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

Phenolic and anthocyanin profile characterization of five disease resistant grape cultivars in septentrional context of France.

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

Front-face synchronous fluorescence spectroscopy coupled with PLSR for rapid quantitative analysis of Chinese five-spice powder composition.

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

Automatic Power Quality Events Classifier based on hybrid CNN–LSTM network and multisine fitting algorithm

Multisynchrosqueezing transform aided transfer learning based approach for diagnosis of single and mixed power quality events

EV charging station with ancillary service for power quality improvement in low voltage distribution network

Fuzzy logic sliding mode controller based solar PV fed UPQC for improvement of dynamic performance and power quality enhancement in distribution power system.

Abstract This work presents a Distributed Static Compensator (DSTATCOM) intended for low-voltage distribution systems, controlled through a Zordan Least Mean Square (ZLMS) optimization framework. Practical deployment of adaptive controllers in power quality conditioners is often constrained by slow convergence, sensitivity to parameter variation, difficulty in handling unbalanced loads, and increased computational effort during real-time implementation. To overcome these limitations, the proposed control approach incorporates parallel processing to enable effective shunt compensation while satisfying IEEE-519 harmonic requirements. An enhanced form of the Adaptive Least Mean Square (ALMS) method is also developed, and its behaviour is evaluated under both balanced and disturbed operating conditions. The neural structure consists of six subnetworks dedicated to extracting active and reactive current components separately, enabling improved estimation reliability. The ZLMS adaptation mechanism refines weight adjustment using learning rate control, normalization factors, and historical weight information, leading to faster convergence and reduced steady-state error. Validation is performed through MATLAB/Simulink simulation and experimental testing using a dSPACE-1108 real-time platform. The results demonstrate notable improvements in power factor, harmonic mitigation, voltage regulation, and load balancing during dynamic operating conditions when compared with conventional ALMS-based control.

With the widespread integration of distributed power systems and renewable energy, power quality disturbance (PQD) problems are becoming increasingly serious. Most existing PQD classification methods rely on the extraction of single time-domain or frequency-domain features, lacking effective cross-domain information fusion, and their classification accuracy significantly decreases under noise interference. To address this issue, this paper proposes a power quality disturbance classification model (MCA-KAN) based on a multi-channel attention mechanism and a KAN network. This model employs parallel time-domain and frequency-domain feature extraction structures, utilizing 1D-CNN and BiLSTM to capture time- and frequency-domain features respectively, and introducing a cross-attention mechanism to achieve adaptive fusion of time-domain and frequency-domain features. Furthermore, the model uses a KANLinear layer for nonlinear mapping to enhance its classification ability and robustness in high-noise environments. To verify the model's robustness, this paper constructs a noisy dataset under three signal-to-noise ratios (SNR=20dB, 30dB, 50dB) and conducts comparative experiments. The results show that the proposed MCA-KAN achieves an accuracy of 97.82% at an SNR of 20dB, and maintains stable recognition performance at higher SNRs. Compared with existing methods, the proposed network outperforms existing methods in terms of classification accuracy and robustness.

ABSTRACT The rapid growth of Electric Vehicles(EVs) and Photovoltaic(PV) systems in modern microgrids has intensified power quality issues, including voltage fluctuations, harmonics, and frequency deviations. Hybrid Energy Storage Systems (HESS), integrating batteries and supercapacitors, provide stability support but require intelligent coordination for optimal performance. Conventional control strategies struggle to manage fast PV variations and sudden EV charging surges,resulting in unstable voltage profiles and degraded power quality. Existing Deep Learning approaches lack effective attention mechanisms and adaptive optimisation for accurate short-term forecasting and energy allocation. This study proposes an Intelligent Animal Migration–optimised Attention-Long Short-Term Memory (IAM-Att-LSTM) framework for microgrid power quality enhancement. A real-time microgrid dataset of 2500 records, including voltage, current, frequency, harmonics, PV generation, EV load, Battery SOC, and SuperCap SOC, was preprocessed using missing-value imputation and Z-score normalisation. The Attention-LSTM captures temporal variations in PV output and EV demand, while Intelligent Animal Migration Optimisation adaptively manages energy sharing between battery and super capacitor units. Simulation results demonstrate superior predictive accuracy with a MAPE of 0.95% and MARPE of 1.85, reducing voltage deviations and improving dynamic response during PV fluctuations and EV charging spikes. IAM-Att-LSTM improves microgrid power quality with intelligent prediction and optimized HESS control for EV-integrated systems.

The fast growth of electric vehicles (EVs) and renewable energy motivates reliable charging infrastructure with balanced energy management and good power quality. However, conventional converter controllers like proportional and integral (PI) and fuzzy logic controllers (FLCs) exhibit slow dynamic response, poor adaptability to varying solar conditions, unbalanced energy management, low power quality, and higher total harmonic distortion (THD). To overcome these limitations, this work proposes an adaptive neuro-fuzzy inference system (ANFIS) controller for balanced energy management and improved power quality in EV charging stations. The ANFIS controller is a combination of a fuzzy inference system (FIS) and a neural network (NN). The FIS provides the best maximum power point tracking and robust control during changing solar PV conditions. The NN optimally controls the flow of power between the solar PV system, energy storage battery (ESB), EV, and utility grid. The entire system is simulated in MATLAB/Simulink. It consists of a PV system with a capacity of 2 kW, an ESB with a capacity of 10 kWh and an EV battery with a capacity of 4 kWh, which are linked by bidirectional DC/DC converters. A 30 kVA bidirectional inverter, along with an LCL filter, is connected between the 500 V DC bus and 440 V utility grid, allowing for both directions. The results validate the effectiveness of the proposed ANFIS controller in terms of DC bus voltage stability, faster dynamic response, enhanced renewable energy utilization, improved efficiency to 98.86%, reduced voltage and current THD to 4.65% and 2.15% respectively, reduced utility grid stress, and enhanced energy management compared to conventional PI and FLCs.

The global energy demand is increasing day by day due to factors such as industrialization, technological advancements, and population growth. In order to meet this growing demand, merely exploring new energy sources is not sufficient; issues such as energy efficiency and power quality are also gaining increasing importance. Harmonics, one of the most critical parameters of power quality, refer to the distortions caused by electrical devices in power systems and can directly affect the efficiency and safety of energy systems. Household appliances such as air conditioners, computers, televisions, washing machines, and dishwashers are among the major contributors to harmonic pollution. In this study, the harmonic effects of three different washing machines manufactured in different years were examined. The primary objective of this study is to analyze the impact of washing machines on power quality at different washing temperatures and to compare their harmonic generation levels. Within the scope of the study, harmonics generated during washing cycles at different temperatures (30°C, 60°C, and 90°C) were analyzed in detail for each washing machine. Measurements were conducted using a Fluke 435 power quality analyzer, and the obtained data were processed using MATLAB software for detailed analysis. The measurements revealed that the harmonic levels of washing machines vary depending on the model year, motor type, and washing temperature. It was observed that older models produced lower harmonic levels, whereas modern inverter motor washing machines exhibited higher harmonic levels. Additionally, it was determined that harmonic distortion changes as the washing temperature increases. The results highlight the significance of design modifications aimed at reducing the harmonic generation of electrical household appliances and the importance of techniques that enhance energy efficiency. This study aims to contribute to raising awareness among consumers and engineers regarding power quality and harmonic management, thereby guiding the development of more efficient and environmentally friendly electrical devices.

Electric mobility has emerged as a cornerstone of global decarbonization strategies, with its successful deployment critically dependent on the coordinated integration of vehicle powertrain engineering, advanced battery technologies, charging infrastructure, power grid interaction, and intelligent control systems. This paper presents a comprehensive system-level critical assessment of electric mobility, providing an integrated analytical framework that unifies electric vehicle (EV) powertrains, electrochemical energy storage, grid impacts, artificial intelligence (AI), and sustainability considerations. The study systematically examines EV propulsion architectures, charging technologies, and the operational characteristics of contemporary and emerging battery chemistries, including lithium-ion variants ( Nickel–Manganese–Cobalt, Nickel–Cobalt–Aluminum, and Lithium Iron Phosphate), solid-state batteries, and sodium-ion batteries, with particular emphasis on degradation mechanisms, thermal safety, second-life utilization, and recycling pathways. The impacts of large-scale EV charging on power distribution networks are rigorously analyzed through power quality and voltage stability modeling, highlighting harmonic distortion, feeder loading, and voltage deviation challenges associated with high-power fast-charging infrastructure. Advanced mitigation strategies, including active filtering and AI-based grid impact prediction, are discussed to enhance grid resilience. AI is positioned as a core enabling technology throughout the EV ecosystem, with detailed coverage of data-driven and physics-informed approaches for battery health estimation, remaining useful life prediction, range estimation, smart charging control, traffic-aware routing, and charging queue optimization. Furthermore, emerging quantum-inspired optimization and quantum machine learning paradigms are identified as promising tools for addressing high-dimensional uncertainty in routing, charging scheduling, and battery diagnostics. A life-cycle sustainability perspective is incorporated to evaluate the environmental performance of EVs, emphasizing the influence of electricity generation mix, battery manufacturing emissions, material criticality, and recycling efficiency on overall greenhouse gas reduction potential. By synergizing engineering models, AI-driven intelligence, grid interaction analysis, and life-cycle assessment, this work delivers a unified blueprint for accelerating the transition toward sustainable electric mobility. The presented framework offers clear technical guidance for researchers, policymakers, and industry stakeholders seeking to design resilient, intelligent, and environmentally responsible electric transportation systems.

Introduction Proton exchange membrane fuel cells (PEMFCs) are highly efficient and environmentally friendly energy conversion devices. Their modular multi-stack FC (MSFC) provides enhanced operational stability and power output. However, during online electrochemical impedance spectroscopy (EIS) testing, multi-frequency sinusoidal current disturbances injected by the DC/DC converter can cause periodic fluctuations in the common DC bus voltage, threatening system stability and measurement accuracy. Existing approaches, such as hardware filtering or improved topology, suffer from high cost and complexity. While the traditional extended state observer (ESO) can estimate the disturbance, it struggles to suppress the fluctuations during multi-frequency co-detection. Methods This paper proposes an enhanced ESO design, namely the multi-resonant ESO (MRESO), for MSFC hybrid systems. By embedding multiple resonant units, the MRESO accurately tracks the EIS disturbance frequency and combines feedforward compensation with battery-assisted control to achieve robust bus voltage stability. The system modeling is based on the principle of energy conservation, and the control scheme adopts a dual-loop structure consisting of an inner current loop and an outer voltage loop. The voltage loop integrates the MRESO to improve interference rejection capability. Results Simulation results show that under load current disturbances and EIS measurement interference, MRESO can reduce voltage fluctuations by over 70% and achieve stability within 0.08 s, outperforming both PI control and traditional ESO, validating its effectiveness in suppressing multi-frequency disturbances. Discussion This study provides a feasible voltage stabilization solution for online EIS monitoring of MSFC, contributing to improve the power quality and reliability of the hybrid system.

This paper proposes an optimized control strategy for a grid-connected cascaded multilevel inverter designed to enhance the grid stability and power quality in smart grid environments. The novelty of this work lies in the implementation of independently regulated low-voltage DC links for each H-bridge cell, which enables modular control, fault tolerance, and improved dynamic performance. In addition, the control scheme operates without the need for reference frame transformation, significantly reducing the computational complexity. The main objective is to achieve selective and flexible compensation of disturbing currents caused by nonlinear, unbalanced, or reactive loads using current decomposition based on the conservative power theory (CPT). This decomposition allows the system to isolate specific current components and apply targeted compensation strategies in real time. Experimental validation under both ideal and degraded grid conditions demonstrates the effectiveness of the proposed control method. Key performance outcomes include the Total Harmonic Distortion (THD) reduction in the source current from 13.2% to 3.1%. The voltage regulation accuracy 1.5% of the reference value across the variable load conditions. Improved dynamic response time, with compensation settling achieved within 20 ms during load transients. Stable operation under voltage sag conditions up to 30% depth, with maintained power compensation functionality using the MATLAB Simulink environment.

A bi-Level collaborative optimization strategy for power quality in distribution networks based on fuzzy triple black hole multi-objective optimization algorithm

Type-2 fuzzy logic optimized bridgeless positive output Luo converter for high power factor and enhanced power quality in low-voltage EV chargers

Abstract The use of wireless charging technology for Electric Vehicles (EVs) is gaining increasing significance due to its ability to charge batteries safely and conveniently without the need for physical connectors. This paper presents the design and implementation of the transmitter-side power conversion system for a wireless EV charging station. The proposed system architecture integrates a front-end Power Factor Correction (PFC) rectifier, an Interleaved Resonant DC-DC converter, and a three-level Neutral Point Clamped (NPC) inverter, providing a highly efficient and stable power transmission path for battery charging. The Interleaved Resonant DC-DC converter, designed with a Zero Voltage Switching (ZVS) mechanism, minimizes switching losses and enhances overall efficiency. Its operation under Constant Current (CC) and Constant Voltage (CV) control modes ensures smooth and reliable mode transition through an auxiliary controller incorporated alongside a conventional PI controller. The inverter stage adopts a Modified Space Vector Pulse Width Modulation (MSVPWM) strategy that significantly reduces Total Harmonic Distortion (THD) in both voltage and current waveforms, thereby maintaining compliance with power quality standards. The complete system's performance has been evaluated through both simulation and experimental validation. MATLAB/Simulink-based analysis demonstrates stable converter dynamics with a regulated output voltage of 325 V and negligible transient disturbances during mode transitions. A 0.2 kW laboratory prototype was developed to confirm the simulation results using PIC24F16KA102 and FPGA-SPARTAN-7 controllers for power and switching signal generation, respectively. Experimental measurements indicate that the proposed Interleaved Resonant DC-DC converter achieved an efficiency of approximately 95.35% at rated load, which aligns closely with theoretical predictions. The three-level NPC inverter produced a 320 V output with voltage and current THD levels of 4.8% and 5.6%, respectively. A 2:1 air-core transformer designed for high-frequency operation (100-200 kHz) effectively transfers power to the EV receiver side. The influence of coupling coefficient and air-gap variation on transfer efficiency was systematically investigated, revealing an optimal separation distance of 10-20 mm. The overall results confirm the efficacy of the proposed design for high-efficiency wireless EV charging. Future work will concentrate on optimizing the receiver-side converters to further enhance system efficiency and dynamic performance.