Computational intelligence in wave energy: Comprehensive review and case study

Cuadra, L.; Salcedo‐Sanz, Sancho; Borge, José Carlos Nieto; Alexandre, Enrique; Rodrı́guez, Germán

doi:10.1016/j.rser.2015.12.253

Cited by 78 publications

(32 citation statements)

References 203 publications

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“…∆ b 1 Coefficient of variation for betweenness. It is defined by Equation (10). f OBJ (G) = f ζ (G) = objective function to be minimized.…”

Section: Imentioning

confidence: 99%

“…Efficiently integrating distributed RE generation systems [3][4][5] is a key research topic because the most used renewable energies-photovoltaic (PV) solar energy [6,7], wind energy [8,9] and marine energy [10]-are intermittent and more difficult to store [11] and integrate without affecting the quality of the electrical network [12] or the electricity prices [13]. The current proliferation of small-scale urban PV in buildings [14] and urban wind generators [15] can help home electricity consumers become also producers ("prosumers") [16] using the smart grid (SG) [17,18] and micro-grids (µ−Gs) [19] concepts.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms

et al. 2017

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Abstract:In this work, we describe an approach that allows for optimizing the structure of a smart grid (SG) with renewable energy (RE) generation against abnormal conditions (imbalances between generation and consumption, overloads or failures arising from the inherent SG complexity) by combining the complex network (CN) and evolutionary algorithm (EA) concepts. We propose a novel objective function (to be minimized) that combines cost elements, related to the number of electric cables, and several metrics that quantify properties that are beneficial for SGs (energy exchange at the local scale and high robustness and resilience). The optimized SG structure is obtained by applying an EA in which the chromosome that encodes each potential network (or individual) is the upper triangular matrix of its adjacency matrix. This allows for fully tailoring the crossover and mutation operators. We also propose a domain-specific initial population that includes both small-world and random networks, helping the EA converge quickly. The experimental work points out that the proposed method works well and generates the optimum, synthetic, small-world structure that leads to beneficial properties such as improving both the local energy exchange and the robustness. The optimum structure fulfills a balance between moderate cost and robustness against abnormal conditions. Our approach should be considered as an analysis, planning and decision-making tool to gain insight into smart grid structures so that the low level detailed design is carried out by using electrical engineering techniques.

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“…∆ b 1 Coefficient of variation for betweenness. It is defined by Equation (10). f OBJ (G) = f ζ (G) = objective function to be minimized.…”

Section: Imentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms

et al. 2017

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“…However, SVM does not belong to ANN [20], and it is a new machine-learning method based on statistical theory. SVM solves the optimal classification hyperplane by using the structural risk minimization principle, overcomes the dimensionality disaster and local minimum problem, and has a small demand for the samples.…”

Section: Svmmentioning

confidence: 99%

“…In existing literature, computational intelligence techniques have been investigated in the field of wave energy [20], financial market [21], and power quality disturbance [22,23]. However, the published review articles about condition monitoring and fault diagnosis have a limited scope, by focusing either on fault feature extraction and classification [24], or on rotating machinery prediction techniques [5,25].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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“…Regarding this, one of the most important problems yet to be solved is Neural Computation (NC) [40] and Fuzzy Computation (FC) [41]. An introduction to the main concepts of bio-inspired CI techniques in energy applications can be found in [20,42]. Examples of NC are Neural Networks (NNs), which are ML algorithms able to learn after training and validation processes.…”

Section: Motivationmentioning

confidence: 99%

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

et al. 2017

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Wind Power Ramp Events (WPREs) are large fluctuations of wind power in a short time interval, which lead to strong, undesirable variations in the electric power produced by a wind farm. Its accurate prediction is important in the effort of efficiently integrating wind energy in the electric system, without affecting considerably its stability, robustness and resilience. In this paper, we tackle the problem of predicting WPREs by applying Machine Learning (ML) regression techniques. Our approach consists of using variables from atmospheric reanalysis data as predictive inputs for the learning machine, which opens the possibility of hybridizing numerical-physical weather models with ML techniques for WPREs prediction in real systems. Specifically, we have explored the feasibility of a number of state-of-the-art ML regression techniques, such as support vector regression, artificial neural networks (multi-layer perceptrons and extreme learning machines) and Gaussian processes to solve the problem. Furthermore, the ERA-Interim reanalysis from the European Center for Medium-Range Weather Forecasts is the one used in this paper because of its accuracy and high resolution (in both spatial and temporal domains). Aiming at validating the feasibility of our predicting approach, we have carried out an extensive experimental work using real data from three wind farms in Spain, discussing the performance of the different ML regression tested in this wind power ramp event prediction problem.

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Computational intelligence in wave energy: Comprehensive review and case study

Cited by 78 publications

References 203 publications

Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms

Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data

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