Numerical Method for Power Losses Minimization of Vector-Controlled Induction Motor

2015 ◽  
Vol 6 (3) ◽  
pp. 486 ◽  
Author(s):  
Alex Borisevich
Keyword(s):  
Induction Motor ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Control Variable ◽  
Control Input ◽  
Power Losses ◽  
Ohmic Losses ◽  
Operation Control ◽  
Dynamic Time ◽  
Motor Operation

<p>The paper devoted to energy efficiency maximizing problem of the induction motor under part-load conditions. The problem is formulated as the minimization of ohmic losses power as a function from flux-producing current in field-oriented motor operation. Control input precompensation which transforms the dynamic time-varying optimization problem to stationary one is introduced. Adjustment rule for control variable is proposed which speeds-up the method convergence in comparison with linear variation of input. Finally a new continuous-time search algorithm for solving the problem of minimizing power consumption was given. The statements on method behavior were formulated and convergence to local minimum was proved. The method verified in simulation and in hardware experimental setup.</p>

scholarly journals A Modified Niching Crow Search Approach to Well Placement Optimization

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 857
Author(s):  
Jahedul Islam ◽  
Md Shokor A. Rahaman ◽  
Pandian M. Vasant ◽  
Berihun Mamo Negash ◽  
Ahshanul Hoqe ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Multimodal Optimization ◽  
Test Functions ◽  
Well Placement ◽  
Suggested Approach ◽  
Placement Optimization ◽  
Well Placement Optimization ◽  
Search Approach

Well placement optimization is considered a non-convex and highly multimodal optimization problem. In this article, a modified crow search algorithm is proposed to tackle the well placement optimization problem. This article proposes modifications based on local search and niching techniques in the crow search algorithm (CSA). At first, the suggested approach is verified by experimenting with the benchmark functions. For test functions, the results of the proposed approach demonstrated a higher convergence rate and a better solution. Again, the performance of the proposed technique is evaluated with well placement optimization problem and compared with particle swarm optimization (PSO), the Gravitational Search Algorithm (GSA), and the Crow search algorithm (CSA). The outcomes of the study revealed that the niching crow search algorithm is the most efficient and effective compared to the other techniques.

scholarly journals Clustering of cancer data based on Stiefel manifold for multiple views

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jing Tian ◽  
Jianping Zhao ◽  
Chunhou Zheng
Keyword(s):  
Optimization Problem ◽  
Nearest Neighbor ◽  
Search Algorithm ◽  
Stiefel Manifold ◽  
Omics Data ◽  
K Nearest Neighbor ◽  
Cancer Data ◽  
Clustering Problem ◽  
Multiple Datasets ◽  
Cluster Class

Abstract Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.

An improved sparrow search algorithm based on levy flight and opposition-based learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Danni Chen ◽  
JianDong Zhao ◽  
Peng Huang ◽  
Xiongna Deng ◽  
Tingting Lu
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Support Vector ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Opposition Based Learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

Induction motor drives with minimal power losses

2012 ◽  
Vol 83 (12) ◽  
pp. 667-671 ◽  
Author(s):  
A. S. Kosmodamianskii ◽  
V. I. Vorob’ev ◽  
A. A. Pugachev
Keyword(s):  
Induction Motor ◽  
Motor Drives ◽  
Power Losses ◽  
Induction Motor Drives

scholarly journals Constructing Synthetic Samples

2016 ◽  
Vol 32 (1) ◽  
pp. 113-127
Author(s):  
Hua Dong ◽  
Glen Meeden
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Random Search ◽  
The United States ◽  
Simulation Studies ◽  
Synthetic Sample ◽  
Population Means ◽  
Adaptive Random Search ◽  
Random Search Algorithm ◽  
Two Populations

Abstract We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.

Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

2016 ◽  
Vol 12 (1) ◽  
pp. 71-78
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya
Keyword(s):  
Particle Swarm Optimization ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Distribution Network ◽  
Reactive Power Optimization ◽  
Power Losses ◽  
Swarm Optimization ◽  
Real Distribution

In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDAŞ (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.

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