Voltage Sag Research Articles

Research on intelligent auxiliary regulation technology of large power grid section based on artificial intelligence

In the modern era, large-scale renewable energy systems are integrated with advanced power systems and provide efficient operations. Also, optimized power systems require accurate energy generation and effective control systems to manage and ensure a stable power supply. Nevertheless, uncertainties are the intermittent balance of supply and high electricity demand. In addition, conventional power sources are not applicable for this challenging task and increase electricity costs. Therefore, an efficient Neuro Fuzzy Single phase Unified Power Quality Conditioner with Maximum Power Point Tracking (NF-SP UPQC -MPPT) strategy is developed for enhancing the grid-connected power systems. Here, Neuro-fuzzy logic is used as dynamic reactive power compensation in the grid. Also, this logic can efficiently handle the Energy storage system (ESS). Then SP UPQC was used to enhance power quality in electrical distribution systems. It combines both series and shunt compensators to mitigate various power quality issues such as voltage sags, harmonics, and unbalance. After that MPPT was utilized to extract the maximum power from the grid system. Model Predictive Control (MPC) controller to determine the overall stability and performance of the system. Moreover, the developed model was implemented on the MATLAB platform and performance is analyzed in terms of voltage deviation, grid current, reactive power fluctuations and Total Harmonic Distortion (THD).

Quasi Z-Source Direct Matrix Converter for Enhanced Resilience to Power Grid Faults in Permanent Magnet Synchronous Motor Applications

In this paper, a voltage control system for a PMSM motor based on the QZSDMC converter is proposed, which allows operation in both buck and boost modes as a possible method to make the drive resistant to power grid voltage sags. The authors presented a method for measuring and transforming the output voltage from QZS, enabling the use of a PI controller to control the voltage supplied to the DMC converter. The publication includes simulation and experimental studies comparing the operation of a PMSM motor powered by DMC and the proposed QZSDMC with voltage regulation. Simulation studies confirm the drive with QZSDMC resistance to voltage sags up to 80% of the rated value. Experimental studies demonstrate the correct operation of PMSM even with a power grid voltage amplitude equal to 40% of the rated value.

Fault-tolerant shunt active power filter with synchronous reference frame control and self-tuning filter

This research proposes a novel method that combines Proportional-Integral (PI) control, Artificial Neural Networks (ANNs), and Synchronous Reference Frame (SRF) theory to improve the performance of a three-phase shunt Active Power Filter (APF) under fault situations. The main goal is to reduce power quality problems in electrical grids that are having problems, such harmonics and voltage sags. To precisely manage the APF, the reference frame in which the grid voltages and currents are synchronized is identified using the SRF theory. In order to provide quick and precise correction of voltage and current distortions, the PI controller is integrated to control the APF's compensatory action. The PI controller offers trustworthy control while running normally, but during errors or disruptions, its functionality may suffer. In order to overcome this difficulty, a self-tuning filter in the form of an Artificial Neural Network (ANN) is presented, which may adaptively modify the PI controller's settings under fault situations. To maintain ideal filter performance, the ANN constantly learns from the system's reaction and modifies in real-time. This self-adjusting feature makes sure that even in the event of grid failures, the APF maintains its ability to mitigate problems with power quality. The suggested method effectively reduces harmonics, voltage sags, and other power quality disturbances under both normal and fault circumstances, as shown by the simulation results. In complicated electrical grid systems, the combination of SRF theory, PI control, and ANN-based self-tuning provides a strong way to improve the dependability and effectiveness of three-phase shunt Active Power Filters.

Comparative Analysis of Various Methodologies for Voltage Swell and Sag Detection in Online and Offline Conditions

Comparative Analysis of Various Methodologies for Voltage Swell and Sag Detection in Online and Offline Conditions

Multi-stage voltage sag frequency evaluation based on process immunity in the distribution network

Abstract The reclosing action of the distribution network may cause multi-stage voltage sag in the grid nodes, which leads to more serious losses for sensitive users. In order to more reasonably characterize the disturbance degree of multi-stage voltage sag on sensitive equipment, a frequency evaluation method based on process immune time (PIT) and improved analytic hierarchy process is proposed. Firstly, multi-stage voltage sag waveforms caused by the reclosing corresponding action under transient fault and permanent fault are analyzed, and the PIT characteristic curve of sensitive equipment under multi-stage voltage sag is characterized. Secondly, the weight function method is used to calculate the influence degree of each stage sag. The analytic hierarchy process is improved by utilizing the correlation and influence degree of each stage sag. The weight of each stage sag is determined, and then the amplitude of the multi-stage voltage sag is calculated equivalently. The multi-stage voltage sag frequency evaluation method is proposed in the distribution network. Finally, the IEEE 30-bus system is used for simulation verification. The results show that the proposed method is more accurate and reasonable for the evaluation of multi-stage voltage sag frequency. This method provides a basis for the frequency evaluation and governance of multi-stage voltage sag.

Voltage sag source location based on power polarity and twin characteristics matching

AbstractPrecisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling.

Research on the Effects of Voltage Sags and Harmonics on the Output Characteristics of Switching Power Supply

Switching power supply is widely used in industrial equipment, household appliances, and other fields due to the advantages of high integration and lightweight. Voltage sags can seriously affect the output voltage of switching power supply and the working condition of its power supply equipment; at the same time, the working scenarios of power equipment using switching power supply often have different level of harmonic problems. Considering the continuous innovation of switching power supply technology, the previous analysis and test results on the effect of voltage sags and harmonics on switching power supplies may no longer be applicable. The paper reveals the mechanism of the effect of harmonic alone as well as voltage sag superimposed harmonics on the output voltage of the switching power supply. Then the test research program was developed, and four different brands switching power supplies were selected for the test research, with which the effects of different amplitude voltage sags, harmonics with different frequencies and distortion, as well as voltage sag superimposed harmonics on the output voltage of switching power supplies were investigated. The results show that the tolerance ability of switching power supplies to voltage sags has been improved compared with previous switching power supplies, and the harmonic itself has less effect on the output voltage of switching power supply, but the joint action of harmonics and voltage sags will have a greater effect on the output voltage of switching power supply. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

A state‐space average model of a three‐level PV inverter for transient short‐circuit currents analysis

AbstractThis paper presents a state‐space average model of a three‐level photovoltaic (PV) inverter to understand short‐circuit currents transient characteristics. Analytical solution of the model is also derived. The proposed model is validated with PV system controller hardware in‐loop (HIL) test method. The errors between simulation and HIL tests results under various conditions are evaluated, testifying the validity of the proposed model. With the model and analytical solution, the decaying time constants of sub‐transient and transient processes are derived, which shows that the time constants of a three‐level PV inverter are not fixed and they vary with voltage dips. The analytical expression of short‐circuit currents is also formulated. The findings from this study are essential to short‐circuit current calculations of a power grid with large‐scale PV plants, coordination of power system protections, and grid planning.

Unified power quality conditioner-based solar EV charging station using the GBDT–JS technique

This manuscript proposes a novel hybrid artificial intelligence (AI) approach for a unified power quality conditioner (UPQC) designed specifically for electric vehicle charging stations (EVCSs). The aim is to integrate multiple vehicle-to-grid (V2G) functionalities, thereby mitigating the challenges associated with electric vehicle (EV) grid integration and the incorporation of distributed energy resources (DERs). The hybrid technique presented in this manuscript combines the gradient boosting decision tree (GBDT) algorithm and the jellyfish search (JS) algorithm, referred to as the GBDT–JS technique. This innovative approach involves utilizing the charging station to offer EV charging services and facilitating the discharge of EVs to the power grid. Integration of the UPQC with DERs, such as photovoltaic (PV), is implemented to decrease the power rating of converters and fulfill power demand requirements. The initial converter within the UPQC is employed to manage the direct current (DC) voltage, while the second converter oversees the power charging or discharging processes of EVs. Additionally, it mitigates the impact of battery voltage fluctuations. The UPQC with vehicle-to-grid functionality minimizes the load pressure on the grid, preventing over-current issues. The presented approach regulates the UPQC converters to mitigate power quality issues such as harmonic currents and voltage sags. Subsequently, the effectiveness of this technique is demonstrated using the MATLAB/Simulink operating platform. The evaluation of GBDT–JS performance involves a comparative analysis with existing techniques. This assessment reveals that the proposed method effectively alleviates power quality issues, specifically reducing total harmonic distortion (THD), and delivers optimal outcomes.

HRES Integrated DSTATCOM Using High Gain Sepic-Zeta Based WOA Optimized ANFIS-MPPT Controller for PQ Issues

The power generated by renewable energy sources (RES), including photovoltaic (PV) and wind energy systems, are a great deal dependent on climate situations that result in Power Quality (PQ) issues, which require rapid adjustment of energy transmission and distribution systems. As a solution, (Distribution Static Synchronous Compensator) DSTATCOM is implemented for reactive power compensation, reducing voltage sag, swell, and THD. A novel High-gain SEPIC-ZETA converter is designed in this study to enhance the voltage obtained from the PV system with high efficiency. On the other hand, the proposed work aims to improve PQ issues using DSTATCOM with PV-Wind and battery systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point (MPPT) controller is adopted for tracking optimal power from the PV and to tune the parameters of this controller adopting Whale optimization algorithm (WOA). Moreover, the PWM rectifier is employed to convert the AC supply from the wind energy system to DC and it is controlled by the Proportional Integral (PI) controller. Hysteresis Current Controller (HCC) is incorporated to generate reference current based on Synchronous Reference Frame (SRF) theory, which is required for the reduction of harmonics. Three phase voltage source inverter is integrated to convert DC-AC supply for distributing the energy supply to load application. Finally, the anticipated work is implemented in MATLAB/Simulink and the comparative analysis is made towards the conventional topologies to authenticate the proficiency of the developed work. As a consequence, the outcomes demonstrate the improved PQ with a lower THD value of 0.72% with high tracking efficiency is accomplished by the proposed technique.

Most influential feature form for supervised learning in voltage sag source localization

The paper investigates the application of machine learning (ML) for voltage sag source localization (VSSL) in electrical power systems. To overcome feature-selection challenges for traditional ML methods and provide more meaningful sequential features for deep learning methods, the paper proposes three time-sample-based feature forms, and evaluates an existing feature form. The effectiveness of these feature forms is assessed using k-means clustering with k = 2 referred to as downstream and upstream classes, according to the direction of voltage sag origins. Through extensive voltage sag simulations, including noises in a regional electrical power network, k-means identifies a sequence involving the multiplication of positive-sequence current magnitude with the sine of its angle as the most prominent feature form. The study develops further traditional ML methods such as decision trees (DT), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), an ensemble learning (EL), and a designed one-dimensional convolutional neural network (1D-CNN). The results found that the combination of 1D-CNN or SVM with the most prominent feature achieved the highest accuracies of 99.37% and 99.13%, respectively, with acceptable/fast prediction times, enhancing VSSL. The exceptional performance of the CNN was also approved by field measurements in a real power network. However, selecting the best ML methods for deployment requires a trade-off between accuracy and real-time implementation requirements. The research findings benefit network operators, large factory owners, and renewable energy park producers. They enable preventive maintenance, reduce equipment downtime/damage in industry and electrical power systems, mitigate financial losses, and facilitate the assignment of power-quality penalties to responsible parties.

Dynamic voltage restorer performance analysis using fuzzy logic controller and battery energy storage system for voltage sagging

Power quality is a major issue in the power transfer process. This is caused by disturbances such as voltage sags, voltage spikes, and harmonics. Voltage sag is the most common disturbance in the electric power system. However, the dynamic voltage restorer (DVR) is the most effective device for voltage sags. This research uses the DVR to overcome voltage sags using fuzzy logic controller (FLC) and battery energy storage system (BESS) to improve the performance of the DVR. The results showed that DVR using FLC improved the quality of voltage recovery compared to BESS because FLC injected a greater voltage of 0.0991 pu than BESS.

Methodology to Estimate the Financial Impacts of the Integration of PV Generators in Distribution Systems on Voltage Sags and Energy Losses

Methodology to Estimate the Financial Impacts of the Integration of PV Generators in Distribution Systems on Voltage Sags and Energy Losses

Enhancing power system reliability: Hydrogen fuel cell-integrated D-STATCOM for voltage sag mitigation

The focus of this study is to investigate the critical matter of voltage sags in power systems, which have a substantial adverse effect on both the functionality of equipment and the quality of power. Central to this investigation is the integration of hydrogen fuel cells and a D-STATCOM system, which is suggested as a substitute approach for mitigating voltage fluctuations. The hydrogen fuel cell serves as the pivotal component, renowned for its ability to significantly improve power quality through the efficient mitigation of voltage sag concerns. The paper provides a comprehensive analysis of the contribution that hydrogen fuel cell integration with D-STATCOM makes to the reliability of power systems. A systematic methodology is employed, supported by simulations and modeling, to emphasize the critical significance of hydrogen fuel cells in surmounting obstacles related to voltage injection and enhancing power quality. By utilizing a Type-3 Fuzzy system, the research evaluates the dependability of the control system and proposed model. The effectiveness of this method, which is focused on hydrogen fuel cells, is verified by means of MATLAB simulations, which demonstrate its superior performance in comparison to conventional PI and ANFIS controllers.

Hres Integrated Dstatcom Using High Gain Sepic-Zeta Based Woa Optimized Anfis-Mppt Controller for Pq Issues

The power generated by renewable energy sources (RES), including photovoltaic (PV) and wind energy systems, are a great deal dependent on climate situations that result in Power Quality (PQ) issues, which require rapid adjustment of energy transmission and distribution systems. As a solution, (Distribution Static Synchronous Compensator) DSTATCOM is implemented for reactive power compensation, reducing voltage sag, swell, and THD. A novel High-gain SEPIC-ZETA converter is designed in this study to enhance the voltage obtained from the PV system with high efficiency. On the other hand, the proposed work aims to improve PQ issues using DSTATCOM with PV-Wind and battery systems. Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point (MPPT) controller is adopted for tracking optimal power from the PV and to tune the parameters of this controller adopting Whale optimization algorithm (WOA). Moreover, the PWM rectifier is employed to convert the AC supply from the wind energy system to DC and it is controlled by the Proportional Integral (PI) controller. Hysteresis Current Controller (HCC) is incorporated to generate reference current based on Synchronous Reference Frame (SRF) theory, which is required for the reduction of harmonics. Three phase voltage source inverter is integrated to convert DC-AC supply for distributing the energy supply to load application. Finally, the anticipated work is implemented in MATLAB/Simulink and the comparative analysis is made towards the conventional topologies to authenticate the proficiency of the developed work. As a consequence, the outcomes demonstrate the improved PQ with a lower THD value of 0.72% with high tracking efficiency is accomplished by the proposed technique.

Voltage and Current Characteristics of Schottky Barrier Diodes and Rectifier Diodes Exposed to Standard Lightning Impulse Voltage

Lightning surges are one of the major reasons of bypass diode (BPD) failure in photovoltaic modules. In this study, standard lightning impulse voltages (SLIVs) were directly applied to Schottky barrier diodes (SKB diodes) and rectifier diodes (PNJ diodes) with different electrical specifications. The critical voltages Vc of SLIV at which the diodes were damaged and the temporal variations of diode current (iD) and voltage (vD) were measured. It was found the iD‐vD characteristics can be categorized into a total of eight patterns: four patterns for the forward SLIV application and the four for the reverse SLIV application, regardless of diode types. The measured results imply that the irreversible voltage dip that occurs before the termination of the reverse breakdown contributes significantly to diode damage in the both forward and reverse SLIV applications. SKB diodes exposed to the reverse SLIV are easily damaged at the lowest magnitude of SLIV among all combinations of diode types and SLIV directions. Once a diode has been damaged due to the first SLIV application, the second application of SLIV to the same diode causes remarkable change in the iD‐vD characteristic even if the magnitude of the second SLIV is smaller than Vc. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Power Quality (PQ) Analyses of DG Utilizing Unified Power Quality Conditioner (UPQC) by White Shark Optimizer and Recalling-Enhanced Recurrent Neural Network

This paper proposes a novel hybrid technique for enhancing power quality (PQ) in distributed generation (DG) systems by deploying a unified power quality conditioner (UPQC). Here, the proposed hybrid method is the joint execution of white shark optimizer (WSO) and recalling-enhanced recurrent neural network (RERNN), called the WSO-RERNN technique. The primary objective of this novel approach is to effectively mitigate voltage sag and reduce voltage harmonics under varying load conditions. It is important to investigate the voltage sag, swell and harmonic distortion of the system to obtain an enhanced PQ of the energy supply. Therefore, this paper shows the brief impact of PQ in DG utilizing the proposed unified PQ conditioner controller. The WSO-RERNN control technique enhances the performance of the UPQC controller by providing the optimal control signal. By then, the efficiency of the proposed approach is done in MATLAB, and the performance is compared with those of existing optimization techniques, including Ant Lion Optimizer (ALO), Grey wolf optimization (GWO) and Salp swarm algorithm (SSA) methods.

Power Quality Improvement of Micro Grid by Using DVR

Power quality is defined as the degree of voltage, current, frequency and waveform of power system under designed specification of the system. While transmitting the power from one end (generation) to another (load) utility has to face some power quality issues such as voltage sag, voltage swell, harmonics, inter-harmonics, voltage imbalance, voltage fluctuation, short duration voltage variation, long duration voltage variation, transient, power frequency variation, waveform distortion etc. These power quality issues directly effect on the efficiency of the power system and performance of the load equipment’s. Problems like voltage sag, voltage swell occurs due to sudden removal of large load from the system, energization of large capacitor bank or system fault like single-line-to-ground fault. These types of problems directly effect on equipment performance and accuracy, especially on sensitive equipment’s as computer, medical instruments, some electronic devices etc. To mitigate voltage sag, voltage swell problem in power system various devices are used such as DVR (dynamic voltage restorer) Key Words: Voltage sag, Voltage swell, Power quality, MATLAB simulation, Single-phase AC system, Single-phase AC load.

Voltage sag sensitive load type identification based on power quality monitoring data

This paper focuses on identifying voltage sag-sensitive loads within unknown load type or when the load is already in operation. To achieve this, a new method of sensitive load identification based on power quality monitoring data is proposed. Firstly, the active power RMS monitoring data is used as the base data. The Hodrick-Prescott filtering and sliding mean segmentation are used to divide the period of the voltage sag event. Next, based on the division result, the differences of steady-state power quality monitoring data before and after each event are calculated as the dataset. The dynamic K-means is used to divide various load action areas. Finally, the voltage tolerance curves of each action area are fitted and compared with the preset curves, then according to the constituted rules in this paper, the type of sensitive load contained by user is recognized. The feasibility and accuracy of the proposed method are verified by analyzing the simulation examples and actual power quality monitoring data.© 2017 Elsevier Inc. All rights reserved.

Compensating voltage waveform distortions using a practical topology of series active power filters

Addressing harmonics, voltage sags, voltage swells, and asymmetrical variations is essential to seamlessly integrate renewable energy sources, electric vehicle charging stations, and power electronics devices into electric power grids. These issues can significantly impact the sinusoidal symmetry of voltage waveforms. Series Active Power Filters (APFs) present a promising solution that can dramatically improve power quality (PQ) by effectively addressing voltage distortions. This paper first identifies several topology-related impracticalities in the existing literature on series APFs, such as using nonlinear loads or linear loads with a current source. Secondly, to understand the motivations behind these impracticalities, commonly used reference extraction methods, namely, Instantaneous Reactive Power Theory (IRPT) and Synchronous Reference Frame (SRF), are tested on a practical topology consisting of a distorted voltage source and a linear RL load. Results conducted in the MATLAB/Simulink environment show that IRPT fails to extract the reference signal under the practical topology, and SRF leads to unacceptable THDs surpassing the 5% threshold mandated by relevant standards set forth for such applications. Thirdly, the matrix pencil method (MPM), a model-based parameter estimation technique that exploits a voltage waveform's underlying exponential signal model to extract the series APF's reference voltage, is proposed. Extensive simulations showcase the superior performance of the MPM-based series APF. It successfully reduces voltage total harmonic distortion (THD) to below 1.13% across various scenarios, including situations involving harmonic-polluted voltage sources, incorporating nonlinear loads, sag and swell phenomenon, and capacitor utilization in DC link implementation.