MultiSpectrum Inspection of Overhead Power Lines

Ronnier Frates Rohrich

and Andre Schneider de Oliveira

Graduate School of Electrical Engineering and Computer Science,

Universidade Tecnol

ogica Federal do Paran

a (UTFPR), Curitiba, Brazil

Keywords:

Inspection, MultiSpectrum Sensor, Power Lines.

Abstract:

Electric power transmission employs an extensive network of transmission and distribution lines to connect

energy production plants with consumers. This architecture limits the extent and frequency of inspections and

implementation of preventive maintenance programs. Robotic systems, which allow movement over trans-

mission cables, have been introduced to address the difﬁculties of inspections in distribution and transmission

lines. This paper introduces a novel method of multispectrum robotic inspection for transmission lines, which

can perform predictive inspection and maintenance, and discusses a new composite sensor that analyzes the

integrity of overhead lines in acoustic, thermal, visual, and reference spectra. The system is particularly de-

signed to be incorporated into cable-inspection robots and moves over cables to provide a direct point of view

of the transmission line components. The proposed method was evaluated using a calibration scenario and

actual overhead power lines.

1 INTRODUCTION

In modern society, efﬁcient and reliable operation of

transmission and distribution lines is crucial for unin-

terrupted electricity supply. Because these infrastruc-

tures span vast distances, often in challenging terrains

and remote locations, ensuring their safety and opti-

mal performance has become a priority. Power line

inspections are vital for identifying potential issues,

preventing failures, and minimizing downtime. Over

time, with the introduction of robotic systems, inspec-

tion and maintenance procedures have evolved signif-

icantly. Such systems have revolutionized inspection

methods and enabled predictive inspections of trans-

mission lines.

Traditionally, power line inspections have relied

on manual processes that are time-consuming, labor-

intensive and pose safety hazards. These methods in-

volve visual inspections by a human on foot or using

specialized vehicles. Although these techniques have

served their purpose, they are limited in their ability

to reach inaccessible areas, particularly in demanding

terrains or adverse weather conditions. These limi-

tations make integrity veriﬁcation a traditional visual

inspection method that is highly dependent on the ex-

perience of the inspector.

https://orcid.org/0000-0002-4523-8536

https://orcid.org/0000-0002-8295-366X

The application of robotics in power line inspec-

tions has the potential to revolutionize these inspec-

tion procedures and have been discussed in several

studies, such as that by (Gonc¸alves et al., 2022).

Robotic systems offer several advantages over tradi-

tional methods, including excellent safety, efﬁciency,

and accuracy. These specialized systems are designed

to navigate challenging environments easily, collect

valuable data, and minimize human interventions

(Yue et al., 2022). Currently, aerial robots, specif-

ically unmanned aerial vehicles (UAVs) or drones,

have gained popularity as tools for conﬁrming the

integrity of transmission lines as they can be used

to inspect inaccessible areas (Wang et al., 2022b).

Aerial robots equipped with high-resolution cameras

and sensors (Li et al., 2023) can capture detailed im-

ages, detect defects, and identify hazards along trans-

mission lines. Ground robots, which are typically

wheeled or tracked, are used in cases wherein over-

head protection may be impractical or unsafe. These

robots are designed to traverse various types of ter-

rains, including rough landscapes and steep slopes,

allowing for comprehensive inspections along trans-

mission lines. They can be equipped with cameras,

sensors, and robotic arms to perform various tasks,

such as tightening screws or making repairs (Cantieri

et al., 2020).

Rohrich, R. and Schneider de Oliveira, A.

MultiSpectrum Inspection of Overhead Power Lines.

DOI: 10.5220/0012176900003543

In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2023) - Volume 2, pages 119-126

ISBN: 978-989-758-670-5; ISSN: 2184-2809

119

Based on this, some advantages of using robotics,

such as higher security, should be highlighted.

Robotic automation eliminates or reduces the need to

send humans into hazardous environments, thereby

minimizing the risks associated with working at

heights or under hazardous conditions. Another ad-

vantage is increased efﬁciency, as robots can oper-

ate autonomously or be remotely controlled, allow-

ing for faster and more efﬁcient inspections. They

can cover long distances and collect data in real-time,

thereby streamlining the assessment process. Accu-

rate data collection is facilitated by advanced sensors

and imaging techniques that enable robots to capture

detailed data, including high-resolution images, ther-

mal images, and 3D maps. This information provides

valuable insights for identifying possible defects in

transmission line structures. Additionally, robotic in-

spections can induce cost savings, lower the reliance

on human labor, minimize downtimes, and enable

predictive maintenance to avoid costly failures.

Predictive analysis of historical pattern data can

be combined with that of real-time monitoring data

to identify patterns and trends. This approach allows

predicting potential failures or performance deterio-

ration, thereby facilitating proactive maintenance and

interventions before critical issues arise. Predictive

analytics allow energy operators to optimize main-

tenance schedules and allocate resources more efﬁ-

ciently. Thus, robotic inspection systems must moni-

tor different magnitudes to understand the current and

future behaviors of transmission lines, enabling pre-

dictive and noncorrective maintenance to optimize the

lifespans of transmission line assets.

This paper proposes a novel MultiSpectrum sen-

sor for robotic inspection of transmission lines that

allows predictive inspection and maintenance. The

sensor is specially designed to cover distinct spectra

of inspection for monitoring the reliability and fail-

ure tendency of power lines, thereby increasing power

grid reliability.

2 RELATED WORK

Robotic inspection of power lines has been discussed

in many studies, with a focus on navigation and ma-

neuverability across overhead lines (Boufares et al.,

2022). However, such systems include visual sensors

that only capture images for ofﬂine analysis by ex-

perienced operators. Employing artiﬁcial intelligence

in transmission line inspection involves using deep

learning techniques to identify the various line com-

ponents (Yang et al., 2022; Souza et al., 2023; Teix-

eira et al., 2020; Zhang et al., 2023). However, these

procedures are limited to visual cracks and faults that

require corrective maintenance.

Another tendency is to incorporate different sen-

sor modalities to perform a more reliable inspection,

as proposed by (Hu and Liu, 2017). Partial discharges

(PDs) are inevitable occurrences in transmission line

components and cannot be identiﬁed using traditional

visual sensors; however, speciﬁc sensors exist to mon-

itor them (Ji et al., 2022; Stone et al., 2021; Pi-

hera et al., 2020). Thermal images are also essential

for identifying normal behavior of transmission lines,

mainly in hotspots (Jin et al., 2020). MultiSpectral

visual sensors are an extension of visual sensors and

have been employed for transmission line inspections

with the aim of automatically extracting superﬁcial

faults (Wang et al., 2022a; Stolper et al., 2005). How-

ever, all of these studies recorded real-time data for

ofﬂine analyses because the sensor only measures a

single spectrum and it is limited to a speciﬁc class of

problems.

This study proposes a multi-sensor approach for

robotic inspection of overhead power lines that senses

various fault spectra and allows predictive mainte-

nance. The paper is focus on multi-sensor composing

to the generation of a unique inspection map.

The paper is organized into ﬁve sections. Section

2 discusses the related works to clarify the contribu-

tions. Section 3 is about the concept of MultiSpec-

trum Sensor. Section 4 discusses the proposed ap-

proach of MultiSpectrum Inspection. At last, section

5 presents the conclusions.

3 MULTISPECTRUM SENSOR

The proposed MultiSpectrum sensor aims to combine

the information of multiple sensors to enable inspec-

tion across multiple spectra, as shown in Figure 1,

for analyzing different aspects of transmission lines

to determine failures reliably.

Figure 1: MultiSpectrum Sensor.

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

120

Thus, the sensor comprises different subsensors

that produce RGB, depth, acoustic, and thermal im-

ages, and distance information, as illustrated in Figure

Figure 2: Features of MultiSpectrum Sensor.

3.1 Acoustic Spectrum

The acoustic camera works in the ultrasonic spec-

trum and can measure fundamental electric phenom-

ena, such as PDs, that occur in power transmission

lines owing to various faults that cause the electrically

stressed insulation area to break down, as shown in

Figure 3.

Figure 3: Example of an acoustic image of distribution line

transformer.

PDs can be harmful and costly if not identiﬁed and

detected in a timely manner and can cause power sup-

ply interruptions. This phenomenon occurs at high

voltages at an area wherein insulation is electrically

strained. PDs can occur in high-voltage equipment

such as transmission or distribution networks and de-

stroy the equipment over time; therefore, detecting

them is crucial. Detecting PDs using ultrasonic meth-

ods allows identifying impending problems long be-

fore they occur because sound is the ﬁrst symptom

of an asset’s deterioration. However, selecting solu-

tions that do not employ PD analytics are selected

may result in more questions than those prior to start-

ing the detection phase (NLAcoustics, 2022). PDs

are a consequence of local electrical stress concentra-

tions within or on the surface of the insulation and

generally appear as pulses with a duration consider-

ably lower than 1µs, as speciﬁed in IEC 60270.

Figure 4: Distance Sensor.

The sound traveling through the air is attenuated

by six decibels with every doubling of the distance

traveled. A medium-sized PD may measure 40 dB(Z).

The sound heard 15 m (approximately 50 feet) away

from the source is 6 dB stronger than that heard at 30

m (approximately 100 feet). To compensate for this,

the acoustic camera employs a microphone array to

increase the detection range (FLIR, 2022) The Multi-

Spectrum sensor comprises a distance sensor that al-

lows attenuation compensation, as shown in Figure 4.

PDs emit ultrasonic sound at a typical frequency

of 40 kHz. Thus, a more comprehensive range of fre-

quencies, from 10–30 kHz, can yield better results

when working from a distance, such as on transmis-

sion lines (Raymond et al., 2015).

3.2 Thermal Spectrum

Anomalies in electrical transmission lines cause an in-

crease in local temperature owing to increased electri-

cal resistance, which results in future failures. Ther-

mal imaging (shown in Figure 5) is a powerful tool

for detecting potential issues in high-voltage power

transmission systems for two reasons: it allows for

non-contact measurements from a distance, guaran-

teeing the inspector’s safety; and it does not interfere

with the system operations, preventing unnecessary

downtime and losses (Raymond et al., 2015). In ra-

diometric images, thermal faults appear as hotspots.

Figure 5: Example of a thermal image of distribution line

transformer.

3.3 Visual and Depth Spectra

The RGB and depth spectra (Figure 6) capture the

visual information of the inspected objects through

color and spatial displacement ([x, y, z]coordinates).

This information allows using artiﬁcial intelligence to

classify and identify the classes of the inspected ob-

MultiSpectrum Inspection of Overhead Power Lines

121

jects. However, the MultiSpectrum sensor uses this

information to correlate other spectra data on a con-

solidated inspection map.

Figure 6: Example of a depth image of distribution line

transformer.

3.4 Reference Spectrum

All spectral information is stamped with a georef-

erence (GPS coordinates), distance of the inspected

object (obtained from the distance sensor), heading

(compass data), and time, as shown in Figure 7. These

data allow correlating the current inspection map with

the previous one, determining the time variations, and

predicting future behavior.

Figure 7: Scheme of Reference Spectrum.

4 MULTISPECTRUM

INSPECTION

MultiSpectrum inspection is executed by composing

the analyses in different spectra to generate a unique

registered inspection map, which promotes informa-

tion correlation between the spectra. Each sensor

source produces information related to its coordinate

frame through a particular ﬁeld-of-view (FoV), which

requires that the spectrum images be transformed into

the same coordinate system that allows interaction be-

tween them. The different resolutions of the spectral

images and the speciﬁc FoV information are listed in

Table 1.

The MultiSpectrum inspection registration and

map generation procedures are summarized in Algo-

Table 1: Characteristics of Spectrum Sensors.

Spectrum Resolution FPS FoV

Thermal 480 x 640 30 25 × 19

RGB 720 × 1280 30 69 × 42

Depth 480 × 848 30 87 × 58

Acoustic 240 × 330 50 70 × –

rithm 1. The entire approach steps are shown in Fig-

ure 8.

Algorithm 1: MultiSpectrum Inspection Map.

Data: ImgAcoustic, ImgThermal, ImgRGB,

ImgDepth

Result: SpectrumMap

1: Acquisition of images from all sensors

outputs(ImgThermal, ImgAcoustic, ImgRGB,

imgDepth);

2: Cut edges of images to get 4:3 aspect ratio

input(ImgAcoustic);

3: Resize images to resolution of 1280x720 pixels

inputs(ImgAcoustic,ImgDepth);

4: Extraction of Green channel of Thermal Image

input(ImgThermal) ;

output(GreenImgThermal);

5: Resizing of Green channel of Thermal Image

to resolution of 1280x720 pixels

input(GreenImgThermal);

6: Do Similarity Filter in Acoustic Image ;

for line=1 to linesize(ImgAcoustic) do

for column=1 to columnsize(ImgAcoustic) do

if ( RedImgAcoustic(line,colunm) ==

GreenImgAcoustic(line,colunm) ==

BlueImgAcoustic(line,colunm)) then

ImgAcousticLeak(line,column) = 0;

else

ImgAcousticLeak(line,column) =

ImgAcoustic(line,column);

end

7: Register ImgAcoustiLeak in ImgRGB

output(RegimgAcousticRGB);

8: Register GreenImgThermal in ImgRGB

output(RegImgThermalRGB);

9: Warp surface in ImgDepth;

10: Warp intensities in registered images

inputs(RegimgAcousticRGB,

RegImgThermalRGB);

11: Stack Spectra Images in MultiSpectrum

Inspection Map

inputs(RegimgAcousticRGB, ImgRGB,

RegImgThermalRGB, ImgDepth);

output(SpectrumMap ;

The ﬁrst step involves changing the conﬁguration

of the spectrum sensors to high-resolution and cor-

recting lens exposure to allow capturing under various

intensities of sunlight inﬂuences and spectrum image

acquisition in the RGB (l × c × 3 matrix) format. The

acoustic spectrum is captured at a particular aspect

ratio and different proportions of other sensor sources

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

122

Figure 8: Approach of MultiSpectrum Inspection.

while performing edge cutting to achieve a 4 : 3 as-

pect ratio. The spectral images are resized to a typ-

ical standard RGB image of 1280 × 720 pixels. An

image resizing process is executed to improve image

similarity. This action is performed through a bicubic

interpolation method proposed by (Acharya and Tsai,

2007), where each output pixel value is a weighted

average of pixels within the nearest 4 × 4 neighbor-

hood.

The next step involves extracting inspection infor-

mation from the acoustic and thermal spectra. Ther-

mal information is obtained from an image, wherein

each pixel represents the thermal intensity. The faults

and damages in the overhead power lines appear as

hot points in the thermal spectrum, which are rep-

resented in yellow color. Thus, hotspots can be

extracted by decomposing thermal images into red,

green, and blue matrices, wherein these points are ob-

tained independently in the green matrix. The acous-

tic spectrum is analyzed using a similarity ﬁlter that

extracts unequal pixels from the red, green, and blue

matrices because the acoustic sensor represents the in-

spection scene information in soft colors (similar to

grayscale but in RGB colors) and faults in saturated

colors, similar to a thermal image.

Subsequently, spectral images are registered in the

same coordinate system to allow information correla-

tion. The RGB image is employed as a standard, and

the thermal image hot points and acoustic image leaks

are registered in the RGB image. Fixed homogeneous

transformations are deﬁned to transform the images

between the reference frames of the spectral images,

as illustrated in Figure 9.

Figure 9: Transformation Tree of MultiSpectrum Sensor.

However, the alignment is not direct because of

the spatial displacement of the capture points and the

angular FOVs of the cameras. The spatial displace-

ment (translation and orientation) between the sensor

sources introduces a dependency on the distance of

the inspected object to execute the image registration

obtained in the time of ﬂight (ToF) module. The trans-

formation is performed using a homogeneous trans-

formation matrix (Equation 1), considering the FoV

angles of the lens and the distance to the inspected

object.

ˆp

(i+n)

= T

(i+1)

ˆp

(1)

MultiSpectrum Inspection of Overhead Power Lines

123

Figure 10: Fault incidence of Overhead Power Lines.

where, p is the spectrum image, T is the homoge-

neous transformation matrix and i is the coordinate

frame.

The last step is image warping, which introduces

spectral intensities into the image. The pixels of the

registered image are transferred onto to a 3D surface,

where the intensities are expressed in Z coordinates.

Spectrum information is normalized to a scale of 0-1

to its proportional adjustment in the inspection map,

and a ﬁxed layers spacing area is added for better 3D

visualization. Subsequently, all spectral images are

stacked on the inspection map, and the distance be-

tween layers represents the intensity axis.

MultiSpectrum inspection was performed on

some electricity distribution poles to validate the pro-

posed approach and identify possible faults. An in-

cidence of acoustic and thermal disturbances was ob-

served, as shown in Figure 10.

The MultiSpectrum sensor is aligned during the

calibration, which includes an aluminum container

ﬁlled with hot water, an electric motor that produces

sound vibrations, and a laser, as shown in Figure

11. Calibration was executed using the maps of this

object generated at different displacements and dis-

tances from the sensor.

The MultiSpectrum Inspection Map delimits the

abnormal incidence of thermal hotspots and acous-

tic emissions, enabling predictive inspections to avoid

future interruptions in electricity distribution. The in-

Figure 11: Calibration scenario of MultiSpectrum Sensor.

spection can be seen in the Y-Z view, where is shown

the spectrum layers spacing and warping zone of each

spectrum data, as illustrated in Figure 12.

5 CONCLUSIONS

This paper proposed a novel method for MultiSpec-

trum inspection of overhead power lines that analyzes

power line components over distinct spectra. This

method consolidates the information on a stacked

MultiSpectrum inspection map, wherein each spec-

trum is represented in a registered layer with a direct

correlation between them. The proposed approach al-

lows analyzing overhead power lines across different

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

124

Figure 12: X-Z overview of MultiSpectrum Inspection

Map.

spectra, thereby enabling more reliable inspections

and predictive maintenance. The system has been de-

signed for and expected to be incorporated into cable

inspection robots and moves over cables to provide a

direct point of view of transmission line components.

Future works will discuss the analysis of the Multi-

Spectrum inspection map to determine faults, dam-

ages and to predict future issues.

ACKNOWLEDGEMENTS

The project is supported by National Council for

Scientiﬁc and Technological Development – CNPq

(process CNPq 407984/2022-4); Fund for Scientiﬁc

and Technological Development – FNDCT; Ministry

of Science, Technology and Innovations – MCTI of

Brazil; Araucaria Foundation; and the General Super-

intendence of Science, Technology and Higher Edu-

cation (SETI).

REFERENCES

Acharya, T. and Tsai, P.-S. (2007). Computational foun-

dations of image interpolation algorithms. Ubiquity,

2007(October):4–1.

Boufares, F., Doudech, I., and Bahrami, M. R. (2022).

Electrical transmission lines robot inspector: Design

challenges. In 2022 International conference on in-

dustrial engineering, applications and manufacturing

(ICIEAM), pages 746–754. IEEE.

Cantieri, A., Ferraz, M., Szekir, G., Ant

onio Teix-

eira, M., Lima, J., Schneider Oliveira, A., and

Aur

elio Wehrmeister, M. (2020). Cooperative uav–

ugv autonomous power pylon inspection: An investi-

gation of cooperative outdoor vehicle positioning ar-

chitecture. Sensors, 20(21):6384.

FLIR (2022). Using acoustic imaging for partial discharge

monitoring. https://www.ﬂir.com/discover/industrial/

using-acoustic-imaging-for-partial-discharge-monitoring/.

Last checked on Jun 04, 2023.

Gonc¸alves, R. S., Souza, F. C., Homma, R. Z., Sud-

brack, D. E. T., Trautmann, P. V., and Clasen, B. C.

(2022). Robots for inspection and maintenance of

power transmission lines. In Robot Design: From The-

ory to Service Applications, pages 119–142. Springer.

Hu, Y. and Liu, K. (2017). Inspection and monitoring tech-

nologies of transmission lines with remote sensing.

Academic Press.

Ji, H., Lei, X., Xu, Q., Huang, C., Ye, T., and Yuan, S.

(2022). Research on characteristics of acoustic signal

of typical partial discharge models. Global Energy

Interconnection, 5(1):118–130.

Jin, Y., Quan, M., Yan, S., Yan, Y., Cui, M., and Liu,

Y. (2020). Analysis of overhead transmission lines

fusing failure due to poor contact between conduc-

tors and clamps. Engineering Failure Analysis,

118:104858.

Li, Z., Zhang, Y., Wu, H., Suzuki, S., Namiki, A., and

Wang, W. (2023). Design and application of a uav

autonomous inspection system for high-voltage power

transmission lines. Remote Sensing, 15(3):865.

NLAcoustics (2022). Partial discharges: Who needs

to detect them and why? https://nlacoustics.com/

partial-discharges-who-needs-to-detect-them-and-why.

Last checked on Jun 04, 2023.

Pihera, J., Hornak, J., Trnka, P., Turecek, O., Zuzjak, L.,

Saksela, K., Nyberg, J., and Albrecht, R. (2020).

Partial discharge detection using acoustic camera.

In 2020 IEEE 3rd International Conference on Di-

electrics (ICD), pages 830–833. IEEE.

Raymond, W. J. K., Illias, H. A., Mokhlis, H., et al. (2015).

Partial discharge classiﬁcations: Review of recent

progress. Measurement, 68:164–181.

Souza, B. J., Stefenon, S. F., Singh, G., and Freire, R. Z.

(2023). Hybrid-yolo for classiﬁcation of insulators

defects in transmission lines based on uav. Interna-

tional Journal of Electrical Power & Energy Systems,

148:108982.

Stolper, R., Hart, J., and Mahatho, N. (2005). The de-

sign and evaluation of a multi-spectral imaging cam-

era for the inspection of transmission lines and sub-

station equipment. In World Insulator Congress and

Exhibition, Hong Kong, China.

Stone, G. C., Sasic, M., Wendel, C., and Shaikh, A. (2021).

Initial experience with acoustic imaging of pd on high

voltage equipment. In 2021 IEEE Electrical Insula-

tion Conference (EIC), pages 13–17. IEEE.

Teixeira, M. A. S., Neves-JR, F., Koubaa, A., de Arruda,

L. V. R., and de Oliveira, A. S. (2020). Deepspatial:

Intelligent spatial sensor to perception of things. IEEE

Sensors Journal, 21(4):3966–3976.

Wang, B., Ren, M., Xia, C., Li, Q., Dong, M., Zhang, C.,

Guo, C., Liu, W., and Pischler, O. (2022a). Evalu-

ation of insulator aging status based on multispectral

imaging optimized by hyperspectral analysis. Mea-

surement, 205:112058.

Wang, Z., Gao, Q., Xu, J., and Li, D. (2022b). A re-

view of uav power line inspection. In Advances in

MultiSpectrum Inspection of Overhead Power Lines

125

Guidance, Navigation and Control: Proceedings of

2020 International Conference on Guidance, Naviga-

tion and Control, ICGNC 2020, Tianjin, China, Octo-

ber 23–25, 2020, pages 3147–3159. Springer.

Yang, L., Fan, J., Huo, B., Li, E., and Liu, Y. (2022). Ple-

net: Automatic power line extraction method using

deep learning from aerial images. Expert Systems with

Applications, 198:116771.

Yue, X., Feng, Y., Jiang, B., Wang, L., and Hou, J. (2022).

Automatic obstacle-crossing planning for a transmis-

sion line inspection robot based on multisensor fusion.

IEEE Access, 10:63971–63983.

Zhang, Z., Li, Z., and Tang, W. (2023). Transmission line

visual inspection method based on neural network on-

line learning. In e-Learning, e-Education, and Online

Training: 8th EAI International Conference, eLEOT

2022, Harbin, China, July 9–10, 2022, Proceedings,

Part I, pages 513–528. Springer.

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

126