Recent advancements across various sectors have resulted in a significant increase in the utilization of smart gadgets. This augmentation has resulted in an expansion of the network and the devices linked to it. Nevertheless, the development of the network has concurrently resulted in a rise in policy infractions impacting information security. Finding intruders immediately is a critical component of maintaining network security. The intrusion detection system is useful for network security because it can quickly identify threats and give alarms. In this paper, a new approach for network intrusion detection was proposed. Combining the results of machine learning models like the random forest, decision tree, k-nearest neighbors, and XGBoost with logistic regression as a meta-model is what this method is based on. For the feature selection technique, the proposed approach creates an advanced method that combines the correlation-based feature selection with an embedded technique based on XGBoost. For handling the challenge of an imbalanced dataset, a SMOTE-TOMEK technique is used. The suggested algorithm is tested on the NSL-KDD and CIC-IDS datasets. It shows a high performance with an accuracy of 99.99% for both datasets. These results prove the effectiveness of the proposed approach.

The global shift toward renewable energy, particularly wind power, underscores the critical need for advanced fault diagnosis systems to optimize wind turbine reliability and efficiency. While traditional diagnostic methods remain foundational, their limitations in addressing the nonlinear dynamics and operational complexity of modern turbines have accelerated the adoption of Artificial Intelligence (AI)-driven approaches. This review systematically examines advancements in AI-based fault diagnosis techniques, including machine learning (ML) and deep learning (DL), from 2019 to 2024, analyzing their evolution, efficacy, and practical challenges. Drawing on a curated selection of 55 studies (identified via structured searches across IEEE Xplore, ScienceDirect, and Web of Science), the paper prioritizes research employing data-driven or model-based methodologies with explicit experimental validation and clearly documented data sources. The excluded works lacked English accessibility, validation, or data transparency. Focusing on high-impact faults in gearboxes, blades, and generators, these components are responsible for over 70% of turbine failures, the review maps prevalent ML and DL algorithms, such as CNNs, LSTMs, and SVMs, to specific fault types, revealing hybrid AI models and real-world data integration as key drivers of diagnostic accuracy. Critical gaps are identified, including overreliance on simulated datasets and inconsistent signal preprocessing, which hinder real-world applicability. This study concludes with actionable recommendations for future research, advocating adaptive noise-filtering techniques, scalable hybrid architectures, and standardized benchmarking using operational turbine data. By bridging theoretical AI advancements with practical deployment challenges, this work aims to inform next-generation fault diagnosis systems, enhancing turbine longevity and supporting global renewable energy goals.

The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is imperative to implement efficient methods for the accurate detection and diagnosis of PV system faults to prevent unexpected power disruptions. This paper introduces a potential strategy for fault identification and classification through the utilization of machine learning (ML) techniques. The study aimed to use ML algorithms to identify and classify normal operations, seven different types of faults, in two operational modes (maximum power point tracking and intermediate power point tracking). Four machine learning algorithms and ensemble methods (decision trees, k‐nearest neighbors, random forest, and extreme gradient boosting) were employed, followed by hyperparameter tuning and cross‐validation to determine the best configuration. The results indicated that ensemble methods, particularly XGBoost, excelled in detecting and classifying faults in PV systems, achieving a 99% accuracy rate after hyperparameter adjustments. The TPR values show a high sensitivity of 0.999, with some achieving a perfect score of 1.000. The FPR shows very low values, with the majority of metrics indicating FPRs at or close to 0%. This performance is crucial in the solar energy context, as failing to detect faults can result in significant energy loss and increased maintenance costs.

The major issue in smart grid (SG) communications networks is the latency management of a vast amount of SG traffic in the access network that connects power substations to a large number of SG monitoring devices. There are three possible deployments for the SG access network considered by power industries: 1) a public access network with a mix of SG and human-to-human (H2H) traffic; 2) a private access network exclusively assigned to SG communications; and 3) a mix of private and public access networks, referred to as hybrid access networks. The SG communications traffic is classified as fixed-scheduling (FS) and event-driven (ED). The FS and ED traffic, generated by SG devices, occur on a periodic basis and as a response to electricity supply conditions, respectively. In this paper, we develop traffic models for public, private, and hybrid SG access networks based on queuing theory. By using these models, we derive an expression for the mean queuing delay for each traffic class in each network. We then propose an optimization problem to find the optimal partitioning of the SG traffic in a hybrid access network. The analytical results obtained from the proposed models agree very well with the simulation results.

The most challenging issue in Machine-to-Machine (M2M) last mile wireless access networks is the management of a large number of M2M devices that generate a vast amount of M2M traffic. The communications traffic for various M2M applications or services is classified as Fixed-Scheduling (FS) or Event-Driven (ED). The FS traffic is an operational traffic, which occurs on a periodic basis, such as reports on the measured data, sent by M2M sensor devices. The ED traffic, which is assumed to have a higher priority, is triggered by occurrence of specific events, such as the traffic generated by M2M devices, due to failure in the monitored systems. To date, we have not seen any traffic model for M2M last mile wireless access networks, which incorporates the different characteristics of ED and FS traffic. Thus, in this paper, we develop an analytical traffic model for M2M last mile wireless access networks based on a priority queuing system, which considers the combination of both periodic FS and random ED traffic. By using the proposed model, we derive expressions for the mean queuing delay and blocking probability of each traffic class. The derived analytical expressions are validated by simulations of a wireless network model and are shown to agree very well.