Power Quality Improvement in Grid Connected PV System using Fuzzy Logic Controller based Maximum Power Point Tracing(MPPT)

The two stage three phase grid connected solar photovoltaic (SPV) system is used to power quality improvement using LCL filter under linear and unbalance load. The DC-DC power converter and DC-AC power converter is used to interfaced between the SPV and grid. Solar PV system is constructed by simplified single diode model (SSDM). The maximize the SSDM solar PV output voltage by using maximum power point (MPP) technique to switching of DC-DC power converter for boosting the SPV voltage. A three phase DC-AC power converter is used to convert boosted DC into AC and fed into the grid. To control the DC-AC power converter is used to controller by abc to dq, PLL, PI controller and PWM technique. The power quality improvement using LCL filter measure by FFT analysis in MATLAB/SIMULIK. The above effectiveness control of proposed technique of grid current THD is validate using simulation results.

In renewable system control engineering, Maximum Power Point Tracking (MPPT) techniques are considered as one of the key steps in photovoltaic (PV) system development. Indeed, some MPPT techniques provide high performance under uniform irradiation and temperature conditions. However, their performance significantly degrades when the two preceding climatic conditions suddenly change. Therefore, this paper proposes a new voltage regulation based on Fuzzy Logic Control (FLC) strategy and small-signal model of DC–DC booster converter. The proposed control scheme is mainly based on the indirect incremental conductance (Inc) technique, equipped with an optimal integrator controller (OIC). It is implemented in a PV system, consisting of a DC–DC boost converter connected to a resistive load, with the main objective of ensuring a good MPP tracking behavior for variable operating conditions. To achieve this objective, the proposed MPPT strategy is designed around two stages. First, the smooth reference voltage is ensured by a Single-Input Single-Output (SISO) fuzzy logic controller using the Inc technique based on the MPPT approach. Then, the optimal integrator controller stabilizing the closed-loop system is integrated with optimization of its parameter according to Routh’s criterion for resistive load variations. Simulation results confirm the effectiveness of the proposed MPPT technique compared to some commonly used MPPT approaches. The superiority of the proposal is proven by using efficiency ratio, which rises more than 98% in all case studies. These objectives are ensured for both uniform and critical sudden change in weather conditions, regardless of resistive load variations.

The efficient interleaved boost converter (IBC) combined with the 3-level neutral point clamped (NPC) inverter for grid-connected photovoltaic systems (GCPVS) maximizes solar energy efficiency are presented. Enhancing power quality at the Point of Common Coupling (PCC) while utilizing the rated capacity of the inverter into consideration is the main goal of the proposed approach. The grid connected photovoltaic system includes an efficient three-level NPC inverter and interleaved boost converter to decrease DC link voltage oscillation. In addition to preventing overheating, the inverter current controls reactive power adjustment, active power injection, and current harmonic filtering. Using MATLAB, the proposed topology is implemented, and the results are contrasted with those of other existing techniques. The proposed method attains a THD of 2.5%. The proposed technique displays the efficiency is 95%. When compared to existing strategies, the proposed technique displays lower THD and high efficiency.

Solar Energy is the clean and renewable source of energy. Solar PV systems are gaining attraction for energy generation in these days of increasing energy demand due to its reducing cost and new researches carried on its efficient use. The solar cell is always operated at its Maximum Power Point for better results. There are different methods to search the maximum power point of PV system. In this paper, a Maximum Power Point Tracking method for Solar PV system using Sliding Mode Control is proposed. The PV system modeling practice under standard test conditions is discussed. The proposed controller strategy is robust technology in steady state and during varying environmental conditions. A DC/DC boost converter is utilized as a control actuator for the MPP tracking using PWM control on the switches. Simulation results in MATLAB/SIMULINK and discussion are provided to demonstrate the effectiveness of the proposed controller design.

Solar Photovoltaic (SPV) power generation system is becoming a popular and alternative technology to full fill the requirement of household electric power. The operation and maintenance cost of a typical SPV power generation system is too low at the household level, whereas the initial investment or installation cost of such systems is comparatively too high. Although the SPV power generation system uses free and renewable energy to generate eco-friendly electricity, due to the high initial investment, slow response of service assistance for technical support during the system failure, maintenance, troubleshooting, lack of basic troubleshooting and maintenance knowledge of the end-users are some of the primary reasons for reducing the popularity and faith on SPV power generation technology in the household level. In this paper, an elaborate explanation of design, installation, commissioning, maintenance, and troubleshooting methods have been briefly described to set up an indigenous and cost-effective solar photovoltaic power generation system to meet the electricity demand for basic household requirements with an individual effort.

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

The proposed work deals with the implementation of a control approach for an active power transfer between solar photovoltaic (PV) array and grid/ load alongwith power quality improvement by eliminating harmonics and compensating reactive power required by the load in the distribution system. The PV array is directly connected across the DC link of a voltage source converter (VSC). The power is transferred to the grid/ load through VSC, which performs the function of distribution static compensator (DSTATCOM) by injecting compensating currents in the proposed system. The PV array is operated at maximum power by employing the usage of perturb and observe (P&O) maximum power point tracking (MPPT) approach in order to extract the available maximum power from the solar PV array. A feed-forward term for the solar PV array power, is incorporated for the enhancement of dynamic response. The prominence of the control algorithm lies in the efficient switching of VSC, by generating reference grid currents, which are obtained through indirect current control technique. The recursive digital filter is utilized for processing the load currents and extracting the active power component of the load currents. Therefore, the active power component of load currents obtained, are used for generating the reference grid currents in the proposed system. A prototype of the system is developed in the laboratory and its performance is studied for varying loads, changing solar insolation, and voltage swell and voltage sag conditions.

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably. In this paper after comparison and analysis common methods used in controller of photovoltaic systems such as Fuzzy and P&O, proposed an approach combined from FLC and particle swarm optimization algorithm (PSO) as an appropriate method to achieve maximum power point tracking (MPPT). Indeed Fuzzy logic control can cope with photovoltaic system using heuristic knowledge rules, but tuning the control parameters is not straightforward. PSO performs an on-line haphazard global search for input and output scaling factors of a PD-type fuzzy controller. The objective function of the PSO algorithm has been defined to minimize slope of P-V curve. The simulation results in SIMULINK of MATLAB indicate that proposed method can effectively eliminate the power oscillation around MPP and raise stability and reach steady state of the system.

Solar photovoltaic (PV) system has gained its own importance in global electrical power generation. The increase in efficiency of PV cell, due to improvement in design parameters has led the world to use solar PV system as one of the reliable alternative energy source. PV system may be connected either in standalone mode or grid connected mode along with single stage configuration or two stage configuration. The intermediate stages present between solar PV system and grid is the boost converter with maximum power point (MPP) tracking controller and the inverter with different control techniques. The present work explains about a 14KW and 10KW solar PV system of two stage configuration simulated in standalone mode and grid connected mode with simple proportional-integral (PI) controller and fuzzy logic controller (FLC) to regulate dc output voltage. The behavior of PV system has been observed with switching on of an additional load for a stipulated time period. The performance of solar PV system under disparate running conditions and its associated power curves were presented.

there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV) production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (PO however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck–Boost) for a better matching between the PV and the load . This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O) control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.

This review presents a synthesis of fuzzy logic-based (FL) controllers applied to photovoltaic (PV) systems over the last decade, with a specific focus on maximum power point tracking (MPPT) and power management. These subsystems are critical for improving the efficiency of PV energy conversion, as they directly address the nonlinear, time-varying, and uncertain behavior of solar generation under dynamic environmental conditions. FL-based control has proven to be a powerful and versatile tool for enhancing MPPT accuracy, inverter performance, and hybrid energy management strategies. The analysis concentrates on three main categories, namely, Mamdani, Takagi–Sugeno (T-S), and Type-2, highlighting their architectures, operational characteristics, and application domains. Mamdani controllers remain the most widely adopted due to their simplicity, interpretability, and effectiveness in scenarios with moderate response time requirements. T-S controllers excel in real-time high-frequency operations by eliminating the defuzzification stage and approximating system nonlinearities through local linear models, achieving rapid convergence to the maximum power point (MPP) and improved power quality in grid-connected PV systems. Type-2 fuzzy controllers represent the most advanced evolution, incorporating footprints of uncertainty (FOU) to handle high variability, sensor noise, and environmental disturbances, thereby strengthening MPPT accuracy under challenging conditions. This review also examines the integration of metaheuristic algorithms for automated tuning of membership functions and hybrid architectures that combine fuzzy control with artificial intelligence (AI) techniques. A bibliometric perspective reveals a growing research interest in T-S and Type-2 approaches. Quantitatively, Mamdani controllers account for 54.20% of publications, T-S controllers for 26.72%, and Type-2 fuzzy controllers for 19.08%, reflecting the balance between interpretability, computational performance, and robustness to uncertainty in PV-based MPPT and power management applications.

This work investigates the performance of a single-phase grid-connected photovoltaic (PV) system under non-ideal source voltage conditions using a fuzzy logic controller. The single-phase instantaneous reactive power theory has been modified to explore the harmonic and reactive power compensation with distorted grid voltage excitation while transferring active and reactive power to the grid and the non-linear load. Instead of a traditional Proportional-Integral (PI) controller, the proposed method implements a fuzzy logic controller for the extraction of the reference compensating current. This method is compared with the traditional single-phase instantaneous reactive power theory. The advantage associated with a fuzzy logic controller is that it does not need accurate mathematical modeling of the system, instead, it is based upon the linguistic characterization of the input and output variables. Besides, care has been taken to provide uninterrupted compensation throughout the variation of the solar irradiation level. The proposed method has also been verified through a laboratory-developed prototype using a DS1103 processor.

Remote areas in India are still not connected to the power grid. But they have mobile network connectivity. The people face problems in charging their cell phones. They are forced to travel a long distances to get access to electrical outlets. The use of new efficient photovoltaic solar cells (PVSCs) has emerged as an alternative measure of renewable green power, energy conservation and demand-side management. The manufacturing of solar cells and photovoltaic arrays has advanced dramatically in recent years. But they have two inherent major problems the first is low conversion efficiency (10to16%for commercially available amorphous silicon solar cells), second is high capital cost [1]. Solar powered systems consist of different parts to be controlled separately. Since the generated power is a function of uncontrollable environmental conditions, it requires extra caution to design controllers that handle unpredictable events and maintain efficient load matching power. In this study, a photovoltaic (PV) solar array model was developed and analyzed using Matlab/Simulink. The model includes converter circuits, a controller using fuzzy logic control (FLC), and a maximum Power point (MPP) tracking control which is used to operate the PVA at its maximum power. The MPP point is tracked using four algorithms perturbation & Observation, Incremental conductance method, Constant Voltage method and Constant current method. Later a comparative performance analysis of the model for MPP algorithms is done.

The perturbation and observation (P&O) maximum power point tracking (MPPT) algorithms are very common in PV systems. Implementations of P&O algorithms based on peak current control and on instantaneous sampled values have shown to provide very fast transients and small oscillations around the maximum power point (MPP). However, operation with fixed variation of the reference current results in a trade-off between speed of response and maximum power yield in the steady state. Variable variation of the reference current can be successfully done with Fuzzy logic but results in significant computation burden for common digital signal processors (DSP). Thus the minimum time step is relatively large what reduces the transient response of the scheme. This paper presents an alternative approach based on non-switching zones and compares by simulation the performance and discusses the implementation difficulties of 4 peak-current controlled P&O MPPT algorithms.

Agricultural Photovoltaic Systems are a key technology to achieve sustainable development goals by reducing competition between land for food and electricity. In addition, Agricultural Photovoltaic Systems are at the heart of the link between power generation, crop production and irrigation water conservation. The main ecophysiological constraint on crop production under photovoltaics is the reduction of light. It is difficult to recommend shade tolerance for some plant varieties due to insufficient information on shading conditions for most plants. The use of shading panels (photovoltaic panels) requires more crop-specific research to determine the optimal percentage of panels and their placement that will not reduce agricultural yields. Crop yield variation versus field shading and availability to maximize the system require extensive research. This study aims to develop a standard procedure for designing an agricultural grid-connected photovoltaic power generation system for solar power generation in an agricultural area in Bahteem, Egypt. The technical and annual performance of the grid-connected PV system was simulated using PV Syst software. The paper started with a pre-feasibility study of a grid-connected photovoltaic system using PV Syst. Software with an extensive database of meteorological data, including global daily horizontal solar irradiance, and a database of various renewable energy system components from different manufacturers. In this work, a comprehensive literature review of agricultural solar photovoltaic systems is conducted, with a particular focus on grid-connected systems, followed by a design procedure for grid-connected solar photovoltaic systems. The planned photovoltaic system will generate a total of 400 KWp of electricity. This generated electricity can drive down electricity prices by exporting excess electricity to the national grid. In addition, solar power systems are fuel-efficient and have a low environmental impact.