A cost-effective placement of power quality monitors for harmonic resonance conditions and voltage sags under system uncertainties

Power quality disturbances reconstruction method based on ATSAMP

Effect of ice coating on thermal characteristics of pantograph-catenary arc and power quality of traction drive system

Design and implementation of a low-cost, open-source power quality analyser for smart grids

Harmonic impedance estimation using small-signal injection with power quality control

Since renewable energy resources are penetrating modern power systems more than ever, the wind energy conversion system (WECS) requires fast dynamic response and robustness as well as reliable operation under strong-grid and weak-grid conditions. One example is the doubly fed induction generator (DFIG), which remains an attractive option within variable-speed wind generation technologies, as it has a low converter rating, high efficiency, and flexible power control capability. Yet, DFIG-based WECSs control methods are still typical in nature and their performance degrades with wind speed fluctuation, grid interference, model instability and parameter perturbation. In this paper, we proposed a robust control approach for DFIG-based WECSs that merges grid-forming and grid-following operation in the same multi-objective optimized sliding mode control (SMC) design framework. To improve the active and reactive power control, rotor speed regulation, dc-link voltage balancing as well as fault ride-through capability with reduced chattering effect encountered in classical SMC, the proposed concept approach is devised. When working in grid-following mode, the controller accurately synchronizes and injects power into the utility grid. It then operates in grid-forming mode, providing voltage and frequency support, thus improving operation under weak-grid and islanded conditions. A multi-objective optimization procedure is applied to find the SMC parameters that minimize settling time, overshoot, steady-state tracking error (SSTE), and control effort simultaneously. Simulation results show that active power settling time is decreased from 0.42 s to 0.18 s and dc-link voltage overshoot is resided from 14.6% to 4.1% in comparison to a traditional PI-based controller using the proposed approach. Moreover, compared to the conventional observer, rotor speed tracking error is reduced by 31.8%, and stator current total harmonic distortion decreases from 4.9% to 2.1%. When subjected to a 30% sag in grid voltage, the proposed controller retains closed-loop stability and recovers nominal operating conditions within 0.12 s versus 0.31 s for the benchmark controller. In addition, these findings verify that the proposed MOO-SMC called upgrade substantially increases the dynamism performance, stabilizing and quality of power with respect to conventional SMC for various operating conditions applied to DFIG-based WECSs

The rapid growth of electric vehicles (EVs) has increased the demand for efficient and high-power quality battery charging systems. Conventional EV chargers typically use diode bridge rectifiers followed by DC–DC converters for AC–DC power conversion; however, these rectifier-based systems suffer from high conduction losses, poor power factor, increased total harmonic distortion (THD), and reduced overall efficiency. These drawbacks not only degrade charger performance but also introduce harmonics into the utility grid, resulting in additional losses and reduced reliability. To address these issues, this paper presents the design and analysis of a modified bridgeless AC–DC Landsman converter for EV charging applications. The proposed topology eliminates the conventional diode bridge rectifier, thereby reducing conduction losses and improving efficiency. The converter operates as a power factor correction (PFC) stage and provides a regulated DC output suitable for EV battery charging. The modified bridgeless configuration reduces the number of conducting devices in each switching cycle, which improves power quality and minimizes input current ripple. A PI controller is employed to regulate the DC-link voltage and maintain a constant output voltage. The proposed converter is modeled and simulated in MATLAB/Simulink using a 230 V single-phase AC input with a switching frequency of 20 kHz, and the output voltage is regulated to 48 V. Simulation results demonstrate improved power factor, reduced harmonic distortion, and stable output voltage. The input current waveform becomes nearly sinusoidal, and conduction losses are significantly reduced compared to conventional rectifier-based chargers. Therefore, the proposed modified bridgeless Landsman converter provides improved efficiency, reduced THD, better voltage regulation, and enhanced power quality, making it suitable for EV battery charging applications.

The Dynamic Voltage Restorer is a custom power device employed to alleviate voltage issues at load terminals. In today's world, power quality has emerged as a significant concern. This is particularly true with the advent of advanced devices that are highly sensitive to the quality of the power supply. Power quality issues manifest as deviations in voltage, current, or frequency, leading to failures in end-user equipment. A prominent issue addressed here is power sag. To tackle this challenge, custom power devices are implemented. Among these devices is the Dynamic Voltage Restorer (DVR), recognized as the most efficient and effective modern custom power device utilized in power distribution networks. The DVR injects the necessary voltage in series with the supply voltage via an injection transformer to correct the voltage amplitude, phase, and harmonic components in the line. This paper discusses the development, simulation, and analysis of a Dynamic Voltage Restorer (DVR) using MATLAB/SIMULINK. To improve the voltage sag restoration capability of the DVR, this paper focuses on the creation of a control structure utilizing a Discrete PWM pulse generator. Furthermore, this paper explores a new control algorithm based on the abc to dq0 transformation for pulse generation. The results indicate that the developed DVR possesses a strong capability to restore voltage levels during sag conditions.

Abstract This article presents a new strategy to enhance the maximum power point tracking (MPPT) method for optimal power extraction from photovoltaic (PV) panels, as well as an inverter control strategy for regulating the active and reactive power injected into the grid in a double-stage singlephase photovoltaic installation. The proposed method is based on a robust Third-Order Super-Twisting Sliding Mode Control (TOSTC).In the first stage, TOSTC is used to control the boost converter in order to ensure fast and accurate tracking of the maximum power point, even under varying environmental conditions, while reducing chattering and minimizing power losses. In the second stage, TOSTC is applied to control the inverter current, enabling precise regulation of both active and reactive power injection, and significantly reducing harmonic distortion, accordingly, improving the overall power quality delivered to the grid. The article compares the performance of the TOSTC with conventional control methods, namely the PI controller for the boost converter and the PR controller for the inverter, all implemented in the MATLAB/Simulink environment. Simulation results demonstrate the superiority of the TOSTC approach in terms of tracking speed, accuracy, and harmonic reduction. The stability of the proposed controller is validated using Lyapunov criteria, and its effectiveness is confirmed through simulations under both dynamic and steady-state conditions, highlighting the robustness and reliability of this innovative control strategy for grid-connected photovoltaic systems

Energy efficiency and power quality are crucial for sustainable campus operations, particularly in higher education institutions seeking to minimize energy waste and ensure compliance with national electrical and safety standards. This study aimed to develop and evaluate a web-based audit platform for optimizing energy efficiency and power quality. Grounded in the Technology Acceptance Model (TAM) and ISO/IEC 25010 software-quality standards, the research employed a survey design complemented by semi-structured interviews to assess platform acceptability among a purposive sample of 35 faculty, staff, and experts. Results indicate robust acceptability, with overall mean ratings exceeding 4.4 on a 5‑point Likert scale across all constructs, and Cronbach’s alpha values ranging from 0.710 to 0.865, confirming instrument reliability. One‑sample t‑tests (p < 0.001) demonstrated significantly positive perceptions. A multiple regression model accounted for 78.2% of the variance in behavioral intention (R² = 0.782), with work compatibility (r = 0.838) and perceived usefulness (r = 0.795) as the strongest predictors. Compliance features effectively embedded PEC and OSHS standards, and platform reliability and maintainability were highly rated (M > 4.5). The findings support the platform’s feasibility as a sustainable infrastructure and environmental performance tracking tool, recommending institutional adoption to enhance campus energy management, system safety, and operational efficiency at the university.

The growing global energy demand and environmental concerns associated with fossil fuels have accelerated the adoption of renewable energy sources, especially solar photovoltaic (PV) systems. Grid-connected PV systems are widely preferred due to their ability to integrate seamlessly with existing power networks. However, their performance is affected by issues such as fluctuating solar irradiance, inefficient power extraction, and poor power quality. This thesis presents the design and control of a grid-connected solar PV system using an advanced Maximum Power Point Tracking (MPPT) technique and power quality enhancement methods. A comprehensive mathematical model of the PV array, DC-DC converter, and grid-connected inverter is developed and simulated in MATLAB/Simulink. The proposed intelligent MPPT algorithm improves tracking speed and minimizes oscillations compared to conventional methods like Perturb and Observe (P&O) and Incremental Conductance. To address power quality concerns, a grid-synchronized inverter with suitable filtering techniques is implemented to reduce harmonic distortion, voltage fluctuations, and poor power factor. Simulation results demonstrate improved MPPT efficiency, reduced Total Harmonic Distortion (THD), and enhanced voltage and current stability. This work offers an efficient solution for reliable grid integration of solar PV systems, supporting sustainable energy development. Keywords: Solar Photovoltaic (PV) System; Maximum Power Point Tracking (MPPT); Grid-Connected Inverter; Power Quality Improvement; Total Harmonic Distortion (THD).

Adaptive and Optimization-Driven Inverter Control for Power Quality Enhancement in Grid-Interactive Renewable Energy Systems: A Review

Emergency locator transmitters (ELTs) are key safety systems for post-crash aircraft localization and search-and-rescue operations. In more electric aircraft (MEA), however, their design and operation are increasingly influenced by complex electrical architectures, tighter equipment integration, and more demanding electromagnetic environments. This paper presents a narrative literature review of ELT technology from a MEA-oriented perspective. A practice-oriented narrative approach is adopted, examining ELTs through a dual lens: the evolution of the search and rescue (SAR) ecosystem and the progressive electrification of aircraft systems. The review addresses ELT fundamentals, classifications, operating principles, and interaction with the Cospas-Sarsat infrastructure, and examines the transition from legacy analog beacons to modern 406 MHz digital systems incorporating GNSS positioning, MEOSAR capabilities, second-generation beacon functionalities, and distress tracking features. Particular attention is given to integration challenges in MEA platforms, including autonomous energy supply, battery endurance, power quality disturbances, electromagnetic compatibility, installation robustness, antenna survivability, and certification constraints. The analysis highlights that ELT performance in MEA depends not only on the beacon itself, but also on the coupled interaction among device design, installation conditions, and the electrical environment. Finally, the review outlines research priorities for next-generation ELTs, including improved survivability assessment, energy-aware architectures, integration strategies based on electromagnetic compatibility, and certification-ready solutions compatible with future aircraft platforms.

Electric vehicles (EVs) are increasingly recognized as an environmentally sustainable transportation option that supports reduced emissions and improved energy utilization. However, integrating EV charging infrastructure into existing power distribution networks presents technical challenges because EV chargers employ power electronic converters that behave as non-linear loads. This study investigates the influence of EV charging on a low-voltage three-phase distribution network (LV3PDN), focusing on key power quality indicators such as voltage deviation, current distortion, and total harmonic distortion (THD) at the point of common coupling (PCC). A three-phase LV network based on a 25-kVA, 415 V/415 V, 1:1 isolation transformer is modelled in the MATLAB Simulink environment, incorporating representative residential and commercial load conditions. To represent EV charging behaviour, a two-wheeler onboard charger model is adopted, selected because its input current harmonic profile lies between those reported for commonly used electric two-wheelers operating in Wardha city, Maharashtra, India. The analysis evaluates three operating conditions: EV charging location along the feeder, battery state of charge (SoC), and DC–DC converter operation mode (average and switched). Simulation results show that EV integration increases feeder current by about 50% and raises THD; distortion grows downstream, while voltage THD stays below 0.1%.

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.

The transportation and energy sectors are increasingly integrating electric vehicles (EVs) as essential components of sustainable development.Photovoltaic (PV)powered charging stations, comprising PV modules interfaced with the public grid, offer a reliable and cost-effective solution for EV battery charging.However, these stations typically introduce nonlinear loads, leading to significant current distortions that degrade power quality.To mitigate these issues, Shunt Active Power Filters (SAPFs) are employed to suppress harmonic currents and ensure clean energy delivery.The inverter in the SAPF operates as a controlled current source, injecting compensating harmonics in parallel with the nonlinear load.A key component in this system is the DC link voltage controller, which directly influences the accuracy of harmonic compensation.This study proposes a Modified Antlion Optimization Algorithm (MALO) for DC link voltage control in a SAPF using a Five-Level Packed U Cell (PUC5) inverter for a grid-connected PV system with EV charging capabilities.The MALO algorithm surpasses ALO in terms of convergence rate and circumvents the local optima.MALO optimises the parameter of the PI controller in SAPF using the objective function of integral time absolute error (ITAE).The performance of the MALO-based controller is compared against a Particle Swarm Optimization (PSO) approach under identical system configurations.Both models are evaluated using MATLAB/Simulink simulations based on a modified instantaneous reactive power (MIRP) theory for harmonic current extraction.Results demonstrate that the MALO-based control achieves superior harmonic mitigation, reducing Total Harmonic Distortion (THD) to as low as 1.53%, thereby outperforming the PSO-based system.Maximum harmonic compensation of around 96.75% is achieved with the help of the proposed system compared to uncompensated power system.The findings validate the effectiveness of MALO for improving power quality in PV-powered EV charging infrastructures.

Abstract — In the modern industrial setup, achieving optimal power quality becomes essential for effective operation, prolonging the life span of equipment, and ensuring the safety of workers. The traditional monitoring system is mostly reactive and lacks cloud integration and environmental hazard detection capabilities. In this study, an intelligent cloud-based power quality and environmental monitoring system is proposed. The system uses a PZEM-004T sensor for measuring the AC voltage, current, active power, energy, frequency, and power factor. Moreover, a DS18B20 digital thermometer is used to measure the temperature while an MQ-2 semiconductor sensor is utilized for detecting gases and smoke. All of the sensing operations are carried out by an Arduino UNO controller which then relays the data to the NodeMCU module for uploading on ThingSpeak IoT platform. The SIM800L GSM module gives instant alert messages for any faults, whereas the relay module automatically disconnects loads when thresholds are exceeded within 200 milliseconds. The I2C 16×2 LCD enables parameter readings locally. Experiments conducted during normal working conditions revealed that the input voltage is 236 V, load current is 0.42 A, effective power is 100 W, power factor is one, ambient temperature lies between 27 °C and 30 °C, and the gas index is 12.0 ppm equivalent. All the simulated fault conditions have been sensed and mitigated. Key Words— Power Quality Monitoring, Internet of Things (IoT), Cloud Computing, PZEM-004T, NodeMCU ESP8266, GSM Communication, ThingSpeak, Fault Detection, Relay Protection, Predictive Maintenance, Industrial Automation

Power quality is a critical factor in maintaining the reliability and efficiency of modern power systems, particularly in distribution networks that extensively utilize power electronic equipment. One of the primary sources of harmonic distortion is the use of Variable Speed Drives (VSDs), which operate as non-linear loads and cause current and voltage waveforms to deviate from their ideal sinusoidal shape. This study aims to analyze the harmonic characteristics generated by VSDs in the distribution system of PT. PLN ULP Tomohon, determine the levels of Total Harmonic Distortion of current (THD-I) and voltage (THD-V), and evaluate the effectiveness of harmonic mitigation using a passive single-tuned harmonic filter. The research employs a descriptive quantitative approach through modeling and simulation of the power system using the Electrical Transient Analyzer Program (ETAP) software. Technical system data, including transformer capacity, distribution network configuration, and load parameters, are modeled in the form of a Single Line Diagram (SLD) and analyzed using the Harmonic Load Flow module. The simulation results indicate that VSD operation produces dominant harmonics at the 5th and 7th orders, which significantly increase the THD levels within the system. The implementation of a passive single-tuned harmonic filter is shown to effectively reduce harmonic distortion, allowing the THD-I and THD-V values to approach or comply with the limits specified in the IEEE 519-2014 standard. Therefore, the application of appropriate harmonic mitigation methods can improve power quality, enhance energy efficiency, and increase the reliability of electrical power systems in distribution networks.

The researchers present a new approach to identify power quality disturbances through unsupervised clustering which uses DBSCAN as its primary analytical techniques. The system uses machine learning models to predict RMS values which enables users to identify different types of power quality disturbances through their unique patterns. The researchers apply scaling preprocessing to the data before they use Random Forest and XGBoost regression models to forecast RMS values. The Random Forest and XGBoost models execute the classification process which determines whether PQD events belong to the "Good" or "Poor" categories. The researchers apply DBSCAN because DBSCAN identifies noise and detects non-linear clusters. The researchers use standard evaluation metrics to assess model performance which includes R² and RMSE and accuracy measurements. The research achieves high accuracy and precision in PQD classification with nearly flawless regression outcomes and exceptional clustering performance. The solution delivers a powerful solution for real-time power quality monitoring which supports the development of sophisticated intelligent grid systems. The study enhances machine learning capabilities for power systems monitoring through its application in fault detection and anomaly classification. Keywords: PQD, Unsupervised Clustering, DBSCAN, Power Quality Disturbances, RMS Prediction, Machine Learning, Random Forest, XGBoost, Clustering, Real-Time Monitoring.

In three-phase PWM rectifiers, abrupt load changes and parameter variations challenge DC-bus voltage regulation and degrade the performance of conventional controllers. To ensure robust regulation under nonlinear and time-varying conditions, this study proposes a type-3 fuzzy logic controller (T3-FLC) for DC-bus voltage regulation. The T3-FLC enhances the conventional type-1 framework by employing a three-dimensional membership structure that captures both vertical and horizontal uncertainties in the fuzzy inference process. This structure improves adaptability and stability in the face of system disturbances. The proposed controller was compared with a conventional proportional-integral (PI) controller and a type-1 fuzzy logic controller (T1-FLC) under different operating conditions: constant reference, reference tracking, load variation, regenerative operation, and grid disturbances. Under reference tracking mode, it settles within approximately 12 ms for the largest reference step, with the overshoot kept below 0.3%, whereas the T1-FLC and PI controllers require noticeably longer settling times and exhibit higher overshoot. In regenerative operation, the T3-FLC maintains tight DC-bus regulation with recovery times of 10–12 ms and an overshoot of about 2.7%, outperforming the benchmark controllers. Power quality analysis further shows that the proposed controller maintains low input-current distortion, with THD approximately 5–13%, and a near-unity power factor across all scenarios. These results confirm the T3-FLC as an effective control strategy for power converters.