Industry 5.0 represents a paradigm shift from Industry 4.0, repositioning the human factor, sustainability and resilience as central values of industrial processes. Although recent literature extensively covers the individual technologies associated with this transition, a systematic approach is lacking that simultaneously maps the enabling technologies onto the three official pillars of Industry 5.0, assesses their maturity level and identifies tensions between the pillars. This paper proposes a pillar-based taxonomy, namely Human-Centricity, Sustainability and Resilience, as an original framework for classifying key technologies for Industry 5.0. The taxonomy integrates three dimensions of analysis: pillar membership, technological maturity level and inter-pillar conflict relationships, providing a structured framework for analyzing Industry 5.0 technologies. Based on the proposed taxonomy, cross-cutting technologies that simultaneously serve multiple pillars are identified, together with four key inter-pillar trade-offs. The paper concludes with a structured agenda of open research directions, derived directly from the taxonomy analysis. The results provide a useful framework for both researchers positioning their contributions in the context of Industry 5.0 and industrial practitioners assessing the technological maturity of available solutions.

Early and reliable diagnosis of inter-turn short-circuit (ITSC) faults is critical to maintaining the reliability, availability, and safe operation of doubly fed induction generators (DFIGs) used in wind energy conversion systems (WECSs). Incipient winding faults are particularly challenging to identify because their electrical signatures can be masked by the inherent spectral complexity of DFIG operation and variations in wind and operating conditions. This study proposes a hybrid Fast Fourier Transform-Adaptive Neuro-Fuzzy Inference System (FFT–ANFIS) diagnostic framework for the detection, localization, and severity assessment of ITSC faults in both stator and rotor windings. The proposed approach employs the FFT method to extract fault-sensitive harmonic components from stator-current signals, which are subsequently used as diagnostic features by an Adaptive Neuro-Fuzzy Inference System (ANFIS). By integrating spectral feature extraction with nonlinear neuro-fuzzy classification, the proposed framework provides an efficient and interpretable mechanism for distinguishing healthy and faulty operating conditions and assessing fault severity. The methodology is evaluated using MATLAB/Simulink simulations under healthy and multiple ITSC fault conditions with different fault locations and severity levels. The results demonstrate 100% classification accuracy for stator faults, rotor faults, and multiple short-circuit (MSC) fault conditions, together with near-zero prediction error in fault-severity estimation. These results confirm the high discriminative capability of the selected FFT-based spectral features and the effectiveness of ANFIS in establishing the nonlinear relationship between fault signatures and fault conditions. In addition, the proposed framework maintains low computational complexity and is therefore suitable for real-time condition-monitoring applications. Compared with existing diagnostic approaches, the proposed method provides a unified framework for multi-fault diagnosis while combining high diagnostic accuracy, computational efficiency, and interpretable decision-making. The proposed FFT–ANFIS framework consequently offers a practical approach for early fault detection and condition-based maintenance of DFIG-based wind turbines, with the potential to reduce unplanned downtime, maintenance requirements, and energy-production losses.

This paper presents an intelligent cascaded fractional-order proportional-integral (CFO-PI) control strategy optimized using a genetic algorithm (GA) for a 1.5 MW DFIG-based multi-rotor wind turbine (MRWT) system. The primary objective is to enhance operational performance and power quality. The proposed method is evaluated against the conventional direct power control scheme using a traditional PI regulator (DPC-PI) to demonstrate its effectiveness. Comparative analysis shows substantial performance improvements achieved by the CFO-PI approach. Specifically, active power ripple is reduced by 61.71% compared to DPC-PI, resulting in smoother power delivery and improved grid compatibility. In addition, the steady-state error of active power decreases by 72.60%, indicating improved tracking accuracy. For reactive power, a 52.03% reduction in ripple is observed, while current ripple is reduced by approximately 56%, reflecting enhanced waveform quality. These results highlight the CFO-PI controller's capability to maintain better power quality and steady-state performance relative to conventional DPC-PI. Overall, the GA-optimized CFO-PI controller provides a promising alternative for improving dynamic performance and power quality in DFIG-based MRWT systems.

ABSTRACT A viable approach to meet rising power demands and mitigate global warming is the installation of wind turbine systems (WTSs). However, variable wind speeds can significantly impact the energy output of these highly interconnected and nonlinear systems. As a result, maintaining energy quality and operational performance remains a major challenge for researchers and decision‐makers. Although proportional–integral (PI) regulators and two‐level converters are commonly used in WTSs, they may struggle under rapidly changing wind conditions. This study proposes a command technique for a WTS that utilizes a doubly fed induction generator (DFIG) to manage energy output amid fluctuating wind conditions. The proposed strategy improves current control and allows for independent management of DFIG power by integrating a matrix converter (MC) with a fractional calculus‐based PI regulator. Unlike usual AC/DC/AC converters, the MC is an advanced AC/AC energy converter that offers enhanced voltage and frequency control along with bidirectional power flow. The effectiveness of the MC and fractional calculus‐based PI regulator is evaluated in terms of minimizing torque ripples, reducing total harmonic distortion (THD), and regulating DFIG energy. MATLAB/Simulink simulations indicate that the new robust control outperforms usual algorithms by reducing torque fluctuations and current THD. A comparative analysis shows improvements in power overshoot, response time, THD, and power ripple mitigation. Furthermore, compared with the two‐level converter systems, the new robust algorithm demonstrates greater strength against variations in wind conditions and system parameters.

Grid-synchronization is a fundamental requirement for three-phase grid-connected power converters, especially in modern converter-dominated systems. Conventional synchronous reference frame phase-locked loops (SRF-PLLs) typically rely on a PI-type loop filter, whose tuning and dynamic behavior can become sensitive to grid distortions and control-loop interactions. This paper proposes an Embedded Machine Learning–based PLL observer that reformulates angle synchronization as an online optimization problem and updates the estimated electrical angle and frequency using a gradient-descent (GD) learning law driven by the SRF phase detector signal. The resulting structure preserves standard Clarke/Park processing while replacing the classical PI loop filter with a compact adaptive update suitable for realtime DSP/MCU implementation (Edge AI). The proposed observer is integrated into a three-phase AC–DC converter control framework, and a large-signal stability analysis is developed using a Lyapunov-based argument to establish boundedness and convergence properties under stated assumptions. Simulation and experimental results verify accurate phase tracking and stable synchronization performance, demonstrating the practical feasibility of the proposed embedded learning PLL observer for grid-connected power-electronic systems.

The rapid proliferation of Internet of Things (IoT) devices in smart agriculture has exposed critical vulnerabilities in irrigation monitoring infrastructure, particularly for time-series sensor streams transmitted over Low-Power Wide-Area Networks (LPWAN). This paper presents a comprehensive review of cybersecurity challenges and countermeasures for agricultural IoT systems, with emphasis on three interconnected domains: (i) TinyML-based anomaly detection for on-device security of irrigation time-series data, (ii) hierarchical edge-fog-cloud security architectures for deployment of intrusion detection and authentication mechanisms, and (iii) communication-layer hardening for Wireless Sensor Networks (WSN) and LPWAN protocols including LoRaWAN, NB-IoT, and ZigBee. We systematically survey attack taxonomies across all IoT layers, from false data injection and replay attacks on sensor streams to cloud-layer ransomware and supply chain threats. Drawing on a curated corpus of recent literature, we identify open gaps in standardized security benchmarks, lightweight cryptographic protocol design, and TinyML model validation under adversarial conditions. Our analysis highlights the emerging role of federated learning and blockchain-based authentication as scalable solutions for distributed agricultural deployments and identifies explainable AI (XAI) as an essential complement for operator trust and auditability of on-device anomaly detection decisions.

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications due to their high efficiency, compact structure, and excellent dynamic performance. However, achieving accurate speed control with high robustness under load disturbances and parameter uncertainties remains a significant challenge. Conventional proportional-integral (PI) controllers often suffer from overshoot, slow dynamic response, and sensitivity to nonlinear operating conditions. To address these limitations, this paper proposes an intelligent control strategy that combines third-order sliding mode control (TOSMC) with the Golden Jackal Optimization (GJO) algorithm for optimal PMSM speed regulation. The proposed TOSMC-GJO approach aims to enhance the operational performance, robustness, and reliability of PMSM drives. The control structure consists of an optimized outer-loop speed controller and an inner-loop predictive current controller to improve current quality and eliminate the need for conventional PI tuning. The controller parameters are optimized using a fitness function designed to minimize tracking error, overshoot, settling time, torque ripples, and total harmonic distortion (THD). Simulation results under variable speed and load torque conditions demonstrate that the proposed TOSMC-GJO controller achieves superior performance compared with PI control and TOSMC optimized using Grey Wolf Optimization (GWO). The proposed strategy eliminates speed overshoot and reduces the response time to 0.0052 s, compared with 0.0056 s for TOSMC-GWO and 0.011 s for PI control. In addition, the THD of stator currents is reduced to 6.12%, improving current quality and reducing harmonic distortion. The proposed controller also provides smoother torque response, better disturbance rejection capability, and improved waveform symmetry. These results confirm that integrating high-order nonlinear control with metaheuristic optimization significantly improves the dynamic performance, operational reliability, and robustness of PMSM drive systems under demanding operating conditions.

The smart city represents a new stage in urban evolution, driven by technological progress, social transformations, and the increasing emphasis placed on sustainability. This metamorphosis generates hub-type architectural models, used not only for data collection and interconnection but also for the management and monitoring of people, resources, and urban services. This discussion addresses how digital urbanism has followed different paths globally by synthesising technological, economic, social, and governance perspectives. Compared with traditional models of urbanisation, new smart cities are built not only for digital interconnection but also to be citizen-centred, environmentally friendly, and resilient to global crises. This article analyses recent scientific literature on the theoretical and practical foundations of technologies that support data-driven decision-making, infrastructure efficiency, and the delivery of inclusive public services. At the same time, major challenges are highlighted, such as the lack of system interoperability, information fragmentation, and the risks associated with excessive surveillance, which can generate social exclusion, as well as financial and political constraints. International examples from Helsinki, Barcelona, Dubai, and Singapore offer both models that have achieved success and critical lessons about the limits of these approaches. This paper is not limited only to the problems faced by smart cities. It also highlights the opportunities they can bring. Finally, based on the conclusions of the analysis carried out and the identified trends, a strategic framework is proposed, oriented towards responsible innovation, collaboration, and sustainability. This approach contributes to informing researchers, decision-makers, urban planners, and the public interested in the transformation of the urban environment.

In recent years, acoustic signal analysis has emerged as a promising approach for fault detection in electromechanical systems, providing a non-invasive, low-cost, and real-time alternative to traditional monitoring techniques. This paper presents the design and implementation of a low-cost embedded system for acoustic fault detection. The proposed system is built on a Raspberry Pi Nano platform, integrating a low-power microphone, real-time audio signal preprocessing, and lightweight machine learning models for classification of normal versus faulty operating conditions. The focus is placed on developing an efficient signal processing pipeline that includes noise reduction, feature extraction (time and frequency domain descriptors, Mel-frequency cepstral coefficients), and on-device classification using compact neural network architectures. The embedded setup enables autonomous monitoring without the need for external computation resources, making it suitable for edge deployment in industrial and IoT environments. Experimental validation is carried out using publicly available datasets such as MIMII (Malfunctioning Industrial Machine Investigation and Inspection), as well as preliminary real-time recordings. The results demonstrate that the system achieves reliable fault detection accuracy while maintaining low computational and energy costs, highlighting its potential for scalable deployment in smart maintenance applications.

The intermittent nature of renewable power sources, nonlinear load effects, and harmonic distortions induced by power electronic converters complicate the maintenance of high energy quality in microgrid-connected hybrid renewable power systems. In a range of operating conditions, conventional strategies-including fractional-order proportional-integral (FOPI) controllers-frequently prove ineffective in delivering both robust harmonic mitigation and expeditious dynamic response. To surmount these constraints, the present paper puts forth an intelligent control solution that is predicated on a fractional-order fuzzy logic (FOFL). The FOFL is integrated into a multi-converter HRPS, comprising a photovoltaic generator, a lithium-ion battery power storage system, and a wind turbine equipped with a permanent magnet synchronous generator. A multifunctional voltage source inverter has been developed to control these parts, which are interfaced via a common DC bus. Through the implementation of MATLAB 2021 simulation studies, the efficacy of the suggested algorithm is verified and evaluated in comparison to the FOPI. The findings indicate that the FOFL enhances system efficacy by minimizing harmonic distortion, improving energy quality, and achieving a faster dynamic response under various circumstances. In the context of grid-connected microgrid environments, the FOFL has been demonstrated to offer superior overall energy management, robustness, and adaptability when compared to other evaluated strategies.

Proton exchange membrane fuel cell (PEMFC) systems typically generate low-voltage and high-current DC power, requiring a step-up converter interface for electric vehicle (EV) and DC microgrid applications. In addition, excessive current ripple and rapid transient loading conditions may increase electrical and thermal stress within the fuel cell stack. Consequently, both converter topology and control strategy play important roles in maintaining stable system operation and favorable PEMFC operating conditions. This paper presents a differential flatness-based nonlinear control strategy for a PEMFC-fed multiphase interleaved boost converter. The proposed control structure combines inner-loop inductor current regulation with outer-loop DC bus energy regulation. This configuration achieves stable voltage control, balanced phase-current sharing, and reduced fuel cell current ripple during transient operating conditions. A two-phase interleaved boost converter prototype was experimentally implemented using a 2.5 kW PEMFC platform and a dSPACE DS1202 MicroLabBox real-time controller. Experimental tests under steady-state and dynamic loading conditions were conducted to evaluate DC bus voltage regulation, transient response, current-sharing capability, and robustness against load disturbances. The experimental results demonstrated that the proposed nonlinear controller achieved faster transient voltage recovery and smaller DC bus voltage deviation compared with a conventional PI-based control approach. In addition, the interleaved converter structure reduced input current ripple at the PEMFC output terminals during dynamic operation. Overall, the results indicate that the proposed control strategy is suitable for PEMFC-powered EV and DC microgrid applications requiring stable DC bus regulation and fast dynamic power control. Experimental results demonstrate that the proposed controller reduces the DC bus voltage recovery time from approximately 150 ms to 50 ms, corresponding to a 66.7% improvement over a conventionally tuned PI controller. In addition, the maximum DC bus voltage deviation is reduced from approximately 1.0 V to 0.5 V while maintaining balanced phase-current sharing with less than 3% mismatch throughout the tested operating conditions.

The increasing penetration of wind energy is a key enabler of the global transition toward low-carbon and sustainable power systems. However, ensuring high efficiency, power quality, and operational reliability under variable wind and grid conditions remains a critical challenge for doubly fed induction generator (DFIG)-based wind energy conversion systems. Conventional direct power control (DPC) strategies based on proportional-integral (PI) regulators are simple and widely implemented, yet their performance degrades in the presence of nonlinear system dynamics, parameter uncertainties, and rapid wind speed fluctuations-factors that directly affect energy yield, component lifetime, and grid stability. To enhance the sustainability and resilience of wind power generation, this study proposes a cascaded neural network-based control architecture for DFIG-driven systems. The outer neural control loop regulates active and reactive power references to optimize energy capture and support grid requirements, while the inner neural loop ensures fast and precise tracking by generating appropriate control signals for the rotor-side converter. Leveraging their adaptive learning capability, the neural controllers effectively model nonlinear dynamics and compensate for uncertainties in real time. Compared with the conventional DPC-PI scheme, the proposed approach achieves improved dynamic response, reduced power and electromagnetic torque ripples, enhanced disturbance rejection, and greater robustness under varying wind and grid conditions. These improvements contribute to sustainable energy production by increasing conversion efficiency, reducing mechanical stress, minimizing maintenance requirements, and extending turbine service life. Furthermore, improved reactive power control enhances grid integration and supports stable operation in renewable-dominated power systems. Simulation results validate the superior performance of the cascaded intelligent control strategy. The findings demonstrate that advanced adaptive control techniques can play a significant role in strengthening the reliability, efficiency, and long-term sustainability of wind energy systems, thereby supporting global decarbonization goals and the broader transition to sustainable energy infrastructures. Future work will focus on real-time implementation, stability assessment, and experimental validation to facilitate practical deployment.

This work investigates integrating a shunt active power filter (SAPF) with renewable energy sources (RESs) to enhance harmonic mitigation and overall power system performance. Hybrid renewable energy sources (HRES) combine RESs and manage them with smart strategies aimed at maximizing power point tracking in wind and PV energy setups. This research, therefore, proposes a new solution: an HRES carefully combined with a SAPF for the efficient mitigation of power quality. The performance of the SAPF largely depends on its control loops, as these determine its ability to generate sufficient compensating current. Fluctuations in the dc link voltage can disrupt the system’s operation. Normally, this voltage is kept high and steady, influenced by the power demand, but under light load conditions, it may rise excessively, causing switching noise and higher losses. To overcome this issue and keep the dc link voltage stable while also reducing total harmonic distortion from both balanced and unbalanced nonlinear loads, RES is integrated on the dc side of the SAPF, which offers better voltage regulation than conventional methods. The anticipated tactic is tested further down both ideal and distorted grid situations, with varying load patterns, and its performance is benchmarked against other existing control strategies.

This study improves the field-oriented control (FOC) strategy used in double-powered induction generator (DPIG) wind-turbine systems by replacing traditional proportional–integral (PI) controllers with fractional-order PI regulators tuned through particle swarm optimization (FOPI-PSO). The proposed FOC–FOPI-PSO framework integrates three optimized controllers that enhance energy extraction and stabilize the power delivered to the grid. In contrast to previous PSO-based fractional or hybrid control approaches—which often add computational burden or focus on different machine configurations—this work embeds a PSO-tuned fractional PI controller directly into the standard FOC architecture of a DPIG. This preserves the simplicity of conventional PI implementation while specifically improving DPIG power-flow behavior, delivering quicker transient response and substantially attenuated power oscillations. MATLAB simulations demonstrate marked performance gains: active-power oscillations are reduced by 90.90%, response time improves by 2.52%, and steady-state error drops by 89.09% relative to traditional FOC–PI control. Additionally, current total harmonic distortion decreases by approximately 99.46%. These results highlight the proposed method as a highly effective and reliable control solution for modern wind-energy systems.

This article investigates the methodology for detection and identification of the electrical faults that can occur in photovoltaic panels. The fault-free operation of photovoltaic systems is an achievable goal for increasingly longer periods of time. Preventing faults, improves the efficiency of electricity supply and increases the lifespan of the panels. This paper highlights the most common panel faults, using the multiple simulation method, based on scenarios that encompass each case of electrical fault. Emphasis is on describe and simulation the types of faults such as open circuit, cell is short-circuited, the rows of photovoltaic cells connected in parallel are short-circuited, system output is short-circuited, increase series resistance between two rows, etc. The simulation results and validation of the proposed technique are implemented by MATLAB/Simulink toolbox and PSpice. The purpose of simulating the types of defects is to demonstrate, based on the results, first of all, the accuracy of the values obtained, but also the direct impact on reducing the electrical power provided by the photovoltaic panels.

ABSTRACT A comprehensive analysis of new technologies, challenges and trends in wastewater treatment plant modeling and control Wastewater treatment is a critical process for protecting water resources and ensuring environmental sustainability. The modeling of key parameters such as dissolved oxygen (DO), nitrogen (in various forms like ammonia, nitrite, and nitrate), plays a fundamental role in understanding and optimizing the performance of wastewater treatment plants (WWTPs). The present document offers a thorough review of modeling strategies for oxygen and nitrogen compounds, emphasizing the importance of predictive accuracy and process control. A comprehensive review of the extant literature reveals the current state of the field, while also analyzing recent research findings to identify gaps and limitations in existing models. This review employed a systematic approach to analyze 64 peer-reviewed studies (2021–2025) using PRISMA criteria to identify emerging trends in AI-driven and hybrid modeling. The paper's conclusions offer a series of recommendations for the refinement of the model and its subsequent implementation in real-time monitoring systems. The review underscores recent advancements in data-driven, classical, and hybrid modeling strategies, with a particular emphasis on the increasing integration of artificial intelligence and machine learning techniques into conventional process models. Key trends, limitations, and research gaps are identified.

Multilevel inverters (MLIs) find a wide range of applications in high and medium-power applications, including electric vehicles, FACTS devices, HVDC, and renewable energy systems. Since it utilizes a large number of semiconductor devices and capacitors, it is prone to open-circuit (OC) and short-circuit (SC) faults, which decrease its reliability. As factories and machinery become increasingly complex and costly, there is diminished tolerance for degradation in performance, reduced productivity, and safety risks. This necessitates the prompt detection and identification of potential abnormalities and faults, as well as the implementation of real-time fault-tolerant operations to minimize performance decline and avert hazardous situations. Subsequent to first defect detections in MLIs, corrective measures can prolong the standard operation of MLIs and, in certain instances, reduce the system's capacity to avert unforeseen shutdowns. The research contributions in this study provide a valuable addition to the field of power electronics. The introduction of an advanced MLI topology with fault-detection, localization and fault-tolerant (FT) capabilities not only addresses existing challenges in MLIs but also paves the way for future developments in high and medium-performance power conversion systems. The integration of real-time fault detection and compensation mechanisms enhances the robustness of MLIs, making them more suitable for deployment in mission-critical applications.

Load-frequency control (LFC) in a small power microgrid (MG) continues to be a major challenge due to the integration of wind energy into small isolated power networks. It creates challenges for LFC due to the low inertia of these systems and production fluctuations. This paper examines an islanded MG system consisting of a diesel generator and wind turbine, considering a realistic wind model. A multi-level PID-(1+PI) controller is designed for the secondary control loop to maintain frequency stability and improve frequency fluctuations in the isolated MG. Optimizing the controller parameters is crucial for improving frequency control performance of the MG and increasing the control mechanism’s adaptability. Thus, to determine optimal values for these parameters, the Prairie Dog Optimization (PDO) algorithm is applied. The Integral Time Squared Error (ITSE) index serves as the objective function. Simulations and studies are conducted using under various operation scenarios. as well as its fast dynamic response capability. Results show that the proposed multi-level controller outperforms the conventional PID and PI controllers, proving it to be an efficient control method for islanded small MG systems with low inertia and high production fluctuations of wind energy as well as its fast dynamic response capability.

Maintaining high energy quality and power stability remains a key challenge in modern wind energy systems due to nonlinear dynamics, operational uncertainties, and generator disturbances. To address these issues, this paper presents a hybrid control approach that combines fractional-order control, a feedback proportional–integral (FPI) regulator, and a genetic algorithm–based optimization scheme. The designed regulator is designed to suppress energy oscillations and overcome the energy quality limitations associated with traditional direct power control and PI-based control methods. Fractional-order control enhances dynamic flexibility and robustness, while the genetic algorithm ensures optimal tuning of controller parameters under varying operating conditions. The effectiveness of the designed method is evaluated on a 1500 kW doubly-fed induction generator–based multi-rotor wind power system using MATLAB simulations. Comparative results demonstrate notable improvements in power stability, energy quality, and overall system performance across diverse scenarios, confirming the suitability of the proposed approach for advanced renewable energy applications.

In this paper, a new multi-stage proportional-integral-derivative (MPID) controller based on the coefficient diagram method (CDM) is proposed to solve the frequency deviation problem in isolated microgrids (MGs) with renewable energy sources (RESs) and plug-in electric vehicle batteries (PHEVs). The MPID controller is implemented with a two-loop proportional-integral (PI) controller and proportional-integral-derivative (PID) structure and uses the CDM method for fine-tuning parameters, improving the dynamic response, stability, and adaptability of the system.Various simulations and comparative evaluations were carried out under different operating conditions, such as load variation and RES disturbances and intermittence, to test the effectiveness and robustness of the proposed method.In contrast to conventional PID tuning approaches, the CDM method dynamically optimizes parameter adaptation, improves system stability, and significantly reduces overshoot and settling time. The results showed that the MPID-CDM controller achieved considerable optimization in terms of performance metrics. These included a 68.40% reduction in integral time absolute error, a 20.7% improvement in settling time, a 58% reduction in overshoot compared to the controller using the standard PID tuned by particle swarm optimization, and a 47% improvement in overshoot compared to the standard PID tuned by cuckoo search algorithms. In addition, integrating PHEVs into the control technique improved frequency regulation by 35% in the case of high load variations, indicating their importance in stabilizing the MG energy management system and improving system efficiency under dynamic and uncertain conditions.