Electrostatic precipitators (ESPs) are among the most widely used devices for removing dust, fumes, and aerosols from gas streams. The efficiency of particle collection depends on several interrelated factors, necessitating continuous monitoring and optimization. Among these, particle resistivity plays a key role in determining ESP performance, especially in relation to the behaviour of the dust layer deposited on the collecting electrode. High-resistivity particles, such as PVC, are difficult to charge and tend to retain their charge upon contact with the grounded electrode. This leads to an accumulation of surface potential and the onset of the back corona phenomenon, which significantly reduces collection efficiency. To optimize filtration, it is essential to maintain a high-intensity electric field that enhances particle charging and collection—particularly for high-resistivity materials—without triggering electrical arcing. This study introduces a novel experimental method for controlling a wire–cylinder type ESP. The method is based on the automatic adjustment of the high-voltage supply using a motorized potentiometer (PM), which ensures the discharge current remains at a predefined set point. The proposed approach allows for the in-situ estimation of the surface potential and resistivity of the pollution layer under electrical discharge conditions, across different particle sizes.

This paper presents a comprehensive study on power management and control strategies for hybrid renewable energy microgrids. The research focuses on a DC microgrid system that integrates photovoltaic (PV) arrays with a hybrid energy storage system (HESS) comprising batteries and supercapacitors. The proposed system addresses key challenges in renewable energy integration, including power fluctuations, energy balance maintenance, and DC bus voltage stability. We develop and analyze a multilayer control strategy for power sharing among the PV system, battery, and supercapacitor components. This strategy enables supercapacitors to handle fast transients while batteries manage steady-state power variations, enhancing overall system stability and extending battery lifespan by preventing overcharging and deep discharging. Three maximum power point tracking (MPPT) techniques—Perturb and Observe (P&O), Incremental Conductance (INC), and Fuzzy Logic Control (FLC)—are implemented and compared through MATLAB/SIMULINK simulations under varying irradiance conditions. The comparative analysis reveals that while P&O and INC methods offer simplicity and ease of implementation, the FLC approach achieves higher accuracy in maximum power tracking despite increased operational complexity. The simulation results confirm the effectiveness of the proposed control methods in ensuring DC-bus voltage stability and power balance throughout the system. This research contributes valuable insights for improving the scalability, resilience, and performance of microgrid systems with hybrid renewable energy sources and storage solutions.

The advancement of power electronics in recent years is largely attributed to the development of semiconductor components and new architectures for energy conversion systems. Among these, multicellular converters — which use a series connection of elementary switching cells — have gained significant attention This connection enables higher power processing without increasing stress on the switches, while also improving the output signal quality. However, managing the voltages across floating capacitors in these systems requires precise control. This paper proposes a hybrid modeling and control approach for a multicellular converter with three cells. The hybrid automaton model integrates both continuous and discrete dynamics, making it an effective tool for modeling power electronic systems with Boolean control inputs. Simulation results highlight the effectiveness of the proposed hybrid approach in overcoming voltage control and operational complexities in multicellular converters. By providing a novel solution to voltage control challenges, this modeling framework paves the way for advanced control solutions in power electronics applications. The findings demonstrate notable improvements in efficiency and response times, reinforcing the potential of hybrid modeling in optimizing power electronic systems.

The Series APF is an efficient and effective advanced power structure used in power distribution networks to compensate voltage harmonics, sags, swells and all voltage disturbances. The conventional system based on two-level voltage source inverter (VSI) presents several drawbacks and it is limited to low power applications. In order to overcome the disadvantages of this topology the tendency is to use high-level converters such as Flying Capacitor (FC), Cascade H-Bridge (CHB) and Neutral Point Clamped (NPC) inverters. This last configuration has gained much attention due to its advantages in lower switching loss, better electromagnetic compatibility, higher voltage capability, and lower harmonics. Among the NPC converters the nine-level (NPC) inverter is a potential candidate to use in various industrial applications mainly wind power generation, motor drives and active power filters. This research paper proposes a novel configuration of series APF based on nine-level (NPC) inverter topologies controlled by multicarrier phase disposition-sinusoidal pulse width modulation (PD-SPWM) combined with fuzzy logic. To verify the accuracy and test the validity of proposed series APF simulation models are developed using MATLAB-Simulink and SimPowerSystem Toolbox. The performance of proposed system is evaluated in term of harmonics and all voltage disturbances compensation using PQ and modified PQ control strategies. The obtained results prove the effectiveness of proposed series APF using nine-level inverter and confirm the superiority of modified p-q compared to p-q method in term of total harmonic distortion (THDv) reduction.

This paper presents a comparative study of two approaches for detecting and analyzing acoustic microwaves in piezoelectric materials, with a focus on Lithium Niobate (LiNbO₃) substrates. The first approach models acoustic microwave propagation in piezoelectric structures using an interdigital transducer (IDT) to excite electro-elastic waves. It examines various wave modes, including secondary surface waves, leaky waves, bulk waves, and skimming bulk waves, while applying wavelet transform for efficient signal detection. Two wavelet functions—Mexican-hat and Morlet—are evaluated in terms of their capability to identify acoustic wave singularities and their effectiveness in microwave signal processing. The second approach introduces a machine learning framework based on Support Vector Machines (SVM) to detect ultrasonic pulses and reveal wave patterns that are typically undetectable using conventional methods. By classifying the real and imaginary components of attenuation coefficients and acoustic velocity, this method yields more accurate values and enhances the modeling of ultrasonic pulse propagation. While the wavelet-based approach emphasizes signal processing for wave detection, the SVM-based method demonstrates superior performance in identifying complex wave patterns and provides higher accuracy in ultrasonic pulse modeling, thereby supporting the development of advanced acoustic microwave devices.

Outer covers 1 and 4 Inside covers 2 and 3 1. 03-10 Electric Power Transmission without Loss: HTS Superconducting Cable Ion DOBRIN, George DUMITRU, Dan ENACHE, Mihai GUȚU 2. 11-17 Photovoltaic System Optimization: Showing the Benefit of Artificial Intelligence Nacera MAZOUZ 3. 18-27 Performance Investigation of a Grid-Connected PV System Utilizing a Novel Ćuk Converter […]

Multilevel inverters have a vast range of research and development opportunities that remain growing in depth and breadth. They also present an amazing amount of problems. Furthermore, to reduce the total harmonic distortion, improve efficiency, reliability, simplicity and reduce the cost of multilevel inverters. The primary motivation of this research is to establish an efficient MLI with reduced active switching elements to more levels in the output in order to get improved waveform quality by reducing THD and also reduce TSV per unit and cost function per level. NLC techniques is used in these inverters to control the switching schemes. Thirty-three level new reduced switch count MLI is recommended with only 17 switches and 4 unsymmetrical voltage sources. The performance of recommended system is verified for R and RL loads. TSV per unit calculated and it is 6.09. The total harmonic distortion is 3.11 %, the cost function per level of the recommended inverter is 4.24 for weight co-efficient 0.5 and 4.61 for weight co-efficient 1. The performance of the recommended inverters is reliable, less complexity, TSV per level is low and cost function per level is less. The THD produced by these 33 level inverters meet the standards of IEEE.

Gear pumps often bear unbalanced radial hydraulic pressure, which is easy to cause serious wear of bearings, and the improvement of working pressure will be limited by many aspects. The built-in double gear pump of hydraulic mechanical automatic transmission is designed, and the structural composition and characteristics are analyzed; the basic parameters of gear pump are designed, and the working process and instantaneous flow characteristics are analyzed; the radial force generated by the hydraulic pressure along the gear circumference, the radial force generated by the gear meshing transmission torque and the synthetic force are analyzed; the structural parameters of gear pump are designed. According to the analysis results, the gear strength is checked by using the finite element model. Based on the gear pump test bench, the characteristics of the gear pump are analyzed under two working conditions: fixed speed (1,500 rpm), regulated pressure (1 MPa - 5 MPa), fixed pressure (2 MPa) and regulated speed (600 rpm – 1,800 rpm) to test the reliability of the design. The results show that the radial force generated by the liquid pressure of the center wheel of the double gear pump is balanced, and the meshing force of the center wheel is also balanced. The maximum stress on the center gear of gear pump occurs at the tooth root, which is 98.453 MPa, far less than the yield limit of gear material. When the pump speed is constant, the total efficiency, mechanical efficiency and volumetric efficiency of the pump show a decreasing trend with the increase of pressure, but the range is small. When the pressure remains unchanged, the flow of the pump increases gradually with the increase of rotating speed. The torque increase shows a downward trend, but the range is small. The mechanical efficiency shows an increasing trend, but the range is small. The volumetric efficiency increases first and then decrease. When the rotating speed is 1,000 rpm ~ 1,500 rpm, the volumetric efficiency of the pump is high. The analysis contents and results provide a reference for the design of this kind of gear pump.

In this research paper an optimized maximum power point tracking (MPTT) method for photovoltaic (PV) system is presented, the proposed technique improves the classical fuzzy logic MPPT by optimizing the fuzzy membership functions using genetic algorithms (GA). As application a photovoltaic water pumping system is chosen. Our system is composed of photovoltaic panels, boost chopper, three levels inverter, induction motors and pump. Fuzzy logic MPPT optimized by genetic algorithms is used to extract the maximum power from the photovoltaic panels and to control the inverter the space vector pulse width modulation (SVPWM) technique has been applied. To validate the proposed technique, simulations have been made using Simulink environment on MATLAB and the results show a better performance of the proposed MPPT compared to the classical fuzzy logic MPPT methods.

Bearings play a critical role in the operation of rotary machines, serving as essential components. Their failure often leads to unexpected shutdowns, posing a significant risk to the entire system. To mitigate these risks, it is imperative to implement proactive maintenance measures and strategic planning to prevent system breakdowns. This article introduces a comparative analysis between two predictive modelling approaches: Bidirectional Long Short-Term Memory (Bi-LSTM) and Adaptive Neuro Fuzzy Inference System (ANFIS) networks, aiming to enhance bearing prognostics. The proposed methodology involves a two-step process. Firstly, data undergoes pre-processing through wavelet packet decomposition (WPD). Subsequently, a degradation model is employed for predicting the remaining useful life (RUL). To validate the accuracy of the proposed approach, extensive testing is conducted using a bearing's life dataset obtained from a run-to-failure experiment. The results demonstrate that the ANFIS model exhibits remarkable capabilities in learning and accurately estimating the system's RUL, achieving this with minimal computation time compared to alternative methods, thus presenting a more efficient and precise solution.