Kanagaraj Nallaiyagounder

The increasing penetration of rooftop photovoltaic (RTPV) systems in low-voltage (LV) distribution networks introduces challenges such as voltage rises, reverse power flow, and reduced hosting capacity, thereby necessitating effective active power regulation (APR) in module-level micro-inverters. This paper proposes a dual-layer control framework for a 250 watt-peak (Wp) three-switch rooftop PV micro-inverter, integrating quantum-behaved particle swarm optimization with reinforcement learning (QPSO-RL) for accurate maximum power point tracking (MPPT) and a linear quadratic regulator (LQR) for reserve-aware APR. The QPSO-RL algorithm improves available-power estimation under varying irradiance, temperature, and partial-shading conditions, while the LQR-based controller ensures fast, well-damped, and grid-compliant power regulation. The proposed framework was developed and validated using MATLAB/Simulink 2024 for simulation studies and LabVIEW with NI myRIO 2022 for real-time hardware implementation. Both simulation and experimental results confirm that the proposed method achieves 99.5% MPPT accuracy, convergence within 20 ms, grid-injected current total harmonic distortion (THD) below 3%, and a near-unity power factor. In addition, the reserve-based regulation strategy improves feeder compliance and reduces converter stress, thereby supporting reliable rooftop PV integration. These results demonstrate that the proposed QPSO-RL + LQR framework offers a practical and intelligent solution for high-performance, grid-supportive rooftop PV micro-inverter applications.

In this paper, a new Nine-Level Single-Phase Bridge Inverter (NLSPBI) with a reduced number of switches using Level-Shifted Carrier Pulse Width Modulation (LSCPWM) is presented. Unlike other DC supply sequences, the proposed topology provides a higher voltage level by requiring a minimum group of components and DC input sources for the supply as it uses trinary series DC sources (1:3). Adding and removing DC input sources in this trinary arrangement results in higher output levels. The trinary supply-fed multilevel concept is implemented in a nine-level inverter with seven power switches and two power diodes. A maximum of four devices can be operated at any one time, operating only two power switches simultaneously with a high frequency, which significantly reduces switching and line losses, thereby increasing the overall system's efficiency. In addition, a level-shifted carrier PWM with Phase Disposition (PD) and an Alternative Phase Opposition (APO) PWM are proposed to optimize the output level and improve the harmonic spectrum. The computation of the power loss for the proposed configuration is described in detail using relevant mathematical formulas. Furthermore, by using MATLAB/Simulink and Hardware-In-The-Loop (HIL) simulations, it has been demonstrated that the presented inverter structure is feasible with the proposed PWM scheme and can provide the desired performance.

A multi‐input single‐output converter that is based on the impedance network and the standard isolated converters is presented. The topology is named quasi Z‐source full‐bridge isolated converter (qZSFBIC). The proposed topology helps to integrate various renewable power generation systems with a common three‐phase grid‐connected inverter. It was also capable of providing isolation between the input and output circuits in addition to the boost function. The integration of multiple energy sources is achieved using fewer components than conventional converters. As a result, it achieves greater conversion efficiency and has improved circuit characteristics. It offers a larger voltage control range as compared to the conventional ZSC and enhances the input‐output voltage transformation ratio. To determine whether or not the suggested converter is technically feasible, the circuit architecture, operating principle, control mechanism, and simulation results are presented. An improved proportional resonant‐second order general integrator (IPR‐SOGI) has been utilized to provide a gating signal for the voltage source inverter. The 1.5 kW, 400 V, 50 Hz model is designed to authenticate the proposed scheme with qZSFBIC. The results showed that the proposed converter offered double the time of boosting than the conventional ZSC converter and the conversion efficiency is around 89%.

Using the Z-network idea and conventional isolated power converters as its foundation, this article introduces a multi-port converter. This article proposes a topology that is called Multi-Port Z-Network Converter (MPZNC). A grid-connected inverter can include N input sources into a single DC bus using the suggested topology. Bypassing the input and output circuits was another capability it possessed with the boost function. Compared to traditional converters, these one-use fewer parts to integrate various energy sources. Consequently, it has better circuit properties and achieves higher conversion efficiencies. In comparison to the standard Z-Source Converter (ZSC), it improves the input-output voltage transformation ratio and provides a wider voltage control range. The circuit design, operating principle, control mechanism, and simulation data have been presented to prove technically possible. To investigate the suggested MPZNC-fed Single Phase Five Level (SPFL) inverter in the given scenario, a 1.5 kW, 230 V, 50 Hz miniature laboratory study model was created. According to the findings, the suggested converter has an efficiency of around 93% and provides double the amount of boosting time as the standard ZSC converter.

Static compensators (STATCOMs) are often used in distribution systems to enhance power quality. There is a need to enhance the performance of STATCOM to optimize its utilization and facilitate the provision of additional ancillary services. This paper employs the multilayer discrete noise-eliminating second order generalized integrator (MDNESOGI) to regulate the quasi-impedance source inverter (qZSI)-STATCOM for power exchange with the grid. Compared to conventional second-order generalized integrator (SOGI), MDNESOGI exhibits a higher capability for rejecting DC offset. In instances of abnormal grid operation or system malfunction, the inclusion of DC rejection capability enhances the robustness and reliability of the system. The suggested control algorithm only requires two integrators, three mathematical operators, and a damping factor, making it far easier to implement than transformation-based methods. The distorted load current is broken down into its active and reactive components using this control mechanism. The reference currents are then calculated by multiplying these parts by their corresponding voltage standards. The DC offset is reduced and transient oscillations in the weight component are eliminated by adjusting the damping factor. The suggested algorithm effectively handles power quality tasks like (a) reducing harmonic distortion, (b) compensating for reactive power, (c) adjusting for power factor, and (d) balancing the load under different conditions in the distribution system. The experimental study results are used to examine the stability of the proposed control scheme in both static and dynamic scenarios. In addition, a comparison to traditional methods is provided to demonstrate the new method’s superiority. Experimentation results show that the suggested controller is superior to its contemporaries in all scenarios where power quality is a factor, meeting the IEEE standard requirements.

Integrating renewable energy sources (RES), such as photovoltaic (PV) systems, into power system networks increases uncertainty, leading to practical challenges. Therefore, an accurate photovoltaic (PV) power prediction model is required to provide essential data that supports smooth power system operation. Hence, the work presented in this paper compares and discusses the results of different machine learning (ML) techniques in predicting the power produced by the 300 MW Sakaka PV Power Plant in the north of Saudi Arabia. The validation of the presented work is performed using real-world operational data obtained from the specified solar farm. Several performance measures, including accuracy, precision, recall, F1 Score, and mean square error (MSE), are used in this work to evaluate the performance of the different ML approaches and determine the most precise prediction model. The obtained results show that the Support Vector Machine (SVM) with a Radial basis function (RBF) is the most effective approach for optimizing solar power prediction in large-scale solar farms.

Converting devices are quickly becoming the most important part of renewable energy-producing systems that are linked to the grid. Applications that are linked to the grid are the most common place to find usage for two-port power converters that are built using single-input and single-output (SISO) ports. The incorporation of SISO power converters into the grid-connected hybrid system results in an increase in both its size and its cost. Multiple power sources may be connected to a single DC bus by means of hybrid power systems, which make use of multi-input power converters. To combine the hybrid wind and PV system with a common DC bus, this study suggests an isolated multi-input single-output (IMISO) Z-Source converter. It has been determined that the suggested system performs well in spite of dynamic load fluctuations and shifting input voltage circumstances. The sliding mode controller (SMC) has also been used to control a single-phase five-level (SPFL) inverter. The purpose of developing the laboratory prototype model was to verify the proposed IMISO Z-source converter-fed single-phase five-level (SPFL) inverter in the context of the circumstance that is being investigated.

Nowadays, renewable energy sources such as solar Photovoltaic (PV) and wind power systems play a significant part in the process of generating electricity. These systems are tremendously reliant on the present and forecasted weather conditions. The inconsistent behavior of the system has an impact on the production as well as the variances in the output. As a result, the requirement for energy transmission and distribution systems to compensate promptly is becoming crucial. This paper proposes a new smart grid application for power system operation. A Static Compensator (STATCOM) device is utilized to improve the power quality and power flow in the distribution system, reduce unwanted harmonics and compensate for reactive power in the power sources. A quasi-Z-Source Inverter (qZSI) based STATCOM is constructed in connection with a Three-Phase Four-Wire (3P4W) distribution system. The combination of a q Z-source and a Photovoltaic (PV) system makes up the suggested compensator circuit that is employed here for switching. To control the suggested compensator, an Adaptive Frequency Fixed (AFF) - Second Order Generalized Integrator (SOGI) has been implemented. The Fuzzy Logic Controller (FLC) is used to optimize the parameters of the Proportional Integral (PI) controller, such as Kp and Ki. The performance is evaluated using a variety of characteristics, including power, current, and voltage, generated reactive power and active power, analysis in non-linear load, and other similar metrics.

The generation of electricity, considering environmental and economic factors is one of the most important challenges of recent years. In this article, a thermoelectric generator (TEG) is proposed to use the thermal energy of an electric water heater (EWH) to generate electricity independently. To improve the energy conversion efficiency of the TEG, a fuzzy logic controller (FLC)-based perturb & observe (P&O) type maximum power point tracking (MPPT) control algorithm is used in this study. An EWH is one of the major electricity consuming household appliances which causes a higher electricity price for consumers. Also, a significant amount of thermal energy generated by EWH is wasted every day, especially during the winter season. In recent years, TEGs have been widely developed to convert surplus or unused thermal energy into usable electricity. In this context, the proposed model is designed to use the thermal energy stored in the EWH to generate electricity. In addition, the generated electricity can be easily stored in a battery storage system to supply electricity to various household appliances with low-power-consumption. The proposed MPPT control algorithm helps the system to quickly reach the optimal point corresponding to the maximum power output and maintains the system operating point at the maximum power output level. To validate the usefulness of the proposed scheme, a study model was developed in the MATLAB Simulink environment and its performance was investigated by simulation under steady state and transient conditions. The results of the study confirmed that the system is capable of generating adequate power from the available thermal energy of EWH. It was also found that the output power and efficiency of the system can be improved by maintaining a higher temperature difference at the input terminals of the TEG. Moreover, the real-time temperature data of Abha city in Saudi Arabia is considered to analyze the feasibility of the proposed system for practical implementation.

The design and analysis of a fractional order proportional integral derivate (FOPID) controller integrated with an adaptive neuro-fuzzy inference system (ANFIS) is proposed in this study. A first order plus delay time plant model has been used to validate the ANFIS combined FOPID control scheme. In the proposed adaptive control structure, the intelligent ANFIS was designed such that it will dynamically adjust the fractional order factors (λ and µ) of the FOPID (also known as PI^λD^µ) controller to achieve better control performance. When the plant experiences uncertainties like external load disturbances or sudden changes in the input parameters, the stability and robustness of the system can be achieved effectively with the proposed control scheme. Also, a modified structure of the FOPID controller has been used in the present system to enhance the dynamic performance of the controller. An extensive MATLAB software simulation study was made to verify the usefulness of the proposed control scheme. The study has been carried out under different operating conditions such as external disturbances and sudden changes in input parameters. The results obtained using the ANFIS-FOPID control scheme are also compared to the classical fractional order PI^λD^µ and conventional PID control schemes to validate the advantages of the controllers. The simulation results confirm the effectiveness of the ANFIS combined FOPID controller for the chosen plant model. Also, the proposed control scheme outperformed traditional control methods in various performance metrics such as rise time, settling time and error criteria.