The Practical Application of Bio-Inspired PMA for the Detection of Partial Discharges in High Voltage Equipment.

In this paper, the practical application of a bio-inspired antenna for partial discharge (PD) detection in high voltage equipment was evaluated in order to validate the efficiency of using this technology for PD monitoring purposes. For this, PD measurements using the bio-inspired antenna were performed on operational 69 kV potential transformers (PT) in a real substation. After the field experiment, laboratory measurements using the IEC 60270 standard method and a bio-inspired antenna were performed, simultaneously, over the evaluated PT. The results obtained at the substation indicated suspicious frequencies of partial discharge activity in two out of three evaluated potential transformers, mainly for the frequencies of 461 MHz, 1366 MHz, 1550 MHz and 1960 MHz. During the laboratory tests, the presence of partial discharge activity over the suspicious potential transformers was confirmed with the detection of PD apparent charge levels above 20 pC. Finally, the frequency spectrum obtained from the PD signals detected by the bio-inspired antenna in the laboratory presented similar frequency values to those obtained during the practical application at the substation, making it a promising indicator for future defect classification studies using artificial intelligence.

Efficient Filter Design to Compensate Fabrication Imperfections in 6G Communication Systems.

In this paper, we present a consistent methodology for the reliable design of 6G-oriented filters with enhanced endurance to construction imperfections. The systematic formulation does not depend on the filter's operating frequency and employs a robust strategy for obtaining new roots and poles of the filtering function. Essentially, it requires that all the local maxima of the filtering function do not fluctuate beyond the design attenuation levels for a set of predefined roots/poles distortions. To this purpose, two novel algorithms for the derivation of the appropriate filtering functions are developed, in the prior basis, together with a versatile optimization criterion and a heuristic comparison approach that guarantee optimal outcomes. Specifically, the principal idea of the first technique is to accurately extract the roots of the new polynomial from a system of equations on condition that the maximum local peaks of the distorted (due to imperfections) initial polynomial are below a prefixed threshold, such as the unit. Conversely, the second method develops an alternative polynomial, compressed in the amplitude and frequency range, so that a similar prerequisite regarding the maximum local peaks, is satisfied. It is stressed that both methods are fully generalized and may be applied to any polynomial combination, without increasing the overall complexity. The proposed framework is successfully verified in terms of theoretical examples and the numerical simulation of realistic waveguide and mictrostrip line filters, operating at frequencies from 2GHz to 65GHz, which unveil its superiority over existing schemes and implementations.

Correction: Che et al. Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)-For Industrial Internet of Things (IIoT). Sensors 2023, 23, 5710.

There was an error in the original publication [...].

Indoor Positioning System (IPS) Using Ultra-Wide Bandwidth (UWB)-For Industrial Internet of Things (IIoT).

The integration of the physical and digital world has become increasingly important, and location-based services have become the most sought-after application in the field of the Internet of Things (IoT). This paper delves into the current research on ultra-wideband (UWB) indoor positioning systems (IPS). It begins by examining the most common wireless communication-based technologies for IPSs followed by a detailed explanation of UWB. Then, it presents an overview of the unique characteristics of UWB technology and the challenges still faced by the IPS implementation. Finally, the paper evaluates the advantages and limitations of using machine learning algorithms for UWB IPS.

Robust FDTD Modeling of Graphene-Based Conductive Materials with Transient Features for Advanced Antenna Applications.

The accurate modeling of frequency-dispersive materials is a challenging task, especially when a scheme with a transient nature is utilized, as it is the case of the finite-difference time-domain method. In this work, a novel implementation for the modeling of graphene-oriented dispersive materials via the piecewise linear recursive convolution scheme, is introduced, while the time-varying conductivity feature is, additionally, launched. The proposed algorithm is employed to design a reduced graphene-oxide antenna operating at 6 GHz. The transient response to graphene's conductivity variations is thoroughly studied and a strategy to enhance the antenna performance by exploiting the time-varying graphene oxide is proposed. Finally, the use of the featured antenna for modern sensing applications is demonstrated through the real-time monitoring of voltage variation.

An empirical investigation on the correlation between solar cell cracks and hotspots.

In recent years, solar cell cracks have been a topic of interest to industry because of their impact on performance deterioration. Therefore, in this work, we investigate the correlation of four crack modes and their effects on the temperature of the solar cell, well known as hotspot. We divided the crack modes to crack free (mode 1), micro-crack (mode 2), shaded area (mode 3), and breakdown (mode 4). Using a dataset of 12 different solar cell samples, we have found that there are no hotspots detected for a solar cell affected by modes 1 or 2. However, we discovered that the solar cell is likely to have hotspots if affected by crack mode 3 or 4, with an expected increase in the temperature from 25[Formula: see text]C to 100[Formula: see text]C. Additionally, we have noticed that an increase in the shading ratio in solar cells can cause severe hotspots. For this reason, we observed that the worst-case scenario for a hotspot to develop is at shading ratios of 40% to 60%, with an identified increase in the cell temperature from 25[Formula: see text]C to 105[Formula: see text]C.

A Review of Techniques for RSS-Based Radiometric Partial Discharge Localization.

The lifespan assessment and maintenance planning of high-voltage power systems requires condition monitoring of all the operational equipment in a specific area. Electrical insulation of electrical apparatuses is prone to failure due to high electrical stresses, and thus it is a critical aspect that needs to be monitored. The ageing process of the electrical insulation in high voltage equipment may accelerate due to the occurrence of partial discharge (PD) that may in turn lead to catastrophic failures if the related defects are left untreated at an initial stage. Therefore, there is a requirement to monitor the PD levels so that an unexpected breakdown of high-voltage equipment is avoided. There are several ways of detecting PD, such as acoustic detection, optical detection, chemical detection, and radiometric detection. This paper focuses on reviewing techniques based on radiometric detection of PD, and more specifically, using received signal strength (RSS) for the localization of faults. This paper explores the advantages and disadvantages of radiometric techniques and presents an overview of a radiometric PD detection technique that uses a transistor reset integrator (TRI)-based wireless sensor network (WSN).

Automatic Annotation of Subsea Pipelines using Deep Learning.

Regulatory requirements for sub-sea oil and gas operators mandates the frequent inspection of pipeline assets to ensure that their degradation and damage are maintained at acceptable levels. The inspection process is usually sub-contracted to surveyors who utilize sub-sea Remotely Operated Vehicles (ROVs), launched from a surface vessel and piloted over the pipeline. ROVs capture data from various sensors/instruments which are subsequently reviewed and interpreted by human operators, creating a log of event annotations; a slow, labor-intensive and costly process. The paper presents an automatic image annotation framework that identifies/classifies key events of interest in the video footage viz. exposure, burial, field joints, anodes, and free spans. The reported methodology utilizes transfer learning with a Deep Convolutional Neural Network (ResNet-50), fine-tuned on real-life, representative data from challenging sub-sea environments with low lighting conditions, sand agitation, sea-life and vegetation. The network outputs are configured to perform multi-label image classifications for critical events. The annotation performance varies between 95.1% and 99.7% in terms of accuracy and 90.4% and 99.4% in terms of F1-Score depending on event type. The performance results are on a per-frame basis and corroborate the potential of the algorithm to be the foundation for an intelligent decision support framework that automates the annotation process. The solution can execute annotations in real-time and is significantly more cost-effective than human-only approaches.

An Efficient Algorithm for Partial Discharge Localization in High-Voltage Systems Using Received Signal Strength.

The term partial discharge (PD) refers to a partial bridging of insulating material between electrodes that sustain an electric field in high-voltage (HV) systems. Long-term PD activity can lead to catastrophic failures of HV systems resulting in economic, energy and even human life losses. Such failures and losses can be avoided by continuously monitoring PD activity. Existing techniques used for PD localization including time of arrival (TOA) and time difference of arrival (TDOA), are complicated and expensive because they require time synchronization. In this paper, a novel received signal strength (RSS) based localization algorithm is proposed. The reason that RSS is favoured in this research is that it does not require clock synchronization and it only requires the energy of the received signal rather than the PD pulse itself. A comparison was made between RSS based algorithms including a proposed algorithm, the ratio and search and the least squares algorithm to locate a PD source for nine different positions. The performance of the algorithms was evaluated by using two field scenarios based on seven and eight receiving nodes, respectively. The mean localization error calculated for two-field-trial scenarios show, respectively, 1.80 m and 1.76 m for the proposed algorithm for all nine positions, which is the lowest of the three algorithms.