JP2016223821A - Diagnostic device of electrical apparatus, diagnostic system of electrical apparatus, diagnostic method of electrical apparatus, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To realize high accurate diagnosis of an electrical apparatus such as a rotary machine.SOLUTION: A diagnostic device comprises: a measurement data storage unit (24) for storing measurement data of an electrical apparatus to be diagnosed; a phenomenon description model storage unit (22) for storing a phenomenon description model representing the state of an electrical apparatus by one or a plurality of model constant values (D1); a model constant value determination unit (26) for determining a model constant value (D1) so as to reproduce the measurement data, and calculating a success level index value (D2) being the degree of matching of the model constant value (D1) with respect to the electrical apparatus; and a judgement unit (28) for obtaining the certainty of occurrence of a phenomenon corresponding to the phenomenon description model and the degree of progress of the phenomenon by using the model constant value (D1) and the success level index value (D2).SELECTED DRAWING: Figure 3

Description

  The present invention relates to a diagnostic apparatus for an electrical device, a diagnostic system for the electrical device, a diagnostic method for the electrical device, and a program.

  As background art of this technical field, the abstract of the following Patent Document 1 states that “the type of partial discharge generation source can be determined from the partial discharge characteristics of the oil immersion insulation device”, “ A generation transition pattern storage unit 81 that applies an AC voltage and obtains and stores a generation transition pattern of a partial discharge charge amount accompanying an increase in the applied voltage, a plurality of partial discharge charge amounts obtained using a plurality of types of particles as a partial discharge generation source A generation transition pattern model storage unit 82 for storing a generation transition pattern model of the current generation, a generation transition pattern storage unit 81, and a generation transition pattern model and a plurality of generation transition pattern models are read out from the generation transition pattern model storage unit 82. By determining which of the generation transition pattern models is the closest, the partial discharge source at the time of the partial discharge It is described as being provided with a particle discrimination unit 83 for discriminating the type of particles "that.

  In addition, the abstract of the following Patent Document 2 states that “the electrical equipment diagnosis system 1 is a sensor that detects the current and voltage of the electrical equipment 2, a sensor and torque sensor that detect the rotational angle position and rotational speed of the rotating machine, temperature, When the feature quantity detected by the sensor 3, which is a generic name of a sensor, a vibration sensor, etc., is input to the learning unit 5 via the A / D conversion circuit 4, the learning unit 5 changes to a normal state, a failure state, or a deteriorated state of the motor. Each corresponding feature amount is parameterized and stored based on the hidden Markov model, and the diagnosis unit 6 stores the output probability of the feature amount pattern detected in the operating state of the electrical equipment 2 in the learning unit 5. After calculating based on the parameters, the state of the electrical equipment 2 is determined by determining the parameter having the maximum output probability, and the state is displayed on the display unit 7 ”.

Japanese Patent Laid-Open No. 11-142467 JP 2005-251185 A

  A high-voltage motor having a rated power supply voltage of 3.3 kV, 6.6 kV, 11 kV, or the like may be applied to the high-output motor. Since the high-voltage motor is applied to basic equipment such as a factory, if it is stopped due to a sudden failure, it has a great influence such as a reduction in the operating rate of the production equipment and a review of the production plan. As a cause of failure of the high-voltage motor, there is an investigation result that about 25% is insulation deterioration of the stator winding.

  In recent years, variable speed drive using an inverter is spreading to high-voltage motors in order to save energy. However, when an inverter is used, there arises a problem that the insulation deterioration is accelerated by an inverter surge. This trend is expected to become even more pronounced with the advent of semiconductor devices using new materials such as SiC (silicon carbide). This is because the frequency of occurrence of the inverter surge is proportional to the output frequency of the inverter, and it is considered that the insulation deterioration is accelerated when the output frequency of the inverter is increased. Under such circumstances, there is a demand for highly accurate insulation deterioration diagnosis in the operating state of the high-voltage motor.

  The technique disclosed in Patent Literature 1 determines which of the plurality of generation transition pattern models the occurrence transition pattern of the partial discharge generated inside or around the insulating member is, and determines the generation source of the partial discharge. It is something to be determined. However, in this technique, it is determined which pattern model is the closest to the pattern model prepared in advance, but there is a problem that information on the degree of progress of the phenomenon expressed by the model and the reliability of the determination cannot be obtained. In particular, there was a high possibility of erroneous determination when an occurrence transition pattern model suitable for an actual phenomenon was not prepared.

The technique disclosed in Patent Document 2 attempts to diagnose the state of electrical equipment using a statistical pattern recognition technique. However, this technique has a problem that it is difficult to quantitatively grasp the degree of progress of deterioration because diagnosis is performed without using a physical model that describes the deterioration phenomenon.
The present invention has been made in view of the circumstances described above, and it is an object of the present invention to provide an electrical device diagnosis apparatus, an electrical device diagnosis system, an electrical device diagnosis method, and a program capable of realizing highly accurate diagnosis of the electrical device. And

  In order to solve the above-described problems, an electrical apparatus diagnostic apparatus according to the present invention includes a measurement data storage unit that stores measurement data for an electrical apparatus that is a diagnosis target, and a phenomenon in which the state of the electrical apparatus is represented by one or more model constant values A phenomenon description model storage unit that stores a description model, and determines the model constant value so as to reproduce the measurement data, and calculates a success level index value that is a degree of conformity of the model constant value to the electrical device A model constant value determination unit; and a determination unit that obtains a certainty of occurrence of a phenomenon corresponding to the phenomenon description model and a degree of progress of the phenomenon using the model constant value and the success level index value. It is characterized by having.

  According to the electrical device diagnostic apparatus, electrical device diagnostic system, electrical device diagnostic method, and program of the present invention, it is possible to achieve highly accurate diagnosis of electrical devices.

1 is a block diagram of a rotating machine diagnostic system according to a first embodiment of the present invention. It is a circuit diagram which shows an example of the equivalent circuit of the insulating member which a partial discharge phenomenon produces. It is a block diagram of the rotary machine diagnostic apparatus in 1st Embodiment. It is a figure which shows the 1st example of distribution of a partial discharge signal. It is a figure which shows the 2nd example of distribution of a partial discharge signal. It is a figure which shows the 3rd example of distribution of a partial discharge signal. It is a figure which shows the 4th example of distribution of a partial discharge signal. It is a block diagram of the rotary machine diagnostic apparatus in 2nd Embodiment of this invention. It is a block diagram of the modification of a rotary machine diagnostic system. It is a block diagram of the other modification of a rotary machine diagnostic system. It is a block diagram of the further another modification of a rotating machine diagnostic system.

[First Embodiment]
(overall structure)
The configuration of the rotating machine diagnosis system according to the first embodiment of the present invention will be described below with reference to the block diagram shown in FIG.
In FIG. 1, electric power is supplied from a power source 1 to a motor 3 that is a three-phase induction motor via feeder lines 2a, 2b, and 2c. A load machine 4 is coupled to the rotating shaft of the motor 3. One end of each of the three measuring capacitors 11 is connected to the feeder lines 2a, 2b, 2c, and the other ends of the measuring capacitors 11 are connected to each other and to the ground potential.

  The current sensor 5 collectively measures the current (partial discharge signal) flowing from each measurement capacitor 11 to the ground potential by partial discharge. The partial discharge generated in and around the insulating member of the winding of the motor 3 mainly appears in a high frequency region of 1 MHz to 100 MHz or more. As the current sensor 5, one having sensitivity in the frequency band is applied. Since the frequency component such as the power supply frequency is considerably lower than 1 MHz, it is not detected by the current sensor 5.

  Further, between the measurement capacitor 11 and the motor 3, a current sensor 6 that collectively measures the currents of all phases is provided on the power supply lines 2 a, 2 b, and 2 c. As the current sensor 6, one having sensitivity in the same frequency band as the current sensor 5 is applied. Therefore, if the partial discharge is not generated in the motor 3, the partial discharge signal detected by the current sensors 5 and 6 becomes zero. However, if the partial discharge occurs in the motor 3, the non-zero partial discharge signal is converted into the current sensor. 5 and 6 are measured.

  The output signals of the current sensors 5 and 6 are analyzed by the rotating machine diagnostic device 8, converted into information related to the sign of insulation deterioration, and transmitted to the user by the information transmission device 10. This information transmission device 10 may be a device that appeals visually, such as a display or a lamp, or may be a device that appeals to hearing, such as a buzzer.

(Equivalent circuit of insulation member)
There are various factors that cause insulation deterioration in the motor 3, but in this embodiment, "occurrence of voids (voids) in the insulating member" is assumed as one factor.
When the motor 3 is operated for a long time, the insulating member undergoes alteration such as oxidation or decomposition due to the heat generated by the motor 3, thereby causing a change in the configuration of the insulating member, that is, generation of a void. . Generally, since the void has a lower dielectric constant than the insulating member, the voltage applied to the void is relatively high, and corona discharge is likely to occur in the void. When a corona discharge occurs, a slight charge transfer occurs, so that a high-frequency partial discharge signal is superimposed on the current flowing in the stator winding of the motor 3.

  FIG. 2 shows a circuit diagram of an equivalent circuit of the insulating member in which the void is generated. In FIG. 2, input terminals 50 and 51 correspond to a pair of conductors sandwiching an insulating member, and a voltage V is applied between them. The capacitor 52 corresponds to an insulating member in a place where no void is generated. A series circuit of the capacitor 53, the void portion 54, and the capacitor 58 corresponds to a portion where a void is generated. The void part 54 includes a capacitor 55, a resistor 56, and a switch 57.

  When the partial discharge is not generated in the void portion 54, the switch 57 can be considered to be in an off state. On the other hand, when partial discharge is occurring, the switch 57 can be considered to be in the on state. In FIG. 2, the capacitances of capacitors 52, 53, 55, and 58 and the resistance value of resistor 56 are obtained by applying various optimization methods to the actually measured values of current sensors 5 and 6 (see FIG. 1). Can be determined.

(Configuration of rotating machine diagnostic device 8)
Next, the configuration of the rotating machine diagnostic apparatus 8 shown in FIG. 1 will be described.
The rotating machine diagnosis device 8 includes hardware as a general computer such as a CPU (Central Processing Unit), a RAM (Random Access Memory), a ROM (Read Only Memory), and an HDD (Hard Disk Drive). Stores an OS (Operating System), application programs, various data, and the like. The OS and application programs are expanded in the RAM and executed by the CPU.

FIG. 3 is a block diagram showing functions realized by the application program developed in the RAM in the rotating machine diagnosis device 8.
In FIG. 3, the phenomenon description model storage unit 22 stores a phenomenon description model whose operation is defined by one or a plurality of constants. The “phenomenon description model” is specifically a mathematical expression or an equivalent circuit. In the example shown in FIG. 2, the configuration of the equivalent circuit itself shown in FIG. 2 is a phenomenon description model. The operation of this equivalent circuit can also be expressed by mathematical expressions.

  Measurement data obtained from the current sensors 5 and 6 (see FIG. 1) is stored in the measurement data storage unit 24. Then, the model constant value determining unit 26 determines one or more constant values that define the behavior of the phenomenon description model, that is, the model constant value D1 so as to reproduce the measurement data most. In the example shown in FIG. 2, the capacitances of the capacitors 52, 53, 55, and 58 and the resistance value of the resistor 56 correspond to the model constant value D1.

  Further, when determining the model constant value D1, the model constant value determining unit 26 also obtains one or a plurality of index values (success level index value D2) indicating the success level for determining the model constant value D1. In the example shown in FIG. 2, the “calculated value” of the current can be obtained based on the phenomenon description model (circuit configuration in FIG. 2) and the model constant value D1 (capacitance, resistance value). On the other hand, a residual is generated between the actual measured value and the calculated value. Therefore, the reciprocal of the residual between the actually measured value and the calculated value can be used as the success level index value D2.

Here, how the model constant value D1 and the success level index value D2 change as the deterioration of the motor 3 progresses is examined.
First, assume a state in which the motor 3 has not deteriorated, that is, a normal state. In a normal state, the model constant value D1 and the success level index value D2 both vary due to variations in measured values due to noise or the like, and no significant fluctuation occurs.

  Next, it is assumed that a deterioration phenomenon occurs in the motor 3 and the phenomenon description model is a correct model that matches the deterioration phenomenon. In this case, as the deterioration phenomenon proceeds and the model constant value D1 changes from the value in the normal state, the success level index value D2 increases from the value in the normal state.

  Next, it is assumed that a deterioration phenomenon has occurred and the phenomenon description model does not conform to the deterioration phenomenon. In this case, the method of changing the success level index value D2 differs depending on the fitness of the model. That is, in the case of a model that does not fit at all, the success level index value D2 is smaller than the value in the normal state. In the case of a model that fits to some extent, the success level index value D2 increases more than the value in the normal state, but the increase is not as great as when the correct model is applied.

  As described above, if there is an increasing tendency in the success level index value D2, it can be determined that there is a relatively high possibility that a deterioration phenomenon that matches the applied phenomenon description model has occurred. On the other hand, if the success level index value D2 shows a decreasing tendency, it can be determined that there is a high possibility that a phenomenon different from the phenomenon described by the applied phenomenon description model has occurred.

  The generation certainty / progression degree determination unit 28 shown in FIG. 3 periodically obtains the model constant value D1 and the success level index value D2 after the use of the motor 3 is started, that is, after the motor 3 is in a normal state. It is obtained and accumulated in the history data storage unit 30 including the width of variation of these values. Thereby, the occurrence certainty / progress degree determination unit 28 quantitatively calculates the certainty that the deterioration phenomenon related to the phenomenon description model has occurred and the deterioration progress degree of the deterioration phenomenon.

  It is conceivable to use machine learning as a method for quantitatively calculating the degree of progress of deterioration. For example, the model constant value D1 and the success level index value D2 in the normal state are stored in the history data storage unit 30 as initial values of the multidimensional data set, and depending on the degree of deviation from the initial value of the subsequent multidimensional data set. The degree of deterioration can be quantified. Thereby, it is possible to increase detection sensitivity even for weak abnormal signs. The certainty degree and the degree of deterioration obtained by the occurrence certainty / progress degree determination unit 28 are transmitted to the user via the information transmission device 10 (see FIG. 1).

(Specific examples of diagnosis)
The partial discharge signals measured by the current sensors 5 and 6 are converted into discharge charge amounts in the rotating machine diagnosis device 8 and stored in the measurement data storage unit 24 as a distribution with respect to the phase angle of the applied voltage, for example, as shown in FIG. Is done. Since the phase angle distribution of the discharge charge amount shows different characteristics depending on the generation site and the generation factor of the partial discharge, in this embodiment, as a phenomenon description model, the feature of the phase angle distribution of the discharge charge amount due to one generation factor Is prepared and stored in the phenomenon description model storage unit 22.

  As described above, the equivalent circuit shown in FIG. 2 is an example of the phenomenon description model, and assumes a partial discharge phenomenon that occurs in a void inside the insulating member of the motor 3. As shown in FIG. 4, the partial discharge signal due to the void is in the period when the absolute value of the voltage V applied to the insulating member increases most, that is, in the vicinity of 0 ° to 90 ° and 180 ° to 270 ° of the voltage phase. There is a tendency to concentrate. This is because the charge continues to be supplied to the void during the period in which the absolute value of the voltage V increases, and therefore, the charge accumulation in the void and the corona discharge tend to be repeated.

  The model constant value determining unit 26 uses an optimization method or the like to determine the model constant value D1 so as to most reproduce the measurement data of the partial discharge signal, obtain the success level index value D2, and determine the degree of occurrence / progression The determination unit 28 performs insulation deterioration diagnosis by using the obtained model constant value D1 and success level index value D2.

  For example, it is assumed that the phase angle distribution shown in FIG. 4 is obtained at a certain point in time and then the motor 3 is further operated to result in the phase angle distribution shown in FIG. Compared to FIG. 4, the phase angle distribution shown in FIG. Since the model constant value determination unit 26 determines the model constant value D1 according to the distribution of FIG. 5, the model constant value D1 is generally different from the value when the distribution of FIG. 4 is obtained. .

  Further, the model constant value determining unit 26 obtains a success level index value D2 based on the determined model constant value D1. Here, since the distribution of FIG. 5 has a larger amount of discharge charge relative to the noise than the distribution of FIG. 4, the success level index value D2 is larger than the value when the distribution of FIG. 4 is obtained. It is thought that it shows a tendency. Thus, when the success level index value D2 shows an increasing tendency, it can be determined that “the phenomenon description model is correct”, that is, “the partial discharge phenomenon that has occurred is a void inside the insulating member”.

  However, the reason why the partial discharge occurs is not limited to the void inside the insulating member, and various factors can be considered. For example, if the binding string that binds the stator windings is loosened by electromagnetic vibration, the wedge that fixes the stator windings falls off due to electromagnetic vibrations, or if dust adheres or accumulates, the gap between the stator and rotor In any case, partial discharge occurs with a distribution according to each factor.

  For example, as shown in FIG. 6, it is assumed that a partial discharge is concentrated near a specific phase angle and a phase angle distribution is measured such that the discharge charge amount is biased to either positive or negative. When the equivalent circuit of FIG. 2 is applied to this measurement data, it is considered that the success level index value D2 decreases as the discharge charge amount increases. In such a case, it can be determined that “the wrong phenomenon description model is applied”, that is, “the partial discharge phenomenon that has occurred is not the partial discharge in the void inside the insulating member”.

  Further, for example, assume that a phase angle distribution as shown in FIG. 7 is measured. In the distribution shown in FIG. 7, the partial discharge signals are concentrated in the sections of the voltage phase near 0 ° to 90 ° and 180 ° to 270 °, but the discharge charge amount in each section gradually decreases as the phase angle increases. doing. When the equivalent circuit of FIG. 2 is applied to this measurement data, the success level index value D2 increases to some extent when the discharge charge amount increases, but does not increase as much as when the correct phenomenon description model is applied. It is done.

  In such a case, “a phenomenon description model that is correct to some extent is applied”, that is, “the partial discharge phenomenon that occurs is similar to the partial discharge in the void inside the insulating member” or “inside the insulating member It can be determined that the partial discharge phenomenon has occurred due to a complex factor including the voids of "."

It is desirable to collect measurement data at a frequency that is greater than or equal to the frequency at which diagnosis is performed. For example, if the diagnosis is made once a day, the measurement data may be collected at a frequency of once or more a day.
As described above, according to the rotating machine diagnostic apparatus 8 of the present embodiment, the degree of deterioration progress can be quantified, so that it is possible to realize a highly accurate insulation deterioration diagnosis while measuring partial discharge during operation of the motor 3. it can.

[Second Embodiment]
Next, the configuration of the rotating machine diagnosis system according to the second embodiment of the present invention will be described.
Although the overall configuration of this embodiment is the same as that of the first embodiment (FIG. 1), a rotating machine diagnostic device 8A shown in the block diagram of FIG. 8 is applied instead of the rotating machine diagnostic device 8. Different. Therefore, the configuration of the rotating machine diagnostic apparatus 8A will be described with reference to FIG. Similar to the rotating machine diagnostic device 8, the rotating machine diagnostic device 8A includes hardware as a general computer having a CPU, a RAM, a ROM, an HDD, and the like. The HDD includes an OS application program and various data. Etc. are stored. FIG. 8 is a block diagram showing functions realized by the application program expanded in the RAM. In FIG. 8, portions corresponding to the respective portions in FIG.

  The phenomenon description model storage unit 42 according to the present embodiment stores a plurality of phenomenon description models respectively corresponding to a plurality of different phenomena. Among these, one phenomenon description model that is actually applied is designated by the model update unit 44. The model update unit 44 designates a plurality of phenomenon description models while switching them cyclically.

  The model constant value determination unit 26 calculates a model constant value D1 and a success level index value D2 for the applied phenomenon description model. The history data storage unit 30 stores the calculated model constant value D1 and the success level index value D2 in association with the applied phenomenon description model, including the range of variation of these values.

  The occurrence certainty / progress degree determination unit 28 is based on the model constant value D1 and the success level index value D2, and the degree of certainty that the deterioration phenomenon related to the applied phenomenon description model has occurred and the deterioration progress of the deterioration phenomenon. The degree is calculated quantitatively. The specific method is the same as that of the first embodiment, but in this embodiment, the certainty factor and the degree of deterioration progress are calculated for each of a plurality of phenomenon description models.

  Based on the certainty level and the degree of deterioration progress for each phenomenon description model, the comprehensive judgment unit 46 is likely to interpret which deterioration phenomenon has occurred in the motor 3, that is, a phenomenon description model most suitable for the measurement data. Determine which model is. This determination result is referred to as a comprehensive determination result. Then, the comprehensive determination unit 46 causes the information transmission apparatus 10 (see FIG. 1) to display the degree of certainty and the degree of deterioration progress for each of the plurality of phenomenon description models, and display the comprehensive determination result.

  As described above, according to the rotating machine diagnostic apparatus 8A in the present embodiment, the degree of deterioration can be quantified in the same manner as the rotating machine diagnostic apparatus 8 in the first embodiment, so that partial discharge is generated during operation of the motor 3. A highly accurate insulation deterioration diagnosis can be realized while measuring. Furthermore, according to the rotating machine diagnostic apparatus 8A of the present embodiment, the comprehensive determination unit 46 makes a comprehensive determination on a plurality of deterioration factors, so that it is possible to increase the accuracy of grasping the deterioration phenomenon.

[Modification]
The present invention is not limited to the above-described embodiments, and various modifications can be made. The above-described embodiments are illustrated for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of an embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of an embodiment. Further, it is possible to delete a part of the configuration of each embodiment, or to add or replace another configuration. Examples of possible modifications to the above embodiment are as follows.

(1) In each of the above-described embodiments, an example in which a three-phase induction motor is applied as the motor 3 has been described. However, the number of phases of the motor 3 may be appropriately determined according to the application. May be applied. Moreover, the electrical device in the present invention is not limited to a motor, and may be a generator. As for the generator, the direction of power flow is reversed compared to the motor, but the insulation deterioration diagnosis can be performed by the same method. In addition, the “electric device” in the present invention is not limited to a rotating machine such as a motor or a generator, but can also be applied to a stationary electric device such as a transformer or a reactor.

(2) The overall configuration of the rotating machine diagnosis system is not limited to that shown in FIG. 1, and various modifications can be made. For example, as shown in the block diagram of FIG. 9, instead of the current sensor 6 (see FIG. 1), current sensors 7a, 7b, which individually detect partial discharge signals flowing in the power supply lines 2a, 2b, 2c of the respective phases. 7c may be provided.
Further, as shown in the block diagram of FIG. 10, instead of the current sensor 5, current sensors 9 a, 9 b, and 9 c that individually detect partial discharge signals flowing through the measurement capacitors 11 of each phase may be provided. Further, as shown in the block diagram of FIG. 11, instead of the current sensors 5 and 6, current sensors 7a, 7b, and 7c for each phase and current sensors 9a, 9b, and 9c may be provided.

(3) Moreover, the sensor which can detect a partial discharge is not restricted to a current sensor. For example, the partial discharge can also be detected by a sound wave sensor that detects an impact sound when the partial discharge occurs or an electromagnetic wave sensor that detects the electromagnetic radiation when the partial discharge occurs. Therefore, these sonic sensors or electromagnetic wave sensors may be applied in place of the current sensors 5, 6 or the like, or may be applied together with the current sensors 5, 6 or the like.

(4) Since the hardware of the rotating machine diagnosis devices 8 and 8A in each of the above embodiments can be realized by a general computer, a program for realizing the block diagrams shown in FIGS. 3 and 8 is stored in a storage medium, Or you may distribute via a transmission line.

(5) Although the processing shown in FIGS. 3 and 8 has been described as software processing using a program in each of the above embodiments, a part or all of the processing is ASIC (Application Specific Integrated Circuit); IC), or hardware processing using an FPGA (field-programmable gate array) or the like.

[Overview of composition and effect]
As described above, according to the electrical device diagnosis apparatus (8, 8A) in the first and second embodiments, the measurement data storage unit (24) that stores the measurement data for the electrical device that is the diagnosis target, A phenomenon description model storage unit (22, 42) for storing a phenomenon description model representing the state of the electrical device by a plurality of model constant values (D1), and the model constant value (D1) to reproduce the measurement data. A model constant value determining unit (26) that determines a success level index value (D2) that is a degree of conformity of the model constant value (D1) to the electrical device, and the model constant value (D1) And a determination unit (28) for obtaining a certainty that the phenomenon corresponding to the phenomenon description model has occurred and a progress degree of the phenomenon using the success level index value (D2). To.

  According to this configuration, the determination unit (28) can quantify the degree of progress of a phenomenon such as deterioration using the model constant value (D1) and the success level index value (D2). Can be diagnosed.

  Furthermore, according to the electrical apparatus diagnosis apparatus (8A) in the second embodiment, the phenomenon description model storage unit (42) stores a plurality of the phenomenon description models. Among them, based on the model update unit (44) that specifies one phenomenon description model to be applied while switching, and the certainty and progress obtained by the determination unit (28) for a plurality of the phenomenon description models. And a comprehensive determination unit (46) for determining a phenomenon description model most suitable for the measurement data.

  Thereby, since the comprehensive determination part (46) performs comprehensive determination with respect to a plurality of phenomenon description models, it is possible to achieve high accuracy such as deterioration diagnosis.

Furthermore, according to the diagnostic apparatus (8, 8A) for the electrical device in the first and second embodiments, the model constant value (D1) and the success level index value (D2) in the normal state of the electrical device are multidimensional. The storage unit (30) stores data as a data set, and the determination unit (28) includes a multidimensional data set based on the current model constant value (D1) and the success level index value (D2), and the storage The degree of progress is obtained by the degree of deviation from the multidimensional data set (initial value) stored in the section (30).
Thus, by comparing the multidimensional data set in the normal state with the subsequent multidimensional data set, the determination unit (28) can increase the detection sensitivity even for a weak abnormality sign.

Furthermore, according to the diagnostic apparatus (8, 8A) of the electrical device in the first and second embodiments, the measurement data is data obtained by measuring a partial discharge signal measured during operation of the electrical device, The determination unit (28) is characterized in that the degree of insulation deterioration of the electrical device is obtained as the progress degree.
Thereby, it is possible to quantitatively determine the degree of insulation deterioration of the electric device while operating the electric device.

Furthermore, according to the diagnostic apparatus (8, 8A) of the electrical equipment in the first and second embodiments, a mathematical expression or an equivalent circuit is applied as the phenomenon description model.
Thus, by describing the phenomenon description model with a mathematical expression or an equivalent circuit, it is possible to quantitatively grasp the progress of deterioration.

Furthermore, according to the diagnostic apparatus (8, 8A) of the electrical equipment in the first and second embodiments, an optimization method is applied as a method for determining the model constant value (D1).
Thereby, an appropriate model constant value (D1) can be obtained according to the measurement data.

Furthermore, according to the diagnostic apparatus (8, 8A) of the electrical equipment in the first and second embodiments, the electrical equipment is a rotating machine.
Thereby, the degree of insulation deterioration of the rotating machine can be obtained quantitatively.

DESCRIPTION OF SYMBOLS 1 Power supply 2a, 2b, 2c Feeding line 3 Motor 4 Load machine 5, 6, 7a, 7b, 7c, 9a, 9b, 9c Current sensor (sensor)
8,8A Rotating machine diagnostic device (diagnostic device for electrical equipment)
DESCRIPTION OF SYMBOLS 10 Information transmission apparatus 11 Capacitor for measurement 22 Phenomenon description model memory | storage part 24 Measurement data memory | storage part 26 Model constant value determination part 28 Generation | occurrence | production reliability and progress determination part (determination part)
30 History data storage unit (storage unit)
42 phenomenon description model storage unit 44 model update unit 46 comprehensive judgment unit 50, 51 input terminals 52, 53, 55, 58 capacitor 54 void unit 56 resistor 57 switch

Claims (10)

A measurement data storage unit for storing measurement data for an electrical device to be diagnosed;
A phenomenon description model storage unit that stores a phenomenon description model representing the state of the electrical device by one or a plurality of model constant values;
A model constant value determining unit that determines the model constant value so as to reproduce the measurement data, and calculates a success level index value that is a degree of conformity of the model constant value to the electrical device;
A determination unit that obtains a certainty of occurrence of a phenomenon corresponding to the phenomenon description model and a degree of progress of the phenomenon using the model constant value and the success level index value; Equipment diagnostic equipment. The phenomenon description model storage unit stores a plurality of the phenomenon description models,
A model update unit that specifies one of the plurality of phenomenon description models to be applied while switching;
And a comprehensive determination unit that determines a phenomenon description model most suitable for the measurement data based on the certainty and the degree of progress obtained by the determination unit for a plurality of the phenomenon description models. The diagnostic apparatus for electrical equipment according to claim 1. A storage unit that stores the model constant value and the success level index value in a normal state of the electrical device as a multidimensional data set;
The determination unit obtains the degree of progress based on a degree of divergence between the multidimensional data set based on the current model constant value and the success level index value and the multidimensional data set stored in the storage unit. The diagnostic apparatus for electrical equipment according to claim 2, wherein: The measurement data is data obtained by measuring a partial discharge signal measured during operation of the electrical equipment,
The diagnostic device for an electric device according to claim 3, wherein the determination unit obtains a degree of insulation deterioration of the electric device as the progress degree. The electrical apparatus diagnosis apparatus according to claim 4, wherein a mathematical expression or an equivalent circuit is applied as the phenomenon description model. The electrical apparatus diagnosis apparatus according to claim 5, wherein an optimization technique is applied as a technique for determining the model constant value. The electrical apparatus diagnosis apparatus according to claim 6, wherein the electrical apparatus is a rotating machine. A diagnostic device for an electric device according to claim 1,
The electrical equipment;
A diagnostic system for electrical equipment, comprising: a sensor that collects the measurement data from the electrical equipment. A measurement data storage process for storing measurement data for the electrical device to be diagnosed;
A phenomenon description model storage process for storing a phenomenon description model representing the state of the electrical device by one or more model constant values;
Determining the model constant value so as to reproduce the measurement data, and calculating a model constant value determining step of calculating a success level index value that is a degree of conformity of the model constant value to the electrical device;
Using the model constant value and the success level index value to determine the certainty that the phenomenon corresponding to the phenomenon description model has occurred and the degree of progress of the phenomenon. Device diagnostic method. Computer
A measurement data storage unit for storing measurement data for an electrical device to be diagnosed;
A phenomenon description model storage unit for storing a phenomenon description model representing the state of the electrical device by one or more model constant values;
A model constant value determining unit that determines the model constant value so as to reproduce the measurement data, and calculates a success level index value that is a degree of conformity of the model constant value to the electrical device,
A determination unit that calculates a certainty that a phenomenon corresponding to the phenomenon description model has occurred and a degree of progress of the phenomenon using the model constant value and the success level index value;
Program to function as.

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