Electrical and Electronic Engineering - Conference Items
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Item Stability analysis of virtual power plant with grid forming converters(Institute of Electrical and Electronics Engineers (IEEE), 2024-10-04) Tozak, Macit; Taskin, Sezai; Sengor, Ibrahim; Hayes, Barry P.A virtual power plant is a system containing multiple distributed generators aggregated and flexibly coordinated to act as a single power source. This study investigates the active power, reactive power, frequency, and voltage support provided by a virtual power plant interconnected with the grid. The investigation encompasses the analysis of grid-forming (GFM)-controlled wind and solar power plant units, considering the fluctuating power generation from solar and wind sources. The real hourly wind and solar generation profiles from currently operational plants are used as the active power set points. The stability and power reference tracking of grid-connected converters are analyzed using dispatchable virtual oscillator control (dVOC) and droop control-based methods. The results show that both control strategies substantially improve the virtual power plant's ability to ensure grid stability and accurately track power references, despite the inherent variability of wind and solar energy generation.Item Neonatal hypoxic-ischemic encephalopathy grading from multi-channel EEG time-series data using a fully convolutional neural network(Institute of Electrical and Electronics Engineers (IEEE), 2023-10-18) Yu, Shuwen; Marnane, William P.; Boylan, Geraldine B.; Lightbody, Gordon; Science Foundation Ireland; Wellcome TrustA deep learning classifier is proposed for hypoxic-ischemic encephalopathy (HIE) grading in neonates. Rather than using any 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 (335h and 338h respectively) multi-center neonatal continuous EEG datasets were used for training and test. 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. 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.Item American Multinomial Option Pricing on FPGA using OneAPI(IEEE, 2023) O'Mahony, Aidan T.; Zeidan, Gil; Hanzon, Bernard; Popovici, Emanuel M.; Dell Technologies; Intel Corporation; Science Foundation IrelandThis paper describes a new method for pricing American options that utilizes the OneAPI framework and a Field-Programmable Gate Array (FPGA) device. By using a multinomial model based on Pascals Simplex, the method is able to price American options with improved performance over traditional methods. The use of the OneAPI framework allows for efficient parallelization of the computations on the FPGA, resulting in improved performance and faster pricing of the options. Our analysis indicates that for 7 assets, the method can process approximately 53.74 options per second at the maximum tree depth of 15, showcasing its potential for real-world applications. The results of the method are validated against existing pricing models, demonstrating its accuracy and viability for use in real-world option pricing applications.Item Behavioral modeling of low-frequency noise in switched-capacitor circuits using Python(Institute of Electrical and Electronics Engineers (IEEE), 2022-08-05) Kalogiros, Spyridon; Salgado, Gerardo; McCarthy, Kevin; O'Connell, Ivan; Science Foundation Ireland; European Regional Development FundIn precision circuits validating the performance in the presence of low-frequency noise is particularly challenging especially at transistor level, as long simulations are required to observe the low frequency performance. However, running such system-level simulations is rarely practical at transistor level as these simulations can take days to weeks to complete. This work presents a high-level model in Python for generating low-frequency noise which can be used for validating the low-frequency performance of a design in a timely manner. Simulation times can be reduced from days to minutes, enabling designers to achieve a high-level simulation coverage. With Python and NumPy this can be achieved using open-source software tools at no cost.Item Lightweight anomaly detection framework for IoT(Institute of Electrical and Electronics Engineers (IEEE), 2020-08-31) Tagliaro Beasley, Bianca; O'Mahony, George D.; Gómez Quintana, Sergi; Temko, Andriy; Popovici, Emanuel; Science Foundation IrelandInternet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.