Oualid Aissa

This study presents an intelligent method for predicting the stopping length of streamers by analysing the current signals accompanying streamer propagation in natural ester insulating liquids (specifically, rapeseed oil and olive oil) under AC voltage conditions. A Random Forest regressor was selected on the basis of its fast prediction speed and computational efficiency. Two characteristics derived from the current signals, time domain energy (E) and frequency domain energy (D), served as input parameters, while the experimentally measured streamer stopping length was the output variable. The performance of the model was subjected to rigorous evaluation, employing a range of statistical metrics, including the coefficient of determination (R²), the mean absolute error (MAE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The findings demonstrate high R² with low MAEs, RMSEs and MAPEs, indicating robust generalisation ability. These results underscore the effectiveness of the Random Forest regressor in achieving near-perfect prediction accuracy for streamer length estimation, highlighting its potential for real-time applications in insulation diagnostics and high voltage engineering.

Introduction. This paper focuses on a renewable energy system coupled to a dual purpose power grid via a parallel active power filter for injecting photovoltaic energy into the grid and improving the power quality in the presence of the non-linear load. The novelty of the work consists in the combination of two advanced techniques – Fuzzy Logic Controller (FLC) and the optimized Anti-Windup Fractional Order Proportional-Integral Differentiator (AW-FOPID) regulator based on Particle Swarm Optimization with the Spreading Factor (PSO-SF) algorithm, applied to the improved Direct Power Control (DPC) strategy under different conditions related to climate changes and healthy or infected electrical network. Purpose. Its main role is to improve the power quality and reject the perturbations deforming the electrical network under distorted, unbalanced and balanced grid voltage conditions. Besides, the FLC is employed the Maximum Power Point Tracking (MPPT) under any weather conditions. In addition, the optimized AW-FOPID controller leads to keep the DC bus voltage at its reference value with small undershoots and overshoots in the voltage with a short response time in steady or dynamic states. Methods. The rejection of disturbances affecting the grid is offered by the improved DPC. On the other hand, an intelligent method based on fuzzy logic was used MPPT under any weather conditions. Furthermore, an AW-FOPID regulator based on PSO-SF algorithm is used to keep the DC bus voltage at its reference value with small undershoots and overshoots in the voltage, while keeping a fast response time. Results. The proposed system control is evaluated in various states of power source: distorted, unbalanced, and balanced by simulation using MATLAB/Simulink. The simulation results illustrate the effectiveness and performance of the studied control strategies. References 26, tables 8, figures 16. Вступ. У цій статті основна увага приділяється системі відновлюваної енергії, що з'єднана з енергосистемою подвійного призначення через паралельний фільтр активної потужності для подачі фотоелектричної енергії в мережу та покращення якості електроенергії за наявності нелінійного навантаження. Новизна роботи полягає у поєднанні двох передових методик – Fuzzy Logic Controller (FLC) та оптимізованого регулятора Anti-Windup Fractional Order Proportional-Integral Differentiator (AW-FOPID) на основі оптимізації рою частинок з коефіцієнтом розширення (PSO-SF), що застосовується до покращеної стратегії прямого управління потужністю (DPC) у різних умовах, пов'язаних зі змінами клімату та справною або зараженою електричною мережею. Мета. Його основна роль полягає в покращенні якості електроенергії та усуненні збурень, що деформують електричну мережу в умовах спотвореної, незбалансованої та збалансованої напруги мережі. Крім того, у FLC використовується система відстеження точки максимальної потужності (MPPT) за будь-яких погодних умов. Крім того, оптимізований контролер AW-FOPID дозволяє підтримувати напругу шини постійного струму на опорному значенні з невеликими відхиленнями і викидами напруги з коротким часом відгуку в стані динамічного стану. Методи. Відмова від перешкод, що впливають на мережу, забезпечує покращений DPC. З іншого боку, інтелектуальний метод, заснований на нечіткій логіці, використовувався MPPT за будь-яких погодних умов. Крім того, регулятор AW-FOPID, заснований на алгоритмі PSO-SF, використовується для підтримки опорного значення напруги постійного струму шини з невеликими відхиленнями і викидами напруги, зберігаючи при цьому малий час відгуку. Результати. Пропоноване управління системою оцінюється у різних станах джерела живлення: спотвореному, незбалансованому та збалансованому шляхом моделювання з використанням MATLAB/Simulink. Результати моделювання ілюструють ефективність та продуктивність вивчених стратегій управління. Бібл. 26, табл. 8, рис. 16.

This paper presents an advanced strategy combining fuzzy logic and artificial neural networks (ANNs) for direct torque control (DTC) and broken-bar fault diagnosis in induction motors. More specifically, a fuzzy-based controller is used to simultaneously minimize the stator flux and the electromagnetic torque ripples. A neural switching table is then proposed to achieve the interface inverter control. Besides, a closed-loop broken-bar fault detection strategy based on the Hilbert technique (HT) with the discrete wavelet transform (DWT) and ANNs is proposed. The fault detection is performed by analyzing the induction motors stator current by using the combined techniques HT-DWT. The effect of a broken-bar fault on the machine varies according to the number and position of the broken bars. The neural detector was used in order to identify the number of broken bars through only one current measurement. The effectiveness of the developed control has been verified using MATLAB/Simulink and real-time simulation in OPAL-RT 4510. Obtained results show improved performances in terms of torque ripple minimization and stator current quality, evaluated, respectively, at 43.75 and 41.26 as well as a rigorous motor health monitoring.

This study proposes a way of designing a reliable voltage controller for buck DC/DC converter in which the terminal attractor approach is combined with an enhanced reaching law-based Fast Terminal Synergetic Controller (FTSC). The proposed scheme will overcome the chattering phenomena constraint of existing Sliding Mode Controllers (SMCs) and the issue related to the indefinite time convergence of traditional Synergetic Controllers (SCs). In this approach, the FTSC algorithm will ensure the proper tracking of the voltage while the enhanced reaching law will guarantee finite-time convergence. A Fuzzy Neural Network (FNN) structure is exploited here to approximate the unknown converter nonlinear dynamics due to changes in the input voltage and loads. The Fuzzy Neural Network (FNN) weights are adjusted according to the adaptive law in real-time to respond to changes in system uncertainties, enhancing the increasing the system’s robustness. The applicability of the proposed controller, i.e., the Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller (AFN-FTSC), is evaluated through comprehensive analyses in real-time platforms, along with rigorous comparative studies with an existing FTSC. A dSPACE ds1103 platform is used for the implementation of the proposed scheme. All results confirm fast reference tracking capability with low overshoots and robustness against disturbances while comparing with the FTSC.

This paper discusses the simulation and experimental validation of a curative solution, called power factor correction (PFC), intended to improve the power quality of the single-phase AC/DC converter. A modern control law combined between two methods which are the fuzzy logic theory and the predictive technique is proposed in this research work. The first is reserved to guarantee an intelligent regulation of the DC output voltage while the second aims to the intuitive power switch governance of the boost chopper constituting the PFC. The adopted solution ensures the delivery of a source current of sinusoidal shape, respecting the imposed international standards and the operation of the treated system under a power factor very close to unity. The effectiveness and practical use of the intelligent intuitive PFC control have been experimentally proven using the dSPACE 1104 card for different operating conditions, resulting from the variation of the load and the reference output voltage. The practical results in terms of power quality identified by a total harmonic distortion of the source current of 2.8% and a power factor of 0.995 respect the international standard IEEE 519-2014. Also, they are very convincing compared to those existing and recently published in the literature.

This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.