Power Quality Research Articles

Research on Voltage Prediction Using LSTM Neural Networks and Dynamic Voltage Restorers Based on Novel Sliding Mode Variable Structure Control

To address the issue of uncertainty in the occurrence time of voltage sags in power grids, which affects power quality, a voltage state prediction method based on LSTM neural networks is proposed for predicting voltage states. For the problem of quickly and accurately compensating for voltage sags, a DVR system based on a new approach law of sliding mode variable structure control is proposed, which significantly reduces chattering, improves response speed, and enhances the robustness of the system. The stability of the system is proven based on Lyapunov stability theory. Simulation experiments are conducted to analyze the voltage state prediction effect based on the LSTM neural network and the compensation effect of the novel reaching law of sliding mode variable structure control under different levels of voltage sag, validating the effectiveness and correctness of the proposed solution.

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%.

A NOVEL FAULT-TOLERANT GENERALIZED SYMMETRICAL TOPOLOGY FOR RENEWABLE ENERGY AND ELECTRIC VEHICLE APPLICATIONS

Multilevel inverters are a versatile and powerful technology for applications such as motor drives, high voltage direct current transmission (HVDC), renewable energy systems, and grid-connected applications. As the demand for high-quality, clean energy and low distortion continues to grow, multilevel inverters are preferred over traditional square wave inverters. This paper proposes a generalized 13-level symmetrical topology to meet the power quality requirements. It consists of six DC sources and twelve semiconductor devices. Further, the topology has cascaded to increase the number of levels, but the cost and circuit complexity increase with the number of units. The gate pulses for switches are produced by the nearest-level modulation technique. The performance comparison of the topology with recent literature provides comprehensive information about the novelty of the configuration. The MLI performance parameters are number of switches, number of gates driving circuits, total harmonic distortion (THD), cost function per level (CCPL), total standing voltage (TSV), and efficiency (η). MATLAB/Simulink investigates the proposed topology, and further, to validate its simulation output, the hardware prototype model has been implemented in the laboratory.

Fuzzy Logic Controller for Power Control of an Electric Arc Furnace

Electric Arc Furnaces (EAFs) are widely used in the steel manufacturing industry to melt scrap steel by employing a large number of electric arcs. EAFs play an important role in ensuring the efficient production of steel. However, their nonlinear and variable load characteristics have a significant impact on power quality. Because the active power of an electric arc depends on its length, a system for controlling the electrode positions is necessary. This paper presents a control system based on a fuzzy logic controller for the active power control of an electric arc furnace. Individual simulation scenarios were chosen with both reference values and the process taken into consideration. The reference, constant value, step variation, and the sequence of step variation were investigated, as well as step disturbances and the sequence of step disturbances from the viewpoint of the process. Furthermore, the procedure of changing the tap on a transformer was investigated. The proposed solution minimizes the time required for charge elaboration, but the main benefit is that there are no additional costs in the implementation process because the installation remains identical, with the only changes being improvements to soft control management.

An overview of Applied Artificial Intelligence in Power Grids

Power grids are networks of lines and equipment used for electrical power generation, transmission, distribution, and consumption. Power grids are undergoing a process of digital transformation based on emerging information and communication technologies, with the aim of optimising the operation of power systems, increasing efficiency, reliability, sustainability, security, and quality of service, while reducing CO2 emissions. In this process of digital transformation, Artificial Intelligence (AI) has become a strategic element within power grids. This paper provides an overview of AI applications in power grids and how they can improve the efficiency, reliability, and availability of their processes. It analyses the main challenges and prospects for the future application of AI in power grids. AI applications help process large volumes of data and generate insights, with a focus on two primary functions: supporting operations and aiding strategic decision-making. These factors highlight the significant potential for AI application in power grids and solidify AI as a strategic key to the development of smart grids.

Terminal Sliding Mode Control of Microgrid Inverter Systems

ABSTRACTTo enhance the power quality of microgrid inverters and reduce the influence of changes in inductance parameters and external disturbances on the direct power control of the inverter system, a terminal sliding mode control strategy with a variable exponential power reaching law has been proposed. The designed new reaching law comprises a variable exponential term and an enhanced power term. The variable exponential term contains an arctangent function, and the power term coefficient is replaced by a function about the sliding mode. Therefore, the convergence rate can be adaptively adjusted based on different stages of the system, ensuring a faster rate of convergence throughout the process of approaching the sliding mode surface. To weaken the effects of changes in inductance parameters, a power disturbance observer is designed to estimate the internal disturbances induced by the filtered inductance in the system. Subsequently, a sliding mode control law containing disturbance observations is derived. Moreover, a variable exponential terminal sliding surface is designed to adjust the convergence rate of system errors on the sliding surface in stages, thereby enhancing the control performance of the system. The simulation results show that the new reaching law has faster convergence rate and better dynamic performance. The convergence speed of the system error can be accelerated by the designed variable exponential terminal sliding surface. The sliding mode control strategy with the variable exponential power reaching law is applicable to the power control system of three‐phase inverters in microgrids, thereby significantly enhancing the dynamic performance and robustness of the system.

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%.

A Hybrid Gallium-Nitride–Silicon Direct-Injection Universal Power Flow and Quality Control Circuit With Reduced Magnetics

A Hybrid Gallium-Nitride–Silicon Direct-Injection Universal Power Flow and Quality Control Circuit With Reduced Magnetics

Photovoltaic inverters experimentally validate power quality mitigation in electrical systems

Photovoltaic inverters experimentally validate power quality mitigation in electrical systems

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.

A Novel Recognition Method for Complex Power Quality Disturbances Based on Rotation Vector and Fuzzy Transfer Field

A Novel Recognition Method for Complex Power Quality Disturbances Based on Rotation Vector and Fuzzy Transfer Field

Experimental performance verification of an intelligent detection and assessment scheme for disturbances and imbalances of three-phase synchronous machine output using coherence estimators

In this article, power quality disturbances, such as voltage and current unbalances, series and shunt faults, are monitored in three-phase synchronous machines. Because the imbalances affect machine efficiency and service life, it is essential to figure out whether the three-phase voltages/currents are balanced or unbalanced. An integrated detection algorithm is proposed to detect the unbalanced voltages/currents and various abnormal conditions precisely and quickly, which is based on the coherence estimator. Most existing detection methods require more than one cycle time to determine the contingency conditions, while the proposed technique detects these situations in a fast and discrete protection system. The suggested scheme acquires instantaneous measurements of the three-phase voltages and currents taken at the synchronous machine terminals in order to calculate fifteen coherence coefficients that are utilized to detect and assess any unbalance or trouble accurately and swiftly. A new proposal for imbalance and disturbance indicators based on the coherence estimators is developed in this study. Multiple test cases have been conducted to validate the proposed algorithm capabilities using a real power system, which includes a motor-generator set, a three-phase load and measurement transformers. The experimental results have been revealed that the relay reliability and accuracy percentages have been found to be 98.28% and 98.52%, respectively. The quantitative findings of the imbalance and disturbance assessment are recorded.

Power Signal Histograms—A Method of Power Grid Data Compression on the Edge for Real-Time Incipient Fault Forensics

Across the power grid infrastructure, deployed power transmission systems are susceptible to incipient faults that interrupt standard operations. These incipient faults can range from being benign in impact to causing massive hardware damage and even loss of life. The power grid is continuously monitored, and incipient faults are recorded by Digital Fault Recorders (DFRs) to mitigate such outcomes. DFR-recorded data allow for power quality forensics and event analysis, but this ability comes at the cost of high data storage and data transmission requirements. It is common for data older than two weeks to be overwritten due to storage limitations, without being analyzed. This inhibits the creation of long-term data libraries that would enable incipient fault forensics and the characterization of behavior that precedes them, which limits the development and implementation of preventive measures; thus, there is a critical need to reduce DFR-recorded data’s storage requirements. This work addresses this critical need by leveraging the cyclic and residual histograms and introducing the frequency and Root Means Squared (RMS) histograms, which alleviate the current high data storage requirements and provide effective Incipient Fault Prediction (IFP). The residual, frequency, and RMS histograms are an extension of the cyclic histogram, reduce the data storage requirement by up to 99.58%, can be generated on the DFR without interrupting its normal operations, and are capable of predicting voltage arcing six hours before it is strong enough to trigger a DFR-recorded event.

Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks—Harmonic State Estimation

In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant nodes is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.

Power quality improvement in three-phase grid tied pv systems with 3-D SVPWM control active power filters

Power quality improvement in three-phase grid tied pv systems with 3-D SVPWM control active power filters

Edge-Driven Linux Platform for Power Quality Solutions

In the modern landscape of data centers, ensuring optimal power quality and efficiency is paramount. This paper describes designing and implementing an edge-driven Linux platform for power quality solutions for real-time measurement, analysis, and conformity with IEC 61000-4-30, IEEE 519, and EN 50160 standards. It employs an AM6442xx processor and has a web interface with the backing of an efficient embedded web server, a NodeJS, and a Nginx server. It supports industrial communication protocols like Modbus TCP to simplify integration into existing systems. In addition, it involves risk assessment, threat modeling, and enhanced ability of the platform to withstand tests that threaten the confidentiality and integrity of data. Thus, the proposed solution increases the efficiency of data center operations and provides reliable power quality metering that meets current international standards.

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.

Integrating canonical correlation analysis with machine learning for power quality disturbance classification

Abstract Recently, the rapid growth of Renewable Energy Resources (RER) in power generation has resulted in the frequent occurrence of Power Quality Disturbances (PQDs) within the power system. The timely and accurate detection of these PQDs is critical for maintaining good power quality while integrating RER into hybrid power systems to make them more robust and stable. In this paper, a multi-view dimensionality reduction approach based on Canonical Correlation Analysis (CCA) is proposed to differentiate different types of PQDs. Here, a dataset of 29 types of PQDs which include nine single types and twenty multiple types of PQDs have been generated using their mathematical model in MATLAB for experimentation. CCA being a multi-view dimensionality reduction technique maximizes the correlation between two different views of the data. Here two cases of datasets have been considered for further exploration, Case 1: PQDs without noise and with 20 dB noise, Case 2: PQDs with 20 dB and 30 dB noise. Furthermore, to test the efficacy of CCA in both cases, the extracted features have been tested using four different classifiers, i.e., K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF). The performance of each of the classifiers has been tested on five different performance metrics such as precision, recall, F1 score, hamming loss, and accuracy, and the results show that the proposed technique of multi-view dimensionality reduction is capable of classifying the PQDs with two different views at a time.

Power Quality Enrichment and Power Management using an Adaptive Control Scheme in Microgrid System

This article proposes an ANFIS-based control scheme for improved power quality and power management that will provide optimal and sustainable energy in a grid-connected renewable energy system. The system consists of a wind energy conversion system (WECS), a photo voltaic system (PVS), and a battery energy storage system (BESS). To increase the PVS's and WECS output powers, a unique control approach is suggested in this paper. The adaptive Neuro-Fuzzy Inference System (ANFIS) controller is incorporated into the proposed controller to generate three-phase reference current signals for harmonic elimination in the system during system dynamic conditions. The proposed control technique can work under voltage quality problems such as voltage sag, voltage swell, neutral currents, and reactive power. Renewable energy sources (PV and Wind) are interfaced to improve the DC-link overall performance by minimizing short-term and long-term voltage problems. The proposed controller regulates the energy flows between the renewable energy sources to the end users with a unity power factor. A supervisory control scheme is implemented for optimal power management in the system. Based on the load requirement, and how the renewable, battery, and grid sources are sharing the power, a detailed analysis is given in the paper. Simulation results from the MATLAB/Simulink platform under several test conditions at the grid side and load side illustrate the efficacy of the proposed control mechanisms in the environment of power optimization and energy management. The comparative analysis is performed to show the efficacy of the proposed system. Finally, the proposed system is validated and the THD content of the grid currents is found good.

Analysis and control of grid-interactive PV-fed BLDC water pumping system with optimized MPPT for DC-DC converter

In this study, a novel water pumping module fed by grid interactive Photo-Voltaic with a bidirectional Power Flow Control was proposed. In addition to improving the pumping system’s reliability, a water pump is powered by a brushless DC motor drive. This method enables the pump to work at its maximum capacity for the entirety of that day, regardless of the weather. The entire system becomes more reliable as a result of the motor’s increased use of photovoltaic (PV) generated power for pumping applications. Maximum Power Point Tracking (MPPT) controller incorporating Machine Learning algorithm drives bridgeless greater static gain DCDC converter to achieve higher power generation point and increment PV efficiency. The PV array’s operation would be managed using the ML back propagation technology to capture the most electricity under any ecological circumstance. A BLDC motor is fed by a Voltage Source Inverter (VSI) that includes a DC bus controlled in both directions by a unit vector template (UVT) approach incorporated in a single-phase voltage source converter (VSC). Additionally, utilizing a PI controller to manage the DC capacitor voltage in the UVT controller at a particular level is not appropriate for the increased PQ capabilities. However, due to tuning problems with the current controller, this controller is unpopular. The aforementioned problems are resolved by employing a unique intelligent-based fuzzy logic controller that achieves good performance features. In this technique, the function of a PV array at its Maximum Power Point (MPP), as well as power quality enhancements and a decrease in Total Harmonic Distortion (THD) of the grid are accomplished. The proposed PI controller attains a significant voltage THD of 3.736. The PI controller, on the other hand, managed to achieve a load voltage THD of 2.629%. The ANFIS method, whose value is 1.739%, is discovered to have a lower THD than all remotes with improved features, it lessens abrupt swings while maintaining steady DC-link voltage.