WO2022003758A1

WO2022003758A1 - Abnormality diagnosis device, power conversion device, and abnormality diagnosis method

Info

Publication number: WO2022003758A1
Application number: PCT/JP2020/025464
Authority: WO
Inventors: 俊彦宮内; 将仁三好; 健開田; 壮太佐野; 烈菅原
Original assignee: 三菱電機株式会社
Priority date: 2020-06-29
Filing date: 2020-06-29
Publication date: 2022-01-06
Also published as: DE112020007369T5; KR20230010708A; CN115885469A; JPWO2022003758A1; JP6824494B1

Abstract

An abnormality diagnosis device (30) detects current flowing through an electric motor (2) driven by pulse width modulation control of a power conversion device (100) to analyze the frequency of the current and has a determination unit (34) that determines an abnormality of the electric motor (2) on the basis of a spectrum peak of at least one side band component of a modulated wave, which is obtained from the result of the frequency analysis. The abnormality diagnosis device (30) is provided with a frequency setting unit (33) for presetting a noise frequency (fnα) within the current. The determination unit (34) determines the abnormality by estimating the presence or absence of noise interference at the spectrum peak of the side band component on the basis of the frequency of the side band component and the set noise frequency (fnα).

Description

Abnormality diagnosis device, power conversion device and abnormality diagnosis method

The present application relates to an abnormality diagnosing device for diagnosing an abnormality in an electric motor, a power conversion device equipped with the abnormality diagnosing device to drive the electric motor, and a method for diagnosing an abnormality in the electric motor.

In order to diagnose the abnormality of the motor during operation, for example, in the conventional method described in Patent Document 1, the frequency analysis is performed on the current flowing through the motor, and the abnormality is diagnosed from the frequency component appearing as a sideband wave of the power supply frequency component. Then, in the current flowing through the motor, the noise component is canceled by subtracting the waveforms of two periods having the same phase, and the pulsating component that appears when the rotor is abnormal is extracted to perform abnormality diagnosis. ..

Further, in the conventional method described in Patent Document 2, the induction motor is driven by the PWM (pulse width modulation) control of the inverter, and the noise component generated in the vibration spectrum is removed and the noise component is removed. The spectrum is inverse Fourier transformed, and the vibration acceleration waveform collected by the induction motor is obtained with the noise component removed.

Japanese Patent Application Laid-Open No. 2003-274691 Japanese Unexamined Patent Publication No. 2016-116251

In the conventional abnormality diagnosis described in Patent Document 1, only noise components having the same phase and the same magnitude can be canceled out in the current flowing through the motor for each cycle. However, the noise component varies depending on the driving condition of the motor or the abnormal state, and the noise component that cannot be reduced remains, and it is difficult to reliably extract the frequency component for the abnormality diagnosis.

In the conventional abnormality diagnosis described in Patent Document 2, the noise signal which is an acceleration component caused by the carrier frequency is avoided, and other noise components, particularly the noise component in the low frequency region, remain, and the abnormality diagnosis is reliable. It was difficult to do.

The present application discloses a technique for solving the above-mentioned problems, and prevents an abnormality of a motor driven by pulse width modulation control of a power converter from the influence of noise including a low frequency region. It is an object of the present invention to provide an abnormality diagnosis device for reliable diagnosis.
Another object of the present invention is to provide a power conversion device provided with such an abnormality diagnosis device, which can reliably diagnose an abnormality of an electric motor and drive the electric motor.
Further, it is an object of the present invention to provide an abnormality diagnosis method for reliably diagnosing an abnormality of an electric motor driven by pulse width modulation control of a power converter by preventing the influence of noise including a low frequency region.

The abnormality diagnosis device disclosed in the present application diagnoses an abnormality of an electric motor driven by pulse width modulation control of a power conversion device. The abnormality diagnosis device includes a detection unit that detects a current flowing through the motor, an analysis unit that frequency-analyzes the current detected by the detection unit, and outputs an analysis result.
A determination unit for determining an abnormality of the motor based on a spectral peak of at least one sideband wave component of the modulated wave obtained from the analysis result, and a frequency setting unit for presetting a noise frequency in the current are provided. .. Then, the determination unit estimates the presence or absence of noise interference in the spectrum peak of the sideband wave component based on the frequency of the sideband wave component and the set noise frequency, and determines the abnormality of the motor. ..

Further, the power conversion device disclosed in the present application includes a power conversion unit that converts DC power into AC power and supplies power to the electric motor, and a control device that outputs and controls the power conversion unit by the pulse width modulation control. The control device includes the abnormality diagnosis device and diagnoses an abnormality of the electric power.

Further, the abnormality diagnosis method disclosed in the present application is a method for diagnosing an abnormality in an electric motor driven by pulse width modulation control of a power conversion device, and is a modulated wave having three frequencies used for the pulse width modulation control. Of the frequency, carrier frequency, and sampling frequency for sampling the modulated wave, the maximum promise number of two or more frequencies including the modulated wave frequency is calculated, and the frequency that is an integral multiple of the maximum promise number is set as the noise frequency. The first step, the second step of detecting the current flowing through the electric motor and frequency analysis, and the abnormality of the electric motor based on the spectral peak of the sideband wave component of the modulated wave obtained from the analysis result in the second step. It is provided with a third step of determining. Then, in the third step, the presence or absence of noise interference in the spectrum peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency set in the first step.

Further, the abnormality diagnosis method disclosed in the present application is a method of diagnosing an abnormality of an electric motor driven by pulse width modulation control of a power conversion device, and the power is obtained from a modulated wave frequency used for the pulse width modulation control. In the first step of setting the frequency deviated by an integral multiple of the frequency of the AC power supply to which the converter is connected as the noise frequency, the second step of detecting the current flowing through the electric motor and analyzing the frequency, and the second step of the second step. The present invention includes a third step of determining an abnormality of the electric motor based on the spectral peak of the sideband wave component of the modulated wave obtained from the analysis result of the above. Then, in the third step, the presence or absence of noise interference in the spectrum peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency set in the first step.

According to the abnormality diagnosis device disclosed in the present application, it is possible to reliably diagnose an abnormality of an electric motor driven by pulse width modulation control of a power conversion device by preventing the influence of noise including a low frequency region. Become.

Further, according to the power conversion device disclosed in the present application, an abnormality of the motor driven by the pulse width modulation control of the power conversion device can be reliably diagnosed by preventing the influence of noise including a low frequency region. Will be possible.

Further, according to the abnormality diagnosis method disclosed in the present application, it is possible to reliably diagnose an abnormality of a motor driven by pulse width modulation control of a power converter by preventing the influence of noise including a low frequency region. It will be possible.

It is a figure which shows the structure of the power conversion apparatus and the abnormality diagnosis apparatus by Embodiment 1. FIG. It is a block diagram which shows the schematic structure of the abnormality diagnosis apparatus by Embodiment 1. FIG. It is a block diagram which shows the hardware structure of a part of the abnormality diagnosis apparatus by Embodiment 1. FIG. It is a figure explaining the frequency spectrum waveform of the electric current in the abnormality diagnosis apparatus by Embodiment 1. FIG. It is a waveform diagram explaining the pulse width modulation control of the power conversion apparatus by Embodiment 1. FIG. It is a flowchart explaining the operation of the abnormality diagnosis apparatus by Embodiment 1. FIG. It is a block diagram which shows the schematic structure of the abnormality diagnosis apparatus by Embodiment 2. FIG. It is a block diagram which shows the schematic structure of the abnormality diagnosis apparatus by Embodiment 3. FIG. It is a figure which shows the structure of the power conversion apparatus and the abnormality diagnosis apparatus by Embodiment 4. FIG. It is a block diagram which shows the schematic structure of the abnormality diagnosis apparatus according to Embodiment 4. 9 is a frequency spectrum waveform of a current for explaining the noise frequency according to the fourth embodiment. It is a flowchart explaining the operation of the abnormality diagnosis apparatus according to Embodiment 4. It is a figure which shows the structure of the power conversion apparatus and the abnormality diagnosis apparatus according to Embodiment 5. It is a figure which shows the carrier wave by another example of Embodiment 5. It is a schematic diagram of the frequency spectrum waveform of the current for demonstrating the effect by Embodiment 6.

Embodiment 1.
FIG. 1 is a diagram showing a configuration of a power conversion device and an abnormality diagnosis device according to the first embodiment.
As shown in FIG. 1, the power conversion device 100 is connected between an AC power supply 1 composed of, for example, a commercial power source and an electric motor 2, and drives and controls the electric motor 2. The power conversion device 100 includes a power conversion unit 10 and a control device 20 that outputs and controls the power conversion unit 10.
Further, the current i flowing from the power conversion unit 10 to the electric motor 2 is detected by the current sensor 3, and the abnormality diagnosis device 30 diagnoses the abnormality of the electric motor 2 based on the current i. The current sensor 3 may be built in the power conversion device 100 or may be externally attached, and the number and position thereof are not limited to those shown in the figure.

The power conversion unit 10 includes a converter unit 10A, an inverter unit 10B, and a smoothing capacitor 10C, which are connected via a DC bus. The converter unit 10A converts the AC power from the AC power supply 1 into DC power and outputs it to the smoothing capacitor 10C, and the inverter unit 10B converts the DC power of the smoothing capacitor 10C into AC power and supplies power to the electric motor 2. ..
In this case, the AC power supply 1, the electric motor 2, and the power conversion device 100 show a three-phase configuration, but the present invention is not limited to this.

The converter unit 10A is composed of a three-phase bridge circuit including six diodes Da, and the input / output lines of each phase are connected to the AC power supply 1. The inverter unit 10B is composed of a three-phase bridge circuit including six switching elements Q in which diodes Db are connected in antiparallel to each other, and input / output lines of each phase are connected to the motor 2. As the switching element Q, for example, an IGBT (Insulated Gate Bipolar Transistor) or a MOSFET (metric-axis-semiconductor transistor) or the like is used.

The AC power from the AC power supply 1 is rectified by the converter unit 10A, converted into DC power, and output to the smoothing capacitor 10C. The control device 20 generates a gate signal G to each switching element Q of the inverter unit 10B by pulse width modulation control (PWM control), and turns the switching element Q on and off to control the switching element Q from the power conversion unit 10 to the electric motor 2. Output the desired power. As a result, the power conversion device 100 drives the electric motor 2.
The configurations of the converter unit 10A and the inverter unit 10B are not limited to those shown in the figure. Further, in this case, the power conversion unit 10 is provided with a converter unit 10A and is connected to the AC power supply 1, but there is an inverter unit 10B that converts DC power into AC power and supplies power to the motor 2. The converter unit 10A may be omitted.

The abnormality diagnosis device 30 has a modulated wave frequency f0 and a carrier frequency fc, which are frequencies of the modulated wave (fundamental wave), the carrier wave, and the clock signal (CLK) for sampling used by the control device 20 in the PWM control of the power conversion unit 10. , The sampling frequency fs is acquired. Then, the abnormality diagnosis device 30 performs frequency analysis on the current i flowing from the power conversion unit 10 to the electric motor 2, and diagnoses the abnormality of the electric motor 2.

FIG. 2 is a block diagram showing a schematic configuration of the abnormality diagnosis device 30. As shown in FIG. 2, the abnormality diagnosis device 30 includes a detection unit 31 for detecting the current i flowing in the motor 2, an analysis unit 32 for frequency analysis of the current i, and a noise frequency (noise frequency fnα) in the current i. A frequency setting unit 33 for presetting the above and a determination unit 34 for determining an abnormality of the electric motor 2 are provided.
The detection unit 31 acquires the output of the current sensor 3 and detects the current waveform in the current i of at least one phase flowing through the motor 2. The analysis unit 32 performs frequency analysis based on the detected current i, and derives the analysis result 32a including the frequency spectrum waveform. The frequency setting unit 33 acquires the modulated wave frequency f0, the carrier frequency fc, and the sampling frequency fs, calculates the greatest common divisor GCD thereof, and sets the greatest common divisor GCD and its integral multiple as the noise frequency fnα.

The determination unit 34 acquires the spectrum peak of the sideband wave component of the modulated wave from the analysis result 32a by the analysis unit 32, determines the abnormality of the motor 2 based on the spectrum peak, and outputs the determination result 34a. At that time, the presence or absence of noise interference in the spectral peak of the sideband wave component of the modulated wave is estimated based on the noise frequency fnα, and the sideband wave component presumed to have noise interference is excluded from the abnormality determination.

As the hardware constituting the abnormality diagnosis device 30, a known dedicated device used for frequency analysis and, for example, the processor 5 and the storage device 6 shown in FIG. 3 can be used in combination.
The processor 5 executes the control program input from the storage device 6. The storage device 6 includes an auxiliary storage device and a volatile storage device. A control program is input to the processor 5 from the auxiliary storage device via the volatile storage device. The processor 5 outputs data such as calculation results to the volatile storage device of the storage device 6, and stores these data in the auxiliary storage device via the volatile storage device as needed.

When there is a sign of abnormality in the motor 2, the sideband wave component of the modulated wave, which is a specific frequency component, increases in the current i. For example, assuming that the rotation frequency of the rotor is fr due to the dynamic eccentricity of the rotor or vibration caused by an abnormality, the sideband wave component of | k1 · f0 ± k2 · fr | increases based on the frequency (f0 ± fr). do. Here, k1 and k2 are positive integers, respectively.
Further, if the conductor bar of the cage rotor is damaged, the sideband wave component of the frequency ((1 ± 2s) · f0) increases when the slip is s.

Further, when the bearing has a scratch, the sideband wave component deviated from the modulated wave frequency f0 increases by a characteristic frequency determined by the location of the scratch and the shape of the bearing. For example, if the outer ring of the bearing is scratched, the characteristic frequency is
N · fr (1-dcosθ / D) / 2
Will be. However, N, d, D, and θ are the number of balls, the diameter of the balls, the pitch diameter, and the contact angle in the bearing, respectively.
Hereinafter, when the term “sideband wave or sideband wave component” is simply used, it refers to the sideband wave of the modulated wave or the sideband wave component of the modulated wave.

FIG. 4 is a diagram illustrating a frequency spectrum waveform of the current i in the abnormality diagnosis device 30 when there is an abnormality in the electric motor 2.
As shown in FIG. 4, a plurality of

spectra

41 and 42 appear on both sides of the spectrum 40 having the modulated wave frequency f0. In this case, the spectrum 41 of the sideband wave component of the modulated wave appears at a frequency (f0 ± fr) deviated by the rotation frequency fr on both sides of the modulated wave frequency f0, and further, the noise component caused by the switching operation of the inverter unit 10B. The spectrum 42 appears.

In FIG. 4, the spectrum 41 of the sideband wave component and the spectrum 42 of the noise component do not overlap with each other, and the spectrum 41 of the sideband wave component showing an abnormal sign can be detected separately from the spectrum 42 of the noise component. Further, when the conditions are changed, the spectrum 42 of the noise component may approach the spectrum 41 of the sideband wave component showing an abnormal sign, causing noise interference at the spectrum peak (not shown).
In this case, only the spectrum 41 in the case of k1 = k2 = 1 at the frequency | k1 · f0 ± k2 · fr | is shown, but if the spectrum peak is included, it is usually k1 = k2 = 1. The spectrum 41 by the combination other than the above also appears.

FIG. 5 is a waveform diagram illustrating PWM control of the power conversion device 100.
As shown in FIG. 5, in PWM control, the modulated wave M is compared with the carrier wave Cr to generate a gate signal G. At that time, the modulated wave M is sampled at the timing of the clock signal (CLK), the value of the modulated wave M is temporarily stored, and the value is compared with the carrier wave Cr.
When the carrier frequency fc or the sampling frequency fs is not a multiple of the modulated wave frequency f0, the spectrum 42 of the noise component caused by the switching operation of the inverter unit 10B is displayed at the greatest common divisor of those values and an integral multiple thereof. Occurs.

Therefore, by calculating the maximum promises of the two frequencies of the carrier frequency fc or the sampling frequency fs and the modulated wave frequency f0, or all three frequencies, it is possible to grasp in advance at which frequency noise can occur. can do.
In this case, the frequency setting unit 33 acquires the modulated wave frequency f0, the carrier frequency fc, and the sampling frequency fs, calculates the greatest common divisor GCD thereof, and sets the greatest common divisor GCD and its integral multiple as the noise frequency fnα. .. When the greatest common divisor GCD is the modulated wave frequency f0, the noise frequency fnα is not set.

Next, the operation of the abnormality diagnosis device 30 will be described with reference to the flowchart shown in FIG.
First, the abnormality diagnosis device 30 detects the current waveform of at least one phase of the current i of each phase current i flowing from the power conversion unit 10 of the power conversion device 100 to the electric motor 2 by the detection unit 31. In this case, the detection unit 31 shall detect the current waveforms for three phases. The current sensor 3 may detect each phase current i of the three phases, or may detect two phases and obtain the current of the remaining phases by calculation (step S1).
Next, the analysis unit 32 performs frequency analysis based on the detected current i, and derives the analysis result 32a including the frequency spectrum waveform (step S2).

On the other hand, the frequency setting unit 33 acquires the modulated wave frequency f0, the carrier wave frequency fc, and the sampling frequency fs from the control device 20 of the power conversion device 100 (step S3). Then, the frequency setting unit 33 calculates the greatest common divisor GCD of the modulated wave frequency f0, the carrier frequency fc, and the sampling frequency fs, and further calculates an integral multiple of the greatest common divisor GCD (step S4).

When the greatest common divisor GCD is not the modulated wave frequency f0, the calculated greatest common divisor GCD and its integral multiples are set as the noise frequency fnα. The noise frequency fnα is set within a range that does not exceed the measurable range.
The greatest common divisor GCD is a value lower than fc / 2. Further, under the normal control conditions of the power converter 100, the greatest common divisor GCD is a value lower than (fc-4f0). Therefore, the noise frequency fnα to be set includes a frequency in the frequency domain lower than fc / 2, and is generally set to include a frequency lower than (fc-4f0) (step S5).

The determination unit 34 estimates the presence or absence of noise interference at the spectral peak of the sideband wave component of the modulated wave based on the analysis result 32a derived in step S2 and the noise frequency fnα set in step S5. Specifically, it is determined whether the frequency of the sideband wave component (spectrum 41) of the modulated wave overlaps or is close to the noise frequency fnα, and it is estimated that there is noise interference.
Since the sideband wave component (spectrum 41) of the modulated wave that increases due to the abnormal sign of the motor 2 is a specific frequency component as described above, the determination unit 34 monitors the specific frequency component and sets the frequency. It is compared with the noise frequency fnα, and when the difference is less than the set value, it is determined that they are duplicated or close to each other. The set value is set to several Hz, for example, 2 Hz. When the difference is equal to or greater than the set value, the peak of the spectrum 41 is not affected by the noise component, and noise interference does not occur (step S6).

If there is a sideband wave component presumed to have noise interference in step S6, the determination unit 34 excludes the sideband wave component from the target of abnormality diagnosis (step S7), and the motor 2 is based on the other sideband wave components. The abnormality is determined and the determination result 34a is output. At that time, if the spectral peak of the sideband wave component exceeds a preset reference value, it is determined to be abnormal. The reference value is set, for example, based on the spectral peak of the modulated wave frequency f0 (step S8).

If the greatest common divisor GCD is the modulated wave frequency f0 in step S5, the frequency setting unit 33 does not set the noise frequency fnα and proceeds to step S8. Then, the determination unit 34 determines the abnormality of the motor 2 based on the spectral peak of the sideband wave component.

As described above, the abnormality diagnosis device 30 according to this embodiment sets the frequency (noise frequency fnα) of the noise component in the current i flowing through the motor 2 in advance, and analyzes the current i by the frequency of the modulated wave. Abnormal diagnosis is performed for the lateral band component. Then, at the time of abnormality diagnosis, the presence or absence of noise interference in the spectral peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency fnα, and the sideband wave component presumed to have noise interference is excluded. The anomaly is determined based on the spectral peaks of the remaining sideband components.
Therefore, erroneous diagnosis due to the influence of noise including a low frequency region can be prevented, and abnormality diagnosis of the motor 2 can be performed with high reliability.

Further, the noise frequency fnα is set to a frequency of the maximum promised GCD of the modulated wave frequency f0, the carrier wave frequency fc, and the sampling frequency fs used for PWM control, and an integral multiple thereof, so that noise including a low frequency region is included. The influence of the ingredients can be reliably prevented.
Further, when the difference between the frequency of the sideband wave component and the set noise frequency fnα is less than the set value and is close to each other, the sideband wave component is estimated to have noise interference, so that noise interference can be estimated with high reliability.

Although the noise frequency fnα is set within a range that does not exceed the measurable range, it may be set only in a frequency region lower than 1/2 of the carrier frequency fc.

Further, when the maximum promise number GCD calculated by the frequency setting unit 33 is the modulated wave frequency f0, the noise frequency fnα is not set, but the modulated wave frequency f0 and its integral multiple are set as the noise frequency fnα as it is. However, there is no problem because the frequency component is not close to the side band component of the modulated wave.

Further, although the carrier wave Cr by the triangular wave is shown for the PWM control of the power conversion device 100, the carrier wave Cr is not limited to the triangular wave, and a sine wave may be used.
Further, in order to improve the voltage utilization rate, a third harmonic may be superimposed on the modulated wave M. In that case, the value of the greatest common divisor GCD does not change, and the noise frequency fnα can be set in the same manner.

Embodiment 2.
FIG. 7 is a block diagram showing a schematic configuration of the abnormality diagnosis device 30A according to the second embodiment.
As shown in FIG. 7, the abnormality diagnosis device 30A includes a detection unit 31, an analysis unit 32, a frequency setting unit 33, and a determination unit 34, and further includes a notification unit 35, as in the first embodiment.
If there is a sideband wave component presumed to have noise interference, the determination unit 34 excludes the sideband wave component from the target of abnormality diagnosis (see step S7 in FIG. 6), and outputs a notification command 34b to the notification unit 35. do. Then, the notification unit 35 outputs a notification signal 35a that notifies the outside that there is noise interference. Other configurations and operations are the same as those in the first embodiment.

In this embodiment, as in the first embodiment, erroneous diagnosis due to the influence of noise components including a low frequency region can be prevented, and abnormality diagnosis of the motor 2 can be performed with high reliability. Further, at the time of diagnosis, the user is notified that there is an estimation that there is noise interference, so that the convenience is improved.

Even if there is a sideband wave component presumed to have noise interference, the sideband wave component may not be excluded from the target of abnormality diagnosis, and the notification signal 35a may only be output from the notification unit 35. In that case, the user is notified to call attention, and the user can consider the influence of the noise component on the determination result 34a from the abnormality diagnosis device 30A, and as a result, erroneous diagnosis can be prevented.

Embodiment 3.
FIG. 8 is a block diagram showing a schematic configuration of the abnormality diagnosis device 30B according to the third embodiment.
As shown in FIG. 8, the abnormality diagnosis device 30B includes a detection unit 31, an analysis unit 32, a frequency setting unit 33, a determination unit 36, a noise detection unit 37, and a storage unit, as in the first embodiment. A unit 38 and a switch 39 are provided. The configuration and operation other than the determination unit 36, the noise detection unit 37, the storage unit 38, and the switch 39 are the same as those in the first embodiment.

Similar to the first embodiment, the detection unit 31 acquires the output of the current sensor 3 and detects the current waveform in the current i of at least one phase flowing through the motor 2. The analysis unit 32 performs frequency analysis based on the detected current i, and derives the analysis result 32a including the frequency spectrum waveform. The frequency setting unit 33 acquires the modulated wave frequency f0, the carrier frequency fc, and the sampling frequency fs, calculates the greatest common divisor GCD thereof, and sets the greatest common divisor GCD and its integral multiple within a range not exceeding the measurable range. Set as the noise frequency fnα.

Then, the noise detection unit 37 detects the magnitude of noise at the noise frequency fnα of the current i, for example, the value of the spectral peak of the noise component from the analysis result 32a by the analysis unit 32 during the normal operation of the electric motor 2. The detection result is stored in the storage unit 38. The noise detection by the noise detection unit 37 is performed in advance during the normal operation of the motor 2 prior to the abnormality diagnosis of the motor 2.
The switch 39 selectively switches the output destination of the analysis result 32a of the analysis unit 32 to one of the noise detection unit 37 and the determination unit 36. The determination unit 36 is selected when the abnormality of the motor 2 is diagnosed, and the noise detection unit 37 is selected when the noise is detected in advance during the normal operation of the motor 2.

The determination unit 36 acquires the spectrum peak of the sideband wave component of the modulated wave from the analysis result 32a by the analysis unit 32, determines the abnormality of the motor 2 based on the spectrum peak, and outputs the determination result 36a. At that time, the presence or absence of noise interference in the spectral peak of the sideband wave component of the modulated wave is estimated based on the noise frequency fnα. Specifically, as in the first embodiment, when the difference between the frequency of the sideband wave component of the modulated wave and the noise frequency fnα is less than the set value, the frequency of the sideband wave component and the noise frequency fnα overlap or are close to each other. It is presumed that there is noise interference.

Subsequently, the determination unit 36 extracts the magnitude of the noise of the noise frequency fnα, which is the noise interference source, from the storage unit 38. Then, with respect to the sideband wave component of the noise interference destination, the abnormality of the electric motor 2 is determined based on the spectral peak of the sideband wave component and the magnitude of the extracted noise. Specifically, for example, when the value obtained by subtracting the spectral peak value of the noise component from the spectral peak value of the sideband wave component exceeds a preset reference value, it is determined to be abnormal. The reference value is set, for example, based on the spectral peak of the modulated wave frequency f0.

As described above, the abnormality diagnosis device 30B according to this embodiment sets the frequency (noise frequency fnα) of the noise component in the current i flowing through the motor 2 in advance, and analyzes the current i by the frequency of the modulated wave. Abnormal diagnosis is performed for the lateral band component. Further, prior to the abnormality diagnosis, the abnormality diagnosis device 30B detects the magnitude of noise at the noise frequency fnα of the current i from the analysis result 32a by the analysis unit 32 during the normal operation of the electric motor 2, and the detection result. Remember. Then, at the time of abnormality diagnosis, the presence or absence of noise interference in the spectrum peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency fnα, and the spectrum peak of the sideband wave component estimated to have noise interference is obtained. , Used for abnormality judgment in consideration of the magnitude of noise.

Therefore, as in the first embodiment, erroneous diagnosis due to the influence of the noise component including the low frequency region can be prevented, and abnormality diagnosis of the motor 2 can be performed with high reliability. Further, since the sideband wave component presumed to have noise interference is also used for the abnormality diagnosis without removing it, the sideband wave component to be monitored for the abnormality diagnosis can be reliably monitored and the abnormality diagnosis of the motor 2 can be surely performed. ..

The noise detection by the noise detection unit 37 is obtained from the analysis result 32a of the current i during normal operation of the motor 2, but is obtained from the result of frequency analysis by detecting the output voltage to the motor 2. You can also do things. In that case, it is not particularly necessary to detect it during normal operation of the motor 2, and the magnitude of noise corresponding to normal operation at the noise frequency fnα of the current i can be calculated from the result of the frequency analysis of the detected voltage. The calculation result can be used by the determination unit 36 without being stored in the storage unit 38, and the storage unit 38 may be omitted.
Further, both the result of the frequency analysis of the detected voltage and the detection result of the noise obtained from the analysis result 32a of the current i during the normal operation of the electric motor 2 can be used by the determination unit 36, and the abnormality determination can be made. Accuracy is improved.

Further, also in the third embodiment, the notification unit 35 may be provided by applying the second embodiment to notify the user that there is an estimation of noise interference.

Embodiment 4.
FIG. 9 is a diagram showing the configuration of the power conversion device 100 and the abnormality diagnosis device 30C according to the fourth embodiment.
As shown in FIG. 9, the power conversion device 100 is configured in the same manner as in the first embodiment, and includes a power conversion unit 10 and a control device 20 that outputs and controls the power conversion unit 10. Further, the current i flowing from the power conversion unit 10 to the electric motor 2 is detected by the current sensor 3, and the abnormality diagnosis device 30 diagnoses the abnormality of the electric motor 2 based on the current i.

The power conversion unit 10 includes a converter unit 10A, an inverter unit 10B, and a smoothing capacitor 10C, which are connected via a DC bus. In this embodiment, the converter unit 10A cannot be omitted, and the AC power from the AC power supply 1 is converted into DC power and output to the smoothing capacitor 10C. The inverter unit 10B converts the DC power of the smoothing capacitor 10C into AC power and supplies power to the motor 2.
Also in this case, the power conversion unit 10 indicates that the AC power supply 1, the electric motor 2, and the power conversion device 100 have a three-phase configuration, but the present invention is not limited to this.

The AC power from the AC power supply 1 is rectified by the converter unit 10A, converted into DC power, and output to the smoothing capacitor 10C. The control device 20 generates a gate signal G to each switching element Q of the inverter unit 10B by PWM control, controls the switching element Q on and off, and outputs desired power from the power conversion unit 10 to the electric motor 2. ..
In this way, the power conversion device 100 drives the electric motor 2. The DC voltage of the smoothing capacitor 10C and the AC voltage output to the electric motor 2 slightly fluctuate at the frequency of the AC power supply 1 and a frequency that is an integral multiple of the frequency, and the sideband wave deviates from the modulated wave frequency f0 by that value. A component (noise component) is generated in the current i.

The abnormality diagnosis device 30C acquires the frequency of the modulated wave (modulated wave frequency f0) used by the control device 20 in the PWM control of the power conversion unit 10 and the frequency of the AC power supply 1 (AC power supply frequency fac). Then, the abnormality diagnosis device 30C performs frequency analysis on the current i flowing from the power conversion unit 10 to the electric motor 2, and diagnoses the abnormality of the electric motor 2.

FIG. 10 is a block diagram showing a schematic configuration of the abnormality diagnosis device 30C. As shown in FIG. 10, the abnormality diagnosis device 30C includes a detection unit 31 for detecting the current i flowing in the motor 2, an analysis unit 32 for frequency analysis of the current i, and a noise frequency (noise frequency fnβ) in the current i. The frequency setting unit 33A is provided in advance, and the determination unit 34 for determining an abnormality of the electric motor 2 is provided.
The detection unit 31 and the analysis unit 32 operate in the same manner as in the first embodiment.

The frequency setting unit 33A acquires the modulated wave frequency f0 and the AC power supply frequency fac, and calculates and sets the following frequencies as the noise frequency fnβ. However, m and n are positive integers, respectively.
｜ m ・ fac ± n ・ f0 ｜
That is, the noise frequency fnβ is an absolute value deviated from an integral multiple of the modulated wave frequency f0 by an integral multiple of the AC power frequency fac.

The determination unit 34 acquires the spectrum peak of the sideband wave component of the modulated wave from the analysis result 32a by the analysis unit 32, determines the abnormality of the motor 2 based on the spectrum peak, and outputs the determination result 34a. At that time, the presence or absence of noise interference in the spectral peak of the sideband wave component of the modulated wave is estimated based on the noise frequency fnβ, and the sideband wave component presumed to have noise interference is excluded from the abnormality determination.

FIG. 11 is a frequency spectrum waveform of the current i for explaining the noise frequency.
As shown in FIG. 11, when the modulated wave frequency f0 and the AC power supply frequency fac are 50 Hz and 60 Hz, respectively, the frequency is a multiple of the modulated wave frequency f0 (100 Hz, 150 Hz, 200 Hz), and the frequencies are 10 Hz and 70 Hz separately. , 110 Hz, 170 Hz, the spectrum of the noise component appears. When the frequencies of noise components other than the multiple times of the modulated wave frequency f0 are expressed by the modulated wave frequency f0 (50 Hz) and the AC power supply frequency fac (60 Hz), respectively,
10Hz = fac-f0
70Hz = 2 ・ fac-f0
110Hz = fac + f0
170Hz = 2 ・ fac + f0
Therefore, the above-mentioned calculation formula of the noise frequency fnβ is satisfied.

Next, the operation of the abnormality diagnosis device 30C will be described with reference to the flowchart shown in FIG.
First, the abnormality diagnosis device 30C detects the current waveform of the current i for at least one phase among the phase currents i flowing from the power conversion unit 10 of the power conversion device 100 to the electric motor 2 as in the first embodiment. Detected by the unit 31 (step S1), the analysis unit 32 performs frequency analysis based on the detected current i, and derives the analysis result 32a including the frequency spectrum waveform (step S2).

On the other hand, the frequency setting unit 33A acquires the modulated wave frequency f0 and the AC power supply frequency fac (step SS3).
Then, as described above, the frequency setting unit 33A has the frequency setting unit 33A.
｜ m ・ fac ± n ・ f0 ｜
Is calculated (step SS4) and set as the noise frequency fnβ. The noise frequency fnβ includes the case of m = n = 1 and is set within a range not exceeding the measurable range.
When m = n = 1, that is, (fac ± f0) is a value lower than fc / 2. Further, under the normal control conditions of the power conversion device 100, (fac ± f0) is a lower value than (fc-4f0). Therefore, the noise frequency fnβ to be set includes a frequency in the frequency domain lower than fc / 2, and is generally set to include a frequency lower than (fc-4f0) (step S5).

The determination unit 34 estimates the presence or absence of noise interference at the spectral peak of the sideband wave component of the modulated wave based on the analysis result 32a derived in step S2 and the noise frequency fnβ set in step S5. Specifically, it is determined whether the frequency of the sideband wave component (spectrum 41) of the modulated wave overlaps or is close to the noise frequency fnβ, and it is estimated that there is noise interference. In this case as well, as in the first embodiment, the determination unit 34 compares a specific frequency component (sideband wave component) that increases due to an abnormality sign with the noise frequency fnβ, and makes a difference. If it is less than the set value, it is judged that they are duplicated or close to each other, and it is estimated that there is noise interference. Also in this case, the set value is set to several Hz, for example, 2 Hz (step S6).

If there is a sideband wave component presumed to have noise interference in step S6, the determination unit 34 excludes the sideband wave component from the target of abnormality diagnosis (step S7), and the motor 2 is based on the other sideband wave components. Judge the abnormality of. At that time, if the spectral peak of the sideband wave component exceeds a preset reference value, it is determined to be abnormal. The reference value is set, for example, based on the spectral peak of the modulated wave frequency f0 (step S8).

As described above, the abnormality diagnosis device 30C according to this embodiment sets the frequency (noise frequency fnβ) of the noise component in the current i flowing through the motor 2 in advance, and the current i is frequency-analyzed to obtain the modulated wave. Abnormal diagnosis is performed for the lateral band component. Then, at the time of abnormality diagnosis, the presence or absence of noise interference in the spectral peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency fnβ, and the sideband wave component presumed to have noise interference is excluded. The anomaly is determined based on the spectral peaks of the remaining sideband components.
Therefore, it is possible to prevent erroneous diagnosis due to the influence of the noise component including the low frequency region, in this case, the noise component caused by the modulated wave frequency f0 and the AC power frequency fac, and the abnormality diagnosis of the motor 2 can be performed with high reliability.

Further, when the difference between the frequency of the sideband wave component and the set noise frequency fnβ is less than the set value and is close to each other, the sideband wave component is estimated to have noise interference, so that noise interference can be estimated with high reliability.

Although the noise frequency fnβ is set within a range that does not exceed the measurable range, it may be set only in a frequency region lower than 1/2 of the carrier frequency fc.

Further, the notification unit 35 may be provided by applying the above-described second embodiment to the fourth embodiment to notify the user that the presence or absence of noise interference has been estimated.

Further, the third embodiment may be applied to the fourth embodiment. In that case, a noise detection unit 37, a storage unit 38, and a switch 39 are provided to detect and store the magnitude of noise at the noise frequency fnβ of the current i during normal operation of the motor 2 prior to the abnormality diagnosis. Keep it. Then, at the time of abnormality diagnosis, the presence or absence of noise interference in the spectrum peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency fnβ, and the spectrum peak of the sideband wave component estimated to have noise interference is obtained. , Used for abnormality judgment in consideration of the magnitude of noise.
As a result, the sideband wave component to be monitored for the abnormality diagnosis can be reliably monitored, and the abnormality diagnosis of the motor 2 can be reliably performed.

Further, when the above embodiment 3 is applied, the noise detection by the noise detection unit 37 is obtained from the result of frequency analysis by detecting the line voltage output to the motor 2 or the DC voltage of the smoothing capacitor 10C. You can also. In that case, it is not particularly necessary to detect it during normal operation of the motor 2, and the magnitude of noise corresponding to normal operation at the noise frequency fnβ of the current i can be calculated from the result of the frequency analysis of the detected voltage. The calculation result can be used without being stored in the storage unit 38, and the storage unit 38 may be omitted.
Further, both the result of the frequency analysis of the detected voltage and the detection result of the noise obtained from the analysis result 32a of the current i during the normal operation of the motor 2 can be used, and the accuracy of the abnormality determination is improved. ..

Further, in the fourth embodiment, the frequency setting unit 33A acquires the modulated wave frequency f0 and the AC power supply frequency fac to set the above-mentioned noise frequency fnβ, which was shown in the first embodiment. The noise frequency fnα may also be set. In that case, the frequency setting unit 33A acquires the modulated wave frequency f0, the carrier wave frequency fc, the sampling frequency fs, and the AC power supply frequency fac, and calculates and sets the noise frequency fnα and the noise frequency fnβ. As a result, the influence of the noise component can be widely suppressed to prevent erroneous diagnosis, and the abnormality diagnosis of the motor 2 can be performed more reliably.

Furthermore, although the

abnormality diagnosis devices

30 and 30A to 30C according to the above embodiments 1 to 4 are shown to be outside the power conversion device 100, they may be inside the control device 20 of the power conversion device 100. , The same effect can be obtained, and the exchange of information necessary for setting the noise frequencies fnα and fnβ becomes easy.

Embodiment 5.
FIG. 13 is a diagram showing the configuration of the power conversion device 100A according to the fifth embodiment.
As shown in FIG. 13, the power conversion device 100A includes a power conversion unit 10 configured in the same manner as in the first embodiment, and a control device 20A that outputs and controls the power conversion unit 10. The control device 20A includes an inverter control unit 21 for controlling the output of the power conversion unit 10 and an abnormality diagnosis device 30D.
Further, the current i flowing from the power conversion unit 10 to the electric motor 2 is detected by the current sensor 3, and the abnormality diagnosis device 30D diagnoses the abnormality of the electric motor 2 based on the current i.

In the control device 20A, the inverter control unit 21 generates a gate signal G to each switching element Q of the inverter unit 10B by PWM control, and controls the switching element Q on and off to control the switching element Q from the power conversion unit 10 to the electric motor 2. Output the desired power. As a result, the power conversion device 100A drives the electric motor 2.

The abnormality diagnosis device 30D has a modulated wave frequency f0, a carrier wave frequency fc, and a sampling frequency fs, which are frequencies of a modulated wave (fundamental wave), a carrier wave, and a clock signal (CLK) for sampling used by the inverter control unit 21 in PWM control. To get. Then, the abnormality diagnosis device 30D performs frequency analysis on the current i flowing from the power conversion unit 10 to the electric motor 2, and diagnoses the abnormality of the electric motor 2.

The abnormality diagnosis device 30D includes a detection unit 31, an analysis unit 32, a frequency setting unit 33, and a determination unit 34, as in the abnormality diagnosis device 30 shown in the first embodiment, and includes the detection unit 31, the analysis unit 32, and the abnormality diagnosis device 30. The frequency setting unit 33 operates in the same manner as in the first embodiment. Similar to the first embodiment, the determination unit 34 estimates the presence or absence of noise interference in the spectral peak of the sideband wave component of the modulated wave based on the frequency of the sideband wave component and the noise frequency fnα.
When there is no noise interference, the determination unit 34 makes an abnormality diagnosis of the motor 2 based on the spectral peak of the sideband wave component, as in the first embodiment. Then, when there is noise interference, the determination unit 34 interrupts the abnormality diagnosis and transmits the notification signal SS1 to the inverter control unit 21.

When the inverter control unit 21 receives the notification signal SS1 notifying the interruption of the abnormality diagnosis from the abnormality diagnosis device 30D, the inverter control unit 21 changes the carrier frequency fc and outputs the power conversion unit 10 by PWM control using the changed carrier frequency fc. It controls and drives the electric motor 2. The carrier frequency fc can be easily changed without directly affecting the output of the power conversion unit 10.
In the abnormality diagnosis device 30D, each part operates again to continue the abnormality diagnosis. Since the noise frequency fnα changes when the carrier frequency fc is changed, the presence or absence of noise interference at the spectral peak of the sideband wave component also changes. As a result, the determination unit 34 can derive an estimation without noise interference, and makes an abnormality diagnosis of the motor 2 based on the spectral peak of the sideband wave component.

Regarding the change of the carrier frequency fc, it is desirable that the determination unit 34 derives the estimation without noise interference by changing it once, but it is possible to change it multiple times.

As described above, in the power conversion device 100A according to this embodiment, the abnormality diagnosis device 30D in the control device 20A determines the sideband wave component based on the frequency of the sideband wave component and the noise frequency fnα at the time of abnormality diagnosis. The presence or absence of noise interference at the spectrum peak is estimated, and if it is estimated that there is noise interference, the carrier frequency fc is changed. As a result, the greatest common divisor GCD of the modulated wave frequency f0, the carrier wave frequency fc, and the sampling frequency fs is changed, and the frequency itself of the noise component caused by the PWM control is changed. Therefore, it is possible to remove noise interference at the spectral peak of the sideband wave component, and it is possible to reliably perform an abnormality diagnosis. In this way, erroneous diagnosis due to the influence of the noise component can be prevented, and abnormality diagnosis of the electric motor 2 can be performed with high reliability.

In the fifth embodiment, the carrier frequency fc is changed, but at least one of the modulated wave frequency f0, the carrier frequency fc, and the sampling frequency fs, which is used for the calculation of the greatest common divisor GCD. Just change one.

Further, when the carrier frequency fc is changed, the carrier frequency fc may be changed with time as shown in FIG. In this case, the carrier wave Cr changes by alternately repeating two kinds of frequencies (1 / t1) and (1 / t2) with two kinds of different periods t1 and t2. The change is not limited to each cycle, and may be changed over time to three or more kinds of frequencies. Further, the frequency may be changed continuously instead of discretely.
When the carrier frequency fc or the sampling frequency fs changes with time as described above, the spectrum of the greatest common divisor GCD and its integral multiple frequency components is dispersed in a plurality of frequency ranges. As a result, the spectral peak of the noise component can be reduced, the noise interference at the spectral peak of the sideband wave component can be removed or suppressed, and the abnormality diagnosis can be performed with high reliability.

Embodiment 6.
In the fifth embodiment, when it is estimated that there is noise interference at the spectral peak of the sideband wave component during the abnormality diagnosis by the abnormality diagnosis device 30D, at least one of the frequencies used for the calculation of the greatest common divisor GCD is used. Shown what to change.
In this embodiment, in the case of the fifth embodiment, the greatest common divisor GCD is further such that the greatest common divisor GCD matches the modulated wave frequency f0 or is 10 Hz or less, preferably several Hz or less. Change at least one of the frequencies used in the calculation of.

FIG. 15 is a schematic diagram of a frequency spectrum waveform of a current for explaining the effect of the sixth embodiment. In FIG. 15, noise components are shown in two cases where the greatest common divisor GCD of the modulated wave frequency f0, the carrier frequency fc, and the sampling frequency fs is several Hz and the comparative example exceeding 10 Hz.

As shown in FIG. 15, apart from the spectrum 40 of the modulated wave frequency f0, the

spectra

42A and 42B of the noise component caused by the switching operation of the inverter unit 10B appear. The spectrum 42A is a case where the greatest common divisor GCD exceeds 10 Hz, and the spectrum 42B is a case where the greatest common divisor GCD is several Hz. The spectrum 42B has a larger number of appearances but a lower spectrum peak than the spectrum 42A.
By reducing the greatest common divisor GCD to several Hz in this way, the number of appearances of the spectrum of the noise component increases, but the spectrum can be dispersed over a plurality of frequency ranges and the spectrum peak can be reduced. As a result, noise interference at the spectral peak of the sideband wave component of the modulated wave can be removed or suppressed, and abnormality diagnosis can be performed with high reliability.

In the sixth embodiment, when at least one of the frequencies used in the calculation of the greatest common divisor GCD is changed so that the greatest common divisor GCD matches the modulated wave frequency f0, the abnormality diagnosis device 30D is used. Since the noise component assumed by the above is removed, the abnormality diagnosis can be performed reliably and reliably.

Embodiment 7.
In this embodiment, the abnormality diagnosis device 30C shown in the fourth embodiment is applied to the abnormality diagnosis device 30D in the power conversion device 100A shown in the fifth embodiment. In this case, the abnormality diagnosis device 30C is provided in the control device 20A of the power conversion device 100A.
The abnormality diagnosis device 30C includes a detection unit 31, an analysis unit 32, a frequency setting unit 33A, and a determination unit 34, as in the fourth embodiment, and the detection unit 31, analysis unit 32, and frequency setting unit 33A are described above. It operates in the same manner as in the fourth embodiment.

Similar to the fourth embodiment, the determination unit 34 estimates the presence or absence of noise interference in the spectral peak of the sideband wave component of the modulated wave based on the frequency of the sideband wave component and the noise frequency fnβ.
When there is no noise interference, the determination unit 34 makes an abnormality diagnosis of the motor 2 based on the spectral peak of the sideband wave component, as in the fourth embodiment. Then, when there is noise interference, the determination unit 34 interrupts the abnormality diagnosis and transmits the notification signal SS1 to the inverter control unit 21.

When the inverter control unit 21 receives the notification signal SS1 notifying the interruption of the abnormality diagnosis from the abnormality diagnosis device 30C, the inverter control unit 21 changes the modulated wave frequency f0 and PWM-controls the power conversion unit 10 using the changed modulated wave frequency f0. The output is controlled by the above to drive the electric motor 2.
In the abnormality diagnosis device 30C, each part operates again to continue the abnormality diagnosis. Since the noise frequency fnβ changes when the modulated wave frequency f0 is changed, the presence or absence of noise interference at the spectral peak of the sideband wave component also changes. As a result, the determination unit 34 can derive an estimation without noise interference, and makes an abnormality diagnosis of the motor 2 based on the spectral peak of the sideband wave component.

As described above, in the power conversion device 100A according to this embodiment, the abnormality diagnosis device 30C in the control device 20A determines the sideband wave component based on the frequency of the sideband wave component and the noise frequency fnβ at the time of abnormality diagnosis. The presence or absence of noise interference at the spectrum peak is estimated, and if it is estimated that there is noise interference, the modulated wave frequency f0 is changed.
As a result, the frequency itself of the noise component caused by the fluctuation of the voltage (DC voltage of the smoothing capacitor 10C and the AC voltage output to the electric motor 2) according to the AC power supply frequency fac is changed. Therefore, it is possible to remove noise interference at the spectral peak of the sideband wave component, and it is possible to reliably perform an abnormality diagnosis. In this way, erroneous diagnosis due to the influence of the noise component can be prevented, and abnormality diagnosis of the electric motor 2 can be performed with high reliability.

In the above embodiments 5 to 7, when it is estimated that there is noise interference, the frequency related to the noise frequency is changed. However, the assumed noise interference is removed or suppressed from the beginning for power conversion. The device 100A can also be operated.
In this case, the modulation wave frequency f0, the carrier wave frequency fc, and the sampling frequency fs are determined so that the difference between the frequency of the sideband wave component to be monitored and the assumed noise frequency becomes equal to or more than the set value, and the power conversion device 100A is used. drive. Alternatively, the power conversion device 100A is operated by determining the modulated wave frequency f0, the carrier wave frequency fc, and the sampling frequency fs so as to reduce the greatest common divisor GCD to several Hz.

Although the present application describes various exemplary embodiments and examples, the various features, embodiments, and functions described in one or more embodiments are applications of a particular embodiment. It is not limited to, but can be applied to embodiments alone or in various combinations.
Therefore, innumerable variations not exemplified are envisioned within the scope of the techniques disclosed in the present application. For example, it is assumed that at least one component is modified, added or omitted, and further, at least one component is extracted and combined with the components of other embodiments.

1 AC power supply, 2 electric motor, 10 power conversion unit, 10A converter unit, 10B inverter unit, 10C smoothing capacitor, 20, 20A control device, 30, 30A to 30D abnormality diagnosis device, 31 detection unit, 32 analysis unit, 32a analysis result , 33, 33A frequency setting unit, 34 judgment unit, 35 notification unit, 36 judgment unit, 37 noise detection unit, 38 storage unit, 100, 100A power converter, f0 modulated wave frequency, fac AC power supply frequency, fc carrier frequency, fs sampling frequency, fnα, fnβ noise frequency, M modulated wave.

Claims

In an abnormality diagnostic device that diagnoses an abnormality in an electric motor driven by pulse width modulation control of a power converter.
A detector that detects the current flowing through the motor,
An analysis unit that frequency-analyzes the current detected by the detection unit and outputs an analysis result,
A determination unit for determining an abnormality of the motor based on a spectral peak of at least one sideband component of the modulated wave obtained from the analysis result, and a determination unit.
A frequency setting unit for presetting the noise frequency in the current is provided.
The determination unit estimates the presence or absence of noise interference in the spectrum peak of the sideband wave component based on the frequency of the sideband wave component and the set noise frequency, and determines the abnormality of the motor.
Abnormality diagnostic device.
The noise frequency set by the frequency setting unit includes a frequency lower than 1/2 of the carrier frequency.
The abnormality diagnostic device according to claim 1.
The frequency setting unit is the maximum of two or more frequencies including the modulated wave frequency among the three frequencies used for the pulse width modulation control, the modulated wave frequency, the carrier frequency, and the sampling frequency for sampling the modulated wave. The promise number is calculated, and the maximum promise number and its integral multiple are set as the noise frequency.
The abnormality diagnostic device according to claim 1 or 2.
The frequency setting unit sets an absolute value of a value deviated by an integral multiple of the frequency of the AC power supply to which the power conversion device is connected from an integral multiple of the modulation wave frequency used for the pulse width modulation control as the noise frequency. do,
The abnormality diagnostic device according to claim 1 or 2.
When the difference between the frequency of the sideband wave component and the noise frequency is less than the set value and is close to each other, the determination unit estimates that there is noise interference with the sideband wave component.
The abnormality diagnosis device according to any one of claims 1 to 4.
The determination unit determines an abnormality in the motor, excluding the sideband wave component presumed to have noise interference.
The abnormality diagnostic device according to claim 5.
A noise detection unit for detecting the magnitude of noise at the noise frequency of the current during normal operation of the motor and a storage unit for storing the detection result by the noise detection unit are provided.
The determination unit determines the abnormality of the motor based on the spectral peak of the sideband wave component estimated to have noise interference and the detection result in the storage unit.
The abnormality diagnostic device according to claim 5.
A notification unit for notifying the presence or absence of the noise interference to the outside is provided.
The abnormality diagnosis device according to any one of claims 1 to 7.
A power conversion unit that converts DC power into AC power and supplies power to the motor,
A control device for controlling the output of the power conversion unit by the pulse width modulation control is provided.
The control device includes the abnormality diagnosis device according to any one of claims 1 to 8, and diagnoses an abnormality of the electric motor.
Power converter.
A power conversion unit that converts DC power into AC power and supplies power to the motor,
A control device for controlling the output of the power conversion unit by the pulse width modulation control is provided.
The control device includes the abnormality diagnosis device according to claim 3, and diagnoses the abnormality of the motor.
When the difference between the frequency of the sideband component and the noise frequency is less than the set value, the control device changes at least one of the two or more frequencies used in the calculation of the greatest common divisor. The pulse width modulation control is performed.
The abnormality diagnosing device diagnoses an abnormality of the electric motor based on the changed frequency.
Power converter.
The change of the frequency is to change the frequency with time.
The power conversion device according to claim 10.
When the difference between the frequency of the sideband component and the noise frequency is less than the set value, the control device causes the greatest common divisor to match the modulated wave frequency or to be 10 Hz or less. Change at least one of the two or more frequencies.
The power conversion device according to claim 10.
The change of the frequency is to change the carrier frequency as the frequency.
The power conversion device according to any one of claims 10 to 12.
A power conversion unit including a converter unit that converts AC power from an AC power source into DC power, a smoothing capacitor, and an inverter unit that converts the DC power of the smoothing capacitor into AC power and supplies power to the electric motor.
A control device for controlling the output of the power conversion unit by the pulse width modulation control is provided.
The control device includes the abnormality diagnosis device according to claim 4, and diagnoses an abnormality in the motor.
Power converter.
When the difference between the frequency of the sideband wave component and the noise frequency is less than the set value, the control device changes the modulated wave frequency to perform the pulse width modulation control.
The abnormality diagnosing device diagnoses an abnormality of the motor based on the changed modulated wave frequency.
The power conversion device according to claim 14.
In the abnormality diagnosis method for diagnosing the abnormality of the motor driven by the pulse width modulation control of the power converter.
Among the three frequencies used for the pulse width modulation control, the modulated wave frequency, the carrier frequency, and the sampling frequency for sampling the modulated wave, the maximum promise number of two or more frequencies including the modulated wave frequency is calculated, and the result is calculated. The first step of setting a frequency that is an integral multiple of the maximum commitment as the noise frequency,
The second step of detecting the current flowing through the motor and analyzing the frequency,
A third step of determining an abnormality of the motor based on the spectral peak of the sideband wave component of the modulated wave obtained from the analysis result in the second step is provided.
In the third step, the presence or absence of noise interference in the spectral peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency set in the first step.
Abnormal diagnosis method.
In the abnormality diagnosis method for diagnosing the abnormality of the motor driven by the pulse width modulation control of the power converter.
The first step of setting the frequency deviated by an integral multiple of the frequency of the AC power supply to which the power conversion device is connected from the modulated wave frequency used for the pulse width modulation control as the noise frequency.
The second step of detecting the current flowing through the motor and analyzing the frequency,
A third step of determining an abnormality of the motor based on the spectral peak of the sideband wave component of the modulated wave obtained from the analysis result in the second step is provided.
In the third step, the presence or absence of noise interference in the spectral peak of the sideband wave component is estimated based on the frequency of the sideband wave component and the noise frequency set in the first step.
Abnormal diagnosis method.