A review on optimization algorithms and application to wind energy integration to grid

Behera, Sasmita; Sahoo, Subhrajit; Pati, Bibhuti Bhusan

doi:10.1016/j.rser.2015.03.066

Cited by 85 publications

(35 citation statements)

References 132 publications

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“…The DE was designed to meet the requirement of practical minimization techniques with consistent convergence to the global minimum in consecutive independent trials. It can solve non-differentiable, nonlinear functions and multimodal cost functions [48].…”

Section: Differential Evolution (De)mentioning

confidence: 99%

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

Júnior¹,

Nunes²,

Nascimento³

et al. 2018

Optimization and Control of Electrical Machines

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The main objective of thermoelectric power plants is to meet the power demand with the lowest fuel cost and emission levels of pollutant and greenhouse gas emissions, considering the operational restrictions of the power plant. Optimization techniques have been widely used to solve engineering problems as in this case with the objective of minimizing the cost and the pollution damages. Heuristic and metaheuristic algorithms have been extensively studied and used to successfully solve this multi-objective problem. This chapter, several optimization techniques (simulated annealing, ant lion, dragonfly, NSGA II, and differential evolution) are analyzed and their application to economic-emission load dispatch (EELD) is also discussed. In addition, a comparison of all approaches and its results are offered through a case study.

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Section: Differential Evolution (De)mentioning

confidence: 99%

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

Júnior¹,

Nunes²,

Nascimento³

et al. 2018

Optimization and Control of Electrical Machines

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“…Problem Specific Methodology Used [3] 2015 Sea wave Ordinal classification SVM, ANN, LR [4] 2015 Solar Classification SVM [5] 2009 Power disturbance Classification SVM, wavelets [10] 2015 Wind Optimization Bio-inspired, meta-heuristics [14] 2015 Wind Classification Fuzzy SVM [15] 2011 Wind Classification DT, SOM [16] 2015 Wind Classification SVM, k-NN, fuzzy, ANN [17] 2010 Solar Classification Semi-supervised SVM [20] 2013 Wind Ordinal classification SVM, DT, LR, HMM [30] 2014 Wind Classification SVM, LR, RF, rotation forest [31] 2011 Wind Classification ANN, LR, DT, RF [32] 2013 Wind Classification k-NN, RBF, DT [33] 2011 Wind Classification, regression BN [34] 2014 Wind Classification, regression Heuristic methodology: WPPT [35] 2011 Wind Classification Bagging, ripper, rotation forest, RF, k-NN [36] 2013 Wind Classification ANFIS, ANN [37] 2012 Wind Classification SVM [38] 2015 Wind Classification ANN, SVM [39] 2015 Wind Classification PNN [40] 2015 Wind Classification DT, BN, RF [41] 2015 Wind Classification, clustering AuDyC [42] 2016 Wind Classification, clustering AuDyC [43] 2010 Power disturbance Classification HMM, SVM, ANN [44] 2015 Power disturbance Classification SVM, NN, fuzzy, neuro-fuzzy, wavelets, GA [45] 2015 Power disturbance Classification SVM, k-NN, ANN, fuzzy, wavelets [46] 2002 Power disturbance Classification Rule-based classifiers, wavelets, HMM [47] 2004 Power disturbance Classification PNN [48] 2006 Power disturbance Classification ANN, RBF, SVM [49] 2007 Power disturbance Classification ANN, wavelets [50] 2012 Power disturbance Classification PNN [51] 2014 Power disturbance Classification ANN Table 3. Summary of the main references analyzed, grouped by application field, problem type and methodologies considered (II)...…”

Section: Reference Year Application Fieldmentioning

confidence: 99%

“…Some recent tutorials have been focused on important RE applications (e.g., wind prediction, solar radiation prediction) using ML approaches [7,8]. Sub-families of algorithms, such as evolutionary computation, neural computation or fuzzy logic approaches, have been also the objective of previous reviews [9,10], which are very useful for researchers or practitioners interested in each specific field. Following this latter trend, this paper is focused on an important specific branch of ML: classification problems and classification algorithms and how these problems arise in different RE applications.…”

Section: Introductionmentioning

confidence: 99%

A Review of Classification Problems and Algorithms in Renewable Energy Applications

et al. 2016

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Classification problems and their corresponding solving approaches constitute one of the fields of machine learning. The application of classification schemes in Renewable Energy (RE) has gained significant attention in the last few years, contributing to the deployment, management and optimization of RE systems. The main objective of this paper is to review the most important classification algorithms applied to RE problems, including both classical and novel algorithms. The paper also provides a comprehensive literature review and discussion on different classification techniques in specific RE problems, including wind speed/power prediction, fault diagnosis in RE systems, power quality disturbance classification and other applications in alternative RE systems. In this way, the paper describes classification techniques and metrics applied to RE problems, thus being useful both for researchers dealing with this kind of problem and for practitioners of the field.

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“…In this context, the variable speed wind turbine control necessitates precise and robust controllers that can adapt to the changes in the external environment and equally in the operational conditions and disturbances . For the afore‐mentioned factors, optimization algorithms have been proposed to mitigate the effects of the potential perturbations of the system .…”

Section: Introductionmentioning

confidence: 99%

Intelligent maximum power tracking control of a PMSG wind energy conversion system

Aicha

Youcef

Hassaine

et al. 2019

Asian Journal of Control

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A comparative control study for a maximum power tracking strategy of variable speed wind turbine is provided. The system consists of a direct drive permanent magnet synchronous generator (PMSG) and an uncontrolled rectifier followed by a DC/DC switch-mode step down converter connected to a DC load. The buck converter is used to catch the maximum power from the wind by generating an efficient duty cycle. Distinct Maximum Power Point Tracking (MPPT) algorithms are analyzed and compared: a classical Proportional-Integral controller (PI) and two based Fuzzy Logic Controllers (FLC), including a conventional Fuzzy-PI and an Adaptive FLC-PI. The main aim of the presented study is to develop an advanced control scheme for wind generators to ensure a high level operating of the system and a maximum power extraction from the wind. This is achieved by analyzing the behavior of the system under fluctuating wind conditions employing Matlab Simpower Systems tool.Simulation results confirm that the Adaptive FLC-PI controller algorithm has better performances in terms of dynamic response and efficiency especially in comparison with the ones of a PI controller under variable wind speed. KEYWORDS DC-DC buck converter, fuzzy logic controller (FLC), maximum power point tracking (MPPT), permanent magnet synchronous generator (PMSG), proportional integral (PI) controller, wind turbine

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A review on optimization algorithms and application to wind energy integration to grid

Cited by 85 publications

References 132 publications

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

Multi-Objective Optimization Techniques to Solve the Economic Emission Load Dispatch Problem Using Various Heuristic and Metaheuristic Algorithms

A Review of Classification Problems and Algorithms in Renewable Energy Applications

Intelligent maximum power tracking control of a PMSG wind energy conversion system

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