Artificial Intelligence and Learning Systems Methods in Supporting ...

ACTA PHYSICA POLONICA A

Vol. 123 (2013)

No. 6

Acoustic and Biomedical Engineering

Articial Intelligence and Learning Systems Methods in Supporting Long-Term Acoustic Climate Monitoring ∗

M. Kªaczy«ski

and T. Wszoªek

AGH  University of Science and Technology, Faculty of Mechanical Engineering and Robotics Department of Mechanics and Vibroacoustics, al. A. Mickiewicza 30, 30-059 Krakow, Poland Developing eective methods for automatic identication of noise sources is currently one of the most important tasks in long-term acoustical climate monitoring of the environment. Manual verication of recorded data, when it comes to proper determination of noise levels, is time-consuming and costly.

A possible solution is to use

pattern recognition techniques for acoustic signal recorded by a monitoring station. This paper presents usefulness of special directed measurement techniques, acoustic signal processing, and classication methods using articial intelligence (the Sammon mapping) and learning systems methods (Support Vector Machines) in the recognition of corona audible noise from ultra-high voltage AC transmission lines. DOI: 10.12693/APhysPolA.123.1024 PACS: 43.60.Lq, 07.05.Mh

near ultra-high voltage AC (UHV AC) transmission lines.

1. Introduction

This material was used in an earlier authors' study [4]. One of the adverse occurrences associated with transmission of electricity through ultra-high voltage AC transmission lines is corona which, among other features, is a source of acoustic signal. Such signal can be a valuable diagnostic symptom of a condition of certain elements of the transmission circuit, while, on the other hand, it is a source of noise, and residents in the immediate vicinity of such high voltage lines often complain about inconvenience associated with said noise.

Large

randomized factors stimulating corona phenomena, especially weather conditions and technical state of the conductors and insulators' surface, cause such generated acoustic signal having a random nature regarding both the level and shape of the spectrum [1]. The above-mentioned randomness causes great diculty in accurate measurement of long-term noise levels

The monitoring station (Fig. 1) registered data by means of an all-weather microphone [5] in the mode of sound level continuous spectrum measurement in 1/3 octave bands in the frequency range between 20 Hz and 20 kHz. Additionally, the A-, C-, and Z-weighted sound levels were registered. The averaged spectrum data was recorded to a buer-type le every 10 s, while other data, including

LAeq

values and statistical levels of the 1/3 oc-

tave spectrum and the A-, C-, and Z- weighted sound levels, were recorded every 15 minutes. In the same time step, the following average weather conditions were registered: temperature, atmospheric pressure, rainfall, humidity, and wind speed and direction.

The registered

data was used for extraction of the distinctive features of CAN, and further for dene the feature vector.

deriving from corona. The indicators can be determined on the basis of short-term measurements in characteristic states of weather, time of day or seasons of the year, either from data gathered in long-term continuous monitoring systems. Manual verication of recorded data is time-consuming and costly. A possible solution is to use pattern recognition methods for identication of acoustic noise sources based on data recorded by a monitoring station [2, 3]. 2. Research methodology

In this study, recordings from a long-term acoustic monitoring station were used for detection and identication of corona audible noise (CAN). The monitoring measurements were carried out in several locations

∗ corresponding

author

Fig. 1.

The long-term acoustic monitoring station near

400 kV transmission line.

(1024)

Articial Intelligence and Learning Systems Methods. . .

1025

In the authors' previous papers [1, 4, 6, 7], several ver-

signal power in a selected frequency band to the signal

sions of the feature vector parameters were presented,

power in the whole 2020000 Hz band. The signal power

and the proper selection of the feature space was exten-

coecient of the

sively discussed. Finally, the space has been reduced from

formula:

fg P

19 to 10 elements [4] and contains:

•

normalized (to der (M1 ,

M2 )

M0 ) spectral moments of 1 and 2 or-

for the whole frequency band (20 Hz

P Cm =

to 20 kHz);

•

coecients of the signal power divided into the

where

tonal components band from 20 Hz to 400 Hz (P C1 )

band.

coecients useful in detection of the tonal components 100 Hz and 200 Hz in the spectra of registered samples (T C100 ,  the

T C200 );

L90

statistical level for A-weighted

the interference level);

WS

 wind speed [m/s],

 relative humidity [%].

The spectral moment of the

m-th order is described by a m

|Gn (fi )| [fi ] ,

(1)

Gn (f ) is the frequency spectrum of the n-th data record, fi  midpoint frequency for the i-th frequency band dened for the spectral analysis, f0  lower band frequency, fN  upper band frequency. The spectral moment of the 0-th order M0 is used for

where

GN (f )

are the limits of the selected frequency

For the purpose of this research, two power coecients

P C1

calculated from 20 Hz to 400 Hz

frequency band (the band where rainfall noise eects and the tonal components of corona noise are encountered) and

P C2 calculated from 1 kHz to 10 kHz frequency band

signal are located). Tonal coecients useful in detection of the tonal components are dened as

T Cf =

∂GN (f ) ∂f

 ,

(7)

f is the frequency of a given tonal component, and f = 100 Hz, 200 Hz. Therefore, the space of the feature vector is based weighted sound level, and the meteorological data and it can be described symbolically as:

Xn = hM1 , M2 , P C1 , P C2 , T C100 , T C200 , RH, RF, W S, LA 90i. (8) 3. Experimental study

3.1. Research material

normalization of the higher order moments and is given by the following relation:

M0 (t) =

(6)

on the spectral coecients, statistical data of the A-

i=0

N X

,

where

general relation (1):

N X

f d, f g



 rain fall [mm/h],

Mm (t) =

20000 P

(the band where the noise components of corona process

sound level (carrying the information concerning

• RF RH

GN (f )

f =fd

have been used:

10 kHz (P C2 );

• LA 90

order can be determined by the

f =20

and the noise components band from 1000 Hz to

•

m-th

The database used in the study contained 6371 records of the feature vector (8) with the imposed recogni-

|GN (fi )| .

(2)

tion qualication thresholds:

0  no corona eect

(6012 records); 0.5  average corona eect signal (215

i=0 It is usually convenient to use spectral moments normal-

records); 1  corona eect signal (144 records). This

ized according to relation:

classication was proposed by subjective decision based

Mu (m) = The

M1

on listening to audio recordings of the data.

M (m) . M (0)

(3)

moment (4) can be interpreted as a spectrum's

center of gravity (weighted average frequency):

N P

M1 (t) = The

M2

|GN (fi )| · fi

i=0

M0 (t)

.

(4)

moment (5) can be interpreted as the spectrum

dispersion around the spectrum's center of gravity:

v uP u N u G (f ) · fi2 t i=0 N i M2 (t) = − M12 (t). M0 (t)

(5) Fig. 2.

Spectrum of intense corona eect.

In addition to the spectral moments, the so-called relative power coecients of the signal spectrum were determined.

These parameters describe the ratio of the

Example spectra registered by the acoustic monitoring stations are shown in Figs. 24, together with respective

M. Kªaczy«ski, T. Wszoªek

1026

The task of the mapping consists in selection of a vector

y

minimizing the error function. To this end, New-

ton's optimization method [11] was used simplied to the form

ypq (t + 1) = ypq (t) − η∆pq (t),

(11)

where

∂E ∂y ∆pq (t) = 2pq , ∂ E ∂y 2 pq

Fig. 3.

(12)

# d∗pj − dpj [ypq − yjq ] , d∗pj · dpj j=1,j6=p " n
2 X ∂2E 1 d∗pj − dpj =− ∗ 2 ∂ypq c d dpj j=1,j6=p pj # 2  d∗pj − dpi (ypq − yjq ) − · 1+ . dpj dpi ∂E 2 =− ∂ypq c

Spectrum of average corona eect.

n X

"

(13)

(14)

Example results of the Sammon representation for the examined group of corona audible noise are presented in Figs. 5 and 6.

Separation of the objects related to 1

(corona eect) and 0 (no corona eect) is clearly seen Fig. 4.

in Fig. 5 and conrms that the proposed 10-parameters

Spectrum of no corona eect.

feature space can be considered useful for identication purposes. However, separation of the objects related to

values of the feature vector and the corona noise classi-

0.5 (average corona eect) and 0 (no corona eect) is

cation. In the spectrum of the line audible noise, two

fuzzy (cf. Fig. 6).

characteristic elements can be distinguished:

a broad-

band component  a noise in the range from about 1 kHz to 10 kHz, described alternately as `frying', `crackle, `wheezing'  and a pure-tone component at frequencies of 100 Hz (for a 50 Hz system) and multiples (e.g. 200 Hz). The pure tones are superimposed on broadband noise. A `hum' of the second harmonic (hum noise) is at the same time considered as the most perceptible.

3.2. The Sammon mapping The feature vectors represent recorded data in a 10dimensional space.

This multidimensional structure is

dicult to interpret by visual inspection.

Therefore, it

is important to develop an understandable and perceptible graphical illustration of such set of data.

In cer-

tain studies [8, 9], solving this problem by applying the

Fig. 5.

Sammon representation [10] is proposed. The Sammon

with Sammon's representation (1  corona eect sig-

representation allows presenting multi-variant data on a

nal, 0  no corona eect).

plane.

Visualization of 10-dimensional feature vectors

Transformation is based upon selection of two-

dimensional vectors created in the way which minimizes

This is related to the problem that often the medium

the error function of the distance between the considered

and low corona noise is masked by background noise;

objects.

therefore, despite assigning the object (record) to the

The error function is dened according to expression:

E=P

1

n X

(d∗ij

∗ i