Power Quality Issues Research Articles

Enhancing the performance of solar-powered EV charging stations using the TOSSI-based CTF technique

In this paper, the design and analysis of a novel solar-powered EV-charging system employing a third-order sinusoidal signal integrator (TOSSI) based-CTF (character of triangular function) is proposed. The TOSSI-based CTF is used to extract fundamental active components by eliminating harmonic distortions from the load currents. This control structure has the unique capability of active current separation, employing simple mathematical operations. Moreover, the response is further refined with the help of optimized gain parameters. The designed system is capable of handling various power quality issues, including harmonic mitigation, current balancing, and power factor improvement. The EV battery is primarily charged by solar-PV power employing a bidirectional DC-DC converter. Alternatively, the EV battery may be charged by the grid supply during the unavailability of sunlight by taking the power quality issues into due consideration. The suggested control topology is used to enhance the dynamic operation of solar-powered EV charging stations experiencing solar power intermittency and variation of load. Using MATLAB, the efficacy of the proposed control topology is tested for different operating scenarios. The suggested control topology is also verified and validated using prototype hardware developed in the laboratory, where the suggested controller has proven its utility over and above existing state-of-the-art controllers in the domain.

FUZZY-PROPORTIONAL INTEGRAL DERIVATIVE CONTROLLER WITH INTERACTIVE DECISION TREE

The STATCOM is extensively used in the power system to address the power quality issues by actively compensating the reactive power requirements of the load. However, the STATCOM performance depends on the underlying controller operation to handle sudden load changes and disturbances optimally with faster response. To solve this issue, this paper presents the intelligent algorithm-optimized fuzzy rule-based Proportional Integral Derivative (PID) controller to enhance the performance of the STATCOM. The proposed controller consists of a fuzzy-PID controller capable of handling non-linear dynamics and sudden changes in the load. The interactive decision tree (IDT) technique detects weaker metrics and improves their influence in control scenarios. The Biogeography-based optimization (BBO) algorithm minimizes the controller's integral absolute error to tune the fuzzy-PID controller parameters. The algorithm terminates once all the metrics are tuned to satisfaction with the operator’s consent. The proposed IDT-based fuzzy PID controller is tested on a power system containing a nonlinear load, and its performance is compared with the existing controller and simulated using the MATLAB/Simulink tool. The proposed controller provides fast control action, reduces the reactive power from the source by 42.5%, and improves the power factor by 1.5%.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

The Use of Hybrid Technology for Power Quality Improvements in Grid-Connected PV Systems

In recent trends, photo-voltaic (PV) is mostly build upon competitive technological development of power quality (PQ) issues. In this article, a hybrid control strategy is implemented with multi-level inverter (MLI) to improve PQ features. As a result, the combination of these controllers with suitable level of MLI could improve the PQ features in a significant way.

Power Quality Analysis of Electrical Installation at Commercial Center

For many years, power quality analysis has constantly been carried out at the power stations or sub-stations using the data collected at the data Centre of the power plant. However, this practice has proven to be erroneous and tedious to determine the exactly power quality problem especially when it comes to commercial facilities. Therefore, a revised approach of analysis for the power quality problems at the sites where they occur has been used in this research. This is because of the sensitivity of modern electronic equipment used in commercial facilities and also the need for accuracy of the data measurements carried out at the sites henceforth, with accurate data measurement, we can easily identify the power quality issues. Through-out this research, load unbalance of 28% was found. The solution to load unbalance was to balance the loads to 3,3% among the three phases and power losses caused by this unbalance current were calculated. Although the neutral conductor remained unchanged, harmonics especially triplen-harmonics was found to be more 10% recommended by IEC standards, therefore active harmonic filter of 200A was installed at the main panel board to reduce the losses caused by harmonics at the facility.

An Efficient Archerfish Hunting Optimizer (AHO)-Based Power Quality Improvement in Distribution System Connected UPQC

These days, because power quality (PQ) issues are costing industrial customers a lot of money, both electrical utilities and end-users are paying a lot of attention to them. The most significant and frequent PQ concerns for sensitive loads are voltage sag and swell. This manuscript proposes an optimization technique for enhancing the control system of a unified power quality conditioner (UPQC) connected solar photovoltaic system in the distributed generation system. The proposed strategy is the archerfish hunting optimizer (AHO). The objective of the AHO approach is to optimizing UPQC operation in distribution systems, focusing on targeted power quality enhancement. The optimal control pulses are produced by the series active power filter and the shunt active power filter, depending on the load-side and source circumstances. During the load variation states the AHO technique manages the power loss, total harmonic distortion (THD), and voltage instability issue respectively. The performance of the AHO methodology is carried out on the MATLAB platform and compared with existing methods, like particle swarm optimization (PSO), latent semantic analysis (LSA), and ant-lion optimizer (ALO). The existing methods THD are 6.35%, 7.85%, and 9.19%. The proposed method of THD is 4.22%. From the outcome, it concluded that the proposed method based on the THD is low contracted to present techniques.

PV Based Single Stage Cascaded Seven Level Modular Multilevel Converter with ZSV Control for Grid Interfaced System

Objectives: Climatic perturbation and environmental factors affect solar power generation. When these solar panels are connected to the grid it causes power quality issues such as voltage harmonics, voltage distortion, etc. These problems are addressed in this paper using a distributed MPPT algorithm. Methods: Multilevel inverters have made remarkable contributions to renewable energy, high voltage, and other high-power applications. Hence, a single-stage Cascaded Seven-Level Modular Multilevel Converter (CSLMMC) is used to address the issues. Zero Sequence Voltage (ZSV) control is proposed for producing balanced power during varying irradiance conditions. The proposed method is simulated using MATLAB SIMULINK software. Partial shading is conquered by using distributed MPPT. Findings: The simulation of the proposed system is performed for a three-phase system. The three-phase Cascaded Modular Multilevel Converter is connected to the grid through two loads which are controlled by a Zero Sequence Voltage Controller. Power quality parameters such as current, harmonics, and power are analyzed for the proposed system. Novelty: THD analysis for a five-level cascaded H bridge converter and the proposed systems are compared and presented. Keywords: Photo voltaic, Cascaded seven-level modular multilevel converter, Zero sequence control, Maximum power point tracking, Voltage Source Converter

Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique

AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

Enhancement of Active Distribution Network Performance with Multiple Distributed Generators and DSTATCOMs using Reptile Search Algorithm

The integration of multiple DGs can introduce power quality issues and instability in the (DN) due to their intermittent and fluctuating nature. Distributed Static Compensators (DSTATCOMs) are devices that effectively manage and regulate both active and reactive powers, thereby maintaining the desired levels of reactive power in the DNs. This paper investigates the problem of determining the optimal number and placement of multiple DSTATCOMs within active DNs with multiple Distributed Generators (DGs) connected to them. To achieve the optimal sizing and allocation of multiple DSTATCOMs, a novel heuristic method called the reptile search algorithm (RSA) is introduced in this study. A combination of the RSA method and the loss sensitivity factor (LSF) are utilized to identify the optimal number, sizes, and locations of DSTATCOMs to enhance the performance of active DNs with different types of DGs and loads. The desired improvements include mitigating voltage deviation at nodes, minimizing system power losses, and alleviating overloading in feeders. The effectiveness of the algorithm is evaluated using an IEEE-33 bus DN, which is modified to incorporate different DGs and load types. To assess the efficiency of the proposed method, a modified particle swarm optimization (MPSO) algorithm is used for comparison. The results demonstrate that the RSA approach presented in this paper is robust in obtaining optimal solutions, offers fast and easy implementation, can be applied to large DNs, and outperforms the MPSO algorithm.

Studies on Mitigating Power Quality Issues Using PV: Transformerless Dynamic Voltage Restorer System with Modified Marine Predator Algorithm

Studies on Mitigating Power Quality Issues Using PV: Transformerless Dynamic Voltage Restorer System with Modified Marine Predator Algorithm

Adaptive Frequency-Based Power Management for Off-Grid Hybrid Photovoltaic Converters

The intermittency in photovoltaic systems (PV) can lead to power quality issues, especially in off-grid applications. In these cases, adding an energy storage element helps to cope with the intermittency to provide adequate power to the local load. In this scenario, combining Li-Ion batteries and supercapacitors as a hybrid energy storage system (HESS) is powerful due to the battery's high energy density and the supercapacitor's high power density. The HESS demands adequate power management to operate adequately. This paper proposes an adaptive frequency-based power management for a Li-Ion battery and supercapacitor HESS applied to an off-grid PV converter. The main idea of this strategy is to guarantee a smoother current in the battery and reduce the power dissipation in the HESS. The smoother battery current transition helps avoid excessive battery stress and improves lifespan. The proposed strategy is compared with two other strategies. The results show that the proposed approach is more efficient in reducing the HESS' power dissipation and providing smooth current variations to the battery.

A voltage sensorless technique for a shunt active power filter adopting band pass filter under abnormal supply conditions

Shunt active power filters (SAPFs) are effective in addressing power quality issues such as harmonics and voltage unbalance. During abnormal grid conditions, there will be a substantial variation in both the actual and reactive power of the system. Various SAPF control approaches were employed to address these issues. This paper presents an improved bandpass filter technique that serves multiple purposes: extracting the reference current harmonic for SAPF, providing reactive current for power factor correction, and functioning as a backup synchronization method in the event of a loss of bus voltage measurement. Furthermore, it should be minimally affected by the unbalance in supply voltage. The case study model and the proposed reference technique have been executed and validated using the MATLAB software environment.

Experimental investigation of adaptive multi-generalized integrator-based controller for electronically interfaced hybrid microgrid system

This research work explores the conceptualization and evaluation of a hybrid microgrid that taps into the potential of solar photovoltaic systems, wind energy conversion systems, and battery energy storage systems. In practice, nonlinear loads pose power quality issues that significantly impact the performance of hybrid microgrids. To tackle these challenges, an adaptive multi-generalized integrator (MGI) filter is proposed. This filter extracts the fundamental signal from the distorted utility grid voltages. It features a pre-filter with a DC-off set rejection loop, effectively minimizing the DC-offsets from the distorted grid voltages. This facilitates the distribution of active power generated by the hybrid microgrid's sources while simultaneously addressing various power quality problems. In addition, a dynamic power management control approach enhances grid resilience by operating the hybrid microgrid in grid-connected and islanding modes. It provides uninterrupted and high-quality power supply to consumers during grid outages. The performance of this filter is comprehensively assessed through numerical simulations in MATLAB®/Simulink® environment. A real-time laboratory prototype is developed with WAVECT® WUC300 FPGA controller, demonstrating the practical application of the proposed filter. It ensures the minimum DC-offsets of 0.02V, fast convergence, and minimum oscillations. The grid synchronization and power quality index of the grid currents simultaneously achieve a THD of 2.82 %, complying with the IEEE-1547 and IEEE-519 standards. Importantly, the proposed adaptive-MGI filter outperforms the conventional SOGI and LMF filters in terms of nonlinear load tracking capabilities, with a response speed of less than 200μs, thereby highlighting its practical relevance and importance.

Sliding mode-based direct power control of unified power quality conditioner

The Unified Power Quality Conditioner (UPQC) is a promising solution for mitigating multiple Power Quality(PQ) issues in distribution systems, including harmonics, poor power factor, voltage sag/swell and voltage imbalance. The conventional Sliding Mode Controller (SMC) in UPQCs suffers from wide switching frequency variations, chattering problems, and inherent active and reactive power coupling. This study proposes a nonlinear control method, Sliding Mode-based Direct Power Control (SMC-DPC), for the simultaneous regulation of the shunt and series compensators in a UPQC. By optimizing voltage vector selection based on real-time power errors, the proposed method effectively mitigates chattering, and reduces switching frequency variations, and ensures precise tracking of instantaneous active and reactive powers even in the presence of coupling effects. The proposed approach simplifies the system design and improves steady state and dynamic power tracking. The simulation results in MATLAB Simulink® on a 20 kVA system demonstrate that the proposed method achieves a source current THD of 1.52%, compared to 2.31% for conventional SMC and 5.11% for linear controller. Furthermore, the method is robust to grid parameter variations and demonstrates satisfactory performance in both strong and weak grids. In weak grids, the proposed method reduces the line losses by 13.71% and 14.54% compared to SMC and linear controller respectively. The reported results comply with international standards such as IEEE-519 and IEC 61000-3-12, confirming effectiveness of SMC-DPC for enhancing PQ in distribution system.

Reactive Power Compensation for Standalone Hybrid Power System Using Facts Devices

Reactive power compensation is essential in hybrid grid-connected systems because power electronic inverters utilized for supplying DC energy into the grid causes a reduction in the overall power factor of the power systems. Due to the ability to provide a reliable and efficient power supply to remote and off-grid areas, hybrid power systems are growing in popularity. It can provide greater opportunities for operational growth by combining economic and technology advancement. To meet the demand for electrical energy in both regular and critical situations, operators must be in charge of the power systems reliable and secure operation. The primary objective of the work is to control the reactive power flow in a grid-connected hybrid renewable energy system (PV-wind-battery). The power quality problems that these systems frequently experience include voltage sags, harmonics, and flicker. A FACTS device Unified Power Quality controller (UPQC) with a Genetic Algorithm based PI controller is suggested to handle these power quality issues. In order to improve the performance of the power system, the proposed optimization approaches are used to tune the UPQC in a multiline transmission system. The model was developed with the help of the MATLAB/Simulink work framework.

Novel nine level switched capacitor multi-level inverter based STATCOM for distribution system

FACTS based devices are mostly used in power systems due to their capacity to improve system stability. The Static Synchronous Compensator (STATCOM), a shunt-connected device in the FACTS device family, is used for power compensation, power balancing, and enhancing dynamic stability in contemporary power systems. In the proposed work, STATCOM based novel nine level switched capacitor based multi-level inverter (MLI) is used to reduce power quality issues. The proposed novel inverter has a few number of switches with one voltage source. The placement of STATCOM based inverters in the wrong places may have detrimental effects on power loss and electricity quality. To overcome these issues, green anaconda optimization (GAO) is used to allocate the proposed system in an optimal place. The performance of the proposed inverter is examined using the MATLAB/Simulink tool. This inverter requires fewer switches and achieves DC link voltage balances in comparison to any other existing conventional topologies. STATCOM based inverter is validated under different faulty conditions to show the effectiveness of this inverter. The proposed new inverter has nine levels and compensates for the poor state while improving power quality. The proposed method has a substantially lower total harmonic distortion (THD) of 1.04 % while maintaining an efficiency of 99.02 %. Experimental validation is established using a dSPACE RTI1104 controller to validate the proposed method. Experimental results obtained 1.04 % of harmonics with the same output voltage as the simulation.

Hybrid Deep Neural Network Approaches for Power Quality Analysis in Electric Arc Furnaces

In this research, we investigate the power quality of the grid where an Electric Arc Furnace (EAF) with a very high load operates. An Electric Arc Furnace (EAF) is a highly nonlinear load that uses very high and variable currents, causing major power quality issues such as voltage sags, flickers, and harmonic distortions. These disturbances produce electrical grid instability, affect the operation of other equipment, and require strong mitigation measures to reduce their impact. To investigate these issues, data are collected from the Point of Common Coupling where the Electric Arc Furnace is fed. The following three main factors are identified for evaluating power quality: apparent power, active and reactive power, and distorted power. Along with these powers, Total Harmonic Distortion, an important indicator of power quality, is calculated. These data are collected during the full process of producing a complete steel batch. To create a Deep Neural Network that can model and forecast power quality parameters, a network is developed using LSTM layers, Convolutional Layers, and GRU Layers, all of which demonstrate good prediction performance. The results of the prediction models are examined, as well as the primary metrics characterizing the prediction, using the following: MAE, RMSE, R-squared, and sMAPE. Predicting active and reactive power and Total Harmonic Distortion (THD) proves useful for anticipating power quality problems in an Electric Arc Furnace (EAF). By reducing the EAF’s impact on the power system, accurate predictions will anticipate and minimize disturbances, optimize energy consumption, and improve grid stability. This research’s principal scientific contribution is the development of a hybrid deep neural network that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) layers. This deep neural network was designed to predict power quality metrics, including active power, reactive power, distortion power, and Total Harmonic Distortion (THD). The proposed methodology indicates an important step in improving the accuracy of power quality forecasting for Electric Arc Furnaces (EAFs). The hybrid model’s ability for analyzing both time-series data and complex nonlinear patterns improves its predictive accuracy compared to traditional methods.

Synergetic UPQC Application for Power Quality Enhancement in Microgrid Distribution System: SCSO Approach

Nowadays, Hybrid renewable energy systems (HRES) are increasingly being integrated into grid-connected load systems to lower losses and boost dependability. When the HRES system is connected to the grid system to satisfy needed load demand, power quality issues arise because of imbalanced load circumstances, non-linear load, and critical load. So this manuscript proposed a synergetic Unified Power Quality Conditioner (UPQC) application for power quality enhancement in microgrid distribution systems. The Sand Cat Swarm Optimization (SCSO) technique is based on the proposed sand cat swarm optimization method. The proposed strategy's primary goal is to regulate power flow, minimize harmonic distortion in both voltage and current waveforms, and maximize UPQC. By then, the proposed method's performance has been implemented into practice on the MATLAB platform and contrasted with several currently used techniques. The proposed technique displays the low Total Harmonic Distortion (THD) and operation time in all existing approaches like Moth Flame Optimization (MFO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), Improved Bat Search Algorithm (IBSA), Lion Algorithm (LA), Artificial Bee Colony (ABC), Atom Search Optimization (ASO) and Crow Search Algorithm (CSA). The proposed method attains THD as before UPQC is 31%, after UPQC is 3%, load current is 1.42%, and load voltage is 2%. It also achieves a low operation time of 506ms and a low settling time of 0.15s compared to the existing methods. The significant improvement in power quality metrics demonstrates the effectiveness of the proposed SCSO technique.

Performance analysis of hybrid metaheuristic assisted collateral FO controller for HRES system integrated UPQC

Due to the fluctuating characteristics of the environment, it is advisable to connect a minimum of two renewable energy sources (RES) to the grid to mitigate unreliable power supply. The integration of Hybrid Renewable Energy Storage (HRES) with nonlinear loads introduces harmonics and other power quality issues, which are significant concerns for both utility providers and customers. Power quality challenges, such as real power fluctuations, voltage interruptions, and reactive power imbalances, can be mitigated through the use of a Unified Power Quality Conditioner (UPQC). For optimal performance, the series and shunt compensators within the UPQC rely on precise PWM signals to control the converters' outputs. These signals are correctly tuned by controllers using updated algorithms. The paper develops an optimized Hybrid Metaheuristic assisted Collateral Controller for the improvement of the 3-phase HRES system-based Distribution Grid incorporated with UPQC. It consists of a Fractional order Proportional Integral derivative (FOPID) controller combined with a proportional integral (PI) controller. As a consequence, the DC link voltage regulation and controller optimization are achieved with the optimal tuning of gain parameters by using a hybrid Metaheuristic Algorithm named Amplified Slime Mould with WildeBeest Herd Optimization (ASM-WHO) Algorithm. The optimization of the weights (W1 and W2) and parameters of the Collateral Fractional Order Controller is achieved using an innovative hybrid metaheuristic method that combines the Wildebeest Herd Optimization (WHO) and Slime Mould Algorithm (SMA). The proposed system is implemented in MATLAB Simulink to analyze the compensation efficiency during voltage sag/swell. The proposed controller showcases robust performance, emphasizing its potential for enhancing power quality and distribution stability in Three-Phase Hybrid Renewable Energy Storage systems integrated with UPQC, outperforming or matching the performance metrics of conventional methods such as PI, FOPID controller, FOPID-PI with various optimization algorithms (CSO,SOA, WHO, SMA, AQO, HBA). The settling minimum and maximum of 170.5668 and 189.4736 for the proposed FOPID-PI controller with ASM – WHO is notably superior to that of the controller without optimization, the proposed collateral controller method (using SOA or CSO), WHO optimization, and the PI controller, improving by 1.7 %, 13.78 %, 1.338 %, and 13.66 %, respectively. The ASM-WHO algorithm shows robust performance and improved convergence, as validated by benchmark function tests, and surpasses competing algorithms.

MITIGATION OF POWER QUALITY PROBLEMS IN STEEL PLANTS USING STATIC SYNCHRONOUS COMPENSATOR

This paper focuses on mitigating various power quality issues from electric arc furnaces (EAF) in steel plants, including voltage flicker, sag, swell, and harmonics. Arc furnaces are recognized as one of the industry's most nonlinear electrically polluting loads. The proposed solution in this article to address all power quality issues caused by EAF is implementing a flexible power supply strategy known as a static synchronous compensator (STATCOM) for injecting reactive energy into the electrical grid. In this study, an experimental model of EAF is utilized based on real measurements of the variation of arc resistance and arc reactance during the melting phase. The pulse width modulation (PWM) technique is employed for current tracking feedback control to generate the required reactive current. A fuzzy logic controller (FLC) is employed for the DC bus.