Power Quality Improvement of Grid Connected PV System

The expansion of the spread of solar photovoltaic power generation systems is fraught with challenges related to the low efficiency and performance of these systems in light of the change in the surrounding weather conditions and the disturbances associated with the loads, which weakens the electrical energy productivity of these photovoltaic systems due to energy loss. To overcome and reduce these challenges as much as possible, it is considered the use of controllers that track the maximum power point of photovoltaic systems is the ideal solution to increase their efficiency and improve the quality of their electrical power, in order to obtain the maximum productive capacity from these systems, and this will be reflected in achieving an optimum benefit from the available solar energy and thus reducing the loss of unused energy in the absence of the control system.This paper presents a study, analysis and simulation of the actual behaviour of the grid-connected solar photovoltaic system model by means of the (MATLAB/Simulink_R2018a) program. The performance of Fuzzy Logic Controller is then evaluated to track the path of the maximum power point, and its work is based on tracking techniques that use the direct control method to control the operating cycle of the DC commutator, which in turn performs by feeding the inverter with the maximum output power from the solar photovoltaic system.A tracking technology was selected that relies on the method of artificial intelligence, which is the technique (fuzzy logic controller), and a fuzzy logic controller of the type (Mamdani) was used. The most suitable for tracking the maximum power point at the steady state of the Grid and under constant and changing weather conditions, as well as choosing the most appropriate controller when transient disturbances occur in the Grid under constant weather conditions only. The results proved the effectiveness of the fuzzy logic controller, and it had good performance in tracking the maximum power point in terms of achieving more stability, reducing error and settling time.

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.

There exist many types and configurations of switched-capacitor converters. However, they are mainly designed for specific applications such as DC-DC or DC-AC (multilevel converters) power conversions. Consequently, they are inapplicable in situations that require multi-applications. A better alternative would be to develop SC converters that are of multi-functions. Different types of power conversions can be achieved by a generalized SC converter, and is possible to fabricate the proposed converter into an IC package. The manufacturing cost can be further reduced through a mass production process. Nevertheless, fundamental studies and clarifications on the feasibility of the idea are still required as relevant literature are not reported. In this work, a preliminary study on a universal SC converter is presented. By programming the control sequence of power switches, the proposed converter is capable of achieving DC-DC, AC-DC, and DC-AC power conversions, while having the properties of bi-directional power flow and variable conversion ratios. The detailed explanations of its circuit operations, control methodology, and basic design procedures, are discussed. Experimental results are included to verify the feasibility of the proposed converter for low voltage applications.

Today, Increase the demand of Renewable energy resources (RES) in distribution systems because total energy demand is supplied by the burning of fossil fuels and it is limited. In this paper presents a control strategy of three phase four wire grid interfacing inverter to effectively utilize the renewable energy Source with grid. Controlling of inverter in such a way that to utilize the following function 1) compensate load current (i.e. reduce harmonics), 2) compensate load voltage (i.e. reduce harmonics), 3) compensate load reactive power and load neutral Current. The Renewable Energy Source may be Solar or Wind depends on distribution system voltage level. All these works of the inverter is done either individually or combined to overcome the unbalanced effects of all types of linear, non-linear, balance or unbalance loads at distribution level. This new control concept is demonstrated with extensive MATLAB/Simulink simulation to validated result.

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.

In order to extract the maximum volume of sun’s radiated energy maximum power point tracking has to be s elected. Due to the obstructions like clouds , ozone layer and on the other hand changes in the climatic conditions such as, water vapor, reflection, absorption and pollution makes the photo voltaic (PV) panel to generate less energy without the maximum power point (MPP) technique. So, in order to gain the highest power from the PV array, even in the extreme conditions, maximum power point incremental conductance (INC) algorithm, Fuzzy (FL) logic controller and Artificial neural network (ANN) techniques are implemented and their performance is analyzed in this paper. These techniques provide efficient power output when compared to ideal PV panel system. Their response is developed in MATLAB/Simulink environment.

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 paper investigates the potential use of the PEBB concept in low-voltage fed power converters, such as those being currently utilized in particular traction drives (e.g. forklift drives or wheelchair drives) or those being envisaged to be soon utilized on board automobiles being equipped with a 42 V electric power system including both engine directly-coupled starter/alternator and ultracapacitor-based storage system. Concerning such specific applications, various power converter topologies having input voltage in the range from 24 V to 80 V and rating power from few kW up to 30 kW are likely to be used in order to accomplish bi-directional DC-DC or DC-AC power conversion. Thereby, the envisaged development of a low-voltage PEBB (i.e. LV-PEBB) module including standardized power, thermal, and control interfaces is of great interest for power converter manufacturers as it would be expected to lead to great simplification in converter design and assembly and thereby should allow substantial reduction of mass-production costs

Photovoltaic (PV) module is the key component in the solar energy system. Fault classification for photovoltaic module is necessary for safety, efficiency and reliability of photovoltaic systems. When faults of PV module occurred, the current-voltage characteristics (I-V curves) of PV module are changed leads to the shift in voltage and current of the maximum power point (MPP). In this paper, fault classification for photovoltaic module based on MPP and machine learning techniques is proposed. Through analysing the location of the MPP in I-V parameters plane, faults of PV module during performance of could be identified and classified. The parameters of voltage and current of PV module in the faults (line-to-line fault, shading, open circuit in one array, open circuit, short circuit during different irradiation levels) are collected by simulations in Simulink/MATLAB software. Then, these parameters would be processed and built into a dataset. This dataset would be fed into fault classification model for PV module using machine learning algorithms included Support Vector Machine (SVM) and k-means. The desired results of machine learning models would be accurately classified faults using parameters of voltage and current of the MPP and survey the fault diagnosis through the borderline of each classification group of the models. The result shows that the fault classification capability and accuracy of the machine learning model using SVM algorithm (98,9%) are better than the k-means algorithm (61,1%).

Photovoltaic (PV) system uses maximum power point tracking (MPPT) technique to maximize its power output. This paper presents a swarm inspired optimization based control method for the MPPT of a PV system. In particular, control methods of perturb and observe (P&O)-proportional integral (PI) and optimisation of this controllers using spider monkey optimisation (SMO) for MPPT control of PV generator are proposed. A well known PI controller is tuned using spider monkey algorithm and tested on a simulated stand alone PV system with local load. The proposed SMO based PI controller enhances the P&O MPPT technique to track the maximum power point (MPP) more rapidly and accurately. The design algorithm of this controller is presented with its simulation results. Simulation results under different environmental conditions are presented and discussed to verify the satisfactory performance of the proposed control, in which the optimised P&O-PI controller by SMO algorithms gives the better performance technique over the traditional control methods.

The concept of electric vehicles has become an emerging trend for the last few years owing to their hefty returns. This article also becomes a part of the emerging electric vehicle technology replacing the conventional vehicle. This article presents the illustration of the hybrid electric vehicle employing the solar and fuel cell as the sources of electric vehicle. It is also involved in the proposal of Maximum Power Point Technique (MPPT) for controlling the output of the sources. It also presents the utilisation of different types of controllers like Proportional Integral Derivative Controller (PID), Fuzzy Logic Controller (FLC) and Neural Network (NN)-based peak power point technique for following the maximum power of the sources. This evaluates the performance of different controllers-based power point technique in terms of voltage, current, power, response time, torque ripples and efficiency of the system and aims to improve the system performance by 10%. The proposed hybrid electric vehicle structure has been simulated and results are demonstrated using MATLAB/Simulink.

In this paper, efficient charging of electric vehicle battery from a considered renewable solar photovoltaic source with the help of a modified Z source with efficient boosting topology. Adapting this Z-source converter to act as a voltage gainer with a boosting function allows a solar Photovoltaic (PV) input voltage of 25VDC (Volts Direct Current) to be increased to a designed output voltage of 75VDC at a low duty ratio, resulting in minimal switching loss. The closed-loop steady-state and transient parameters at the output were analyzed and compared using modern evolutionary algorithms. The power range upheld throughout the circuit is around (300–350) W. The battery is assumed to have an impedance model of Resistor-Capacitor (RC) load with a serial range of 12 V and 7 Ah. The proposed converter achieves higher conversion efficiency by the Maximum Power Pont Tracking (MPPT) and NSGA-II/MNSGA-II (non-dominated sorting genetic algorithm) based controller algorithm for tuning the optimal design value and is validated in a MATLAB Simulink platform. In this work, we analyze closed-loop systems under the mentioned power range. The MPPT with an algorithm-based controller tends to trigger the switch in the closed-loop system to get the optimized output. The Maximum Power Point (MPP) technique implemented is an incremental conductance method for extracting solar PV power and improving load performance. Consequently, the proposed evolutionary optimization algorithm steady-state ripple factor response of the proposed MNSGA-II has a lower output side, thus achieving around 98% of the controller implementation efficiency.

This paper helps us analyze three different MPPT techniques like Perturb and Observe, Incremental Conductance and Particle Swarm Optimization method. As the output characteristic depends on temperature and irradiance, therefore the maximum power point (MPPT) is not always constant. Hence it is necessary to ensure that the PV panel is operating at its maximum power point. There are many different MPPT techniques but, the confusion lies in selecting which MPPT technique is best as every algorithm has its own merit and demerit. In order to extract maximum power from PV arrangement, PSO algorithm is proposed. Algorithms are implemented using the DC-DC converter as well as SEPI converter. Results of simulations are presented in order to demonstrate the effectiveness of PSO algorithm, when compared to Perturb and Observe (P&O) and Incremental Conductance (INC). To simulate the proposed system MATLAB/SIMULINK power system tool box is used.

A novel maximum-power-point tracking (MPPT) method was proposed for 10 KW single-stage three-phase photovoltaic (PV) grid-connected system. First, the step and direction of disturbance voltage, which was added to the voltage reference of the maximum power point, was intelligently adjusted by the fuzzy control and the PV array differential power to voltage. Therefore, the voltage reference of the maximum power point (MPP) of PV array could be obtained quickly at any time. Then, a double loop control system, which included the voltage loop PI controller and the synchronous current vector PI controller with feed-forward compensation, was used to control the output voltage of the PV array to track the voltag reference of MPP. Finally, the simulation was performed with the help of Matlab/Simulink & SimPowerSystems. Simulation results show that the proposed MPPT control method can track the MPP exactly and quickly so that the PV system always generates maximum power.

The decay of power quality is one of the principal issues of electrical distribution systems. One of the main reasons for this is the use of non-linear devices like the switching devices and the power electronics converters. Non-linearity leads to detrimental effects on the utility of the power supply, system efficiency, reduction in power factor, etc. The reactive power is increased due to the reduction in power factor and it does not have any contribution in the transfer of energy, hence its compensation is needed. In this paper, the Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) based control methods for a Three-Phase Distribution Static Compensator (DSTATCOM) are proposed. This is used for compensation of the reactive power along with the current related power quality issues that include harmonics. These control methods are simulated in the MATLAB environment with the help of the SIMULINK toolbox. The simulation results suitably demonstrate the performance of these methods and it is found that source current waveforms with lesser distortion are achieved with ANFIS as compared to ANN.