scholarly journals Optimization of Two-Stage IPD-(1+I) Controllers for Frequency Regulation of Sustainable Energy Based Hybrid Microgrid Network

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 919
Author(s):  
Abdul Latif ◽  
S. M. Suhail Hussain ◽  
Dulal Chandra Das ◽  
Taha Selim Ustun
Keyword(s):  
Sustainable Energy ◽  
Research Field ◽  
Optimization Techniques ◽  
Frequency Regulation ◽  
Water Heater ◽  
Two Stage ◽  
Domain Specific ◽  
Hybrid Microgrid ◽  
Main Challenge ◽  
Algorithmic Technique

Sustainable energy based hybrid microgrids are advantageous in meeting constantly increasing energy demands. Conversely, the intermittent nature of renewable sources represents the main challenge to achieving a reliable supply. Hence, load frequency regulation by adjusting the amount of power shared between subsystems is considered as a promising research field. Therefore, this paper presents a new stratagem for frequency regulation by developing a novel two stage integral-proportional-derivative with one plus integral (IPD-(1+I)) controller for multi sources islanded microgrid system (MS-IμGS). The proposed stratagem has been tested in an MS-IμGS comprising of a wind turbine, parabolic trough, biodiesel generators, solid-oxide fuel cell, and electric water heater. The proposed model under different scenarios is simulated in MATLAB environment considering the real-time recorded wind data. A recently developed sine-cosine algorithmic technique (SCA) has been leveraged for optimal regulation of frequency in the considered microgrid. To identify the supremacy of the proposed technique, comparative studies with other classical controllers with different optimization techniques have been performed. From the comparison, it is clearly evident that, SCA-(IPD-(1+I)) controller gives better performance over other considered stratagems in terms of various time domain specific parameters, such as peak deviations (overshoot, undershoot) and settling time. Finally, the robustness of the proposed stratagem is evaluated by conducting sensitivity analysis under ±30% parametric variations and +30% load demand. The lab tests results validate the operation of the proposed system and show that it can be used to regulate the frequency in stand-alone microgrids with a high penetration of renewable energy.

Two-Stage Configurable Decoder Model for Domain Specific FEC Decoder Design

2011 ◽  
Vol E94-A (12) ◽  
pp. 2659-2668
Author(s):  
Ittetsu TANIGUCHI ◽  
Ayataka KOBAYASHI ◽  
Keishi SAKANUSHI ◽  
Yoshinori TAKEUCHI ◽  
Masaharu IMAI
Keyword(s):  
Two Stage ◽  
Domain Specific ◽  
Decoder Design

scholarly journals Optimum Synthesis of a BOA Optimized Novel Dual-Stage PI − (1 + ID) Controller for Frequency Response of a Microgrid

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3446 ◽  
Author(s):  
Abdul Latif ◽  
S. M. Suhail Hussain ◽  
Dulal Chandra Das ◽  
Taha Selim Ustun
Keyword(s):  
Control Strategies ◽  
Frequency Regulation ◽  
Power Generator ◽  
Control Approach ◽  
Proportional Integral ◽  
Optimum Synthesis ◽  
Unknown Disturbances ◽  
Dual Stage ◽  
Algorithmic Technique

A renewable and distributed generation (DG)-enabled modern electrified power network with/without energy storage (ES) helps the progress of microgrid development. Frequency regulation is a significant scheme to improve the dynamic response quality of the microgrid under unknown disturbances. This paper established a maiden load frequency regulation of a wind-driven generator (WG), solar tower (ST), bio-diesel power generator (BDPG) and thermostatically controllable load (heat pump and refrigerator)-based, isolated, single-area microgrid system. Hence, intelligent control strategies are important for this issue. A newly developed butterfly algorithmic technique (BOA) is leveraged to tune the controllers’ parameters. However, to attain a proper balance between net power generation and load power, a dual stage proportional-integral- one plus integral-derivative PI − (1 + ID) controller is developed. Comparative system responses (in MATLAB/SIMULINK software) for different scenarios under several controllers, such as a proportional-integral (PI), proportional-integral-derivative (PID) and PI − (1 + ID) controller tuned by particle swarm optimization (PSO), grasshopper algorithmic technique (GOA) and BOA, show the superiority of BOA in terms of minimizing the peak deviations and better frequency regulation of the system. Real recorded wind data are considered to authenticate the control approach.

Genetic Algorithm to Solve Multi-Period, Multi-Product, Bi-Echelon Supply Chain Network Design Problem

2009 ◽  
Vol 2 (4) ◽  
pp. 24-42 ◽  
Author(s):  
R. Dhanalakshmi ◽  
P. Parthiban ◽  
K. Ganesh ◽  
T. Arunkumar
Keyword(s):  
Supply Chain ◽  
Supply Chains ◽  
Raw Materials ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Production Plant ◽  
Two Stage ◽  
Cost Structures ◽  
Multi Stage ◽  
Product Costs

In many multi-stage manufacturing supply chains, transportation related costs are a significant portion of final product costs. It is often crucial for successful decision making approaches in multi-stage manufacturing supply chains to explicitly account for non-linear transportation costs. In this article, we have explored this problem by considering a Two-Stage Production-Transportation (TSPT). A two-stage supply chain that faces a deterministic stream of external demands for a single product is considered. A finite supply of raw materials, and finite production at stage one has been assumed. Items are manufactured at stage one and transported to stage two, where the storage capacity of the warehouses is limited. Packaging is completed at stage two (that is, value is added to each item, but no new items are created), and the finished goods inventories are stored which is used to meet the final demand of customers. During each period, the optimized production levels in stage one, as well as transportation levels between stage one and stage two and routing structure from the production plant to warehouses and then to customers, must be determined. The authors consider “different cost structures,” for both manufacturing and transportation. This TSPT model with capacity constraint at both stages is optimized using Genetic Algorithms (GA) and the results obtained are compared with the results of other optimization techniques of complete enumeration, LINDO, and CPLEX.

scholarly journals Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5504
Author(s):  
Hyang-A Park ◽  
Gilsung Byeon ◽  
Wanbin Son ◽  
Hyung-Chul Jo ◽  
Jongyul Kim ◽  
...  
Keyword(s):  
Real Time ◽  
Industrial Revolution ◽  
Storage System ◽  
Research Field ◽  
Optimization Techniques ◽  
Virtual Space ◽  
Machine Learning Techniques ◽  
Time Data ◽  
Digital Twin ◽  
Learning Techniques

Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern.

On the control of a single flexible arm robot via Youla-Kucera parameterization

Robotica ◽  
2014 ◽  
Vol 34 (1) ◽  
pp. 150-172 ◽  
Author(s):  
Habib Esfandiar ◽  
Saeed Daneshmand ◽  
Roozbeh Dargahi Kermani
Keyword(s):  
Closed Loop ◽  
Optimization Techniques ◽  
Loop System ◽  
Flexible Manipulator ◽  
Order System ◽  
Flexible Beam ◽  
Closed Loop System ◽  
Stabilizing Controller ◽  
Main Challenge ◽  
Tip Mass

SUMMARYIn this paper, based on the Youla-Kucera (Y-K) parameterization, the control of a flexible beam acting as a flexible robotic manipulator is investigated. The method of Youla parameterization is the simple solution and proper method for describing the collection of all controllers that stabilize the closed-loop system. This collection comprises function of the Youla parameter which can be any proper transfer function that is stable. The main challenge in this approach is to obtain a Youla parameter with infinite dimension. This parameter is approximated by a subspace with finite dimensions, which makes the problem tractable. It is required to be generated from a finite number of bases within that space and the considered system can be approximated by an expansion of the orthonormal bases such as FIR, Laguerre, Kautz and generalized bases. To calculate the coefficients for each basis, it is necessary to define the problem in the form of an optimization problem that is solved by optimization techniques. The Linear Quadratic Regulator (LQR) optimization tool is employed in order to optimize the controller gains. The main aim in controller design is to merge the closed-loop system and the second order system with the desirable time response characteristic. The results of the Youla stabilizing controller for a planar flexible manipulator with lumped tip mass indicate that the proposed method is very efficient and robust for the time-continuous instances.

An End-User Oriented Visual Programming Language

2014 ◽  
Vol 513-517 ◽  
pp. 2141-2144
Author(s):  
Ying Jia ◽  
Be Jun Shen ◽  
Tian Yu Yu ◽  
Jian Gang Zhu
Keyword(s):  
Information Systems ◽  
Field Studies ◽  
Visual Programming ◽  
End Users ◽  
Research Field ◽  
End User ◽  
Domain Specific ◽  
Web Information ◽  
Develop Software ◽  
Web Information Systems

With the promotion of IT applications and the rise of Web 2.0, mass users' individual requirements continue to emerge. How to quickly meet increasing development and maintenance requirements has been a critical problem of software development. Is it possible for end-users to develop software? This paper chooses Web information systems as the research field, studies the end-user programming technology, and designs an end-user oriented visual domain-specific language VUDSL for university Web information systems. VUDSL programming tools are also implemented, to support end-users without the knowledge of software engineering to develop target information systems by visual programming.

A FAST TWO-STAGE CONTENT-BASED IMAGE RETRIEVAL APPROACH IN THE DCT DOMAIN

2008 ◽  
Vol 22 (04) ◽  
pp. 765-781 ◽  
Author(s):  
TIENWEI TSAI ◽  
YO-PING HUANG ◽  
TE-WEI CHIANG
Keyword(s):  
Image Retrieval ◽  
High Efficiency ◽  
Feature Space ◽  
Content Based Image Retrieval ◽  
Two Stage ◽  
Feature Extraction Method ◽  
Domain Specific ◽  
Retrieval Scheme ◽  
Domain Specific Knowledge ◽  
Distinct Features

In this paper, a two-stage content-based image retrieval (CBIR) approach is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing the database, the DC coefficients of Y, U and V components are quantized such that the feature space is partitioned into a finite number of grids, each of which is mapped to a grid code (GC). When querying an image, at coarse classification stage, the grid-based classification (GBC) and the distance threshold pruning (DTP) serve as a filter to remove those candidates with widely distinct features. At the fine classification stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that both high efficacy and high efficiency can be achieved simultaneously using the proposed two-stage approach.

scholarly journals Fuzzy Controller Based Reserve Management in Hybrid Microgrid for Frequency Regulation

2019 ◽  
Vol 9 (1) ◽  
pp. 652-663
Keyword(s):  
Fuzzy Controller ◽  
Power Sharing ◽  
Frequency Regulation ◽  
Solar Photovoltaic ◽  
Diesel Generator ◽  
Power Sources ◽  
Hybrid Microgrid ◽  
Central Controller ◽  
Power Point ◽  
Power Outputs

Power quality is a growing concern with sensible and critical loads asking for stringent frequency regulations. The distributed generation sources like Solar Photovoltaic, Diesel Generator, Fuel Cells, and Battery driven Electric Vehicles (BEV) are considered as power sources in the system. For the power-sharing among these generators, a central controller with a novel simple adaptive-additive control strategy is used. Each source has a local controller to regulate the power outputs. This paper presents a Neuro-Fuzzy controller for operating Solar Photovoltaic power at a Limited Power Point while a novel Neuro-Tuned-Fuzzy Controller decides which BEVs to be connected to the microgrid by giving a priority value to each BEV. The simulation results show the coordinated operation of the central and local controllers to regulate the power outputs of the sources was effective to manage reserves and achieve frequency regulation.

scholarly journals Genetic Algorithm Parameter Effect on 3D Truss Optimization with Discrete Variable

2018 ◽  
Vol 5 (1) ◽  
pp. 61-70 ◽  
Author(s):  
Akshay Kumar ◽  
H K Rangavittal
Keyword(s):  
Genetic Algorithm ◽  
Population Size ◽  
Global Optimum ◽  
Research Field ◽  
Optimization Techniques ◽  
Truss Optimization ◽  
Function Evaluation ◽  
Selection Function ◽  
Nonlinear Constraint ◽  
Discrete Area

The Genetic Algorithm is one of the advanced optimization techniques frequently used for solving complex problems in the research field, and there are plenty of parameters which affect the outcome of the GA. In this study, a 25-bar truss with the nonlinear constraint is chosen with the objective to minimize the mass and variables being the discrete area. For the same, GA parameter like Selection Function, Population Size, Crossover Function, and Creation Function are varied to find the best combination with minimum function evaluation. It is found that the Uniform selection gives the best result irrespective of the creation function, population size or crossover functions. But this is at the cost of a large number of function evaluations, and the other selection function fails to reach the global optimum and has a smaller number of function evaluation count. If the analysis of selection function is done one at a time, it is seen that all Cases performs better in Roulette but, Case A which is non-integer type with 200 population size being computationally cheaper than Case B and C of population size 300. In the Tournament selection, Case A, B with smaller population size and Case C with higher population size performs better. Case C performs better at Remainder selection with smaller population size, and Case A and B for Stochastic Uniform with higher population size. And, it is clear that the function evaluation count increases with the population size in every Case from this study.

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