# Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise

Denis Stanescu, Dragos Nastasiu, Angela Digulescu, Cornel Ioana, Cristina Despina-Stoian

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Denis Stanescu, Dragos Nastasiu, Angela Digulescu, Cornel Ioana, Cristina Despina-Stoian. Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise. IET Smart Grid, 2025, 8 (1), 10.1049/stg2.12187. hal-04942832

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# Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise

Denis Stanescu<fnref n="1" />,<fnref n="2" />

Dragos Nastasiu<fnref n="1" />,<fnref n="3" />

Angela Digulescu<fnref n="1" />,<fnref n="2" /> ID

Cornel Ioana<fnref n="2" />,<fnref n="3" />

Cristina Despina-Stoian<fnref n="1" />,<fnref n="2" />

## Correspondence

Angela Digulescu, 39-49 George Cosbuc boulevard, district 5, Bucharest, Romania.

Email: angela.digulescu@mta.ro;

angeladigulescu87@gmail.com

## Funding information

Class_T project; I-lab 2021 GOLTEN (GIObal

Localization of Transient Phenomena in Electrical Networks) project

## Abstract

With the development of the power distribution systems demands, the grid reliability becomes a strategic issue. Continuous monitoring of power transmission networks is a key component in addressing this issue. Partial discharge (PD) characterisation instruments provide a reliable solution and an important indicator of the state of cable insulation. An increase in the PD level usually indicates the development of a fault that affects the integrity of the cable and can lead to various problems related to the quality and quantity of the transmitted energy. In this context, the use of Internet of Things (IoT) systems for power networks monitoring can provide a significant improvement to the localisation and detection of faults. In this paper, the authors show the advantages of continuous monitoring of power grid cables using a new IoT based framework using an advanced signal processing technique, namely the phase diagram. Also, the authors show that variations in temperature can impact the initiation and progression of PD, affecting the reliability and lifespan of electrical insulation systems. Understanding this temperature dependence is crucial for accurate PD detection and effective maintenance of power cables. Our results show that higher temperatures can accelerate the PD activity, while lower temperatures may suppress it.

## KEYWORDS

distribution networks, fault diagnosis, power grids, transient analysis

# 1 INTRODUCTION

In the nowadays integration of renewable energy sources with the traditional ones, the smart grids are a very useful tool [1]. However, until the implementation of smart grids reaches the desired integration level, we still have to rely on traditional energy grids. The current transmission and distribution power networks are exhibiting an increase in the length of electrical cables used. Within this expansion, it is important to recognise the occurrence of failures in electrical equipment. This can lead to significant losses in the delivered electrical energy, as well as the quality of service provided. In this sense, a special attention has been given to the development of power grid monitoring techniques for the evaluation and diagnosis of the network, particularly

to the quality of the insulation in high voltage cables and equipment.

Monitoring the insulation quality of electrical equipment is a powerful tool to ensure the safe operation of the power grid. In the search for an effective early warning for potential electrical fault, monitoring the Partial discharge (PD) activity is a critical step. PDs are the most common sources of faults in the electrical cables within the network and they can lead to complete insulation failure [2]. PDs occur due to imperfections or insulation defects. They represent failure precursors, as over time, PDs can cause damage to the cable, potentially leading to a complete network failure. This is why PD phenomena is considered the most appropriate indicator to identify the general state of the electric energy delivery network. The absence of a reliable and economically advantageous (i.e. low

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is
properly cited.

© 2024 The Author(s). *IET Smart Grid* published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

<footnote n="1">¹Telecommunications and Information Technology Department, Military Technical Academy “Ferdinand I”, Bucharest, Romania</footnote>

<footnote n="2">²Altrans Energies, Grenoble, France</footnote>

<footnote n="3">³GIPSA-LAB, Université Grenoble-Alpes, Grenoble, France</footnote>
cost of monitoring per km of cable) method for monitoring and locating the PD sources means that currently the maintenance of a power grid is in fact based on the failure prevention aspect. This prevention consists of replacing equipment based on experience and planning according to empirical laws, which do not provide a satisfactory solution.

Given the growing number of use cases and increased demand, network operators need an improved monitoring system to ensure optimal and safe operating conditions, to maintain the quality of supply and to improve the efficiency of the network. Conventional PDs monitoring techniques, such as traditional offline and preventive maintenance, mostly based on topology identification [3–5] have several limitations represented by a high number of drawbacks when set in place. These involve temporary power outages, line cuts, manual control and successive measurements, leading to operational losses, high cost and increased human resources. The difficulties increase when dealing with underground power cables, where the access is limited. Additionally, these offline techniques cannot highlight the connection between PDs and temperature.

Power grid status monitoring based on PD analysis consists of acquiring the PD signal, separating it from noise or other transient signals, followed by a data processing yielding meaningful results. In this way, a decision can be made regarding the amount of PD that exists in each cable in the power system.

A continuous online PD monitoring system provides a better diagnosis of operating conditions. Continuous PD monitoring has a number of advantages over periodic PD monitoring. For example, periodic PD monitoring may not reveal significant PD activities because PD activities differ over time. Another advantage is that online monitoring provides more accurate information than offline testing, as offline testing conditions can differ greatly from actual operating conditions. Also, continuous online monitoring effectively reduces maintenance costs. Once acquired, PD signals can be stored and accessed anytime, anywhere with modern means of communication.

In this paper, we address the issue of monitoring power grid cables. Our main motivation is to enhance the monitoring and maintenance of power grid cables through the implementation of an Internet of Things (IoT) system for continuous surveillance and we show the condition of the network in terms of the analysis of PDs. Using a detection method derived from the phase diagram concept, we eliminate the drawbacks given by conventional detection methods, which are generally energetic, with thresholds determined for each type of configuration, such as the spectrogram method and provide a more accurate analysis of partial discharges. These drawbacks stem from the fact that low-amplitude transients are likely ignored by current detection procedures. We will also demonstrate that the evolution of PDs is closely related to the environmental conditions, particularly temperature changes.

At the moment, several IoT systems with applications in power grid systems measurements have been proposed. Tehrani and Atarodi [6] proposed an IoT system for smart measuring energy consumption based on voltage regulating

control loops, zero offset amplifiers and high precision analogue to digital converters supporting current measurement range from 1µA to 150 mA with maximum sampling rate of 5 MHz. Elsisi et al. [7] proposed an IoT system for fault diagnosis of power transformers against cyberattacks, approaching a perspective based on deep learning and online monitoring. However, we consider that to ensure the reliability of power cables, a fundamental analysis that must be carried out is that of monitoring PD activities, which we propose to do using the system proposed in this paper.

Our first contribution is the implementation of a monitoring system for both reactive and predictive maintenance of the power grid, highlighting the innovative aspects of our approach. Reactive maintenance is based on the ability to detect and locate possible faults, which is essential to minimise their impact on customers. Our previous work for reactive maintenance of power grids has resulted in the implementation of modern fault detection methods based on phase diagram representation [8, 9]. The phase diagram-based detection approach has proved more accurate than conventional detection methods [9, 10] and the use of this method constitutes the second aspect of our contribution. Conventional detection techniques rely generally on an energy criterion, with thresholds determined for each type of cable configuration [11]. The spectrogram-based detection is also widely used. By analysing the frequency content of the transient signals, PDs moments of occurrence can be detected. Another type of approach is based on the PD signal projection on dictionaries (wavelets, for example), used to detect the presence of a transient via its resemblance to the waveforms found in the dictionary [12–14].

The localisation part is a more complex problem, deeply studied. Various methods of solving this problem have been identified so far [15]. The most commonly used methods for locating PDs are methods relying on the travelling waves theory, analysing the propagation time associated with fault effects [16, 17]. High frequency-based methods are also used to locate faults, relying on the high frequency information contained within voltage and current measurements [18, 19]. Unfortunately, these methods have a low accuracy in complex topologies such as distribution networks. Distribution networks are more complex and require more equipment to deploy an infrastructure than transmission networks. Also, these networks are not compatible with underground distribution networks.

In our previous work, we have implemented new ways of approaching the localisation issue [20, 21]. Currently, our system works based on the innovation described in ref. [22]. This technique is based on the use of the relative distortion of the PD signals in the phase diagram. The PD signals propagating in the cable exhibit a series of effects (reflection but also dispersion). This natural behaviour is then highlighted by the phase diagram successfully.

From the predictive maintenance perspective, our system is able to monitor the ageing and degradation of cables, allowing the identification of critical sections that could suffer future problems. In this scope, this paper recommends the use of the developed instrumentation for highlighting the PD activity during the cable's lifetime based on environmental factors, this
aspect being another aspect of the novelty of our work and
contribution. Currently, the main method of studying cable
ageing and degradation is based on the principle that any fault
or degradation causes distinctive changes in the broadband
power line communication (PLC) channel frequency response
(CFR). While it is obvious that changing cable characteristics
lead to variations in the CFR for PLC signals, it remains un-
clear whether these variations are significant enough to indicate
cable degradations [23, 24].

To conclude with the motivation and proposed approach,
our system allows the rapid detection and localisation of faults,
thus reducing their impact on consumers. Using the phase dia-
gram method for analysing PDs provides early identification of
faults, allowing maintenance teams to react promptly, minimise
downtime, and repair costs. The IoT system monitors cable
ageing and degradation based on PD analysis, identifying critical
sections that could develop future problems. The ability to
anticipate failures based on continuous analysis of PD activities
and environmental conditions, especially temperature variations,
allows for efficient and preventive maintenance planning. This
advantage comes from the hardware configuration used within
the system. These aspects were put into practice, after a careful
analysis of the existing systems and methods.

This paper is organised as follows: Section 2 presents the description of the power grid analysed in the context of functionality and location. Section 3 details the online continuous monitoring system and the processing technique used for PD detection. Section 4 describes the results obtained from the processing of the data collection and highlighting the relationship between PD signals and temperature. Finally, Section 5 presents our conclusion and perspectives.

## 2 | REAL CASE STUDY

The tests carried out were based on a real case study where, for
12 months, the system was monitored with the analysis of a
real in-service medium voltage power grid located in France's
Auvergne-Rhône-Alpes region. The network consists of an
underground power line network between two stations: a pri-
mary and a secondary one. The distance between the two
stations is 3183 m. In Figure 1, we see the location of the two
stations on a satellite map.

Figure 2 shows a block diagram of the system. This power
grid is continuously monitored from 1 July 2020 and it is still
ongoing. The analysis performed in this paper uses the data
collected during 417 days, until 21 August 2021.

The analysed segment is a three-phase medium-voltage
level power grid at 20 kV, which serves electricity in the sur-
rounding area.

## 3 | MONITORING TECHNIQUE

In this section, we present the power grid underground power
cable monitoring system and the collected data processing
method.

<figure><img src="image_2.png" /><figcaption>FIGURE 1 The location of the two stations of the segment.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>FIGURE 2 Block diagram of the power grid.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>FIGURE 3 The monitoring system.</figcaption></figure>

### 3.1 | The monitoring system

A continuous power grid cables monitoring system should be
defined by a high degree of accessibility. Thus, it should be
designed in sufficient numbers of layers. Our framework of
IoT-based condition monitoring system consists of four layers:
sensing layer, network layer, data layer and application layer.
The continuous monitoring system is highlighted as a block
diagram in Figure 3.

The sensing layer is the basis of this IoT monitoring, which
allows the system to acquire data in the form of electrical
signals transmitted through the power grid cables. In terms of
equipment, in this layer we have both the sensors and the data
acquisition board. The data acquisition board used is Handy-
scope HS4 [25], a powerful Universal Serial Bus controlled
board with four input channels capable of performing
continuous streaming measurements. It has a 50 MHz band-
width, a maximum sampling rate of 50 MSa/s and a maximum
streaming rate of 500 kSa/s. The electrical transients are sensed
by High Frequency Current Transformer (HFCT) sensors [26]
as illustrated in Figure 4. This HFCT sensor has a transfer
function of 4.8 V/A, a frequency response between 60 kHz and 70 MHz and a load impedance of 50 Ω.

The central element of the network layer is given by a mini computer. We use a mini PC intel Next Unit of Computing (NUC) with i5-4590T 2.00 GHz, 8 GB RAM and 256 GB SSD. This computer is used as an acquisition node and it is connected to a TP-Link nano router. This layer has the role of ensuring the functionality of the sensing layer. It can also be used as an intermediate point of data storage. When a larger amount of data is collected, the information is transferred to the data layer. The data layer consists of a server for data storage and management, as well as advanced data processing. The advantage of the data layer of the system is that through a standard protocol of remote desktop connection the data can be moved to the server without the need to be in the same room with the monitoring system. The server consists in a mini PC intel NUC with i5-4440S 2.80 GHz, 8 GB RAM and 256 GB SSD.

<figure><img src="image_5.png" /><figcaption>**FIGURE 4** The data acquisition board (left) and the HFCT sensor (right). HFCT, High Frequency Current Transformer.</figcaption></figure>

In the application layer, we included the final application provided to the user. This consists in a monitoring interface capable of providing information on the PD level as well as the number and location of detected PD sources.

Figure 5 displays the monitoring and data acquisition IoT system connected to the three phases of the power grid used to monitor the segment and to obtain the necessary data.

This system was used to collect the signals transmitted through the power cables daily, 16 times a day, between 7:00 and 22:00. In Figure 6, we can see the architecture of the proposed system, being highlighted the four layers.

## 3.2 The processing method

A first visualisation of the collected data highlights the existence of a periodic transient signal, transmitted on the cables with a pulse repetition rate $T = 21$ us, highlighted by the green rectangle and a signal specific to the PD, highlighted by the red rectangles. The specific signals of the three phases are shown in Figure 7. As can be seen, these two classes of transient signals are present in all three phases of the power system.

The existence of other signals besides those specific to PDs makes it difficult to detect and analyse them. Generally, the source of these signals is found somewhere near the sensing point, so their level will be higher than a PD signal. This means that there is a possibility that many PDs will remain undetected. This creates false information about the status of the system.

Unfortunately, visual detection is not an option in this case due to the large amount of data. For this purpose, we have

<figure><img src="image_6.png" /><figcaption>**FIGURE 5** The continuous monitoring system connected to the three phases.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>FIGURE 6 The architecture of the continuous monitoring system.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>FIGURE 7 The signals acquired from the three phases.</figcaption></figure>

developed an automatic technique for detecting and separating the transient signals coming from the electrical cables.

The detection method used is based on the entropy of the phase diagram [9, 10]. This method is based on the advantages of the phase diagram representation that highlights the slightest change in the state of the analysed system. Through this, we perform a scan of the entire cable system to look for transient signals, thus being able to identify various disruptive sources that do not occur in the normal operating conditions.

Starting from the signal described as a time series in Equation (1), we transpose it in a multidimensional representation space, through the phase diagram vectors shown in Equation (2).

$$x = \{x[1], x[2], \dots, x[N]\} \qquad (1)$$

$$\vec{v}_{[i]} = \sum_{k=1}^{m} x[i + (k - 1)d] \cdot \vec{e}_{k}, i = 1, \dots, M \qquad (2)$$

In the definition of the phase diagram vectors, *m* is the embedding dimension, *d* is the delay between the samples, *N* is the length of the time series, and $\vec{e}_k$ are the axis unit vectors and $M = N - (m - 1) d$. The most used method for estimating the delay is represented by the mutual information and the

<figure><img src="image_9.png" /><figcaption>FIGURE 8 The signals acquired from the three phases.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>FIGURE 9 Inscription of the phase diagram representation in an ellipse.</figcaption></figure>

embedding dimension is generally determined with the false nearest neighbour method [27, 28]. Figure 8 shows an example of the transition from the time series to the representation in the phase diagram.

The next step in the processing method is given by the quantification of the information contained in the phase diagram, based on the phase diagram vectors distribution. In this quantification, we consider the number of vectors, which are close within a distance $\sigma$ in the phase diagram representation based on Equation (3).

$$T_j(d, m, \sigma) = \frac{1}{N - (m - 1)d} \sum_{j=1, j \neq i}^{N - (m - 1)d} \Theta \left( \left\| \vec{v}_{[i]} - \vec{v}_{[j]} \right\| - \sigma \right) \qquad (3)$$

where $\Theta$ is the Heaviside function, $\sigma$ is the threshold distance used to determine the range in which data fluctuations are considered close and $\|\cdot\|$ is the operator of Euclidean distance. The choice of the tolerance threshold $\sigma$ must be considered. The distance $\sigma$ is chosen as the ratio between the perimeter and the area of the ellipse in which the representation can be included as shown in Figure 9 and defined in Equation (4).

$$\sigma = \frac{ab}{(a + b)} \qquad (4)$$

Afterwards, the number of all points, which satisfy the mentioned criterion, is determined and normalised to the number of all vectors in the phase diagram as described in Equation (5).
$$P(d, m, \sigma) = \frac{1}{N - (m - 1)d} \sum_{i=1}^{N - (m - 1)d} \log(T_j(d, m, \sigma)) \quad (5)$$

In the next step, we are going to an upper dimension and repeating previous steps. Then, the phase diagram-based entropy [9, 10] is defined as in Equation (6).

$$PDE = P(d, m, \sigma) - P(d, m + 1, \sigma) \quad (6)$$

We do this because the distribution of vectors from one dimension to another might be different, and this might reveal interesting aspects about the analysed signal. To emphasise the usefulness of the method, we compare the result of the detection process with a conventional method used at this time in such operations, namely the spectrogram approach. In Figure 10, we show the result of the detection process using the spectrogram. As it can be seen, this only highlights the existence of the periodic signal. Using this approach, PD specific signals are not detected.

Regardless of the threshold we choose, PD signals are not detected, because the spectrogram-based approach cannot separate PDs from the power cable noise. Using the phase diagram entropy-based approach the partial discharges are highlighted as shown in Figure 11.

As it can be seen in the comparison, the detection technique used must be chosen carefully. An inability to distinguish PD signals due to the existence of other external transients is a major difficulty in the analysis of the power cables PD activity.

Based on our approach, we eliminate the high values of the entropy corresponding to the existing periodic signal, leaving only the values of the entropy corresponding to the PDs. Moreover, considering that each detected transient has a unique representation in the phase diagram, we also correlate the information extracted from here to separate the PD signals

from the other transients. More details about this process can be seen in ref. [8], where we showed the ways to identify PDs using phase diagram features. Finding out when PDs appear, left-right windows are created so that we can capture the entire PD signal. Figure 12 shows the block diagram of the steps that the PD analysis takes within the four layers of the system.

All these steps allow us to reach a decision on the influence of the outside temperature on the PD level, which we will highlight in the next section.

<figure><img src="image_11.png" /><figcaption>FIGURE 11 Phase diagram entropy-based detection.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>FIGURE 10 Spectrogram based detection.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>FIGURE 12 Block diagram of the activities undertaken.</figcaption></figure>
# 4 | RESULTS

In this section, we present the results of the evolution of PDs obtained from continuous monitoring from a dual perspective: daily evolution and monthly evolution. In this way, we can have a global vision on the obtained results.

## 4.1 | Daily evolution

Recording a large number of data allowed us to visualise the evolution of the PD level daily. Figure 13 illustrates an example of the evolution of the PD level for a typical day of continuous monitoring, 2 July 2021.

As shown, the level of PD has a high value in the afternoon and a lower value in the morning and evening. This variation allowed us to assume that the temperature of the environment influences the PD level. Thus, in Figure 14, the temperature in Auvergne-Rhône-Alpes region for 2 July 2021 is highlighted.

Analysing Figures 11 and 12, we conclude that the environment conditions and PD level are correlated, a higher temperature lead to higher PD levels. To confirm the above, we also analyse a case in which the rainy weather led to lower temperatures on July 15, 2021.

<figure><img src="image_14.png" /><figcaption>FIGURE 13 Partial discharge (PD) level evolution for July 2, 2021.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>FIGURE 14 Temperature in Auvergne-Rhône-Alpes region on July 2, 2021.</figcaption></figure>

In Figure 15, we see the evolution of the PD level for Phase 1 of the power grid and the temperature of the environment. In this case, the PD level is lower. If at 27°C the PD level is 2 mV, at 17°C the PD level is 1.4 mV. Also, when the temperature is constant, the PD level variation is extremely small around the same global value.

We are also analysing a winter day in which the outside temperatures were quite low, as in 15 January 2021. The results are displayed in Figure 16. In this case, the PD level exhibits a decreasing tendency, closer to the variation of the outside temperature. If at 7°C the PD level is 0.92 mV, at 1°C the PD level is almost 0.82 mV.

## 4.2 | Monthly evolution

In this subsection, we present the results obtained in the monthly evolution of the PD level. Figure 17 shows the PD level on each phase during the 417 days at 3 p.m. On the plots, each point represents an average performed over 10 days of continuous monitoring.

<figure><img src="image_16.png" /><figcaption>FIGURE 15 Partial discharge (PD) level evolution and temperature for 15 July 2021.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>FIGURE 16 Partial discharge (PD) level evolution and temperature for 15 January 2021.</figcaption></figure>
It is shown that starting with July 2020 the PD level has an increase until September 2020, then it exhibits a decrease until January 2021 due to the lower outside temperatures. A proof of the influence of temperature rise on partial discharges is given

<figure><img src="image_18.png" /><figcaption>**FIGURE 17** Partial discharge (PD) level evolution for 1 July 2020-21 August 2021.</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>**FIGURE 18** Partial discharge (PD) level evolution for phase 1 for 1 July 2020 - 21 August 2021.</figcaption></figure>

<figure><img src="image_20.png" /></figure>

by the continuous increase of the level starting with January 2021. We also identify three moments in which the PD level has a significant increase: between 20 April and 30 April 2021, 20 June and 30 June 2021, as well as 20 July and 30 July 2021.

An example of the analysis and the global visualisation of the continuous monitoring is shown in Figures 18–20. We illustrate the total evolution during the entire period, for the 16 moments of established time for the three phases.

We notice that the aspects highlighted in terms of increasing PD levels for 3 p.m. are similar for the other 15 moments of time in a day. We aim to see how the PD level has evolved over the year. Figures below present a comparison between the analysis and the results obtained in July 2020 and July 2021.

Figure 21 represents a snapshot of the average PD level of the month at 16 moments of time. The increase of the PD level in a year is obvious, having the same pattern of variation. A lower level is noticed in the morning and in the evening, with a maximum in the afternoon. Based on this comparison, we can determine the rate of increase of the PD level in the power cables. Figure 22 shows the percentage increase over a year for 16 recording moments.

For phase 1 there is an increase between 19% and 53%, for phase 2 between 48% and 119% and between 41% and 96% for phase 3. These differences between the cables are due to the fact that the ageing of the cables evolves over time. As can be seen, these are quite significant increases. The largest ones are found in the afternoon, due to the higher recorded temperatures.

These findings are important for the future evolution of energy networks and smart grids in France. Although these results were obtained from a unique experiment (to our knowledge, there has been no similar endeavour) and involved accumulating a very large amount of data, they still represent a single model result. In order to obtain more accurate projections, the results should be verified using a larger range of situations and power grid. For this reason, we are currently managing seven more systems installed in different locations in France to integrate the information from seven more power grids. Based on this new information, we intend to carry out a more detailed analysis of the energy distribution in France.

<caption>**FIGURE 19** Partial discharge (PD) level evolution for phase 2 for 1 July 2020 - 21 August 2021.</caption>
<figure><img src="image_21.png" /><figcaption>**FIGURE 20** Partial discharge (PD) level evolution for phase 3 for 1 July 2020 - 21 August 2021.</figcaption></figure>

<figure><img src="image_22.png" /><figcaption>**FIGURE 21** Partial discharge (PD) level evolution comparison for July 2020 and July 2021.</figcaption></figure>

# 5 | CONCLUSION

Power distribution networks are one of the most critical infrastructures that ensure the proper functioning of our society. Due to their extreme complexity and size, it is difficult to achieve a fully monitored and reliably controlled level.

In this paper, we have implemented an IoT system based on a modern technique for continuous power grid underground cables monitoring. This was extremely useful to identify the evolution of the PD level in a real and operational segment of a power network.

The extended period of surveillance allowed us to identify a link between the partial discharges found in underground cables and the surrounding environment. Understanding the impacts of temperature variations within electrical networks is important for future energy management.

Continuous monitoring can alert operators when of a potential critical system breakdown. Consequently, preventive

<figure><img src="image_23.png" /><figcaption>**FIGURE 22** The increase of the Partial discharge (PD) level for 1 year.</figcaption></figure>

measures can be implemented a long time in advance before a major power outage.

From a technological point of view, our paper demonstrates, using a real power grid, that the adaptation of a simple IoT system at a primary underground distribution grid is possible. Moreover, it can be easily installed and it is completely autonomous.

Our future work will also be based on the failure forecasting in power cables by means of phase diagram features and environment information such as humidity and temperature to model cable ageing.

## AUTHOR CONTRIBUTIONS

**Denis Stanescu:** Data curation; formal analysis; investigation; software; writing - original draft. **Dragos Nastasiu:** Data curation; formal analysis; resources; validation; visualization; writing - original draft. **Angela Digulescu:** Conceptualisation; funding acquisition; investigation; methodology; project administration; resources; supervision; validation; writing - review & editing. **Cornel Ioana:** Conceptualization; data curation; formal analysis; funding acquisition; investigation; project administration; resources; supervision; validation; writing - review & editing. **Cristina Despina-Stoian:** Investigation; validation; visualization.

## ACKNOWLEDGEMENTS

This work has been funded by the CLASS_T project on behalf of GIPSA-lab, respectively I-lab 2021 GOLTEN project on behalf of ALTRANS Energies.

## CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

## DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.
ORCID

Angela Digulescu https://orcid.org/0000-0002-1561-3286

REFERENCES

1. Di Santo, K.G., et al.: Active demand side management for households in smart grids using optimization and artificial intelligence. Measurement 115, 152–161 (2018). https://doi.org/10.1016/j.measurement.2017.10.010

2. Raymond, W.J.K., et al.: Partial discharge classifications: review of recent progress. Measurement 68, 164–181 (2015). https://doi.org/10.1016/j.measurement.2015.02.032

3. Giannakis, G.B., et al.: Monitoring and optimization for power grids: a signal processing perspective. IEEE Signal Process. Mag. 30(5), 107–128 (2013). https://doi.org/10.1109/MSP.2013.2245726

4. Liu, Y., et al.: A distribution level wide area monitoring system for the electric power grid–FNET/GridEye. IEEE Access 5, 2329–2338 (2017). https://doi.org/10.1109/ACCESS.2017.2666541

5. Daisy, M., et al.: Fault location in power grids using substation voltage magnitude differences: a comprehensive technique for transmission lines, distribution networks, and AC/DC microgrids. Measurement 220, 113403 (2023). https://doi.org/10.1016/j.measurement.2023.113403

6. Tehrani, Y.H., Atarodi, S.M.: Design & implementation of a high precision & high dynamic range power consumption measurement system for smart energy IoT applications. Measurement 146, 458–466 (2019). https://doi.org/10.1016/j.measurement.2019.06.037

7. Elsisi, M., et al.: Effective IoT-based deep learning platform for online fault diagnosis of power transformers against cyberattacks and data uncertainties. Measurement 190, 110686 (2022). https://doi.org/10.1016/j.measurement.2021.110686

8. Stanescu, D., et al.: Transient power grid phenomena classification based on phase diagram features and machine learning classifiers. In: 2022 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, pp. 1676–1680. IEEE (2022). https://doi.org/10.23919/EUSIPCO55093.2022.9909687

9. Stanescu, D., et al.: On the existing and new potential methods for partial discharge source monitoring in electrical power grids. In: Zhang, Y.-D., et al. (eds.) Smart Trends in Computing and Communications, vol. 286, pp. 155–166. Springer Singapore, Singapore (2022). https://doi.org/10.1007/978-981-16-4016-2_15

10. Stanescu, D., et al.: Entropy-based characterization of the transient phenomena—systemic approach. Mathematics 9(6), 648 (2021). https://doi.org/10.3390/math9060648

11. Van Der Wielen, P., Steennis, F.: First field experience of on-line partial discharge monitoring of MV cable systems with location. In: IET Conference Publications, Prague, Czech Republic (2009). https://doi.org/10.1049/cp.2009.0535

12. Gottin, B., et al.: Detection and localization of transient sources: comparative study of complex-lag distribution concept versus wavelets and spectrogram-based methods. EURASIP J. Appl. Signal Process. 2009(1), 864185 (2009). https://doi.org/10.1155/2009/864185

13. Allahbakhshi, M., Akbari, A.: A method for discriminating original pulses in online partial discharge measurement. Measurement 44(1), 148–158 (2011). https://doi.org/10.1016/j.measurement.2010.09.036

14. Rauscher, A., Hufnagel, M., Endisch, C.: Pareto optimization of wavelet filter design for partial discharge detection in electrical machines. Measurement 205, 112163 (2022). https://doi.org/10.1016/j.measurement.2022.112163

15. Personal, E., et al.: A comparison of impedance-based fault location methods for power underground distribution systems. Energies 9(12), 1022 (2016). https://doi.org/10.3390/en9121022

16. Hizam, H., Crossley, P.A.: Fault location on a distribution feeder using fault generated high frequency current signatures. AJSTD 25(2), 191–203 (2017). https://doi.org/10.29037/ajstd.242

17. Duan, J., Zhou, Q., An, L.: Directional relay based on traveling-wave comparison using intrinsic mode function for EHV transmission lines. In: 2010 5th International Conference on Critical Infrastructure (CRIS), Beijing, China, pp. 1–5 (2010). https://doi.org/10.1109/CRIS.2010.5617568

18. Azirani, M.A., et al.: Optimal frequency selection for detection of partial discharges in power transformers using the UHF measurement technique. Measurement 172, 108895 (2021). https://doi.org/10.1016/j.measurement.2020.108895

19. Shafiq, M., et al.: Electromagnetic sensing for predictive diagnostics of electrical insulation defects in MV power lines. Measurement 73, 480–493 (2015). https://doi.org/10.1016/j.measurement.2015.05.040

20. Digulescu, A., et al.: Advanced signal processing techniques for detection and localization of electrical arcs. In: 2014 10th International Conference on Communications (COMM), Bucharest, Romania, pp. 1–4 (2014). https://doi.org/10.1109/ICComm.2014.6866749

21. Digulescu, A., et al.: Electrical arc surveillance and localization system based on advanced signal processing techniques. In: 2014 IEEE International Energy Conference (ENERGYCON), Cavtat, Croatia, pp. 426–430 (2014). https://doi.org/10.1109/ENERGYCON.2014.6850462

22. Ioana, C.: Procédé de Localisation d'une Source d'impulsions dans un Milieu Dispersif. France Patent G01R 31/08 22 July 2015.

23. Lehmann, A.M., et al.: A diagnostic method for power line networks by channel estimation of PLC devices. In: 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, Australia, pp. 320–325 (2016). https://doi.org/10.1109/SmartGridComm.2016.7778781

24. Artale, G., et al.: A new PLC-based smart metering architecture for medium/low voltage grids: feasibility and experimental characterization. Measurement 129, 479–488 (2018). https://doi.org/10.1016/j.measurement.2018.07.070

25. https://www.tiepie.com/en/usb-oscilloscope/handyscope-hs4 (accessed November 21, 2023).

26. https://ipec.co.uk/pd-products/high-frequency-current-transformer-hfct (accessed November 21, 2023).

27. Marwan, N., et al.: Recurrence plots for the analysis of complex systems. Phys. Rep. 438(5–6), 237–329 (2007). https://doi.org/10.1016/j.physrep.2006.11.001

28. Marwan, N.: A historical review of recurrence plots. Eur. Phys. J. Spec. Top. 164(1), 3–12 (2008). https://doi.org/10.1140/epjst/e2008-00829-1

How to cite this article: Stanescu, D., et al.: Continuous monitoring of partial discharge activities in power cables and their stimulation due to the temperature rise. IET Smart Grid. e12187 (2025). https://doi.org/10.1049/stg2.12187