scholarly journals Optimal integration of wind energy with a shunt-FACTS controller for reductions in electrical power loss

2021 ◽  
Vol 23 (1) ◽  
pp. 41
Author(s):  
I Made Wartana ◽  
Ni Putu Agustini ◽  
Sasidharan Sreedharan
Keyword(s):  
Genetic Algorithm ◽  
Wind Energy ◽  
Power Loss ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Test System ◽  
Optimal Size ◽  
Voltage Profile ◽  
Nsga Ii ◽  
Multi Objective

The integration of distributed generators (DGs) with flexible alternating current transmission systems (FACTS) can improve the performance of the grid system. In this study, we determine the location and optimal size of one type of DG, based on wind energy, with a shunt-FACTS control device called a static var compensator (SVC). The voltage profile is increase and the power loss reduced due to an improvement in performance from the maximizing load bus system scenario. Newton-Raphson power flow with a wind turbine generator (WTG) and SVC are formulated as a multi-objective problem called MLB system and minimizing system power loss (Ploss) by satisfying various system constraints, namely the loading limits, generation limits, voltage limits, and the small-signal stability. A variant of the genetic algorithm, called the non-dominated sorting genetic algorithm II (NSGA-II), is used to solve these conflicting multi-objective optimization problems. Modifications to the IEEE 14-bus standard and practical test system integrated to the WTG and SVC in the PSAT software are used as a test system. The simulation results indicate that the optimal allocation of the WTG and SVC, determined using the proposed technique, results in improved system performance, since all the specified constraints are met.

scholarly journals Recloser-Fuse settings in distribution systems with optimizing multiple distributed generation considering technical aspects

2020 ◽  
Vol 17 (3) ◽  
pp. 1135
Author(s):  
Ahmed Mohamed Abdelbaset ◽  
AboulFotouh A. Mohamed ◽  
Essam Abou El-Zahab ◽  
M. A. Moustafa Hassan
Keyword(s):  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Distribution Systems ◽  
Test System ◽  
Optimal Size ◽  
Loss Reduction ◽  
Voltage Profile ◽  
Power Loss Reduction ◽  
System Power

<p><span>With the widespread of using distributed generation, the connection of DGs in the distribution system causes miscoordination between protective devices. This paper introduces the problems associated with recloser fuse miscoordination (RFM) in the presence of single and multiple DG in a radial distribution system. Two Multi objective optimization problems are presented. The first is based on technical impacts to determine the optimal size and location of DG considering system power loss reduction and enhancement the voltage profile with a certain constraints and the second is used for minimizing the operating time of all fuses and recloser with obtaining the optimum settings of fuse recloser coordination characteristics. Whale Optimizer algorithm (WOA) emulated RFM as an optimization problem. The performance of the proposed methodology is applied to the standard IEEE 33 node test system. The results show the robustness of the proposed algorithm for solving the RFM problem with achieving system power loss reduction and voltage profile enhancement.</span></p>

scholarly journals Optimal Integration of the Renewable Energy to the Grid by Considering Small Signal Stability Constraint

2017 ◽  
Vol 7 (5) ◽  
pp. 2329
Author(s):  
I Made Wartana ◽  
Ni Putu Agustini ◽  
Jai Govind Singh
Keyword(s):  
Genetic Algorithm ◽  
Renewable Energy ◽  
Wind Turbine ◽  
Optimal Allocation ◽  
Optimization Problems ◽  
Small Signal ◽  
Small Signal Stability ◽  
Nsga Ii ◽  
Signal Stability ◽  
Optimal Integration

In recent decades, one of the main management’s concerns of professional engineers is the optimal integration of various types of renewable energy to the grid. This paper discusses the optimal allocation of one type of renewable energy i.e. wind turbine to the grid for enhancing network’s performance. A multi-objective function is used as indexes of the system’s performance, such as increasing system loadability and minimizing the loss of real power transmission line by considering security and stability of systems’ constraints viz.: voltage and line margins, and eigenvalues as well which is representing as small signal stability. To solve the optimization problems, a new method has been developed using a novel variant of the Genetic Algorithm (GA), specifically known as Non-dominated Sorting Genetic Algorithm II (NSGA-II). Whereas the Fuzzy-based mechanism is used to support the decision makers prefer the best compromise solution from the Pareto front. The effectiveness of the developed method has been established on a modified IEEE 14-bus system with wind turbine system, and their simulation results showed that the dynamic performance of the power system can be effectively improved by considering the stability and security of the system.

scholarly journals Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm

2018 ◽  
Vol 8 (11) ◽  
pp. 2253 ◽  
Author(s):  
Yang Xue
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Path Planning ◽  
Optimization Problems ◽  
Planning Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Mutation Operators ◽  
Comparison Results ◽  
Path Planning Problem

In many areas, such as mobile robots, video games and driverless vehicles, path planning has always attracted researchers’ attention. In the field of mobile robotics, the path planning problem is to plan one or more viable paths to the target location from the starting position within a given obstacle space. Evolutionary algorithms can effectively solve this problem. The non-dominated sorting genetic algorithm (NSGA-II) is currently recognized as one of the evolutionary algorithms with robust optimization capabilities and has solved various optimization problems. In this paper, NSGA-II is adopted to solve multi-objective path planning problems. Three objectives are introduced. Besides the usual selection, crossover and mutation operators, some practical operators are applied. Moreover, the parameters involved in the algorithm are studied. Additionally, another evolutionary algorithm and quality metrics are employed for examination. Comparison results demonstrate that non-dominated solutions obtained by the algorithm have good characteristics. Subsequently, the path corresponding to the knee point of non-dominated solutions is shown. The path is shorter, safer and smoother. This path can be adopted in the later decision-making process. Finally, the above research shows that the revised algorithm can effectively solve the multi-objective path planning problem in static environments.

Evolutionary Algorithms for Multi-Objective Scheduling in a Hybrid Manufacturing System

2018 ◽  
pp. 162-187 ◽  
Author(s):  
Ömer Faruk Yılmaz ◽  
Mehmet Bülent Durmuşoğlu
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Manufacturing System ◽  
Optimization Problems ◽  
Multiple Objectives ◽  
Hybrid Manufacturing ◽  
Nsga Ii ◽  
Multi Objective ◽  
Comprehensive Information ◽  
Manufacturing Environments

Problems encountered in real manufacturing environments are complex to solve optimally, and they are expected to fulfill multiple objectives. Such problems are called multi-objective optimization problems(MOPs) involving conflicting objectives. The use of multi-objective evolutionary algorithms (MOEAs) to find solutions for these problems has increased over the last decade. It has been shown that MOEAs are well-suited to search solutions for MOPs having multiple objectives. In this chapter, in addition to comprehensive information, two different MOEAs are implemented to solve a MOP for comparison purposes. One of these algorithms is the non-dominated sorting genetic algorithm (NSGA-II), the effectiveness of which has already been demonstrated in the literature for solving complex MOPs. The other algorithm is fast Pareto genetic algorithm (FastPGA), which has population regulation operator to adapt the population size. These two algorithms are used to solve a scheduling problem in a Hybrid Manufacturing System (HMS). Computational results indicate that FastPGA outperforms NSGA-II.

scholarly journals Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Liheng Liu ◽  
Miaomiao Niu ◽  
Dongliang Zhang ◽  
Li Liu ◽  
Dietmar Frank
Keyword(s):  
Genetic Algorithm ◽  
Optimal Allocation ◽  
Environmental Benefits ◽  
Nsga Ii ◽  
Multi Objective ◽  
Calculation Results ◽  
Key Issues ◽  
Multi Agent ◽  
Evolution Algorithm ◽  
Simulation Results

Abstract The optimal configuration and allocation of a microgrid are one of the key issues to guarantee the economic and reliable working of a microgrid. This is a multi-objective optimisation problem within which the economic index and the load power shortage rate index should be considered when optimising the configuration. In this article, a differential multi-agent multi-objective evolutionary algorithm (DMAMOEA) was designed to optimise the capacity configuration of a microgrid system, which includes three kinds of equipment: wind turbine, photovoltaic equipment and battery. The final optimisation results were compared with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm. Simulation results showed the effectiveness of the algorithm. At the end of this article, the representative solutions in the calculation results are compared and explained and the environmental benefits are analysed, which show the effectiveness of the implementation of the microgrid system.

scholarly journals Continuous domain ant colony optimization for distributed generation placement and losses minimization

2020 ◽  
Vol 9 (2) ◽  
pp. 261
Author(s):  
Zulkiffli Abdul Hamid ◽  
Ismail Musirin ◽  
Ammar Yasier Azman ◽  
Muhammad Murtadha Othman
Keyword(s):  
Ant Colony Optimization ◽  
Distributed Generation ◽  
Distribution System ◽  
Power Loss ◽  
Test System ◽  
Ant Colony ◽  
Optimal Size ◽  
Voltage Profile ◽  
Radial Distribution System ◽  
Continuous Domain

This paper proposes a method for distributed generation (DG) placement in distribution system for losses minimization and voltage profile improvement. An IEEE 33-bus radial distribution system is used as the test system for the placement of DG. To facilitate the sizing of DG capacity, a meta-heuristic algorithm known as Continuous Domain Ant Colony Optimization (ACO<sub>R</sub>) is implemented. The ACO<sub>R</sub> is a modified version of the traditional ACO which was developed specially for solving continuous domain optimization problem like sizing a set of variables. The objective of this paper is to determine the optimal size and location of DG for power loss minimization and voltage profile mitigation. Three case studies were conducted for the purpose of verification. It was observed that the proposed technique is able to give satisfactory results of real power loss and voltage profile at post-optimization condition. Experiment under various loadings of the test system further justifies the objective of the study.

Assessment of Multi-Objective Genetic Algorithms With Different Niching Strategies and Regression Methods for Engine Optimization and Design

2009 ◽  
Author(s):  
Yu Shi ◽  
Rolf D. Reitz
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Prediction Accuracy ◽  
Optimization Problems ◽  
Design Parameters ◽  
Hybrid Mode ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Methods ◽  
Objective Space

In previous study [1] the Non-dominated Sorting Genetic Algorithm II (NSGA II) [2] performed better than other popular Multi-Objective Genetic Algorithms (MOGA) in engine optimization that sought optimal combinations of the piston bowl geometry, spray targeting, and swirl ratio. NSGA II is further studied in this paper using different niching strategies that are applied to the objective-space and design-space, which diversify the optimal objectives and design parameters accordingly. Convergence and diversity metrics are defined to assess the performance of NSGA II using different niching strategies. It was found that use of the design niching achieved more diversified results with respect to design parameters, as expected. Regression was then conducted on the design datasets that were obtained from the optimizations with two niching strategies. Four regression methods, including K-nearest neighbors (KN), Kriging (KR), Neural Networks (NN), and Radial Basis Functions (RBF), were compared. The results showed that the dataset obtained from optimization with objective niching provided a more fitted learning space for the regression methods. The KN, KR, outperformed the other two methods with respect to the prediction accuracy. Furthermore, a log transformation to the objective-space improved the prediction accuracy for the KN, KR, and NN methods but not the RBF method. The results indicate that it is appropriate to use a regression tool to partly replace the actual CFD evaluation tool in engine optimization designs using the genetic algorithm. This hybrid mode saves computational resources (processors) without losing optimal accuracy. A Design of Experiment (DoE) method (the Optimal Latin Hypercube method) was also used to generate a dataset for the regression processes. However, the predicted results were much less reliable than results that were learned using the dynamically increasing datasets from the NSGA II generations. Applying the dynamical learning strategy during the optimization processes allows computationally expensive CFD evaluations to be partly replaced by evaluations using the regression techniques. The present study demonstrates the feasibility of applying the hybrid mode to engine optimization problems, and the conclusions can also extend to other optimization studies (numerical or experimental) that feature time-consuming evaluations and have highly non-linear objective-spaces.

Progressive-Stepping-Based Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization

2016 ◽  
Vol 7 (3) ◽  
pp. 17-49 ◽  
Author(s):  
Akshay Baviskar ◽  
Shankar Krishnapillai
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Multi Objective Optimization ◽  
Step Size ◽  
Nsga Ii ◽  
Multi Objective ◽  
Performance Metric ◽  
Mechanical Design Problem ◽  
Utopia Point

This paper demonstrates two approaches to achieve faster convergence and a better spread of Pareto solutions in fewer numbers of generations, compared to a few existing algorithms, including NSGA-II and SPEA2 to solve multi-objective optimization problems (MOP's). Two algorithms are proposed based on progressive stepping mechanism, which is obtained by the hybridization of existing Non-dominated Sorting Genetic Algorithm II (NSGA-II) with novel guided search schemes, and modified chromosome selection and replacement mechanisms. Progressive Stepping Non-dominated Sorting based on Local search (PSNS-L) controls the step size, and Progressive Stepping Non-dominated Sorting based on Utopia point (PSNS-U) method controls the number of divisions to generate better chromosomes in each generation to achieve faster convergence. Four multi-objective evolutionary algorithms (EA's) are compared for different benchmark functions and PSNS outperforms them in most cases based on various performance metric values. Finally a mechanical design problem has been solved with PSNS algorithms.

Multi-Objective Optimization of a Piezoelectric Sandwich Ultrasonic Transducer by Using Elitist Non-Dominated Sorting Genetic Algorithm

2011 ◽  
Vol 474-476 ◽  
pp. 1808-1812
Author(s):  
Bo Fu ◽  
Yi Jing ◽  
Xuan Fu ◽  
Tobias Hemsel
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Ultrasonic Transducer ◽  
Analytical Models ◽  
Continuous Variables ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

The multi-objective optimal design of a piezoelectric sandwich ultrasonic transducer is studied. The maximum vibration amplitude and the minimum electrical input power are considered as optimization objectives. Design variables involve continuous variables (dimensions of the transducer) and discrete variables (material types). Based on analytical models, the optimal design is formulated as a constrained multi-objective optimization problem. The optimization problem is then solved by using the elitist non-dominated sorting genetic algorithm (NSGA-II) and Pareto-optimal designs are obtained. The optimized results are analyzed and the preferred design is proposed. The optimization procedure presented in this contribution can be applied in multi-objective optimization problems of other piezoelectric transducers.

scholarly journals Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks

2020 ◽  
Vol 7 (6) ◽  
pp. 830-863
Author(s):  
R V Rao ◽  
R B Pawar
Keyword(s):  
Genetic Algorithm ◽  
Heat Sink ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Heat Sinks ◽  
Microchannel Heat Sink ◽  
Nsga Ii ◽  
Multi Objective ◽  
Bee Colony ◽  
Teaching Learning

Abstract In this paper, an endeavor is made to enhance the convergence speed of the recently proposed Rao algorithms. The new upgraded versions of Rao algorithms named as “quasi-oppositional-based Rao algorithms” are proposed in this paper. The quasi-oppositional-based learning is incorporated in the basic Rao algorithms to diversify the searching process of the algorithms. The performance of the proposed algorithms is tested on 51 unconstrained benchmark functions. Also, three multi-objective optimization case studies of different heat sinks such as a single-layered microchannel heat sink (SL-MCHS), a double-layered microchannel heat sink (DL-MCHS), and a plate-fin heat sink (PFHS) are attempted to investigate the effectiveness of the proposed algorithms in solving real-world complex engineering optimization problems. The results obtained using the proposed algorithms are compared with the results obtained using the well-known advanced optimization algorithms such as genetic algorithm (GA), artificial bee colony (ABC), differential evolution (DE), particle swarm optimization (PSO), teaching-learning-based algorithm (TLBO), Jaya algorithm, multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA-II), real-coded GA (RCGA), direction-based GA, self-adaptive multi-population (SAMP) Rao algorithms, and basic Rao algorithms. The proposed quasi-oppositional-based Rao algorithms are found superior or competitive to the other optimization algorithms considered.

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