Patent Application
20240288512
This invention relates to a transformer diagnostic system.
As a technique for diagnosing transformer anomalies, patent document 1 uses a temperature sensor at the bottom and moisture content to estimate the degree of polymerization.
The patent document 2 monitors the load factor and other temperature information without incorporating it by means of upper and lower temperature sensors.
The patent document 3 diagnoses deterioration by temperature rise.
[patent document 1] Japanese unexamined patent publication Tokkai2019-102694
[patent document 2] Japanese unexamined patent publication Tokkaihei5-283240
[Patent document 3] Japanese unexamined patent publication Tokkai2006-24800
Since the amount of moisture in oil can vary depending on its temperature, if the moisture content is measured at the bottom of the transformer, the moisture content will be lower than during operation, and the error in the degree of polymerization estimation value may be larger. In patent document 1, where the degree of polymerization is estimated by the temperature sensor at the bottom and the moisture content, the error in the estimated degree of polymerization value may become larger.
There is a time constant between the winding and the oil, and that time constant varies depending on the oil type and winding structure. The temperature rise of the winding is key in considering damage to the insulation paper, and the monitoring of abnormal diagnosis is insufficient in patent document 2, which monitors without incorporating the load factor.
Patent document 3 diagnoses abnormalities mainly by temperature rise, but the parameters that determine the life of the transformer should consider abnormality diagnosis for other factors such as deterioration of insulating oil and the presence or absence of discharge in addition to the deterioration of insulating paper.
The techniques in patent document 1, patent document 2, and patent document 3 are not sufficient to diagnose the lifetime of a transformer with high accuracy.
The purpose of this invention is to provide a transformer diagnostic system that enables highly accurate diagnosis.
One example of this invention is a transformer diagnostic system having an insulating oil and a winding comprising a detection part detects a temperature at the top and bottom of the insulating oil and hydrogen, an arithmetic unit determines abnormality based on the detected values from the detection part.
According to the present invention, it is possible to realize a diagnostic system for transformer that can perform highly accurate diagnosis.
The following is an example of the invention, illustrated with drawings.
The transformer 1 is an oil-filled transformer with an iron core, a winding attached to the iron core and insulated by insulation paper (winding insulation paper), and insulating oil that soaks the winding and iron core, although the figure is omitted.
Hydrogen detector 2 detects hydrogen gas components based on data from a sensor that detects hydrogen gas components in the insulating oil at the bottom of transformer 1. Here, it is also the detector that detects the temperature in the lower part of the insulating oil of transformer 1. The hydrogen detector and the temperature detector for detecting the temperature at the bottom of the transformer 1 can be separate sensors.
The temperature detector 3 detects temperature based on data from a temperature sensor (such as a resistance thermometer) that detects the temperature at the top of the insulating oil of the transformer 1.
The current detector 4 detects the load current (secondary current) flowing in the load.
Converter 6 performs data conversion of detection data from hydrogen detector 2, temperature detector 3, and current detector 4 for processing on an arithmetic pc (personal computer) 7.
The arithmetic PC 7 (arithmetic section) is a computer device equipped with a processor (processing unit) such as a CPU, main memory, memory device, communication device, etc., and processes various types of information. The processor (processing unit) of the PC 7 executes the operations described below to diagnose abnormalities in the transformer and to predict its service life.
This example transformer diagnostic system comprising a temperature detector 2 that detects hydrogen and the temperature of the bottom part of the insulating oil, a detector 3 that detects the temperature of the top part of the insulating oil, a current detector (CT) 4 that detects the load current (secondary current) flowing in the load, an insulated paper pocket 5 that takes samples of the insulating paper, a converter 6 that converts the data, and an arithmetic PC 7 that performs diagnostics and life prediction of the transformer.
The diagnostic system of the transformer in this example measures the maximum winding temperature at the load factor, upper oil temperature, and lower oil temperature, and acquires the temperature history information by wired or wireless communication with an arithmetic PC 7. At the same time, the arithmetic PC 7 obtains information from the hydrogen sensor and diagnoses abnormalities based on its behavior. The load factor can be calculated from the load current from current detector 4. The current to be detected is not limited to the load current, but the load current can be detected from the primary current of the transformer.
The maximum winding temperature θH is defined by the following equation 1 (Transformer Reliability Investigation Technical Committee, “Oil-immersed transformer operation guidelines,” IEEJ Technical Report (Part 1) No. 143, November 1986, p. 1-2).
The parameters in equation 1 that can be directly measured are θa and K. The parameters that are transformer-specific and can be specified in the shipment are θ0N, R, m, and n. Thus, the unknown value is θgN, and this estimation technique has been defined in various ways.
Many techniques were employed to estimate using pre-measured values, but in the case of new designs, there was a problem that the previous knowledge was not utilized and differed from the results of the pre-measurement.
In this example, the maximum winding temperature can be estimated with high accuracy in addition to estimation by machine learning by analyzing the parameters attributed to them in addition to the actual measurement results.
The vertical axis in
The solid circled area indicates the area where the temperature is actually measured. Dotted circles indicate areas where temperatures are calculated from the measured temperatures.
The average winding temperature in
The arithmetic PC 7 processor calculates the average oil temperature from the measured upper oil temperature, lower (bottom) oil temperature, and the known winding height, and calculates the “average temperature difference between winding and oil” Δθwo. from the difference between the measured average winding temperature and the average oil temperature.
The processing unit of arithmetic PC 7 then calculates the “average winding upper temperature” from the measured upper oil temperature (maximum oil temperature) and the “average temperature difference between winding and oil” Δθwo. The “average winding top temperature” can be calculated from the measured temperatures.
From information such as the “average winding top temperature” and the measured upper oil temperature (maximum oil temperature), the processing unit of the arithmetic PC 7 can accurately estimate the maximum winding temperature.
Until now, the maximum oil temperature of the transformer and the average winding temperature have usually been measured, and the difference between the maximum point oil temperature and the maximum winding temperature was defined as 15° C. Different oil types and designs cause changes in the slope of the winding temperature rise and the slope of the oil temperature rise, resulting in cases where this value cannot be applied as it is.
The processing unit of arithmetic PC 7 in this example measures the oil temperature at the top and bottom, calculates the average oil temperature from the winding height information, and takes the difference from the average winding temperature obtained by actual measurement, so that the aforementioned “difference between the maximum winding temperature at rated load and the maximum oil temperature” θgN can be defined based on actual measurement rather than a constant 15° C.
The arithmetic PC 7 processor in this example can then calculate the difference θgN between the maximum oil temperature and the maximum winding temperature according to the slope of the winding temperature rise and the slope of the oil temperature rise for different oil types and designs. The maximum winding temperature : can then be calculated from equation 1 with higher accuracy.
Furthermore, the processing unit of arithmetic PC 7 in this example uses multiple regression curves to estimate the maximum winding temperature from information on various parameters (oil type, capacity, winding height, number of cooling ducts, wire cross-sectional area, wire shape, loss, etc.) determined during design, actual upper oil temperature and lower (bottom) oil temperature, and average winding temperature, using machine the maximum winding temperature is estimated by machine learning. The estimated maximum winding temperature is reflected in the new design to enable highly accurate life prediction at the design stage.
Using the maximum winding temperature obtained above, the arithmetic PC 7 processor in this example predicts the life of the transformer. The life of the transformer is defined as the degradation of the insulation paper and can be approximated by the Arrhenius law using the Montsinger equation within the temperature range of 80° C. to 150° C. and the following equation 2 can be derived by defining it by the 6° C. half rule.
The processing unit of the arithmetic PC 7 in this example calculates Y/Y, according to equation 2. Equation 2 allows the remaining life of the transformer to be estimated from the information of the actual thermal history used.
Furthermore, the actual degradation of the insulating paper is also measured by collecting and analyzing a sample of insulating paper from the insulated paper pocket 5 attached to the top of the transformer 1 to measure the average degree of polymerization. If the lifetime estimation does not agree with the analysis results, etc., the average degree of polymerization measured is used as the corrected equation 2, and the processing unit of the arithmetic PC 7 in this example performs the estimation calculation according to the corrected equation. In this way, the system can be doubly monitored by on-line and off-line monitoring. Such a configuration allows a more reliable diagnostic system to be configured.
In this example, a hydrogen detector 2 is installed in the lower part of the tank of the transformer 1 to prevent a serious accident from occurring due to a sudden abnormality such as local heating that is left unnoticed. The processing apparatus of the arithmetic PC 7 diagnoses an abnormality of the transformer by determining that the hydrogen component acquired from the hydrogen detector 2 exceeds a predetermined value (threshold).
Hydrogen is the cause of all abnormal phenomena and exerts its effects on discharge, heating, and deterioration of insulation paper. In some cases, the degradation of insulation paper can be measured using the moisture content in oil as a proxy, but in this case, if the temperature at the time of collection changes, the saturated moisture content in the oil will differ, and the deposited moisture will migrate to the insulation paper, so there is a risk of misjudging the measurement results. On the other hand, in the case of hydrogen, which is a gas, changes in solubility due to changes in temperature exist, but the changes are several times smaller than those of water content, so the accuracy of analysis is dramatically improved.
The above-mentioned transformer detection data is imported into an arithmetic PC 7 through converter 6, and various calculations are performed to determine abnormalities in the transformer and to diagnose its remaining life.
According to this example, it is possible to realize a transformer's diagnostic system that can perform highly accurate diagnosis.
In example 2, the touch panel 8, first wireless communication equipment 9, second wireless communication equipment 11, and data server 10 are provided. The data server 10 can diagnose the abnormalities of the transformer and predict the service life of the transformer via the first wireless communication equipment 9 and second wireless communication equipment 11 without arithmetic PC 7 in example 1. In this example, detection data such as temperature at the top and bottom of the insulating oil, hydrogen, and load current can be stored in the data server 10. Hereinafter, explanations of the same items as in example 1 will be omitted.
The touch panel 8 obtains detection data such as temperature at the top and bottom of the insulating oil, hydrogen, and load current via converter 6. The user can monitor these detection data with the touch panel 8.
First wireless communication equipment 9 and second wireless communication equipment 11 communicate data between touch panel 8 and data server 10.
Based on the detection data from the transformer 1 obtained from the second wireless communication equipment 11, the data server 10 performs the same as the arithmetic PC 7 in example 1 to determine the abnormality of the transformer and to diagnose the remaining life of the transformer.
According to this example, in addition to the effects of example 1, the detection data of multiple transformers can be stored in data server 10, and the diagnosis of each transformer can be centrally managed.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2021-135779 | Aug 2021 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2022/017216 | 4/7/2022 | WO |
