A clustering approach for the wind turbine micro siting problem through genetic algorithm

Rodrigues, Sílvio; Bauer, Pavol; Pierik, J.

doi:10.1109/iecon.2013.6699428

Cited by 6 publications

(9 citation statements)

References 10 publications

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“…They are widely used in optimization and search problems e.g. they are used in [15] to find an optimal layout for a large off-shore wind farm and in [16] to find the optimal scale of a renewable energy farm.…”

Section: Evolutionary Input Selectionmentioning

confidence: 99%

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

Sala

Kampouropoulos

Giacometto

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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A model of power demand represents the foundation of any intelligent Energy Management System, and its accuracy is the key factor determining the performance of such system. In order to improve the accuracy of the modeling process, a multi-model approach based on a Hierarchical Clustering of similar load behaviors is presented. The clustering algorithm joins similar data subsets in groups that are modelled separately using Adaptive Neuro-Fuzzy Inference Systems. Thus, each of the obtained models addresses only the characterization of one behavior, which provides better accuracy than classical approaches based on a single model, in addition to being easier and faster to train. During the training process of the models, an input selection technique based on Genetic Algorithms is proposed to search and select the best combination of inputs. The use of search algorithms allows to reduce the complexity of this task while maintaining the system performance, which represents a significant time saving of expert staff. The proposed approach is validated by means of experimental data from an automotive manufacturing plant. In addition to improving the forecasting accuracy, this methodology automates the segmentation of the load profiles into models and the selection of their inputs, as well as improving parallelization to effectively reduce the computation time.Keywords-adaptive neuro-fuzzy inference systems; demand side management; genetic algorithms; hierarchical clustering; intelligent energy management system.

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Section: Evolutionary Input Selectionmentioning

confidence: 99%

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

Sala

Kampouropoulos

Giacometto

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

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“…However, it is known that turbines influence the energy production of neighboring turbines and that this influence might be considered negligible for turbines situated far enough apart [126,127]. For this reason, in this work the distance between the possible locations is used as a measure of dependency instead of the standard statistical analysis used in MOGOMEA [33].…”

Section: Multi-objective Gene-pool Optimal Mixing Evolutionary Algorimentioning

confidence: 99%

“…This is of high importance especially because the evaluation time of a solution varies with the complexity of the OWF layout it represents. For example, the wake loss evaluation highly depends on the number of turbines [126,127] and wind directions [107], whereas the collection system design also depends on the number of turbines of the project [72,75].…”

Section: Multi-objective Gene-pool Optimal Mixing Evolutionary Algorimentioning

confidence: 99%

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

et al. 2016

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Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts designed with standard sequential strategies were obtained through the framework, meaning that the proposed framework has the capability to create different OWF layouts that would have been missed by the designers. In conclusion, the proposed multi-objective optimization framework represents a mind shift in design tools for OWFs which allows cost savings in the design and operation phases.

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“…This work uses a similar optimization goal, the maximization of the wind farm efficiency, which is calculated as the ratio between the wind farm production with and without wake losses [7,30]. The wind farm production is computed as the mean power output for all wind directions and then scaled to account for a given wind direction frequency of occurrence.…”

Section: Optimization Goalmentioning

confidence: 99%

“…This model, due to this ease of implementation and fast computation has been widely adopted in wind farm modeling [49,30,[50][51][52][53][54].…”

Section: Jensen Modelmentioning

confidence: 99%

Wake losses optimization of offshore wind farms with moveable floating wind turbines

Rodrigues

Pinto

Soleimanzadeh

et al. 2015

Energy Conversion and Management

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a b s t r a c tIn the future, floating wind turbines could be used to harvest energy in deep offshore areas where higher wind mean speeds are observed. Currently, several floating turbine concepts are being designed and tested in small scale projects; in particular, one concept allows the turbine to move after installation. This article presents a novel layout optimization framework for wind farms composed of moveable floating turbines. The proposed framework uses an evolutionary optimization strategy in a nested configuration which simultaneously optimizes the anchoring locations and the wind turbine position within the mooring lines for each individual wind direction. The results show that maximum energy production is obtained when moveable wind turbines are deployed in an optimized layout. In conclusion, the framework represents a new design optimization tool for future offshore wind farms composed of moveable floating turbines.

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A clustering approach for the wind turbine micro siting problem through genetic algorithm

Cited by 6 publications

References 10 publications

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures

Wake losses optimization of offshore wind farms with moveable floating wind turbines

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