ABSTRACT The global surge in Electric Vehicle (EV) adoption has made the development and deployment of EV Charging Stations (EVCS) crucial for enabling a sustainable transportation infrastructure. However, this increased integration also presents significant challenges for Radial Distribution Systems (RDS). The integration of EVCS presents significant challenges for distribution networks, primarily in the form of increased Energy Loss (EL) and degraded power quality, measured by Total Harmonic Distortion (THD i ). This work introduces a planning framework to strategically install EVCS integrated with multiple renewable and energy storage technologies, including Photovoltaics (PV), Wind Turbines (WT), Battery Energy Storage Systems (BESS), and Compressed Air Energy Storage (CAES). This work has been conducted on a 33‐bus RDS, aiming to minimize EL and THD i while satisfying operational constraints. This work is modeled as a bi‐objective optimization problem and utilize the Symbiotic Organisms Search (SOS) algorithm to identify optimal solutions. The robustness of the SOS‐derived solution is further confirmed through a comparative analysis with two other metaheuristic techniques: the Ant Lion Optimizer (ALO) and the Gray Wolf Optimization (GWO). The findings indicate that the prosumer‐integrated EVCS model not only supports the increased adoption of EVs but also strengthens the power distribution network by improving its efficiency, stability, and sustainability, making it a crucial step toward a greener energy future.

This study proposes a three-layer gateway control framework for a behind-the-meter virtual power plant (VPP) comprising vehicle-to-grid (V2G)-capable electric vehicle (EV) chargers, battery energy storage systems (BESS), and rooftop photovoltaic (PV) generation in multi-dwelling residential developments, creating a sustainable future through maximising distributed energy resource (DER) utilisation. In particular, the first layer performs day-ahead scheduling to determine the hourly grid import baseline and frequency regulation ancillary service capacity for the following day. In the second layer, real-time regulation dispatch is performed by following the dynamic regulation signal from the grid operator, wherein V2G-capable EVs are coordinated alongside BESS as active demand-side participants in frequency regulation ancillary services, enabling the aggregated behind-the-meter fleet to respond to regulation signals in real time. The third layer performs per-minute three-phase load balancing to maintain network power quality compliance across the multi-dwelling site. The overall goal is to coordinate distributed energy resources behind a single network connection point to simultaneously reduce peak demand, maximise renewable self-consumption, and provide demand-side frequency regulation as a dispatchable VPP asset.

Large-scale and high-proportion access of distributed generation to distribution network breaks the original power flow structure of distribution network, which affects the power quality and safe operation of distribution network system. Voltage stability can be maintained through reactive power optimization, and the system operation economy can be improved. In this paper, the influence of distributed generation access on the stability of distribution network is analyzed. On this basis, the mathematical model of reactive power optimization is established with the comprehensive consideration of active power loss and node voltage deviation of distribution network with distributed generation access, and the improved ant lion algorithm with dynamic weight coefficient is used to solve the model. Finally, the simulation analysis is carried out in IEEE33-bus system, and the improved ant lion algorithm and standard ant lion algorithm are used to solve the reactive power optimization of distribution network. The comparison of the optimization results of the two algorithms proves the feasibility and superiority of the improved ant lion algorithm in solving the reactive power optimization problem.

The increasing incorporation of DERs (Distributed Energy Resources) in Radial Distribution Networks (RDNs) has raised power quality issues, especially voltage sag, swell, and harmonic distortion. The effectiveness of conventional voltage regulation equipment has been proven to be limited in addressing rapid and random voltage fluctuations in DERdominated systems. Although the Dynamic Voltage Restorer (DVR) has been proven to provide better performance in voltage regulation through better series compensation, its effectiveness is still limited by its energy storage and static control system capabilities. Although various researchers have explored and discussed DVR and BESS technology in isolation, there has been a lack of a coordinated and multi-objective framework. In this regard, this thesis has proposed an integrated framework of DVR and BESS (Battery Energy Storage System) for voltage stability improvement in radial distribution systems through the development of two metaheuristic optimization algorithms: Self-Adaptive Learning Osprey Optimization Algorithm and Hybrid Golden Jackal-Hippopotamus Algorithm. In addition, the Self-Adaptive Learning Osprey Optimization Algorithm (S-OOA) has been applied for real-time tuning of the proportional and integral parameters of the DVR through real-time simulations using MATLAB-Simulink on a 14-bus radial distribution feeder system, achieving voltage stability improvement on the load side and achieving voltage values of 0.95-1.05 per unit according to the IEEE 1159 standard within half a cycle. In addition, the power quality index was achieved at 0.95, outperforming other algorithms such as Coati, Crayfish, Pelican, and Osprey Optimization Algorithm. Furthermore, the effectiveness of the proposed system has been proven through comparative analysis of recent literature on voltage stability improvement and harmonic distortion mitigation. The proposed system has been proven to provide better performance in power quality management in modern power systems dominated by renewable energy sources and variability.

Learning-Assisted Model Reference Adaptive Control of Unified Power Flow Controller for Enhanced Power Quality

Grid-tied Microgrids (MGs) are gaining popularity as a solution to the increasing demand for reliable and affordable energy. Integration of these MGs into the grid is challenging due to the fluctuating nature of Renewable Energy Sources (RESs), which is coupled with grid instability, faults and changing loads. Traditional control and optimisation methods cannot fully address the dynamic requirements of controlling these grid-tied MGs. In recent years, Artificial Intelligence (AI) techniques have emerged as a viable approach to improve the control and optimisation of MGs. This paper reviews grid-tied MGs, focusing on renewable energy integration, control and optimisation strategies. The methodology for the review process was preceded by a structured identification of a sample size of 312 relevant studies from major scientific databases, screening based on predefined inclusion and exclusion criteria, eligibility assessment, and final inclusion of high-quality peer-reviewed articles that were published between 2000 and 2025. The objective of this study was to analyse the evolution and performance of various types of microgrid control strategies and optimisation methods, with emphasis on AI techniques like Machine Learning and Reinforcement Learning and specifically on hybrid control architectures which combine model-based approaches with data-driven intelligence to enhance system stability and adaptability. Reviewed sources indicate that AI-controlled microgrid systems improve the performance of traditional control systems when confronted with non-linear dynamics and uncertainties in renewable energy resources, as well as load fluctuations. The results of the analysis also indicate that AI-based control systems contribute to improved power quality, regulation and efficiency through analysing total harmonic distortion and voltage/frequency regulation. Additionally, the emerging trends in predictive maintenance and fault detection are identified as key contributors to the reliability and resilience of these networks. AI control and optimisation methodologies create a comprehensive framework for the control and optimisation of the next-generation grid-tied microgrid, thereby supporting the transition to intelligent, sustainable, and decentralised energy systems.

ABSTRACT This work introduces a control technique based on ANFIS-PID for a bridgeless boost converter functioning in discontinuous conduction mode to operate a BLDC motor with built-in power factor adjustment. The proposed method is implemented in MATLAB/Simulink and validated with a real-time hardware prototype employing a Xilinx Spartan-6 FPGA. Experimental findings indicate a significant improvement in power quality, with the Total Harmonic Distortion (THD) decreased to 2.51% at a 400 V DC connection, signifying a considerable reduction relative to traditional PI/fuzzy-based methods. The power factor remains near unity (≥0.98) at universal mains circumstances (90–230 V) and across different load levels. The hybrid ANFIS-PID controller increases DC-link voltage stability, reduces input current distortion, and boosts dynamic speed regulation under fast load and supply fluctuations. Thorough comparisons between simulated and measured waveforms validate the controller’s efficacy in harmonic suppression, enhancement of input current shape, and facilitation of smoother motor performance. The suggested FPGA-based implementation confirms the practical viability of the control method for high-performance, energy-efficient BLDC motor drives.

The rapid penetration of renewable energy sources such as solar photovoltaic (PV) and wind power into modern power grids introduces significant operational challenges, including intermittency, voltage instability, harmonic distortion, and fault vulnerability. Conventional control strategies are often insufficient for handling dynamic grid disturbances and nonlinear system behavior. This study proposes an Artificial Intelligence (AI)-enhanced control framework for grid-connected renewable energy systems to enable adaptive control, predictive fault detection, and resilient operation. The proposed architecture integrates machine learning-based fault classification, adaptive inverter control, and real-time grid condition monitoring. A hybrid dataset composed of simulated grid disturbances and real operational parameters is used to train and validate the AI model. Results demonstrate improved fault detection accuracy, reduced system recovery time, enhanced voltage stability, and improved power quality under dynamic grid conditions. The proposed AI-driven framework enhances grid reliability, supports high renewable penetration, and contributes to resilient and sustainable energy infrastructure.

Introduction: A flexible traction substation provides power to the trains and the communication and signal facilities at the same time, taking into account the scarcity of strong power sources in the weak power grids. However, the severe impact of single-phase traction loads gives rise to negative sequence (NS) and voltage sag (VS) problems, which compromise the safe operation of electrical equipment. Methodology: To address these issues, this paper proposes a flexible traction power supply topology that integrates a power flow controller, a quasi-Z-source inverter, a dynamic voltage restorer, and photovoltaic (PV) cells, together with a comprehensive compensation strategy for NS and VS based on power quality standards. Results: The strategy employs the three-phase voltage unbalance factor (VUF) as a constraint and quantifies the influence of NS on VS detection accuracy, thereby achieving optimal NS compensation and in-phase VS compensation. Discussion: Hardware-in-loop (HIL) experiments confirm that the proposed structure and strategy reduce compensation power demand, maintain the VUF within 2%, and stabilize the grid voltage at 1 p.u. by providing active power support via the PV system, thereby reducing the capacity requirement of the primary compensation devices. Conclusion: These results demonstrate the capability of the integrated system to mitigate the impacts of single-phase traction loads and enhance power quality in weak grids.

A hybrid AI framework for identification of power quality disturbances in electrical network.

Abstract The increased adoption of nonlinear loads, particularly in the renewable energy and automotive sectors, generates harmonics that directly affect electricity transmission systems, influencing the operation of the power grid and potentially leading to a significant deterioration in the quality of electrical energy, as well as socioeconomic repercussions. This means that it is essential to improve the quality of electrical energy while ensuring that nonlinear loads are integrated in a manner that is compatible with the grid. To overcome these problems, the use of active power filters is one of the most effective solutions for improving electrical power quality. In this article, we propose an integral backstepping control technique for a three-phase active power filter to improve electrical power quality by providing better harmonic compensation. The integral action integrated into backstepping improves the robustness of the system against parametric uncertainties and external and/or internal disturbances, thus ensuring the optimization of the active power filter, and consequently improving the quality of electrical energy in the network by compensating for harmonics. This control technique is compared to classic backstepping in order to verify the effectiveness of the integral action backstepping controller proposed for the APF. The results of simulations and experimental tests carried out in various cases demonstrate its performance. Our system perfectly compensates for harmonics in accordance with standards. The proposed integral backstepping controller is more robust than the classic backstepping controller in terms of harmonic distortion reduction, stability, and speed thanks to a reduced response time and minimized ripple. The results obtained demonstrate its increased performance and efficiency in improving power quality, as well as its socioeconomic impact for industries.

On the impact of voltage unbalance on distribution locational marginal prices

Integrating renewable energy sources into power grids introduces complex challenges, particularly in accurately detecting and classifying power quality (PQ) disturbances due to the variability and intermittency of renewable energy generation. This study proposes a classification framework that employs data balancing techniques and ensemble learning models to classify key PQ events, such as voltage sags, waveform distortions, and over-under frequency disturbances. A comprehensive dataset was collected from a solar farm in Norfolk, England, covering January to December 2023, to perform this analysis. By investigating this high-resolution, high-fidelity big PQ data, the research explored real disturbances and provided insights into the solar site’s operational behavior, contributing to improved grid reliability. This work offers valuable insights into solar farm operations, helping utility-scale owners and operators implement more effective and cost-efficient condition monitoring strategies.

Power quality improvement of microgrids using unified power quality conditioner (UPQC): A scoping review

Electric vehicles (EVs) can act as distributed energy storage units in smart grids through vehicle-to-grid (V2G) and grid-to-vehicle (G2V) operations. However, large-scale bidirectional EV charging introduces power quality issues, including harmonic distortion and DC-link voltage fluctuations. This paper presents a PSO-tuned modified dq (MDq) control strategy for a bidirectional EV charging system operating under V2G and G2V modes. A transformer-less bidirectional DC–DC converter and a grid-connected voltage source inverter with an LCL filter are modeled to enable controlled power exchange between the EV battery and the grid. Particle swarm optimization (PSO) is employed to optimally tune the controller gains using a multi-objective fitness function that minimizes grid current harmonics, DC-link voltage error, current ripple, and settling time. Simulation results obtained in MATLAB/Simulink demonstrate that the proposed MDq controller significantly outperforms conventional PI and MDq-PI controllers, achieving a grid current total harmonic distortion (THD) of 2.39% while maintaining stable DC-link voltage and fast dynamic response. The proposed approach enhances power quality, grid stability, and operational reliability, making it suitable for intelligent EV charging in smart grid applications.

Climate change has prompted the wine industry's growing interest in disease resistant interspecific hybrid wine grapes. Phenolic compounds, and specifically anthocyanins, play a powerful sensory and quality perception role in wine, but the anthocyanin profile of hybrid grapes can differ from those of traditional species Vitis vinifera L. To optimize winemaking strategies, there is a need for a better understanding of hybrid grape and wine composition. In this work, the phenolic compositions, anthocyanin profiles, and color parameters were evaluated and quantified for the five interspecific hybrid red wine grapes Vidoc, Coliris, Artaban, Chambourcin, and Divico (Vitis spp.) and their wines. These cultivars were assessed concurrently with Pinot noir, an emblematic septentrional V. vinifera cultivar known for its unique anthocyanin composition, sourced from multiple locations in Burgundy, France. This characterization sets the groundwork for further exploration of anthocyanin and phenolic interactions through wine color evolution in disease resistant wines.

Power efficient signal conversion and quality signal compression using LDS-ADC and hybrid DCT for biomedical signals

The enhancement of power quality in grid-connected photovoltaic (PV) systems requires the development of effective harmonic mitigation techniques. This paper addresses the design and evaluation of specific passive filters (RC, LC, and LCL filters) for a three-phase grid-tied PV system, aiming to mitigate harmonics in the power system. The paper also systematically calculates and optimally solves for the components required for the given system. The design of the parameters for all filter topologies within the 100-kW grid-connected PV array is thoroughly elaborated. Each topology is evaluated based on the total harmonic distortion (THD) content, which is obtained using fast fourier transform (FFT), as well as DC voltage and system efficiency. The results are presented to identify the best solutions for harmonic mitigation. The modified filter model demonstrated in this study effectively limits harmonic distortion at the output. It is shown that the proposed design addresses the issue of harmonic distortion in grid-connected inverters for PV systems. The goal of this paper is to identify the most reliable filter for extending the system’s lifespan. The results suggest that the LCL filter is superior, as the system’s DC voltage remained within the rated value and the system efficiency was higher compared to the RC filter. The performance and functionality of these filters were tested using MATLAB/Simulink.

Abstract This article presents a battery charging system using an asymmetrical Cuk-Buck-Boost bridgeless (A-CukBB-BL) converter to improve the supply-side power quality. For the negative and positive half-cycles of the mains voltage, the A-CukBB-BL converter utilises the Buck-Boost converter and the cuk converter, respectively. The A-CukBB-BL converter is used in the current study in discontinuous conduction mode (DCM) to enhance the power factor (PF) profile. The input side inductor of the Cuk converter, along with capacitor C B (inductor L A1 and capacitor C B constitute low pass filter (LPF)), also acts as a filter element, reducing the disturbances in the source/mains current throughout the positive half cycle. The input inductor of the Cuk converter also decreases the distortion in the supply mains current during the negative half cycle. Formation of LPF with an inductor of Cuk converter eradicates extra inductor requirement and this LPF provides the liberty to use Buck-Boost converter without an additional filter. The combination of filter-less second order (bb) converter and Cuk (fourth-order) converter results in a net lower order system. The Bridgeless schemes have inbuilt advantage of diode based bridge rectifier (DbBR) stage elimination as BL scheme have inbuilt rectifier in it. So elimination of DbBR and the DbBR associated losses results in component count reduction and enhanced efficiency. Normally, the BL schemes uses two separate path completing diodes to complete the conduction path during both the half cycles of the mains voltage but in this case the same purpose is served by the antiparallel diodes of the switches and thus the requirement of two extra/external back feeding diode is also eliminated. A high-frequency driven flyback converter is deployed in the second stage, that not only improves the battery (load) current profile but also offers electrical and physical seperation in between the load terminal and supply. Filterless BL converters with lower order, the elimination of the entire DbBR stage, two external back-connecting diodes, and the requirement for a voltage sensor for DCM mode operation as opposed to three (2 voltage and 1 current) for CCM operation all imply a reduction in the number of components and the associated losses, which also implies a decrease in the cost and volume of the charging system. Using a pole-zero (P-Z) and a bode graph, this research also gives a thorough stability and mathematical modelling of the A-CukBB-BL converter that is being presented. The charging system for DCM mode operation using A-CukBB-BL converter has been developed at laboratory and on Simulink platform of MATLAB software and results from both platforms are presented to substantiate the EV charging system.

A power quality disturbance classification method using a hybrid transformer and discrete wavelet transform model