Power Quality Problems Research Articles

A Novel Power Quality Management Method of MGC Based on Distributed Consensus Algorithm

ABSTRACTA novel power quality management method of microgrid cluster based on distributed consensus algorithm is proposed in this paper. First of all, when power quality problems arise, converters with suitable positions and capacity can be selected for compensation by using the distributed consensus algorithm proposed in this paper, so as to realize power quality management of microgrid cluster. Further, when a converter in microgrid cluster cannot continue to participate in the task, other converters can be flexibly switched through the distributed consensus algorithm to ensure the continuity and stability of power supply and power quality management. Finally, the remaining capacity of each converter can be adjusted in real time through the distributed consensus algorithm proposed in this paper, which reduces the negative influence of the uneven capacity distribution caused by converter switching on the power quality in microgrid cluster. The results show that multiple converters of microgrid cluster can be effectively coordinated by adopting the power quality management method based on distributed consensus algorithm proposed in this paper, so that the comprehensive power quality management of microgrid cluster can be optimized.

Power Quality Improvement in a Grid Connected Solar-PV System with Adaptive Neuro-Fuzzy Controller Based UPQC

Power quality (PQ) problems are frequently encountered in power grids, and these problems become more severe as the number of devices increases. Therefore, one of the primary responsibilities of utility systems remains to supply people with electricity by standard sinusoidal voltage and current configurations at appropriate amplitudes and frequencies adjoining the point of common connection (PCC). This research examines a solar-photovoltaic (PV) coordinated unified power quality controller (UPQC) to enhance the PQ in the distribution grid network. This paper proposes a UPQC control method relied on the Adaptive Leakage Least Mean Square (AL_LMS) algorithm. This approach uses both the Adaptive Neural Fuzzy Inference System (ANFIS) controller and Proportional-Integral (PI) controller to compare between the serial and shunted active converter switching of UPQC. The technique switches the UPQC's shunt and series voltage source converters (VSCs) to obtain a reference signal through iterative weight update. Through iterative weight update, the method obtains a reference signal by switching the shunted and serial VSCs of the UPQC. Therefore, the PQ disturbances such as load voltage drop and ripple and harmonic distortion of the grid’s current are abridged after the control technology is applied to the solar PV-UPQC. Conferring to the simulation results, the proposed ANFIS control AL_LMS algorithm stands the utmost operative in improving the PQ after applied to the solar-PV sourced UPQC, while complying with the IEEE-519 standards. The work was done in using MATLAB/Simulation software.

Investigation and Efficiency Analysis of Dual-Boost Bridgeless PFC Converter

The increase in electricity consumption makes the electrical efficiency of the systems more important day by day. This situation necessitated the search for high efficiency in Power Factor Correction (PFC) converters, which offer solutions to power quality problems caused by rectifiers. In this study, Bridgeless Boost PFC Converters with high efficiency advantage are examined. The advantages and disadvantages of these converters are presented. Dual-Boost Bridgeless PFC Converter (D-BBPFC), which is a type of bridgeless converters, is introduce and converter is simulated in the PSIM environment. The obtained total harmonic distortion of input current (Iin(THD)), power factor and efficiency values are compared with the Conventional Boost PFC Converter (CBPFC), which is designed in our previous studies with the same parameters. By operating the Dual-Boost Bridgeless PFC Converter at different power levels, the change of power factor and total harmonic distortion is presented in graphic form. For a more detailed efficiency analysis, the converters are rerun by using MOSFETs which have different ON-resistances. As a result, the efficiency advantage of Bridgeless Boost PFC Converters is proven. In addition, the importance of MOSFETs with low ON-resistance such as SiC has been understood for these converters.

Effect of Transformer Rating on the Power Quality Problems in 11kv/440v Distribution System

This study explains in detail how the transformer rating affects the proposed distribution system. Also analysis of the suggested distribution system would react when the transformer rating is changed to reduce harmonic interference, this information is essential. The harmonic in the voltage waveform is changed by changing the transformer's rating. The nonlinear and imbalanced load is taken into account in this approach. To reduce harmonics caused by nonlinear and imbalanced loads along with by adjusting the transformer's rating up or down, an SRF-controlled algorithm is suggested. An 11kV/400V distribution system framework, the SRF- controlled DSTATCOM with four-leg VSI is proposed and placed into the system. The proposed DSTATCOM is evaluated for its ability to regulate the voltage at the PCC as the transformer rating rises and falls in nonlinear and imbalanced load conditions. The outcomes of the suggested work are acquired using the MATLAB/SIMULINK program.

Photovoltaic Power Prediction with Teaching Learning Based Optimization Algorithm

The need for electrical energy has increased considerably due to technological developments. Reducing costs and losses, especially in the supply of electrical energy, is among the goals of energy companies. Photovoltaic energy has been an important alternative in reducing energy costs. However, there are significant power quality problems in transferring the generated photovoltaic energy to the grid. Therefore, the generated photovoltaic energy needs to be accurately estimated to be transferred to the grid smoothly. In the literature, many forecasting models have been used for photovoltaic power forecasting. Each of these forecasting models has estimated photovoltaic power using different input parameters, different estimation intervals, and different estimation algorithms. This paper was conducted using the Teaching-Learning Based Optimization (TLBO) algorithm as an alternative approach to photovoltaic power forecasting models. According to the forecasting results, the root mean square error (RMSE) for the test subset was obtained as 270.32 kW, and the mean absolute percentage error (MAPE) was found to be 3.87%. These results indicate that the TLBO algorithm demonstrates high accuracy for photovoltaic power forecasting and provides an effective alternative model in this field.

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

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

Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization

Optimal sizing and placement of STATCOM, TCSC and UPFC using a novel hybrid genetic algorithm-improved particle swarm optimization

Power Quality Analysis of Electrical Installation at Commercial Center

For many years, power quality analysis has constantly been carried out at the power stations or sub-stations using the data collected at the data Centre of the power plant. However, this practice has proven to be erroneous and tedious to determine the exactly power quality problem especially when it comes to commercial facilities. Therefore, a revised approach of analysis for the power quality problems at the sites where they occur has been used in this research. This is because of the sensitivity of modern electronic equipment used in commercial facilities and also the need for accuracy of the data measurements carried out at the sites henceforth, with accurate data measurement, we can easily identify the power quality issues. Through-out this research, load unbalance of 28% was found. The solution to load unbalance was to balance the loads to 3,3% among the three phases and power losses caused by this unbalance current were calculated. Although the neutral conductor remained unchanged, harmonics especially triplen-harmonics was found to be more 10% recommended by IEC standards, therefore active harmonic filter of 200A was installed at the main panel board to reduce the losses caused by harmonics at the facility.

Solar PV powered DC charger for electric vehicles with reconfigurable power electronic interface

ABSTRACT Global expansion of the EV market has led to widespread DC charging infrastructure. Consumers prefer DC charging over AC to reduce charging time per kilometer. However, grid-connected DC chargers can cause issues such as grid instability, power quality problems, demand imbalance, and conversion losses. This paper proposes an off-grid photovoltaic (PV) fed DC charger for EVs along with a battery energy storage system (BESS) to mitigate these challenges. The work aims to increase the use of clean energy for EV charging, to ensure uninterrupted EV charging with minimum power conversion stages, and to improve PV utilization. BESS serves as a backup power source that charges or discharges based on PV availability. Additionally, the system powers the non-critical adjustable loads of domestic/industrial sites with the available PV power when both EV and BESS are fully charged. The system can be reconfigured to operate in six distinct modes using two DC-DC converters and five relays. Among six modes, four modes operate in single-stage power conversion. A buck converter is used to extract power from PV. A bidirectional buck-boost converter is used to charge and discharge the BESS. The proposed system is validated through hardware prototype for both steady-state and transient conditions.

Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique

AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

Experimental investigation of adaptive multi-generalized integrator-based controller for electronically interfaced hybrid microgrid system

Experimental investigation of adaptive multi-generalized integrator-based controller for electronically interfaced hybrid microgrid system

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.

Joint probabilistic modeling approach for harmonic and three-phase unbalanced disturbance sources

Harmonics and three-phase imbalance are two common disturbances in power systems that interact with and affect each other, leading to more complex impacts on power system performance. To comprehensively and accurately analyze the distribution and interaction mechanisms of harmonics and three-phase imbalance, this study investigated the joint emission level of harmonic and three-phase unbalanced disturbance sources (H-TU-DS). This paper proposes a joint probabilistic emission level modeling approach. The power, harmonic, and three-phase unbalanced data of H-TU-DS are multidimensional, and the feature dataset is filtered by calculating multiple correlation coefficients to achieve dimensionality reduction without losing data information. Considering the different operating characteristics of the H-TU-DS, an improved fuzzy C-means (FCM) algorithm is applied to classify operating conditions, enabling a comprehensive and accurate analysis of the joint emission level. Based on the nonparametric kernel density function, joint probability modeling of H-TU-DS is performed using an adaptive bandwidth improvement method, effectively enhancing the accuracy of the modeling process. Finally, the effectiveness and accuracy of the proposed methods for feature dataset filtering, operating condition classification, and joint probability modeling are demonstrated through comparisons with both simulated and actual data. The proposed model facilitates the prediction and assessment of the impact of H-TU-DS on power quality after grid connection, enabling the effective prevention of power-quality problems through advanced treatment.

Hybrid Deep Neural Network Approaches for Power Quality Analysis in Electric Arc Furnaces

In this research, we investigate the power quality of the grid where an Electric Arc Furnace (EAF) with a very high load operates. An Electric Arc Furnace (EAF) is a highly nonlinear load that uses very high and variable currents, causing major power quality issues such as voltage sags, flickers, and harmonic distortions. These disturbances produce electrical grid instability, affect the operation of other equipment, and require strong mitigation measures to reduce their impact. To investigate these issues, data are collected from the Point of Common Coupling where the Electric Arc Furnace is fed. The following three main factors are identified for evaluating power quality: apparent power, active and reactive power, and distorted power. Along with these powers, Total Harmonic Distortion, an important indicator of power quality, is calculated. These data are collected during the full process of producing a complete steel batch. To create a Deep Neural Network that can model and forecast power quality parameters, a network is developed using LSTM layers, Convolutional Layers, and GRU Layers, all of which demonstrate good prediction performance. The results of the prediction models are examined, as well as the primary metrics characterizing the prediction, using the following: MAE, RMSE, R-squared, and sMAPE. Predicting active and reactive power and Total Harmonic Distortion (THD) proves useful for anticipating power quality problems in an Electric Arc Furnace (EAF). By reducing the EAF’s impact on the power system, accurate predictions will anticipate and minimize disturbances, optimize energy consumption, and improve grid stability. This research’s principal scientific contribution is the development of a hybrid deep neural network that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) layers. This deep neural network was designed to predict power quality metrics, including active power, reactive power, distortion power, and Total Harmonic Distortion (THD). The proposed methodology indicates an important step in improving the accuracy of power quality forecasting for Electric Arc Furnaces (EAFs). The hybrid model’s ability for analyzing both time-series data and complex nonlinear patterns improves its predictive accuracy compared to traditional methods.

A Novel NR-DA-Based ANN for SHEPWM in Cascaded Multilevel Inverters for Renewable Energy Applications

Harmonics is the major power quality problem caused by nonlinear devices, leading to malfunction or operational halt of the system. Low-order harmonics increase vibration and heat generation in motors. Therefore, controlling harmonics in the output waveform is the prime target for industrial applications to avoid economic loss. Selective harmonic elimination pulse width modulation (SHEPWM) is one of the techniques used for eliminating or minimizing selected harmonics in the output voltage waveform. This paper utilizes the Newton-Raphson method and Dragonfly Algorithms to calculate optimum switching angles for a Cascaded H-bridge Multilevel inverter (CHBMLI). The algorithms use non-linear equations to calculate the Switching Angles of MLI. The Dragonfly algorithm requires several iterations to reach an optimum solution. For complex problems, this algorithm becomes computationally expensive and time-consuming. A lookup table addresses the limitation by offline training an Artificial Neural Network (ANN) to generate the optimum switching angle for a given modulation index. Neural Fitting Tool in MATLAB software is used to train the ANN model. The simulation is performed using MATLAB SIMULINK software for both 5-level and 7-level CHBMLI configuration. The Dragonfly algorithm-based ANN achieves THD 8.84% when the modulation index (M) equals 0.8 for a 7-level inverter and THD 14.91% for a 5-level inverter and effectively minimizes third and fifth-order harmonics.

Sliding mode control based dynamic voltage restorer for voltage sag compensation

Sliding mode control based dynamic voltage restorer for voltage sag compensation

Decision making methods used for improving power quality to meet the sustainable energy targets

A higher quality energy demand in line with sustainable goals has led researchers to new studies. The concept of power quality has become very popular in this direction in the last decade. Decision making concept, which is another important and popular concept, has become an indispensable tool in solving difficult decision making problems in many areas. The main starting point of this study was to unearth the crucial studies on “decision making for a better power quality,” which is at the intersection of these two important issues. In this direction, the deficiency in the literature was noticed, the related studies were examined, and the methodological features and prominent aspects of the studies in different categories were examined. The active use of many decision-making methods in the search for solutions to different power quality problems has been demonstrated. The conclusion that decision-making mechanisms offer effective solutions in solving power quality problems has been more clearly emphasized in this study. This review furnishes researchers with crucial insights into current trends in power quality and multi-criteria decision-making research. It also delineates potential avenues for additional research in this increasingly popular field.

A dynamic reference voltage adjustment strategy for Open-UPQC to increase hosting capacity of electrical distribution networks

A dynamic reference voltage adjustment strategy for Open-UPQC to increase hosting capacity of electrical distribution networks

AI based UPQC control technique for power quality optimization of railway transportation systems

Metro trains have non-linear load characteristics, which means that the power sent to them gets distorted. Problems are caused by changes in power, swells, harmonics, and other disturbances. In this research, an artificial intelligence-driven control method was used on a unified power quality conditioner (UPQC) to help reduce power quality problems and improve power quality. Three advanced control methods are built and compared using MATLAB Simulink. Some of these methods are the ANN Controller, the NARMA-L2 Controller, and the PI Controller, improved using the Adaptive Lizard Algorithm. The controls' usefulness is judged by how well they lower the total harmonic distortion (THD) in the source current. The results show that all three AI-based controls work much better than the system that was not paid for. The ANN Controller works the best, followed by the NARMA-L2 Controller, and the PI Controller improved with the Adaptive Lizard Algorithm. These AI-driven control methods can enhance power quality and ensure that metro rail systems run smoothly and efficiently, as shown by how well they work. Modern transportation networks need more advanced ways to handle power quality, and this research helps make those solutions come together.

Power quality improvement of unipolar-input-bipolar-output DC transmission system via load power balancing

In DC transmission and distribution systems, both unipolar and bipolar transmission modes exist, and DC transformers used in these systems are also available in either unipolar or bipolar configurations. In actual systems, due to requirements such as economy, land occupation, and reliability, there is a tendency to use a system with unipolar input and bipolar output. However, the bipolar loads, if unbalanced, will lead to increased equipment costs and voltage imbalance, causing power quality problems. This paper defines the Power Unbalance Factor (PUF) to describe the power quality of the studied DC transmission system and presents an improved DC transformer topology based on a power balancing system. This topology realizes bipolar voltage balance and improves the power quality of the DC transmission system when the load is unbalanced. The influence of the proposed solution on the power design of the DC system is demonstrated through theoretical analysis, and its effectiveness for improving the DC power quality is verified by both simulations in MATLAB/Simulink environment and physical experiments. When the power electronic transformer needs to be overloaded, the proposed topology can reduce the design power of the two branches by using the difference power, which is economical.