The problem of fault diagnosis in electrical motors has an important impact on the supervision of dynamic systems, and model-based methods are efficient tools for this purpose. In this regard, this work presents a novel method for detecting inter-turn short circuits (ITSCs) in the stator windings of permanent magnet synchronous motors (PMSMs). The approach is based on algebraic identification to process the motor voltage signals, estimating the offsets, amplitudes, and phases of the fundamental and third-harmonic components. Fault detection is performed in two steps: first, a voltage imbalance index is evaluated to determine the presence of abnormal operating conditions. Subsequently, characteristic patterns in the estimated parameters are analyzed to identify both the fault type and the affected phase(s). The experimental results show that single-phase ITSC faults produce a reduction in the offset of the faulted phase together with an increase in its third-harmonic amplitude, whereas phase-to-phase ITSC faults lead to an increase in the offsets of the affected phases and nearly identical third-harmonic amplitudes between them. In both cases, only minor variations are observed in the estimated phase angles. The effectiveness of the proposed methodology is supported through theoretical analysis and validated experimentally using voltage measurements acquired from a PMSM test bench. The results demonstrate that the proposed technique can accurately identify fault conditions through voltage imbalance and harmonic-pattern analysis, providing a practical and computationally efficient methodology for PMSM stator winding fault diagnosis.

Brushless DC (BLDC) motors are widely applied in electric mobility, robotics, and energy-efficient systems, promoting the transition toward cleaner technologies. This paper presents a hybrid Electronic Speed Controller (ESC) capable of operating with both Hall-effect and sensorless strategies using a single microcontroller-based platform. Unlike conventional ESCs designed for a specific configuration, the proposed hardware integrates signal conditioning and power stages to process feedback from either type of motor, while the software unifies both control algorithms within a single framework. This dual-mode design enhances scalability, enabling flexible use across a wide range of BLDC motors without additional circuitry. Numerical and experimental results using high-power motors validate the proposed ESC demonstrating good performance in various speed ranges. The proposed ESC offers an educational and research-oriented tool for rapid prototyping and testing of BLDC drives, demonstrating potential for applications in smart mobility, industrial automation, and energy-efficient systems for smart cities.

As the electrical grid modernizes, grid reconfiguration and optimization problems become increasingly complex, significantly increasing the computational burden. Faster and more efficient algorithms are required to operate without specialized hardware in both offline and on-site applications. A critical step in grid reconfiguration is evaluating the radiality of every potential solution proposed by the optimization algorithm in each iteration. Alternatives for this evaluation include Prim's algorithm---the most widespread---eigenvalue analysis with the Fiedler theorem, Depth-First Search~(DFS), and the zero-eigenvalue Multiplicity in the Laplacian matrix. This work first proposes efficient alternatives to calculate the Fiedler value and evaluate matrix multiplicity, and then evaluates the performance of these four strategies in MATLAB and in a dedicated micro-computer under the Processor-in-the-Loop premise. The results indicate that DFS has a mean execution time nearly eight times less than Prim's algorithm, with the Multiplicity algorithm performing similarly on the embedded device. In MATLAB, the Multiplicity algorithm performs slightly better than DFS for grids with fewer than 50 buses. Although the Fiedler algorithm reduces execution time by half compared to Prim's, its performance is still higher than both DFS and the Multiplicity algorithm.

Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO2 emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.

The transition to smart grids is advancing through the integration of grid-connected devices such as distributed energy resource (DER) inverters, distribution static synchronous compensators (D-STATCOMs), and electric vehicle (EV) charging stations, all of which play a vital role in voltage regulation within active distribution networks (DNs). Yet, these devices face growing exposure to cyber threats. This paper proposes a cybersecurity framework embedded directly within their control systems to address these risks by design. Drawing on the widespread deployment of distribution phasor measurement units (D-PMUs) for voltage monitoring, the framework incorporates a digital twin for real-time assessment, supported by data fidelity tests. This approach enables the timely detection and mitigation of false-data injection (FDI) and denial-of-service (DoS) attacks, preserving voltage stability without requiring hardware modifications. Simulations on the IEEE 33-node test feeder confirm the frameworks effectiveness in strengthening the resilience and security of grid-connected systems, representing a meaningful step forward in the protection of smart grids.

Signal analysis is a fundamental field in engineering and data science, focused on the study of signal representation, transformation, and manipulation. The accurate estimation of harmonic vibration components and their associated parameters in vibrating mechanical systems presents significant challenges in the presence of very similar frequencies and mode mixing. In this context, a hybrid strategy to estimate harmonic vibration modes in weakly damped, multi-degree-of-freedom vibrating mechanical systems by combining Empirical Mode Decomposition and Variational Mode Decomposition is described. In this way, this hybrid approach leverages the detection of mode mixing based on the analysis of intrinsic mode functions through Empirical Mode Decomposition to determine the number of components to be estimated and thus provide greater information for Variational Mode Decomposition. The computational time and dependency on a predefined number of modes are significantly reduced by providing crucial information about the approximate number of vibratory components, enabling a more precise estimation with Variational Mode Decomposition. This hybrid strategy is employed to compute unknown natural frequencies of vibrating systems using output measurement signals. The algorithm for this hybrid strategy is presented, along with a comparison to conventional techniques such as Empirical Mode Decomposition, Variational Mode Decomposition, and the Fast Fourier Transform. Through several case studies involving multi-degree-of-freedom vibrating systems, the superior and satisfactory performance of the hybrid method is demonstrated. Additionally, the advantages of the hybrid approach in terms of computational efficiency and accuracy in signal decomposition are highlighted.

The electrical power system is composed of three essential sectors, generation, transmission, and distribution, with the latter being crucial for the overall efficiency of the system. Enhancing the capabilities of active distribution networks involves integrating various advanced technologies such as distributed generation units, energy storage systems, banks of capacitors, and electric vehicle chargers. This paper provides an in-depth review of the primary strategies for incorporating these technologies into the distribution network to improve its reliability, stability, and efficiency. It also explores the principal metaheuristic techniques employed for the optimal allocation of distributed generation units, banks of capacitors, energy storage systems, electric vehicle chargers, and network reconfiguration. These techniques are essential for effectively integrating these technologies and optimizing the active distribution network by enhancing power quality and voltage level, reducing losses, and ensuring operational indices are maintained at optimal levels.

This paper presents a study related to a recently proposed quadratic boost converter topology: the so-called Low Energy Storage Quadratic Boost (LES-QB) converter. Unlike traditional quadratic boost converters, the LES-QB converter achieves high voltage gain with reduced energy storage in its passive components, enabling more compact designs. Recently, an improved operation was proposed based on the optimized selection of capacitors. This work focuses on the implementation and validation of the optimized design. The converter was built and tested, and its operation was compared against that of a non-optimized configuration. The results demonstrate that the correct selection of capacitors leads to a reduced switching ripple without increasing the size of the converter. Experimental results are provided.

This paper presents a decentralised, data-driven voltage control strategy designed to coordinate multiple photovoltaic (PV) inverters operating as a cluster, with a focus on mitigating local voltage deviations. The proposed framework is fully data-driven, obviating the need for prior knowledge of system parameters. A power-sharing mechanism is integrated to facilitate effective coordination among PV inverters within the cluster, dynamically adapting to both local and global voltage conditions. Simulation results on an actual distribution network confirm the method’s effectiveness in maintaining voltage regulation at the point of common coupling (PCC) while ensuring that all local voltages remain within permissible operational limits. The approach exhibits robust adaptability to system variations, addressing challenges posed by high PV penetration and dynamic network changes. Numerical simulations conducted in MATLAB/Simulink highlight the method’s potential to enhance grid stability and support the integration of renewable energy into modern distribution networks.

Lactobacillus amylovorus alpha-amylase is an endoenzyme with a peculiar starch-binding domain containing five identical family 26 carbohydrate-binding modules (CBM26). To investigate the impact of CBMs on catalytic activity, C-terminally truncated derivatives were constructed. The catalytic domain alone shows low affinity for the substrate and a very slow reaction rate, highlighting the importance of CBMs in maintaining the enzyme's optimal conformation and dynamics for efficient catalysis. CBMs enhance enzyme performance, as indicated by improved catalytic efficiency (kcat/Km). Interestingly, the enzyme variant LaCD3CBM, with three CBMs, exhibits the best catalytic efficiency on soluble starch, outperforming the wild-type amylase, with five CBMs. In the case of insoluble starch, the catalytic domain alone could not hydrolyze it and even adding a CBM, the release of reducing sugars was very inefficient. However, this efficiency was significantly improved by two orders of magnitude for the three-CBM variant and the wild-type amylase. CBMs also played a crucial role in protein thermostability, contributing to a higher melting temperature of the catalytic domain with just a single CBM. Notably, thermostability did not increase with the number of CBMs. In conclusion, spatial arrangement and interactions between the catalytic domain and CBMs significantly influenced enzymatic efficiency with both soluble and insoluble substrates. These interactions optimize enzymatic activity and improve thermostability.

AbstractThe integration of alternative energy sources, storage systems, and modern loads into the distribution grid is complicating its operation and maintenance. Variability in individual generation and consumption elements dynamically affects voltage profiles, which in turn undermines efficiency and power quality. This study proposes to address this dynamical variability using an online reconfiguration approach that involves opening and closing switches to modify the grid's topology and adjust voltage levels in response to load/generation variations. Other grid optimization techniques, based on reconfiguration, typically focus on static, fully instrumented grids with predictable parameters and homogeneous changes, aiming to minimize power losses but overlooking the dynamics of variable grid elements. This study proposes a testing approach that is dependent on the estimated transient status of the grid only using a limited number of measurement units and considering the individual‐stochastic variations of loads and generators. The proposed approach was tested on the IEEE 33‐bus test feeder with up to five varying distributed generators. The results confirm that the algorithm consistently finds a reconfiguration alternative that could enhance system efficiency and voltage profiles, even in the face of dynamic load/generator behavior, demonstrating its effectiveness and online adaptability for grid operation and management tasks.

The offline protection coordination problem in the interconnected network has always been one of the important tasks for the researchers. This problem is remarkably constrained and non-linear when considering both the time dial and pickup settings. In addition, the consistent load and topology changes as well as the stochastic renewable distributed generation (DG) made the fixed protection settings become more vulnerable to coordination loss when a fault occurs. Hence, this proposal consists of an adaptive protection scheme that adjust the pickup current and time dial relay settings in a decoupled manner. Where the pickup current settings for each DOCR are determined by the maximum N-1 contingency current that passes through the relay. In addition, the DOCRs time dial settings are optimized alone using the EVOlutionary Algorithm of Random Variables with NORMal Distributions (Evonorm) algorithm. The proposed Evonorm algorithm which is an easy and effective method along with the seeding technique become suitable for adaptive protection coordination strategy. The proposal has been tested on the modified IEEE 14 and 30 bus systems. And results have shown that coordination is achieved.

This paper introduces a novel modelling framework for conducting dynamic analysis of DC microgrids (MG), considering components such as battery energy storage systems (BESS), combined heat and power (CHP) cogeneration, photovoltaic (PV) plants and DC/DC converters. The MG dynamics are accurately captured using well-founded modelling practices of the power system transient analysis field. Firstly, the power device models are derived based on fundamental operating principles using a lumped-type parameter modelling approach. Secondly, the DC microgrid is formulated using nodal power injections, which is combined with Newton–Raphson and implicit trapezoidal methods to efficiently solve fair-sized networks of arbitrary topology. The dynamic outcomes of a 13-bus MG were compared with those of an electromagnetic transient (EMT) simulation conducted in Simscape Electrical for load and solar irradiance changes and for a DC/DC converter outage. For these events, errors smaller than 2.5% were yielded by the new approach, only requiring 3.1% of the computing time employed by the EMT simulation. The method’s applicability was also showcased by assessing the dynamic performance of a larger DC microgrid containing 118 buses, twelve distributed generators and five BESS, which was subjected to typical contingencies such as load increases and distribution line disconnections.

Fault location has been crucial in minimizing fault restoration time. Various techniques and methodologies have been deployed to enhance the performance of fault location algorithms, especially in light of the increasing integration of renewable energy sources. In this context, this paper describes a graph-theory-based method for fault location in power networks with renewable energy sources. This novel technique is designed to provide accurate fault distance estimates, even in the presence of severe noise and fault resistance. It takes advantage of graph theory and equivalent impedances applying Kirchhoff's laws systematically to ensure accurate fault location even in the presence of fault resistances. To showcase the improved accuracy of the proposed methodology, a comparison with typical impedance-based two-terminal fault location methods is carried out. The effectiveness of the proposed algorithm was proven with different electrical systems. Average errors inferior to 0.22% and 0.48% were obtained for single-phase faults and three-phase faults with resistances up to 200 Omega respectively, which confirms the improved performance with respect to conventional algorithms implemented in typical impedance relays.

This paper presents a data-driven dynamic voltage regulation approach that coordinates medium-voltage distributed energy resources (DERs) and distribution static synchronous compensators (D-STATCOMs) in active distribution networks. Using data generated by distribution phasor measurement units (D-PMUs), a data-driven voltage performance index is calculated and a control method is proposed to ensure optimal voltage performance across network nodes. This control method requires minimal data, using only voltage and reactive power measurements to generate control signals. The performance of this approach is validated through simulation tests on IEEE-33 node test feeders, demonstrating efficient voltage regulation under challenging conditions such as solar energy integration, grid faults, and topology changes. The paper also explores additional contributions to system identification and digital twin techniques. This work highlights the potential of data-driven control in distribution networks for effective Volt/Var control, using modern smart-grid technologies for improved grid management.

This article presents a data-driven methodology for modeling DC–DC power electronic converters. Using the proposed methodology, the dynamics of a converter can be captured, thereby eliminating the need for explicit theoretical modeling methods. This approach only requires the acquisition of fundamental measurements: currents through inductors and voltages across capacitors. The acquired data are used to construct a linear difference system, which is algebraically manipulated to form a state–space representation of the converter under analysis. Three DC–DC converter topologies were analyzed, and their resulting models were tested and compared with simulation data, yielding an average error deviation of approximately 2% for current signals and 4% for voltage signals, demonstrating precise tracking of the actual dynamics. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions or in the interconnection of multiple converters.

This article presents a data-driven methodology for modeling lithium-ion batteries, which includes the estimation of the open-circuit voltage and state of charge. Using the proposed methodology, the dynamics of a battery cell can be captured without the need for explicit theoretical models. This approach only requires the acquisition of two easily measurable variables: the discharge current and the terminal voltage. The acquired data are used to build a linear differential system, which is algebraically manipulated to form a space-state representation of the battery cell. The resulting model was tested and compared against real discharging curves. Preliminary results showed that the battery’s state of charge can be computed with limited precision using a model that considers a constant open-circuit voltage. To improve the accuracy of the identified model, a modified recursive least-squares algorithm is implemented inside the data-driven method to estimate the battery’s open-circuit voltage. These last results showed a very precise tracking of the real battery discharging dynamics, including the terminal voltage and state of charge. The proposed data-driven methodology could simplify the implementation of adaptive control strategies in larger-scale solutions and battery management systems with the interconnection of multiple battery cells.

Electric power systems may exhibit fault currents with high asymmetry when generators are close to large motors and loads. In this condition and depending on the fault inception instants, the fault current waveforms may present Delayed Current Zeros (DCZ). During transient studies, in some cases, breakers do not see the current zero-crossing for several cycles. Indeed, if the breaker opens just after a transient when the current has not reached steady-state and may be offset by a slow exponentially decaying DC component, then, the breaker will fail to open as the current will not cross zero within the interruption time. Here, the main concern associated with DCZ is however related to the interruption of the circuit breakers on a current wave crossing zero. At this moment, the interruption is accompanied of an electrical arc having the potential to damage equipment. Resistive superconducting fault current limiters (r-SFCL) are a known solution to reduce the magnitude of short-circuit current in electrical systems. It is a versatile technology that can tackle both fault and stability issues in aging and modern power grids alike. As a side benefit, it may also address the DCZ problem. The present work aims at investigating the impact that an r-SFCL may have on DCZ and, in particular, its ability to eliminate this issue altogether. To that end, a r-SFCL based on an existing design was modeled using the consolidated thermal–electrical analogy. This approach has proven to be fairly accurate to model the transient behavior of the superconducting device. Here, the case study simulates a solid short-circuit at the terminal of a generator in a specific bus, simulating an industrial power system, that includes a circuit breaker, a motor load, and a r-SFCL. The electromagnetic transient computations were carried out considering load and no-load conditions with and without the r-SFCL. Eight scenarios were considered, showing that r-SFCLs can actually address DCZ in addition to limit the fault level.