Voltage Sag Research Articles

Power oscillations cancellation strategy control of DFIG wind turbine during grid voltage dip

Power oscillations cancellation strategy control of DFIG wind turbine during grid voltage dip

Power oscillations cancellation strategy control of DFIG wind turbine during grid voltage dip

Power oscillations cancellation strategy control of DFIG wind turbine during grid voltage dip

Modified SEPIC DC-DC Converter with Wide Step-up/Step-down Range for Fuel Cell Vehicles

The high efficient buck-boost DC-DC converter with wide step-up/step-down range is the way to solve the issue of voltage matching for commercial fuel cell vehicles with a strong hydrogen-electric hybrid. Based on the conventional SEPIC converter, the negative of one of the capacitors is connected with the negative of the power source to get more energy. A modified SEPIC buck-boost DC-DC converter with the advantages of wide voltage conversion range, continuous input current, common ground, and high conversion efficiency is proposed in this paper, meeting all the requirements of DC-DC converters for commercial fuel cell vehicles. On the basis of the steady-state analysis and comparison, the controller is designed based on the small-signal model. To verify the efficiency of the proposed converter, experimental prototypes are built using Si-based and SiC-based devices respectively. The experimental results show that the proposed buck-boost converter can effectively realize 2/3 times step-down and 5 times step-up. When disturbance and voltage dip occur, the adjust time of the control system is less than 200ms. The controller is verified to be robust and have fast responses. The maximum calculation, simulation and experimental efficiencies all exceed 96%, and the maximum efficiency of the SiC prototype under rated power is 96.1%.

Issues of Power Quality

Abstract: In this modern era, the electricity dependence for domestic, commercial and industrial purposes has been increasing day to day. The non-linear loads associated will leads to some of the power quality issues. Such Power Quality Issues viz., voltage sag, Interruptions, spikes in voltage, fluctuations etc. briefly discussed in this paper.

Modeling, Integration and Simulation of the Photovoltaic Power Plant Considering LVRT Capability and Transient Voltage Stability

This paper investigates how high photovoltaic energy penetration impacts dynamic performance and voltage regulation of the modified IEEE-9 bus grid. The transmission power system was modeled and simulated using PSCAD-EMTDC software to conduct the study. Load flow analysis is implemented to explore the power system’s capability to incorporate the desired photovoltaic power. Moreover, the study is based on time response simulations to grid disturbances. The supply and control of reactive power from solar power generation plants are becoming critical issues to study because they can facilitate the integration of PV in power grids under different operating conditions. Network-related faults like a PV solar power plant event outage, a three-phase short-circuit at a conventional bus, and a voltage dip at the PV solar power plant have been considered. The results will help identify the protective devices and strategies needed to maintain the stability and reliability of the system operation and transient analysis of the network under external power network fault and recovery operation. Thus, it has practical significance for real utility studies. Moreover, this comprehensive study will be a valuable guide for assessing and improving the grid’s performance under the study of any other grids, which also gives the vast potential and need for solar energy penetration into the grid systems.

An Improved Power Quality in a Renewable Energy-based Microgrid System Using Adaptive Hybrid UPQC Control Strategy

Due to its ability to integrate renewable energy, improve energy efficiency, and fortify the power system's resilience, microgrids are widely used as regional energy systems. But these advantages do have certain drawbacks in terms of control and Power Quality (PQ). In order to guarantee the proper operation of equipment that is connected and the system's general health, PQ is crucial in microgrids. For microgrids to operate successfully, governing stratagems in concurrence with front-line power electronics devices bid a firm context to knob PQ challenges like Voltage Sag and Swell, Source/Grid current harmonics, Voltage imbalances, Active and Reactive power compensation etc. Multifunctional systems that can integrate clean energy generation and as well as enhance PQ are necessary to meet the demands of complex loads that have the capability of operating in the situation of an unavailable network grid and power electronics devices, which necessitates the need for clean energy. Hence, this paper provides enactment of an adaptive hybrid control strategy based on an Adaptive Leaky Least Mean Square (AL_LMS) algorithm combined with Fuzzy Logic (FL) to the Unified Power Quality Conditioner (UPQC) in a Solar-PV energy based Microgrid system in improving microgrid power quality. The concert of UPQC is assessed by the conventional PI controller and Fuzzy Logic control with MATLAB/SIMULINK software platform, simulation results are conversed with proportionate studies in improving the PQ by minimizing Total Harmonic Distortion (THD), the Voltage Sag & Swell and the result comparison shows the effectiveness of the Fuzzy Logic in coordination with the AL_LMS algorithm resulting improved power quality within the IEEE Power Quality-519 Standards.

ANFIS Controlled MMC-UPQC to Mitigate Power Quality Problems in Solar PV Integrated Power System

This paper focuses on the implementation of a Modular Multilevel Converter (MMC) based Unified Power Quality Conditioner (UPQC) in a grid-integrated Photovoltaic (PV) system. The integration of photovoltaic (PV) systems into the grid brings numerous benefits in terms of renewable energy utilization like sustainable energy generation, energy security, grid resilience, and economic growth. However, the voltage source converters (VSCs) employed in PV systems can introduce power quality (PQ) issues, including voltage fluctuations, harmonics, and reactive power imbalances and grid instability. The objective of this work is to enhance power quality by mitigating voltage and current fluctuations and harmonics generated due to VSC, compensating reactive power requirements by the load and PV system, and regulating the DC link voltage. For DC voltage regulation, an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller is employed to control for MMC-UPQC. The proposed control strategy is simulated using MATLAB/SIMULINK software and the results are compared with conventional Proportional Integral (PI) and fuzzy controllers. Various dynamic conditions such as voltage sag and swell at the point of common coupling (PCC), changes in irradiance for the PV system, and load variations are considered. The simulation results demonstrate that the ANFIS-controlled MMC-based UPQC outperforms the PI and fuzzy controllers in terms of power quality improvement. It effectively suppresses harmonics, maintains a stable DC link voltage, and compensates for reactive power fluctuations. The proposed control strategy provides superior performance and achieves better power quality compared to conventional controllers. The findings of this work highlight the potential of using an ANFIS controller in MMC-based UPQC systems for grid-integrated PV systems.

L-M Based ANN for Predicting the Location of DG under Contingency Condition

The continuing monitoring of online voltage stability and the increased loadability of the transmission lines for the existing electrical power system are the two major challenges that today's energy management systems must deal with. As a result, evaluating online voltage stability under various loading situations is extremely challenging and time-consuming. The line voltage stability indices using an Artificial Neural Network (ANN), the system describes online voltage monitoring and warns the operator before voltage dips. The simulation's findings show that the proposed system may boost system loadability while also lowering the cost of installing an electrical power system and guaranteeing the security of power system operation.

Valproate treatment induces age- and sex-dependent neuronal activity changes according to a patch clamp study.

Autism spectrum disorder is a heterogeneous neurodevelopmental disorder characterized by impaired social interactions, restricted, and stereotyped behaviors. The valproic acid model is one of the most recognized and broadly used models in rats to induce core symptoms of this disorder. Comorbidity of epilepsy and autism occurs frequently, due to similar background mechanisms that include the imbalance of excitation and inhibition. In this series of experiments, treatment was performed on rat dams with a single 500mg/kg dose i.p. valproate injection on embryonic day 12.5. Intracellular whole-cell patch clamp recordings were performed on brain slices prepared from adolescent and adult offspring of both sexes on pyramidal neurons of the medial prefrontal cortex and entorhinal cortex. Current clamp stimulation utilizing conventional current step protocols and dynamic clamp stimulation were applied to assess neuronal excitability. Membrane properties and spiking characteristics of layer II-III pyramidal cells were analyzed in both cortical regions. Significant sex-dependent and age-dependent differences were found in several parameters in the control groups. Considering membrane resistance, rheobase, voltage sag slope, and afterdepolarization slope, we observed notable changes mainly in the female groups. Valproate treatment seemed to enhance these differences and increase network excitability. However, it is possible that compensatory mechanisms took place during the maturation of the network while reaching the age-group of 3 months. Based on the results, the expression of the hyperpolarization-activated cyclic nucleotide-gated channels may be appreciably affected by the valproate treatment, which influences fundamental electrophysiological properties of the neurons such as the voltage sag. Remarkable changes appeared in the prefrontal cortex; however, also the entorhinal cortex shows similar tendencies.

Reconfigurable Converter Topologies for EV Fast Charging Stations

Infrastructure for charging electric vehicles (EVs) is highly demanded due to the rising number of EVs on the road. Stations for charging electric vehicles are necessary for the ongoing transportation e-mobility. In particular, fast charging infrastructures increase the computing ability of transmission grids that are already under a lot of pressure. The market's current energizing foundation takes a ton of room and incidentally causes gridlocks, which raises the risk of mishaps and hinders crisis vehicles. The cost of installing this charging infrastructure increases significantly because the current system needs a lot of room. To resolve these issues, this work depicts a Reconfigurable Multidevice Interleaved Boost Converter Topologies with Placement Algorithm for adjusting and a space-proficient charging foundation. Because of its insignificant electromagnetic impedance, low input and result voltage swells, bidirectionality, high efficiency and dependability, delicate switching, commotion free activity, low exchanging loss, and high effectiveness, this topology is the most ideal decision for high-power EVs.

Analysis and mitigation of PQ disturbances in grid connected system using fuzzy logic based IUPQC

Renewable energy integration introduces grid instability due to variable and intermittent sources like solar and wind, impacting reliability. This paper provides a thorough discussion of recent advancements and emerging trends in grid-integrated wind energy systems (GIWES) and grid-integrated solar energy systems (GISES). More than 70 research articles have been rigorously assessed and listed the technological and economic challenges. The increase in installations of grid-Integrating systems gives rise to challenges like as grid strain, peak shaving impacts, unpredictability of renewable energy sources (RES), and power quality disturbances. A variety of custom power devices, such as dynamic voltage restorers (DVR), static synchronous compensators (STATCOM), active power filters (APF), and unified power quality conditioners (UPQC), have gained popularity in response to these challenges. Among the various challenges, power quality disturbances, including voltage sag, swell, current and harmonics pose significant issues. To address these disturbances this work present a novel approach utilizing fuzzy logic (FL) to develop multi-feeder interline unified power-quality conditioners (MF-IUPQCs). The MF-IUPQC has three legs and three levels, each of which has four diode-clamped inverters. Switching is carried out through the use of space vector pulse width/duration modulation (SVPWM). Total harmonic distortion (THD) induced by nonlinear loads is reduced by the FLC-based MF-IUPQC, which also improves dynamic performance and offers a smooth DC-link voltage. The proposed control mechanism is implemented using MATLAB/Simulink. The fuzzy-based controller is compared to the industry-standard proportional-integral (PI) controller to determine its efficacy. Among them, the MF-IUPQC based on FLC delivers the smoothest voltage profile and the lowest THD.

Analysis of Distribution Static Compensator Control Strategies for Mitigating Voltage Dip Impact on Distribution Network

Voltage dip, over-voltage, load unbalanced, and current and voltage harmonics distortions are the key characteristics of poor power quality (PQ) issues on the power distribution network with a significant negative impact. The performance of a custom power device, distribution static compensator (D-STATCOM), in reducing current total harmonic distortion (THD) during the mitigation process of voltage dip with fault is investigated in this study. To control the load side voltage, the D-STATCOM utilizes a three-phase voltage source converter and is coupled at the point of common coupling (PCC). To mitigate voltage dip effects, this study implements and compares the effectiveness of the conventional PI controller with an intelligent optimization-based PI controller using the dynamic gravitational search algorithm (DGSA). The performance of these controllers is validated by the MATLAB/Simulink simulation results obtained. Analysis of the results demonstrates that D-STATCOM operates flawlessly with an intelligently optimized PI control strategy reducing the current THD from 11.68% to 3.74%.

Solar PV-Fed Multilevel Inverter with Series Compensator for Power Quality Improvement in Grid Connected Systems

Power quality problems arise as a result of RES integration into the grid. Voltage fluctuations, harmonic distortion, and spikes all impact customers as power quality issues on the grid. A cheap series compensator, such as the Dynamic Voltage Restorer (DVR), is the best approach to fix these problems. In order to enhance the grid's power quality, this research introduces a novel multilayer inverter that uses solar PV as a distributed voltage rectifier. The main objective of the proposed work is to enhance power quality through the development of a DVR that simultaneously functions as an integrated 23-level multilevel inverter. Beyond that, the boost converter has an improved INC-MPPT implementation. Despite their numerous benefits, multilayer inverters encounter reliability concerns such as reduced total harmonic distortion (THD) and fewer components. The suggested design has the potential to produce 23 distinct output voltage levels by use of asymmetrical DC sources. An inverter that is both smaller and cheaper with fewer parts is one of the advantages of the MLI. On top of that, the dq reference frame rotation adjustment is well studied mathematically. To test the DVR's flexibility with an equal load, simulation approaches are used. The simulation results of the suggested structure are generated using MATLAB/Simulink. Results from the Fuzzy Logic System show that the proposed technique improves power quality and compensates better for voltage sag.

Intelligent Voltage Sag Compensation Using an Artificial Neural Network (ANN)-Based Dynamic Voltage Restorer in MATLAB Simulink

An innovative Dynamic Voltage Restorer (DVR) system based on Artificial Neural Network (ANN) technology, implemented in MATLAB Simulink, accurately detects, and dynamically restores voltage sags, significantly improving power quality and ensuring a reliable supply to critical loads, contributing to the advancement of power quality enhancement techniques. Voltage sags are a prevalent power quality concern that can have a significant impact on sensitive electrical equipment. An innovative approach to address voltage sags through the operation of a Dynamic Voltage Restorer (DVR) based on Artificial Neural Network (ANN) technology. The proposed system, developed using MATLAB Simulink, leverages the ANN's capabilities to accurately detect voltage sags and dynamically restore the voltage to the affected load. The ANN is trained using a comprehensive dataset comprising voltage sag events, enabling it to learn the intricate relationships between sag characteristics and optimal compensation techniques. By integrating the trained ANN into the DVR control scheme, real-time compensation for voltage sags is achieved. The effectiveness of the proposed system is rigorously evaluated through extensive simulations and performance analysis. The results demonstrate the superior performance of the ANN-based DVR in terms of voltage sag detection accuracy and restoration precision. Consequently, the proposed system presents an intelligent and adaptive solution for voltage sag compensation, ensuring a reliable and high-quality power supply to critical loads. This research contributes to the advancement of power quality enhancement techniques, facilitating the implementation of intelligent power system.

Comparative Analysis between Dynamic Voltage Restorer and PWM-Switched Autotransformer in Voltage Sag Mitigation

This research aims to address the problem of voltage sag, which is a significant power quality issue in power systems. Voltage sag can lead to power transmission and distribution line tripping and cause damage to electrical equipment. Various voltage sag mitigation devices have been developed to reduce the impact of voltage sag. In this study, we analyze the effectiveness of three types of voltage sag mitigation devices under different voltage sag conditions: three-phase fault, multi-phase fault, and energizing transformer inrush current. The three types of mitigations analyzed are Dynamic Voltage Restorer (DVR) with Park's transformation, DVR with Artificial Neural Network (ANN) controller, and PWM-switched autotransformer with Proportional-Integral (PI) controller. The simulation results using MATLAB demonstrate that all voltage sag conditions have been mitigated except for PWM-switched autotransformer during voltage sag caused by energizing transformer inrush current due to limitations of the PI controller scheme. Based on the analysis of input and output voltage waveforms, three-phase voltage magnitude before and after mitigation, and Total Harmonic Distortion (THD) value after mitigation, DVR with ANN controller is identified as the most effective voltage sag mitigation device, followed by DVR with Park's transformation and PWM-switched autotransformer.

Voltage Sag Data Generation Based on the Probabilistic Diffusion Model

To effectively analyze and mitigate voltage sag phenomena using deep learning techniques, a diverse and representative dataset of voltage sag events is essential. However, the available voltage sag dataset obtained from monitoring systems is often limited in quantity and lacks comprehensive coverage of various sag types. In this paper, the achievement of generating high-quality voltage sag waveforms that closely resemble real-world scenarios is accomplished by leveraging the diffusion model’s ability to capture complex data distributions. Our study focuses on modifying the diffusion model to capture the specific characteristics of voltage sag waveforms. The results demonstrate the effectiveness of the proposed approach in generating realistic voltage sag datasets, highlighting its potential for various applications in power system analysis and planning.

Dynamic voltage restorer quality improvement analysis using particle swarm optimization and artificial neural networks for voltage sag mitigation

Power quality is one of the problems in power systems, caused by increased nonlinear loads and short circuit faults. Short circuits often occur in power systems and generally cause voltage sags that can damage sensitive loads. Dynamic voltage restorer (DVR) is an efficient and flexible solution for overcoming voltage sag problems. The control system on the DVR plays an important role in improving the quality of voltage injection applied to the network. DVR control systems based on particle swarm optimization (PSO) and artificial neural networks (ANN) were proposed in this study to assess better controllers applied to DVRs. In this study, a simulation of voltage sag due to a 3-phase short-circuit fault was carried out based on a load of 70% of the total load and a fault location point of 75% of the feeder’s length. The simulation was carried out on the SB 02 Sibolga feeder. Modeling and simulation results are carried out with MATLAB-Simulink. The simulation results show that DVR-PSO and DVR-ANN successfully recover voltage sag by supplying voltage at each phase. Based on the results of the analysis shows that DVR-ANN outperforms DVR-PSO in quality and voltage injection into the network.

A Centralized Battery Energy Storage-Based Medium-Voltage Multiwinding DVC for Balance and Unbalance Operations

This paper proposes a centralized battery energy storage based medium-voltage multi-winding dynamic voltage compensator (DVC) for balance and unbalance operations. In this topology, the compensation voltage is added to the grid side through the transformer, and the primary side of the transformer is shunted by multiple windings to support the voltage sag on the medium-voltage distribution systems. According to the symmetrical and asymmetrical grid-side voltage sags, the positive and negative sequence mathematical models of DVC as well as their voltage-current double closed-loop control strategies are developed. As for the DC bias caused by circulating current between the primary windings of each phase of the multi-winding transformer, circulating current suppression algorithm is added to the positive and negative sequence inner current loops. Simulation and experiments results are performed to verify the effectiveness and superiority of the proposed methodology on voltage support.

Flexible Adaptive Integrated Compensation Strategy for Voltage Sag

A dynamic voltage restorer (DVR) is often used for voltage compensation to reduce or eliminate the harm of voltage sag. The existing combined compensation strategy does not consider the specific process of voltage sag, so it cannot adapt to the change according to the drop depth and eliminate the phase abrupt change. In this paper, a flexible, adaptive combination compensation strategy is proposed, which adopts the appropriate compensation strategy according to the actual voltage change process, sets the transition period to avoid phase jump, and adopts the full voltage compensation strategy to fully recover the phase at the end of the voltage dip, to realize the adaptive, flexible compensation of voltage. The simulation results show that the compensation strategy varies with the voltage drop depth. Both the intermediate phase fluctuation and the energy consumption are smaller. The final compensation is complete.

An Enhanced Filtering Generalized Integrator-Based Control for Improved Performance of a Grid-Tied PV System at Adverse Grid Voltages

This paper deals with a single stage photovoltaic (PV) system with improved resilience against adverse grid conditions. This is majorly achieved in two ways. First, it presents an enhanced filtering generalized integrator (EFGI) based filtering stage, to process the prevalent grid voltages and derive their positive sequence components free of harmonics components, unbalance, and DC offset. The presented EFGI delivers a considerable improvement over the conventional filters and helps to improve the power quality (PQ) in case of unequal or distorted voltage conditions. Second, to address large voltage dip or rise scenarios, a dual mode mechanism is applied for the selection of the DC link voltage ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> ) reference and the corresponding generated PV array power. At regular voltage scenario and at availability of PV power, the reference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> is governed by the necessity to keep the PV array operating at its peak generation levels. However, at loss of PV power or divergent grid voltages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> control is quickly transferred to an alternate approach, varying <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> as per the existing grid voltages. Using this, the injected currents are limited, and system de-synchronization is prevented at voltage dips. The increased <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_dc</i> at grid voltage rise avoids deterioration in the grid PQ. The grid PQ is also assured despite the presence of local non-ideal loads. The EFGI benefits and the real time performance of the presented system, are assessed using test results.