Fast Transient Stability Assessment of Power Systems Using Optimized Temporal Convolutional Networks

© 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/The transient power grid stability is greatly affected by the unpredictability of inverter-based resources of today's interconnected power grids. This article introduces an efficient transient stability status prediction method based on deep temporal convolutional networks (TCNs). A grey wolf optimizer (GWO) is utilized to fine-tune the TCN hyperparameters to improve the proposed model's accuracy. The proposed model provides critical information on the transient grid status in the early stages of fault occurrence, which may lead to taking the proper action. The proposed TCN-GWO uses both synchronously sampled values and synthetic values from various bus systems. In a postfault scenario, a copula of processing blocks is implemented to ensure the reliability of the proposed method where high-importance features are incorporated into the TCN-GWO model. The proposed algorithm unlocks scalability and system adaptability to operational variability by adopting numeric imputation and missing-data-tolerant techniques. The proposed algorithm is evaluated on the 68-bus system and the Northeastern United States 25k-bus synthetic test system with credible contingencies using the PowerWorld simulator. The obtained results prove the enhanced performance of the proposed technique over competitive state-of-the-art transient stability assessment methods under various contingencies with an overall accuracy of 99% within 0.64 s after the fault clearance.Peer reviewe

Multiple resonances in arrays of spiral resonators designed for magnetic resonance imaging

This article analyzes a planar metamaterial made of spiral resonators (SRs) printed on a thin dielectric layer, and operating around 123 MHz for medical applications. The design is based on an analytical model using equivalent circuits. This approach enables to explain the origin of multiple resonant frequencies in SR metamaterials. Theoretical predictions are successfully validated by measurements carried out in the frequency range [100-140 MHz]. These properties lead to verb, interesting applications, such as compact antennas with high directivity or medical magnetic resonance imaging at 123 MHz. In particular our simulations show that a metamaterial slab, formed by an array of spiral resonators is able to transport the magnetic image information across the slab without distortion. (C) 2008 Wiley Periodicals, Inc

Intrusion Detection Method Based on SMOTE Transformation for Smart Grid Cybersecurity

© 2022 IEEE.Real-Time Intrusion Detection Systems (IDSs) have attracted greater attention for secured and resilient smart grid operations. IDSs are employed to identify unknown cyberattacks and malware from network traffics. In this paper, an efficient model-based machine learning is proposed to detect a variety of cyberattacks. The proposed method enhanced Extremely randomized Trees (ET) classifier based on Synthetic Minority Oversampling Technique (SMOTE) accurately classifies imbalanced IDSs data. The proposed ET-SMOTE uses a virtue of data processing blocks to enable multi-layer network cyber-security assessment in smart grids by acquiring the essential knowledge of attack dynamics. The proposed computing framework provides an accurate multiclass classification of five network traffic categories: denial of service attacks, probing attacks, root to local attacks, user to root attacks, and normal. The experimental results demonstrate the high accuracy of the proposed ET-SMOTE algorithm in detecting various types of cyber threats compared to benchmark models with an accuracy of 99.79% using the NSL-KDD networks data set

An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting

The integration of Photovoltaic (PV) systems requires the implementation of potential PV power forecasting techniques to deal with the high intermittency of weather parameters. In the PV power prediction process, Genetic Programming (GP) based on the Symbolic Regression (SR) model has a widespread deployment since it provides an effective solution for nonlinear problems. However, during the training process, SR models might miss optimal solutions due to the large search space for the leaf generations. This paper proposes a novel hybrid model that combines SR and Deep Multi-Layer Perceptron (MLP) for one-month-ahead PV power forecasting. A case study analysis using a real Australian weather dataset was conducted, where the employed input features were the solar irradiation and the historical PV power data. The main contribution of the proposed hybrid SR-MLP algorithm are as follows: (1) The training speed was significantly improved by eliminating unimportant inputs during the feature selection process performed by the Extreme Boosting and Elastic Net techniques; (2) The hyperparameters were preserved throughout the training and testing phases; (3) The proposed hybrid model made use of a reduced number of layers and neurons while guaranteeing a high forecasting accuracy; (4) The number of iterations due to the use of SR was reduced. The presented simulation results demonstrate the higher forecasting accuracy (reductions of more than 20% for Root Mean Square Error (RMSE) and 30 % for Mean Absolute Error (MAE) in addition to an improvement in the R2 evaluation metric) and robustness (preventing the SR from converging to local minima with the help of the ANN branch) of the proposed SR-MLP model as compared to individual SR and MLP models

Theoretical and experimental investigations of planar metamaterials at radio frequencies for magnetic resonance imaging

Left-Handed Materials (LHMs) are artificial structures which present a negative permittivity epsiv and a negative permeability mu simultaneously. They can be realized using a periodic disposition of metallic rods and split rings resonators (SRRs). One of the most interesting properties of these LHMs is the emergence of negative refraction. This property may be explored for several applications at microwaves. The aim of this work is to study theoretically and experimentally some properties of metamaterials at radio-frequencies using planar and compact structures. The choice of the radio-frequency range is related to several applications such as compact antennas with high directivity or magnetic resonance imaging. We investigate the properties of spiral resonators between 100 MHz and 500 MHz, either coupled to strip lines to form a left handed (LH) metamaterial, or disposed into a 3x3 array to form a Single Negative (SNG) metamaterial.Anglai

Theoretical and experimental investigations of multiple resonant frequencies in single negative metamaterials

In this paper, the properties of a Single Negative (SNG) planar metamaterial are analysed, designed and measured. The Single Negative structure is made of Spiral Resonators printed on a thin dielectric layer. The analysis is based on an analytical modelling using equivalent circuits. This approach enables to explain the origin of multiple resonant frequencies in Single Negative Metamaterials. The calculations are successfully validated by measurements carried out in the frequency range [80 - 150 MHz]. These properties lead to very interesting applications, such as compact antennas with high directivity or enhancing of magnetic resonance imaging resolution.Anglai