Distortion Power Research Articles

Current Compensation for Faulted Grid-Connected PV Arrays Using a Modified Voltage-Fed Quasi-Z-Source Inverter

Large-scale photovoltaic (PV) systems are being widely deployed to meet global environmental goals and renewable energy targets. Advances in PV technology have driven investment in the electric sector. However, as the size of PV arrays grows, more obstacles and challenges emerge. The primary obstacles are the occurrence of direct current (DC) faults and shading in a large array of PV panels, where any malfunction in a single panel can have a detrimental impact on the overall output power of the entire series-connected PV string and therefore the PV array. Due to the abrupt and frequent fluctuations in power, beside the low-PV systems’ moment of inertia, various technical problems may arise at the point of common coupling (PCC) of grid-connected PV generations, such as frequency and voltage stability, power efficiency, voltage sag, harmonic distortion, and other power quality factors. The majority of the suggested solutions were deficient in several crucial transient operating features and cost feasibility; therefore, this paper introduces a novel power electronic DC–DC converter that seeks to mitigate these effects by compensating for the decrease in current on the DC side of the system. The suggested solution was derived from the dual-source voltage-fed quasi-Z-source inverter (VF-qZSI), where the PV generation power can be supported by an energy storage element. This paper also presents the system architecture and the corresponding power switching control. The feasibility of the proposed method is investigated with real field data and the PSCAD simulation platform during all possible weather conditions and array faults. The results demonstrate the feasibility and capability of the proposed scheme, which contributes in suppressing the peak of the transient power-to-time variation (dP/dt) by 72% and reducing its normalized root-mean-square error by about 38%, with an AC current total harmonic distortion (THD) of only 1.04%.

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.

The Implications of Pre-Existing Market Power Distortions for Electric Vehicle Tax Policies: Evidence from Colombia

The Implications of Pre-Existing Market Power Distortions for Electric Vehicle Tax Policies: Evidence from Colombia

Calculation and optimization of China's power distortion under carbon peaking target

Based on Hahn & Metcalfe (2021), we deduced the theoretical framework and calculated China's power distortion index (PDI) using the city-level industrial and residential power consumption and price data. We further studied the relationship between PDI and carbon emissions, and optimized the PDI under the carbon peaking target through scenario analysis. We find that: (1) China's PDI showed an inverted “U” shape between 2006 and 2019. (2) On average, one-unit decrease in PDI could result in a decrease of 3.72 % in carbon emissions. (3) The scenario simulation indicates that to achieve the target PDI, the average industrial (residential) power price in 2030 needs to be reduced (increased) to 0.59 (0.58) yuan.

A Joint Logarithmic based nonlinear Adaptive filter for load balancing in electric power distribution system

When nonlinear loads are introduced to a distribution system, various power quality challenges emerge, such as load unbalance, harmonic distortion, and reactive power mismatch. This study presents a novel solution utilizing a joint logarithmic hyperbolic cosine adaptive filter-operated distribution static compensator to mitigate these issues. The cost function of the adaptive filter is designed to be smooth for minor errors and resilient to significant errors. The proposed control technique computes fundamental active and reactive currents, generating reference signals for the distribution static compensator. A comparative analysis with conventional adaptive control techniques is also conducted. The distribution system undergoes comprehensive testing under various dynamics to ensure compliance with IEEE-519 standards. Real-time simulation-based experiments are employed to showcase the algorithm's performance.

Power Components Mean Values Determination Using New Ip-Iq Method for Transients

This paper deals with the quasi-instantaneous determination (in a single-step response time) of apparent, active, and reactive (i.e., blind and distortion) power mean values including the total power factor, total harmonic distortion, and phase shift of fundamentals of a power electronic and electrical system (PEES) using the ip-iq method, which is the main contribution of the paper. The power components’ mean values are investigated during the transient and steady states. The power components’ mean values can be determined directly from phase current and voltage quantities, using an integral calculus over one period within the next calculation step and using moving average and moving rms techniques (or digital filtering). Consequently, the power factor can be evaluated with known values of a phase shift of fundamentals (using a Fourier analysis). The results of this study show how a distortion power component during transients is generated even under a harmonic supply and linear resistive–inductive load. The paper contains a theoretical base, modeling, and simulation for the three and single phases of the transients in power electronic systems. The worked-out results can be used to determine and size any PES. The presented approach brings a detailed time waveform verified by simulations in Matlab/Simulink 2022a and the Real-time HW Simulator Plecs RT Box 1.

Control algorithm for an island microgrid under DSTATCOM using a Third Order Sinusoidal Integrator (TOSSI) to improve power quality for a local nonlinear load

Energy delivery to end-user customers is critically dependent on the distribution system. The system must synchronize correctly during island operation while connected as a microgrid to provide better power quality. The consumer experiences distortion and uneven power quality during these activities, raising severe research concerns. In order to mitigate the power quality issues at the end-user side, the modified Third Order Sinusoidal Integrator (TOSSI)-based DSTATCOM is the main topic of this research work. In order to instruct the DSTATCOM, the TOSSI receives feedback on phase, voltage, and frequency via the phase-locked loop (PLL) system. Through the use of the harmonic component of the load current, the enhanced TOSSI-based feedback enhances the dynamics of the distribution side. Enhancing DSTATCOM’s performance in distorted and unbalanced system circumstances is the aim of the suggested research. This work explores power factor correction and harmonic reduction in unbalanced and non-linear load settings utilizing modified TOSSI feedback, achieving an accuracy of 92%. The planned microgrid is modelled and simulated in MATLAB’s Simulink environment to demonstrate the importance of the suggested mitigation technique.

Enhancement of Power Quality through the Implementation of a Hybrid Artificial Intelligence Model within Solar-Wind Energy Systems

The generation of electricity is significantly assisted by hybrid solar-wind power systems, which also play a crucial role in the advancement of intelligent grids. Furthermore, the integration of wind and solar energy storage control systems, in conjunction with energy markets, has rendered the electricity grid more economically feasible. To meet the growing demand for electricity, hybrid renewable energy systems connected to microgrids consist of substantial identifying elements. The issue of harmonic distortion in microgrids arising from nonlinear loads is a fundamental area of research. Understanding the impact of microgrids on power quality is also of great importance. This study focuses on enhancing power quality in solar-wind hybrid systems by incorporating emerging Artificial Intelligence (AI) methodologies. The proposed approach comprises two main components: (i) detection of distortions caused by voltage fluctuations and (ii) improvement of power quality through an AI-based control system. Initially, power distortions are identified and eliminated using the Enhanced Kalman Filter (EKF) algorithm. A Hybrid Artificial Neural Network with Fuzzy Intelligence (HANFI) model is then developed based on this power data. This model integrates two prominent neural network architectures with fuzzy systems. Experimental validation of the system is conducted using MATLAB, which facilitates the simulation of hybrid energy systems.

Single-Phase Robust Charger with High Power Quality for Electric Vehicle Application

The widespread use of electric vehicles (EVs) necessitates the development of a robust charging infrastructure. The goal of this research paper is to create a high-quality, single-phase charger for electric cars. The purpose of this design is to provide a charger that is both fast and efficient but also protects against power quality problems including harmonics, power factor correction, and voltage variations. A single-phase–single-stage improved power quality EV charger for small and medium power applications has been designed and simulated. A single DC-DC converter is utilized for constant charging and improved power quality operation. The charger presented exhibits improved power quality as sinusoidal current is drawn from the utility grid supply with the total harmonic distortion (THD) in compliance with the IEEE 519 and IEC 61000-3-2 standards. In order to charge the battery of an electric vehicle, most two-stage converters first use a boost converter for power factor correction (PFC), and then they use a DC-DC converter that can accept any input voltage. These two-stage conversions are inefficient and use more parts than necessary. This work proposes a PFC converter based on a single-stage switching inductor Cuk converter, which has the advantages of high step-down gain, low current stress, high efficiency, and a small number of components. The suggested converter’s operational analysis and design equations are performed in continuous current mode. The proposed converter is the subject of extensive mathematical modelling, analysis, simulation, and experimentation presented in this study. With both constant voltage (CV) and constant current (CC) loads, the proposed converter’s performance is analysed with regard to power quality indices like voltage total harmonic distortion (THD), current THD, and total power factor. Additionally, in CV mode and CC mode, the suggested converter’s dynamic performance with battery charging is evaluated in relation to the extensive supply changes. The developed charger presents a reliable and efficient solution for EV charging, fostering the transition to a cleaner and more sustainable transportation system.

New Goodness-of-Fit Tests for the Kumaraswamy Distribution

The two-parameter distribution known as the Kumaraswamy distribution is a very flexible alternative to the beta distribution with the same (0,1) support. Originally proposed in the field of hydrology, it has subsequently received a good deal of positive attention in both the theoretical and applied statistics literatures. Interestingly, the problem of testing formally for the appropriateness of the Kumaraswamy distribution appears to have received little or no attention to date. To fill this gap, in this paper, we apply a “biased transformation” methodology to several standard goodness-of-fit tests based on the empirical distribution function. A simulation study reveals that these (modified) tests perform well in the context of the Kumaraswamy distribution, in terms of both their low size distortion and respectable power. In particular, the “biased transformation” Anderson–Darling test dominates the other tests that are considered.

Integration of electric vehicle into smart grid: a meta heuristic algorithm for energy management between V2G and G2V

Recently, Electric Vehicles (EV) have been providing fast response and substantial progress in the power generation model. Further, EVs are exploited as adaptable Energy Storage Systems (ESSs) and show a promising performance in ancillary service markets to increase the demand of Smart Grid (SG) integration. The expansion of Vehicle-to-Grid concept has created an extra power source when renewable energy sources are not available. Yet, numerous operational problems still are required to be considered for EV implementation to turn out to be extensive. Even the development of Photo-Voltaic (PV) technology creates a problem in SGs when used for EV charging. Because of this, the Energy Management System (EMS) is required to handle charging requirements and deal with the intermittent generation. Here, in this research, an Improved Honey Badger algorithm (IHBA) is proposed for integrating SGs with EV parking lot, solar panels, and dynamic loads at the Point of Common Coupling (PCC). The proposed IHBA uses a dynamic programming method to optimize the charging Grid-to-Vehicle (G2V) or discharging Vehicle-to-Grid (V2G) profiles of the EVs using the forecasts of PV generation. This algorithm considers user preferences while also lowering reliance on the grid and maximizing SG effectiveness. The study’s findings show that the Honey Badger method is efficient in resolving issues involving large search spaces. The developed method is used to optimize charging and discharging of EV which is tested in MATLAB to obtain a stable load profile. From the evaluation of obtained results, it is evident that the IHBA controller outperforms the WOA and EHO controllers in terms of total harmonic distortion voltage (3.12%), power loss (0.197 kW) and efficiency (98.47%).

Selective PQD Power Control Strategy for Single-Phase Grid-Following Inverters

This paper proposes the selective PQD power control strategy based on proportional-integral controllers devised in stationary <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">abc</i> -reference frame for single-phase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grid-following</i> inverters. Its physical behavior and mathematical model are presented in detail and the system output impedance is derived. It is shown that the proposed control accurately tracks active, reactive, and selective distortion power terms. Experimental results show that the proposed control strategy effectively rejects source disturbances, achieving low values of current distortion even under heavily distorted grid voltages. Stability analysis under stiff and weak grids and DC-link voltage control for photovoltaic applications are evaluated. It is also shown that the implemented technique requires 26% less execution time in the main interruption than conventional solutions using proportional-integral plus resonant controllers.

Virtual Power Grid Flux-Oriented Vector Control for Three-Phase Voltage-Type PWM Rectifier

More considerable power distortions are produced by the non-reversible diode-bridge rectifiers in the power system’s AC-DC conversion technology. In addition, bridge-rectifiers worsen energy quality and decrease power factor. Three-Phase Pulse Width Modulation (3phase-PWM) rectifiers have seen rapid growth in use due to recent breakthroughs in power device technology. Its benefits include a unity power factor, no harmonic distortion, and pulsating direct currents. An actual power regulation technique based on a novel virtual power grid controller is suggested to cheaply regulate a 3phase-PWM converter with good performance. The study introduces a new sensorless control scheme to improve the 3phase-PWM-performance rectifier. The proposed control technique employs the virtual power grid flux-oriented vector control with a Second-Order Generic Integral with Instantaneous Phase Lock Loop (SOGI-IPLL) to overcome the problems inherent in relying only on low-pass filter estimates. The virtual power grid flux value may then determine the phase difference. The IPLL takes the phase angle as an input and is utilized to construct the power-oriented vector control. The proposed power grid flux-oriented control strategy combines the VFidea with SOGI-IPLL to circumvent the restrictions imposed by relying only on an integrator of low-pass filters (LPF) to provide an estimate. A refined Virtual flux (VF) estimator with direct power control can improve management at a cheaper cost and more efficiently. The modeling findings demonstrate that the DC voltage input and power grid throughput of the 3phase-PWM rectifier can be successfully regulated in both the rectification and inversion states, allowing for the effective functioning of the 3phase-PWM rectifier.

Budeanu’s Distortion Power Components Based on CPC Theory in Three-Phase Four-Wire Systems Supplied by Symmetrical Nonsinusoidal Voltage Waveforms

Budeanu’s power theory, in its fundamental version, describes single-phase sinusoidal and nonsinusoidal systems. Over time, this elementary description has been extended to three-phase three-wire and four-wire systems, regardless of power conditions. Initially, three-phase systems were considered as three independent single-phase systems. A distinct approach was introduced by Czarnecki in his power theory (Currents’ Physical Components—CPC). The energy description and reference of the equivalent parameters of the load are comprehensive in the context of three-phase systems; Czarnecki treats such systems as a whole. This paper introduces a mathematical model to expand the basic Budeanu theory for three-phase four-wire (3-p 4-w) systems powered by symmetrical and nonsinusoidal voltage sources. The proposed approach is based on mutual elements between the fundamental Budeanu theory and the CPC theory, treating the 3-p 4-w system as a whole. In the extended Budeanu theory model, equations for the Budeanu reactive current and the Budeanu complemented reactive current are derived. The article also demonstrates their orthogonality concerning the remaining components, indicating that each of the seven components can exist independently of the others. Furthermore, in the extended Budeanu theory, it is possible to identify which equivalent parameters of the load are responsible for the individual currents (powers) and which components are associated with the total distortion power proposed by Budeanu in 1927. All of the calculations were performed in Matlab/Simulink 2023b software.

Flowing Heating System for Pipeline of Electromagnetic Coupling of Power Frequency Without the Iron Core and Its Structure Optimization

Abstract In response to the current problems such as electromagnetic coupling heating equipment relying on the limitations of electronic devices mostly and difficulty in achieving uniform heating of flowing material, this paper proposed a flowing heating system for pipelines of electromagnetic coupling of power frequency without the iron core. Using the heating system of 500 kW/10.5 kV, structural and electrical parameters were obtained from theoretical calculations, and a finite element simulation model was established. Aiming at the problems of voltage waveform distortion and low power factor, the factors affecting the heating system such as pipe wall thickness and coupling gap were analyzed, and the influence laws on the heating system were obtained. The structure of the conductive ring was proposed for system optimization. In the case of the no-iron heart, the heat efficiency can reach 89.01%, the power factor increased to 0.915, and the voltage distortion was also significantly reduced. Based on the finite element simulation results, the structure of the spoiler ball was proposed to address the problem of uneven heating, and the simulation showed that the spoiler balls can optimize the heating uniformity of the heating system. This system can realize the uniform heating of material without the cost of the iron core and has the characteristics of high voltage and high power, which can provide an effective way of thinking for the electric heating of hot water, steam, hot air, etc.

A Multiple-Sensor Fault-Tolerant Control of a Single-Phase Pulse-Width Modulated Rectifier Based on MRAS and GPI Observers

Due to their advantages in ensuring low harmonic distortion and high power factors, single-phase Pulse-Width Modulated (PWM) rectifiers are widely employed in several industrial applications. Generally, the conventional control loop of a single-phase PWM rectifier uses both voltage and current sensors. Hence, in case of sensor fault, the performance and the availability of the converter can be seriously compromised. Therefore, diagnosis approaches and fault-tolerant control (FTC) strategies are mandatory to monitor these systems. Accordingly, this paper introduces a novel multiple-sensor FTC scheme for a single-phase PWM rectifier. The proposed fault diagnosis approach relies on joining several Generalized Proportional Integral (GPI) and Model Reference Adaptive System (MRAS) observers with a residual generation technique to detect and isolate sensor faults in a simple and reliable manner. While conventional sensor FTC methods dedicated to PWM rectifiers can only deal with single faults, the suggested approach guarantees a very good effectiveness level of sensor fault detection, isolation (FDI) and FTC of multiple-sensor fault occurrence scenarios. Consequently, the single-phase PWM rectifier can work with only the survivable single sensor with the guarantee of very good performance as in healthy operation mode. The effectiveness of the proposed sensor FDI approach and its control reconfiguration performance are demonstrated through both extensive simulation and experimental results.

Bundling of Power Quality Indices as Method for Correlating Load Susceptibility and Disturbances Superposition

This paper presents a neuro-fuzzy approach to power quality assessment. Electrical equipment susceptibility to disturbances varies from type to type. Additionally, not only one physical phenomenon (e.g. harmonics) can cause equipment malfunctioning or damage, but more likely a superposition of some different disturbances. The authors propose an automated neuro-fuzzy approach to power quality indicia bundling which is correlated to equipment susceptibility. Whereby distortion power is seen as one of the power quality indices. The neuro-fuzzy analyzer matches logically different indices and gives as output one value describing the possible thread to electrical equipment. A reduction of data amount to be analysed by a system engineer is aimed.

Investigating the Power Quality of an Electrical Distribution System Stressed by Non-Linear Domestic Appliances

: Power quality has become a matter of growing concern in recent years owing to a daily rise in use of non-linear loads at domestic level. In addition, fast growing technologies like distributed generation and electric vehicles are emerging as part of modern distribution systems. Therefore, it is necessary to evaluate and analyze the power quality issues due to various non-linear home appliances to give a clear picture of the current scenario. So that future technologies can be accommodated in the distribution systems while coping with existing power quality issues. The experimentally developed harmonic models, of various commonly deployed domestic appliances, are used for the simulation of a practical distribution system in Electrical Transient Analyzer Program (ETAP). Experimental results combined with simulation results show an alarmingly high level of harmonic distortion, breaching the recommended standard practices, in both current and voltage at not only point of common coupling (PCC) but also at the consumer’s end. In addition to increase in losses, this also degrades power factor giving rise to distortion power. While discussing other impacts of harmonic distortion, true power factor (PFtrue) and distortion power (D) has also been evaluated at PCC.

A new fractional integration approach based on neural network nonlinearity with an application to testing unemployment hysteresis

This paper proposes a nonlinear fractional unit root approach which is known as the autoregressive neural network–fractional integration (ARNN–FI) test. This new fractional integration test is based on a new multilayer perceptron of a neural network process, proposed in Yaya et al. (Oxf Bull Econ Stat 83(4):960–981, 2021). The asymptotic theory and the properties of the proposed test are given. By setting up a Monte Carlo simulation experiment, the simulation results reveal that as the number of observations increases, size and power distortions would disappear in the test. The empirical application based on this new test reveals that the unemployment rates of three European countries are neither stationary nor mean-reverting in line with the hysteresis hypothesis.

Centroidal Clustering of Noisy Observations by Using r th Power Distortion Measures.

We consider the problem of clustering a dataset through multiple noisy observations of its members. The goal is to obtain a clustering that is as faithful to the clustering of the original dataset as possible. We propose a centroidal approach whose distortion measure is the sum of r th powers of the distances between the cluster center and the noisy observations. For r=2 , our scheme boils down to the well-known approach of clustering the average of noisy samples. First, we provide a mathematical analysis of our clustering scheme. In particular, we find formulas for the average distortion and the spatial distribution of the cluster centers in the asymptotic regime where the number of centers is large. We then provide an algorithm to numerically optimize the cluster centers in the finite regime. We extend our method to automatically assign weights to noisy observations. Finally, we show that for various practical noise models, with a suitable choice of r , our algorithms can outperform several other existing techniques over various datasets.