Electrical and Electronic Engineering - Journal Articles

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    Energy management strategy of a green port serving emission-free ferries considering EV parking lot availability
    (Institute of Electrical and Electronics Engineers (IEEE), 2023-12-20) Ozdemir, Hilal; Güldorum, Hilmi Cihan; Erenoğlu, Ayşe Kübra; Sengor, Ibrahim; Hayes, Barry P.; Erdinç, Ozan; Science Foundation Ireland; Junta de Comunidades de Castilla-La Mancha; European Regional Development Fund; Universidad de Castilla-La Mancha
    While the role of transportation is indispensable in daily life, the transportation sector is one of the major contributors to greenhouse gas (GHG) emissions. With the utilization of green vehicles in transportation systems and the adoption of renewable energy sources in power systems, it becomes possible to achieve an environmentally friendly transportation system. In this paper, an energy management algorithm for a port power system is presented, including a solar-based local power production unit serving hybrid ferries having fuel cell/battery, as well as an electric vehicle (EV) parking lot, allowing for bi-directional power flow between the port and the grid. The proposed concept enables dual integrated green transportation with the possibility of parking EVs to the lot and then continuing to journey via another green transportation option. The stochasticity regarding the photovoltaic plant (PVP) and EV behaviors are taken into consideration under a real-time rolling horizon optimization framework. Minimizing the operational cost of the port related to the energy consumption is the objective function in this novel transportation-to-transportation integration concept. Numerous case studies have been developed and tested to demonstrate the effectiveness of the suggested algorithm.
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    Neonatal hypoxic-ischemic encephalopathy grading from multi-channel EEG time-series data using a fully convolutional neural network
    (MDPI, 2023-10-25) Yu, Shuwen; Marnane, William P.; Boylan, Geraldine B.; Lightbody, Gordon; Science Foundation Ireland; Wellcome Trust
    A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture simpler, and deeper, but with fewer parameters. Here, two large (335 h and 338 h, respectively) multi-center neonatal continuous EEG datasets were used for training and testing. The model was trained based on weak labels and channel independence. A majority vote method was used for the post-processing of the classifier results (across time and channels) to increase the robustness of the prediction. A dimension reduction tool, UMAP, was used to visualize the model classification effect. The proposed system achieved an accuracy of 86.09% (95% confidence interval: 82.41–89.78%), an MCC of 0.7691, and an AUC of 86.23% on the large unseen test set. Two convolutional neural network architectures which utilized time-frequency distribution features were selected as the baseline as they had been developed or tested on the same datasets. A relative improvement of 23.65% in test accuracy was obtained as compared with the best baseline. In addition, if only one channel was available, the test accuracy was only reduced by 2.63–5.91% compared with making decisions based on the eight channels.
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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.
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    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.
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    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.