This paper deals with the direct torque control (DTC) of an induction motor. This control allows torque flux to be decoupled without the need for coordinate transformations. It has the advantage of being robust to parametric variations of the machine in tracking mode, and to load disturbances in regulation mode. However, in addition to torque and flux ripples, this control in its conventional version proves insufficient at low speeds due to the demagnetizing effect of the stator flux caused by the ohmic drop across the stator resistor. This demagnetization considerably increases torque and flux ripples and causes current distortions. To alleviate this problem, it is necessary to shift back the flux positions in the (α, β) plane by an appropriate angle. The main objective of this study is to find this offset angle without affecting the entire order in its original version. In speed loop, the parameters of the PI controller are optimized using the Particle Swarm Optimization (PSO) algorithm.
This study evaluates the potential of the lead-free double perovskites CsGeI3 and CsSnI3 for high-performance photovoltaic applications through a combined approach integrating SCAPS–1D solar cell modeling with density functional theory (DFT). Using the Au/CuI/CsGeI3/CsSnI3/ZnSe/FTO device configuration, SCAPS–1D simulations identified optimal absorber-layer parameters, including thicknesses of 1000 nm for CsGeI3 and 700 nm for CsSnI3, a doping level of 1019 cm−3, and a defect density of 1014 cm−3. Under these optimized conditions, the device achieved high performance, delivering a Voc of 1.17534 V, Jsc of 30.54 mA·cm−2 FF of 88.08%, and a PCE of 31.61%. To complement these device-level results, DFT calculations were performed to examine the structural, mechanical, thermodynamic, and electronic characteristics of both materials. The optimized lattice parameters, elastic constants, and derived mechanical moduli confirmed structural integrity and ductile behavior, while the calculated sound velocities and Debye temperatures supported their thermal stability. Electronic-structure analyses revealed narrow mBJ band gaps of 0.71 eV for CsGeI3 and 0.69 eV for CsSnI3, indicating strong absorption capabilities across the visible-NIR region. This work presents the first application of a CsGeI3-CsSnI3 dual-absorber solar cell, offering a promising strategy for improving light absorption, carrier transport, and overall device performance. Furthermore, these combined results demonstrate that CsGeI3 and CsSnI3 possess favorable optoelectronic properties and high simulated photovoltaic efficiency, making them excellent candidates for future eco-friendly solar-energy technologies.
This research explores machine learning (ML) techniques, with an emphasis on neural networks (NNs), to predict household energy consumption in the context of smart grids. The study evaluates algorithms such as CNN, LSTM, RF, AdaBoost, and CatBoost with a primary focus on hybrid models to enhance forecast precision. The research adopts a comprehensive data collection strategy while dividing the time series data into training (80%) and testing (20%) segments for rigorous model evaluation. Hybrid approaches that combine several algorithms significantly outperform single models, and LSTM-CatBoost is at the forefront by exhibiting lower error rates and higher R2 values. To avoid overfitting, the research employs cross-validation in conjunction with early stopping, thus, producing robust and trustworthy models. The paper provides a wealth of information on energy forecasting that paves the way for the energy efficiency and sustainability of smart grids, which are essentially energy management systems based on the latest technology and consumer feedback mechanisms.
This study proposes an anomaly detection framework that combines CNN–LSTM feature extraction with a boosting-based ensemble strategy to improve the reliability of photovoltaic (PV) system monitoring. Real multi-source PV operational data are first preprocessed using the ISODATA clustering algorithm, which automatically adjusts the number of clusters and reduces redundancy. Principal component analysis (PCA) is then applied to lower data dimensionality while retaining key variability. A hybrid CNN-LSTM network is developed, where CNNs extract spatial features from heterogeneous PV measurements and LSTMs capture temporal dependencies in power sequences. Based on the learned representations, an ensemble model integrates the outputs of Gaussian Mixture Models (GMM), Isolation Forest (IF), and Interquartile Range (IQR) through a boosting-inspired weighting mechanism to enhance robustness under complex operating conditions. Experiments conducted on real PV datasets show that the proposed method achieves nearly 97% anomaly detection accuracy, with an average F1-score of 0.89 ± 0.03 and a recall rate of 0.91 ± 0.02. Compared with single-model baselines, the framework provides more stable performance and maintains a false positive rate below 2.1%, demonstrating its practical value for real-world PV anomaly detection.
Multiphase induction machines have revolutionized the propulsion and traction sector, thanks to their fault tolerance. This paper is focused on the analysis of electrical faults in the squirrel cage Seven-Phase Induction Machine (SPIM). This study provides a solid basis for diagnosing various faults by analyzing their distinct signatures for the electric faults that can affect this machine. To this end, a transient finite element model is elaborated to study these several electrical faults in SPIM. This paper will be devoted to the study of opening faults in various stator phases (one phase, two adjacent phases, two non-adjacent phases and two far phases), and shortcircuiting between turns of stator phases (5%, 10%, 15% of the first phase). An analysis of electromagnetic torque ripple, joule loss and efficiency is undertaken in this paper.
Speaker identification is a crucial topic in various fields, including linguistics, speech acoustic technology, and artificial intelligence. Despite the progress, speaker identification remains a challenge, particularly in acoustically noisy contexts or when the speakers are phonetically similar. Moreover, concerns regarding privacy and data protection frequently arise in speaker identification, particularly concerning the use of personal audio data. Signal processing and machine learning techniques have significantly advanced, improving the accuracy and resilience of voice recognition systems. New methods, including Convolutional Neural Networks (CNN), are advancing voice information extraction performance. This study aims to develop a Speaker Identification System based on deep learning techniques. These techniques have gained widespread recognition in the field of automatic acoustic signal processing. Many researchers have used convolutional neural networks, and the recognition phase is based on the cross-entropy criterion. This article proposes an advanced technique to combine convolutional neural networks with the maximum likelihood criterion. This proposed technique has yielded promising results when compared to traditional systems, such as Vector Quantization (VQ), and Gaussian Mixture Model (GMM). The suggested approach achieves an accuracy of 87.97% using all the data from the LibriSpeech corpus.
Various types of faults can occur in a Permanent Magnet Synchronous Machine (PMSM) system, including bearing faults, electrical short/open circuits, eccentricity faults, and demagnetization faults (DFs). A DF occurs when the magnetic strength of the PMSM's permanent magnets weakens, resulting in reduced output torque, which is undesirable in electric vehicles (EVs). This fault can be attributed to physical damage, high-temperature stress, reverse magnetic fields, and aging. Motor current signature snalysis (MCSA) is a traditional method for detecting motor faults, relying on the extraction of signal features from the stator current. In this study, a simulation model of the PMSM was developed to represent both partial and uniform DFs, allowing for the simulation of varying degrees of demagnetization. Harmonic analysis using fast Fourier transform (FFT) demonstrated that the fault diagnosis method based on harmonic wave analysis is effective only for partial DFs in PMSMs, and not applicable to uniform DFs.
The ongoing development of modern telecommunication technologies is leading to a steady increase in electromagnetic pollution on Earth and space. This pollution in Low Earth Orbit (LEO) can impact space-based systems and operations, making it difficult to control small satellites. Furthermore, it can interfere with scientific measurements and experiments conducted in space. The primary objective of this paper is to demonstrate the ability to design and develop a 3-unit PocketQube-class student satellite, MRC-100, as an extension of the SMOG-1 one-unit PocketQube satellite, the fourth satellite of Hungary. The MRC-100 satellite comprises several scientific payloads. The main one is a wide-band spectrum analyzer that operates in the frequency range of 30 MHz to 2.6 GHz and is used to measure electromagnetic pollution in Low Earth Orbit. This paper's measurements were conducted on an extended band ranging from (2–3.1 GHz). We present the capabilities of the extended band spectrum analyzer to measure electromagnetic pollution with the designed system's limited size 40 × 40 mm, weight, and power consumption of less than 400 mW. The working extended band spectrum analyzer was tested on the satellite flight module in the laboratory.
In the context of global energy transition towards low-carbonization, direct-drive permanent magnet synchronous wind power generation systems have become one of the mainstream power generation equipment due to their unique advantages. However, traditional control algorithms are difficult to cope with the complex operating conditions of the grid-side converter. This paper aims to improve the grid connection control algorithm of the direct-drive wind power system. It proposes a collaborative algorithm based on sliding mode control (SMC) for DC link voltage control and model-free predictive control (MFPC) for current control. Additionally, a discrete extended state observer (DESO) is designed to observe the unknown state variables in the system to avoid the influence of model mismatch. By comparing the simulation results of different algorithms of the control system under internal and external disturbances, it is verified that the proposed algorithm can more effectively track the d-q axis target current and stabilize the DC link voltage. The proposed method can help wind turbine generators operate efficiently in harsh environments, and has certain theoretical and engineering value for improving the grid connection performance of direct-drive wind power and promoting the consumption of new energy.
Hexagonal planar arrays are useful in radar, sonar, and wireless communications due to their ability to provide complete coverage in the azimuth plane. On the other hand, hybrid arrays that combine two different array structures, like a small central square subarray surrounded by a number of rings, are capable of providing better performance than the conventional array architectures. This paper introduces two new planar array structures that are efficiently optimized to best cope with these aforementioned applications. The first proposed design is a planar array with hexagonal structure based on discrete hexagonal-ring amplitude distributions, while the second design structure is the hybrid array architecture with a small central square subarray surrounded by a number of elements in the shape of a ring. The idea of first design structure is to re-represent the conventional element-based excitation amplitudes by discrete hexagonal-based excitation amplitudes in which they are ordered in descending from the center to the array edges. By this way, the amplitude excitations of the array elements become more compatible and practicable with the needed real-life discrete RF attenuators or amplifiers that are used for configuring the targeted excitation amplitudes. Moreover, the discrete hexagonal-based excitation amplitudes need a simpler feeding network than its element-based counterpart, thus, the array cost and complexity are greatly reduced. An optimization scheme based on a genetic algorithm is used to optimize these two proposed array structures to achieve ultra-low sidelobe levels while preserving mainlobe directivity. Simulation results confirm the effectiveness of the proposed array structures.
This paper presents an advanced direct torque control (DTC) strategy incorporating artificial intelligence and a speed Model Reference Adaptive System (MRAS) observer for permanent magnet synchronous motors (PMSMs) used in electric vehicle traction. The studied electric vehicle is equipped with four in-wheel PMSMs, requiring an electric differential to ensure balanced torque distribution and vehicle stability, especially during cornering maneuvers. To reduce system weight and enhance efficiency, two machines on the same vehicle side are powered by a single three-leg inverter, forming a multi-machine single-inverter configuration. A master–slave control structure is adopted to manage this architecture and ensure synchronized operation of all motors. The proposed AI-based DTC combined with the MRAS speed observer significantly improves torque accuracy, dynamic response, and robustness against disturbances. Simulation results obtained using MATLAB/Simulink confirm that the proposed strategy achieves high performance in both transient and steady-state conditions, ensuring reliable traction, enhanced vehicle stability, and improved overall dynamic behavior.
The aim of this paper is to study the possibility of use of the Halbach array magnets in the design of a magnetic drum separator and to address their performance and efficiency in removing fine iron particles transported by conveyor belt. Such a study is based on the estimation and comparison between the particle capture efficiencies of two configurations of the drum separator, one designed by using conventional arrangement of magnets and other based on the use of Halbach array magnets. To check the capture efficiency, we computed the particle trajectory. For this, we solved numerically the magnetic and particle dynamic governing equations by coupling the finite element (FE) and Runge-Kutta (RK4) methods. The obtained results show that the Halbach configuration can give better capture performance.
Driven by the "dual-carbon" goals, the large-scale integration of wind power into distribution networks poses challenges to voltage stability due to its inherent volatility and uncertainty. To address it, this paper proposes a probabilistic assessment method based on the total probability formula that incorporates wind speed correlation to effectively evaluate voltage deviation. Firstly, the probability model of wind power is established according to the uncertainty of wind speed considering the correlation. Secondly, the wind power output is discretized and aggregated to ensure that the resulting random variables in the combined state approximately follow a normal distribution. Spatial correlations in wind speed are accounted for using the Nataf transformation. Furthermore, the probability of each aggregated wind power state determines its weight. These weights are then used to accumulate and integrate the probabilistic power flow (PPF) results. The total PPF calculation accounts for wind power uncertainty, following the Total Probability Formula (TPF) framework. Finally, considering the indexes with the probability and severity of voltage deviation, the comprehensive risk indicator for voltage exceeding limits is constructed. Based on the IEEE-33 bus test system, the proposed TPF method is compared with Monte Carlo Simulation (MCS) and the Two-Point Estimation Method (2PEM). The comparison demonstrates its superior computational accuracy and efficiency, establishing it as an effective tool for assessing the impact of wind power integration on distribution networks.
This paper presents an efficient stator power control algorithm for a grid-connection wind turbine system based on a doubly fed induction generator (DFIG) operating under variable wind speed conditions. The proposed approach focuses on controlling the stator power of the DFIG under varying operating conditions. In order to extract maximize wind power extraction, the stator-side power factor is maintained at unity through maximum power point tracking (MPPT). An advanced fuzzy-type controller, known as Type-2 Fuzzy logic control (T2-FLC), is proposed to regulate the rotor's direct and quadrature currents. Simulation results obtained in the MATLAB/Simulink environment demonstrate that the T2-FLC controller offers robust performance, superior dynamic response, and enhanced operational efficiency for the wind energy and drive applications in steady-states conditions.
The disordered integration of high-penetration distributed photovoltaics (DPVs) into smart distribution networks has caused critical challenges including transformer reverse overloading and degraded power quality. Strategically deploying grid-level energy storage systems (ESSs) presents an effective solution to address these issues while enhancing operational efficiency and power quality. This paper proposes a non-cooperative game theory-driven optimal siting and sizing method for DPVs and ESSs in smart distribution networks. A tri-objective optimization model is formulated to mitigate grid vulnerability, reduce power losses, and minimize life-cycle carbon emissions of PV generation. To resolve conflicting interests among multiple stakeholders (DPV owners, ESS operators, and grid companies), a non-cooperative game framework with equilibrium strategies is established. An improved multi-objective particle swarm optimization (IMOPSO) algorithm is developed to solve the Nash equilibrium point that maximizes benefits for all participants. Case studies on IEEE 33 bus and IEEE-69 bus distribution systems demonstrate that the proposed method achieves: 2.43% reduction in grid vulnerability index, 4.29% decrease in network losses, and 44.44% reduction in PV life-cycle carbon emissions – all while maintaining voltage quality requirements and realizing Pareto-optimal allocation solutions for multi-stakeholder interests.
In this paper, an efficient nonlinear control algorithm, called Constrained Neural Networks based Model Predictive control using Sine Cosine Algorithm (CNNMPC-SCA) is developed to control the dynamics of quadrotors. The main objective is to design an efficient controller for quadrotors that ensures satisfactory performance while minimizing the gap between the quadrotor positions and the reference trajectories. Indeed, a novel dynamic model architecture of the quadrotor is developed using several Nonlinear Autoregressive Exogenous (NARX) neural networks, this model aims to accurately predict the future behavior of the quadrotor within a short and acceptable time frame, making it suitable for implementation in the control process. The designed model was validated and then integrated into the CNNMPC-SCA algorithm. Furthermore, the metaheuristic algorithm known as the Sine Cosine Algorithm (SCA) was modified and employed to solve the non-convex, nonlinear optimization problem of the proposed predictive controller. To assess the efficiency of the proposed CNNMPC-SCA algorithm, a comparative study was conducted using the Adaptive Fuzzy PID controller and the hybrid Fuzzy PID controller. The obtained results demonstrate that the proposed control algorithm achieves better control performances compared to those obtained using the other considered controllers.
In this paper, a novel adaptive hybrid control structure for field-oriented control (FOC) of six-phase induction motor (SPIM) drives is proposed. The controller effectively integrates backstepping (BS) control for the outer loops with a novel adaptive higher-order sliding mode (NAHOSM) controller for the inner current loop. This control strategy also proposes load disturbance estimator using a predictive model to estimate the component required for the BS_NAHOSM control strategy to help identify and proactively eliminate disturbances. The proposed approach demonstrates robust and stable performance under parameter uncertainties and load disturbances, while significantly reducing the chattering phenomenon. Simulation results obtained using MATLAB/Simulink confirm the effectiveness and superiority of the proposed control strategy.
Predictive current control (PCC) is considered as an effective and efficient strategy for controlling multiphase drives, offering superior flexibility, fast dynamic response, and reduced computational complexity compared to conventional control methods. This paper presents a robust PCC approach for a dual-star induction machine (DSIM), integrated with direct field-oriented control (DFOC), using proportional-integral (PI) controllers to control mechanical speed and flux to analyze the efficiency of the drive system's behavior in complex challenging scenarios caused by motor's external perturbations and parameters uncertainties. The proposed PCC algorithm incorporates a two-step-ahead prediction horizon to evaluate a cost function that minimizes the deviation between reference and predicted stator currents. The control signal is selected from a finite set of voltage vectors (VVs) provided by a two-level voltage source inverter (2L-VSI), and the optimal switching states combination is selected to ensure precise control and improved performance. The proposed framework is validated through comprehensive simulations conducted in the Simulink/MATLAB environment. The findings achieved superior disturbances rejection capabilities, and minimized steady-state error. Furthermore, the system highlights an effective performance and robustness against simultaneous extreme parameters variations, especially under full load for very low speed scenario.
This paper addresses a critical challenge in the field of attitude control of fixed-wing Unmanned Aerial Vehicles (UAVs), focusing on the leader and multi-followers′ problem. It introduces Twin Delayed Deep Deterministic Policy Gradient (TD3) based on Cascade-forward ANN networks approach to control the leader and multiple followers in autonomous navigation and path tracking. The TD3 component is initially trained off-line and then applied in real-time. The control system incorporates a novel adaptive Laplacian consensus protocol using an undirected communication graph model that adjusts inter-UAV connection weights in real time based on relative positions. This approach was implemented on a leader-follower formation consisting of one leader and three follower UAVs. The paper includes a stability analysis of the proposed method, demonstrating the system′s overall stability. The effectiveness of this approach is validated through a MATLAB simulation which demonstrates that the TD3 based on ANN (Cascade-forward networks), which is used to train the actor and the twin critics networks, has superior performance with low tracking error, good formation keeping during aggressive maneuvers, and reduced control surface oscillations. The application exhibited improved adherence to the prescribed distance during sharp turns, with fewer formation deviations at the trajectory end point. The findings result verify that combining strategies leads to the formation integrity. The TD3 based on ANN which uses Cascade-forward networks implementation offers also significant improvement in stability, accuracy, and control efficiency for practical UAV formation control applications.
A significant challenge in the operation of Photo-Voltaic (PV) array is partial shading, which drastically reduces the power output. Shaded arrays exhibit numerous local Maximum Power Point (LMPP) alongside a single Global Maximum Power (GMP). Reconfiguring shaded modules to extract the GMP is crucial, and achieving this relies heavily on a uniform shadow distribution across the PV surface. This paper proposes a novel strategy, Diverse Emperor Penguin (DEP) algorithm, for uniform shadow distribution across shaded PV arrays. It is a onetime static reconfiguration technique based on the commonly occurring shading patterns in a given location. The proposed strategy was tested on 9 × 9 PV array at ten distinct shading patterns in MATLAB/Simulink platform. The analytical results observe the power enhancement at all the shading patterns and highest power improvement is 23.01%, 10.20% when compared with standard TCT and SuDoKu configurations respectively. The same were tested experimentally on 3 × 3 PV array and compared with standard TCT and SuDoKu configurations and the results obtaind confirm that the proposed technique has superior performance compared with stated techniques.
