The growing digitalization of chemical-process-industries (CPIs) has brought benefits in efficiency and product quality, but it has simultaneously introduced new vulnerabilities. Cyber-threats represent new risks to CPIs, and compromised measurements can lead to hazardous situations. This study examines cybersecurity in CPIs, emphasizing the importance of process-domain knowledge in identifying and mitigating cyber-threats. While current detection strategies, such as anomaly detectors, network-centric intrusion systems, and secure-control methods, provide valuable protection, many suffer from key limitations, including limited adaptability and inadequate integration of heterogeneous cyber–physical information sources. These shortcomings reduce their practicality in large-scale, nonlinear, safety-critical CPIs. This work highlights important knowledge gaps, including a lack of scalable validation platforms, realistic cyber–physical datasets, adaptive control structures, and detection methods. To move beyond these limitations, this paper puts forward an interdisciplinary cybersecurity-framework that draws on expertise from chemical engineering, industrial communication engineering, data science, and computer science. This approach enables the development of cyber–physical co-simulation environments, multi-source-hybrid detection architectures, standardized interfaces, and human-in-the-loop decision tools. The study concludes that active engagement of chemical and process safety engineers, together with coordinated cross-domain collaboration, is vital for creating resilient, scalable, and forward-thinking cybersecurity solutions for CPIs safety and security.

Distribution automation solutions enhance reliability, minimize customer outages, and optimize system performance. In the distribution systems of the Taiwan Power Company, these solutions help reduce outage durations. This article proposes a graph reinforcement learning framework for power restoration while considering the operational and topological constraints of a distribution network. A graph convolutional network is used to extract environmental information necessary for restoring the distribution system. The extracted features are then utilized in a deep Q-network to give a better comprehension of massive graph-structured data and the status of automatic switches. Furthermore, service zone features derived from graph convolutional layers over the distribution network topology are used to develop an optimal switching strategy for network restoration using deep reinforcement learning. The effectiveness of the proposed method is validated through case studies on a practical 94-node-cell distribution system operated by Taipower.

Transactive energy systems are transforming traditional distribution grids into dynamic, market-based environments. However, fully achieving this vision requires addressing key challenges in distributed optimization and decentralized coordination. To this end, this paper proposes a novel framework to realize the benefits of transactive systems using targeted economic mechanisms and collaborative optimization methods. The proposed framework introduces a novel economic concept termed the transaction impact factor that links trading incentives with distribution locational marginal prices. By considering this factor, market decisions reflect the true operational impact on the grid. Moreover, to enable independent agents to collaborate iteratively and maximize their individual benefits, this paper develops a cyclic pattern identification-driven convergence algorithm. Simulation tests on an IEEE feeder system validate the effective performance of the proposed method in improving market efficiency, grid reliability, and integration of distributed resources.

System parameter uncertainty, variable nature of renewable energy sources (RESs), and false data injection (FDI) due to cyberattacks represent the main challenges against the resilient operation of microgrids and cybersecurity. These factors can push microgrid frequencies beyond safe limits, leading to system instability. This article presents a novel architecture integrating an unknown input observer (UIO) with adaptive model predictive control (AMPC) and an Internet of Things (IoT) platform for effective microgrid frequency control. The UIO, employing linear matrix inequalities (LMI) and quasi-oppositional learning-based equilibrium optimizer (QO-EO), estimates disturbances caused by FDI and RES variations. The AMPC adjusts generator output and manages electric vehicle (EV) charging/discharging to mitigate these disturbances and maintain stable system frequencies. Real-time system status visualization and alerts via the IoT dashboard enhance operational oversight. The study evaluates the proposed UIO-based AMPC against various cyberattacks, load fluctuations, wind power variability, and parameter uncertainties. Comparative analysis with model predictive control (MPC), proportional-integral-derivative (PID), and fuzzy logic controllers demonstrates superior performance in estimating FDI and managing RES variability, thereby ensuring microgrid stability under challenging conditions. Quantitatively, the proposed AMPC reduces settling time to less than 0.3 s, whereas conventional methods require more than 5 s under parameter uncertainty. In addition, the maximum overshoot is limited to below 0.008, compared to 0.012-0.0246 for traditional controllers. Simulation and experimental results affirm the effectiveness of the approach in suppressing cyberattack impacts and maintaining frequency stability within operational thresholds.

The electrification of maritime electrical power systems has progressed from isolated onboard upgrades to integrated shipboard power systems, shore-to-ship interfaces, and port microgrids operating under strict safety, reliability, and regulatory constraints. In parallel, artificial intelligence, machine learning, and advanced control techniques have been increasingly reported in literature to enhance energy management, resilience, and operational efficiency. However, their technical suitability and deployment readiness remain insufficiently assessed from a system-level and operational perspective. This paper presents a system-level technical assessment of control, optimization, and AI/ML-based approaches for maritime power systems, with exclusive focus on IEEE Transactions on Industry Applications. Using a rigorously selected dataset of forty-two journal papers, the study classifies methods by maritime system context, operational layer, and method role, audits formulation transparency, and evaluates deployment realism under non-ideal operational assumptions. To support more structured comparison, a semi-quantitative rubric is used to summarize how explicitly formulation and deployment realism criteria are addressed across method classes. The results show that optimization and model predictive control dominate safety-critical decision layers due to explicit constraint enforcement, while learning-based methods are limited to auxiliary roles such as forecasting, monitoring, and decision support. The rubric-based comparison further indicates that optimization, MPC, and hybrid AI–optimization approaches achieve the strongest formulation scores, whereas supportive learning-based methods remain weaker in deployment realism because sensing non-idealities, communication constraints, and real-world feasibility are rarely treated explicitly.

Recently, the cybersecurity of harbor-integrated shipboard microgrids (SMGs) has become a significant concern for ensuring the integrity and resilience of maritime energy systems within intelligent grids. However, the increasing reliance on internet-based technologies exposes SMGs to false data injection attacks (FDIAs), denial of service (DoS) attacks, and other cyber threats, which can compromise power management and operational stability. To address these threats, this paper proposes a hybrid detection framework that integrates Hilbert-Huang Transform (HHT) for signal decomposition and anomaly identification, Long Short-Term Memory Variational Autoencoder (LSTM-VAE) for deep feature extraction and latent attack pattern detection, and a Bidirectional Gated Recurrent Unit (Bi-GRU) for sequential dependency analysis and real-time FDIA detection. The HHT-based empirical mode decomposition (EMD) enables time-frequency analysis by extracting intrinsic mode functions (IMFs), improving the detection of nonstationary attack patterns in maritime environments. The LSTM-VAE identifies subtle attack signatures within SMG data, while the Bi-GRU enhances temporal modeling and classification, ensuring accurate and real-time detection with minimal false positives. Extensive validation against various cyberattack scenarios, including component-level intrusions, demonstrates the framework's robustness in detecting and mitigating cyber threats. By optimizing the deep LSTM-VAE-based Bi-GRU model with carefully tuned hyperparameters, the proposed method achieves a detection accuracy of 99.80%, surpassing existing techniques. This work introduces a scalable, adaptive, and maritime-specific cybersecurity solution, significantly enhancing shipboard microgrid security and operational resilience in intelligent maritime SMGs.

The increasing integration of inverter-based resources (IBRs), such as Distributed Generation Units (DGUs), in shore-power cyber-physical DC shipboard microgrids (SMGs) enhances maritime energy autonomy and operational flexibility but also introduces critical vulnerabilities to cyberattacks. In particular, bounded and unbounded false data injection attacks (FDIAs) and man-in-the-middle (MiTM) attacks targeting measurement units and communication links within shore-ship inverters and DGU converters can destabilize voltage regulation, disrupt power-sharing coordination, and compromise system integrity. To address these challenges, a novel distributed, resilient control strategy is proposed in this paper for the operation of DC SMGs in dynamic maritime environments. The approach integrates decentralized voltage regulation with adaptive load-sharing mechanisms and embedded compensation terms to autonomously detect and mitigate malicious input perturbations in IBRs. The controller dynamically suppresses corrupted feedback signals and ensures resilient coordination among DGUs without relying on centralized supervision or prior knowledge of attack models. The proposed method guarantees uniformly ultimately bounded convergence for global voltage regulation and proportional load sharing objectives, even under unbounded attack settings. A Lyapunov-based stability analysis is also performed to formally establish these guarantees, ensuring robust performance against a wide range of adversarial scenarios. Test results of a practical shore-power DC SMG are presented to validate and demonstrate the performance of proposed method in terms of voltage stability, power-sharing accuracy, and cyber-resilience compared with those obtained from standard control schemes against multiple and combined attacks. The results have indicated that the proposed method can be a practical and adaptive solution for secure, stable, and resilient operation of IBRS-based shore-power DC SMGs under advanced threats.

The rapid electrification of maritime transport demands reliable emission monitoring systems to guarantee data integrity, regulatory compliance, and environmental sustainability. This study introduces a digital twin-based Internet of Things (IoT) framework that integrates hybrid machine learning models, including Support Vector Regression (SVR), Random Forest (RF), and Deep Neural Network (DNN), with the Isolation Forest (IF) anomaly detection algorithm for real-time ship emission assessment and cyberattack resilience. Through extensive experimentation, the proposed models achieved training and testing mean squared error (MSE) values of 0.0352–0.0387 and R² scores above 0.96, confirming high prediction accuracy in modeling complex emission behaviors. Under false data injection and adversarial perturbation scenarios, the hybrid approaches, including RF-IF, SVR-IF, and DNN-IF demonstrated superior anomaly detection accuracy ranging from 92.44% to 95.64%, outperforming state-of-the-art thresholding, isolation-based methods, Variational Autoencoder (VAE), and Long Short-Term Memory Autoencoder (LSTM-AE). The DNN-IF model exhibited the highest detection precision and resilience against cyber threats. These findings highlight the novel contribution of combining predictive modeling with anomaly detection in a digital twin environment, offering a secure and intelligent monitoring solution for next-generation low-emission marine vessels.

This paper proposes a hybrid control scheme that integrates the horned lizard optimisation algorithm (HLOA) with a super-twisting sliding mode control (ST-SMC) for robust global maximum power point tracking (GMPPT) in photovoltaic (PV) systems under partial shading conditions (PSC). The method employs a dual-loop structure: The HLOA performs a global search of the power-voltage curve in the outer loop, while the inner-loop ST-SMC ensures finite-time convergence of the converter's duty cycle to the computed reference. This decouples global exploration from fast tracking, achieving both high accuracy and rapid response. The framework's superiority is validated through simulation and an experimental prototype. In a comparative analysis against advanced metaheuristics including the grey wolf optimiser (GWO), whale optimisation algorithm (WOA), flower pollination algorithm (FPA), and enhanced leader particle swarm optimisation (ELPSO), the proposed HLOA-ST-SMC technique converges within 0.5 s, exceeding ELPSO by 29% and achieving over 50% faster convergence than GWO, more than 67% faster convergence compared with PSO, and over 69% faster convergence relative to WOA and FPA, while consistently maintaining a high tracking accuracy of 99.87%. Experimental results confirmed a tracking efficiency of 99.6% with negligible steady-state oscillations. The proposed HLOA-ST-SMC framework thus sets a new benchmark for dynamic performance and robustness in GMPPT applications.

Seaports play a crucial role in global trade, acting as key hubs in international logistics networks. However, the growth of maritime activities has led to significant environmental challenges, including elevated carbon emissions and pollution. This paper presents an in-depth bibliometric analysis of sustainable and smart port initiatives, utilizing data from the Web of Science database to identify trends, key contributors, and technological advancements. The study focuses on the adoption of innovative technologies, such as All-Electric Ships, smart grids, and renewable energy systems, which are essential for modernizing port operations and reducing environmental impact. The analysis categorizes initiatives into five main areas: Energy & Fuels, Climate & Air, Port & City, Waste & Circular Economy, and Environment & Biodiversity. The findings highlight the potential of hybrid microgrid systems and advanced digital solutions in enhancing energy management and operational efficiency. Policy frameworks, including differentiated carbon pricing and global port incentive programs, are proposed as effective measures to align seaport sustainability with global decarbonization goals. This research provides a concise overview of current developments and outlines future strategies for achieving sustainable port infrastructure.

Cyber resilience is paramount in modernizing maritime transportation; however, cyberattacks pose significant challenges in deploying resilience, remote control, and monitoring technologies. Shipboard microgrids (SMGs) are not immune to cyber threats, given their tightly integrated cyber-physical processes managed through advanced software. With extensive power electronic components, SMGs are highly vulnerable to attacks, compromising their security and stability. False data injection attacks and potential malware, spoofing, and jamming attacks threaten the integrity and safety of SMGs. Robust attack detection and prevention techniques are crucial for enhancing SMG resilience, stability, and adaptive capability and stabilizing them at their optimum operating state. This study addresses cutting-edge cybersecurity issues concerning SMGs, offering an inclusive review of attacks and protective measures across critical maritime systems for attack detection, prevention, and countermeasures for SMGs. To illustrate the cyber resilience in the SMG using artificial intelligence methods, a novel cyberattack detection method integrating the Hilbert–Huang transform (HHT) and deep learning (DL) is proposed and used as a case study. An advanced one-dimensional convolutional neural network (1D-CNN) with the Adam optimizer and HHT for signal feature extraction has been employed along with the construction of multiple-input basis models within the DL framework for automatic intrinsic feature extraction from raw signal fluctuations. Various basis patterns are generated using a 1D-CNN-based autoencoder–decoder system for deep feature extraction, optimizing the ensemble’s performance to detect attack types using weighted 1D-CNN models. The simulation results demonstrate the efficiency and reliability of the proposed method, achieving a 94.75

The integration of renewable energy sources in shipboard microgrids introduces challenges such as model uncertainties, external disturbances, and measurement noise, which impact system stability and performance. To address these challenges and support reliable operation, this paper proposes a continuous adaptive barrier function technique based on a non-singular integral-type terminal sliding mode stabilizer for load frequency control in heterogeneous renewable shipboard microgrids. First, a nonlinear dynamic model of shipboard microgrid systems is developed, capturing the effects of uncertainties and disturbances. A novel non-singular integral-type terminal sliding mode control method is then introduced to ensure finite-time convergence. Additionally, an adaptive gain strategy is proposed to estimate the upper bounds of unknown disturbances and noise. To suppress the chattering phenomenon and ensure a smooth control law, a continuous adaptive barrier function is introduced as an innovative alternative to conventional discrete adaptive gains. The proposed method, grounded in Lyapunov stability theory, ensures finite-time stability and robust performance while reducing transient oscillations and disturbances without requiring prior knowledge of their upper bounds. Simulation results and Speedgoat hardware-in-the-loop testing, compared with existing methods, validate the superior performance and practical applicability of the proposed approach.

Grid-connected inverters play a pivotal role in the integration of renewable energy sources. However, their dynamic interaction with the equivalent grid impedance at the point of common coupling (PCC) is a major contributor to system instability. Therefore, real-time identification of grid impedance under time-varying operating conditions is critical for ensuring stable and reliable system performance. Recently, the use of pseudo-random binary sequence (PRBS)-based excitation for wideband impedance identification has gained more attention due to its favorable frequency-domain characteristics and ease of implementation. This paper extends the application of the online PRBS-based impedance estimation framework beyond the conventional equivalent RL model to include the more realistic and accurate lumped-T and lumped-π equivalent transmission models. Furthermore, a dedicated phase-locked loop (PLL) is integrated into the estimation process to accurately capture the low-frequency impedance dynamics. The proposed methodology is validated in a MATLAB/Simulink environment, demonstrating high fidelity and real-time estimation accuracy when compared to the analytical solutions across all considered models. The accurately estimated models have potential applications in grid stability analysis, adaptive control of power converters, and real-time monitoring in power system operation and protection.

This paper presents an adaptive deep neural network (DNN) approach for intrusion detection and prevention in shipboard AC/DC microgrids, focusing on shore-to-ship power connections and power quality (PQ) challenges. The method addresses false data injection attacks (FDIAs) that disrupt secondary control, leading to voltage and current regulation failures. By integrating Fast Fourier Transform (FFT) for frequency-domain feature extractions such as voltage sag/swell, harmonics, and transient distortions with DNN classification, the model achieves high accuracy in detecting cyberattacks under PQ disturbances. Optimized using the Adam optimizer and ReLU-sigmoid activation, the framework enhances detection accuracy while reducing false positives. The obtained results demonstrate superior performance of FFT-DNN over state-of-the-art methods, achieving 97.7% accuracy across attack scenarios. The approach effectively classifies between cyber-intrusions and power quality disturbances, offering a scalable cybersecurity solution for maritime systems. This study advances secure and resilient shore-ship DC microgrids by addressing cybersecurity vulnerabilities and power quality challenges in shore-power connections.

Renewable energy and energy storage systems rely on power converters, which lack the inherent inertia response of synchronous generators. As their penetration increases, system inertia is influenced by their control strategy. Without effective control mechanisms, inertia declines, challenging frequency stability and heightening the risk of rapid frequency drops and outages. Accurate inertia estimation is crucial for maintaining grid stability. To address this challenge, this paper proposes a variable structure recursive dynamic regressor extension and mixing for estimating inertia in renewable power system. By incorporating primary frequency control effects and simulating droop control in converters, the proposed method improves estimation accuracy. The test results from both a modified IEEE 9 bus system and a practical island renewable power system confirm its effectiveness.

The sine-cosine algorithm (SCA) is a metaheuristic algorithm based on the trigonometric functions sine and cosine to explore and exploit the searching area. However, typical SCA faces serious limitations, in particular, premature convergence, exploration-exploitation disequilibrium, and a susceptibility to being trapped in local maxima, making it inefficient for complex optimization challenges. To address and overcome these challenges, a multiple vector-based sine-cosine algorithm (MTV-SCA) is proposed for robust tracking of the global maximum power point (GMPP) in photovoltaic (PV) systems under partial shading conditions (PSC). The MTV-SCA integrates multiple search strategies with three adaptive control parameters through a MTV mechanism, significantly enhancing convergence speed and tracking accuracy. Furthermore, an Integral Derivative Sliding Mode Control (IDSM) is incorporated to refine the tracking process, ensuring stability and resilience against rapid environmental fluctuations. Simulation results, validated using a Boost converter, that the proposed MTV-SCA-IDSM approach achieves a fast-tracking time and high tracking accuracy across various PSC. These results confirm that the MTV-SCA-IDSM approach is a highly efficient and reliable solution for real-world PV energy harvesting applications.

Enhancing cybersecurity in DC shipboard microgrid (SMG) systems is crucial for maintaining the resilience of energy operation in maritime transportation systems (MTS). However, increasing cyber threats pose significant challenges to deploying resilient technologies in intelligent DC SMGs. These intricate cyber-physical systems, comprising power electronics, distributed generation units, sensors, and monitoring technologies, are managed remotely, making them vulnerable to attacks that jeopardize their stability and security. Existing solutions often require additional support due to low detection and high false alarm rates, primarily from manual analysis. To address these issues, this study presents a sophisticated deep learning-driven cyber-attack detection and identification model designed to effectively enhance the security of DC SMGs in MTS. The proposed framework leverages Long Short-Term Memory (LSTM) with variational autoencoder (VAE) architectures for deep feature extraction (DFE) under attack scenarios. VAE schemes automatically uncover hidden patterns within the DC SMG network, and their outputs are utilized by deep learning (DL) schemes for accurate attack detection. The proposed model incorporates a deep artificial neural network (ANN)-based encoder-decoder scheme to identify attacks in DC SMGs, contributing to an efficient operation. Additionally, DL-based LSTM-VAE and ANN are meticulously designed with appropriate hyperparameters to counter cyber threats. The data-driven DL method achieved the testing accuracy of 99.81%, outperforming various state-of-the-art (SOTA) DL and machine learning (ML) methods. Extensive testing scenarios have been conducted to demonstrate the performance and robustness of the proposed DL methods under different levels of attacks, ensuring their efficacy in enhancing the cybersecurity of DC shipboard microgrids.

Circuit breakers play a crucial role in power system protection, ensuring the safety and reliability of electrical networks. However, their performance can degrade over time due to mechanical wear, environmental conditions, and operational stress. Early detection is essential to prevent protection system failures. To this end, this paper presents a novel online monitoring framework for assessing the health condition of circuit breakers in real-time, enabling predictive maintenance and improving system reliability. The proposed approach utilizes advanced signal processing, and data analytics to analyze circuit breaker operation parameters such as closing and opening times, coil current, and transient signals. Then, it uses those data to train a novel Kernal density Isolation Forest based algorithm for status identification of on grid circuit breakers. Case studies and experimental results demonstrate the effectiveness of the proposed method in identifying early signs of circuit breakers degradation and preventing unexpected failures.