Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability

Pathan, M Ilius; Shahriar, Mohammad Shoaib; Rahman, Mohammad Mominur; Hossain, Md. Sanwar; Awatif, Nadia; Shafiullah, Md

doi:10.1002/9781119893998.ch9

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…The GMDH [ 19 ] is an early example of a feedforward network for DL. It is part of a heuristic family of algorithms that, by identifying correlations between input attributes, may automatically create self-organization models of optimal complexity.…”

Section: Proposed Modelmentioning

confidence: 99%

Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments

Aldaej,

Ahanger,

Ullah

2023

Sensors

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The Internet of Things (IoT) technology has seen substantial research in Deep Learning (DL) techniques to detect cyberattacks. Critical Infrastructures (CIs) must be able to quickly detect cyberattacks close to edge devices in order to prevent service interruptions. DL approaches outperform shallow machine learning techniques in attack detection, giving them a viable alternative for use in intrusion detection. However, because of the massive amount of IoT data and the computational requirements for DL models, transmission overheads prevent the successful implementation of DL models closer to the devices. As they were not trained on pertinent IoT, current Intrusion Detection Systems (IDS) either use conventional techniques or are not intended for scattered edge–cloud deployment. A new edge–cloud-based IoT IDS is suggested to address these issues. It uses distributed processing to separate the dataset into subsets appropriate to different attack classes and performs attribute selection on time-series IoT data. Next, DL is used to train an attack detection Recurrent Neural Network, which consists of a Recurrent Neural Network (RNN) and Bidirectional Long Short-Term Memory (LSTM). The high-dimensional BoT-IoT dataset, which replicates massive amounts of genuine IoT attack traffic, is used to test the proposed model. Despite an 85 percent reduction in dataset size made achievable by attribute selection approaches, the attack detection capability was kept intact. The models built utilizing the smaller dataset demonstrated a higher recall rate (98.25%), F1-measure (99.12%), accuracy (99.56%), and precision (99.45%) with no loss in class discrimination performance compared to models trained on the entire attribute set. With the smaller attribute space, neither the RNN nor the Bi-LSTM models experienced underfitting or overfitting. The proposed DL-based IoT intrusion detection solution has the capability to scale efficiently in the face of large volumes of IoT data, thus making it an ideal candidate for edge–cloud deployment.

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Section: Proposed Modelmentioning

confidence: 99%

Deep Learning-Inspired IoT-IDS Mechanism for Edge Computing Environments

Aldaej,

Ahanger,

Ullah

2023

Sensors

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“…Therefore, a great amount of research has been conducted in recent decades by researchers in perspective to safe, reliable, and efficient motor drive systems with electric vehicles [5]- [9]. Recently, artificial intelligence has not only been employed in improving the EV systems but also in enhancing the power systems to supply reliable power to customers [10]- [14].…”

Section: Introductionmentioning

confidence: 99%

Securing Electric Vehicle Performance: Machine Learning-Driven Fault Detection and Classification

Ul Islam Khan,

Pathan,

Mominur Rahman

et al. 2024

IEEE Access

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Electric vehicles (EVs) are commonly recognized as environmentally friendly modes of transportation. They function by converting electrical energy into mechanical energy using different types of motors, which aligns with the sustainable principles embraced by smart cities. The motors of EVs store and consume electrical power from renewable energy (RE) sources through interfacing connections using power electronics technology to provide mechanical power through rotation. The reliable operation of an EV mainly relies on the condition of interfacing connections in the EV, particularly the connection between the 3-ϕ inverter output and the brushless DC (BLDC) motor. In this paper, machine learning (ML) tools are deployed for detecting and classifying the faults in the connecting lines from 3-ϕ inverter output to the BLDC motor during operational mode in the EV platform, considering double-line and three-phase faults. Several machine learning-based fault identification and classification tools, namely the Decision Tree, Logistic Regression, Stochastic Gradient Descent, AdaBoost, XGBoost, K-Nearest Neighbour, and Voting Classifier, were tuned for identifying and categorizing faults to ensure robustness and reliability. The ML classifications were developed based on the datasets of healthy and faulty conditions considering the combination of six critical parameters that have significance in reliable EV operation, namely the current supplied to the BLDC motor from the inverter, the modulated DC voltage, output speed, and measured speed, as well as the output of the Hall-effect sensor. In addition, the superiority of the proposed fault detection and classification approaches using ML tools was assessed by comparing the detection and classification efficiency through some statistical performance parameter comparisons among the classifiers.

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