Grid Quality Research Articles

A Low-Cost Evaluation Tool for Synchronization Methods in Three-Phase Power Systems

The use of renewable energy sources (RESs) together with energy storage systems (ESSs) allows for smoothing power variations, thus improving power backup capabilities and power quality in the electric power grid. These applications require power converters to transfer energy between the renewable generator or energy storage and the power grid. In any case, the control algorithm of the power converter requires the synchronization method to provide a correct estimation of the instantaneous voltage of the power grid. This work provides engineers and researchers with an accessible platform at a low cost (less than USD 100) and a methodology for the experimental validation of digital synchronization algorithms as a step before their implementation in grid-connected equipment. The methodology evaluates the performance of the digital algorithms when there are variations in amplitude, frequency, phase, and harmonic content in the emulated three-phase power grid, as well as the execution times (tex), while a digital platform emulates the electrical signals and generates reference signals for the evaluation. To illustrate this proposal, two synchronization algorithms—SRF-PLL and DSOGI-PLL with a low-pass filter—are implemented in a digital controller and tested. The evaluation tool confirms the algorithms’ performance and shows that the execution time of DSOGI-PLL is 91% longer than that of SRF-PLL, which is well known in the literature.

TagGen: Diffusion-based generative model for cardiac MR tagging super resolution.

The aim of the work is to develop a cascaded diffusion-based super-resolution model for low-resolution (LR) MR tagging acquisitions, which is integrated with parallel imaging to achieve highly accelerated MR tagging while enhancing the tag grid quality of low-resolution images. We introduced TagGen, a diffusion-based conditional generative model that uses low-resolution MR tagging images as guidance to generate corresponding high-resolution tagging images. The model was developed on 50 patients with long-axis-view, high-resolution tagging acquisitions. During training, we retrospectively synthesized LR tagging images using an undersampling rate (R) of 3.3 with truncated outer phase-encoding lines. During inference, we evaluated the performance of TagGen and compared it with REGAIN, a generative adversarial network-based super-resolution model that was previously applied to MR tagging. In addition, we prospectively acquired data from 6 subjects with three heartbeats per slice using 10-fold acceleration achieved by combining low-resolution R = 3.3 with GRAPPA-3 (generalized autocalibrating partially parallel acquisitions 3). For synthetic data (R = 3.3), TagGen outperformed REGAIN in terms of normalized root mean square error, peak signal-to-noise ratio, and structural similarity index (p < 0.05 for all). For prospectively 10-fold accelerated data, TagGen provided better tag grid quality, signal-to-noise ratio, and overall image quality than REGAIN, as scored by two (blinded) radiologists (p < 0.05 for all). We developed a diffusion-based generative super-resolution model for MR tagging images and demonstrated its potential to integrate with parallel imaging to reconstruct highly accelerated cine MR tagging images acquired in three heartbeats with enhanced tag grid quality.

An enhanced interface-sharpening algorithm for accurate simulation of underwater explosion in compressible multiphase flow on complex grids

In underwater explosion simulations, the accuracy is critically affected by numerical diffusion and the quality of the computational grid. An enhanced interface-sharpening algorithm, based on the five-equation model and an anti-diffusion source term, is proposed to simulate the strongly compressible multiphase flow with an arbitrary number of phases in underwater explosions. To enhance the precision of interface and maintain computational stability on nonuniform and unstructured grids, an adaptive factor algorithm and a continuous interface capture function are developed. Furthermore, a redesigned fractional step method incorporating a special iteration stop criteria is proposed to solve the volume fraction equation. A pressure–density cutoff model is used to calculate the mixture density at the saturation pressure to account for the effect of cavitation. The quadratic monotone upstream-centered scheme for conservation laws reconstruction scheme and the improved advection upstream splitting method (AUSM+-up) flux scheme are also adopted to compute the flux across the grid interfaces. The overall computational framework is constructed upon a density-based explicit finite volume method. To assess the accuracy and robustness of the proposed numerical model, four representative test cases were chosen. The results reveal that the model refines the phase interface, adeptly capturing the complexities inherent in underwater explosion scenarios with both precision and efficiency across a diverse array of conditions.

An Energy Storage System for Regulating the Maximum Demand of Traction Substations

With the development of electrified railways towards high speed and heavy load, the peak power of traction loads is increasing, and the maximum demand and negative sequence current of traction substations are also increasing. Therefore, this article proposes an energy storage system (ESS) based on Li-ion batteries for regulating the maximum demand of traction substations. An ESS is connected to the DC bus of a railway power conditioner (RPC), which is connected to the two power supply arms of the traction substation. In response to the large fluctuation of traction load, this paper proposes a maximum demand active regulation method based on short-term prediction of traction load. The short-term prediction of traction load adopts a time series short-term load prediction method based on BP neural network error correction. Then, based on the load prediction value of the traction substation and the state of charge of the ESS, a collaborative control strategy for ESS and RPC is formulated to enable RPC to achieve a negative sequence suppression function simultaneously. Finally, simulation experiments were conducted using MATLAB, and the results showed that compared with the traditional maximum demand regulation method based on peak power reference values, the method proposed in this paper significantly reduces the number of ESS charging and discharging cycles, improves the regulation effect of maximum demand, and has a higher net income during the lifecycle. At the same time, it also takes into account the negative sequence current suppression function, thereby improving the comprehensive economic benefits of railways and the quality of power grids.

Artificial intelligent fuzzy control and LAPO algorithm for enhancement LVRT and power quality of grid connected PV/wind hybrid systems

Low Voltage Ride Through (LVRT) is considered one of the main and serious problems facing the electrical grid. It occurs due to three-phase symmetric faults and asymmetric faults such as a double line to ground fault that applies in this system. This paper applies Static Synchronous Compensators (STATCOM) to improve the LVRT capability and dynamic performance of an electrical grid linked to a Photovoltaic (PV)/Wind hybrid system through grid disturbances. A hybrid power system containing a PV station that produces 1 MW and a wind farm from type Doubly Fed Induction Generator (DFIG) that produces 9 MW is connected to STATCOM with 48 pulses at PCC bus and energized load. It compensates reactive power to improve LVRT that occurred due to fault. The applied STATCOM controller adjusts the voltage of the PCC bus during an occuring fault on the grid by compensating reactive power. STATCOM is controlled by a Proportional–Integral–Derivative (PID) and is compared with STATCOM controlled by Artificial Intelligence Control (AIC)-based on Proportional—Integral Fuzzy Logic Control (PI FLC). The Lightning Attachment Procedure Optimization Algorithm (LAPO) optimization method is used to adjust the parameters of the PI controller to reduce error signals. A simulation model of the suggested hybrid power system has been performed using Matlab/Simulink. The simulation results of STATCOM proved powerful and the effectiveness of STATCOM with PI FLC in reducing voltage dip, compensating active power of wind and PV farm, protecting DC-link voltage of PV and wind from overvoltage and oscillation that happens at three-phase fault and double line to ground fault as compared with PID STATCOM in enhancement LVRT capability, and power quality.

A multi-stage balancing scheduling method for flexible loads of large-scale electric vehicles

To address the degradation of grid quality and charging efficiency associated with the large-scale integration of electric vehicles (EVs), a multi-stage balanced flexible load scheduling method is proposed. This approach is designed to facilitate peak shaving and valley filling, balance intermittent energy fluctuations, and provide auxiliary services, thereby significantly altering system load characteristics, smoothing energy fluctuations, reducing operational costs, and enhancing the regulatory capabilities of power grid dispatching operations. A multi-objective optimization mathematical model is developed, focusing on key indicators that impact the scheduling process, including network loss, operational cost, and user satisfaction. A multi-stage flexible load scheduling framework is introduced within the competitive swarm optimization (CSO) algorithm, resulting in the design of an advanced CSO algorithm. This algorithm is distinguished from traditional methods by the implementation of advanced learning based on grouping after a random competitive learning phase, which enhances the efficiency of particle swarm learning while ensuring stable population convergence throughout the optimization process. Furthermore, the CSO framework is maintained to ensure effective population diversity, greatly improving the optimization performance. Simulation results indicate that the voltage fluctuation index of the proposed algorithm is 1.8% lower than that of the standard CSO algorithm, while network loss and operational costs are reduced by 2.83 and 5.81%, respectively, thereby validating the effectiveness and efficiency of the proposed approach.

Research on the Quality of Adaptive Grids for Calculating Separate Flows on a Two-Fluid Turbulence Model

Research on the Quality of Adaptive Grids for Calculating Separate Flows on a Two-Fluid Turbulence Model

Selective Harmonic Compensation in SPC Grid-Forming Converters for Improving Power Quality in Weak Grid

Selective Harmonic Compensation in SPC Grid-Forming Converters for Improving Power Quality in Weak Grid

A Q‐Learning and Fuzzy Logic Control of Hybrid Energy Storage System Using Two Stage Low‐Pass Filter to Smooth Power Fluctuations in Microgrid

ABSTRACTThe intermittent and fluctuating nature of wind turbine output power is increasingly being recognized as a significant issue adversely impacting the power quality and stability of electrical grids. With the increasing integration of wind power, this challenge cannot be underestimated. However, a potential solution for mitigating the adverse effects of such fluctuations lies in the Hybrid Energy Storage System (HESS), which encompasses battery energy storage systems (BESS) and supercapacitors (SC). The HESS is equipped with flexible operational modes for charging and discharging, thus enabling grid‐connected microgrids to possess the ability to counteract these oscillations. In this article, a control strategy based on the combination of Q‐learning and fuzzy logic control approaches is presented for tuning the parameters of a utilized two‐stage variable time constant low‐pass filter (LPF) in a grid‐connected microgrid. The proposed strategy adaptively tunes the time constants of LPFs to mitigate wind power fluctuations. Furthermore, practical constraints for the energy storage systems and their interfaced converters, such as preventing overcharge/discharge, ramp rate requirements, and certain maximum power conversion ranges, have been taken into account. Numerical simulation results verify the effectiveness of the proposed two‐stage variable time constant LPF for output wind power fluctuation reduction considering practical constraints of HESS.

Physics-Informed Sampling Scheme for Efficient Well Placement Optimization

Abstract Well placement optimization is a crucial task in terms of oil and gas recovery and economics in the field development plan. It poses significant challenges due to the multitude of local optima, which demand massive computational cost for global search algorithms. To address this, many proxy models have been applied for replacing reservoir simulations in many cases. Among these, convolutional neural network-based proxy models utilizing streamline time of flight maps as input demonstrated excellent performances. Nevertheless, these models exhibit diminishing performances during optimization processes, so additional retraining processes are required for successful results. In this study, we propose an initial sampling scheme using physics-informed quality maps incorporating static and dynamic information. The quality maps combine drainage area with permeability to represent the quality of each reservoir grid. The proposed scheme provides better performance than other sampling schemes. We demonstrate that the proposed scheme provides efficient well placement optimization regardless of the number of samples without retraining.

Improve power quality and stability of grid - Connected PV system by using series filter

As the world increasingly focuses on renewable energy sources, grid-connected PV systems will continue to play a critical role in meeting our energy needs. This integration has brought many benefits, but has also created various problems related to power quality and stability at the connection points. Various techniques are used to improve the power quality, such as passive filters, tuned passive harmonic filters, and active filters. This paper presents the use of a series active filter on the DC side of grid-connected PV systems to improve their power quality, stability, and dynamic performance. The proposed filter consists of an inductor, two capacitors, and four transistor-diode pairs, the operation of which is controlled by a sinusoidal pulse width modulation (SPWM) scheme. The MATLAB/Simulink is used to build and validate a comprehensive mathematical model of the studied system. The transfer function of the system is identified and its transient response is studied under various test input signals. A Bode plot is also generated to assess the impact of the integration of this component on the stability of the system. The results showed that incorporating the filter resulted in a significant reduction in the total harmonic distortion factor (Thd%) for both the voltage and current waves at the inverter output. Moreover, it improved the transient response characteristics of the system by significantly reducing the maximum overshoot value of the system response with respect to the test input signals. The results indicate that the stability of the system is improved after the filter addition, as evidenced by the increased gain margin and phase margin in the system Bode plot.

An Ultra‐High Voltage AC/DC Isolated Matrix Converter Applied to V2G Electric Vehicle Charging Piles

ABSTRACTIn recent years, in order to alleviate global environmental problems, renewable energy power generation and the electric vehicle industry have been vigorously promoted by many countries. During this background, many scholars have proposed the development of vehicle‐to‐grid (V2G) systems to alleviate the impact of renewable energy generation on grid quality, and electric vehicle charging piles are the core part of realizing V2G systems. This article proposes an ultra‐high voltage AC/DC isolated matrix converter applied to V2G electric vehicle charging piles, which can achieve bidirectional flow of energy, and proposes the control strategy that can reduce the current stress of the device. In addition, the ultra‐high voltage module of the proposed topology can provide wide voltage range and high voltage charging power for different types of electric vehicles. The proposed topology and control strategy are verified by simulation.

Innovative simplified approach and numerical investigation for flow and heat transfer in internally threaded tubes

Internally threaded tubes exhibit exceptional heat transfer capabilities, holding paramount significance in enhancing energy efficiency, reducing energy consumption, and tackling thermal challenges across aerospace, automotive, and environmental domains. Nonetheless, their intricate structural features, software modelling intricacies, extensive grid requirements, and challenges in maintaining grid quality render numerical simulation investigations a demanding endeavor. This study elucidates the influence mechanisms of thread spacing on the distribution of single-phase and two-phase flows. It delves into the condensation heat transfer of the R32 refrigerant and the single-phase heat transfer of water within internally threaded tubes. At the same Reynolds number, the heat transfer rate can be improved by approximately 19.61% with the optimized internally threaded parameters. When the Reynolds number varies from 18,626 to 51,222, the heat transfer rate per unit length for Tube-5 remains around 2,478.9 W/m, with a variation of less than 5.19%. Furthermore, it presents a simplified numerical simulation approach tailored for internally threaded pipes. This method manipulates wall roughness and regulates wall rotation speed to mimic fluid dynamics phenomena akin to conventional numerical simulation techniques. The findings reveal that the proposed simplified numerical simulation approach yields heat transfer and pressure drop predictions with less than 3% discrepancies compared to traditional numerical simulation predictions. Simultaneously, compared to directly simulating threaded pipes through conventional means, this approach significantly mitigates grid generation complexities, slashing computational time costs by at least 85% and facilitating efficient thermal design and structural optimization processes.

Inversion of creep parameters and their application in underground salt cavern energy storage systems for enhancing wind power integration

While the development of wind energy presents great potential and opportunities, the high level of uncertainty inherent in wind farms also poses considerable challenges for operators, especially when it comes to participating in the energy market. The intermittent and unpredictable fluctuation characteristics of wind power generation may cause significant deviations in the frequency and voltage of the grid, which pose a certain threat to the stability of the grid and power quality and need to be mitigated by advanced energy storage solutions. Salt caverns, these geological structures deep in underground space, are widely used in oil and gas storage, compressed air energy storage(CAES), and other projects around the world. In the operation of underground salt caverns as storage, there is no effective technical means to monitor the deformation of deep caverns in real-time. Therefore, developing a high-precision real-time prediction method for cavern volume deformation is of great significance for the safety assessment during the underground salt cavern operation. In this context, this paper proposes a comprehensive technical approach to meet the needs of real-time monitoring of the cavern status, including volume changes, in the intelligent construction of underground salt cavern facilities in China. Firstly, through the shut-in pressure test of the brine well, the volume shrinkage of the cavern during the monitoring period is obtained, and the creep parameters of the salt rock strata in the regional salt mine are inversely derived. Secondly, for a typical cavern in operation in this region, the creep deformation is predicted using numerical simulation methods. By analyzing the cavern volume deformation under different pressures, an empirical equation for cavern volume deformation is established. Finally, by combining the volume changes obtained from sonar tests conducted during the operation of the cavern, the accuracy of the simulated results and the theoretical model derived from the inverted creep parameters is verified. The real-time analysis method for cavern volume deformation developed from this research will be applied to the intelligent construction of underground salt cavern facilities, ensuring safe and controllable operation management of the storage facilities.

Enhancing power quality in a smart grid using dynamic voltage restorer

Enhancing power quality in a smart grid using dynamic voltage restorer

The influence of traction load on power quality of weak power grid

The influence of traction load on power quality of weak power grid

Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.

Research on 3D Geological and Numerical Unified Model of in Mining Slope Based on Multi-Source Data

As mining engineering progresses into the deep excavation phase, the intensification of high pressure, high temperature, strong disturbances, and complex geological conditions becomes increasingly prominent. Researchers perform stability analysis on the excavation area to reduce potential safety hazards during the extraction process. Developing a detailed numerical calculation model that accurately reflects the true geological structure is essential for numerical simulation analysis in mining engineering. Based on the excellent 3D geological modeling capabilities of 3D Mine software, this paper introduces a new 3D geological and numerical unified modeling method (3DMine-Rhino-HyperMesh) involving multi-software coupling and details the specific steps and concepts of this modeling approach. Subsequently, using a certain open-pit mine in Panzhihua as a backdrop, a detailed geological and numerical unified model is established, reflecting the true geological structure of the mining area, and the potential failure mechanisms of the mine slope are analyzed. The results indicate that the modeling method aligns well with the actual geological conditions, enhancing the grid quality of the numerical model and offering a new modeling approach for simulating and analyzing large complex geological entities in mining operations.

Tetrahedral grids in Monte Carlo radiative transfer

Context. To understand the structures of complex astrophysical objects, 3D numerical simulations of radiative transfer processes are invaluable. For Monte Carlo radiative transfer, the most common radiative transfer method in 3D, the design of a spatial grid is important and non-trivial. Common choices include hierarchical octree and unstructured Voronoi grids, each of which has advantages and limitations. Tetrahedral grids, commonly used in ray-tracing computer graphics, can be an interesting alternative option. Aims. We aim to investigate the possibilities, advantages, and limitations of tetrahedral grids in the context of Monte Carlo radiative transfer. In particular, we want to compare the performance of tetrahedral grids to other commonly used grid structures. Methods. We implemented a tetrahedral grid structure, based on the open-source library TetGen, in the generic Monte Carlo radiative transfer code SKIRT. Tetrahedral grids can be imported from external applications or they can be constructed and adaptively refined within SKIRT. We implemented an efficient grid traversal method based on Plücker coordinates and Plücker products. Results. The correct implementation of the tetrahedral grid construction and the grid traversal algorithm in SKIRT were validated using 2D radiative transfer benchmark problems. Using a simple 3D model, we compared the performance of tetrahedral, octree, and Voronoi grids. With a constant cell count, the octree grid outperforms the tetrahedral and Voronoi grids in terms of traversal speed, whereas the tetrahedral grid is poorer than the other grids in terms of grid quality. All told, we find that the performance of tetrahedral grids is relatively poor compared to octree and Voronoi grids. Conclusions. Although the adaptively constructed tetrahedral grids might not be favourable in most media representative of astrophysical simulation models, they still form an interesting unstructured alternative to Voronoi grids for specific applications. In particular, they might prove useful for radiative transfer post-processing of hydrodynamical simulations run on tetrahedral or unstructured grids.

Smart grid power quality monitoring and regulation based on photoelectric sensors and optical system signal processing

With the rapid development of smart grids, power quality monitoring and regulation have become an important task to ensure the stable operation of the power grid. Traditional power quality monitoring methods have problems such as low monitoring accuracy and poor real-time performance. Therefore, this article proposes a smart grid power quality monitoring and adjustment method based on photoelectric sensors and optical system signal processing to improve monitoring accuracy and real-time performance. This article uses photoelectric sensors to monitor the power quality in the power grid in real-time, and transmits the monitoring data to the optical system. Signal processing of monitoring data through optical systems to extract key parameters related to power quality. Finally, by adjusting the relevant equipment of the smart grid, the regulation and optimization of power quality can be achieved. The experimental results show that the smart grid power quality monitoring and adjustment method based on photoelectric sensors and optical system signal processing can effectively improve monitoring accuracy and real-time performance. Through real-time monitoring and regulation of power quality, the stability and reliability of the power grid have been significantly improved.