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In light of the reliable operation of renewable shipboard microgrids, this paper implements a fractional-order theory for flexible modeling for an accurate study of shipboard microgrids. Then, it introduces a novel variable structure control strategy based on a fractional-order reduced chatter nonlinear sliding mode. This strategy incorporates a time delay estimation and an optimization routine for parameter adjustments, aiming to achieve nearly time-optimal control performance and faster frequency accommodation. The proposed method exploits the benefits of fractional order modeling, reduced chatter sliding mode, and variable structure approaches. While the adapted reduced chatter strategy may decrease convergence speed, the variable structure strategy compensates for this by accelerating settling speed. These features, along with continuous, soft, and limited control signals, make the proposed method suitable for the challenging conditions of low inertia, high dynamics, uncertainty, and a noisy environment in nonlinear renewable shipboard microgrids. The Lyapunov stability proof and simulation results demonstrate the proposed method's fast, high accuracy, and reliable performance. Furthermore, the validation process, which involves comparisons with conventional sliding-mode strategies, fractional-order proportional-integral-derivative controllers, and experimental test results using the Speedgoat real-time target machine in conjunction with Simulink real-time, serves to further establish the superiority of the proposed approach. • Introduces a novel approach using fractional-order theory for a unique variable structure control strategy tailored for shipboard microgrids. • Presents a cutting-edge control strategy based on fractional-order chatter-free nonlinear sliding mode for nearly time-optimal control performance. • Exploits the advantages of fractional order modeling, chatter-free sliding mode, and variable structure approaches to tackle challenges faced by renewable shipboard microgrids. • Mitigates the convergence speed reduction caused by chatter free solution using continuous, soft variable structure, and limited control signals. [ABSTRACT FROM AUTHOR]

- In recent years, the aviation industry has witnessed a substantial integration of power electronics technology within Aircraft Microgrids (AMs). Consequently, the extension of electrical wiring networks has expanded, resulting in heightened intricacies within these systems. Therefore, the identification and location of faults in wiring networks have become an important topic in AMs to guarantee the safety of the electrical power systems. Time Domain Reflectometry (TDR) is widely used to locate and recognize electric wire faults. However, soft fault location on complex electrical networks using TDR is perplexing due to its weak effect on the reflected signal. Moreover, the existence of noise in the environment can worsen the TDR's performance. In this paper, a new approach based on TDR, along with the Subtractive Correlation Method (SCM) and Neural Network (NN), is proposed. The TDR response of the complex wiring network is determined using a newly proposed model based on the Finite Difference Time Domain (FDTD) method. The validity of the proposed model is established through experimentation including two distinct cable types also the introduced model notably enhances computational efficiency, a fact substantiated by our experimental findings and an extensive benchmarking against recent publications. These evaluations collectively underscore a significant reduction in computational time. Then, the reflected signal undergoes processing through SCM, a technique employed to amplify the subtle influence of the soft fault in two scenarios: one accompanied by noise and the other noise-free. Furthermore, NN is used to handle the inverse problem of localizing and characterizing the soft faults by their exact resistance values. Even within noisy environments, the proposed methodology excels in accurately locating and characterizing soft faults with a high degree of precision, all in real-time diagnostic scenarios. • Novel FDTD-based model for determining the TDR response of intricate wiring networks. • Empirically validated model enhancing computational efficiency and reducing time. • SCM approach amplifies subtle soft fault impacts, even in noisy environments. • Neural Networks excel in precisely localizing and characterizing soft faults by resistance values. • Real-time diagnostic capability for pinpointing and characterizing soft faults with exceptional precision. [ABSTRACT FROM AUTHOR]

The energy consumption of major equipment in residential and industrial facilities can be minimized through a variety of cost-effective energy-saving measures. Most saving strategies are economically viable where several algorithms can be employed to reduce energy consumption to reduce costs to a considerable extent. Machine learning (ML) is one of these techniques. A review of recent research efforts concerning the application of ML strategies to energy conservation and management problems is presented in this study. In addition, ML approaches and strategies for energy-saving problems, management, technologies, and control methods have been discussed. A comprehensive review of all available publications is also used to make observations about past considerations. As a result, it has been concluded that ML is capable of solving a wide range of decision and management problems within a short period of time with minimal energy consumption. In addition, ML perspectives have been viewed from the perspective of emerging communication technologies, instruments, and cyber-physical systems (CPSs), along with the advancement of ultra-durable and energy-efficient Internet-of-Things (IoT) based communication sensors technology. Moreover, a comprehensive review of recent developments in ML algorithms is also included, including safe reinforcement learning (RL), Deep RL, path integral control for RL, and others not previously. Lastly, critical ML considerations such as emergency and remedial measures, integrity protection, fusion with existing robust controls, and combining preventive and emergent measures have been discussed. The implementation of recently applied ML, RL, and IoT strategies for energy management, conservation, and resilient operation is clarified in this paper. The proposed review highlights the advantages and drawbacks of the recent energy conservation strategies. Finally, the perspective solutions have been clarified to cope with the world direction for zero energy buildings. • A comprehensive review of AI techniques in energy applications has been presented. • Empirical analysis of the energy management and conservation methods. • Describes the ML's and IoT's role in energy control and how to implement it. • Explains the challenges of demand-side data pre-processing of machine learning. • Offers possible directions for future research in the resilient operation of smart grids. [ABSTRACT FROM AUTHOR]

• Investigating and revealing potential risks of poor energy quality for shipboard microgrids. • Providing a clear view of the importance of selecting the accurate algorithms for analyzing the quality of energy and delivering a comparative study. • Proposing a developed algorithm, which is an offline-based energy quality analyzing technique developed for the short-term protective action stage to assess the energy quality of shipboard microgrids. • Proposing an enhanced algorithm, which is an online-based energy quality assessing technique, Which is very suitable for the long-term preventive action stage. Retrofitting of shipboard microgrids is receiving much attention nowadays due to the flexibility it offers to adapt existing ships with rapid market variations to move towards all-electric ships (A-ESs). This modernity mainly relies on incorporating the power electronics converters that unfortunately draw a large number of harmonics, which affect the energy quality and threaten the system stability and crew/passengers safety. The contribution of this paper lies in proposing two developed open-loop algorithms to assess the harmonics distortion of A-ESs without relying on signal periodicity. The first algorithm is an offline-based analyzing technique developed for the short-term protective action stage, which incorporates the eigenvalue solution inside the short-time fast Fourier transform (ST-FFT) to adapt its window size. Consequently, this algorithm can provide a comprehensive harmonics analysis with a fast transient response even under large system frequency drifts. The second algorithm is an online-based assessing technique, which is very suitable for the long-term preventive action stage to provide accurate harmonics distortion assessment with the fast transient response and efficient computation burden. This approach relies on cascading three moving average filters (MAFs) with particular window sizes to filter and estimate the mean value inside the discrete Fourier transform and the true RMS blocks without the need of frequency information. Hence, it results in enhancing the harmonics rejection capability of the algorithm even under the existence of non-characteristic harmonics and frequency drifts. Simulation and experimental results are provided to validate the efficacy of the proposed algorithms and the results are compared with traditional methods. [ABSTRACT FROM AUTHOR]