JP2022012217A - Diagnosis method and diagnosis system for transformer - Google Patents

Abstract

To provide a diagnosis method and a diagnosis system which enable current abnormality of a transformer causing failure thereof and future abnormality thereof to be diagnosed.SOLUTION: A diagnosis method for diagnosing abnormality of a transformer includes the steps of: measuring quantity of gas in insulating oil of the transformer with time; performing a regression analysis on the basis of the measured quantity of the in-oil gas at each operation period of the transformer, and acquiring an approximate function indicating an increase quantity of the in-oil gas per unit time; comparing the quantity of the gas in the insulating oil of the transformer with the quantity of the gas estimated on the basis of the approximate function, at each operation period of the transformer. A diagnosis system 100 diagnosing the abnormality of the transformer comprises a regression analysis unit 13 performing the regression analysis on the basis of the quantity of the gas in the insulating oil measured with time at each of the operation period of the transformer, and obtaining the approximate function indicating the increase quantity of the in-oil gas per unit time, and a diagnosis unit 15 comparing the quantity of the gas in the insulating oil of the transformer with the quantity of the in-oil gas which is estimated on the basis of the approximate function, at each operation period of the transformer.SELECTED DRAWING: Figure 1

Description

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

The power system development plan is based on the forecast of power demand and power 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. In addition, the aging of society, the declining working population, and the transition to an information-oriented society are progressing. Therefore, in line with these trends, flexible maintenance of the electric power system is required.

As renewable energy becomes more widespread, it is expected that the optimization of power flow will become more important. When wind power generation, solar power generation, etc. are added in addition to thermal power generation and nuclear power generation, there is a possibility that the power flow will be concentrated on a specific power system and line overload will occur. Depending on the state of the equipment on the power system, an unexpected event may occur at the planning stage to destabilize the power system, so it is important to predict the abnormality of the equipment in advance.

Transformers installed in the power system include oil-filled type and dry type (mold type). The oil-filled transformer has a structure in which the coil is immersed in insulating oil, and is more widely used than the dry type. As the insulating oil, mineral oil, vegetable oil and the like are used. By natural convection or forced circulation of mineral oil with high withstand voltage, both insulation performance and cooling performance are satisfied.

Conventionally, time-planned maintenance (TBM) has been adopted as a method for maintaining and managing a transformer. The general expected life of a transformer is about 30 years. When the insulating oil or the insulating material deteriorates, the characteristic gas of the internal abnormality is generated. Therefore, the gas analysis in the oil is performed about once a year by electric power companies, railway companies, general business companies, and the like.

Non-Patent Document 1 describes a reference value of a gas allowed in insulating oil as an index used for maintenance and management of an oil-immersed transformer. Hydrogen (H ₂ ) ≧ 400 ppm, methane (CH ₄ ) ≧ 100 ppm, ethane (C ₂ H ₆ ) ≧ 150 ppm, ethylene (C ₂ H ₄ ) ≧ 10 ppm, acetylene (C ₂ H ₂ ) ≧ 0.5 ppm, monoxide One of carbon (CO) ≥300 ppm and total flammable gas (TCG) ≥500 ppm, "Caution required I", C ₂ H ₂ ≥ 0.5 ppm, C ₂ H ₄ ≥ 10 ppm and TCG ≥ 500 ppm. One of "Caution II", C ₂ H ₂ ≧ 5 ppm, C ₂ H ₄ ≧ 100 ppm and TCG ≧ 700 ppm, C ₂ H ₄ ≧ 100 ppm and TCG increase ≧ 70 ppm / month It is stated that it is determined to be.

General Incorporated Association Electric Cooperative Research Association, "Electrical Cooperative Research", Vol. 54, No. 5 (Part 1) "Maintenance and management of oil-immersed transformers"

According to the conventional transformer maintenance method, since the oil gas analysis by TBM is performed, it is possible to know the amount of gas generated in the insulating oil at the time of collecting the insulating oil. Therefore, it is possible to diagnose the presence or absence of the current abnormality of the transformer from the current deterioration state of the insulating oil and the insulating material. Moreover, since the average operating life of the transformer is known, it is possible to predict the remaining life that can be operated after that from the current state of deterioration.

However, there is a problem that the conventional maintenance management method cannot predict the future abnormality that occurs in the transformer. The operating life of a transformer depends on the aging deterioration of insulating oil and insulating material, accidental abnormalities, etc., but in reality, there are few cases where the life is exhausted due to aging deterioration.

Since transformer failures are likely to occur when an abnormality progresses, it is desirable to predict in advance an abnormality that will lead to an accidental failure in order to operate the power system in a stable manner. If an abnormality that leads to an accidental failure can be predicted in advance, it is possible to prevent the operation from being stopped due to the failure by carrying out repairs, inspections, and the like. However, although a method for grasping the current abnormality leading to an accidental failure is known, a method for accurately grasping the future abnormality has not been known so far.

In the future, if renewable energy becomes more widespread and the power sources on the power system are diversified, there is a possibility that failures due to instantaneous load fluctuations and harmonic currents that are not assumed in the maintenance plan will increase. In the conventional maintenance management method, since the gas in oil is analyzed by TBM, it is difficult to promptly take measures to stop the transformer even when an abnormality leading to a failure is confirmed. Therefore, there is a need for a means for accurately grasping current abnormalities and future abnormalities as precursory phenomena leading to transformer failure.

Therefore, it is an object of the present invention to provide a diagnostic method and a diagnostic system capable of diagnosing current abnormalities and future abnormalities leading to transformer failure.

That is, the diagnostic method according to the present invention in order to solve the above problems 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 and a transformer. For each operating period of the transformer, a step of performing regression analysis based on the measured amount of the gas in the oil to obtain an approximate function representing the amount of increase in the gas in the oil per unit time, and each operating period of the transformer. Includes a step of comparing the amount of gas in the insulating oil of a transformer with the amount of gas in the oil estimated based on the approximation function.

Further, the diagnostic system according to the present invention is a diagnostic system for diagnosing an abnormality in a transformer, and is based on the amount of gas in the insulating oil of the transformer measured over time for each operating period of the transformer. Based on the approximation function, the regression analysis unit that performs regression analysis to obtain an approximation function that represents the amount of increase in the gas per unit time, and the amount of gas in the insulating oil of the transformer for each operating period of the transformer. It is provided with a diagnostic unit for comparing with the estimated amount of gas in the oil.

According to the present invention, it is possible to provide a diagnostic method and a diagnostic system capable of diagnosing current abnormalities and future abnormalities leading to transformer failure.

It is a figure which shows the structure of the diagnostic system which concerns on 1st Embodiment of this invention. It is a figure which shows the result of having measured the amount of gas in the insulating oil of a transformer with time. It is a figure which shows the result of having measured the amount of gas in the insulating oil of a transformer with time. It is a figure which shows the failure rate curve of a general transformer. It is a figure explaining the approximate function obtained in the range of the initial failure period. It is a figure explaining the approximate function obtained in the range of the accidental failure period. It is a figure explaining the approximate function obtained in the range of the wear failure period. It is a flowchart which shows an example of the process of diagnosing the abnormality of a transformer. It is a figure which shows the diagnostic system which concerns on 2nd Embodiment of this invention. It is a figure which shows the structure of the diagnostic system which concerns on 2nd Embodiment of this invention. It is a figure which shows an example of the measurement database based on the measurement data obtained by a plurality of transformers. It is a flowchart which shows an example of the process of diagnosing the abnormality of a plurality of transformers.

Hereinafter, a diagnostic method and a diagnostic system according to an embodiment of the present invention will be described. In each of the following figures, common configurations are designated by the same reference numerals and duplicated description will be omitted.

The diagnostic method according to the present embodiment relates to a method for diagnosing an abnormality in a transformer. Diagnosis of transformer anomalies includes diagnosing current anomalies occurring in transformers and predicting future anomalies occurring in transformers. The transformer to be diagnosed is an oil-filled transformer, but the type of insulating oil as an insulating medium, the type of insulating material, the rated capacity of the transformer, the rated voltage, and the like are not particularly limited.

Examples of the abnormality of the transformer include an event in which the insulating medium or the insulating material needs to be replaced, an event in which the operation of the transformer needs to be stopped, and a precursory phenomenon thereof. Specific examples of the abnormality of the transformer include deterioration or breakdown of the insulating property of the insulating medium or the insulating material, short circuit or overheating of the coil, progress of the flow charging phenomenon of the insulating medium, generation of partial discharge, and the like.

The diagnostic method according to the present embodiment is a step of measuring the amount of gas in the insulating oil of the 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 process of finding an approximate function that represents the amount of increase in gas per unit time, and the amount of gas in the insulating oil of the transformer for each operating period of the transformer are estimated based on the approximate function. Includes a step of comparing with the amount of.

In the diagnostic method according to the present embodiment, the insulating oil enclosed in the transformer is analyzed for gas in oil over time, and the amount of gas in the insulating oil is measured over time. Then, by performing regression analysis of the measurement results measured over time and grasping the tendency of abnormalities occurring in the transformer, maintenance management for condition monitoring maintenance (CBM) becomes possible.

From the measurement results of oil gas analysis, an approximate function that expresses the amount of increase (increase rate) of oil gas per unit time by performing regression analysis with time as the independent variable and the amount of oil gas as the dependent variable. Is required. It can be said that the approximation function reflects the influence of the aged deterioration of the insulating medium and the insulating material and the influence of the accumulation of repeated abnormalities. From the approximation function, it can be seen whether the measured value of the current amount of oil gas and the predicted value of the future amount of oil gas have the same tendency as the past aging deterioration and the past repeated abnormality.

Therefore, by determining whether the measured value of the amount of gas in the insulating oil of the transformer deviates from the current estimated value of the amount of gas in the oil estimated from the approximate function, it is accidental at present. The presence or absence of abnormalities can be diagnosed. In addition, the conventional prediction is made by determining whether the estimated value of the future amount of gas in oil estimated from the approximate function deviates from the upper limit value allowed as the amount of gas in the insulating oil of the transformer. Can diagnose the possibility of future abnormalities that were difficult.

When diagnosing the current anomaly of a transformer, substitute the current transformer operating time into an approximate function that represents the increase in oil gas per unit time to obtain an estimate of the current amount of oil gas. Ask. Then, the current measurement value of the amount of gas in the insulating oil of the transformer corresponding to the same operation time is compared with the estimated value of the amount of gas in oil estimated based on the approximate function.

As a result of the comparison, the measured value of the amount of gas in the insulating oil of the transformer was equal to or higher than the preset predetermined value with respect to the estimated value of the amount of gas in the oil estimated based on the approximate function. At that time, it is possible to determine that an abnormality has occurred in the transformer. In such a case, a warning is displayed or notified to the transformer operator to replace the insulating oil or insulating material or stop the operation of the transformer.

In addition, when diagnosing future abnormalities of the transformer, the operating time of the future transformer is substituted into the approximate function representing the increase amount of the gas in the oil per unit time to estimate the amount of the gas in the oil in the future. Find the value. Then, the estimated value of the amount of gas in oil estimated based on the approximate function is compared with the upper limit of the amount of gas in the insulating oil of the transformer corresponding to the same operation time.

As a result of the comparison, the estimated value of the amount of gas in oil estimated based on the approximate function is equal to or higher than the preset predetermined value with respect to the upper limit of the amount of gas in the insulating oil of the transformer. At that time, it is possible to determine 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 to inspect the transformer, replace the insulating oil or insulating material, or stop the operation of the transformer.

The predetermined value used for the determination can be arbitrarily set according to the type of the gas in the oil within a range exceeding the measurement error of the amount of the gas in the oil and the natural fluctuation of the amount of the gas in the oil. The predetermined value is preferably a value of 20% or more with respect to the estimated value of the amount of gas in oil estimated based on the approximate function. Generally, it is said that the device error and the measurement error of the transformer are about 20% at the maximum. Therefore, if a value of 20% or more is set with respect to the amount of gas in oil indicated by the approximate 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 together with the details of the diagnostic method.

FIG. 1 is a diagram showing a configuration of a diagnostic system according to the 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 a process of diagnosing an abnormality in a transformer, a storage unit 20 that stores data used for the diagnostic process, and an input unit 30. , A display unit 40, and a communication unit 50.

The diagnostic system 100 is a device having a function of executing the above-mentioned diagnostic method, and is used for diagnosing current abnormalities and future abnormalities of transformers. The transformer to be diagnosed is an oil-filled transformer, which is installed on a power system away from the diagnostic system 100.

The diagnostic system 100 is configured to output a diagnosis result of an abnormality of the transformer for one input representing the amount of gas in the insulating oil of the transformer. The diagnostic system 100 may be configured to manually input measurement data representing the amount of gas in the insulating oil of the transformer by reading the measured value, or may be configured to input from a gas sensor via a communication line. You may.

The line connecting the diagnostic system 100 and the gas sensor may be either a wired communication line or a wireless communication line. When the measurement data is manually input, 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 the target gas for gas analysis in oil, 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 further preferable to analyze at least hydrogen.

The concentration of these gases increases in the insulating oil when the insulating oil or the insulating paper is thermally decomposed by abnormal discharge or abnormal overheating, or when the insulating oil or the insulating paper deteriorates over time. Methane, acetylene, ethylene, ethane and hydrogen are known to be mainly derived from the deterioration of insulating oil. It is known that carbon monoxide and carbon dioxide are mainly derived from deterioration of insulating paper and press board which are insulating materials.

Therefore, by measuring the amount of these gases, the current state of insulating oil and insulating material and the degree of abnormalities that have occurred inside the transformer in the past can be known. That is, it is possible to diagnose the current abnormality occurring in the transformer from the current amount of gas in oil. In addition, since the future state can be estimated from the tendency of the increase amount of gas in oil per unit time up to now, future abnormalities occurring in the transformer can be diagnosed.

2 and 3 are diagrams showing the results of measuring the amount of gas in the insulating oil of the transformer over time.
2 and 3 show the results of oil gas 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 represents the operating time [year] of the transformer, and the vertical axis represents the gas amount (concentration) [ppm].

In Fig. 2, 〇 is the result of acetylene (C ₂ H ₂ ), Δ is the result of methane (CH ₄ ), □ is the result of ethane (C ₂ H ₆ ), and ◇ is ethylene (C ₂ H ₄ ). Is the result of. In FIG. 3, □ is the result of carbon dioxide (CO ₂ ), ◇ is the result of carbon monoxide (CO), and 〇 is the result of hydrogen (H ₂ ).

As shown in FIGS. 2 and 3, when the transformer is continuously operated, the amount of gas increases according to the rated capacity and voltage of the transformer, the type of insulating medium and insulating material, the type of the cause of abnormality, and the like. do. The increase in the amount of gas occurs for each type of gas, and the degree of increase also differs from each other. However, unlike aging deterioration, accidental abnormalities occur as relatively rapid changes, so that a large change in the amount of gas is brought about in a short time.

Therefore, when an approximate function representing the amount of increase in oil gas per unit time is obtained for each type of oil gas, it deteriorates over time due to the difference from the estimated value of the amount of oil gas estimated based on the approximate function. Abnormalities due to factors other than the above can be diagnosed. Moreover, since the result of the comparison for each type of gas in oil shows a characteristic pattern, the type of the cause of the abnormality can be estimated.

In the diagnostic method and the diagnostic system 100 according to the present embodiment, an approximate function representing an increase amount of gas in oil per unit time is obtained for each operating period of the transformer. As the operating period of the transformer to be the target range of the regression analysis, a period set based on the tendency of the time change of 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 general transformer.
In FIG. 4, the horizontal axis represents the operating time of the transformer, and the vertical axis represents the failure rate [%] of the transformer. The broken line is the failure rate of the initial failure caused by the design and manufacture of the transformer, the one-dot chain line is the failure rate of the accidental failure that occurs accidentally in the transformer, and the two-dot chain line is the cause of aging deterioration such as wear. Represents the failure rate of wear failure. The solid line represents the combined failure rate curve.

As shown in FIG. 4, it is known that the probability that a general transformer fails within a unit time shows a characteristic tendency depending on the operating time and is represented by a bathtub-shaped failure rate curve. There is. The failure rate curve can be divided into three regions according to the tendency of the failure rate to change. The first period in which the failure rate decreases over time is called the initial failure period, the intermediate period in which the failure rate is steady is called the accidental failure period, and the last period in which the failure rate increases over time is called the wear failure period. ..

FIG. 5 is a diagram illustrating an approximate function obtained in the range of the initial failure period. FIG. 6 is a diagram illustrating an approximate function obtained in the range of the accidental failure period. FIG. 7 is a diagram illustrating an approximate function obtained in the range of the wear failure period.
In FIGS. 5 to 7, the horizontal axis represents the operating time [year] of the transformer, and the vertical axis represents the amount (concentration) [ppm] of gas in oil. The solid line is the result of measuring the amount of gas in the insulating oil over time, and the broken line is the approximate function obtained by regression analysis of the measurement result, that is, the approximation representing the increase amount of the 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. The linear approximation function by linear regression is expressed as Y = aX + b. The coefficients a and b are obtained for each type of gas in oil and for each operating period of the transformer. As a method of linear regression for obtaining such an approximate function, for example, the least squares method can be used.

The initial failure period of the transformer includes a period of 3 years or more and 6 years or less from the start of operation of the transformer, although it depends on the rated capacity and the rated voltage 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 oil gas per unit time tends to be relatively small. The difference in the amount of oil gas for each type of oil gas is usually small, although it depends on the initial degassing conditions and the like. In FIG. 5, the period up to 5 years of operation time is defined as the initial failure period.

The accidental failure period of the transformer includes a period of 20 to 30 years from the end of the initial failure period, although it depends on the rated capacity and the rated voltage of the transformer. The accidental failure period is a period in which the failure rate of the transformer is steady, and shows a tendency that the amount of increase in oil gas per unit time begins to increase in response to an accidental abnormality. In FIG. 6, the period from 5 to 25 years of operation time is defined as an accidental failure period.

The wear failure period of the transformer includes the period after the end of the accidental failure period. The wear failure period is a period in which the failure rate of the transformer increases over time, and the amount of increase in oil gas 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 influence of aging deterioration and the influence of accumulation of abnormalities. In FIG. 7, the period from the operating time of 25 to 30 years is defined as the wear 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 to be the target range of the regression analysis, the cause of the abnormality is narrowed down for each operating period of the transformer. You get the function. Since an approximate function that can accurately diagnose an accidental abnormality is obtained, it is possible to more accurately diagnose the current abnormality that leads to the failure of the transformer and the future abnormality that leads to the failure of the transformer.

As shown in FIG. 1, in the diagnostic system 100, the calculation unit 10, the storage unit 20, the input unit 30, the display unit 40, and the communication unit 50 are connected to the 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 embodied by the arithmetic unit 10 executing a predetermined program.

Further, the storage unit 20 stores measurement data 21 for the amount of gas in oil, diagnostic condition data 22, operation period data 23, approximate function data 24, estimation data 25, and diagnostic result data 26. .. The diagnostic condition data 22 and the operation period data 23 are input to the diagnostic system 100 in advance.

The calculation unit 10 reads various data and programs, executes a program for diagnosing an abnormality of a transformer, performs calculations, and the like. The arithmetic unit 10 is composed of, for example, an arithmetic unit such as a CPU (Central Processing Unit) and a storage device such as a RAM (Random Access Memory) and a ROM (Read Only Memory). The calculation unit 10 can be configured by 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 an abnormality of the transformer. The storage unit 20 is composed of, for example, a storage device such as a ROM, a hard disk, a flash memory, a magnetic disk, or an optical disk. The storage unit 20 may be configured by one hardware or may be configured by a plurality 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 by the operator of the diagnostic system 100. The input unit 30 is composed of, 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 measurement data 21 representing the amount of gas in the insulating oil of the transformer, the input unit 30 can be used for inputting the measurement data 21.

The display unit 40 is a device that displays the operation information of the diagnostic system 100, the contents of various data, the diagnostic status, the diagnostic result, and the like. The display unit 40 is composed of, 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 the 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 for the input of 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 the oil 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.

The diagnostic condition data 22 is data representing various conditions used for diagnosis, such as the timing of the abnormality to be diagnosed, the type of gas in oil used for diagnosing the abnormality, the upper limit of the amount of gas in oil used for diagnosis, and the diagnosis of abnormality. Includes information such as sensitivity (threshold 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 the future abnormality by comparison with the preset upper limit value. As the upper limit value, a reference value permitted in the maintenance management of the transformer, for example, a numerical value of "Caution I" described in Non-Patent Document 1 can be set.

The operation period data 23 is data representing the operation period of the transformer to be the target range of the regression analysis, and includes information on the start and end of the operation period such as the initial failure period, the accidental failure period, and the wear failure period. The operating period of the transformer to be the target range of the regression analysis can be set to an arbitrary period for each rated capacity and rated voltage of the transformer to be diagnosed, depending on the knowledge of the time change of the failure rate and the like.

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

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

The diagnosis result data 26 is data representing the diagnosis result of the abnormality of the transformer, 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 it is equal to or more than the predetermined value with respect to the estimated value of, and whether or not the estimated value of the amount of gas in oil estimated based on the approximate function is equal to or more than the predetermined value with respect to the preset upper limit value. Includes information on whether or not. The diagnosis result data 26 is obtained, for example, as a difference between the measured value and the estimated value of the amount of gas in oil and a difference between the estimated value and the 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 be provided with a common interface function for manual input of measurement data 21 or input from a gas sensor.

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

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

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

The diagnostic unit 15 reads out the measurement data 21, the diagnostic condition data 22, and the estimation data 25, and estimates the amount of gas in the insulating oil of the transformer in the oil for each operation period of the transformer based on the approximate function. Compare with the amount of gas. The diagnosis result data 26 representing the diagnosis result is output to the storage unit 20 and the image generation unit 16.

Based on the diagnosis result data 26, the image generation unit 16 represents the result of the diagnosis as a warning 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. Generate image data. The image data is output to the output unit 50. The image data may include measurement data 21, diagnostic condition data 22, operation period data 23, approximation function data 24, and estimation data 25 as display contents.

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

When diagnosing an abnormality in a transformer, first, the operating period of the transformer, which is the target range of regression analysis, and the diagnostic conditions are set. As the operating period of the transformer, specific start and end periods such as an initial failure period, an accidental failure period, and a wear failure period are set. As the diagnostic conditions, the timing of the abnormality to be diagnosed, the type of the gas in the oil used for the diagnosis of the abnormality, the upper limit of the amount of the gas in the oil used for the diagnosis, the sensitivity of the diagnosis of the abnormality, and the like are set.

Subsequently, 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, every day, every week, every month, every year, etc. by the gas sensor. The latest measured measurement data 21 is input to the primary diagnosis unit 12 together with the data of the upper limit value of the amount of gas in oil set in advance.

Subsequently, it is determined whether or not the amount of gas in the insulating oil of the transformer to be diagnosed is equal to or greater than the preset upper limit value of the amount of oil 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 equal to or greater than the upper limit value (step S102; YES), the process proceeds to step S106 to warn that the transformer has an abnormality. 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.

Then, for each operating period of the transformer, regression analysis is performed based on the measurement data of the amount of gas in the insulating oil of the transformer, and an approximate function representing the amount of increase in the amount of gas in the oil per unit time is obtained (step). S103). The regression analysis unit 13 performs fitting such as linear regression by 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.

Subsequently, 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 the current abnormality, the estimation unit 14 substitutes the operating time of the current transformer into the approximate function to obtain the 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 operating time of the future transformer into the approximation function to obtain an estimated value of the amount of gas in the oil in the future.

Then, the amount of gas in the insulating oil of the transformer is compared with the estimated value of the amount of gas in oil estimated based on the approximate function, and the amount of gas in the insulating oil of the transformer is equal to or more than a predetermined value. (Step S105).

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

On the other hand, when diagnosing a future abnormality, the diagnosis unit 15 compares the estimated value of the amount of gas in oil estimated based on the approximate function with the upper limit of the amount of gas in the insulating oil of the transformer. It is determined whether or not the estimated value of the amount of gas in oil estimated based on the approximation function is equal to or more than a predetermined value with respect to the upper limit of the amount of gas in the insulating oil of the transformer.

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

If the amount of gas in the oil is less than a predetermined value, the calculation unit 10 may indicate that no abnormality has occurred in the transformer to be diagnosed, or an abnormality may occur in the transformer to be diagnosed in the future. An image showing that it is low or the like may be generated and displayed as a diagnosis result image on the display unit 40. Further, the operating time of the transformer, which is predicted to exceed the upper limit value in the future, may be obtained and displayed as a diagnosis result image on the display unit 40.

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

When the current abnormality is diagnosed, the image generation unit 16 generates a diagnosis result image indicating that the transformer to be diagnosed has an abnormality as a diagnosis result of the abnormality of the transformer, and the diagnosis result image is displayed on the display unit 40. Is displayed to give a warning. The diagnosis result image showing the result of diagnosing the current abnormality is an image showing the operating period of the diagnosed transformer, an image showing the type of gas in the oil showing the abnormality, and an image estimated based on an approximate function in the oil. An image or the like showing the difference from the estimated value of the amount of gas can be included.

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 transformer to be diagnosed in the future as a diagnosis result of the abnormality of the transformer, and displays the image on the display unit 40. A diagnosis result image is displayed and a warning is given. The diagnosis result image showing the result of diagnosing the future abnormality is an image showing the operating period of the diagnosed transformer, an image showing the type of oil gas showing the abnormality, and a preset amount of oil gas. It is possible to include an image or the like showing the difference from the upper limit value of.

According to such a diagnostic method and the diagnostic system 100, regression analysis is performed for each operating period of the transformer based on the amount of gas in oil measured by the transformer, and the amount of gas in the insulating oil of the transformer is determined. In order to compare with the amount of gas in oil estimated based on the approximate function obtained for each operation period, it is possible to accurately detect accidental abnormalities that are different from the aged deterioration of insulating media and insulating materials and the accumulation of repeated abnormalities. Can be diagnosed. Since transformer failures are likely to occur when abnormalities accumulate, it is possible to accurately grasp current and future abnormalities as precursory phenomena leading to transformer failures. Since the approximation function is obtained for each type of gas in oil, it is possible to predict the amount of gas in oil in the future for each type as a pattern, and it is also possible to estimate the type of cause of future failure. Become.

In addition, the primary diagnosis by comparing with the preset upper limit of the amount of oil in oil and the secondary diagnosis by comparing with the estimated value of the amount of gas in oil estimated based on the approximate function by regression analysis. Since the two-step diagnosis is performed, it is possible to individually diagnose a clear abnormality that has already occurred and an accidental abnormality that will lead to a failure in the future. If a clear abnormality has already occurred, the process is completed at the stage of the primary diagnosis, and regression analysis and comparison with the estimated value are not required, so that the transformer abnormality can be diagnosed promptly.

In the process shown in FIG. 8, a two-step diagnosis of a primary diagnosis and a secondary diagnosis is performed, but the primary diagnosis may not be executed and only the secondary diagnosis may be executed. Further, the upper limit of the amount of gas in oil used for diagnosis may be the same between the numerical value used in the primary diagnosis and the numerical value used in the secondary diagnosis when diagnosing a future abnormality. 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 the second embodiment of the present invention.
As shown in FIG. 9, the diagnostic system 200 according to the present embodiment is connected to a plurality of terminals 140 provided for each of the plurality of transformers 1 via a communication line, and monitors an abnormality of the plurality of transformers 1. It is configured to function as a diagnostic server for diagnosis.

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 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 the gas sensor 110.

The communication line is composed of the Internet, a LAN (Local Area Network), a fixed leased line, and the like. The communication line may be a wired communication line, a wireless communication line, or a line in which these are combined.

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

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

FIG. 10 is a diagram showing a 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 stores a calculation unit 10 that performs a process of diagnosing an abnormality in a transformer and data used for the diagnostic process, similarly to the diagnostic system 100. It includes a storage unit 20, an input unit 30, a display unit 40, and a communication unit 50.

The difference between the diagnostic system 200 and the diagnostic system 100 is that measurement data 21 representing the measured value of the amount of gas in the insulating oil of the transformer is collected from a plurality of transformers 1 and obtained by the plurality of transformers 1. Based on the measured value of the amount of gas in oil obtained, an approximate function representing the amount of increase in gas in oil per unit time is obtained, and the current or future abnormality of the transformer 1 to be diagnosed can be detected by multiple transformers. This is a point of diagnosis using the approximation function obtained for the vessel 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. I have.

Further, in the diagnostic system 200, the storage unit 20 stores the measurement database 211, the diagnostic condition data 221, the operation period data 23, the approximate function data 24, the estimation data 25, and the diagnostic result data 26. The diagnostic condition data 221 and the operation period data 23 are input to 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 the measurement data 21 associated with the identifier for each transformer 1 and the identifier for distinguishing 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 by a plurality of transformers.
As shown in FIG. 11, the measurement data 21 obtained by the plurality of transformers 1 is stored in the storage unit 20 of the diagnostic system 200 as the measurement database 211. The measurement database 211 contains 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 gas in oil. Contains information on the date and time when the quantity was measured.

The diagnostic condition data 221 includes information such as the timing of the abnormality to be diagnosed, the type of gas in oil used for diagnosing the abnormality, the upper limit of the amount of gas in oil used for diagnosis, the sensitivity of diagnosing the abnormality, and the transformation of the diagnosis target. Includes information for instrument 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 operation time or the like.

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

The regression analysis unit 131 reads out the measurement data 21 and the operation period data 23 for each transformer in the measurement database 211 in which the operation period of the transformer belongs to the same range, and reads the measurement data 21 and the operation period data 23 for each transformer on the power system and of the transformer. Regression analysis is performed based on the amount of gas in oil measured by multiple transformers for each operation period. The approximate function data 24 obtained for each transformer is output to the storage unit 20.

The calculation unit 132 reads out the approximation function data 24 obtained for each transformer on the power system, and averages a plurality of approximation 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 approximate function using a plurality of transformers that have measured the measurement data 21 as a population. The approximate function data 24 representing the averaged approximate function is output to the storage unit 20.

FIG. 12 is a flowchart showing an example of a process of diagnosing an abnormality of a plurality of transformers.
As shown in FIG. 12, in the diagnostic system 200, similarly to the diagnostic system 100, the primary diagnosis by comparison with the preset upper limit of the amount of gas in oil and the approximate function by regression analysis are used. A two-step diagnosis can be made with a secondary diagnosis by comparing with the estimated value of the estimated amount of gas in the oil. As the secondary diagnosis, the diagnosis of the current abnormality or the diagnosis of the future abnormality can be performed.

In the diagnostic system 200, similarly to the diagnostic system 100, measurement data of the amount of gas in the insulating oil of the transformer is acquired (step S201), and a transformer to be diagnosed for abnormality is selected (step S202). ), It is determined whether or not the amount of gas in the insulating oil of the transformer to be diagnosed is equal to or greater than the preset upper limit of the amount of oil in oil (step S203).

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

Subsequently, a regression analysis is performed based on the measurement data of the amount of gas in the insulating oil of the transformer for each transformer and for each operation period of the transformer, and the increase amount of the gas in the oil per unit time is shown. The approximate function for each transformer is obtained (step S204). The regression analysis unit 131 performs fitting such as linear regression by the least squares method based on the measurement data 21 for each transformer whose operating periods of the transformers belong to the same range, and creates a plurality of approximate functions.

Subsequently, a plurality of approximate functions obtained for each transformer and each operation 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 additive average of the coefficients of the approximation function.

Subsequently, similarly to the above-mentioned diagnostic system 100, an estimated value of the amount of oil gas for each operating time of the transformer is obtained based on an approximate function representing an increase amount of oil gas per unit time (step S206). ), Compare 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 approximate function, and if the amount of gas in the insulating oil of the transformer is above the specified value It is determined whether or not there is (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 proceeds to step S209. On the other hand, as a result of the determination, if the amount of gas in the insulating oil of the transformer is equal to or higher than a predetermined value (step S207; YES), the abnormality is warned as a diagnosis result of the abnormality of 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 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 give a warning.

Subsequently, it is determined whether or not all the diagnoses to be diagnosed have been completed (step S209). Whether or not the diagnosis of the diagnosis target is 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 all the diagnoses to be diagnosed have not been completed (step S208; NO), the process returns to step S201. After that, the diagnosis target is switched and the diagnosis process is repeated. On the other hand, when all the diagnoses to be diagnosed have been completed (step S208; YES), the diagnosis process is terminated.

According to such a diagnostic method and the diagnostic system 200, a regression analysis is performed for each operating period of the transformer based on the amount of gas in oil measured by 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 approximate function obtained for each operation period, anomalies that occur accidentally under a common power flow are found. It can be diagnosed with high accuracy. The tendency of past aging deterioration and the tendency of past repetitive anomalies that occurred in the same power system will be added by regression analysis for multiple transformers.

In addition, since the approximation function obtained for each transformer is averaged between the transformers, the approximation function used for diagnosis is different from the case of regression analysis within the range of all measurement data obtained on the power system. , Can be obtained sequentially for each transformer. Even when it is difficult to measure the amount of gas in the insulating oil, or when the operating times of each transformer do not match, the diagnosis as a whole can be efficiently performed.

In the diagnostic system 200, the approximation function may be obtained from the measurement data 21 of all the 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 or may not be included as a target of regression analysis.

That is, in the diagnostic system 200, the regression analysis unit 131 obtains one approximate function based on the amount of gas in oil measured by some or all of the transformers 1 among the plurality of transformers 1, and the diagnostic unit 15 obtains one approximate function. However, it is possible to configure the amount of gas in oil measured by the transformer for which the approximate function is obtained to be compared with the amount of gas in oil estimated based on the approximate function for each operation period of the transformer. can.

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

Alternatively, in the diagnostic system 200, the regression analysis unit 131 obtains one approximate function based on the amount of gas in oil measured by some of the plurality of transformers 1, and the diagnostic unit 15 determines. For each period of operation of the transformer, the amount of gas in oil measured by the rest of the transformer for which the approximate function is not obtained shall be compared with the amount of gas in oil estimated based on the approximate function. You can also.

According to 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 obtained based on the amount of gas in oil measured by other transformers. Be done. Therefore, even if it is difficult to measure the amount of gas in the insulating oil or the operating time of the transformer does not match, it is necessary to diagnose the current abnormality and future abnormality of the transformer to be diagnosed. Can be done.

Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and various modifications are included as long as they do not deviate from the technical scope. For example, the embodiments described above are not necessarily limited to those having all the configurations described. In addition, it is possible to replace a part of the configuration of one embodiment with another configuration, or to add another configuration to the configuration of one embodiment. It is also possible to add, delete, or replace some of the configurations of one embodiment with other configurations.

For example, in the above-mentioned diagnostic method and diagnostic systems 100 and 200, an approximate function is obtained for each type of gas in oil, and a singular point deviating from the approximate function by a predetermined value or more is used as an estimated value to perform diagnosis for each type of gas. However, a plurality of types of oil-in-oil gas may be compared, and the types of oil-in-oil gas that deviate from a predetermined value or more may be ranked and aggregated for diagnosis for each transformer. Further, multivariate analysis according to the type of gas in oil may be combined. As a multivariate analysis, there is a method of clustering each measurement data on a multidimensional space for a plurality of types of gas in oil and diagnosing an abnormality from a mathematical distance to a cluster pattern.

Further, the diagnostic systems 100 and 200 are configured to warn the abnormality when the abnormality of the transformer is diagnosed, but the abnormality is diagnosed together with the warning of the abnormality or instead of the warning of the abnormality. It may be configured to control to stop the operation of the transformer. As a warning of abnormality, a voice warning may be given instead of the diagnosis result image.

10 Calculation unit 11 Data acquisition unit 12 Primary diagnosis unit 13 Regression analysis unit 14 Estimating unit 15 Diagnosis unit 16 Image generation unit 20 Storage unit 21 Measurement data 22 Diagnostic condition data 23 Operation period data 24 Approximate function data 25 Estimated data 26 Diagnostic result data 30 Input unit 40 Display unit 50 Communication unit 100 Diagnostic system

Claims (15)

It is a diagnostic method for diagnosing abnormalities in transformers.
The process of measuring the amount of gas in the insulating oil of a transformer over time, and
A step of performing regression analysis based on the measured amount of the gas in the oil for each operating period of the transformer to obtain an approximate function representing the amount of increase in the gas in the oil per unit time.
A diagnostic method comprising a step of comparing the amount of gas in the insulating oil of the transformer with the amount of the gas in the oil estimated based on the approximate function for each operating period of the transformer. The diagnostic method according to claim 1.
When the measured value of the amount of gas in the insulating oil of the transformer is equal to or more than a predetermined value with respect to the estimated value of the amount of gas in the oil estimated based on the approximate function, an abnormality occurs in the transformer. A diagnostic method that determines that the product is used. The diagnostic method according to claim 1.
When the estimated value of the amount of gas in the oil estimated based on the approximate function is equal to or more than a predetermined value with respect to the preset upper limit of the amount of gas in the insulating oil of the transformer, the transformer is concerned. A diagnostic method that determines that an abnormality will occur in the future. The diagnostic method according to claim 2 or claim 3.
The diagnostic method in which the predetermined value is a value of 20% or more with respect to the estimated value. The diagnostic method according to claim 1.
The diagnostic method in which the operating period of the transformer is a period set based on the failure rate curve of the transformer. The diagnostic method according to claim 5.
The operating period of the transformer includes an initial failure period in which the failure rate of the transformer decreases over time, an accidental failure period in which the failure rate of the transformer is steady, and a failure rate of the transformer over time. A diagnostic method that is a period set as one of the increasing wear failure periods. The diagnostic method according to claim 6.
The initial failure period is a period from the start of operation of the transformer to 3 years or more and 6 years or less.
The accidental failure period is a period from the end of the initial failure period to 20 years or more and 30 years or less.
The diagnostic method in which the wear failure period is a period after the end of the accidental failure period. The diagnostic method according to claim 1.
A diagnostic method in which 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 process of measuring the amount of the gas over time is performed for a plurality of transformers having the same rated capacity and rated voltage.
The step of obtaining the approximation function is performed for some of the plurality of transformers.
For each operating period of the transformer, the amount of the gas in the oil measured by the transformer for which the approximation function has not been obtained is estimated based on the approximation function obtained by another transformer. A diagnostic method to compare with the amount of. The diagnostic method according to claim 1.
The process of measuring the amount of the gas over time is performed for a plurality of transformers having the same rated capacity and rated voltage.
The step of obtaining the approximation function is performed for all or some of the plurality of transformers.
The approximation function is averaged between the transformers and
A diagnostic method that compares the amount of gas in the insulating oil of a transformer with the amount of gas in the oil estimated based on the averaged approximation function for each operating period of the transformer. It is a diagnostic system that diagnoses abnormalities in transformers.
Regression analysis is performed based on the amount of gas in the insulating oil of the transformer measured over time for each operating period of the transformer, and a regression function is obtained to obtain an approximate function representing the amount of increase in the gas in the oil per unit time. With the analysis department
A diagnostic system including a diagnostic unit that compares the amount of gas in the insulating oil of the transformer with the amount of gas in the oil estimated based on the approximate function for each operating period of the transformer. The diagnostic system according to claim 11.
When the measured value of the amount of the gas in the insulating oil of the transformer is equal to or more than a predetermined value with respect to the estimated value of the amount of the gas in the oil estimated based on the approximate function, the transformer has an abnormality. A diagnostic system that displays a warning that it is occurring. The diagnostic system according to claim 11.
When the estimated value of the amount of gas in oil estimated based on the approximate function is equal to or more than a predetermined value with respect to the preset upper limit value of the amount of gas in the insulating oil of the transformer, the transformer is concerned. A diagnostic system that displays a warning that an abnormality will occur in the future. The diagnostic system according to claim 11.
The operating period of the transformer is a period set based on the failure rate curve of the transformer.
A diagnostic system including a storage unit that stores data at the beginning and end of the period. It is a diagnostic system that diagnoses abnormalities in transformers.
A data acquisition unit that acquires measurement data of the amount of gas in the insulating oil of the transformer measured over time from multiple transformers with the same rated capacity and rated voltage.
Regression analysis is performed based on the amount of gas in the insulating oil of the transformer measured over time for each transformer and each operating period of the transformer, and the increase amount of the gas in the oil per unit time is calculated. Regression analysis unit that finds the approximate function to represent,
A calculation unit that averages the approximation function between the transformers,
A diagnostic unit that compares the amount of gas in the insulating oil of the transformer with the amount of gas in the oil estimated based on the averaged approximate function for each transformer and for each operating period of the transformer. And equipped with a diagnostic system.

Images