The advent of power electronic devices for the control of non-linear loads has made an impact on the power quality at the Distribution side of the utility grid. The Distribution side must be compensated for both reactive and real power compensation while simultaneously improving the power quality. Unified Power Quality Conditioner is a FACTS device with back-to-back converters coupled together with a DC link element that improves the power quality at the Distribution side. The DC link element sizing and the converter ratings are the challenges faced during the design of UPQC. The Distributed Energy Resource (DER) integrated at the DC link element, supports for power exchange and minimization of the converter ratings. UPQC integrated with a DER simultaneously improves the fault-ride-through capabilities at the Point of Common Coupling. The PV integrated UPQC with bidirectional series and shunt converters are controlled by a hybrid combination of Unit Vector Template and p-q theory respectively. The control unit of the proposed system has to be more precise in a closed-loop structure. The conventional mathematical based PI controllers fail to perform well during the transient oscillations for the UPQC system. The proposed PV-UPQC system has been analysed with an Adaptive Neuro-Fuzzy controller that embeds a reinforced learning algorithm. The Fuzzy-Model-Based (FMB) controller improves the performance of the system by inferencing the system parameters through linguistic rules and helps in reference current generation. The PV-UPQC performed significantly well even during various load conditions. The implementation of an Adaptive Neuro-Fuzzy Inference System leads to minimization in the percentage of Total Harmonic Distortion level.

The utility grid is prone to power quality issues due to the advent of power electronic devices and integration of Distributed Energy Resources. The power quality has to be maintained despite variation in load under steady and fault conditions. Unified Power Quality Conditioner is a FACTS device that improves the power quality at the Distribution side of the utility grid. In Unified Power Quality Conditioner, reactive and real power compensation is carried out simultaneously improving the power quality. The proposed control algorithm for Unified Power Quality Conditioner is a hybrid combination of Reactive Power Control and Unit Vector Template. The Unified Power Quality Conditioner is integrated with Distributed Energy Resources like solar energy to minimize the power rating of converters and meet the power demand. The reinforced learning algorithms have been effectively increasing the performance of the power electronic devices; among them, the most commonly used is the Neural-Network algorithm. The Artificial Neural Network controller for the solar integrated Unified Power Quality Conditioner system improves the power quality when compared to the conventional controllers by self-adapting themselves to the environmental needs. The system is tested under both balanced and unbalanced load conditions with MATLAB-SIMULINK. The per-unit system has been used for the analysis purpose to minimize the complexity. The hybrid control of the series and shunt converters supported by solar integration at the DC link proves to eliminate distortion caused by the non-linear load. The system is subjected to a momentary voltage sag/swell as per IEEE 1159 standards. The momentary sag/swell that varies between 0.5 seconds to 3 seconds has been made to ride through less than 50 milliseconds as per the Computer and Business Equipment Manufacture Association curve. The load side current harmonics have been minimized inevitably by the proposed control methodology to ensure reliability and good power factor at the Distribution side. A comparative analysis between the conventional Proportional-Integral controller and Artificial Neural Network controller in solar integrated Unified Power Quality Conditioner proved the Artificial Neural Network controller improved the harmonic reduction of various orders by an average of 12.72%. The Total Harmonic Distortion minimization for various harmonic orders was brought under the limits of IEEE 519 standards with Artificial Neural Network controller.

Fossil fuels are being replaced by renewables. Most of the renewables are intermittent, to have reliable power the renewables have to be conditioned before injecting into the utility grid. The DC-DC converters are perfect power electronic devices for conditioning the renewables. The single ended primary inductor converter (SEPIC) performs the conditioning with a very high voltage transfer gain and minimum ripples. The maximum power extraction from the PV panels is required for providing good quality DC power. Intelligent controllers can make use of optimization techniques. The particle swarm optimization (PSO) technique can optimize the controller to extract the maximum power. The SEPIC converter duty variation is optimized and a comparative analysis with the Buck-Boost converter is done in a MATLAB/Simulink environment. The proposed SEPIC converter system performed well by improving the power tracking by 40% and the system has been analysed in a battery charging environment.

This paper introduces a B-type inverter and an improved Y source DC-DC converter that use a selective harmonic elimination (SHE) method based on whale optimization to inject power into the grid with reduced harmonic content. The suggested topology contains four unidirectional switches, two bidirectional switches and three DC sources to produce the required output voltage level. The proposed topology can be functioned with equal and unequal DC sources. The developed structure can generate the negative voltages without the use of an H-bridge. It has more redundant states, and it is partially fault tolerant in nature. The proposed structure provides a high value of level to switch ratio. A single input multiple output high gain modular Y-source DC-DC converter is implemented for effective utilization of the PV modules in the suggested system. The triggering angles for the suggested system is generated using the SHE based whale optimization algorithm (WOA). Using the SHE-WOA method a total harmonic distortion (THD) of $4.31\%$ is obtained. The developed system is integrated with the grid and for grid synchronization PI based WOA method is used. Simulation study has identified the presence of only 2.47% of harmonics in the grid current and 2.03% in experimental analysis. Both these harmonic distortions are less than the IEEE-519 standards.