• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    A method for detecting an electric arc in an electrical system from a signal (104) from at least one sensor sensing acoustic waves in the system, comprising: a) computing by means of a device processing, on a sliding window (W [i]) of signal samples, at least one statistical parameter chosen from the dissymmetry coefficient (SK [i]) and the flattening coefficient (KU [i]) of the signal ; b) detecting a possible occurrence of an event taking into account said at least one statistical parameter (SK [i], KU [i]); and c) performing a frequency analysis of the signal to identify an electric arc when an event is detected in step b).
    公开号:FR3046232A1
    申请号:FR1563381
    申请日:2015-12-28
    公开日:2017-06-30
    发明作者:Diego Alberto；Vincent Heiries；Pierre Perichon；Jerome Genoulaz
    申请人:Commissariat a lEnergie Atomique CEA；Commissariat a lEnergie Atomique et aux Energies Alternatives CEA；Labinal Power Systems SAS；
    IPC主号:
    专利说明:

    METHOD FOR DETECTING AN ELECTRIC ARC BY ANALYSIS OF SA
    ACOUSTIC SIGNATURE
    Field
    The present application relates to a method for detecting the formation of an electric arc in an electrical system from its acoustic signature.
    Presentation of the prior art
    In many electrical systems, such as high-voltage systems such as electrical cabinets, transformers, electric batteries, electrical distribution networks, etc., arcing can occur, for example as a result of a surge or loss of voltage. a failure such as breaking a cable or wearing an insulation. If maintained, an electric arc can cause serious damage, including a fire. Electrical arcing is particularly dangerous in continuous-supply electrical systems, since a "natural" zero crossing of the supply voltage can not be relied on to cause the arc to go out. Early detection of the formation of an electric arc is a major issue for the safety of many electrical systems.
    Various solutions have been proposed to detect the formation of an electric arc in an electrical system. In particular, detection methods based on current and voltage measurements in the system, detection methods based on optical signal measurements, detection methods based on electromagnetic signal measurements, and detection methods have been proposed. based on acoustic signal measurements.
    Of particular interest here are detection methods based on acoustic signal measurements. summary
    Thus, an embodiment provides a method for detecting an electric arc in an electrical system from a signal from at least one sensor sensing acoustic waves in the system, comprising: a) computing by means of a device processing, on a sliding window of signal samples, at least one statistical parameter chosen by the asymmetry coefficient and the flattening coefficient of the signal; b) detecting a possible occurrence of an event taking into account said at least one statistical parameter; and c) performing a frequency analysis of the signal to identify an electric arc when an event is detected in step b).
    According to one embodiment, step b) comprises the detection of a characteristic peak or amplitude variation of said at least one statistical parameter.
    According to one embodiment, the method further comprises calculating a magnitude representative of the instantaneous energy of the signal.
    According to one embodiment, in step b), said magnitude representative of the instantaneous energy of the signal is taken into account to detect a possible occurrence of an event.
    According to one embodiment, step c) comprises the calculation of the 3-order derivative of the signal, and the search for a characteristic peak in the derived signal.
    According to one embodiment, step c) comprises calculating the spectral power density of the signal.
    According to one embodiment, step c) comprises the calculation of a quantity representative of the signal energy in a spectral band characteristic of electric arcs, and the detection of the possible crossing of an energy threshold in this band. .
    According to one embodiment, the magnitude representative of the energy of the signal in a spectral band characteristic of the electric arcs is normalized with respect to a quantity representative of the energy of the signal in another spectral band.
    Another embodiment provides a computing device for detecting an electric arc in an electrical system from a signal from at least one sensor detecting acoustic waves in the system, comprising a processing device arranged for: a) calculating by means of a processing device, on a sliding window of samples of the signal, at least one statistical parameter chosen from the dissymmetry coefficient and the signal flattening coefficient; b) detecting a possible occurrence of an event taking into account said at least one statistical parameter; and c) performing a frequency analysis of the signal to identify an electric arc when an event is detected in step b).
    Another embodiment provides a system comprising: an electrical system; at least one sensor arranged to detect acoustic waves in the electrical system; and a computing device as defined above arranged to process an output signal of the sensor.
    Brief description of the drawings
    These features and their advantages, as well as others, will be set forth in detail in the following description of particular embodiments made without implied limitation in relation to the appended figures among which: FIG. 1 illustrates an electrical system provided with a an arc detection device according to an example of an embodiment; Fig. 2 is a flowchart illustrating steps of a method of detecting an electric arc according to an example of an embodiment; and FIG. 3 illustrates a device adapted to the implementation of a method of detecting an electric arc according to an example of an embodiment.
    detailed description
    The same elements have been designated with the same references in the various figures. For the sake of clarity, only the elements that are useful for understanding the described embodiments have been shown and are detailed. In particular, the electrical systems in which it is desired to detect arcs have not been detailed, the detection solutions described being compatible with all electrical systems in which electric arcs can occur.
    FIG. 1 represents an electrical system 100 to be monitored, for example an electric amoire, an electricity distribution network in an airplane, an electric battery, or any other electrical system in which it is desired to be able to detect the possible appearance of a electric arc.
    The electrical system 100 is equipped with an arcing detection device comprising an acoustic sensor 102, for example an ultrasonic sensor, adapted to detect acoustic waves in the electrical system 100. The formation of an electric arc 'accompanies indeed the emission of characteristic acoustic waves whose detection can identify the presence of the arc. The sensor 102 may be disposed on a wall of a housing of the electrical system 100, or in physical contact with an electrical conductor that it is desired to monitor particularly. Although only one sensor 102 is illustrated in FIG. 1, in alternative embodiments, several sensors 102 may be provided to monitor different parts of the system 100. The output of each sensor 102 is for example treated separately in a manner similar or identical to which will be described in more detail below.
    The sensor 102 provides an output signal 104 to a computing device 106 of the arcing device. By way of example, the output signal 104 of the sensor 102 is a digital signal, and the computing device 106 is a digital processing circuit comprising for example a microprocessor. The output signal 104 is a signal in the time domain, representing the evolution as a function of time of the amplitude of the acoustic waves picked up by the sensor 102. The computing device 106 is adapted to analyze the signal supplied by the sensor 102 to detect the possible presence of an electric arc in the system 100.
    The computing device 106 provides, for example, an output signal 108 to an output module 110 of the arcing device, which may be an alarm, a display, or any other interface element making it possible to inform an user of the presence of an arc in the system 100.
    The computing device 106 can provide, in addition to or instead of the output signal 108, an output signal 112 sent back to the system 100, which can for example control the safety of the system 100 when an electric arc is detected. for example by interrupting the supply current in all or part of the system 100.
    The implementation of a robust detection of an electric arc from its acoustic signature passes through a frequency analysis of the acoustic signals detected by the sensor 102. Indeed, the detection of acoustic energy peaks in frequency bands particular, for example between 80 and 120 kHz, provides good performance of arcing detection and minimize the risk of false detection related to other phenomena likely to generate acoustic signals, such as mechanical shocks. The real-time frequency analysis of the output signal of the sensor 102 however requires significant computing resources, and results in a high electrical consumption of the computing device 106.
    Fig. 2 is a flowchart illustrating steps of a method of detecting an electric arc according to an example of an embodiment. This method can be implemented by the computing device 106 of the electric arc detection device of FIG. 1 to carry out a continuous monitoring of the system 100 and to detect as quickly as possible the formation of an electric arc. An advantage of the method of FIG. 2 lies in its relatively low computational complexity, which makes it possible in particular to limit the electrical consumption of the computing device 106.
    The method of FIG. 2 comprises a step 201 of computation, on a window W [i] of consecutive samples of the output signal 104 of the sensor 102, of at least one of the following statistical parameters of the signal 104: the coefficient dissymmetry (often referred to as the Anglo-Saxon "skewness"); and the flattening coefficient (also called kurtosis).
    The dissymmetry coefficient SK [i] and the flattening coefficient KU [i] of the signal 104 in the window W [i] can be defined as follows:
    where n is the number of samples of the window W [i], j is an integer from 1 to n, yj is the value of the sample of rank j of the window W [i], ÿ is the average of n samples yj of the window W [i] and s is the standard deviation of the n samples yj of the window W [i].
    The window W [i] is a sliding window, and the calculation of the statistical parameters SK [i] and / or KU [i] can be performed in real time as the samples arrive. As an illustrative example, the window W [i] comprises n = 2000 samples and the sampling period of the signal 104 is equal to 1 μs, so that each window W [i] covers a period of 2 ms. The sliding step of the window is for example 1 sample, that is to say that the window W [i + 1] contains the last n-1 samples yj of the window W [i], plus one additional sample following the last sample yn of the window W [i].
    The inventors have found that the formation of an electric arc in the system 100 results in the appearance of a peak or a large amplitude variation in one and / or the other of the statistical signals SK [ i] and KU [i].
    Thus, the method of FIG. 2 comprises a step 203 for detecting a peak or a large amplitude variation in the statistical signal SK [i] and / or KU [i]. By way of example, step 203 may comprise, after the step of calculating the statistical parameter SK [i] and / or KU [i], a step of detecting the possible crossing of a high or low threshold by the statistical parameter SK [i] and / or KU [i].
    If no peak and no significant amplitude variation are detected in step 203, steps 201 and 203 are again implemented for the next window W [i + 1] of signal 104.
    If a peak or a large amplitude variation of the statistical parameter SK [i] and / or KU [i] are detected in step 203, the detection device can deduce that an event that can correspond to the formation of an electric arc has occurred in the system 100. The only statistical analysis of the signal 104 does not make it possible to determine with certainty that the event detected corresponds to the appearance of an electric arc. Indeed, other events such as a mechanical shock can be at the origin of the peak detected in the signal SK [i] and / or KU [i]. Thus, when an event that can correspond to an electric arc is detected from the statistical signal SK [i] and / or KU [i] in step 203, the method of FIG. step 205, a frequency analysis of the signal 104, for discriminating the formation of an electric arc among different types of events. Examples of signal frequency analysis methods 104 that may be implemented in step 205 will be described hereinafter.
    When an electric arc is detected during the frequency analysis step 205, an output signal signaling this detection can be generated, for example in order to trigger an alarm and / or a safety of the electrical system 100.
    An advantage of the method of FIG. 2 is that in the absence of a remarkable event in the system 100, its computational complexity is limited to the implementation of a calculation of one or more statistical parameters of the signal 104 in the system. time domain. Only when a remarkable event is detected from this statistical analysis of the signal 104, a frequency analysis (potentially heavier in computations) is implemented to allow to more finely discriminate an electric arc among different types events and thus limit the risk of false detections. This approach reduces the overall power consumption of the electric arc detection device.
    Note that in step 203, the detection of an event can be performed from the single parameter SK [i], from the single parameter KU [i], or from the two parameters SK [i] and KU [i]. By way of example, the implementation of a frequency analysis of the signal 104 can be triggered when a peak or a significant amplitude variation is detected in the signal SK [i], or when a peak or a variation significant amplitude are detected in the signal KU [i], or when a peak or a significant amplitude variation is detected in the product signal SK [i] * KU [i].
    In addition to the statistical parameters SK [i] and / or KU [i], the detection of an event that can correspond to an electric arc can be based on other characteristic quantities of the signal 104, for example on the instantaneous energy of the signal By way of example, step 201 may further comprise, for each sample of the signal 104, the calculation of the instantaneous energy of the normalized signal 104 over the standard deviation of the acoustic noise, defined as follows:
    where σηοι3θ is the standard deviation of the acoustic noise measured by the sensor 102 on a reference window in the absence of any remarkable event (electric arc, mechanical shock, etc.) capable of generating acoustic waves, and where Pnoise is the average acoustic noise in this same reference window.
    During step 203, it can then for example be considered that a remarkable event that can correspond to an electric arc has occurred, and consequently decide to implement step 205 frequency analysis of the signal 104, when a peak or a significant variation in amplitude has been detected in the statistical signal SK [i] and / or KU [i], and the normalized instantaneous energy EIN [i] of the current sample is greater than a threshold S. The threshold S is for example between 4 * anoj_se and 6 * σηοί3θ, for example equal to equal to 5 * σηοί3θ. Frequency analysis - first example
    In this example, the step 205 comprises the calculation of the derivative of order 3 of the signal 104. The operation of derivation to the order 3 of the signal 104 corresponds to a high-pass filtering of the signal 104. This operation offers a good compromise between computational complexity and efficiency of detection / discrimination of electric arcs. The derivative of order 3 of the signal 104 has a remarkable peak in the presence of an electric arc, and has no peak in the presence of a mechanical shock. Thus, step 205 may include the search for a possible peak characteristic of an electric arc in the signal 104. For example, the calculation of the derivative of order 3 of the signal 104 and the search for a eventual peak in the derived signal can be implemented on the same window W [i] of samples of the signal 104 that led to detect a remarkable event in step 203, and / or on subsequent windows. The described embodiments are however not limited to this particular case.
    Frequency analysis - second example
    In this example, step 205 includes a conversion of the time signal 104 provided by the sensor 102 into the frequency domain. For this, step 205 comprises for example the calculation of a local Fourier transfomer or STFT (in English "Short Time Fourier Transfom") on a window of consecutive samples of the signal 104, for example the same window W [i] of samples of the signal 104 that has led to detecting a remarkable event in step 203. The power spectral density or DSP in the processing window can then be estimated, for example by calculating the squared modulus of the standardized STFT. on the total energy of the treated window Knowing the frequency band (s) of the acoustic waves generated by an electric arc, it is then possible to effectively detect / discriminate an electric arc among different types of events. , step 205 may comprise calculating the energy of the signal 104 in a spectral band characteristic of electric arcs, for example the band ranging from 80 to 120 kHz, and the detection of the possible crossing of an energy threshold in this band. The spectral processing of the signal 104 can be continued by dragging the processing window so as to build the spectrogram of the signal 104, that is to say a matrix in which each column contains the DSP of the signal 104 for a time window. The samples of the signal 104. The calculation of the spectrogram makes it possible to follow the temporal evolution of the spectrum of the signal 104, which makes it possible to further improve the detection / discrimination performance of the electric arcs. Step 205 may in particular include analyzing the temporal evolution of the energy of the signal 104 in one or more specific spectral bands. This analysis can be performed differentially, for example by comparing the energy of the signal 104 in a spectral band characteristic of electric arcs to the energy of the signal 104 in one or more other spectral bands.
    The STFT calculated in step 205 can be defined as follows:
    where X [m, ω] is the STFT, m and ω are the discretized variables in time (m) and in frequency (ω) of the STFT, x [n] is the signal in time, n is the discrete time, and wind [] is the sliding window used to select and weight the subset of the samples on which the STFT is estimated.
    For a time m fixed, the DSP calculated in step 205 can be defined as follows:
    where X [co] is the STFT for a time m fixed, and where the sum at the denominator corresponds to the total energy of the signal contained in the computation window in the band going from 0 to cos / 2, with ω3 = 2πί3, fs being the sampling frequency of the signal 104.
    Figure 3 illustrates an example of a processing device 300 of the computing device 106 of Figure 1. The processing device 300 is arranged to implement the electric arc detection method described above. The device 300 may comprise one or more processors 302 (P) receiving instructions stored in an instruction memory 304. The processing device 300 may further comprise a memory 306 configured to store the various quantities calculated during implementation. of the arcing detection method, for example the statistical parameters SK [i] and / or KXJ [i], the instantaneous energy EIN [i], if applicable, the order 3 derivative of the signal 104, if applicable , STFTs and DSPs of signal 104 if any, etc. The memory 306 can further store the time-varying signal 104 from the sensor 102, which is, for example, received by the processor 302 via an input / output interface 308. The input interface output 308 may further provide the output signal 112 of the device 106 adapted to control the operation of the electrical system 100 in the event that an electric arc is detected. The processing device 300 further comprises, for example, a display 310, which for example provides a user interface and means for alerting a user if an electric arc is detected.
    Particular embodiments have been described. Various variations and modifications will be apparent to those skilled in the art. In particular, the embodiments described are not limited to the examples of frequency analysis methods of the signal 104 (step 205 of the method of FIG. 2) described above. More generally, any other method of frequency analysis allowing the detection of an electric arc from its acoustic signature can be implemented during step 205 of the method of FIG.
    In addition, the electric arc detection method described in connection with FIG. 2 may include additional optional steps to improve its performance. For example, a pretreatment of the signal 104 may be implemented, consisting in performing a band-pass filtering of the signal, according to a bandwidth including the emission frequencies typical of electric arcs and mechanical shocks, so as to reduce the energy contribution of noise. It will be noted that the inclusion in the bandwidth of the pretreatment filter of the characteristic frequencies of the mechanical shocks (in addition to the characteristic frequencies of the electric arcs) is optional, but has the advantage of allowing the implementation of an arc detection. by differential analysis (difference between the energy at the characteristic frequencies of the arcs and the energy at the characteristic frequencies of the shocks).
    权利要求:
    Claims (10)
    [1" id="c-fr-0001]
    A method for detecting an electric arc in an electrical system (100) from a signal (104) from at least one sensor (102) for detecting acoustic waves in the system, comprising: a) computing by means of a processing device, on a sliding window (W [i]) of samples of the signal (104), at least one statistical parameter selected from the dissymmetry coefficient (SK [i]) and the flattening coefficient (KU [i]) of the signal (104); b) detecting a possible occurrence of an event taking into account said at least one statistical parameter (SK [i], KU [i]); and c) performing a frequency analysis of the signal (104) for identifying an electric arc when an event is detected in step b).
    [2" id="c-fr-0002]
    2. The method of claim 1, wherein step b) comprises detecting a peak or amplitude variation characteristic of said at least one statistical parameter (SK [i], KU [i]).
    [3" id="c-fr-0003]
    The method of claim 1 or 2, further comprising calculating a magnitude representative of the instantaneous energy of the signal (104).
    [4" id="c-fr-0004]
    4. The method of claim 3, wherein, in step b), said magnitude representative of the instantaneous energy of the signal (104) is taken into account to detect a possible occurrence of an event.
    [5" id="c-fr-0005]
    The method of any one of claims 1 to 4, wherein step c) comprises calculating the first order derivative of the signal (104), and searching for a characteristic peak in the derived signal.
    [6" id="c-fr-0006]
    The method of any one of claims 1 to 4, wherein step c) comprises calculating the power spectral density of the signal (104).
    [7" id="c-fr-0007]
    The method according to claim 6, wherein step c) comprises calculating a magnitude representative of the signal energy (104) in a spectral band characteristic of the electric arcs, and detecting the possible crossing of a energy threshold in this band.
    [8" id="c-fr-0008]
    The method of claim 7, wherein said magnitude representative of the signal energy (104) in a spectral band characteristic of the electric arcs is normalized with respect to a magnitude representative of the signal energy (104) in another spectral band.
    [9" id="c-fr-0009]
    A computing device (106) for detecting an electric arc in an electrical system (100) from a signal (104) from at least one sensor (102) for sensing acoustic waves in the system, comprising a device processing apparatus arranged for: a) computing by means of a processing device, on a sliding window (W [1]) of samples of the signal (104), at least one statistical parameter selected from the dissymmetry coefficient (SK [ i]) and the flattening coefficient (KU [i]) of the signal (104); b) detecting a possible occurrence of an event taking into account said at least one statistical parameter (SK [i], KU [i]); and c) performing a frequency analysis of the signal (104) for identifying an electric arc when an event is detected in step b).
    [10" id="c-fr-0010]
    A system comprising: an electrical system (100); at least one sensor (102) arranged to detect acoustic waves in the electrical system (100); and a computing device (106) according to claim 9 arranged to process an output signal (104) of said at least one sensor (102).
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1563381|2015-12-28|
    FR1563381A|FR3046232B1|2015-12-28|2015-12-28|METHOD FOR DETECTING AN ELECTRIC ARC BY ANALYSIS OF ITS ACOUSTIC SIGNATURE|FR1563381A| FR3046232B1|2015-12-28|2015-12-28|METHOD FOR DETECTING AN ELECTRIC ARC BY ANALYSIS OF ITS ACOUSTIC SIGNATURE|
    EP16206506.4A| EP3187842B1|2015-12-28|2016-12-22|Method for detecting an electric arc by analysing the acoustic signature thereof|
    US15/390,510| US10690713B2|2015-12-28|2016-12-25|Method of detecting an electric arc by analysis of its acoustic signature|
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