scholarly journals Model-based integration of constrained search spaces into distributed planning of active power provision

2013 ◽  
Vol 10 (4) ◽  
pp. 1823-1854 ◽  
Author(s):  
Jörg Bremer ◽  
Michael Sonnenschein
Keyword(s):  
Optimization Methods ◽  
Active Power ◽  
Electricity Sector ◽  
Energy Resources ◽  
Feasible Region ◽  
Support Vector ◽  
Energy Unit ◽  
Distributed Planning ◽  
Search Spaces ◽  
The Individual

The current upheaval in the electricity sector demands distributed generation schemes that take into account individually configured energy units and new grid structures. At the same time, this change is heading for a paradigm shift in controlling these energy resources within the grid. Pro-active scheduling of active power within a (from a controlling perspective) loosely coupled group of distributed energy resources demands for distributed planning and optimization methods that take into account the individual feasible region in local search spaces modeled by surrogate models. We propose a method that uses support vector based black-box models for re-constructing feasible regions for automated, local solution repair during scheduling and combine it with a distributed greedy approach for finding an appropriate partition of a desired target schedule into operable schedules for each participating energy unit.

Geographical aspects of the diffusion of passive houses

2013 ◽  
Vol 4 (2) ◽  
pp. 151-156 ◽  
Author(s):  
G. Kozma ◽  
E. Molnár ◽  
K. Czimre ◽  
J. Pénzes
Keyword(s):  
Energy Consumption ◽  
Energy Resources ◽  
Substantial Part ◽  
Renewable Energy Resources ◽  
Passive House ◽  
The Earth ◽  
Passive Houses ◽  
The Individual ◽  
Geographical Locations ◽  
Over Time

Abstract In our days, energy issues belong to the most important problems facing the Earth and the solution may be expected partly from decreasing the amount of the energy used and partly from the increased utilisation of renewable energy resources. A substantial part of energy consumption is related to buildings and includes, inter alia, the use for cooling/heating, lighting and cooking purposes. In the view of the above, special attention has been paid to minimising the energy consumption of buildings since the late 1980s. Within the framework of that, the passive house was created, a building in which the thermal comfort can be achieved solely by postheating or postcooling of the fresh air mass without a need for recirculated air. The aim of the paper is to study the changes in the construction of passive houses over time. In addition, the differences between the geographical locations and the observable peculiarities with regard to the individual building types are also presented.

scholarly journals Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6069
Author(s):  
Sajjad Haider ◽  
Peter Schegner
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Optimization Methods ◽  
Monte Carlo Analysis ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Worst Case ◽  
Voltage Drops ◽  
The Individual ◽  
Nodal Voltage

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

scholarly journals An Improved Forecasting Method and Application of China’s Energy Consumption under the Carbon Peak Target

2021 ◽  
Vol 13 (15) ◽  
pp. 8670
Author(s):  
Xiwen Cui ◽  
Shaojun E ◽  
Dongxiao Niu ◽  
Dongyu Wang ◽  
Mingyu Li
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Optimization Methods ◽  
Support Vector ◽  
The Sustainable Development ◽  
Consumption Data ◽  
Forecasting Method ◽  
Forecasting Performance ◽  
Carbon Peak

In the process of economic development, the consumption of energy leads to environmental pollution. Environmental pollution affects the sustainable development of the world, and therefore energy consumption needs to be controlled. To help China formulate sustainable development policies, this paper proposes an energy consumption forecasting model based on an improved whale algorithm optimizing a linear support vector regression machine. The model combines multiple optimization methods to overcome the shortcomings of traditional models. This effectively improves the forecasting performance. The results of the projection of China’s future energy consumption data show that current policies are unable to achieve the carbon peak target. This result requires China to develop relevant policies, especially measures related to energy consumption factors, as soon as possible to ensure that China can achieve its peak carbon targets.

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

2021 ◽  
pp. 016173462199809
Author(s):  
Dhurgham Al-karawi ◽  
Hisham Al-Assam ◽  
Hongbo Du ◽  
Ahmad Sayasneh ◽  
Chiara Landolfo ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Empirical Evaluation ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Simple Majority ◽  
Ultrasound Scan ◽  
Histograms Of Oriented Gradients ◽  
The Individual ◽  
Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

scholarly journals Scalable Multiport Converter Structure for Easy Grid Integration of Alternate Energy Sources for Generation of Isolated Voltage Sources for MMC

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1779
Author(s):  
Syed Rahman ◽  
Irfan Khan ◽  
Khaliqur Rahman ◽  
Sattam Al Otaibi ◽  
Hend I. Alkhammash ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Energy Resources ◽  
Energy Sources ◽  
Renewable Energy Resources ◽  
Component Level ◽  
Storage Devices ◽  
Multiport Converter ◽  
Detailed Simulation ◽  
The Individual

This paper presents a novel, scalable, and modular multiport power electronic topology for the integration of multiple resources. This converter is not only scalable in terms of the integration of multiple renewable energy resources (RES) and storage devices (SDs) but is also scalable in terms of output ports. Multiple dc outputs of a converter are designed to serve as input to the stacking modules (SMs) of the modular multilevel converter (MMC). The proposed multiport converter is bidirectional in nature and superior in terms of functionality in a way that a modular universal converter is responsible for the integration of multiple RES/SDs and regulates multiple dc output ports for SMs of MMC. All input ports can be easily integrated (and controlled), and output ports also can be controlled independently in response to any load variations. An isolated active half-bridge converter with multiple secondaries acts as a central hub for power processing with multiple renewable energy resources that are integrated at the primary side. To verify the proposed converter, a detailed design of the converter-based system is presented along with the proposed control algorithm for managing power on the individual component level. Additionally, different modes of power management (emulating the availability/variability of renewable energy sources (RES)) are exhibited and analyzed here. Finally, detailed simulation results are presented in detail for the validation of the proposed concepts and design process.

scholarly journals Landslide Mapping Using Multiscale LiDAR Digital Elevation Models

2020 ◽  
Vol 26 (4) ◽  
pp. 405-425
Author(s):  
Javed Miandad ◽  
Margaret M. Darrow ◽  
Michael D. Hendricks ◽  
Ronald P. Daanen
Keyword(s):  
Image Classification ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Support Vector ◽  
Classification Approach ◽  
Resolution Image ◽  
Kappa Value ◽  
The Individual ◽  
Segmentation Image

ABSTRACT This study presents a new methodology to identify landslide and landslide-susceptible locations in Interior Alaska using only geomorphic properties from light detection and ranging (LiDAR) derivatives (i.e., slope, profile curvature, and roughness) and the normalized difference vegetation index (NDVI), focusing on the effect of different resolutions of LiDAR images. We developed a semi-automated object-oriented image classification approach in ArcGIS 10.5 and prepared a landslide inventory from visual observation of hillshade images. The multistage work flow included combining derivatives from 1-, 2.5-, and 5-m-resolution LiDAR, image segmentation, image classification using a support vector machine classifier, and image generalization to clean false positives. We assessed classification accuracy by generating confusion matrix tables. Analysis of the results indicated that LiDAR image scale played an important role in the classification, and the use of NDVI generated better results. Overall, the LiDAR 5-m-resolution image with NDVI generated the best results with a kappa value of 0.55 and an overall accuracy of 83 percent. The LiDAR 1-m-resolution image with NDVI generated the highest producer accuracy of 73 percent in identifying landslide locations. We produced a combined overlay map by summing the individual classified maps that was able to delineate landslide objects better than the individual maps. The combined classified map from 1-, 2.5-, and 5-m-resolution LiDAR with NDVI generated producer accuracies of 60, 80, and 86 percent and user accuracies of 39, 51, and 98 percent for landslide, landslide-susceptible, and stable locations, respectively, with an overall accuracy of 84 percent and a kappa value of 0.58. This semi-automated object-oriented image classification approach demonstrated potential as a viable tool with further refinement and/or in combination with additional data sources.

scholarly journals A Control Strategy for Smooth Power Tracking of a Grid-Connected Virtual Synchronous Generator Based on Linear Active Disturbance Rejection Control

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 3024 ◽  
Author(s):  
Yaya Zhang ◽  
Jianzhong Zhu ◽  
Xueyu Dong ◽  
Pinchao Zhao ◽  
Peng Ge ◽  
...  
Keyword(s):  
Disturbance Rejection ◽  
Synchronous Generator ◽  
Active Power ◽  
Energy Resources ◽  
Active Disturbance Rejection Control ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
New Energy ◽  
Simulation Results ◽  
Virtual Synchronous Generator

The power quality of new energy resources has received tremendous attention recently. The control approach for the inverter, an interface between the new energy resources, and the infinite bus system is of vital importance. For the virtual synchronous generator (VSG), one of the research hotspots in the inverter control field, there are some challenges remaining to be dealt with. First is the contradiction between the rapid response and overshoot of active power output if VSG is connected to the grid. Secondly, the active power is deeply influenced by the fluctuation of gird frequency and this may bring power oscillation to VSG in weak grids. In this article, an active power controller for power tracking of grid-connected VSG is designed based on linear active disturbance rejection control (LADRC) by compensating for the lumped disturbance in a feedforward fashion. The parameters of the controller are analyzed and tuned in the frequency domain to acquire a desirable control performance. Moreover, the robustness of the control system is also considered. Simulation results show that the designed control system can transmit active power to the grid in a timely manner with no overshoot, as demanded. Additionally, it can output active power steadily according to the power reference without using a phase-locked loop (PLL) when the grid frequency has different features of fluctuation. In addition, the simulation results demonstrate that the improved VSG has strong robustness to the model parameter perturbation and mismatch.

Nondegenerate Piecewise Linear Systems: A Finite Newton Algorithm and Applications in Machine Learning

2012 ◽  
Vol 24 (4) ◽  
pp. 1047-1084 ◽  
Author(s):  
Xiao-Tong Yuan ◽  
Shuicheng Yan
Keyword(s):  
Linear Systems ◽  
Optimization Problems ◽  
Piecewise Linear ◽  
Optimization Methods ◽  
Coefficient Matrix ◽  
Learning Problems ◽  
Support Vector ◽  
Data Sets ◽  
Piecewise Linear Systems ◽  
Vector Machines

We investigate Newton-type optimization methods for solving piecewise linear systems (PLSs) with nondegenerate coefficient matrix. Such systems arise, for example, from the numerical solution of linear complementarity problem, which is useful to model several learning and optimization problems. In this letter, we propose an effective damped Newton method, PLS-DN, to find the exact (up to machine precision) solution of nondegenerate PLSs. PLS-DN exhibits provable semiiterative property, that is, the algorithm converges globally to the exact solution in a finite number of iterations. The rate of convergence is shown to be at least linear before termination. We emphasize the applications of our method in modeling, from a novel perspective of PLSs, some statistical learning problems such as box-constrained least squares, elitist Lasso (Kowalski & Torreesani, 2008 ), and support vector machines (Cortes & Vapnik, 1995 ). Numerical results on synthetic and benchmark data sets are presented to demonstrate the effectiveness and efficiency of PLS-DN on these problems.

Sign in / Sign up

Export Citation Format

Share Document