Electrical and Electronic Engineering - Journal Articles

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SIW-DGS bandpass filter design for C band satellite communications
(Springer Nature Switzerland AG, 2023-03-29) Nasser, Mohammed; Celik, Ali Recai; Helhel, Selcuk; Akdeniz Üniversitesi
In this paper, a bandpass filter is designed and fabricated for C-band satellite communication applications. The substrate integrated waveguide and defected ground structures are used in the design process. CST Microwave Studio software is used to analyze and design the proposed filter. It is built over DiClad 880 laminate having a thickness of 0.508 mm, and formed by etching three cascaded DGS cells on the SIW’s top plane. There is a good agreement between the simulated and measured results. The filter is centered at 6.175 GHz with 500 MHz bandwidth (8.1% fractional bandwidth) in line with applicable US Federal Communications Committee Rules. The simulated insertion loss at the center frequency is around 0.80 dB and the return loss in the passband is better than 30 dB. The measured minimum insertion loss is 1.4 dB, and the measured return loss in the passband is better than 14.5 dB. The obtained results are presented, discussed, and compared with other studies. It can be said that the features of the proposed filter such as size, order, return loss, insertion loss, upper band rejection, etc. are better than those of many other filters given in the literature.
Revenue-based allocation of electricity network charges for future distribution networks
(Institute of Electrical and Electronics Engineers (IEEE), 2022-05-20) Cuenca, Juan J.; Jamil, Emad; Hayes, Barry P.; Department of Business, Enterprise and Innovation, Ireland
This paper investigates the economic implications that high penetrations of distributed energy resources (DER) have in future distribution networks, and proposes a novel scalable scheme for the assignment of use of network charges based on individual participant nodes' revenue. For validation purposes, a techno-economic simulation is proposed to understand how power and revenue flows will change. A year-long high-resolution quasi-static time series (QSTS) simulation, two price schemes, four trading environments, and four DER allocation methods from the literature are used to study economic benefits for individual participants and the supplier. Testing is performed using the IEEE 33-bus and 123-bus networks, and an Irish urban medium voltage feeder. Revenue flow is presented as an indicator of which participant nodes are profiting more from grid usage, and therefore should be responsible for greater network charges, this is validated against traditional and alternative schemes. Important reductions in use of network charges are seen especially by participant nodes with a higher PV generation-to-load and self-consumption rates. The proposed method is only relevant when dynamic tariffs are in place and/or local trading is enabled. Ultimately, results suggest that the income from network charges received by the supplier is increased when dynamic tariffs are used.
Upper-layer post-processing local energy bids and offers from neighbouring energy communities
(Institute of Electrical and Electronics Engineers (IEEE), 2022-11-28) Cuenca, Juan J.; Hosseinnezhad, Vahid; Hayes, Barry P.; Department of Business, Enterprise and Innovation, Ireland
Future local energy trading schemes represent an important economic incentive for inclusion of distributed energy resources (DER) and flexibility in local energy communities. Nonetheless, trading schemes at the low voltage level are envisioned to result in unattended bids and offers of energy. In the absence of an alternative, these leftovers are expected to be captured by the supplier at a low price (in case of excess energy) and at a high price (in the case of energy requirements), which can represent significant economic benefits. This paper proposes a decentralised offline trading method to transfer this benefit from the supplier to the local energy communities using a minimum electrical distance criterion. Validation is made by running a year-long quasi-static time-series (QSTS) simulation with a resolution of one minute, using PV generation profiles, and four state-of-the-art DER allocation methods in the IEEE 33bus distribution test network. Results suggest that transferring these benefits can increase incomes up to 227% and decrease expenses up to 6.1% for local energy communities. Additionally, the sensitivity of the method to energy prices and market time step is studied.
GaAs MMIC nonreciprocal single-band, multi-band, and tunable bandpass filters
(Institute of Electrical and Electronics Engineers (IEEE), 2023-01-02) Simpson, Dakotah; Psychogiou, Dimitra; National Science Foundation; Science Foundation Ireland
This article reports on the RF design and practical development of active MMIC single-band, multi-band, and tunable bandpass filters (BPFs) with lossless and nonreciprocal transfer functions. They are based on series-cascaded lumped-element frequency-selective cells that are coupled with MMIC-based FETs. The FETs introduce gain and counteract the loss of the lossy elements. Furthermore, due to their unilateral behavior, nonreciprocal transfer functions can be obtained. This allows for an RF codesigned filtering isolator functionality to be created within a single RF component. By cascading multiple frequency-selective cells, both single-band and multi-band transfer functions with and without transmission zeros (TZs) can be realized. The basic operating principles of the MMIC concept are first described through parametric studies on different types of frequency-selective cells. These are followed by tunable and higher selectivity design methodologies. For practical demonstration purposes, four MMIC prototypes were designed, built, and measured using a commercially available GaAs process. They include a three-cell frequency-tunable BPF, two dual-band BPFs, and a quasi-elliptic BPF.
Development of an EEG artefact detection algorithm and its application in grading neonatal hypoxic-ischemic encephalopathy
(Elsevier Ltd., 2022-10-21) O'Sullivan, Mark E.; Lightbody, Gordon; Mathieson, Sean R.; Marnane, William P.; Boylan, Geraldine B.; O'Toole, John M.; Wellcome Trust; Science Foundation Ireland
Objective: The primary aim of this study is to develop and evaluate algorithms for neonatal EEG artefact detection. The secondary aim is to subsequently assess its application as a post-processing routine for automated EEG grading of background abnormalities in neonatal hypoxic-ischemic encephalopathy (HIE). Methods: A database of neonatal EEG with expertly annotated artefacts was used to train and validate machine learning models to automatically identify EEG epochs containing artefacts. Three approaches were developed and compared, specifically, a simple threshold-based digital signal processing (DSP) method, a machine learning method, and a deep learning method. The artefact detection classifier was subsequently assessed as a post-processing tool to assist in the application of automated EEG grading of HIE. A new deep learning model for grading the EEG was developed by training an existing network on a large, multi-centre dataset. The artefact detection algorithm was integrated into the grading algorithm through a post-processing routine. Results: Using a database containing 19 h of EEG from 51 patients with per-channel and per-second annotations of artefacts, a deep learning convolutional neural network solution achieved best performance for artefact detection with an area under the operating characteristic curve (AUC) of 0.84, compared to an AUC of 0.68 and 0.82 for a DSP method and a random-kernel ridge-classifier model, respectively. The automated EEG grading algorithm was trained and tested on 653 h of EEG from 181 patients, which achieved an accuracy of 82.8 % (95 % CI: 80.5 % to 85.2 %). The percentage of detected artefacts in the misclassified epochs was not statistically different (p = 0.568) compared to that of correctly classified epochs. Using artefact detection, a small number of epochs were removed from grading, resulting in a minor increase in accuracy for the EEG grading algorithm from 82.6 % to 83.6 %. Conclusion: Deep learning methods achieved highest classification performance for neonatal EEG artefact detection, although a ridge classifier using random kernels achieved comparable performance without significant parameter tuning or training time. The inclusion of artefact detection in automated EEG grading does not significantly improve accuracy in our curated dataset, but does allow for a quality measure to be presented alongside the automated EEG grades which may increase user confidence in its real-world application.

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