PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Acoustic cloud based approach for Corona early detection onHydropower Equipment
Jose Manuel Nieto Diaz1, Paulo Henrique Teixeira1 and Manuel Sobreira Seoane2
1
2

J.M. Voith SE & Co. KG

AtlantTIC Research Center, Universidad de Vigo

ABSTRACTThe Hydropower Generation market is facing globally high operational flexibility, compelling theoperator to look forward to reach maximum availability and performance.
The early detection of incipient electrical failures at the generator winding, transformers andconventional substations will help reach these targets.
This paper presents a monitoring system which identifies through sound emission the acousticemissions produced through small electrical discharges, called “Corona”. The Corona has acharacteristic acoustic signature, containing specific frequency components in the range between 7 to19 kHz. Acoustic Corona monitoring can also be used in combination with Partial Discharge
Monitoring Systems, improving the early detection of incipient failures.
The approach along with using control system data, including historical machine behavior, andapplying cloud based machine learning algorithms allows the continuous monitoring of the Coronaeffect. This allows to cluster machine “normal” behavior and to detect corona anomalies, allowing
early warning before failures.
The output provides a web-based visualization to the operator, indicating unusual behavior that thesupport engineer can investigate possible issues in an early phase. The automated system provides acontinuous monitoring of the equipment and raises a warning or alarm just in case of a corona acousticbehavior.
This solution allows an easy retrofit of existing hydropower plants as it requires no physicalmodifications of the equipment.
Keywords: acoustic, Corona, Machine learning

1. INTRODUCTION
Acoustic monitoring and cloud computing introduce a new approach of a monitoring system for
Hydro Power plants: the “integral” approach.
This approach is based on the use of non-localized measurements which promotes the use of
information from a larger set of equipment, e.g. inside the generator, turbine pit, conventional
substations, or in the area of auxiliary systems such as high-pressure units.
The acoustic monitoring system is capable of detecting anomalies or deviations from typicalbehavior of the machine (1). This approach has advantages over classical monitoring systems, wheresensors are mounted to fixed locations. Integral sensors like microphones supervise a larger space andthus supervise more equipment with a smaller number of sensors. It is very interesting, for example,
how the vibration signals of classical monitoring systems relate to signals of acoustic monitoring. (1).
Although the use of microphones will lead to obtain signals which will be a mixture of thecontribution of several sound sources, it can be possible to detect specific events if a proper set of
acoustic features is selected and techniques of blind source separation is used.
Most of the malfunctions detected in the hydro power units are caused by following type ofphenomena:

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Mechanical: Due to shaft misalignment, anisotropy of bearings, instability of t he bearing
oil film, mechanical or magnetic imbalance and friction.
Hydraulic: Due to self-generated forces, hydrostatic, cavitation, instabilities in the draft
tube or flow in the hydraulic ways. They can also come from transient regime period
excitations
Electric: Due to unbalanced magnetic forces, lack of uniformity in the generator gap or
phenomena known as Partial Discharge or Corona.

Mechanical phenomena and their translation into vibrations and correlated acoustic effects havebeen studied and published in previous papers (1). Our new goal is to investigate Corona as electricmalfunction potentially causing degradation of the insulation system in the power unit (generator) andas part of the future investigations we will focus on one of the most typical hydraulic phenomena:
cavitation (2).
The Corona effect can occur in generators or transformers. The corona is produced by partial
discharges which may occur in high voltage equipment due to unforeseen high electric field strengths .
The occurrence of partial discharge does not mean the total loss of the insulation system as it does notcause a complete conductor to ground connection. Partial discharge can also occur along the surface ofsolid insulating materials. This happens if the surface tangential electric field is high enough to cause
a breakdown along the insulator surface. However, with the passage of time the Corona may end up
deteriorating the insulation, causing increased aging of the insulation material.

Figure 1 – Corona on generators (source ofilsystems.com)

Evidence of the Corona is often visible to the eye with a white powder also appearing that sprinkles
the surface of the stator winding. Past research also talks about the possibility of detecting the coronaat the same time by means of acoustic (3), visual monitoring and through the detection of gases, sincea secondary product of the Corona is the appearance of ozone (4).
In practice, the Corona is evaluated during two different situations:
1.1

During Commissioning

During the dry commissioning of the generator, a high-power test (executed in accordance with e.g.
IEEE 56-1979 (§ 8.1.5.2)) is carried out manually, which ends with the visual inspection of themachine. Thus, the test is normally executed by night with reduced illumination; if necessary, a dark
covering must be provided.
Under certain circumstances, this test involves the presence of personnel in the generator pit, whichentails risks. For safety reasons, all metallic non-energized parts of the machine as well as the HV
power supply should be grounded and the windings must be free from dust or other kind of dirty. It will2597

be also necessary to isolate the area around the stator and provide security/safety advertises. Only
qualified personnel should be allowed to execute the test.
Due to all these points, an acoustic monitoring system detecting corona and avoiding the presenceof personnel in the generator pit and the generator stator during the test would be a significant
improvement for hydropower plants commissioning and maintenance personnel.
A similar scheme to the wiring schematic (Figure 2) for testing corona during commissioning has also
been used for the laboratory tests of our system.

Figure 2 – Wiring schematic for alternating voltage test of the stator winding
Where:HIPOT
TPVA, B e C
F
1.2

-

HV test equipment;
Voltage transformer;
Voltmeter AC;
stator winding phases;
rotor winding.

During Operation period

In the medium and long term, special equipment is needed to detect the Corona by means ofcouplers. These couplers are installed mainly to detect Partial Discharge, but they also measure theCorona. For diagnosis, it is essential to make a temporary tendency of the measure Partial Discharge
because the machines have different reference values.

Figure 3 – Mounted couples inside a generator pit (source sparks instruments)

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The doubling of Corona levels over a six-month period is cause for concern. The machine must beopened and inspected visually.
The acoustic cloud-based approach could be an easy way to equip the generator and both do
stand-alone Corona monitoring and correlate the results and analysis of the classical Corona
Monitoring system.

2. PROCEDURES FOR EVALUATING THE CORONA
2.1

Tests description

For Corona generation, both in the laboratory and in the field, it is necessary to apply the highvoltage source defined previously and shown in figure 2.
In both cases, the high voltage source is connected to the voltage transformer. In the case of thelaboratory, the experiment is closed by connecting the high voltage source with an electrical conductor.
In the case of the field, the voltage source is connected to one of the bars of the stator winding , on thegenerator output bus
In both cases, special attention must be paid to conveniently storing the unused elements. The
metallic surfaces must be clean and free of dust and to achieve the Corona special conditions oftemperature and humidity are required.
Once the electric part is assembled, the voltage of the high-power source is raised in steps until theCorona appears. As a result it appears a time signal like the one in figure 4 containing multiple Coronas
over time. This Corona must be very similar for the experiment done in the laboratory as in the field
experiment.

Figure 4 – Pure Corona Signal

As the purpose of this work is the separation and detection of Corona in running machineconditions, and to achieve an effect similar to that of the generator running in a hydraulic power station,in the laboratory a simulated background noise with similar intensity and shape to the real one wasadded.

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The superposition of the Corona signal to the background noise of the machine in nominal
operation can be seen in Figure 5. In addition, in other experiments other noises , such as fans, areadded.

Figure 4 – Corona Signal and Running Machine background noise added
For all these scenarios, different samples of different lengths are taken to better perform thetraining of the algorithms.
For the analysis and separation of the Corona, two different techniques have been chosen that havegiven very interesting and promising results:xx

2.2

In the first one, MFCCs (Mel Frequency Cepstral Coefficients) were used as features andreduced in dimensions under the PCA. Finally, 3 dimensions were used to visualize the dataset.
In the second one, a CNN (Convolutional Neural Network) was established using grayscale spectrograms of input size 300X300 for binary classification of test data

MFCCs (Mel Frequency Cepstral Coefficients)

The first classification strategy that we have carried out is MFCCs. Mel Frequency Cepstral
Coefficients (MFCCs) are widely used features for automatic speech and speaker recognition. Inaddition, applications in environmental noise are becoming more frequent.
The cepstral coefficients are derived from the Fourier transform (FFT – Fast Fourier Transform) or
the discrete cosine transform (DCT), but the basic feature is that in MFCC the frequency bands arelogarithmically located, according to the Mel scale.
The short time Fourier transform of the signal is evaluated to follow the temporal changes of thefrequency components. Then the next step is the calculation of the periodogram of the power spectrumof each frame.
Following, the filter bank according to the Mel scale is applied to the power spectra and theresulting energy is summed.
Then the logarithm of all filter bank energies is calculated, and through the DCT we obtain thecepstral coefficients of the Mel frequencies, which will form the MFCC vector.

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After the MFCCs were extracted as features, the dimensions of the data set were reduced using thePCA (Principal Component Analysis) clustering. Finally, we only used three dimensions of the dataset
to represent the data in three-dimensional space.

Figure 5 – PCA Clustering of the Corona estimation after MFCCs calculation

2.3

Convolutional Neural Network

The second classification strategy that we have carried out is CNN (Convolutional NeuralNetwork). CNN is a type of Artificial Neural Network with supervised learning that processes its
layers imitating the visual cortex of the human brain to identify different characteristics in the inputsthat ultimately make it possible to identify objects and "see".
In our case, we will work with spectrograms of the different noises as input for the CNN. Figure 6

Figure 6 – Corona spectrogram (Left) and Corona+ Machine spectrogram (Right)
The CNN contains several specialized hidden layers and a hierarchy: this means that the first layers
can detect lines, curves and are specialized to reach deeper layers that recognize shapes . These consistof taking "groups of nearby pixels" from the input image and operating mathematically (scalarproduct) against a small matrix called a kernel. That kernel suppose s a of size 3 × 3 pixels "runs" allinput neurons (left-right, top-bottom) and generates a new output matrix which will ultimately be ournew layer of hidden neurons.
In our case, our CNN Model (containing 5 layers) was trained with the same number of samples
from the running power unit and from the running power unit + Corona.
In addition, the concept of data augmentation was used hereby artificially generated data was added2601

to the original training data. Finally, the trained model was tested on the data of a second experiment
under the same conditions as the first experiment. Figure 7

Figure 7 – CNN Training Data Set and Test Data Set
The results of the bi-class decision model are very positive, giving rise to very small decision errors,with very high accuracy and precision. Figure 8

Figure 8 – Results of the application of the CNN Model on Corona

3. CONCLUSIONSIt has been possible to separate and detect the Corona by means of a bi-class detector based onneural networks with very high hit rates and by means of clustering based on the MFCCs algorithmwith very promising results.
The next step is to integrate it into Voith's standard acoustic monitoring algorithms and apply thisapproach also to the special Corona tests performed during the start -up of hydraulic machines.
The acoustic monitoring is conceived as an integral monitoring system. Not only the components
from the vibrations can be isolated (1), but also the spectral components of other aggregates of ahydroelectric power plant (air compressors, etc.), or electrical effects as Corona, allowing monitoringof the anomalies of these systems.

ACKNOWLEDGEMENTS
The authors would like to thank various colleagues contributing to the overall success of this new
development: Rudolf Münch, who is a highly experienced senior expert in Automation, Analysis andAcoustic by Voith; Daniel Schröder, who advised Artificial Intelligence and Analysis; and Thomas
Blank, who performed a lot of the in-depth analysis, created graphs, and did the number crunching
during his time as student at Voith Digital Ventures.

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REFERENCES
1. "Correlation between Vibrations and Acoustic Emissions of a Hydropower Plant". HYDRO 2017
Conference Paper. Jose Manuel Nieto Diaz, Felix Flemming, Klaus Engels, Franz Kunkel.
2. "Investigation of the Relationship Between Acoustic Emission, Vibration, Noise, and Cavitation
Structures on a Kaplan Turbine". Rus T., Dular M., Irok B., Hoevar M., Kern I. An Journal ofFluids Engineering, Vol 129-1112-20, 2007
3. "Acoustic diagnoses of AC corona on the surfaces of insulators". Vahidi, M. J. Alborzi, H.
Aghaeinia, and M. Abedi. IEEE Bologna PowerTech Conference, Bologna, Italy, June 23- 26,
2003.
4. "A Fiber-Optic Multisensor System for Predischarges Detection on Electrical Equipment ". Letizia
De Maria and Daniele Bartalesi. IEEE Sensors Journal, VOL. 12, NO. 1, JANUARY 2012.

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