Grid Algorithm Research Articles

Underwater-to-air distorted image correction based on the reconstructed water surface

Abstract The lights with spatial target information received by the underwater camera are refracted at the intersection with the water surface, resulting in geometric distortion of the image. Existing methods for correcting the water-to-air distorted images typically rely on a large amount of data, such as image sequences, making the restoration using a single frame challenging. To address the issue, we propose a spatial pixel correction algorithm based on the reconstructed water surface. Firstly, we introduce a gradient water surface reconstruction algorithm based on the discrete normal vector, ensuring high accuracy in the spatial position and amplitude of the reconstructed water surface. Thus, intersections of the lights with the reconstructed water surface can be solved based on the constructed water surface. Subsequently, we propose a camera’s reverse tracking algorithm, which skillfully links the images with the spatial pixel coordinates. Finally, based on the characteristics of pixel arrangement, we propose a spatial grid algorithm to separate the spatial coordinates obtained by the reverse tracking algorithm. This part can better handle the highly concentrated and over-dispersed pixels in the spatial coordinate system. The proposed correction algorithm has better correction performance. The similarity between the restored and real images is more than 80%, and the mean square error is less than 0.01.

An effective ensemble electricity theft detection algorithm for smart grid

AbstractSeveral machine learning and deep learning algorithms have been presented to detect the criminal behaviours in a smart grid environment in recent studies because of many successful results. However, most learning algorithms for the electricity theft detection have their pros and cons; hence, a critical research issue nowadays has been how to develop an effective detection algorithm that leverages the strengths of different learning algorithms. To demonstrate the performance of such an integrated detection model, the algorithm proposed first builds on deep neural networks, a meta‐learner for determining the weights of detection models for the construction of an ensemble detection algorithm and then uses a promising metaheuristic algorithm named search economics to optimise the hyperparameters of the meta‐learner. Experimental results show that the proposed algorithm is able to find better results and outperforms all the other state‐of‐the‐art detection algorithms for electricity theft detection compared in terms of the accuracy, F1‐score, area under the curve of precision‐recall (AUC‐PR), and area under the curve of receiver operating characteristic (AUC‐ROC). Since the results show that the meta‐learner of the proposed algorithm can improve the accuracy of deep learning algorithms, the authors expect that it will be used in other deep learning‐based applications.

Optimal control algorithms for Gabon’s Smart Grid: Enhancing efficiency and sustainability

The adoption of smart grid technologies offers Gabon several opportunities to enhance energy efficiency and sustainability. This study investigates the use of optimum control algorithms to increase grid stability and enhance the utilization of renewable energy sources. Mathematical models and simulations are used to assess genetic algorithms in light of Gabon's unique energy situation. Systems known as electrical smart grids supply electricity to users directly from power plants in an effort to reduce costs, cut down on blackouts, and improve energy efficiency. Smart grids, or SGs, are well-known pieces of equipment because of their amazing capabilities, which include bi-directional communication, stability, power failure detection, and interconnectivity with appliances for monitoring. Many modern data and security management systems, including modeling, monitoring, optimization, and/or artificial intelligence, lead to SGs. Energy was once cheap, easily managed, and dependent only on fundamental elements. Demand has significantly increased as a result of the current circumstances, which has also increased Gabon's electricity expenses, the likelihood of a contingency, and the complexity of the electrical network. In Gabon, smart grids have shown to be the most practical and clever way to lessen these problems in recent years. An electrical network with information technology integrated is called a smart grid. According to this study, using genetic algorithms in a smart grid could lower Gabon's overall electricity prices. In order to meet demand, the proposed approach makes use of off-grid battery banks and renewable energy sources. The goal of GA optimization is the short-term time averaged power cost; factors that affect this include grid energy to satisfy load, battery discharge, and other factors. MATLAB software is used to solve the optimization problem over a 24-hour period of renewable input, real-time energy pricing, and load. The results are presented.

Design of ant colony planning algorithms for multi-stage grids of distribution networks considering network resilience

Network Destruction Resilience is the ability of a system to maintain good operation in the event of an external attack or internal failure. The resilience of distribution network is crucial to guarantee the reliability of power supply. In this paper, we design a planning algorithm considering network destructibility for the multi-stage ant colony planning problem of distribution networks. The method establishes the multi-stage network planning objective function of distribution network from the perspectives of total investment cost and annual operation cost of multi-node network frame, destruction resistance, active power and reactive network loss of distribution network, etc. Then based on the constraints of the model, the improved ant colony algorithm is used to solve the multi-stage network planning objective function of distribution network, and the results of the anti-termite colony planning of multi-stage network frame of distribution network are obtained. In order to verify the effectiveness of the algorithm, simulation experiments are carried out on real distribution network data. The results show that the proposed ant colony planning algorithm for multi-stage grid frames can effectively improve the destruction resistance of distribution networks, and reduce the total investment cost and annual operation cost of multi-stage grid frames, and reduce the network loss rate data of multi-stage grid frames of distribution networks after application. It provides an effective method for planning the distribution network.

Assessment of Envelope- and Machine Learning-Based Electrical Fault Type Detection Algorithms for Electrical Distribution Grids

This study introduces envelope- and machine learning (ML)-based electrical fault type detection algorithms for electrical distribution grids, advancing beyond traditional logic-based methods. The proposed detection model involves three stages: anomaly area detection, ML-based fault presence detection, and ML-based fault type detection. Initially, an envelope-based detector identifying the anomaly region was improved to handle noisier power grid signals from meters. The second stage acts as a switch, detecting the presence of a fault among four classes: normal, motor, switching, and fault. Finally, if a fault is detected, the third stage identifies specific fault types. This study explored various feature extraction methods and evaluated different ML algorithms to maximize prediction accuracy. The performance of the proposed algorithms is tested in an emulated software–hardware electrical grid testbed using different sample rate meters/relays, such as SEL735, SEL421, SEL734, SEL700GT, and SEL351S near and far from an inverter-based photovoltaic array farm. The performance outcomes demonstrate the proposed model’s robustness and accuracy under realistic conditions.

Framework for Numerical 6DOF Simulation with Focus on a Wing Deforming UAV in Perch Landing

The perch landing maneuver of a wing-deforming unmanned aerial vehicle (UAV) was investigated through a framework that uses the free, open-source OpenFOAM with 6 degrees of freedom (6DOF) simulations. The framework uses a moving grid to follow the trajectory of the UAV, reducing computational resources. Together with the ability to allow internal grid deformation, sliding mesh, and algorithm addition, it can accurately mimic the entire landing process. Different wing deformation speeds, additional elevator rotation and emulated propeller lift were added to the 6DOF simulations to investigate their effects on the landing maneuver. The results showed that the wing deformation retraction speed has a considerable effect on the trajectory and velocity of the UAV. The wing deformation reduced the forward velocity of the UAV by 32%, from 13.89 to 9 m/s. With the elevator control, the velocity was reduced to 5 m/s. Lastly, and an activation time of 1 s for the emulated propeller lift can further decrease the velocity to around 4.2 m/s. A better algorithm for the emulated propeller lift may be able to give a superior performance. This framework allows us to understand the underlying perch landing maneuver aerodynamics. It can also be used on problems like fast-turning agile and flapping wing flight.

Fault detection and mitigation in MVDC grids using a Non-Unit Voltage Ratio Derivative Algorithm

Future MVDC grids will be used to reinforce the grid infrastructure enhancing transmission capabilities, minimizing losses, and providing capability to compensate reactive power as well as to control load flows. This paper addresses the protection of MVDC grids, which is one of the main challenges to be overcome, before taking advantage of all the inherent benefits that this technology will bring. The main objective of this paper is to validate a non-unit voltage derivative fault detection algorithm in a MVDC grid with green hydrogen production. The performance is analyzed with different fault cases and fault locations. As a main conclusion, the distance is the most relevant factor, which affects detection and tripping times.

Research on the Application of Node Importance Assessment based on HITs Algorithm in Power Grid Planning

Research on the Application of Node Importance Assessment based on HITs Algorithm in Power Grid Planning

Observational Limitations to the Emergence of Climate Signals

AbstractUsing model projections to study the emergence of observable climate signals presumes omniscient knowledge about the climate system. In reality, observational knowledge suffers from data quality and availability issues, for instance data gaps, changes in instrumentation, issues due to gridding and retrieval algorithms. Overlooking such deficiencies leads to misrepresentations of the time of emergence (ToE). We introduce a new definition of ToE that accounts for observational limitations, and show that significant corrections to the ToE may be necessary to achieve the same statistical confidence as would be afforded by omniscient knowledge. We also show how our method can inform future observational needs and observing systems design.

Task scheduling using glowworm-based optimal heterogeneous earliest finish time algorithm for mobile grid

Task scheduling using glowworm-based optimal heterogeneous earliest finish time algorithm for mobile grid

Numerical Research on Impacting Load and Structural Response for a Model Experiment of High-speed Craft

Abstract High-speed craft generally applies hy droplane boat-type and large power engine to obtain high speed in water as results that significant hydrodynamic nonlinearities, high-speed craft has complex the impact load characteristics and structural response which attract considerable attentions of ship mechanics researchers. This paper studies impacting load and structural response of a high-speed craft by means of numerical approach coupling CFD and FEM. A model experiment of high-speed craft is selected to simulate impact load and structural response, the craft model consists of two ship bodies and a keel beam, a number of pressure sensors and strain gauges are arranged to capture the impacting pres s ure and VBM. A numerical FSI approach coupling CFD and FEM is used to study the model experiment of the high-speed craft. CFD is used to solve the hydrodynamics of the high-speed craft, DFBI and overset grid algorithm are employed to simulate the craft motion. Dy namic FEM is us ed to calculate structural response of high-speed craft, structural VBM is obtained. Two-way FSI is employ ed to realize coupling of CFD and FEM, wave pres s ures and structural displacement are iterated to progress. At last, numerical res ults and experimental res ults including impacting pressure, craft motion and VBM are compared and analyzed, they agree with closely. The numerical approach can be used to study the impacting load and structural respons e for high-speed craft.

Hybrid Grid Pattern Star Identification Algorithm Based on Multi-Calibration Star Verification.

In order to solve the star identification problem in the lost space mode for scientific cameras with small fields of view and higher instruction magnitudes, this paper proposes a star identification algorithm based on a hybrid grid pattern. The application of a hybrid pattern generated by multi-calibration stars in the initial matching enables the position distribution features of neighboring stars around the main star to be more comprehensively described and avoids the interference of position noise and magnitude noise as much as possible. Moreover, calibration star filtering is adopted to eliminate incorrect candidates and pick the true matched navigation star from candidate stars in the initial match. Then, the reference star image is utilized to efficiently verify and determine the final identification results of the algorithm via the nearest principle. The performance of the proposed algorithm in simulation experiments shows that, when the position noise is 2 pixels, the identification rate of the algorithm is 96.43%, which is higher than that of the optimized grid algorithm by 2.21% and the grid algorithm by 4.05%; when the magnitude noise is 0.3 mag, the star identification rate of the algorithm is 96.45%, which is superior to the optimized grid algorithm by 2.03% and to the grid algorithm by 3.82%. In addition, in the actual star image test, star magnitude values of ≤12 mag can be successfully identified using the proposed algorithm.

Modeling Pharmacokinetics in Individual Patients Using Therapeutic Drug Monitoring and Artificial Population Quasi-Models: A Study with Piperacillin.

Population pharmacokinetic (pop-PK) models constructed for model-informed precision dosing often have limited utility due to the low number of patients recruited. To augment such models, an approach is presented for generating fully artificial quasi-models which can be employed to make individual estimates of pharmacokinetic parameters. Based on 72 concentrations obtained in 12 patients, one- and two-compartment pop-PK models with or without creatinine clearance as a covariate were generated for piperacillin using the nonparametric adaptive grid algorithm. Thirty quasi-models were subsequently generated for each model type, and nonparametric maximum a posteriori probability Bayesian estimates were established for each patient. A significant difference in performance was found between one- and two-compartment models. Acceptable agreement was found between predicted and observed piperacillin concentrations, and between the estimates of the random-effect pharmacokinetic variables obtained using the so-called support points of the pop-PK models or the quasi-models as priors. The mean squared errors of the predictions made using the quasi-models were similar to, or even considerably lower than those obtained when employing the pop-PK models. Conclusion: fully artificial nonparametric quasi-models can efficiently augment pop-PK models containing few support points, to make individual pharmacokinetic estimates in the clinical setting.

TWO-GRID VIRTUAL ELEMENT DISCRETIZATION OF QUASILINEAR ELLIPTIC PROBLEM

In this paper a two grid algorithm for quasilinear elliptic problem based on virtual element method (VEM) discretization is proposed. With this new algorithm the solution of a quasilinear elliptic problem on a fine grid is reduced to the solution of a quasilinear elliptic problem on a much coarser grid, and the solution of a linear system on the fine grid. A priori error estimate in H1 norm is derived. Numerical experiments are carried out to illustrate the theoretical findings.

A secure certificateless ring signcryption scheme based on SM2 algorithm in smart grid

In the context of smart grids, bidirectional transmission of electricity information enables real-time electricity generation tailored to consumer needs. However, ensuring user privacy during data collection has emerged as a significant concern with the proliferation of data collection and transmission capabilities. Existing solutions such as group signature and pseudonym systems have limitations, such as a lack of trustworthiness in group signature administrators and increased system costs associated with pseudonym storage. To address these drawbacks, this paper proposes a certificateless ring signcryption scheme with conditional privacy protection based on the SM2 algorithm. The scheme efficiently enables users to ring signcryption-transmitted messages, thereby concealing the sender’s identity from the message receiver. This approach resolves the privacy concerns mentioned earlier. In addition, tracking algorithm and batch verification algorithm have been designed to improve computational efficiency while also providing the ability for trusted parties to track malicious users. This scheme achieves conditional privacy preservation while avoiding substantial storage costs for power resources. Compared to the latest available programs, our proposed scheme offers enhanced efficiency and lower communication costs.

Research on the Application of Joint Optimization Strategy of Energy Storage System and Demand Side Response Based on Tianqun Algorithm in Renewable Energy Grid

Research on the Application of Joint Optimization Strategy of Energy Storage System and Demand Side Response Based on Tianqun Algorithm in Renewable Energy Grid

A comprehensive review on application of ai algorithms for GRID connected Solar Photovoltaic Systems

A comprehensive review on application of ai algorithms for GRID connected Solar Photovoltaic Systems

Surface unequal charging effect of GEO satellite

When a satellite is in orbit, the plasma environment interacts with the surface material, and the surface charge accumulation of different structures is inconsistent,resulting in an unequal charging effect. In serious cases, this leads to electrostatic discharge and affects the normal operation of the satellite. In this study, a three-dimensional model of a geosynchronous orbit (GEO) satellite is constructed, and the particle cloud grid algorithm (Particle-In-Cell,PIC) was used to simulate the charging process and the unequal charged state between the satellite panels in the solar wind plasma environment and the hot plasma environment. The simulation results show that in the solar wind plasma environment, the charging balance potential of solar panels is low and the charging difference between panels is small, which does not affect the normal operation of the satellite. However, when the satellite is in an hot plasma environment, if some parts of the satellite suddenly encounter extremely strong interference, the charging balance of the satellite in the solar wind plasma will be destroyed, and there will be a large potential difference between the irradiated part and the adjacent structure, which can easily cause electrostatic discharge to harm the normal operation of the satellite.

Two-grid finite element method with an H2N2 interpolation for two-dimensional nonlinear fractional multi-term mixed sub-diffusion and diffusion wave equation

<abstract><p>In this paper, we studied the two-grid method (TGM) for two-dimensional nonlinear time fractional multi-term mixed sub-diffusion and diffusion wave equation. A fully discrete scheme with the quadratic Hermite and Newton interpolation (H2N2) method was considered in the temporal direction and the expanded finite element method is used to approximate the spatial direction. In order to reduce computational time, a dual grid method based on Newton iteration was constructed with order $ \alpha\in(0, 1) $ and $ \beta\in(1, 2) $. The global convergence order of the two-grid scheme reaches $ O(\tau^{3-\beta}+h^{r+1}+H^{2r+2}) $, where $ \tau $, $ H $ and $ h $ are the time step size, coarse grid mesh size and fine grid mesh size, respectively. The error estimation and stability of the fully discrete scheme were derived. Theoretical analysis shows that the two grid algorithms maintain asymptotic optimal accuracy while saving computational costs. In addition, numerical experiments further confirmed the theoretical results.</p></abstract>

Distributed economic dispatch algorithm in smart grid based on event-triggered and fixed-time consensus methods

In this paper, a distributed economic dispatch algorithm based on event-triggered and fixed-time consensus is proposed for optimal dispatching among generators in smart grid. Aiming to quickly achieve economic dispatch in distributed smart grid and significantly reduce the frequency of communication between generators, the algorithm can address the supply–demand balance between the generation side and the consumption side, as well as the capacity constraints of the generators. Different from the relevant work, the proposed incremental cost can converge in fixed-time and the settling time is independent of the initial state of the generators. It will provide more flexibility for the system. Meanwhile, a fully distributed event-triggered strategy is adopted, which can reduce the interactive information between generators and avoid the waste of communication resources. Furthermore, the proposed algorithm is robust to the time-varying communication network topology. Finally, several examples are presented that validate the effectiveness of the algorithm.