Power Quality Parameters Research Articles

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

Review of methods for prediction parameters of electricity quality and electric consumption

Subject of research: methods for predicting power consumption parameters. Purpose of research: to determine the optimal method for predicting power consumption and power quality parameters based on methods of analysis and synthesis. Object of research: methods for predicting parameters of power consumption and power quality based on neural networks. Main results of research: In recent years, forecasting power consumption and power quality parameters has become a very important topic, both from a technological and economic point of view. Forecasting electrical energy consumption ensures the most balanced operation of the electrical network, as well as correct work with electricity suppliers. As a consequence, such forecasting must be carried out with high accuracy. The paper presents a study of the state of the issue in forecasting power consumption and power quality parameters. Forecasting methods are considered and the most optimal ones for forecasting power consumption and power quality parameters are determined. It has been established that the most suitable method for solving problems of forecasting power consumption and power quality parameters at the point of connection is the forecasting method using neural networks.

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.

Smart Internet of Things Power Meter for Industrial and Domestic Applications

Considering the widespread presence of switching devices on the power grid (including renewable energy system inverters), network distortion is more prominent. To maximize network efficiency, our goal is to minimize these distortions. Measuring the voltage and current total harmonic distortion (THD) using power meters and other specific equipment, and assessing power factor and peak currents, represents a crucial step in creating an efficient and stable smart grid. In this paper, we propose a power meter capable for measuring both standard electrical parameters and power quality parameters such as the voltage and current total harmonic distortion factors. The resulting device is compact and DIN-rail-mountable, occupying only three modules in an electrical cabinet. It integrates both wired and wireless communication interfaces and multiple communication protocols, such as Modbus RTU/TCP and MQTT. A microSD card can be used to store the device configuration parameters and to record the measured values in case of network fault events, the device’s continuous operation being ensured by the integrated backup battery in this situations. The device was calibrated and tested against three industrial power meters: Siemens SENTRON PAC4200, Janitza UMG-96RM, and Phoenix Contact EEM-MA400, obtaining an overall average measurement error of only 1.22%.

Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.

Optimal allocation of distributed generation on DC Networks

A consistent increase in the installation of distributed generation (DG) in response to rising energy cost and demand throughout the world, especially for the last two decades, primarily aims to obviate upgrading investments for transmission and distribution lines on the existing power grid while ensuring sustainable, resilient, and reliable service to end users. Random installation of DG, on the other hand, can invalidate the existing grid’s designated objectives, and operational boundaries due to the changes in network topology resulting in unforeseen load flow. Appropriate integration of DG units on DC networks should be remedy to high transmission/distribution losses in lines, instabilities in voltage profiles, issues in power quality parameters, and derogation in the protection schemes. Based on the behavior of load profiles, e.g., 24 h or longer period, operational and technical constraints, this study proposes a framework that minimizes transmission/distribution losses on a DC network, in which discrete variables stand for the location of DGs. Optimal power flow (OPF) problem formulation underpins the proposed optimization framework. Overall, this computationally challenging planning problem refers to a mixed-integer nonlinear program (MINLP) including discrete variables with non-convex power balance/flow representations. To deal with the non-convexities and discrete form of the formulation, a mixed-integer second-order cone programming (MISOCP) relaxation, and branch-and-bound search are assigned. The validation of the introduced approach is carried out on the IEEE modified benchmark systems. With the help of proposed method, optimal allocation of DG units on 9-bus, 14-bus, and 30-bus systems, respectively, lead up to 99.93%, 94.38%, and 79.09% total power losses reduction.

Sample-by-sample Power Quality Disturbance classification based on Sliding Window Recursive Discrete Fourier Transform

Power Quality Disturbances (PQDs) are caused by large-scale grid connections of nonlinear loads and Distributed Generations (DG). They affect the electrical and electronics equipment performance and may cause serious accidents and economic losses. The classification of PQDs becomes a key issue for end-users in order to enhance the Power Quality (PQ) and comprises an important step in monitoring the Electric Power System (EPS). Accurate PQDs classification is crucial for improving the quality of power supply, monitoring the condition of power equipment, and mitigating power grid issues. In the literature, several methods for detecting and classifying PQDs have been proposed. However, these methods consume a significant amount of processing time due to the detection and sample storage steps required to classify each disturbance class. In this work, we propose a method for classifying PQDs on a sample-by-sample basis. The innovation of this method lies in its ability to classify PQDs for each individual sample, thereby eliminating the need for window-based (batch) processing. To do so, it is applied the Sliding Window Recursive Discrete Fourier Transform (SWRDFT) to real-time estimation of PQ parameters. For the classification stage, decision tree, Artificial Neural Networks (ANN), Ensemble and the Naive Bayes classifiers were employed. The proposed method using Ensemble achieved the most satisfactory results for six different types of PQDs (oscillatory transients, harmonics, notches, sags, spikes and swells), in which oscillatory transients and spikes were identified in the first monitored samples (sample 2 and 11 respectively), sags were identified in the 14th sample counting from the beginning of the disturbance, notches were captured from the 35th sample, and harmonics and swells were identified after the 113th sample.

Enhancing electromagnetic compatibility and energy efficiency of electric vehicle charging stations

Problem. The article proposes a single-link structure for an electric vehicle charging station utilizing an active four-square rectifier with power factor correction. A Matlab model of the proposed charging station is developed, taking into account parameters such as the power network, the switches of the active rectifier, its automatic control system, and an equivalent model of the battery compartment. Additionally, a mathematical model for calculating static and dynamic losses is created based on polynomial approximation of the energy dependencies of IGBT modules. The analysis investigates power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Goal. The aim of this study is to propose a single-link structure for an electric vehicle charging station using an active four-square rectifier with power factor correction. It includes an analysis of power quality parameters, components of energy losses, and efficiency of the charging station at different charge currents and PWM frequencies during a full battery charge interval. Methodology. To achieve the goal, several key steps are considered. These include theoretical substantiation of the scheme of the electric microgrid charging station for electric vehicles with one-stage energy conversion, analysis of the battery connection scheme in the Tesla Model S electric car, research and calculation of efficiency, modeling of the charging station, development of a Matlab model of a microgrid system for the charging station, SAC analysis of battery charge voltage and current of a three-phase AV with PWM, modeling of losses in IGBT modules by polynomial approximation of dependencies, distribution of losses in the charging station system, and analysis of energy efficiency parameters. Results. The study presents the energy efficiency parameters of an external DC EV charging station using an active rectifier. It reveals that maximum efficiency of the system is achieved at minimum charge current. However, decreasing the charge current prolongs the charge process and slightly affects power quality parameters. Originality. A mathematical model for calculating static and dynamic losses was developed based on polynomial approximation of the energy dependencies of IGBT modules. The analysis encompasses power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Practical value. This study contributes to the further development of electric vehicles by improving the energy indicators of electric vehicle batteries and converters of electric vehicle charging stations, enabling fast charging modes. Active development is observed in each of these directions.

Enhancing NILM classification via robust principal component analysis dimension reduction

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on aggregated reading from a centralized meter. Usually, NILM techniques are shown to be improved when various power features and additional power quality parameters are included. However, adding power features leads to increased time complexity which is a disadvantage to real-time operation. Previous attempt to operate a principal component analysis (PCA) method to reduce the dimension of the problem managed to improve the run time but with considerably low accuracy. To this end, we utilize a robust PCA approach, to mitigate the influence of outliers in the data as a measure for improved performance. The proposed procedure achieves extraordinary results with accuracy over 96% for 600 hours long record of power quality measurements of the consumption of seven appliances from the standard AMPds dataset.

Power quality improvement of PWM AC Chopper fed capacitor run induction motor using BFO, PSO and CSO Algorithm

This paper exhibits performance enhancement of PWM AC chopper fed capacitor run induction motor by using optimization algorithm. The PWM AC chopper utilizes enhanced asymmetrical pulse width modulation technique by incorporating four pulses in quarter cycle. The switching instants are obtained optimally by bacteria foraging optimization, particle swarm optimization and cuckoo search optimization algorithm. The power quality parameters considered in this paper are total harmonic distortion of current, total harmonic distortion of voltage, power factor and efficiency. The simulation result shows that proposed algorithm shows better performance compared to other optimization techniques.

Development of an IoT smart energy meter with power quality features for a smart grid architecture

Electricity consumption has been intensifying due to population growth, climate change, urbanization, and the growing use of electronic devices, which are increasingly non-linear loads that cause poor power quality conditions. The trend of the Internet of Things has led to the creation of devices that encourage the efficient and effective utilization of electrical power. This in turn facilitates the development of modern power distribution structures such as smart grids. Consequently, this paper presents in detail the design, construction, and validation of a three-phase IoT smart meter intended to form part of the end-user demand side of a smart grid. The compact embedded system, with a manufacturing cost below $80 USD, features a unique electronic design that enables its installation in any load center and employs a straightforward IoT structure that includes WiFi technology for Internet communication. Also, a deployed web application was developed specifically to display the smart meter measurements. Unlike other smart meters, the proposed meter not only provides the amount of active energy consumption, but total and fundamental RMS current and voltage, active, reactive, and apparent power, reactive energy, power factor, and some power quality parameters such as, line frequency, amplitude of 64 current harmonics, and total harmonic distortion. Additionally, this study shows that the prototype achieves an absolute error of less than 1% in all its measurements. Finally, real-life applications of the developed device are demonstrated in residential environments.

Individual emission assessment of harmonics in DSO environment

The paper presents the propagation assessment and power quality parameters improvement system in power distribution grid. In this work main functionalities of the system are described focusing on the individual assessment module. The module is using CIRED/CIGRE C4.109 method which is based on the 10-minutes aggregated data. Three cases of individual emission assessment using real measurement data in the distribution system operator environment ware analyzed. The obtained results confirmed the legitimacy of using 10-minute data to assess the emissions of harmonics.

Measurement and Evaluation of Voltage Unbalance in 2 × 25 kV 50 Hz High-Speed Trains Using Variable Integration Period

This article addresses the need for a standardized method to measure power quality in railroad systems, which differ from distribution and transmission networks. It evaluates the applicability of existing standards in detecting variations and short-term disturbances in railroad networks powered by the 50/60 Hz AC grid or the 2 × 25 kV AC network used for high-speed trains. The objective is to propose a standardized algorithm capable of accurately identifying disturbances to assess power quality on railway traction substations. A new method is proposed to characterize voltage imbalances more precisely. Practical demonstrations confirm that a short integration period, as used in existing standards, provides a more accurate estimation of disturbance amplitude and duration. Field experiments validate the proposed solution, embedded in equipment installed on the 225 kV line supplying the 2 × 25 kV AC substation for high-speed rail. Comparative analysis of results obtained during high-speed train journeys confirms the algorithm’s potential to aid standards committees in reviewing and updating existing standards, as well as expediting the creation, approval, and implementation of new standards for railway installations. Experimental comparisons of other power quality parameters, such as frequency and voltage harmonics, also underscore the algorithm’s utility in railway power quality assessment.

Analysis and Experimental Verification of Improving the EEDI of a Ship using a Thruster Supplied by a Hybrid Power System

Abstract In this study, the authors present a theoretical analysis and experimentally verified methods to improve the Energy Efficiency Design Index (EEDI) of ships. The improvements were studied via the application of an innovative solution of a thruster supplied by a hybrid power system on board a passenger-car ferry. The authors performed sea trials of a ship’s electrical power system supplied by battery packs with diesel generating set power units. The experimental study focused on energy balance and management, which were considered together with related power quality issues. The authors found that the application of an energy storage system to the ferry, such as batteries, with the simultaneous adaption of the operation modes of the electrical power system for current exploitation, significantly improved energy efficiency. Fuel consumption and CO2 emission were reduced, while adequate parameters of electrical power quality were maintained to meet classification standards.

Power Supply Status Analysis and Scheme Design of High Efficiency and Rapid Driving Equipment

In the view of the problems of frequent movement and change in the practical application of complete sets of high efficient and rapid driving equipment, a scheme of extending the power supply distance is designed. In this scheme, an intelligent comprehensive regulation device is arranged in the shifting area and a power quality parameter monitoring device is arranged in the combination switch of the device. Real-time and accurate monitoring of the three-phase line voltage and phase Angle during the operation of the equipment and feedback to the intelligent integrated regulation device through optical fiber communication mode. The PLC controller in the intelligent integrated regulation device implements different voltage and phase regulation control strategies according to the voltage and phase Angle values monitored at the head and end. The test results in the plant show that the scheme can improve the terminal voltage of the equipment and achieve the purpose of “shift and change, long distance power supply.

Kinerja Inverter 500 W untuk PLTS Skala Kecil

At Electrical Engineering Department of Politeknik Negeri Malang, there is an EBT learning module in the form of solar panel with small capacity, namely 80 Watts. The only load that can be supplied is DC load because there isn’t installed inverter. Therefore, it is necessary to determine the inverter specifications to be installed in small-scale solar system. This research aims to determine the specifications and test the performance of a 500 W inverter integrated with a 50 Wp solar system. Also analyze power quality parameters and efficiency when connected to various load. The test method is no-load and load testing with the parameters measured including current, voltage, power, frequency and THD. Analysis was also carried out on the correlation between efficiency and loading and inverter pins with THDV and THDI. In the no-load test, the inverter output voltage waveform was obtained as a pure sine wave with a frequency of 50.10 Hz. In testing the LED and LHE loads, it was found that the inverter was good for use when the load was 50-80% of its capacity. The lowest THDV value is at a Pin value of 26 W, namely a THDV value of 0.009%. The highest THDV on the Pin is 130 W with THDV value of 1.9%. The lowest THDI value is at Pin 9.75 W, namely 27.6%. The highest THDI value on the 130 W Pin is 80.9%. The THD limit is still within the IEEE standard limit, namely 5% - 10%.

Probabilistic and Harmonic Assessment of Power Quality Parameters from Distributed Energy Resources in a Distribution Network

Distributed energy resources (DERs) are leading actors in energy transition worldwide. Despite the environmental benefits, DERs are intermittent (consumption/generation power changes according to the input variables), and the use of power electronic devices affects the power quality in distribution networks. Typically, models and simulations (deterministic or probabilistic) are proposed to estimate such impacts. However, previous studies have analyzed the main effects caused by one or two DERs during a day. These studies had a reduced level of detail in the applied methodology in modeling and impact evaluation, or they did not consider that the harmonic distortion level depends on input variables. The aim of this article was to develop a probabilistic harmonic simulation approach to characterize the hourly effect of photovoltaic systems (PVS), battery energy storage systems, and electric vehicle charging stations in a 34-node IEEE test feeder. The individual and collective effect estimation of the DERs consists of the hourly quantification of the effective voltage value, voltage unbalance, power factor, and harmonic distortion, with data obtained using Python and PowerFactory co-simulation for 23 operation scenarios. These impacts were assessed through indicators proposed in the literature and hypothesis tests (inferential statistics). Each DER generally operates at a specific time, making it easy to identify the individual effects of network parameters. The PVS increased the effective voltage value and total harmonic distortion. However, electric vehicles and batteries increase the voltage unbalance and power factor due to the two-phase connection in some network nodes.

Analysis of the Impact of a Photovoltaic Farm on Selected Parameters of Power Quality in a Medium-Voltage Power Grid

Over the last few years, a dynamic increase in the installed capacity of distributed energy sources has been observed, with the largest share being photovoltaic sources. The power grid is a system of connected vessels, and changing the structure of electricity production has a specific impact on the operation of this network, which makes it necessary to study the impact of the sources on the power system. The current and projected increase in the number of connected installations will make the issues of interaction and cooperation of distributed sources with the network extremely important. The article presents an analysis of the impact of a photovoltaic farm on selected parameters of the quality of electricity supply. This analysis was made on the basis of simulation results in a computer program and measurement tests carried out on a real photovoltaic farm with a capacity of 1.8 MW connected to the medium voltage power grid. The impact of the farm-generated power on the values of fundamental indicators of the quality of electricity supply, such as voltage deviations, voltage asymmetry factors, and voltage distortions factors, is presented. These relationships were determined based on the correlation and regression analysis of individual electrical quantities.

Design and installation of a novel multi-point measurement system for a renewable energy grid

New power generation systems will be characterized by multiple small-scale units, integrating non conventional or renewable sources, such as wind turbines, photovoltaic power plants or even hydrogen generation and storage. Monitoring this kind of installations is of vital importance for security of supply and efficiency issues. The integration of these energy sources with the actual centralized ones involves great challenges for engineers to correctly plan the grid without any problem of stability. Therefore, successful implementations of smart electrical networks require the development of robust systems of analysis for electrical parameters. This paper shows a multipoint measurement system, installed in a relevant renewable-supplied technology park, ready to monitor all power quality parameters and their influence into the distribution grid. Important results will be shown applied to the existent power quality (PQ) at the technology park.

Energy Efficiency and Distributed Generation: Case Study

The challenges and opportunities of the Brazilian electric sector show the necessity of the diversification of the energy matrix with other renewable energy sources, to attend the growing demand and improving energy security. In this context, energy efficiency and distributed generation represent an important purpose to meet the challenges related to the growing consumption of electricity in Brazil and to mitigate its impacts. The emphasis given in this work is the preparation of a project of energy efficiency with the implementation of a photovoltaic plant in a teaching and research institution, subject to the guidelines established by the National Electric Energy Agency - ANEEL. The results show that the energy saved and the reduction in peak demand, as well as the installation of the photovoltaic plant, will make the facility self-sufficient and result too improvement in the power quality parameters.