The use of photovoltaic (PV) systems as the energy source of electrical distributed generators (DG) is gaining popularity, due to the progress of power electronics devices and technologies. Large-scale solar PV power plants are becoming the preferable solution to meet the fast growth of electrical energy demand, as they can be installed in less than one year, as compared to around four years in the case of conventional power plants. Modular multilevel inverters (MMIs) are the best solution to connect these large-scale PV plants to the medium-voltage (MV) grid, due to their numerous merits, such as providing better power quality, having higher efficiency, providing better reliability, and their scalability. However, MMIs are still progressing and need some improvement before they can be implemented safely in the industrial, medium, and high voltage networks. The main purpose of this paper is to review the present MMIs topologies when used in PV applications. The review aims to present a comprehensive study of the various recent submodule circuits associated with MMI topologies. Maximum power point tracking (MPPT) control schemes for PV inverters will be explored extensively. Then, the different control strategies of PV MMIs will be presented and compared to give a holistic overview of the submodules balancing techniques, ranges, and capabilities under balanced and unbalanced grid conditions. In addition, the paper will discuss the future of PV MMIs systems in electricity networks.

This paper presents a modular grid-connected single-phase system based on series-connected current-source module integrated converters (MICs). The modular configuration improves the reliability, redundancy and scalability of photovoltaic (PV) distributed generators. In this system, each PV panel is connected to a dc/ac inverter to permit individual Maximum Power Point Tracking (MPPT) operation for each panel. Thus, the harvested power from the PV system will increase significantly. There are four different inverter topologies suitable to be used as MICs with different performances in terms of filtering elements size, power losses, efficiency, output voltage range, and high frequency transformers’ size. For the MPPT control, the oscillating even order harmonic components should be eliminated from the inverter’s input side otherwise the maximum power cannot be extracted. The proposed modulation scheme in this paper will ease the control of inverter’s input and output sides. Therefore, the 2nd order harmonic in the input current can be eliminated without adding new active semiconductor switches. A repetitive controller coupled with proportional-resonant controllers are employed to achieve accurate tracking for grid side as well as input side currents. Comparisons and performance evaluations for the proposed MICs are presented and validated with 1 kVA prototype controlled by TMS320F29335 DSP.

Poetry is a significant aspect of any language. Many cultures and the history of nations are recognized in poems. Compared to prose, each poem has a rhythmic structure that is quite different. The language has its set of lyrical structures for poems, known as meters. Detecting the meters of Arabic poems is a complicated and lengthy procedure. The text must be encrypted using the Arudi method to classify the poem's meter, which requires complex rule-based transformation before another set of rules classifies the meters. Applying deep learning (DL) to meter classification in Arabic poems includes constructing a neural network to discern rhythmic patterns inherent in various meters. The model can extract essential features, like word lengths or syllable patterns, by tokenizing and preprocessing text datasets. Architectures such as Long Short-Term Memory Networks (LSTM) or Recurrent Neural Networks (RNNs) are fitting solutions to capture temporal relations in poetic verses. This research introduces a Hybrid Meta-heuristics with Deep Learning for the Arabic Poem Meter Detection and Classification (HMDL-APMDC) model. The main intention of the HMDL-APMDC system is to recognize various kinds of meters in Arabic poems. The HMDL-APMDC technique primarily preprocesses the input dataset to make it compatible with the classification process. Besides, the HMDL-APMDC technique applies Convolution and Attention with a Bi-directional Gated Recurrent Unit (CAT-BiGRU) for the automated recognition of meter classes. Furthermore, the adaptive sine-s-cosine particle swarm optimization (ASCA-PSO) algorithm is applied to optimize the hyperparameter tuning of the CAT-BiGRU model, enhancing the meter detection results. A detailed simulation analysis is made to highlight the improved performance of the HMDL-APMDC technique. The empirical outcomes stated that the HMDL-APMDC technique had a superior outcome of 98.53% over recent models under the MetRec dataset.

This paper presents a novel modular integrated on-board charger (MIOBC) topology and control scheme for dual-motor electric vehicle (EV) applications. Designed for effective power management across various operational states (driving, regenerative braking, and charging), the MIOBC modularises both the battery and converters, improving fault ride-through (FRT) capability, system flexibility, safety, and efficiency. The architecture features a single-stage bidirectional isolated Cuk converter as its submodule (SM), providing inherent power factor correction (PFC), reduced current ripple, and enhanced power quality. The control strategy integrates finite control set model predictive control (FCS-MPC) with classical proportional-integral (PI) controllers in a hierarchical multi-loop framework. The FCS-MPC dynamically predicts and regulates switching states, minimising a defined cost function to achieve real-time current and voltage tracking, while also suppressing second-order harmonic components through an innovative capacitor-based energy buffering technique. The paper further explores the impact of the prediction horizon on stability, employing state-space modelling to analyse robustness under parameter variations. Experimental validation is conducted on a 20 kW dual-motor system controlled by a TMS28335fezdsp, demonstrating robust performance under normal and fault conditions, including mode switching and secondorder harmonic suppression.

This paper presents an improved structure for the submodules (SMs) in the three-phase modular inverter (TPMI) based on a dual isolated SEPIC/CUK (DISC) converter for large-scale photovoltaic (LSPV) plants. The DISC SMs can offer several advantages, including increased efficiency, reduced passive elements, and galvanic isolation via compact-size high-frequency transformers. The SMs can also provide a wide range for the output voltage and draw continuous currents with low ripples from the input source. However, the high dv/dt value across the switches during hard switching can cause current oscillations and voltage spikes, which will impair the operation of complementary switches and affect the safety of the power devices. For this challenge, the DISC SM is improved by replacing the output switches with diodes and adding a bypassing switch. In comparison to the conventional DISC SM, the improved DISC SM reduces the switch’s voltage spikes; hence, it can increase the overall efficiency. Thus, the DISC SM’s will be able to suppress voltage spikes in the TPMI inverter and therefore the total reliability will be improved. The work will detail the analysis of the proposed system along with design guidelines. Additionally, the simulation and experimental results to validate the operation of proposed DISC SM are presented using MATLAB/SIMULINK as well as a scaled-down experimental prototype.