Voltage Sag Research Articles

An identical bipolar buck‐boost AC‐AC converter based on a coupled‐inductor with safe‐commutation, high gain, and high efficiency

AbstractThis article proposes a new identical bipolar buck‐boost AC‐AC converter with integrated coupled inductors. It possesses all the critical features of a bipolar buck‐boost topology, providing both in‐phase and anti‐phase output voltages. Compared to its counterparts, a high boost factor is achieved without large duty ratios due to using magnetic coupling. Also, a high efficiency is obtained. A unipolar half‐cycle modulation is employed in which only half of the switches of a phase‐leg are modulated per half‐cycle. Therefore, only two out of eight power switches operate at high frequency at any instant, ensuring high‐efficiency operation. The safe‐commutation operation is achieved by completely turning on the auxiliary switches of the main phase leg for a half‐cycle, ensuring a continuous inductor current path during dead times. This results in voltage‐spike‐free operation. Additionally, this converter benefits from continuous input current and performs well with inductive and non‐linear loads. The proposed topology can mitigate both voltage sags and swells due to bipolar operation when employed as a dynamic voltage restorer (DVR). Its high boost factor helps compensate for deep voltage sags (above 50%). This article presents the detailed theoretical aspects of the proposed topology, followed by experimental verifications on laboratory‐built hardware.

Model Predictive Control for Three-Phase, Four-Leg Dynamic Voltage Restorer

Dynamic voltage restores (DVRs) are usually used to mitigate the effect of voltage sag and guarantee sufficient power supply for sensitive loads. However, three-phase voltage cannot be compensated to the desired balance voltage under unbalanced three-phase loads by traditional DVRs with a three-phase, three-leg inverter. To address this problem, a three-phase, four-leg inverter-based DVR is first introduced in this paper, and then the state space model in its continuous form and discrete form are established, respectively. A two-step predictive method is proposed for the prediction of the output voltage in each switching state by the established voltage prediction model. Finite-control-set model predictive control (MPC) is developed to be used in the three-phase, four-leg inverter-based DVR. Its dynamic response is effectively improved by the proposed MPC method under various voltage sag conditions. The proposed DVR control strategy is validated via MATLAB/Simulink-R2022b simulations, which demonstrate its effectiveness in voltage compensation under various sag conditions.

An Analysis of Case Study Utilizing Distributed Power Flow Controllers Which Improves and Mitigates Power Quality

Abstract—Providing high-quality electrical power should be taken into consideration in light of the rising demand for electricity and the rise in non-linear loads in power systems. This research examines power quality concerns related to voltage sag and swell and uses a distributed power flow controller (DPFC) to reduce voltage deviation and enhance power quality. A novel FACTS device, the DPFC shares structural similarities with the unified power flow controller (UPFC). Despite UPFC, DPFC separates the three-phase series converter into many single-phase series dispersed converters via the line and removes the common dc-link between the shunt and series converters. A DPFC located in a single-machine infinite bus power system with two parallel transmission lines is included in the case study. The MATLAB/Simulink environment was to model the system. The simulation findings that are shown confirm that DPFC may raise power quality.

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.

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%.

Performance Evaluation of Solar PV Integrated with Custom Power Device under Various Load Conditions

The non-linear properties and rapid switching of power electronic equipment are the primary causes of power quality issues in power systems, particularly in the power distribution systems. The widespread use of delicate equipment, which continuously pollutes the environment, is making power quality problems worse. The increasing integration of renewable energy sources into the generation mix and the decarburization of the economy have created new challenges for smart grid technology, requiring creative solutions such as energy storage systems and smart transformers. This article describes a solar photovoltaic integrated unified power quality conditioner (UPQC) that uses a deep learning method based on neural networks and a novel compensating technique. Here, two Deep Neural Network algorithms are used, one in the solar PV system to obtain the maximum power under various irradiance situations, and the other to manage the UPQC under various load conditions. When compared to the conventional UPQC based on PQ Theory, DNN-UPQC produces superior results in terms of reducing total harmonic distortion. This iterative strategy, which is focused on soft computing, provides faster convergence to the target condition while maintaining the updating weight within a predetermined limit. PV-based UPQC has been connected individually to reduce voltage sag, swell, and unbalance in variable load conditions. The system's dynamic and steady state performance are assessed by modelling it with a MATLAB-Simulink in different load condition.

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.

Identification of transmission line voltage sag sources based on multi‐location information convolutional transformer

AbstractConventional methods for identifying voltage sag sources are difficult to categorize accurately due to the complexity of transmission lines and the influence of noise. In order to solve the problem of difficulty in recognizing voltage sag sources of transmission lines under different locations, this paper proposes a new method for transmission line fault diagnosis based on modified wavelet denoising and multi‐location information convolution transformer. The improved wavelet denoising method proposed in this paper solves the problems of discontinuity and bias of the traditional wavelet denoising method, and is able to better reconstruct the original voltage signal in a strong noise environment. In addition, multi‐location information convolution transformer adopts a new model combining multi‐location information convolution and multi‐scale convolution transformer, which realizes the combination of global information and local context information capturing ability under multi‐location faults. This paper validates the method through simulation experiments and practical situations, and the results show that the method can well classify and identify the type and location of voltage sag sources in transmission lines.

Ensuring Stable Operation of Wind Farms Connected to Distribution Networks

Wind farms with type IV wind turbines from various manufacturers are being massively put into operation. These wind turbines comply with the requirements of the grid codes of the countries where they are designed and/or manufactured, but do not factor in the specific features of the distribution networks of other countries to which they are connected. The study at issue involves a comparative analysis of the requirements of grid codes of different countries for the stable operation of wind turbines under standard disturbances. The low voltage ride through (LVRT) characteristic makes it possible to prevent wind turbine shutdowns in case of short-term voltage dips of a given depth and duration. The calculations of transient processes indicate that wind turbines may not meet the requirements of the grid code of a particular country for their stable operation. As a result, standard disturbances will block the reactive current injection and the wind turbine will be switched off. This is often caused by the relay protection devices with a time delay of 1–2 s, which are used in distribution networks and implement the functions of long-range redundancy. Excessive shutdowns of wind turbines lead to emergency rises in the loads for the generating units of conventional power plants, aggravating the post-accident conditions and disconnecting consumers of electricity. This article presents a method for checking the LVRT characteristic settings for compliance with the technical requirements for wind turbines. To prevent wind turbine outages, one should either change the configuration of the LVRT characteristic, upgrade the relay protection devices in the distribution network adjacent to the wind farm, or implement group or individual technical solutions at the wind farm. The performance of the proposed technical solutions is confirmed by the calculations of transient processes.

Review of Low Voltage Ride-Through Capabilities in Wind Energy Conversion System

The significance of low voltage ride-through (LVRT) capability in wind energy conversion systems (WECSs) is paramount for ensuring grid stability and reliability during voltage dips. This systematic review delves into the advancements, challenges, and methodologies associated with LVRT capabilities in WECSs. By synthesizing recent research findings, this review highlights technological innovations, control strategies, and regulatory requirements that influence LVRT performance. Key insights include the efficacy of various LVRT techniques, the role of grid codes in shaping LVRT standards, and the integration of advanced control algorithms to improve system resilience. The study offers a comprehensive understanding of the current landscape of LVRT in WECSs and pinpoints future research directions to optimize their performance in increasingly complex grid environments. During the LVRT process, the stator of a double-fed induction generator (DFIG) is directly linked to the power grid. When the external power grid experiences a failure, the stator flux produces a significant transient component, resulting in substantial overvoltage and overcurrent on the rotor side of the DFIG. Failure to implement preventative measures may result in damage to the converter, therefore compromising the safety and stability of how the power system functions.

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.

Nonlinear integral backstepping control based on particle swarm optimization for a grid-connected variable wind energy conversion system during voltage dips

Double-fed induction generator (DFIG) wind turbines connected to the grid are particularly subject to grid problems such as voltage dips. Because of this, it might be challenging to maintain system stability and prevent system disconnections when using a Proportional-Integral (PI) Controller to operate this system. This paper applies the Integral Backstepping Control for the wind power plant system connected to the power grid. The Integral Backstepping is a nonlinear and recursive method that employs the Lyapunov theory to ensure the system's stability. The best selection of gain values for the Lyapunov function guarantees improved system control. These gains are frequently adjusted using the trial-and-error strategy, which is time-consuming and inefficient. This technique becomes more complex when many parameters need to be determined. It also limits the system's performance and restricts this nonlinear approach's advantages. The objective is to apply the particle swarm optimization for computing several constant values of the nonlinear approach by minimizing an integral absolute error criterion index. The weighted sum of errors is employed to solve the multiple objective problems. The proposed controller tracks the maximum power point, maintains the voltage of the DC-Link constant, and controls active and reactive power. The robustness of this method is verified in critical conditions, including system parameter variation and asymmetrical and symmetrical grid faults. The simulation findings highlight the effectiveness and robustness of the combination of Integral Backstepping with Particle Swarm Optimization in terms of reducing response time from 10.6 (ms) to 2 (ms), canceling static error, and improving overshoot compared to the vector control based on PI regulator. Besides, the DC-Link voltage ripples during the asymmetrical grid faults are reduced to ±1 (V) using the suggested controller. The latter can be implemented thanks to advancements in Central Processor Unit technology.

Power quality enhancement of grid-integrated solar photovoltaic system with unified power quality conditioner

Introduction. To enhance the quality of power and ensure a consistent electricity supply, this study proposes the utilization of a unified power quality conditioner (UPQC) system integrated with solar photovoltaic (PV) technology. The innovation involves single DC-link connecting back-to-back voltage-compensating components arranged in series and shunt, forming the PV-UPQC. The shunt compensator utilizes energy from a PV array to address harmonics in the load current. The objective is to mitigate voltage dips and spikes by injecting voltage that is either in phase with or out of phase with the common coupling point through a series compensator. The method combines the benefits of generating renewable energy to enhance electrical quality. The goal of the paper is the power quality enhancement of grid-integrated solar PV system. The novelty of the proposed work consists of enhancement of grid-integrated solar PV system with UPQC. The purpose of integrating a UPQC into a grid-connected solar PV system is to enhance power quality by mitigating issues such as voltage fluctuations, harmonics and reactive power imbalance. Methods. The proposed topology is implemented in MATLAB/Simulink with grid-integrated solar PV system with UPQC. Results. Integrating UPQC with a grid-connected solar PV system yields substantial improvements in power quality. This includes effectively mitigating voltage fluctuations and harmonics, resulting in smoother operation and reduced disturbances on the grid. Practical value. The proposed topology has proven to be extremely useful for grid-integrated solar PV system with UPQC applications. References 15, table 2, figures 9.

Protection of rotor side converter of doubly fed induction generator based wind energy conversion system under symmetrical grid voltage fluctuations

This paper presents the protection of the rotor side converter in a grid-connected doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) during a symmetrical voltage dip. In order to manage rotor current and regulate DC link voltage, an efficient active crowbar protection circuit is implemented in the rotor side converter (RSC). To improve the low-voltage ride-through capability for grid-connected wind turbine systems, an integrated DFIG-based WECS role is crucial because wind turbines must remain connected to the utility grid during faults to ensure continuity and reliability of power supply. This paper aims to design and implement an efficient active crowbar protection technique to protect the RSC and avoid excessive rotor current during a symmetrical voltage dip. Therefore, the efficient active crowbar protection circuit is designed using MATLAB-Simulink software, and its performance is validated using a real-time (RT) simulator. Finally, the existing methods are compared with the proposed work outcomes, and a conclusion is made.

Hilbert-Huang Transform and machine learning based electromechanical analysis of induction machine under power quality disturbances

Monitoring and predicting Power Quality (PQ) is crucial for quickly minimizing risk, protecting induction machines (IMs), and increasing productivity. This paper proposes an electromechanical analytical framework for IM working with various PQ disturbances based on machine learning (ML). This work collects the motor characteristics and PQ data, motor vibration signals, and other sensor data to identify the failures. The complicated electromechanical data is broken down into intrinsic mode functions (IMFs) using the Hilbert-Huang Transform (HHT), which reveals the modes present in the signals. This frequency-domain data is retrieved, paired with time-domain characteristics, and used as input features for ML algorithms. Support Vector Machines (SVMs) trained on labeled datasets in which features extracted from PQ disturbances are used to categorize the disturbances into several groups. The PQ disturbances caused by voltage sags, swells, harmonics, and transients can all be efficiently classified using SVM classifiers, allowing for real-time determination of the kind of disturbance influencing the IM. The accuracy for the SVM in the proposed scheme is 97.2 %. The SVM method is trained with PQ data and classification results using MATLAB classifier and Python software.

Enhancing DC Microgrids Stability by Integrating DAB Converters with Consensus Algorithms for Bus Voltage Drop Mitigation

Renewable energy sources (RESs) have become a prominent trend in the development of energy systems in many countries. To manage RESs, microgrids have emerged as an efficient and optimal solution. However, a microgrid might occasionally have problems like overloading and voltage sag. To address this problem, this paper proposes a technique that combines a consensus algorithm and a dual active bridge (DAB) to transfer energy between two different microgrids. A detection technique for overloading and voltage sag is part of the proposed control approach. In these circumstances, the DAB facilitates consensus adjustments to the stable microgrid current to bring it into balance with the overloaded current. By applying this proposed control method, voltage drops are significantly reduced, leading to an improvement in the power system quality. The simulation results of the microgrid and a scaled‐down laboratory system are conducted to verify the feasibility of the proposed control scheme. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A novel dual hysteresis controller for the management of a three phase dynamic voltage restorer

In this work a novel control scheme for the three-phase dynamic voltage restorer has been proposed and validated. The proposed control strategy guarantees the source current waveform to be sinusoidal and at near unity power factor. Further, the voltage sag and swell occurring in the source are overcome and the load gets a smooth power supply at a voltage closer to the rated voltage even in the face of voltage sag or swell occurring in the source. The proposed system uses a power factor corrected front end rectifier that regulates a defined voltage in a DC link. The DVR derives power from the DC link. The PFC rectifier and the DVR converter both are controlled by individual PI and hysteresis current controllers. Further the control system has been adopted in per phase method with actual current estimation system for each phase of the rectifier and the DVR sides done individually. The main advantage of the proposed system is that it does not require the usually required DQ transformation scheme. The proposed idea has been proved to be suitable for balanced and unbalanced loads. The proposed idea has been validated with dynamic simulations carried out in the MATLAB SIMULINK environment and with a scaled down experimental prototype.

Parameter Estimation by Considering Both Voltage Dip and Phase Angle Jump for DFIG-Based WTGs in Distribution Grids

Parameter Estimation by Considering Both Voltage Dip and Phase Angle Jump for DFIG-Based WTGs in Distribution Grids

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.

An improved dual vector control for a doubly fed induction generator based wind turbine during asymmetrical voltage dips

An improved dual vector control for a doubly fed induction generator based wind turbine during asymmetrical voltage dips