, and then record the harmonics of this associated device. In this way, the harmonics of each associated device corresponding to the transformer are obtained. Obtain the environmental data when the transformer is working. Use a temperature sensor to obtain the corresponding temperature when the transformer is working. If the temperature corresponding to the transformer does not exceed the reference temperature stored in the database, exclude the environmental abnormality. If the temperature corresponding to the transformer exceeds the reference temperature stored in the database, determine that the temperature data is abnormal, and then record the temperature corresponding to the transformer to obtain the winding information when the winding data is abnormal in each detection time period of the windings in the transformer.

8. The detection method of an electrical automation power supply system according to claim 7, characterized in that, In step three, analyze and obtain the abnormal reasons corresponding to the windings in the transformer. The analysis process is as follows: Import the winding information when the winding data is abnormal in each detection time period of the transformer into the transformer status analysis model: where K is the abnormal value corresponding to the winding in the transformer, B is the oil leakage amount corresponding to the transformer, B′ is the set reference oil leakage amount, M′ is the set reference harmonic, M i is the harmonic of the i-th associated device corresponding to the transformer, G′ is the set reference temperature, G u is the temperature corresponding to the transformer, and τ is the set reference abnormal value of the transformer; When the abnormal value corresponding to the windings in the transformer is 1, it is determined that the abnormal reason corresponding to the windings in the transformer is not a combined abnormality. When the abnormal value corresponding to the windings in the transformer is 0, it is determined that the abnormal reason corresponding to the windings in the transformer is a combined abnormality.

9. A method for detecting an electrical automation power supply system according to claim 1, characterized in that, In step three, judge whether the transformer is faulty. The judgment process is as follows: Obtain the transformer information of the abnormal reasons corresponding to the windings in the transformer. The transformer information includes short-circuit impedance, winding temperature, DC resistance, and dissolved gas content. Then import the transformer information after the abnormal reasons corresponding to the windings in the transformer into the transformer winding fault assessment model, and then analyze and obtain the fault result value corresponding to the windings in the transformer. If the fault result value corresponding to the windings in the transformer is 1, it is determined that the transformer is not faulty and there is a transient abnormal fluctuation of the windings in the transformer. If the fault result value corresponding to the windings in the transformer is 0, it is determined that the transformer is faulty, and then an alarm is sent to notify the staff. In this way, it is judged whether the transformer is faulty.

10. A method for detecting an electrical automation power supply system according to claim 9, characterized in that, The analysis process of obtaining the fault result value corresponding to the windings in the transformer is as follows: Through the transformer winding fault assessment model: The fault result value ψ corresponding to the winding in the transformer is obtained through analysis. v′ is the set reference short-circuit impedance, x′ is the set reference winding temperature, g′ is the set reference DC resistance, j′ is the set reference dissolved gas content, v is the short-circuit impedance corresponding to the winding in the transformer, x is the winding temperature corresponding to the winding in the transformer, g is the DC resistance corresponding to the winding in the transformer, j is the dissolved gas content corresponding to the winding in the transformer, and c is the set reference fault value of the winding in the transformer.

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