Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
  • G01R31/1209—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing using acoustic measurements
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
  • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
  • G01R31/34—Testing dynamo-electric machines
  • G01R31/343—Testing dynamo-electric machines in operation

Definitions

• the invention relates to an electrical diagnostic installation of an electric machine such as a motor or a generator.
• an electric machine such as a motor or a generator.
• Such electrical machines are used in almost all drive systems and especially in wind turbines.
• the implementation of the method may require means for storing important data, in particular for storing a multiplicity of fault signatures, and important calculation means for comparing on the fly a measurement of current with the stored signatures.
• the invention proposes a new electrical fault diagnosis method on an electrical machine, characterized in that it is based on a frequency study of a temporal acoustic signal representative of the noise emitted by the machine during its operation.
• the inventors have found in tests on the one hand that the frequency spectrum of the acoustic signal corresponding to the noise emitted by an electric machine during operation is superimposed on the frequency spectrum of a current flowing in a winding.
• the short-circuit between turns has been modeled by a contact resistance Rk between two turns of a stator winding, impedance contact resistance that varies over time as a function of the evolution of the fault; as an indication, and for the machine studied, Rk is chosen between a few tens of Ohm (appearance of the short circuit) and zero (short-circuit franc), intermediate values of Rk being chosen to simulate the evolution over time of the default.
• harmonics at frequencies fcc (k + (ls) * n / p) * fo, fo being the frequency of the electrical network supplying the machine, where p is the number of pairs of poles of the machine, s being the slip of the machine, k being an odd integer and n being an even integer; these harmonics are present only when the machine has an electrical fault, and their amplitude increases with the importance of the present fault; experience has shown that harmonics at frequencies above a few hundred Hz (about 10% for the machine studied and tested) have a fairly low amplitude and can therefore be neglected; identifying the harmonics below this value is thus sufficient for a reliable diagnosis of an electrical fault;
• the determination of the frequency spectrum of the acoustic signal may comprise the identification of the frequencies of the harmonics present in the acoustic signal, for example by applying to the acoustic signal a Fast Fourier Transform, a Short Term Fourier Transform or a Continuous Wavelet Transform. .
• the predefined frequencies to be searched have for example been defined beforehand by a theoretical study of the monitored electrical machine. The search for the predefined frequencies is performed directly in the frequency spectrum of the acoustic signal.
• the method according to the invention therefore does not require a reference acoustic signal specific to the machine to be monitored and therefore does not require an initialization step for measuring a reference acoustic signal linked to the machine to be monitored.
• the results provided by the method according to the invention are thus totally independent of the state of the machine at the time of implementation of the method. This is all the more interesting that it can not be completely guaranteed that an electric machine, even new, is free of electrical fault.
• the implementation of the method according to the invention requires having a measurement of the acoustic signal emitted by an electrical machine to be diagnosed. Also, according to one embodiment of the method, there may be provided a step of measuring an acoustic signal emitted by an electric machine in operation, the measuring step being performed prior to the step of determining the frequency spectrum of said acoustic signal. According to another mode of implementation, the acoustic signal is measured elsewhere, and possibly saved in a memory; the measured signal is then simply made available at the appropriate time for the execution of the method according to the invention.
• an electrical fault is signaled if at least one predefined frequency characteristic of an electrical fault is identified. It thus becomes possible to trigger a preventive maintenance operation of the machine to eliminate the fault before it causes a failure.
• the amplitude of the corresponding harmonic can be determined.
• a state of progress of said electrical fault can be specified as a function of the amplitude of the harmonic or of the harmonics present at the predefined frequency (s). s) of said electrical fault.
• s the predefined frequency
• a state of progress of said electrical fault is specified, said state of progress being a function of the amplitude of the harmonic or the harmonics present (s) at the predefined frequency or at the predefined frequencies characteristic (s) of said electrical fault and / or a function of the amplitude of a harmonic present at a predefined frequency characteristic of a mechanical vibration of the electric machine, said mechanical vibration being generated by said electrical fault.
• the state of the defect can thus be determined more accurately and a failure can be anticipated more effectively taking into account that vibration and short-circuit current can lead to faster deterioration of the machine.
• the state of progress of the electrical fault is defined by a fuzzy logic method taking into account the amplitude of the harmonic or harmonics present at the predefined frequency or at the predefined frequencies characteristic ( s) of the identified electrical fault and the amplitude of the harmonic present at a predefined frequency characteristic of the mechanical vibration of the electric machine.
• Short circuit between turns is one of the most common electrical faults in electrical machines.
• the invention is of course not limited to the diagnosis of short circuits between turns.
• Other defects can be detected, as for example a short-circuit between two phases, a short-circuit between a phase and a housing of the machine, a fault of eccentricity between the rotor and the stator, ...
• the invention also relates to a product embodied by a computer program downloadable from a communication network and / or recorded on a computer readable medium and / or executable by a processor, characterized in that it comprises code instructions of adapted program for the implementation of a method as described above.
• the invention relates to a diagnostic installation comprising computer means adapted to implement a method as described above.
• the installation may also include means for measuring an acoustic signal emitted by an electric machine in operation.
• the installation is easy to set up since it does not require any physical connection with the machine; it also requires very little space and very little investment.
• the invention can thus be easily implemented including for the automatic monitoring and diagnosis of machines already in operation on site.
• the measuring means a microphone for example, compact, is installed closer to the generator and the computer means can be deported to the foot of the wind turbine, or remotely in a monitoring site of a wind farm.
• FIG. 1 is a simplified diagram of a diagnostic installation according to the invention.
• FIG. 2 is a diagram of the main steps of a diagnostic method according to the invention
• the invention relates to a method of electrical fault diagnosis on an electrical machine, based on the study of an acoustic signal emitted by an electric machine in operation, and more particularly on the study of the frequency spectrum of said acoustic signal.
• An installation according to the invention comprises (FIG. 1) essentially a sound measurement sensor, for example a microphone 10, and a data acquisition and processing system 20 essentially comprising computer means such as a computer equipped with means for storing and executing a computer program comprising a plurality of code lines adapted for carrying out a method of processing data according to the invention and as described below.
• the system 20 includes in particular a data memory in which are stored the calculated predefined frequency or frequencies characteristic of an electrical fault on the machine to be monitored.
• the predefined frequency (s) are specific to the machine to be monitored and are calculated from electrical and mechanical parameters of the machine, as detailed below.
• the microphone is positioned as close to the electrical machine 1 to diagnose to limit the recording of noise.
• the machine is an asynchronous motor.
• the invention can be implemented for other types of electrical machines such as a generator.
• Figure 2 shows schematically the steps of an exemplary method according to the invention. These steps are repeated in a loop, on the fly, during the entire time of the monitoring of a machine in operation. The steps represented in dotted lines are optional.
• the acoustic signal corresponding to the noise emitted by the electric machine is measured (step 31).
• the acoustic signal is not saved in the example shown.
• the acoustic signal can be filtered (step 32) to eliminate any unwanted noise due to the external environment of the machine.
• the wind can generate parasitic noise due to mechanical vibrations of a housing surrounding the electric machine.
• parasitic noises are preferably filtered to facilitate the realization of the following steps.
• a spectral analysis of the acoustic signal is then performed to determine the spectrum of the harmonic frequencies present in the acoustic signal (step 34).
• the spectral analysis of the acoustic signal is carried out for example by implementing a Fast Fourier Transform, a Short Term Fourier Transform or a Continuous Wavelet Transform.
• any predefined frequencies characteristic of an electrical fault (36) are then sought in the frequency spectrum (step 36).
• the amplitude of the harmonic is determined.
• the amplitude of each harmonic may be normalized (step 37), for example by taking as a reference the main harmonic of rank 1.
• the progress of said electrical fault is specified according to the amplitude of the harmonics present at the predefined frequencies.
• fcc (k + (ls) * n / p) * fo characteristics of an electrical fault and as a function of the amplitude of a harmonic present at a predefined frequency close to the frequency 2 * fo characteristic of a mechanical vibration of the electric machine.
• the state of progress of the electrical fault is defined by a fuzzy logic method taking into account the amplitude of the harmonic or harmonics present at the predefined frequency (s). (s) the electrical fault identified and the amplitude of the harmonic present at a predefined frequency characteristic of the mechanical vibration of the electric machine.
• the fuzzy logic method here makes it possible to qualitatively calculate the state of progress of the State_M defect by providing a set of rules formulated in natural language; the implemented method gives a qualitative value of State_M (state of the machine) among a set of qualitative values ⁇ SAIN, INDEFINED, DEBUT, EVOLVED, CRITICAL ⁇ according to qualitative values of the input variables H_D and H_V (respectively l the amplitude of the harmonic of default and the amplitude of the harmonic of vibration) taken among a set of qualitative values ⁇ NONE, ZERO, SMALL, MEDIUM, LARGE ⁇ .
• the method according to the invention will thus provide a qualitative value among SAIN / INDEFINI / DEBUT / EVOLUE / CRITICAL /, to define a degree of urgency for a preventive maintenance action and effectively plan said preventive maintenance action.
• the state SAIN of the machine corresponds, in the mathematical model of an asynchronous machine with short-circuit fault, to a contact resistance between turns Rk much greater than 40 Ohms
• the state DEBUT corresponds to Rk between 20 and 40 Ohms
• the EVOLUE state corresponds to Rk between 5 and 15 Ohms
• the CRITICAL state corresponds to Rk between 0 and 5 Ohms.
• the INDEFINED state corresponds to a situation between SAIN and DEBUT where, in the frequency spectrum of the measured acoustic signal, the harmonics at the characteristic frequencies seem to appear but have an amplitude too small to be detected with certainty.

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• Physics & Mathematics (AREA)
• Acoustics & Sound (AREA)
• General Physics & Mathematics (AREA)
• Tests Of Circuit Breakers, Generators, And Electric Motors (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=57861143

Family Applications (1)

Country Status (3)

Cited By (1)

Families Citing this family (2)

Family Cites Families (8)

• 2016
  • 2016-12-06 FR FR1662025A patent/FR3059784A1/fr not_active Withdrawn
• 2017
  • 2017-12-05 EP EP17821683.4A patent/EP3552033A1/de not_active Withdrawn
  • 2017-12-05 WO PCT/FR2017/053395 patent/WO2018104651A1/fr unknown

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