JP7405707B2 - Transformer diagnostic method and system - Google Patents

Description

The present invention relates to a diagnostic method and system for diagnosing current abnormalities and future abnormalities in a transformer.

Power system development plans are made based on forecasts of power demand and power source development for more than 10 years. However, in recent years, the use of renewable energies that do not emit greenhouse gases, such as sunlight, solar heat, hydropower, wind power, biomass, and geothermal heat, has been expanding. Furthermore, society is aging, the working population is decreasing, and the transition to an information society is progressing. Therefore, flexible maintenance of power systems is required in line with these trends.

As renewable energy becomes more widespread, it is expected that optimization of power flow will become more important. When wind power generation, solar power generation, etc. are expanded in addition to thermal power generation and nuclear power generation, there is a possibility that power flow will be concentrated in a specific power system, causing track overload. Depending on the state of equipment on the power system, events that are not anticipated at the planning stage can destabilize the power system, so it is important to predict equipment abnormalities in advance.

There are two types of transformers installed in power systems: oil-immersed type and dry type (molded type). Oil-immersed transformers have a structure in which the coil is immersed in insulating oil, and are more widely used than dry transformers. Mineral oil, vegetable oil, etc. are used as the insulating oil. Both insulation and cooling performance are achieved by natural convection or forced circulation of mineral oil, etc., which has high voltage resistance.

Conventionally, time-based maintenance (TBM) has been adopted as a method for maintaining and managing transformers. The general expected lifespan of a transformer is said to be about 30 years. When insulating oil and insulating materials deteriorate, gases characteristic of internal abnormalities are generated, so gas-in-oil analysis is performed at electric power companies, railway companies, general business companies, etc. about once a year.

Non-Patent Document 1 describes a standard value of gas allowed in insulating oil as an index used for maintenance management of an oil-immersed transformer. Hydrogen (H ₂ )≧400ppm, Methane (CH ₄ )≧100ppm, Ethane (C ₂ H ₆ )≧150ppm, Ethylene (C ₂ H ₄ )≧10ppm, Acetylene (C ₂ H ₂ )≧0.5ppm, Monoxide Either carbon (CO) ≧300ppm, total combustible gas (TCG) ≧500ppm, “Caution required I”, C ₂ H ₂ ≧0.5ppm, C ₂ H ₄ ≧10ppm and TCG ≧500ppm. "Caution required II" for one of the following, "Abnormal" for any one of C ₂ H ₂ ≥ 5 ppm, C ₂ H ₄ ≥ 100 ppm and TCG ≥ 700 ppm, and C ₂ H ₄ ≥ 100 ppm and TCG increase ≥ 70 ppm/month. ”.

Electric Cooperative Research Association, “Electric Cooperative Research”, Volume 54, No. 5 (Part 1) “Maintenance and Management of Oil-immersed Transformers”

According to the conventional method of maintenance and management of transformers, since gas in oil analysis is performed using TBM, it is possible to know the amount of gas generated in the insulating oil at the time the insulating oil is sampled. Therefore, it is possible to diagnose whether there is a current abnormality in the transformer based on the current state of deterioration of the insulating oil or insulating material. Furthermore, since the average operational life of the transformer is known, the remaining operational life can be predicted from the current state of deterioration.

However, conventional maintenance management methods have a problem in that they cannot predict future abnormalities that will occur in transformers. The operating life of a transformer is affected by aging deterioration of the insulating oil and insulating material, accidental abnormalities, etc., but in reality, there are very few cases where the life of a transformer ends due to aging.

It is thought that transformer failures are likely to occur when abnormalities progress, so in order to operate power systems stably, it is desirable to predict abnormalities that may lead to accidental failures in advance. If abnormalities that can lead to accidental failures can be predicted in advance, it becomes possible to prevent operation stoppages due to failures by carrying out repairs, inspections, etc. However, although methods are known for determining current abnormalities that may lead to accidental failures, there has been no known method for accurately determining future abnormalities.

In the future, if renewable energy becomes more widespread and the power sources on the power system become more diverse, there is a possibility that failures due to instantaneous load fluctuations and harmonic currents that are not anticipated in maintenance plans will increase. In conventional maintenance management methods, gas in oil analysis is performed using TBM, so even if an abnormality leading to a failure is confirmed, it is difficult to promptly take measures such as stopping the transformer. Therefore, there is a need for a means to accurately grasp current abnormalities and future abnormalities as precursory phenomena that lead to transformer failure.

SUMMARY OF THE INVENTION Therefore, an object of the present invention is to provide a diagnostic method and system capable of diagnosing current abnormalities and future abnormalities that may lead to a failure of a transformer.

That is, in order to solve the above problems, the diagnostic method according to the present invention is a diagnostic method for diagnosing an abnormality in a transformer, and includes a step of measuring the amount of gas in the insulating oil of the transformer over time ; Based on the amount of gas in the insulating oil measured for each operating period of the transformer, the operating time of the transformer is divided into a plurality of periods according to the tendency of change in failure rate in the failure rate curve of the transformer. Then , a regression analysis was performed with time as an independent variable and the amount of gas in the insulating oil of the transformer as a dependent variable , and an approximate function representing the relationship between the operating time of the transformer and the amount of gas in the insulating oil was calculated. and a step of comparing the amount of gas in the insulating oil of the transformer with the amount of gas in the insulating oil estimated based on the approximation function for each operating period of the transformer. The operation period of the transformer includes an initial failure period in which the failure rate in the failure rate curve of the transformer decreases over time, an occasional failure period in which the failure rate in the failure rate curve of the transformer is steady, and This is a period set as one of the wear-out failure periods in which the failure rate in the failure rate curve of the transformer increases over time .

Further, the diagnostic system according to the present invention is a diagnostic system for diagnosing an abnormality in a transformer, and the operating time of the transformer is divided into a plurality of periods according to a tendency of change in failure rate in a failure rate curve of the transformer. Based on the amount of gas in the insulating oil of the transformer measured over time for each operating period of the divided transformer , we set time as the independent variable and the amount of gas in the insulating oil of the transformer as the dependent variable. a regression analysis unit that performs regression analysis to calculate an approximate function representing the relationship between the operating time of the transformer and the amount of gas in the insulating oil; a diagnostic unit that compares the amount of gas in the insulating oil with the amount of gas in the insulating oil estimated based on the approximation function, an early failure period in which the failure rate decreases over time, a random failure period in which the failure rate in the failure rate curve of the transformer is steady, and a wear-out failure period in which the failure rate in the failure rate curve of the transformer increases over time. This is the period set as one of the following .

According to the present invention, it is possible to provide a diagnostic method and a diagnostic system that can diagnose current abnormalities and future abnormalities that lead to transformer failure.

1 is a diagram showing the configuration of a diagnostic system according to a first embodiment of the present invention. It is a figure showing the result of measuring the amount of gas in the insulating oil of a transformer over time. It is a figure showing the result of measuring the amount of gas in the insulating oil of a transformer over time. FIG. 3 is a diagram showing a failure rate curve of a general transformer. It is a figure explaining the approximation function calculated|required in the range of an early failure period. FIG. 3 is a diagram illustrating an approximation function obtained within a range of random failure periods. FIG. 3 is a diagram illustrating an approximation function obtained within a wear-out failure period range. It is a flowchart which shows an example of the process of diagnosing abnormality of a transformer. It is a figure showing the diagnostic system concerning a 2nd embodiment of the present invention. It is a figure showing the composition of the diagnostic system concerning a 2nd embodiment of the present invention. FIG. 3 is a diagram showing an example of a measurement database based on measurement data obtained from a plurality of transformers. 2 is a flowchart illustrating an example of a process for diagnosing abnormalities in a plurality of transformers.

A diagnosis method and a diagnosis system according to an embodiment of the present invention will be described below. In addition, in each of the following figures, common components are given the same reference numerals and redundant explanations will be omitted.

The diagnostic method according to this embodiment relates to a method for diagnosing abnormality in a transformer. Diagnosis of abnormalities in a transformer includes diagnosing current abnormalities occurring in the transformer and predicting future abnormalities occurring in the transformer. Although the transformer to be diagnosed is an oil-immersed transformer, the type of insulating oil that is the insulating medium, the type of insulating material, the rated capacity and rated voltage of the transformer, etc. are not particularly limited.

Examples of abnormalities in transformers include events that require replacement of the insulating medium or insulating material, events that require stopping the operation of the transformer, and precursory phenomena thereof. Specific examples of transformer abnormalities include a decrease in the insulation properties or dielectric breakdown of the insulating medium or insulating material, a short circuit or overheating of the coil, progression of the flow charging phenomenon of the insulating medium, and occurrence of partial discharge.

The diagnosis method according to the present embodiment includes a step of measuring the amount of gas in the insulating oil of a transformer over time, and a regression analysis based on the measured amount of gas in the oil for each operating period of the transformer. the amount of gas in the insulating oil of the transformer estimated based on the approximation function. and comparing the amount of.

In the diagnostic method according to the present embodiment, gas in oil analysis is performed over time on insulating oil sealed in a transformer, and the amount of gas in the insulating oil is measured over time. Then, by regression-analyzing the measurement results measured over time and grasping the tendency of abnormalities occurring in the transformer, maintenance management for condition-based maintenance (CBM) is enabled.

From the measurement results of gas-in-oil analysis, by performing regression analysis with time as an independent variable and the amount of gas in oil as a dependent variable, an approximate function representing the amount of increase (increase rate) of gas in oil per unit time can be calculated. is required. It can be said that the approximate function reflects the effects of aging deterioration of the insulating medium and insulating material, as well as the effects of accumulation of repeated abnormalities. From the approximation function, it can be determined whether the current measured value of the amount of gas in oil and the predicted value of the future amount of gas in oil have the same tendency as past aging deterioration or past repeated abnormalities.

Therefore, by determining whether the measured amount of gas in the transformer's insulating oil deviates from the current estimate of the amount of gas in the oil estimated from the approximation function, It is possible to diagnose the presence or absence of an abnormality. In addition, by determining whether the estimated value of the future amount of gas in oil estimated from the approximate function deviates from the upper limit allowed for the amount of gas in the insulating oil of the transformer, the conventional prediction It is possible to diagnose the possibility of future abnormalities, which was previously difficult.

When diagnosing a current abnormality in a transformer, the estimated value of the current amount of gas in oil can be obtained by substituting the current operating time of the transformer into the approximate function that represents the increase in the amount of gas in oil per unit time. demand. Then, the current measured value of the amount of gas in the insulating oil of the transformer corresponding to the same operating time is compared with the estimated value of the amount of gas in the oil estimated based on the approximation function.

As a result of the comparison, the measured value of the amount of gas in the insulating oil of the transformer exceeds a predetermined value with respect to the estimated value of the amount of gas in the oil estimated based on the approximation function. In this case, it can be determined that an abnormality has occurred in the transformer. In such a case, a warning is displayed or notified to the transformer operator, and the insulating oil or insulation material is replaced or the transformer operation is stopped.

In addition, when diagnosing future abnormalities in a transformer, the future amount of gas in oil can be estimated by substituting the future operating time of the transformer into an approximate function that represents the increase in the amount of gas in oil per unit time. Find the value. Then, the estimated value of the amount of gas in the oil estimated based on the approximate function is compared with the upper limit value of the amount of gas in the insulating oil of the transformer corresponding to the same operating time.

As a result of the comparison, the estimated value of the amount of gas in oil estimated based on the approximation function is greater than or equal to a preset value with respect to the upper limit of the amount of gas in insulating oil of the transformer. In this case, it can be determined that an abnormality will occur in the transformer in the future. In such a case, a warning is displayed or notified to the transformer operator, and the transformer is inspected, the insulating oil and insulating material are replaced, and the transformer operation is stopped.

The predetermined value used for the determination can be arbitrarily set according to the type of gas in oil within a range that exceeds measurement errors in the amount of gas in oil and natural fluctuations in the amount of gas in oil. The predetermined value is preferably a value that is 20% or more of the estimated value of the amount of gas in oil estimated based on the approximation function. Generally, it is said that the equipment error and measurement error of a transformer is about 20% at most. Therefore, if a value of 20% or more is set for the amount of gas in oil indicated by the approximation function, misdiagnosis can be suppressed.

Next, a diagnostic system using the diagnostic method according to the present embodiment will be described with reference to the drawings and the details of the diagnostic method.

FIG. 1 is a diagram showing the configuration of a diagnostic system according to a first embodiment of the present invention.
As shown in FIG. 1, the diagnostic system 100 according to the present embodiment includes a calculation unit 10 that performs processing for diagnosing an abnormality in a transformer, a storage unit 20 that stores data used for diagnostic processing, and an input unit 30. , a display section 40, and a communication section 50.

The diagnostic system 100 is a device that has a function of executing the above-described diagnostic method, and is used to diagnose current abnormalities and future abnormalities in the transformer. The transformer to be diagnosed is an oil-immersed transformer, and is installed on the power system away from the diagnostic system 100.

The diagnostic system 100 is configured to output a diagnosis result of a transformer abnormality in response to one input representing the amount of gas in the insulating oil of the transformer. The diagnostic system 100 may be configured to input measurement data representing the amount of gas in the insulating oil of the transformer manually by reading measured values, or may be configured to input measurement data from a gas sensor via a communication line. It's okay.

The line connecting the diagnostic system 100 and the gas sensor may be either a wired communication line or a wireless communication line. Further, when manually inputting the measurement data, the measurement data may be acquired by a gas sensor attached to the transformer, or the insulating oil of the transformer may be sampled and acquired by an analyzer or the like.

As target gases for gas-in-oil analysis, it is preferable to analyze one or more of methane, acetylene, ethylene, ethane, hydrogen, carbon monoxide, and carbon dioxide. As the target gas, it is more preferable to analyze one or more of methane, hydrogen, and carbon dioxide, and it is even more preferable to analyze at least hydrogen.

The concentration of these gases in the insulating oil increases when the insulating oil or insulating paper is thermally decomposed due to abnormal electrical discharge or overheating, or when the insulating oil or insulating paper deteriorates over time. It is known that methane, acetylene, ethylene, ethane and hydrogen are mainly derived from the deterioration of insulating oil. It is known that carbon monoxide and carbon dioxide mainly originate from the deterioration of insulating materials such as insulating paper and pressboard.

Therefore, by measuring the amount of these gases, it is possible to determine the current condition of the insulating oil and insulation material, as well as the extent of abnormalities that have occurred inside the transformer in the past. That is, the current abnormality occurring in the transformer can be diagnosed from the current amount of gas in oil. Furthermore, since the future state can be estimated from the trend of the increase in the amount of gas in oil per unit time up to the present, it is also possible to diagnose future abnormalities that may occur in the transformer.

FIGS. 2 and 3 are diagrams showing the results of measuring the amount of gas in the insulating oil of a transformer over time.
2 and 3 show the results of gas-in-oil analysis of an oil-immersed transformer using mineral oil as an insulating medium and insulating paper as an insulating material. In FIGS. 2 and 3, the horizontal axis shows the operating time of the transformer [years], and the vertical axis shows the gas amount (concentration) [ppm].

In Figure 2, ○ is the result of acetylene (C ₂ H ₂ ), △ is the result of methane (CH ₄ ), □ is the result of ethane (C ₂ H ₆ ), and ◇ is the result of ethylene (C ₂ H ₄ ). This is the result. In FIG. 3, □ is the result for carbon dioxide (CO ₂ ), ◇ is the result for carbon monoxide (CO), and ○ is the result for hydrogen (H ₂ ).

As shown in Figures 2 and 3, as the transformer continues to operate, the amount of gas increases depending on the rated capacity and rated voltage of the transformer, the type of insulating medium and material, the type of cause of the abnormality, etc. do. The amount of gas increases depending on the type of gas, and the degree of increase also differs from one another. However, unlike aging deterioration, an accidental abnormality occurs as a relatively rapid change, resulting in a large change in the amount of gas in a short period of time.

Therefore, when an approximate function representing the increase in the amount of gas in oil per unit time is obtained for each type of gas in oil, the difference between the estimated value of the amount of gas in oil and the amount estimated based on the approximate function causes aging degradation. It is possible to diagnose abnormalities caused by other factors. Furthermore, the results of the comparison for each type of gas in oil show a characteristic pattern, so it is possible to estimate the type of cause of the abnormality.

In the diagnostic method and diagnostic system 100 according to the present embodiment, an approximate function representing the amount of increase in gas in oil per unit time is determined for each operating period of the transformer. As the operating period of the transformer that is the target range of the regression analysis, a period set based on the trend of time change in the failure rate of the transformer, for example, a period set based on the failure rate curve can be used. .

FIG. 4 is a diagram showing a failure rate curve of a typical transformer.
In FIG. 4, the horizontal axis shows the operating time of the transformer, and the vertical axis shows the failure rate [%] of the transformer. The broken line shows the failure rate for early failures caused by transformer design and manufacturing, the one-dot chain line shows the failure rate for random failures that occur accidentally in the transformer, and the two-dot chain line shows the failure rate due to aging such as wear and tear. Represents the failure rate of wear-out failures. The solid line represents the combined failure rate curve.

As shown in Figure 4, it is known that the probability that a typical transformer will fail within a unit time shows a characteristic tendency that depends on operating time, and is expressed by a bathtub-shaped failure rate curve. There is. The failure rate curve can be divided into three regions depending on the tendency of change in failure rate. The first period in which the failure rate decreases over time is called the early failure period, the intermediate period in which the failure rate is steady is called the random failure period, and the final period in which the failure rate increases over time is called the wear-out failure period. .

FIG. 5 is a diagram illustrating an approximation function determined within the range of the initial failure period. FIG. 6 is a diagram illustrating an approximation function obtained within the range of the random failure period. FIG. 7 is a diagram illustrating an approximation function obtained within the wear-out failure period range.
In FIGS. 5 to 7, the horizontal axis shows the operating time of the transformer [years], and the vertical axis shows the amount (concentration) of gas in oil [ppm]. The solid line is the result of measuring the amount of gas in the insulating oil over time, and the broken line is an approximation function obtained by regression analysis of the measurement results, that is, an approximation representing the increase in the amount of gas in the oil per unit time. It is a function.

As shown in FIGS. 5 to 7, an approximate function representing the amount of increase in gas in oil per unit time can be obtained by linear regression for each operating period of the transformer. A first-order approximation function by linear regression is expressed as Y=aX+b. The coefficients a and b are determined for each type of gas in oil and for each operating period of the transformer. As a linear regression method for obtaining such an approximate function, for example, the least squares method can be used.

The initial failure period of a transformer includes a period of 3 to 6 years from the start of operation of the transformer, depending on the rated capacity, rated voltage, etc. of the transformer. The initial failure period is a period in which the failure rate of the transformer decreases over time, and the amount of increase in gas in oil per unit time tends to be relatively small. Although the difference in the amount of gas in oil for each type of gas in oil depends on the initial degassing conditions, etc., it is usually small. In FIG. 5, the period up to 5 years of operating time is defined as the initial failure period.

The random failure period of a transformer includes a period of 20 to 30 years from the end of the initial failure period, although it depends on the rated capacity, rated voltage, etc. of the transformer. The random failure period is a period in which the failure rate of the transformer is steady, and the amount of increase in gas in oil per unit time tends to increase in response to random abnormalities. In FIG. 6, the period from 5 to 25 years of operating time is defined as the random failure period.

The wear-out failure period of a transformer includes the period after the end of the random failure period. The wear-out failure period is a period in which the failure rate of the transformer increases over time, and the amount of increase in gas in oil per unit time tends to be relatively large. The difference in the amount of gas in oil for each type of gas in oil tends to increase depending on the effects of aging and accumulation of abnormalities. In FIG. 7, the period from 25 to 30 years of operating time is defined as the wear-out failure period.

If the period set based on the failure rate curve divided in this way is used as the operating period of the transformer that is the target range of regression analysis, an approximation that narrows down the causes of abnormalities for each operating period of the transformer can be made. A function is obtained. Since an approximate function that can accurately diagnose accidental abnormalities is obtained, it is possible to more accurately diagnose current abnormalities that lead to transformer failure and future abnormalities that lead to transformer failure.

As shown in FIG. 1, in the diagnostic system 100, the calculation section 10, the storage section 20, the input section 30, the display section 40, and the communication section 50 are connected to a bus.

The calculation unit 10 includes a data acquisition unit 11, a primary diagnosis unit 12, a regression analysis unit 13, an estimation unit 14, a diagnosis unit 15, and an image generation unit 16. These functions are realized by the calculation unit 10 executing a predetermined program.

The storage unit 20 also stores measurement data 21 of the amount of gas in oil, diagnostic condition data 22, operating period data 23, approximate function data 24, estimated data 25, and diagnostic result data 26. . The diagnostic condition data 22 and the operating period data 23 are input into the diagnostic system 100 in advance.

The calculation unit 10 reads various data and programs, executes programs for diagnosing abnormalities in the transformer, performs calculations, and the like. The calculation unit 10 is configured by, for example, a calculation device such as a CPU (Central Processing Unit), and a storage device such as a RAM (Random Access Memory) or a ROM (Read Only Memory). The calculation unit 10 can be configured with appropriate hardware such as an IC, a computer, a calculation server, and a cloud.

The storage unit 20 stores various data and programs for diagnosing abnormalities in the transformer. The storage unit 20 is configured by a storage device such as a ROM, a hard disk, a flash memory, a magnetic disk, an optical disk, etc., for example. The storage unit 20 may be composed of one piece of hardware, or may be composed of a plurality of pieces of hardware. A part or all of the functions of the storage unit 20 may be realized by an external storage device or the like.

The input unit 30 is a device that receives input from the operator of the diagnostic system 100. The input unit 30 includes, for example, a keyboard, a mouse, a touch panel, and the like. The input unit 30 is connected via an input interface (not shown). When manually inputting the measured data 21 representing the amount of gas in the insulating oil of the transformer, the input unit 30 can be used to input the measured data 21.

The display unit 40 is a device that displays operation information of the diagnostic system 100, contents of various data, diagnostic status, diagnostic results, and the like. The display unit 40 is configured by, for example, a liquid crystal display, an organic EL display, a cathode ray tube, or the like. The display unit 40 is connected via an output interface (not shown).

The communication unit 50 transmits and receives various data via a communication interface. When the measurement data 21 representing the amount of gas in the insulating oil of the transformer is input from the gas sensor, the communication unit 50 can be used to input the measurement data 21.

The measurement data 21 is data representing a measured value of the amount of gas in the insulating oil of the transformer, and includes information such as the amount and concentration of gas in oil for each type of gas in oil associated with time. The measurement data 21 is collected as time-series data for each rated capacity and rated voltage of the transformer to be diagnosed, and for each type of gas in oil.

Diagnostic condition data 22 is data representing various conditions used for diagnosis, including the timing of abnormality to be diagnosed, the type of gas in oil used for diagnosis of abnormality, the upper limit value of the amount of gas in oil used for diagnosis, and diagnosis of abnormality. Contains information such as sensitivity (threshold value used for determination). As the timing of the abnormality to be diagnosed, it is set whether to diagnose the current abnormality based on the latest measurement data 21 or to diagnose the future abnormality by comparison with a preset upper limit value. As the upper limit value, it is possible to set a standard value that is permissible in maintenance management of the transformer, for example, a value of "Caution I" described in Non-Patent Document 1, etc.

The operating period data 23 is data representing the operating period of the transformer that is the target range of the regression analysis, and includes information on the beginning and end of the operating period, such as the initial failure period, random failure period, and wear-out failure period. The operation period of the transformer that is the target range of the regression analysis can be set to any period depending on the rated capacity and rated voltage of the transformer to be diagnosed, depending on the knowledge of the change in failure rate over time.

The approximate function data 24 is data representing the amount of increase in gas in oil per unit time, and includes information on the approximate function determined by regression analysis. The approximate function data 24 is obtained as data such as coefficients of the approximate function for each type of gas in oil and for each operating period of the transformer.

Estimated data 25 is data representing an estimated value of the amount of gas in oil for each operating time of the transformer estimated based on an approximation function, and represents the amount of gas in oil for each type of gas in oil associated with time. Contains information such as amount and concentration. The estimated data 25 is obtained by setting the time of the abnormality to be diagnosed and substituting the set time into an approximation function.

Diagnosis result data 26 is data representing a diagnosis result of a transformer abnormality, and the measured value of the amount of gas in oil corresponding to the time of the abnormality to be diagnosed is the amount of gas in oil estimated based on the approximate function. Whether or not the estimated value of the amount of gas in oil is greater than or equal to a predetermined value, and whether the estimated value of the amount of gas in oil estimated based on the approximation function is greater than or equal to a predetermined upper limit value. Contains information on whether or not. The diagnosis result data 26 is obtained, for example, as a difference between a measured value and an estimated value of the amount of gas in oil, or a difference between an estimated value and an upper limit value of the amount of gas in oil.

The data acquisition unit 11 acquires the measurement data 21 input to the diagnostic system 100 as time-series data for each type of gas in oil. The measurement data 21 is associated with an identifier that distinguishes the type of gas in oil, and is output to the storage unit 20 and the primary diagnosis unit 12. The data acquisition unit 11 may have a common interface function for manual input of the measurement data 21 or input from a gas sensor.

The primary diagnosis unit 12 reads out the latest measurement data 21 and diagnostic condition data 22, and calculates the latest measurement value of the amount of gas in oil measured by the gas-in-oil analysis for each operating period of the transformer. Perform a primary diagnosis by comparing the amount of gas in oil with the upper limit value. Diagnosis result data 26 representing the results of the primary diagnosis is output to the storage section 20 and the image generation section 16.

The regression analysis unit 13 reads measurement data 21 and operation period data 23 that belong to the same range of operating periods of the transformer, and calculates the amount of gas in oil measured by gas in oil analysis for each operating period of the transformer. Perform regression analysis based on The approximate function data 24 is output to the storage section 20 and the estimation section 14.

The estimation unit 14 reads out the measurement data 21, the diagnostic condition data 22, and the approximation function data 24, and calculates the amount of gas in oil corresponding to the period of the abnormality to be diagnosed from the approximation function representing the amount of increase in gas in oil per unit time. Find the estimated value of . Estimated data 25 is output to storage section 20 and comparison section 15.

The diagnostic unit 15 reads the measured data 21, the diagnostic condition data 22, and the estimated data 25, and calculates the amount of gas in the insulating oil of the transformer for each operating period of the transformer. Compare with the amount of gas. Diagnosis result data 26 representing the diagnosis results are output to the storage section 20 and the image generation section 16.

Based on the diagnosis result data 26, the image generation unit 16 displays the diagnosis result as a warning that an abnormality has occurred in the transformer to be diagnosed or that an abnormality will occur in the future in the transformer to be diagnosed. Generate image data. The image data is output to the output section 50. The image data may include measurement data 21, diagnostic condition data 22, driving period data 23, approximate function data 24, and estimated data 25 as display contents.

FIG. 8 is a flowchart illustrating an example of a process for diagnosing an abnormality in a transformer.
As shown in FIG. 8, the diagnostic system 100 performs a primary diagnosis by comparing the amount of gas in oil with a preset upper limit value, and the amount of gas in oil estimated based on an approximation function by regression analysis. It is possible to perform a two-step diagnosis including a secondary diagnosis based on comparison with estimated values. As a secondary diagnosis, a diagnosis of a current abnormality or a diagnosis of a future abnormality can be performed.

When diagnosing an abnormality in a transformer, first, the operating period of the transformer to be subjected to regression analysis is set, and diagnostic conditions are set. As the operating period of the transformer, specific starting and ending periods such as initial failure period, random failure period, wear-out failure period, etc. are set. As the diagnosis conditions, the timing of the abnormality to be diagnosed, the type of gas in oil used for diagnosis of abnormality, the upper limit value of the amount of gas in oil used for diagnosis, the sensitivity of diagnosis of abnormality, etc. are set.

Next, measurement data of the amount of gas in the insulating oil of the transformer is acquired (step S101). The amount of gas in the insulating oil of the transformer can be measured at appropriate time intervals, such as every sampling interval by a gas sensor, every day, every week, every month, every year, etc. The latest measured data 21 is input to the primary diagnosis section 12 together with data on the upper limit value of the amount of gas in oil set in advance.

Next, it is determined whether the amount of gas in the insulating oil of the transformer to be diagnosed is greater than or equal to a preset upper limit for the amount of gas in oil (step S102). The primary diagnosis unit 12 compares the latest measured value of the amount of gas in oil with a preset upper limit value for each type of gas in oil.

As a result of the determination, if the amount of gas in the insulating oil of the transformer is greater than or equal to the upper limit value (step S102; YES), the process proceeds to step S106, where a warning is issued to the effect that an abnormality has occurred in the transformer. On the other hand, if the amount of gas in the insulating oil of the transformer is less than the upper limit value (step S102; NO), the process proceeds to step S103.

Next, for each operating period of the transformer, a regression analysis is performed based on the measured data of the amount of gas in the insulating oil of the transformer, and an approximate function representing the increase in the amount of gas in the oil per unit time is determined (step S103). The regression analysis unit 13 performs fitting such as linear regression using the least squares method based on the measurement data 21 in which the operating periods of the transformers belong to the same range, and updates the approximation function used for diagnosis.

Next, an estimated value of the amount of gas in oil for each operating time of the transformer is obtained based on an approximate function representing the amount of increase in gas in oil per unit time (step S104).

When diagnosing a current abnormality, the estimation unit 14 substitutes the current operating time of the transformer into the approximation function to obtain an estimated value of the current amount of gas in oil. On the other hand, when diagnosing a future abnormality, the estimation unit 14 substitutes the future operating time of the transformer into the approximation function to obtain an estimated value of the future amount of gas in oil.

Next, the amount of gas in the insulating oil of the transformer is compared with the estimated value of the amount of gas in the oil estimated based on the approximation function, and the amount of gas in the insulating oil of the transformer is greater than or equal to a predetermined value. It is determined whether or not (step S105).

When diagnosing the current abnormality, the diagnostic unit 15 compares the current measured value of the amount of gas in the insulating oil of the transformer with the estimated value of the amount of gas in the oil estimated based on the approximation function, It is determined whether the current measured value of the amount of gas in the insulating oil of the transformer is greater than or equal to a predetermined value with respect to the estimated value of the amount of gas in the oil estimated based on the approximation function.

On the other hand, when diagnosing future abnormalities, the diagnostic unit 15 compares the estimated value of the amount of gas in oil estimated based on the approximation function with the upper limit value of the amount of gas in the insulating oil of the transformer, It is determined whether an estimated value of the amount of gas in oil estimated based on the approximation function is greater than or equal to a predetermined value with respect to an upper limit value of the amount of gas in insulating oil of the transformer.

As a result of the determination, if the amount of gas in the insulating oil of the transformer is less than a predetermined value (step S105; NO), the diagnostic process is ended.

Note that when the amount of gas in oil is less than a predetermined value, the calculation unit 10 determines that there is no abnormality in the transformer to be diagnosed, or that there is a possibility that an abnormality will occur in the transformer to be diagnosed in the future. An image indicating that the condition is low may be generated and displayed on the display unit 40 as a diagnosis result image. Alternatively, the operation time of the transformer that is predicted to exceed the upper limit in the future may be determined and displayed on the display unit 40 as a diagnosis result image.

On the other hand, if the result of the determination is that the amount of gas in the insulating oil of the transformer is greater than or equal to the predetermined value (step S105; YES), an abnormality is warned as a result of the diagnosis of abnormality in the transformer (step S106). After that, the diagnostic process is finished.

When the current abnormality is diagnosed, the image generation unit 16 generates a diagnosis result image indicating that an abnormality has occurred in the transformer to be diagnosed as a diagnosis result of the abnormality of the transformer, and displays the diagnosis result image on the display unit 40. Displays a warning. The diagnosis result image showing the result of diagnosing the current abnormality includes an image showing the operating period of the diagnosed transformer, an image showing the type of gas in oil that showed an abnormality, and an image showing the type of gas in oil that showed an abnormality, and an image showing the type of gas in oil that showed an abnormality. It can include an image or the like showing the difference from the estimated gas amount.

On the other hand, when a future abnormality is diagnosed, the image generation unit 16 generates a diagnosis result image indicating that an abnormality will occur in the future in the transformer to be diagnosed as a diagnosis result of the abnormality in the transformer, and displays it on the display unit 40. Displays the diagnosis result image and issues a warning. Diagnosis result images showing the results of diagnosing future abnormalities include images showing the operating period of the diagnosed transformer, images showing the type of gas in oil that showed an abnormality, and images showing the preset amount of gas in oil. It is possible to include an image showing the difference between the maximum value and the upper limit value.

According to such a diagnostic method and diagnostic system 100, regression analysis is performed for each operating period of the transformer based on the amount of gas in oil measured in the transformer, and the amount of gas in the insulating oil of the transformer is calculated. Because the amount of gas in oil is compared with the amount estimated based on the approximate function obtained for each operating period, it is possible to accurately detect accidental abnormalities that are different from aging deterioration of the insulating medium or insulation material or accumulation of repeated abnormalities. can be diagnosed. Since transformer failures tend to occur when abnormalities accumulate, it is possible to accurately grasp current abnormalities and future abnormalities as precursory phenomena that lead to transformer failures. Since the approximate function is obtained for each type of gas in oil, it is possible to predict the future amount of gas in oil for each type as a pattern, and it is also possible to estimate the type of cause of future failures. Become.

In addition, primary diagnosis is performed by comparing with a preset upper limit value of the amount of gas in oil, and secondary diagnosis is performed by comparing with the estimated value of the amount of gas in oil estimated based on an approximation function by regression analysis. Because this two-step diagnosis is performed, it is possible to separately diagnose clear abnormalities that have already occurred and accidental abnormalities that may lead to failures in the future. If a clear abnormality has already occurred, the process ends at the primary diagnosis stage, eliminating the need for regression analysis or comparison with estimated values, allowing for prompt diagnosis of transformer abnormalities.

Note that in the process shown in FIG. 8, a two-stage diagnosis of a primary diagnosis and a secondary diagnosis is performed, but the configuration may be such that only the secondary diagnosis is performed without performing the primary diagnosis. Further, the upper limit value of the amount of gas in oil used for diagnosis may be the same between the value used in primary diagnosis and the value used in secondary diagnosis when diagnosing future abnormalities, They may be different from each other.

Next, a diagnostic method and a diagnostic system according to another embodiment of the present invention will be described.

FIG. 9 is a diagram showing a diagnostic system according to a second embodiment of the present invention.
As shown in FIG. 9, the diagnostic system 200 according to the present embodiment is connected via a communication line to a plurality of terminals 140 provided for each of the plurality of transformers 1, and monitors abnormalities in the plurality of transformers 1. It is configured to function as a diagnostic server that performs diagnostics.

The plurality of transformers 1 are installed on the same power system, and are composed of oil-immersed transformers having the same rated capacity and the same rated voltage. Each of the plurality of transformers 1 includes a gas sensor 110 that measures the amount of gas in the insulating oil. The amount of gas in the insulating oil of each transformer 1 is measured online over time by a gas sensor 110.

Communication lines include the Internet, LAN (Local Area Network), fixed dedicated lines, and the like. The communication line may be a wired communication line, a wireless communication line, or a combination thereof.

The terminal 140 is equipped with an application that collects measurement data measured by the gas sensor 110 and a communication device that transmits and receives data between the diagnostic system 200 and the diagnostic system 200 . Terminal 140 associates measurement data measured over time by gas sensor 110 with an identifier for each transformer 1, and transmits the data to diagnostic system 200 at predetermined time intervals.

Although two transformers 1 and two terminals 140 are shown in FIG. 9, the number of transformers 1 and terminals 140 is not particularly limited. One terminal 140 may be provided for each transformer 1, or one terminal 140 may be provided for a plurality of transformers 1.

FIG. 10 is a diagram showing the configuration of a diagnostic system according to a second embodiment of the present invention.
As shown in FIG. 10, the diagnostic system 200 according to the present embodiment, like the diagnostic system 100 described above, includes a calculation unit 10 that performs processing for diagnosing an abnormality in a transformer, and a calculation unit 10 that stores data used in the diagnostic processing. It includes a storage section 20, an input section 30, a display section 40, and a communication section 50.

The diagnostic system 200 differs from the diagnostic system 100 described above in that it collects measurement data 21 representing the measured value of the amount of gas in the insulating oil of the transformer from a plurality of transformers 1, and Based on the measured value of the amount of gas in oil obtained by The point is that the diagnosis is made using the approximation function obtained for device 1.

In the diagnosis system 200, the calculation unit 10 includes a data acquisition unit 111, a primary diagnosis unit 12, a regression analysis unit 131, a calculation unit 132, an estimation unit 14, a diagnosis unit 15, and an image generation unit 16. We are prepared.

In the diagnostic system 200, the storage unit 20 also stores a measurement database 211, diagnostic condition data 221, driving period data 23, approximate function data 24, estimated data 25, and diagnostic result data 26. The diagnostic condition data 221 and the operating period data 23 are input into the diagnostic system 200 in advance.

The diagnostic system 200 is configured to input measurement data 21 representing the amount of gas in the insulating oil of the transformer from the gas sensor 110. When the gas sensor 110 measures the amount of gas in oil, the terminal 140 transmits measurement data 21 associated with an identifier for each transformer 1 and an identifier that distinguishes the type of gas in oil to the diagnostic system 200 via a communication line. Send.

FIG. 11 is a diagram showing an example of a measurement database based on measurement data obtained from a plurality of transformers.
As shown in FIG. 11, measurement data 21 obtained from the plurality of transformers 1 is stored in the storage unit 20 of the diagnostic system 200 as a measurement database 211. The measurement database 211 includes information on transformer names that distinguish transformers on the power system, measured values of the amount of gas in insulating oil for each transformer and for each type of gas in oil, and information on gas in oil. Contains information on the date and time when the amount was measured.

Diagnosis condition data 221 includes information such as the timing of the abnormality to be diagnosed, the type of gas in oil used for abnormality diagnosis, the upper limit of the amount of gas in oil used for diagnosis, and the sensitivity of abnormality diagnosis, as well as the transformation voltage to be diagnosed. Contains information for equipment selection. Among the plurality of transformers 1, the transformer to be diagnosed may be selected according to a diagnosis schedule in a predetermined order, or may be selected according to a priority setting based on the length of operating time, etc.

The data acquisition unit 111 collects measurement data 21 that is input to the diagnostic system 200 via a communication line from a plurality of transformers having the same rated capacity and rated voltage, for each transformer on the power system, and for each transformer submerged in oil. Obtain time-series data for each type of gas. The measurement data 21 is stored as a measurement database 211 in association with an identifier that distinguishes a transformer on the power system and an identifier that distinguishes the type of gas in oil.

The regression analysis unit 131 reads measurement data 21 and operation period data 23 for each transformer whose operation period falls within the same range in the measurement database 211, and calculates the measurement data 21 and operation period data 23 for each transformer on the power system and for each transformer. Regression analysis is performed based on the amount of gas in oil measured at multiple transformers during each operating period. The approximate function data 24 obtained for each transformer is output to the storage section 20.

The calculation unit 132 reads approximate function data 24 obtained for each transformer on the power system, and averages the plurality of approximate functions obtained for each transformer on the power system among the plurality of transformers. do. The averaging process can be performed, for example, as a process of calculating the arithmetic mean of the coefficients of the approximation function, using a plurality of transformers that have measured the measurement data 21 as a population. Approximate function data 24 representing the averaged approximate function is output to the storage unit 20.

FIG. 12 is a flowchart illustrating an example of a process for diagnosing abnormalities in a plurality of transformers.
As shown in FIG. 12, the diagnostic system 200, like the diagnostic system 100 described above, performs a primary diagnosis based on a comparison with a preset upper limit value of the amount of gas in oil and an approximate function based on regression analysis. A two-step diagnosis can be performed, including a secondary diagnosis based on comparison with the estimated amount of gas in oil. As a secondary diagnosis, a diagnosis of a current abnormality or a diagnosis of a future abnormality can be performed.

In the diagnostic system 200, similarly to the diagnostic system 100 described above, measurement data of the amount of gas in the insulating oil of a transformer is acquired (step S201), and a transformer to be diagnosed for abnormality is selected (step S202). ), it is determined whether the amount of gas in the insulating oil of the transformer to be diagnosed is greater than or equal to a preset upper limit for the amount of gas in oil (step S203).

As a result of the determination, if the amount of gas in the insulating oil of the transformer is greater than or equal to the upper limit value (step S203; YES), the process proceeds to step S208, where a warning is issued to the effect that an abnormality has occurred in the transformer. On the other hand, if the amount of gas in the insulating oil of the transformer is less than the upper limit (step S203; NO), the process proceeds to step S204.

Next, a regression analysis is performed for each transformer and each transformer operating period based on the measurement data of the amount of gas in the insulating oil of the transformer, and the amount of increase in gas in the oil per unit time is expressed. An approximate function for each transformer is determined (step S204). The regression analysis unit 131 performs fitting such as linear regression using the least squares method based on the measurement data 21 for each transformer whose operating period falls within the same range, and creates a plurality of approximation functions.

Subsequently, the plurality of approximate functions obtained for each transformer and each operating period of the transformer are averaged among the transformers (step S205). The calculation unit 132 obtains one averaged approximation function from a plurality of approximation functions created for each transformer on the power system by calculating the arithmetic mean of the coefficients of the approximation functions.

Next, similarly to the diagnosis system 100 described above, an estimated value of the amount of gas in oil for each operating time of the transformer is determined based on an approximation function representing the amount of increase in gas in oil per unit time (step S206 ), the amount of gas in the insulating oil of the transformer is compared with the estimated value of the amount of gas in the oil estimated based on the approximate function, and it is determined that the amount of gas in the insulating oil of the transformer is greater than or equal to a predetermined value. It is determined whether there is one (step S207).

As a result of the determination, if the amount of gas in the insulating oil of the transformer is less than a predetermined value (step S207; NO), the process advances to step S209. On the other hand, if the result of the determination is that the amount of gas in the insulating oil of the transformer is equal to or greater than the predetermined value (step S207; YES), an abnormality is warned as a result of the diagnosis of abnormality in the transformer (step S208). The image generation unit 16 generates a diagnosis result image indicating that an abnormality has occurred in the transformer to be diagnosed or that an abnormality will occur in the transformer to be diagnosed in the future as a diagnosis result of the abnormality for each transformer, A diagnosis result image is displayed on the display unit 40 to issue a warning.

Subsequently, it is determined whether or not the diagnosis of all the diagnosis targets has been completed (step S209). Whether or not the diagnosis of a diagnosis target has been completed can be managed by recording log data for each of a plurality of transformers on the power system.

As a result of the determination, if the diagnosis of all the diagnosis targets has not been completed (step S208; NO), the process returns to step S201. Thereafter, the diagnostic target is switched and the diagnostic process is repeated. On the other hand, if all the diagnoses for the diagnosis target have been completed (step S208; YES), the diagnostic process is ended.

According to such a diagnostic method and diagnostic system 200, a regression analysis is performed for each operating period of a transformer based on the amount of gas in oil measured in a plurality of transformers having the same rated capacity and rated voltage. In order to compare the amount of gas in the insulating oil of the transformer with the amount of gas in the oil estimated based on the approximation function determined for each operating period, abnormalities that occur accidentally under a common power flow are detected. Can be diagnosed with high accuracy. Past trends in deterioration over time and trends in past repeated abnormalities that have occurred within the same power system are taken into consideration through regression analysis of multiple transformers.

In addition, because the approximation function obtained for each transformer is averaged between transformers, unlike regression analysis in the range of all measurement data obtained on the power system, the approximation function used for diagnosis is , can be obtained sequentially for each transformer. Even if it is difficult to measure the amount of gas in the insulating oil, or if the operating hours of each transformer do not match, the overall diagnosis can be performed efficiently.

In the diagnostic system 200, the approximation function may be obtained from the measurement data 21 of all transformers 1 installed on the same power system, or may be obtained from the measurement data 21 of some transformers 1. good. The transformer 1 to be diagnosed may be included as a subject of regression analysis, or may not be included as a subject of regression analysis.

That is, in the diagnostic system 200, the regression analysis unit 131 calculates one approximate function based on the amount of gas in oil measured in some or all of the plurality of transformers 1, and the diagnostic unit 15 However, it is possible to configure the system to compare the amount of gas in oil measured by the transformer for which the approximation function was obtained every operating period of the transformer with the amount of gas in oil estimated based on the approximation function. can.

According to such a configuration, since the amount of measurement data 21 used for the regression analysis increases, abnormalities in the transformer to be diagnosed can be diagnosed with higher accuracy. In particular, since the accuracy of detecting random abnormalities is improved, it is advantageous in predicting sudden failures that may occur due to accumulation of abnormalities.

Alternatively, in the diagnostic system 200, the regression analysis unit 131 calculates one approximation function based on the amount of gas in oil measured in some of the transformers 1, and the diagnostic unit 15 calculates the following: The system is configured to compare the amount of gas in oil measured in the remaining transformers for which the approximation function has not been obtained for each operating period of the transformer with the amount of gas in oil estimated based on the approximation function. You can also do it.

With such a configuration, even if the amount of gas in oil is not measured in the transformer to be diagnosed, an approximate function can be calculated based on the amount of gas in oil measured in other transformers. It will be done. Therefore, even if it is difficult to measure the amount of gas in the insulating oil or the operating hours of the transformers do not match, it is possible to diagnose current and future abnormalities in the transformer being diagnosed. I can do it.

Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and includes various modifications without departing from the technical scope. For example, the embodiments described above are not necessarily limited to having all the configurations described. Furthermore, it is possible to replace a part of the configuration of a certain embodiment with another configuration, or to add another configuration to the configuration of a certain embodiment. Furthermore, it is also possible to add other configurations, delete configurations, and replace configurations of some of the configurations of a certain embodiment.

For example, in the above-described diagnostic method and diagnostic system 100, 200, an approximate function is obtained for each type of gas in oil, and a singular point that deviates from the approximate function by a predetermined value or more is used as an estimated value to diagnose each type of gas. However, it is also possible to perform a diagnosis for each transformer by comparing a plurality of types of gas in oil and ranking the types of gas in oil that deviate by a predetermined value or more. Additionally, multivariate analysis depending on the type of gas in oil may be combined. Examples of multivariate analysis include a method of clustering measurement data of multiple types of gas in oil in a multidimensional space and diagnosing abnormalities from mathematical distances to the cluster patterns.

Further, the diagnostic systems 100 and 200 are configured to issue an abnormality warning when an abnormality in the transformer is diagnosed. The configuration may be such that control is performed to stop the operation of the transformer that has been turned off. As an abnormality warning, an audio warning may be provided instead of the diagnosis result image.

10 Arithmetic unit 11 Data acquisition unit 12 Primary diagnosis unit 13 Regression analysis unit 14 Estimation unit 15 Diagnosis unit 16 Image generation unit 20 Storage unit 21 Measurement data 22 Diagnosis condition data 23 Operating period data 24 Approximate function data 25 Estimated data 26 Diagnosis result data 30 input section 40 display section 50 communication section 100 diagnostic system

Claims (13)

A diagnostic method for diagnosing an abnormality in a transformer,
A process of measuring the amount of gas in the insulating oil of the transformer over time,
The amount of gas in the insulating oil measured for each operating period of the transformer is divided into a plurality of periods according to the tendency of change in failure rate in the failure rate curve of the transformer. Based on this, a regression analysis is performed with time as an independent variable and the amount of gas in the insulating oil of the transformer as a dependent variable , and an approximate function representing the relationship between the operating time of the transformer and the amount of gas in the insulating oil is calculated. The process of finding
Comparing the amount of gas in the insulating oil of the transformer with the amount of gas in the insulating oil estimated based on the approximation function for each operating period of the transformer ,
The operating period of the transformer includes an initial failure period in which the failure rate in the failure rate curve of the transformer decreases over time, an occasional failure period in which the failure rate in the failure rate curve of the transformer is steady, and A diagnostic method that is a period set as one of the wear-out failure periods in the failure rate curve of a device where the failure rate increases over time . The diagnostic method according to claim 1,
When the measured value of the amount of gas in the insulating oil of a transformer is greater than a predetermined value with respect to the estimated value of the amount of gas in the insulating oil estimated based on the approximation function, an abnormality occurs in the transformer. A diagnostic method that determines that this is occurring. The diagnostic method according to claim 1,
When the estimated value of the amount of gas in the insulating oil estimated based on the approximation function is greater than or equal to a predetermined value with respect to the preset upper limit of the amount of gas in the insulating oil of the transformer, A diagnostic method that determines whether an abnormality will occur in the transformer in the future. The diagnostic method according to claim 2 or 3,
The diagnostic method, wherein the predetermined value is 20% or more of the estimated value. The diagnostic method according to claim 1 ,
The initial failure period is a period of 3 years to 6 years from the start of operation of the transformer,
The random failure period is a period of 20 to 30 years from the end of the initial failure period,
The wear-out failure period is a period after the end of the random failure period. The diagnostic method according to claim 1,
The diagnostic method wherein the gas is one or more of methane, acetylene, ethylene, ethane, hydrogen, carbon monoxide, and carbon dioxide. The diagnostic method according to claim 1,
The step of measuring the amount of gas over time is performed on a plurality of transformers having the same rated capacity and rated voltage,
performing the step of determining the approximate function for some transformers among the plurality of transformers;
For each period of operation of a transformer, the amount of gas in the insulating oil measured in the transformer for which the approximate function has not been determined is calculated based on the insulating oil estimated based on the approximate function determined in another transformer. A diagnostic method that compares the amount of gas in the oil. The diagnostic method according to claim 1,
The step of measuring the amount of gas over time is performed on a plurality of transformers having the same rated capacity and rated voltage,
performing the step of determining the approximate function for all or some of the plurality of transformers;
averaging the approximate function between the transformers;
A diagnostic method that compares the amount of gas in the insulating oil of the transformer with the amount of gas in the insulating oil estimated based on the averaged approximation function for each operating period of the transformer. A diagnostic system for diagnosing an abnormality in a transformer,
Insulating oil of the transformer measured over time for each operating period of the transformer, which is divided into multiple periods according to the tendency of change in failure rate in the failure rate curve of the transformer. Based on the amount of gas of a regression analysis unit that calculates an approximate function representing the relationship ;
a diagnostic unit that compares the amount of gas in the insulating oil of the transformer with the amount of gas in the insulating oil estimated based on the approximation function for each operating period of the transformer ,
The operating period of the transformer includes an initial failure period in which the failure rate in the failure rate curve of the transformer decreases over time, an occasional failure period in which the failure rate in the failure rate curve of the transformer is steady, and A diagnostic system is a period set as one of the wear-out failure periods in a device's failure rate curve where the failure rate increases over time . The diagnostic system according to claim 9 ,
When the measured value of the amount of gas in the insulating oil of the transformer is greater than or equal to a predetermined value with respect to the estimated value of the amount of gas in the insulating oil estimated based on the approximation function, A diagnostic system that displays a warning that something is wrong. The diagnostic system according to claim 9 ,
When the estimated value of the amount of gas in the insulating oil estimated based on the approximation function is greater than or equal to a predetermined value with respect to the preset upper limit of the amount of gas in the insulating oil of the transformer, A diagnostic system that displays a warning that an abnormality will occur in the transformer in the future. The diagnostic system according to claim 9 ,
A diagnostic system comprising a storage section that stores data at the beginning and end of the operating period of the transformer. A diagnostic system for diagnosing an abnormality in a transformer,
a data acquisition unit that acquires measurement data of the amount of gas in the insulating oil of the transformers measured over time from a plurality of transformers having the same rated capacity and voltage;
The operating time of the transformer was measured over time for each transformer and for each operating period of the transformer, which was divided into a plurality of periods according to the tendency of change in failure rate in the failure rate curve of the transformer. Based on the amount of gas in the insulating oil of the transformer , regression analysis was performed with time as an independent variable and the amount of gas in the insulating oil of the transformer as a dependent variable , and the operating time of the transformer and the amount of gas in the insulating oil were a regression analysis unit that calculates an approximate function representing the relationship between the amount of gas and the amount of gas ;
a calculation unit that averages the approximate function between the transformers;
For each transformer and for each operating period of the transformer, the amount of gas in the insulating oil of the transformer is equal to the amount of gas in the insulating oil estimated based on the averaged approximation function. a diagnostic section for comparison ;
The operating period of the transformer includes an initial failure period in which the failure rate in the failure rate curve of the transformer decreases over time, an occasional failure period in which the failure rate in the failure rate curve of the transformer is steady, and A diagnostic system is a period set as one of the wear-out failure periods in a device's failure rate curve where the failure rate increases over time .

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