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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Dindar, Burak Saner, Can Berk Polat, Dogukan Yigit Çakmak, Huseyin Kemâl Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany National University of Singapore Department of Electrical and Computer Engineering Singapore National University of Singapore School of Computing Singapore

ISBN: (纸本)9789531842976

In order to achieve the goal of a carbon-neutral power system, significant changes to the power grid are underway, necessitating enhanced interoperability between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) for effective grid operation, particularly in light of the growing number of distributed generators (DGs). Data privacy concerns, however, complicate decision-making processes, particularly when integrating DGs into system-wide dispatch decisions. In this paper, we propose a machine-learning (ML)-based method to incorporate DGs located within the distribution system (DS) into dispatch decisions, adhering to data privacy by mitigating the exchange of sensitive data, such as system topology and demand profiles, between TSOs and DSOs. The methodology involves training three ML models to represent the behavior of the DS, thereby replacing the standard power flow model that contains sensitive information. Notably, we utilize a novel tailored neural network (NN) architecture to enhance computational efficiency in mapping the feasible region of the DS. Additionally, we employ open-source data to construct the Baden-Wü rttemberg (Germany) electricity grid, allowing to test our method not only on standard systems but also on a model that more accurately represents real-world power systems. The numerical case studies verify that the proposed method achieves results comparable to standard AC optimal power flow (AC-OPF) in both cost-optimality and computational time. © 2024 IEEE.

关键词： Differential privacy

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Impact of Daily Irradiance Profiles on Intra-Day Solar Forecasting 49

Impact of Daily Irradiance Profiles on Intra-Day Solar Forec...

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49th IEEE Photovoltaics Specialists Conference, PVSC 2022

作者： Lopez-Lorente, Javier Theocharides, Spyros Makrides, George Georghiou, George E. Foss Research Centre for Sustainable Energy University of Cyprus Pv Technology Laboratory Department of Electrical and Computer Engineering Nicosia1678 Cyprus

ISBN: (数字)9781728161174

ISBN: (纸本)9781728161174

The analysis of solar energy integration requires capturing the temporal variability of solar irradiance, which can highly increase the errors of solar forecasting models. In this paper, a classification of day types for solar energy applications is investigated and the impact on intra-day solar generation forecasting is assessed. The proposed classification approach uses unsupervised learning based on a combination of self-organized maps and mean-shift clustering with six location-independent metrics related to irradiance variability and energy yield. Two types of forecasts (a deterministic forecast and a probabilistic one) are used as a basis to illustrate the impact of the resulting daily irradiance profiles. The forecasts are emulated by adding white noise to historic hourly irradiance observations and their performance is compared to state-of-the-art benchmarking models. The results illustrate the magnitude of dispersion between intra-hour observations and hourly forecasts, where deterministic forecasts observed 16% to 64% of deviation and probabilistic forecasts observed prediction intervals from 11% to 36%, depending on the daily typology. The findings of this study provide useful information on the impact of intra-day irradiance variability on the performance of forecasting models and how these can be adapted based on each day type to support the integration of solar generation in electricity systems. © 2022 IEEE.

关键词： Solar energy

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Comparative Analysis of Machine Learning Models for Short-Term Net Load Forecasting in Renewable Integrated Microgrids 2

Comparative Analysis of Machine Learning Models for Short-Te...

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2nd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2022

作者： Tziolis, Georgios Lopez-Lorente, Javier Baka, Maria-Iro Koumis, Anastasios Livera, Andreas Theocharides, Spyros Makrides, George Georghiou, George E. University of Cyprus Pv Technology Laboratory Foss Research Centre for Sustainable Energy Department of Electrical and Computer Engineering Nicosia Cyprus Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece

ISBN: (数字)9781665461078

ISBN: (纸本)9781665461078

Accurate net load forecasting (NLF) is crucial in modern power systems and microgrids to ensure optimal operation and management. At the microgrid level, the increasing penetration of renewable energy sources requires more efficient methodologies for NLF, since statistical approaches fail to provide accurate forecasts. Performance limitations of existing statistical approaches can be overcome by leveraging machine learning (ML) models. The purpose of this work is to compare the forecasting performance of six ML models (artificial neural network, extreme gradient boosting, k-nearest neighbors, random forest, recurrent neural network and support vector regression) and identify the best-performing model for short-term net load forecasting (STNLF). The comparative analysis was carried out using historical net load and weather data from the renewable integrated microgrid of the university of cyprus. The results demonstrated accuracies below 10% for all STNLF ML models. Random forest was the best performing model, achieving a normalized root mean square error of 4.32%. The findings illustrate the applicability of STNLF ML models in renewable integrated microgrids, which can benefit microgrid operators in managing and controlling their various assets. © 2022 IEEE.

关键词： Mean square error

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, J.E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications 15772 Greece Keio University Department of Information and Computer Science Kanagawa 2238522 Japan Tohoku University International Research Institute of Disaster Science Sendai9808576 Japan

As synthetic aperture radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. Second, a two-stage Weighted Center Constrained Fuzzy C-Means clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Third, a Convolutional Squeeze-and-Excitation Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model is introduced into the CSWNN to expand pseudolabeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2% –9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Location-enabled IoT (LE-IoT): Indoor Localization for IoT Environments using Machine Learning

Location-enabled IoT (LE-IoT): Indoor Localization for IoT E...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Tanveer Ahmad Xue Jun Li Muhammad Ashfaq Michalis Savva Iacovos Ioannou Vasos Vassiliou Department of Computer Science University of Cyprus Nicosia Cyprus Dep of Electrical and Electronic Engineering Auckland University of Technology Auckland New Zealand Department of Electronics Engineering University of Engineering & Technology Taxila Pakistan Department of Computer Science University of Cyprus & CYENS Centre of Excellence Nicosia Cyprus

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

In recent years, the concept of the Internet of things (IoT) has become ubiquitous among several applications. However, traditional localization methods face challenges in accurately measuring sensor node positions, particularly in scenarios involving wireless signal instability, energy harvesting, anchor mobility, latency, and dense environmental conditions. However, these problems open the new ways to utilize machine learning (ML) and self-calibration for localization. To defeat these problems, in this paper, we have proposed a new localization technique, which reduces the localization error caused by the iterations in a triangulation method and drastically shortens the computation time for full network coverage despite noisy environments. First, we present a localization technique based on a looped network. The link quality induction (LQI) is used to formulate multiple feature vectors for the localization problem, solved using linear regression. Second, we examine the appropriateness of a few algorithms under different tradeoffs by testing the localization accuracy for different network parameters, including the anchor node density, additive noise, and quality of the wireless channel. The simulation achieves exciting results that the adoption of a support vector machine (SVM) and nonlinear regression model applied with a radial basis function (RBF) kernel leads to an enhanced localization error with Root-Mean Square Error (RMSE) of 0.25m. Furthermore, we gain 48.5% accuracy in the localization error by implementing two models.

关键词： Location awareness Support vector machines Wireless communication Wireless sensor networks Accuracy Machine learning algorithms Vectors

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Machine Learning-Driven Prediction of Load Shedding During Cascading Outages

Machine Learning-Driven Prediction of Load Shedding During C...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： George Paphitis Balaji V. Venkatasubramanian Mathaios Panteli Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus School of Technology Woxsen University Telangana India

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

The complexity of modern power systems makes predicting and mitigating cascading outages challenging. Quantifying their impact requires extensive simulations, which are computationally expensive and impractical for real-time applications. To address this issue, we introduce a novel methodology that integrates different machine learning regressors to create a stacking ensemble regressor along with a cascading failure model enabling rapid quantification of cascading-driven nodal load shedding through prediction. The effectiveness of the proposed model is evaluated using the R 2 metric, with studies conducted on the IEEE 39-bus system. Additionally, the model is tested on the IEEE 24-bus Reliability Test System to demonstrate the effect of network reliability on cascading failures and its impact on model fitness. The outcomes of this study indicate that the proposed model fits better on the IEEE 39-bus system, achieving an R 2 of 0.88. In contrast, on the IEEE 24-bus Reliability Test System, the R 2 is 0.42; the reasons behind this discrepancy are examined and discussed.

关键词： Analytical models Computational modeling Contingency management Power system protection Load shedding Predictive models Power system reliability Reliability Power system faults Load modeling

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Identifying critical components in power networks through machine learning

Identifying critical components in power networks through ma...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion (MEDPOWER 2024)

ISBN: (数字)9781837242689

As modern power systems become more complex, identifying critical components for their reliable operation is becoming increasingly important, especially in dealing with widespread cascading outages from large-scale disturbances. In this context, this paper introduces a novel method for identifying critical components in terms of their contribution to cascading-induced demand not served (DNS), utilizing feature selection techniques integrated with a machine learning classifier model. While existing methods predominantly identify critical assets using DNS, they often overlook the effects of cascading failures. Furthermore, those that do consider cascading failures typically focus solely on line overloading. This work, therefore, introduces a machine learning-based methodology coupled with a cascading failure model, considering protection mechanisms like excitation limits, under/over frequency, and line overloading. Tested on the IEEE 24-bus system, the validation method exhibits strong sensitivity (80.35%-90.03%) and specificity (80.35%-94.10%). Additionally, SHapley Additive exPlanations (SHAP) is used for validation by comparing its results with the proposed method.

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Dynamics of cold random hyperbolic graphs with link persistence

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Physical Review E 2022年第6期106卷 064312-064312页

作者： Sofoclis Zambirinis Harrison Hartle Fragkiskos Papadopoulos Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 3036 Limassol Cyprus Network Science Institute Northeastern University Boston Massachusetts 02115 USA

We consider and analyze a dynamic model of random hyperbolic graphs with link persistence. In the model, both connections and disconnections can be propagated from the current to the next snapshot with probability ω∈[0,1). Otherwise, with probability 1−ω, connections are reestablished according to the random hyperbolic graphs model. We show that while the persistence probability ω affects the averages of the contact and intercontact distributions, it does not affect the tails of these distributions, which decay as power laws with exponents that do not depend on ω. We also consider examples of real temporal networks, and we show that the considered model can adequately reproduce several of their dynamical properties. Our results advance our understanding of the realistic modeling of temporal networks and of the effects of link persistence on temporal network properties.

关键词： Evolving networks Random graphs

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Exploring the Use of Particle and Kalman Filters for Obstacle Detection in Mobile Robots

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica Electrical engineering and computer science 2023年第4期67卷 384-393页

作者： Gyenes, Zoltán Bölöni, Ladislau Szádeczky-Kardoss, Emese Gincsainé Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Muegyetem rkp. 3. BudapestH-1111 Hungary Department of Computer Science University of Central Florida 4328 Scorpius St OrlandoFL32816-2362 United States

The present study aims to explore the adaptation of estimation methodologies, specifically Particle filters and Kalman filters, for the purpose of determining the position and velocity vector of obstacles within the operational workspace of mobile robots. These algorithms are commonly employed in the motion planning tasks of mobile robots for the estimation of their own position. The proposed methodology utilizes LiDAR sensor data to estimate the position vectors and calculate the velocity vectors of obstacles. Additionally, an uncertainty parameter can be determined using the introduced perception method. The performance of the newly adapted algorithms is evaluated through comparison of the absolute error in position and velocity vector estimations. © 2023 Budapest university of technology and Economics. All rights reserved.

关键词： Mobile robots

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Development of IoT-Healthcare Model for Gastric Cancer from Pathological Images 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Belgaum, Mohammad Riyaz Momina, Shaik Maryam Farhath, L. Nousheen Nikhitha, K. Naga Jyothi, K. Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The diagnosis of stomach cancer automatically in digital pathology images is a difficult problem. Gastric cancer (GC) detection and pathological study can be greatly aided by precise region-by-region segmentation. On a technical level, this issue is complicated by the fact that malignant zones might be any size or shape and have fuzzy boundaries. The research employs a deep learning-based approach and integrates many bespoke modules to cope with these issues. The channel refinement model is the attentional actor on the chin channel. While implementing the feature channel, the learnt channel weight can be used to eliminate unnecessary features. Calibration is essential to improve classification precision. The results of channel recalibration can be improved with the help of a re-calibration (MSCR) model. The top pooling layer of the network is where the multiscale attributes are sent. The outcomes of channel recalibration may be enhanced by using the channel weights found at various scales as input to the next channel recalibration perfect. Our unique gastric cancer segmentation dataset, carefully glossed down to the pixel level by medical authorities, is used for extensive experimental comparisons. The numerical comparisons with other approaches show that our strategy is superior. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diagnosis

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