METHOD AND SYSTEM OF PARTIAL DISCHARGE RECOGNITION FOR DIAGNOSING ELECTRICAL NETWORKS

Abstract

The method of the present invention makes it possible to recognize partial discharges acquired by means of sensors in electrical networks, comprising a series of steps, among which are a post-processing step (13) of the acquired signals and a recognition step (17) of said signals by means of a convolutional neural network (CNN). The method also includes adaptation (15) and training (16) steps of the neural network, as well as a step to build a library (14) of partial discharge signals from known sources that serve as training of the convolutional neural network (CNN).

Claims

1. Method of partial discharge recognition for diagnosing live electrical networks which comprises the following steps: acquisition (11) of at least one partial discharge (PD) signal through at least one sensor (1), pre-processing (12) of the PD signal acquired through the sensor (1), in order to delimit the PD signal within a frequency range and to eliminate electrical noise. post-processing (13) of the pre-processed PD signal acquired through the sensor (1), and recognition (17) of the post-processed partial discharge signal by a neural network, being the method characterised in that the post-processing step (13) of the PD signal comprises a step for obtaining a PD signal scalogram based on the Wavelet Transform.

2. Method of partial discharge recognition according to claim 1, characterised in that the recognition neural network is a convolutional neural network.

3. Method of partial discharge recognition according to claim 2, characterized in that it further comprises an adaptation step (15) of the convolutional neuronal network.

4. Method of partial discharge recognition according to claim 1, characterized in that it further comprises a step of construction of a library (14) of partial discharge signals from known sources.

5. Method of partial discharge recognition according to claim 4, characterized in that it further comprises a training step (16) of the convolutional neural network through the library of partial discharge signals from known sources.

6. Method of partial discharge recognition according to claim 5, characterized in that it further comprises a verification step (18) of the partial discharge recognized by the convolutional neural network.

7. Method of partial discharge recognition according to claim 6, characterised in that the verified partial discharge signals are incorporated into the partial discharge signal library.

8. Partial discharge recognition system (2) for diagnosing live electrical networks that carries out the method according to the previous claims 1 to 7, characterized in that it comprises a recognition unit (3) that in turn comprises a first post-processing module (4) of partial discharge signals and a second neural network module (5).

9. Partial discharge recognition system (2) according to claim 8, characterised in that the second module (5) of the neural network comprises a convolutional neural network.

10. Partial discharge recognition system (2) according to claim 8 or 9, characterised in that the recognition unit (3) comprises a third module (6) of partial discharge signal library from known sources.

11. Partial discharge recognition system (2) according to claim 10, characterized in that the recognition unit (3) comprises a fourth module (7) of convolutional neural network training.

12. Partial discharge recognition system (2) according to claim 11, characterized in that the recognition unit (3) comprises a fifth module (10) of verification of the partial discharges recognized by the second module (5) of the neural network.

13. Partial discharge recognition system (2) according to claim 8, characterised in that it comprises a partial discharge signal acquisition unit (8).

14. Partial discharge recognition system (2) according to claim 13, characterised in that the partial discharge signal acquisition unit (8) comprises at least one sensor (1).

15. Partial discharge recognition system (2) according to claim 13 or 14, characterised in that it comprises a pre-processing unit (9) of the partial discharge signals acquired by the acquisition unit (8).

Description

FIGURE DESCRIPTION

[0030] FIG. 1.Shows a block diagram of the partial discharge recognition method of the present invention.

[0031] FIG. 2.Shows a block diagram of the partial discharge recognition system where the recognition method of FIG. 1 applies.

PREFERRED EMBODIMENT OF THE INVENTION

[0032] An example of a preferred embodiment is described below, mentioning the figures above, without limiting or reducing the scope of protection of the present invention. FIG. 1 shows a method of recognizing partial discharge signals based on the use of an existing convolutional neural network (CNN), so that this method also defines the steps to be followed for the adaptation (15) and training (16) of the existing neural network by means of partial discharge signals from known sources.

[0033] In the adaptation step (15) of the existing convolutional neural network (CNN), the input parameters of the neural network are adapted according to the format of the input signals and the output parameters of the neural network according to the desired objectives.

[0034] The method comprises an acquisition step (11) of at least one real PD signal through at least one sensor (1) in the field. This acquired signal is then subjected to a first filtering in a pre-processing step (12) and then to a second filtering in a post-processing step (13) using the Wavelet Transform, thus obtaining a high resolution scalogram or graphical representation (image) of the PD signal in the frequency and time spectrum.

[0035] In the case of PD signals from known sources, once the signals have passed through the post-processing step (13), in a following step (14) a library is built which includes all these PD signals from known sources. This library of PD signals from known sources is used in a subsequent training step (16) of the convolutional neural network (CNN), so that, with the trained CNN, inputs (images) of PD signals from unknown sources can be received and provide outputs or results with a high degree of accuracy in the identification of such sources.

[0036] As can be seen in FIG. 1, the partial discharge recognition method of the invention comprises the following application steps in signals from unknown sources and for the identification of the same: [0037] Acquisition (11) of at least one partial discharge signal through at least one sensor (1), [0038] Pre-processing (12) of the PD signal acquired through the sensor (1), [0039] Post-processing (13) of the pre-processed and acquired partial discharge signal through the sensor (1), [0040] Recognition (17) by the convolutional neural network (CNN) of the post-processed partial discharge signal, and [0041] Verification (18) of the partial discharge recognized by the convolutional neural network (CNN).

[0042] Once the PD signals have been verified and accepted, they are incorporated into the library of PD signals from known sources, expanding that library with new data that will ensure greater accuracy in future recognitions. This verification step (18) of partial discharges recognized by the convolutional neural network (CNN) refers to the verification by an on-site operator of the result provided by the CNN. If the operator confirms that the result is accurate, it is included in the library along with the known partial discharge signals. If the operator confirms that the result is not successful, it will also be included in the library as a new source of PD signals.

[0043] FIG. 2 shows the partial discharge recognition system (2) where the method described above is applicable. The partial discharge recognition system (2) comprises a recognition unit (3) that in turn comprises a first post-processing module (4) of partial discharge signals and a second module (5) corresponding to the neural network, such as a convolutional neural network (CNN). The first post-processing module (4) feeds the second module (5) of the convolutional neural network (CNN) through high-resolution inputs (images) of PD signals, so that the combination of both modules (4, 5) allows to obtain highly accurate results. In addition, the recognition unit (3) comprises a third module (6) corresponding to the library of partial discharge signals from known sources, as well as a fourth module (7) for training of the convolutional neural network (CNN) and a fifth module (10) for verification of the partial discharges recognized by the second neural network module (5).

[0044] Finally, the PD recognition system (2) comprises a PD signal acquisition unit (8), such as a sensor (1), and a pre-processing unit (9) of the PD signals acquired by the acquisition unit (8).