The increasing demand for renewable energy sources necessitates advanced methods to ensure grid stability and operational efficiency. Smart grids present a viable solution by enabling the seamless integration of renewable energy systems, such as solar photovoltaic (PV) and other sources. However, the performance and reliability of such systems are critically dependent on the performance and functionality of power inverters (e.g., solar PV inverters). This paper proposes the application of several decision tree algorithms, which are traditional decision tree (TDT), iterative dichotomizer tree (IDT), C4.5 algorithm, and CART algorithm, for fault prediction and diagnostics in solar PV inverters. The training and prediction phases of the decision tree models employ both key inverter specifications, such as nominal operating ranges, abnormal readings, and fault conditions, alongside critical operational parameters, including voltage, current, temperature, conversion efficiency, power factor (PF), and total harmonic distortion (THD). Also, a cost-benefit analysis is made to consider the cost-effectiveness of predictive maintenance of solar PV inverters with the presence of significant economic benefits. A substantial cost advantage is shown, where 12,000 $/year in losses is saved with an investment of merely 4000 $/year in support of a predictive maintenance model with a first year ROI of 200%. Overall, the results of all decision tree models, implemented in MATLAB, confirm that both TDT algorithm and CART algorithm attain a high classification accuracy of about 95% with TDT possessing the shortest fault prediction time of about 50 ms. Markedly, the CART algorithm proves effective when it comes to dealing with an up to 90% missing values. Additionally, the analysis demonstrates a direct proportional relationship between voltage, current, temperature, and conversion efficiency, while revealing an inverse relationship between conversion efficiency and THD. The proposed algorithms for fault prediction hold significant potential for enhancing the reliability of solar PV systems integrated within smart grid frameworks.

Multimode control of a variable-speed wind energy system coupled to standalone DC microgrid

Total harmonic distortion and unbalance factor estimator by three-phase quadrature signal generator based on multiresonant linear oscillator

With widespread proliferation of power electronic devices, harmonic pollution has become increasingly severe in power distribution network. Owing to its simple structure and high compensation accuracy, source-current-detected active power filter (APF) has been employed for harmonic cancellation. This type of APF generates the current reference through closed-loop control scheme without sensing the load current. However, this operational principle renders conventional current-limiting strategies, such as reference truncated limiting, impractical. Because they will destroy the inner closed-loop relationship. Effective residual capacity utilization can not be achieved while maintaining fast response. Therefore, this article proposes an improved fast current-limiting strategy for source-current-detected APF. By output current feedforward, closed-loop relationship is modified to achieve partial compensation. Further, this article optimizes multi-frequency feedforward coefficients for different cases. Fast-limiting control and total harmonic distortion (THD) optimization control are integrated in a cascade to form a hybrid strategy, which exhibits a more balanced performance. By instantaneous current triggering, proposed strategy substantially reduces the response time and overcurrent risk. On this basis, available capacity of APF is better utilized. Finally, proposed strategy is verified by simulations and experiments in which a 2.2kW load is compensated.

An original bio-inspired approach, modeled after the magnificent frigatebird, is proposed in this study to optimize Maximum Power Point Tracking (MPPT) for photovoltaic arrays operating in grid-tied configurations, in response to the growing demand for higher efficiency during the ongoing transition toward sustainable energy. By modeling the frigatebird’s strategic shifts between wide-range scouting and target-focused behavior, the algorithm maintains a dynamic equilibrium between exploration and exploitation, ensuring robust MPPT performance even under rapidly changing irradiance and temperature conditions. The photovoltaic setup under study consists of a 50 kW SunPower panel array, paired with a boost-type DC–DC converter and a three-phase inverter. System behavior was examined in MATLAB/Simulink across three operating scenarios: standard test benchmarks, fast-changing irradiance conditions, and real-world solar measurements collected in Tetouan, Morocco. Simulation outcomes reveal that the MFB-based control achieves a high energy conversion efficiency of 99.5% with a rapid response time of 0.27 s, providing improved performance compared with widely used MPPT methods such as P&O and ABC in terms of dynamic response, tracking precision, and total harmonic distortion (THD). The proposed algorithm relies on a simple computational structure with a limited number of control parameters, contributing to reduced computational burden and supporting its suitability for real-time embedded MPPT applications. A performance comparison against fourteen other MPPT approaches reported in recent studies further supports the effectiveness and adaptability of the proposed method. The findings indicate that MFB represents a promising and scalable solution for advanced smart PV systems, with potential applications in real-time embedded platforms and hybrid renewable energy networks. While the present validation is based on detailed simulation results, experimental implementation and hardware-based assessment are considered as natural extensions of this work. • Novel bio-inspired MPPT based on magnificent frigatebird foraging behavior. • Fast and accurate MPPT under rapid irradiance and temperature variations. • High tracking efficiency of 99.49% with 0.27 s dynamic response. • Superior performance compared with P&O, ABC and recent MPPT methods. • Low computational complexity suitable for real-time embedded systems.

This article proposes a novel deadbeat sliding mode predictive control (DB-SMPC) strategy for T-type three-phase four-leg three-level voltage source inverters (3P-4L-3L-VSIs). Developed in the αβγ frame, the control strategy integrates the advantages of traditional sliding mode control (SMC) and deadbeat predictive control (DBPC), offering enhanced robustness, stability, and superior dynamic and steady-state performance. It outperforms traditional SMC and DBPC in terms of voltage regulation and dynamic response under varying load conditions. This strategy also reduces system chattering, ensures a fixed switching frequency, thereby simplifying the design of LC-filters. The four-leg inverter topology effectively addresses unbalanced and nonlinear loads. When integrated with the proposed DB-SMPC strategy, it enables robust system performance under such conditions. In addition, it significantly reduces total harmonic distortion (THD) for nonlinear loads and improves the dynamic and steady-state performance of balancing neutral-point voltage under unbalanced load conditions. Experimental results verify the effectiveness of the proposed strategy.

The three-level neutral point clamped (3L-NPC) inverter is widely used in permanent magnet synchronous motor (PMSM) drive systems for its low total harmonic distortion (THD) and high efficiency but suffers from neutral point voltage (NPV) drift and third-order harmonic fluctuations. With a relatively small DC-link capacitance, the NPV exhibits more pronounced high-frequency fluctuations. Although conventional carrier-based pulse width modulation (CBPWM) schemes effectively mitigate NPV drift, the regulation latency, due to the injected zero sequence voltage cannot be adjusted within each switching cycle, inherently limits their ability to suppress high-frequency NPV fluctuations. Therefore, this paper investigates the mechanism of NPV fluctuation in three-level NPC inverters and establishes a linearized model describing the relationship between the injected zero-sequence voltage and NPV dynamics. Based on this model, a CBPWM control strategy incorporating active disturbance rejection control (ADRC) is proposed to enhance both steady-state and dynamic NPV regulation. An extended state observer (ESO) is employed to estimate and compensate for the third-harmonic disturbances responsible for NPV oscillations in real time, enabling active suppression of voltage ripple without increasing switching frequency or computational complexity. Experimental results verify that the proposed method achieves stable NPV regulation and smooth torque response under both steady-state and dynamic conditions, thereby enhancing the overall performance of a 3L-NPC inverter-based PMSM drive.

The paper presents a new converter topology for a Transmission-type Static Synchronous Compensator (T-STATCOM), which is composed of two sections: a three-level, neutral-point clamped (3LC) converter using silicon based Insulated-Gate Bipolar Transistors (Si-IGBTs), and a two-level converter (2LC) using Silicon Carbide Metal-Oxide-Semiconductor Field-Effect Transistors (SiC-MOSFETs). These two converters are connected to each other using a coupling transformer featuring an open-end secondary winding. Taking advantage of the high switching frequency of SiC devices, the 2LC performs harmonic compensation and reactive power control, thus eliminating the need for bulky passive filters and enhancing the system overall efficiency. The proposed topology has been validated through the simulation of a 8 MVAr, 154 kV T-STATCOM system. Moreover, the consistence of the concept has been practically confirmed by a scaled-down experimental prototype rated at 6 kVAr. Up to 50% reduction in total losses and component costs is achieved by the proposed topology in comparison with state-of-the-art designs, as well as voltage Total Harmonic Distortion (THDv) compliance with IEEE Std. 519-2014.

This paper proposes an optimal voltage- vector-sequence-based model predictive control (MPC) method for permanent magnet synchronous motor (PMSM) drives that exploits the properties of optimized pulse patterns (OPPs). Specifically, by analyzing OPPs from a space vector perspective, the optimal voltage vector sequence and the corresponding duration are determined in an efficient way, bringing about satisfying steady-state performance. Moreover, the switching pulses are modified in real time to make the current track the optimal current trajectory that is easily obtained according to the harmonic model of the machine, thus achieving fast dynamic responses. First, the reference voltage vector is calculated and the candidate solutions are obtained based on the allowable switching frequency and the fundamental frequency. Second, the optimal solution is selected by evaluating the designed cost function, which expresses the weighted total harmonic distortion (WTHD) of phase voltage in a closed form. Finally, the optimal current is calculated and the switching instants are modified. To verify the validity of the proposed method, experiments are carried out on a 2.5 kW PMSM drive. At nominal speed and rated load, the proposed method reduces the current THD by 54% and 30% when compared with space- vector-modulation-based MPC (SVM-MPC) and finite-control-set MPC (FCS-MPC) at 625 Hz switching frequency, achieving the steady-state performance nearly as good as that of the OPPs-based control method.

Abstract: Power quality has become a significant issue in modern electrical power systems due to the widespread use of nonlinear and power electronic-based loads such as rectifiers, inverters, adjustable speed drives, and industrial converters. These nonlinear loads generate harmonics, reactive power burden, voltage distortion, and poor power factor, which reduce system efficiency, stability, and reliability. Conventional passive filters provide limited harmonic compensation and suffer from resonance problems, while active power filters offer better compensation with increased complexity and cost. To overcome these limitations, this work proposes a PSO-GWO Optimized Tilt Integral Derivative (TID) Controller Based Hybrid Shunt Active Power Filter (HSAPF) for effective power quality improvement. The proposed HSAPF combines passive and active filtering techniques to compensate both lower-order and higher-order harmonics. The passive filter suppresses dominant harmonic frequencies, while the active filter dynamically injects compensating currents through a Voltage Source Inverter (VSI) to improve source current quality and maintain stable operation. A Tilt Integral Derivative (TID) controller is employed to regulate the DC-link voltage and generate switching signals for harmonic compensation. To achieve optimal controller performance, a hybrid Particle Swarm Optimization–Grey Wolf Optimization (PSO-GWO) algorithm is used to tune the controller parameters automatically. The complete system is modeled and simulated in MATLAB/Simulink under three operating conditions: without filter, with passive filter, and with the proposed HSAPF. Simulation results demonstrate that the uncompensated system exhibits severe harmonic distortion with THD of 31.6%, while the passive filter reduces THD moderately to nearly 14%–18%. The proposed PSO-GWO optimized HSAPF significantly reduces THD to nearly 2%–4%, improves power factor close to unity, enhances source current waveform quality, and increases overall system stability and efficiency. The results confirm that the proposed system provides superior harmonic mitigation and effective power quality enhancement for modern electrical power systems Keywords: Hybrid Shunt Active Power Filter (HSAPF), Power Quality Improvement, Total Harmonic Distortion (THD), Tilt Integral Derivative (TID) Controller, Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), PSO-GWO Algorithm, Harmonic Compensation, Active Power Filter (APF), Passive Power Filter (PPF), Voltage Source Inverter (VSI), Nonlinear Loads, Reactive Power Compensation, MATLAB/Simulink, Harmonic Mitigation.

The increase in non-linear loads in the electrical distribution system has resulted in harmonics, which affect the power system’s stability and performance. The project focuses on mitigating the current harmonics of the system by implementing a Shunt Active Power Filter (SAPF), optimized using an Artificial Neural Network (ANN). The proposed system ensures the generation of compensating current through the Shunt Active Power Filter (SAPF), which helps in mitigating the harmonics present in the system. The project showcases the gradual improvement in power quality using different techniques for optimization in the conventional Shunt Active Power Filter (SAPF). This paper presents a comparative analysis of harmonic reduction techniques for a non-linear load system. The uncompensated system shows a Total Harmonic Distortion (THD) of 28.34%. Applying a conventional Shunt Active Power Filter (SAPF), PI-tuned SAPF, and PSO-optimized PI-SAPF reduces THD to 14.01%, 3.43%, and 1.64%, respectively. An Artificial Neural Network (ANN) further enhances the PSO-PI controller through adaptive real-time optimization. MATLAB/Simulink simulations demonstrate the proposed ANN-based SAPF achieves a THD of 1.41%, offering superior harmonic suppression compared to traditional methods. This demonstrates the significant improvement in the power quality by effectively mitigating the harmonics present in the system.

This research focuses on the performance analysis of an AC Voltage Controller (ACVC) utilizing various control techniques—specifically firing angle control and Pulse Width Modulation (PWM) control for different load conditions. The AC Voltage Controller is a power electronic device that regulates AC voltage magnitude without altering its frequency. Simulation models were developed using MATLAB/Simulink for resistive, inductive and single-phase induction motor loads under both firing angle and PWM control techniques. The results highlight that PWM control significantly reduces Total Harmonic Distortion (THD) and improves power factor compared to conventional firing angle control, thereby ensuring enhanced performance and efficiency.

To suppress AC-side oscillation and improve steady-state current quality in current-source-inverter-fed permanent magnet synchronous motor (CSI-PMSM) systems, this paper proposes a predictive current control method based on steady-state characteristics. An equivalent model of the CSI-PMSM system is developed in the synchronous rotating dq reference frame, and the steady-state characteristics of the filter capacitor current are analyzed. The analysis shows that the capacitor current is generally nonzero under steady-state operation, whereas its deviation from the steady-state reference component should converge to zero. Based on this property, a discrete predictive model is constructed, and the stator current tracking error and the capacitor current deviation are incorporated into the cost function to achieve coordinated current tracking and LC oscillation suppression. In addition, a deadbeat preselection and a local finite-candidate optimization scheme are adopted to reduce the online computational burden. Experimental results obtained from a 3.7 kW CSI-PMSM platform demonstrate that, compared with conventional multi-loop PI control, the proposed method significantly reduces dq-axis current ripples and DC-link current fluctuations, while decreasing the stator current total harmonic distortion from 9.87% to 2.25%. These results verify the effectiveness and engineering feasibility of the proposed steady-state-consistent predictive current control method.

Multilevel inverter structures, due to their merits such as step voltage generation, reduction of total harmonic distortion, no need for passive filters, reduction of magnetic interference, and ability to withstand high voltages, can be used in various industries such as energy conversion, electric vehicles, and electric machine drives because of these positive features. This work proposes an innovative multi-level inverter architecture designed to overcome constraints inherent in conventional approaches. The structure facilitates the use of either symmetrical or asymmetrical DC sources. Notably, it features a significant reduction in the required number of switches for generating a given number of voltage steps compared to traditional inverters. The possibility of designing an asymmetric inverter with different voltage source selection algorithms is another feature of the proposed structure. For verification, the system performance has been investigated in MATLAB software. Furthermore, an experimental prototype was constructed. Test results confirm the inverter's capability to produce high-quality voltage waveforms.

Abstract The development of solar photovoltaic (PV) home systems continues to be challenging, particularly in developing countries, where economic and technological constraints persist. This study aims to model a grid-connected solar PV home system using MATLAB/Simulink environment and investigate its performance under varying environmental and load conditions. The system comprises a 2.4 KW PV array connected to a two-stage power conversion system, including a boost DC-DC converter and a single-phase grid-tied inverter. An enhanced perturb and observe (P&O) maximum power point tracking (MPPT) algorithm with scanning capability is proposed to address partial shading challenges. Simulation results demonstrate the system’s ability to maintain a stable DC-link voltage of 380 V with minimal ripple (< 2%) and achieve low total harmonic distortion (THD) in voltage (0.16%) and current (2.27%), complying with IEEE standards. The system efficiently manages power flow between the PV array, household loads, and the utility grid, eliminating the need for energy storage and reducing costs. The findings highlight the system’s robustness under partial shading and varying irradiance conditions, demonstrating its suitability for widespread adoption in grid-connected urban homes.

Introduction. Recently, multilevel inverters (MLIs) have been widely investigated for industrial and renewable energy systems as they are valuable in applications where they can produce clean, high-fidelity electrical signals that minimize harmonic content and distortion. Problem. Among the modulation strategies, selective harmonic elimination pulse width modulation (SHE-PWM) is highly effective, but solving its nonlinear transcendental equations requires accurate numerical methods. Goal. To improve the performance of the 7-level packed U-cell (PUC) inverter by applying the Newton–Raphson method to compute optimal switching angles for SHE-PWM, thereby minimizing total harmonic distortion (THD), improving waveform quality, and achieving a more compact and cost-effective design with fewer components. Methodology. The Newton–Raphson iterative algorithm was implemented in MATLAB/Simulink to solve the nonlinear equations of SHE-PWM, and a hardware prototype of the 7-level PUC-MLI was fabricated and tested to validate real-world performance. Results. The application of the Newton–Raphson algorithm significantly improved the system’s performance. After implementation, the THD was reduced to 13.19 % in the simulation and 18.14 % in the hardware prototype, whereas both initially exhibited considerably higher THD levels. Scientific novelty. The proposed method demonstrates the capability of the Newton–Raphson algorithm as a reliable numerical solution for selective harmonic elimination in the 7-level PUC MLI, ensuring rapid convergence and precise determination of switching angles. Practical value. The study shows that significant harmonic reduction can be achieved without additional hardware or complex circuitry, making the approach applicable to other inverter topologies and suitable for advanced power electronic and renewable energy systems. References 22, tables 4, figures 9.

Introduction. Nowadays, the most widely used wind energy conversion system in wind farms is based on a doubly fed induction generator (DFIG); it has a large speed range and can function in multiple modes. Problem. Harmonic distortion in wind energy conversion system can degrade output waveform quality, reduce power conversion efficiency. Goal. This study investigates the dynamic performance of a wind energy conversion system comprising a grid-connected load, a 13-level hybrid multilevel converter and a doubly fed induction generator (DFIG), using a PI controller. The study aims to evaluate the dynamic performance and power quality of wind energy conversion systems, and to develop a novel hybrid metaheuristic method combining differential evolution (DE) and grey wolf optimization (GWO)-based selective harmonic elimination pulse-width modulation (SHEPWM) control strategies. This method reduces total harmonic distortion (THD) and ensures compliance with IEEE 519 standards, while increasing the power transferred to the grid. Methodology. The system, which includes a grid-connected load, a 13-level converter, and a DFIG, is modeled and simulated in MATLAB/Simulink under steady-state wind conditions. Vector control via stator flow orientation was used to modify the energy quality provided by the DFIG, making the system comparable to the DC machine. Our approach was to use a PI controller in order to directly control the active and reactive DFIG power through multi-level converter then a hybrid metaheuristic algorithm combining DE and GWO is implemented to solve the SHEPWM nonlinear transcendental equations. The proposed algorithm is evaluated based on its ability to suppress lower-order harmonics and improve THD performance, these converters increase the power transmitted to the power grid by reducing harmonic content of the output voltages. Results. By using the DE-GWO hybrid method and a PI controller, lower-order harmonics were effectively removed and THD was reduced to meet IEEE 519 standards. Simulations showed an improvement in output wave quality and better energy conversion efficiency compared to conventional optimization methods. Scientific novelty of the proposed work lies in the fact that the study introduces a novel DE-GWO hybrid optimization method for PWM (SHEPWM) in 13-level hybrid multilevel converter applied to wind energy systems. Practical value. The novel method demonstrates that constant high performance in wind energy systems may be achieved by combining intelligent optimization algorithms with complex multilevel converter designs This means it can be effectively integrated into contemporary wind farms where meeting grid standards, adjusting to varying sizes, and ensuring long-term reliability are crucial. References 26, table 1, figures 19.

The modeling and simulation of single phase five level t-type multilevel is presented in the paper. Solar photovoltaic inverter tailored inverter. The suggested topology is intended to improve. conversion efficiency and power quality and minimized total harmonic distortion. The inverters modeled in grid-connected and islanded operating conditions with PSIM, showing that it is flexible and can perform in different load and environmental conditions. The switching strategy and output waveform shaping is analyzed in detail. emphasizing the benefits of a lower number of switches and a better pulse width. modulation techniques. Sinusoidal output voltage generation is provided by the control system with. strong voltage regulation of both operating modes. The simulation findings support the use of the five-level. T-MLI has a stable operation, good power transfer and low harmonics hence making it. a promising technology to next-generation PV grid integration and standalone energy systems

The rapid growth of electric vehicles (EVs) has increased the demand for efficient and high-power quality battery charging systems. Conventional EV chargers typically use diode bridge rectifiers followed by DC–DC converters for AC–DC power conversion; however, these rectifier-based systems suffer from high conduction losses, poor power factor, increased total harmonic distortion (THD), and reduced overall efficiency. These drawbacks not only degrade charger performance but also introduce harmonics into the utility grid, resulting in additional losses and reduced reliability. To address these issues, this paper presents the design and analysis of a modified bridgeless AC–DC Landsman converter for EV charging applications. The proposed topology eliminates the conventional diode bridge rectifier, thereby reducing conduction losses and improving efficiency. The converter operates as a power factor correction (PFC) stage and provides a regulated DC output suitable for EV battery charging. The modified bridgeless configuration reduces the number of conducting devices in each switching cycle, which improves power quality and minimizes input current ripple. A PI controller is employed to regulate the DC-link voltage and maintain a constant output voltage. The proposed converter is modeled and simulated in MATLAB/Simulink using a 230 V single-phase AC input with a switching frequency of 20 kHz, and the output voltage is regulated to 48 V. Simulation results demonstrate improved power factor, reduced harmonic distortion, and stable output voltage. The input current waveform becomes nearly sinusoidal, and conduction losses are significantly reduced compared to conventional rectifier-based chargers. Therefore, the proposed modified bridgeless Landsman converter provides improved efficiency, reduced THD, better voltage regulation, and enhanced power quality, making it suitable for EV battery charging applications.

Since renewable energy resources are penetrating modern power systems more than ever, the wind energy conversion system (WECS) requires fast dynamic response and robustness as well as reliable operation under strong-grid and weak-grid conditions. One example is the doubly fed induction generator (DFIG), which remains an attractive option within variable-speed wind generation technologies, as it has a low converter rating, high efficiency, and flexible power control capability. Yet, DFIG-based WECSs control methods are still typical in nature and their performance degrades with wind speed fluctuation, grid interference, model instability and parameter perturbation. In this paper, we proposed a robust control approach for DFIG-based WECSs that merges grid-forming and grid-following operation in the same multi-objective optimized sliding mode control (SMC) design framework. To improve the active and reactive power control, rotor speed regulation, dc-link voltage balancing as well as fault ride-through capability with reduced chattering effect encountered in classical SMC, the proposed concept approach is devised. When working in grid-following mode, the controller accurately synchronizes and injects power into the utility grid. It then operates in grid-forming mode, providing voltage and frequency support, thus improving operation under weak-grid and islanded conditions. A multi-objective optimization procedure is applied to find the SMC parameters that minimize settling time, overshoot, steady-state tracking error (SSTE), and control effort simultaneously. Simulation results show that active power settling time is decreased from 0.42 s to 0.18 s and dc-link voltage overshoot is resided from 14.6% to 4.1% in comparison to a traditional PI-based controller using the proposed approach. Moreover, compared to the conventional observer, rotor speed tracking error is reduced by 31.8%, and stator current total harmonic distortion decreases from 4.9% to 2.1%. When subjected to a 30% sag in grid voltage, the proposed controller retains closed-loop stability and recovers nominal operating conditions within 0.12 s versus 0.31 s for the benchmark controller. In addition, these findings verify that the proposed MOO-SMC called upgrade substantially increases the dynamism performance, stabilizing and quality of power with respect to conventional SMC for various operating conditions applied to DFIG-based WECSs