Evaluation of the effect of harmonic and interharmonic distortions on inverse time protective relays

The lack of control of the occurrence of harmonic distortion results to the power system equipment overheating thereby increasing in energy loss, premature aging of the equipment, inaccuracy and reliability issues in sensitive and critical electronic devices. The harmonic distortions are majorly caused by non-linear loads and injection of renewable energy devices mainly, solar system network which are necessary for day-to-day activities. Hence, the paper utilized a standalone single-tuned passive filter and a hybrid of a passive filter and ANN model control mechanism for the mitigation of the impact of harmonic distortion on the Nigerian 330kV transmission network situated in the North central region of the country. The total harmonic distortion (THD) metrics were utilized for the determination of the performance of the filters utilized sequentially in mitigating the impact of the harmonic’s distortion on the current and voltage signals. From the outcome of the simulation, the utilization of the hybrid filter had the best performance as it reduced the THD of the current signal to 1.072% and 1.213% for the Benin kebbi and Kainji bus stations respectively and voltage signal to 1.772% and 1.993% for the Benin kebbi and Kainji bus stations respectively.

This project focuses on an advanced strategy for optimizing power quality using a Five-Level Modified Inverter-Based Static Synchronous Compensator (STATCOM) Cascaded H-Bridge (CHB) combined with an Artificial Neural Network (ANN) controller. The system is designed to address prevalent power quality challenges such as harmonic distortions, voltage sags and reactive power discrepancies, which are crucial in contemporary power grids. The CHB inverter is engineered to minimize Total Harmonic Distortion (THD) while enhancing power conversion efficiency. To optimize its performance further, the ANN controller adjusts the operations of the STATCOM in response to real-time grid conditions. In contrast to traditional controllers, the ANN controller learns from past data, modifying its reaction to varying load and fault situations, ultimately improving voltage stability, reducing harmonics, and ensuring a quick response from the system. Simulation outcomes illustrate the effectiveness of the ANN-based STATCOM in delivering enhanced harmonic mitigation and voltage regulation when compared to standard control techniques. This method proves to be particularly advantageous for smart grids, industrial power systems, and the integration of renewable energy, where sustaining high power quality is critical. The study concludes that the integration of ANN control with the modified CHB inverter-based STATCOM presents a highly effective and adaptable solution for enhancing power quality in intricate electrical networks

This work optimizes the PI controllers of a three-phase bidirectional AC/DC converter to increase Vehicle to-Grid (V2G) system reliability and efficiency. This study aims to solve the limitations of traditional trial-and-error or heuristic tuning methods, which often result in suboptimal performance in dynamic V2G environments. The Grey Wolf Optimiser (GWO) is used to determine optimal controller gains for several objective functions, including Integral Square Error (ISE), Integral Time Absolute Error (ITAE), and Integral Squared Time Error (ISTE). This study shows that a simple, systematically optimised PI controller can compete with more complex techniques in performance. The GWO-tuned controller is closely compared to a standard PI controller and an Adaptive Neuro-Fuzzy Inference System (ANFIS) in MATLAB ® (2023a) under challenging conditions, such as load step changes and sudden power flow reversals. Offline-optimized benefits are confirmed robust by implementing them on a Xilinx Spartan-6 FPGA hardware prototype. Both hardware and simulated results demonstrate the superiority of the GWO-tuned controller. The proposed approach reduces average error reduction by 15%, grid current Total Harmonic Distortion (THD) by 20%, and DC link voltage surge during load transients from 12.5% to 2.3% compared to typical PI controllers. The GWO-PI controller consistently demonstrates improved dynamic response and robustness, proving its suitability for demanding V2G scenarios.

This paper presents a real-time hybrid intelligent control framework for a Positive Output Super-Lift Re-Lift Luo Converter (POSLRLC), developed to enhance voltage regulation, efficiency, and power quality in electric vehicle (EV) charging networks. The proposed architecture integrates a Neuro-Fuzzy C-Means (Neuro-FCM) clustering mechanism with an Adaptive Neuro-Fuzzy Inference System (ANFIS) for dynamic control optimization. The Neuro-FCM module continuously performs online fuzzy clustering on the error [E(k)] and change of error [ΔE(k)], generating adaptive Gaussian membership functions, while the ANFIS layer refines rule consequents using hybrid learning combining least-squares estimation and gradient descent. This dual-layer adaptation provides self-tuning control action without manual gain adjustment, overcoming the limitations of static fuzzy and PI-type controllers. The proposed model was implemented and verified on a Xilinx Zynq UltraScale+ MPSoC platform under a bare-metal C environment, with real-time fixed-point inference executed on FPGA fabric. Simulation and hardware validation were conducted under varying PV input (10–20 V) and dynamic load (10–50 Ω) conditions. Results demonstrate significant performance improvements compared to conventional PID, MPC, SMC, and AGA-FLC controllers. The proposed system achieves a rise time of 6.7 ms, settling time of 7.2 ms, and steady-state error of 0.12 %, with converter efficiency maintained at 96.7–97.2 % and total harmonic distortion (THD) reduced to 2.48 %. The FPGA implementation yields sub-microsecond computation latency with <50 ns jitter, enabling deterministic high-speed control. Overall, the hybrid Neuro-FCM + ANFIS control system demonstrates superior dynamic response, efficiency stability, and harmonic suppression, offering a scalable and intelligent solution for next-generation EV power conversion and renewable energy integration.

The need for multilevel inverters has grown rapidly in recent years, particularly in applications involving high power and high voltage. Their ability to generate output waveforms that closely resemble a pure sine wave makes them highly effective in minimizing harmonic distortion and enhancing overall power quality.This paper presents the design and development of a five-level cascaded H-bridge multilevel inverter, realized through MATLAB/Simulink simulations as well as a hardware prototype driven by a microcontroller. The cascaded H-bridge configuration is chosen because of its modular structure, ease of control, and capability to produce higher output voltages using medium-voltage switching devices. By producing multiple voltage steps that shape a smoother sinusoidal waveform, the proposed system successfully lowers switching losses, improves output waveform quality, and offers strong potential for use in high-power industrial systems and renewable energy applications

The increasing integration of photovoltaic (PV) systems into modern power grids poses significant operational challenges, including variability in solar generation, fluctuations in demand, degradation of power quality, and reduced reliability under uncertain conditions. Addressing these challenges requires advanced control strategies that can manage uncertainty while coordinating storage, inverter-level actions, and power quality functions. This paper proposes a unified stochastic Model Predictive Control (SMPC) framework for the optimal management of photovoltaic (PV) systems under uncertainty. The approach integrates chance-constrained optimization with Value-at-Risk (VaR) modeling to ensure system reliability under variable solar irradiance and demand profiles. Unlike conventional deterministic MPCs, the proposed method explicitly addresses stochastic disturbances while optimizing energy storage, generation, and power quality. The framework introduces a hierarchical control architecture, where a centralized SMPC coordinates global energy flows, and decentralized inverter agents perform local Maximum Power Point Tracking (MPPT) and harmonic compensation based on the instantaneous power theory. Simulation results demonstrate significant improvements in energy efficiency from 78% to 85%, constraint satisfaction from 85% to 96%, total harmonic distortion reduction by 25%, and resilience (energy supply loss reduced from 15% to 5% under fault conditions), compared to classical deterministic approaches. This comprehensive methodology offers a robust solution for integrating PV systems into modern grids, addressing sustainability and reliability goals under uncertainty.

Multilevel inverters (MLIs) have emerged as a foundational component in modern power electronics, particularly in medium- and high-voltage applications where conventional two-level inverters are no longer sufficient to meet performance, efficiency, and reliability standards. One of the primary motivations behind the widespread adoption of MLIs is their ability to synthesize high-quality output voltages with lower total harmonic distortion (THD), thereby improving power quality and system compatibility. This harmonic reduction is crucial in industrial and utility-scale applications, where precise voltage waveforms are essential for the reliable operation of motors, transformers, and sensitive loads. This paper introduces a novel multilevel inverter topology based on the cascaded connection of fundamental inverter modules. The proposed configuration is designed to operate efficiently in both symmetrical and asymmetrical modes, making it highly suitable for integration with renewable energy sources such as fuel cells and photovoltaic systems. In the symmetrical arrangement, each module utilizes identical DC source magnitudes, whereas in the asymmetrical configuration, unequal DC voltage levels—derived through binary or trinary progression—are employed to generate a greater number of output voltage levels using fewer components. The comparative analysis demonstrates that the proposed topology significantly reduces the number of power switches and passive components required, leading to lower power losses and enhanced overall inverter efficiency. Additionally, the total standing voltage stress on the semiconductor switches remains within acceptable limits, thereby improving reliability and operational safety compared to conventional multilevel inverter designs. The flexibility and simplicity of the proposed structure make it an ideal candidate for low- to medium-power renewable energy applications. To validate the functionality and effectiveness of the design, both simulation and experimental results are presented for 11-level, 15-level, and 19-level inverter configurations. The results confirm that the proposed inverter achieves high-quality output voltage waveforms with minimized harmonic distortion and improved performance across various load conditions.

Signal generators are essential instruments for testing, measurement, and embedded system validation. Commercial function generators, however, are often expensive and non-customizable for educational or prototyping environments. This study presents the design and realization of a low-cost, microcontroller-based multi-waveform generator capable of producing sine, triangular, sawtooth, and square waveforms with adjustable frequency, phase, and duty cycle. The system integrates an Arduino Mega 2560 controller with an AD9833 Direct Digital Synthesis (DDS) module for high-precision sine and triangular outputs, while hardware-timed PWM channels generate sawtooth and square waveforms. Three potentiometers provide real-time user control of frequency (50 Hz-1 kHz), phase (0°-360°), and duty ratio (0-100%), and a 16×2 I²C LCD displays the selected waveform parameters. Experimental characterization demonstrates frequency accuracy of ±0.05% and phase error within ±2° for AD9833-based signals, and total harmonic distortion (THD) below 0.8% for sine output up to 1 kHz. PWM-derived waveforms exhibit amplitude linearity of 96-98% and negligible drift across 8 h continuous operation. Compared with conventional analog Wien-bridge or XR2206-based function generators, the proposed system offers higher frequency stability, lower power consumption (≈310 mW), and greater flexibility for digital control at less than 15 USD total cost. The developed prototype successfully reproduces clean, noise-free waveforms observable on an oscilloscope and matches reference laboratory generators with an RMS amplitude deviation under 0.03 V (5 V scale). The compact and modular design enables rapid educational deployment and portable instrumentation. Future enhancements may include amplitude modulation through DAC expansion, frequency sweep automation, and PC-linked waveform visualization. The proposed design thus bridges the gap between low-cost educational tools and professional waveform generation, demonstrating the potential of open-source microcontroller architectures for accurate, user-interactive signal synthesis.

This study presents the design of a high-efficiency pulse width modulation (PWM) power amplifier for marine biological sound reproduction. Due to the capacitive nature of underwater transducers and step-up transformers, output LC filter design is constrained, making it difficult to achieve a flat frequency response and low total harmonic distortion (THD). To address this, the electrical characteristics of these components were measured and modeled to construct equivalent circuits for the PSPICE simulator. Based on these models, an optimized LC filter was designed, and its performance was validated through simulation and experiments. The cause of THD occurring in specific frequency bands was analyzed, and two types of notch filters were applied to improve THD and switching signal attenuation. The proposed methodology offers a practical approach to improving PWM amplifier performance in underwater acoustic systems, supporting the development of compact, efficient, and reliable SONAR transmitters.

Traditional diode-based rectifiers (TDRs) in railway traction substations (TSSs) are inefficient at handling bidirectional power flow and cannot recover regenerative braking energy (RBE). Replacing these conventional systems with reversible traction substations (RTSSs) requires detailed modeling, extensive simulations, and validation using real data. This paper presents a DT-oriented real-time modeling and Hardware-in-the-Loop (HIL) platform for the analysis and performance assessment of RTSSs in DC railway systems. The integration of interleaved PWM rectifiers enables bidirectional power flow, allowing efficient RBE recovery and its return to the main grid. Modeling railway networks with moving trains is complex due to nonlinear dynamics arising from continuously varying positions, speeds, and accelerations. The proposed approach introduces an innovative multi-train simulation method combined with low-level transient and power-quality analysis. The validated DT model, supported by HIL emulation using OPAL-RT, accurately reproduces real-world system behavior, enabling optimal component sizing and evaluation of key performance indicators such as voltage ripple, total harmonic distortion, passive-component stress, and current imbalance. The results demonstrate improved energy efficiency, enhanced system design, and reduced operational costs. Meanwhile, experimental validation on a small-scale RTSS prototype, based on data from the Italian 3 kV DC railway system, confirms the accuracy and applicability of the proposed DT-oriented framework.

ABSTRACT Managing renewable energy systems involves challenges such as optimising power flow, balancing generation with demand, addressing power variations, and reducing computational complexity. Ensuring efficient integration of renewable sources while maintaining power quality (PQ) remains a key concern. As a solution, this research introduces an advanced controller designed to enhance harmonic mitigation and maintain PQ in distribution systems using Renewable Energy Sources (RES). A novel hybrid control strategy is proposed for Shunt Active Filters (SHAFs), combining a Proportional Integral Derivative Accelerometer (PIDA) controller with a Puma Optimiser (PO). The PIDA controller adjusts SHAF fundamental and harmonic loop parameters, including terminal and DC voltages. A comprehensive dataset is generated and optimised using a minimum-error objective function, enabling PO to produce refined control signals and accurate parameter predictions. The proposed method is implemented in MATLAB/Simulink and evaluated against established approaches. Results show improved harmonic mitigation accuracy, with relative accuracy values approaching zero for most test cases, outperforming methods such as SSO, DA, and BFO while reducing system complexity. Experimental validation further confirms the model’s superiority. Total Harmonic Distortion (THD) is reduced from 4.42 to 1.04 in simulations and from 4.85 to 1.051 in experiments, demonstrating enhanced power quality and system stability.

With the development of distributed energy sources such as photovoltaic and wind power, power grids have imposed increasingly higher requirements on power quality. As common nonlinear loads in power grids, multi-pulse rectifiers (MPRs) inject significant harmonics into the grid side. To reduce harmonic pollution at the source, this paper proposes a novel triple-passive harmonic suppression method to reduce the input current harmonics of MPRs. The proposed 48-pulse rectifier comprises a main circuit based on delta-connected auto-transformer (DCT) and a triple-passive harmonic suppression circuit (TPHSC). The TPHSC consists of two interphase reactors (IPRs) and eight diodes. Based on Kirchhoff’s Current Law (KCL), the output currents of the main circuit are calculated, and the operating modes of the TPHSC are analyzed. From the main circuit’s output currents and the DCT topology, the rectifier’s input currents are derived, and the optimal turns ratio of the IPRs for minimizing the input current total harmonic distortion (THD) is determined. The total capacity of the IPRs accounts for only 2.3% of the output load power. Experimental results show that the measured input current THD is close to the theoretical value of 3.8%. Overall, the proposed rectifier offers a cost-effective solution with stronger harmonic suppression capability, making it suitable for applications requiring low grid harmonic pollution.

Electric vehicles operate with rechargeable battery systems, which are critical in achieving zero carbon emission targets. EV chargers, which enable the conversion of alternating current, which is the energy source of these vehicles, into direct current, include various rectifier circuits according to different charging modes (Mode 1, Mode 2, and fast DC charging). This study investigated the harmonic effects of rectifier circuits on the electrical system in single-phase, three-phase, and DC charging modes. As a result, it was determined that three-phase six-pulse rectifier circuits have lower harmonic content compared to single-phase rectifiers by keeping the total harmonic distortion (THD) value of the current drawn from the grid below approximately 20%. This is possible thanks to the elimination of 3rd and multiples of 3 harmonic components in three-phase systems and the achievement of lower fluctuations in the DC link voltage. These findings show that three-phase rectifier circuits provide a cleaner and more efficient power source, even without a filter capacitor.

This project aims to explore and implement effective strategies for the reduction of harmonics in three phase squirrel cage induction motors using the VFD method. By leveraging advanced pulse width modulation (PWM) techniques within VFDs, it is possible to significantly lower total harmonic distortion (THD), thereby enhancing the efficiency and lifespan of the motor. The study includes an analysis of harmonic reduction methods, simulation results, and practical recommendations, with a goal to improve overall power quality and maintain the stable operation of industrial motor drives.Harmonic distortion is a well-known challenge in the operation of three phase squirrel cage induction motors, which are central to many industrial applications due to their robust construction, efficient performance, and low maintenance needs. When these motors are powered using Variable Frequency Drives (VFDs), while variable speed control and energy efficiency are achieved, the inverter switching process inadvertently introduces harmonic components into the current and voltage supplied to the motor. Such harmonics, especially of lower order like the 5th and 7th, can cause increased heating, additional core losses, torque pulsations, and noise, ultimately reducing motor efficiency and lifespan

The increasing demand for reliable DC fast-charging stations in electric vehicle (EV) infrastructure necessitates efficient fault detection mechanisms to ensure operational stability and user safety. This paper will present the development of a diagnostic method for identifying open-circuit faults and short-circuit faults in DC charging stations by leveraging Total Harmonic Distortion (THD) analysis combined with a Second-Order Generalized Integrator (SOGI). The proposed approach uses the THD method to detect anomalies in the current and voltage waveforms, while the Frequency Locked Loop (FLL) serves to track the frequency of the grid and keep the SOGI tuned to it, and SOGI-FLL provides the rectifier with the capability of tracking the frequency, amplitude, voltage, and phase of the grid and monitoring these parameters of the grid. The ability to measure the THD is the kernel of the detection of faults. Detailed simulation confirms the method’s high sensitivity and robustness in detecting open/short circuit faults with minimal false positives. This technique offers a cost-effective, non-invasive diagnostic solution suitable for modern DC charging systems, contributing to improved reliability and efficiency of EV charging infrastructure.

As the primary interface for integrating renewable energy sources such as wind and solar power into the grid, inverters are prone to inducing sub-/super-synchronous or medium-to-high-frequency oscillations during grid-connected operation under weak grid conditions. Optimizing the control structure of a single wind turbine inverter struggles to address multi-mode resonance issues comprehensively. Therefore, a cooperative control strategy for parallel-coupled inverters is proposed. First, a frequency-domain impedance reconstruction method for parallel wind turbines is proposed based on the phase-neutralizing characteristics and damping variation patterns of parallel-coupled impedances. Second, the damping characteristics of inverters are enhanced through the design of an additional damping controller, while the phase-frequency characteristics of wind turbines are improved using active damping based on notch filters. Finally, simulation models based on 2.5 MW permanent magnet synchronous generator (PMSG) units validate the effectiveness of the control strategy. Research results demonstrate that this cooperative control strategy effectively suppresses sub-/super-synchronous and medium-to-high-frequency oscillations: In the 0~300 Hz key oscillation band, the amplitude suppression rate of oscillating current reaches ≥60%, the total harmonic distortion (THD) of the 5th harmonic at the grid connection point decreases from 4.465% to 3.518%.

To address the issues of large cogging torque and poor sinusoidal waveform of induced electromotive force in traditional pure rare earth permanent magnet generators for extended-range electric vehicles, a novel permanent magnet-assisted reluctance generator with hybrid magnetic poles formed by rare-earth and non-rare-earth permanent magnets for series excitation is proposed. The topological structure and operating principle of the generator are introduced. Based on this, an equivalent magnetic circuit model is established, and the analytical expressions of the cogging torque and induced electromotive force of the permanent magnet generator are derived to analyze their main influencing factors. A finite element model of a three-phase 8-pole 36-slot interior dual radial combined magnetic pole permanent magnet generator is established. Sensitivity hierarchical optimization is carried out on the relevant parameters such as the size and position angle of the combined magnetic poles and their corresponding magnetic barriers, so as to obtain the structural parameters affecting the generator’s cogging torque, the amplitude of the induced electromotive force, and the total harmonic distortion (THD) of the induced electromotive force waveform. Then, the data is normalized and a weighted evaluation index is used to obtain the optimal solution combination. Finally, the feasibility of this method is verified through finite element simulation analysis. The results indicate that the amplitude of the no-load induced electromotive force of the generator increased by 6.97%, the THD decreased by 16.2%, and the cogging torque was weakened by 43.8%, effectively improving the output performance of the generator.

Voltage instability and power quality degradation represent critical barriers to the reliable operation of modern wind farm-based microgrids. As the share of distributed wind generation continues to grow, fluctuating wind speeds and variable reactive power demands increasingly challenge grid stability. This study proposes an adaptive decentralized framework integrating a Dynamic Distribution Static Compensator (DSTATCOM) with an Artificial Neuro-Fuzzy Inference System (ANFIS)-based control strategy to enhance dynamic voltage and frequency stability in wind farm microgrids. Unlike conventional centralized STATCOM configurations, the proposed system employs parallel wind turbine modules that can be selectively switched based on voltage feedback to maintain optimal grid conditions. Each turbine is connected to a capacitive circuit for real-time voltage monitoring, while the ANFIS controller adaptively adjusts compensation signals to ensure minimal voltage deviation and reduced harmonic distortion. The framework was modeled and validated in the MATLAB/Simulink R2023a environment using the Simscape Power Systems toolbox. Simulation results demonstrated superior transient response, voltage recovery, and power factor correction compared with traditional PI and fuzzy-based controllers, achieving a total harmonic distortion below 2.5% and settling times under 0.5 s. The findings confirm that the proposed decentralized DSTATCOM–ANFIS approach provides an effective, scalable, and cost-efficient solution for maintaining dynamic stability and high power quality in wind farm based microgrids.

The power quality of the electrical supply system has been impacted by the increase in non-linear loads and renewable energy sources such as the solar and the wind power integrated into the grid. The poor power quality results in equipment damage, system shutdown, and data loss, leading to lower operational efficiency and higher maintenance costs. The voltage sag, voltage swell, voltage flicker and voltage harmonic distortions are the most common and severe issues. The dynamic voltage restorer is the most effective solution for these voltage quality problems, which injects necessary voltage levels at the time of faults in order to maintain the load side voltage within specified boundaries. The appropriate control technique should be adopted for the generation of firing pulses for power electronic switches used to construct this device. The classical sliding mode control has the disadvantage of taking a long time to minimize errors. The main objective of this paper is to improve the accuracy and durability the conventional algorithm by adding nonlinear quantity, resulting in the fast terminal sliding mode controller. Furthermore, the artificial neural network is combined with proposed methodology to improve performance of double feeder power system for nonlinear load conditions. The system is modeled and simulated in a MATLAB/Simulink environment with a proportional integral controller optimized by particle swarm optimization, genetic algorithm and mind blast algorithm followed by non-linear controllers. The faults are simulated to mimic voltage sags A, E and B with different load conditions like linear, dynamic and non-linear. The power quality indices like total harmonic distortion, harmonic compensation ratio, sag score and voltage sag lost energy index are considered for assessment of compensation percentage. It is observed that Sag Score is improved by 80% thus increasing voltage sag lost energy index to more than 95%. Therefore, quantitative data demonstrate the efficacy of the proposed method in mitigating voltage sag while simultaneously reducing grid voltage imbalance and distortion, irrespective of the fault type.