16 results on '"Neshat, Mehdi"'
Search Results
2. [formula omitted][formula omitted]ave [formula omitted]earner: Predicting wave farms power output using effective meta-learner deep gradient boosting model: A case study from Australian coasts.
- Author
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Neshat, Mehdi, Sergiienko, Nataliia Y., Rafiee, Ashkan, Mirjalili, Seyedali, Gandomi, Amir H., and Boland, John
- Subjects
- *
MACHINE learning , *OCEAN wave power , *FARM mechanization , *DEEP learning , *WAVE energy , *COASTS - Abstract
Precise prediction of wave energy is indispensable and holds immense promise as ocean waves have a power capacity of 30–40 kW/m along the coast. Utilising this energy source does not generate harmful emissions, making it a superior substitute for fossil fuel-based energy. The computational expense associated with simulating and computing intricate hydrodynamic interactions in wave farms restricts optimisation methods to a few thousand evaluations and makes a challenging situation for training in deep neural prediction models. To address this issue, we propose a new solution: a Meta-learner gradient boosting method that employs four multi-layer convolutional dense neural network surrogate models combined with an optimised extreme gradient boosting. In order to train and validate the predictive model, we used four wave farm datasets, including the absorbed power outputs and 2D coordinates of wave energy converters (WECs) located along the southern coast of Australia, Adelaide, Sydney, Perth and Tasmania. Furthermore, the capability of the transfer learning strategy is evaluated. The WECs used in this study are of the fully submerged three-tether converter type, similar to the CETO prototype. The effectiveness of the proposed approach is assessed by comparing it with 15 well-established and effective machine learning (ML) methods. The experimental findings indicate that the proposed model is competitive with other ML and deep learning approaches, exhibiting considerable accuracy of 88.8%, 90.0%, 90.3%, and 84.4% in Adelaide, Perth, Sydney and Tasmania and improved robustness in predicting wave farm power output. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. A hybrid cooperative co-evolution algorithm framework for optimising power take off and placements of wave energy converters.
- Author
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Neshat, Mehdi, Alexander, Bradley, and Wagner, Markus
- Subjects
- *
WAVE energy , *COEVOLUTION , *EVOLUTIONARY algorithms , *FARM size , *ALGORITHMS - Abstract
Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this work, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys and also the power-take-off (PTO) parameters for each buoy. The search space for these parameters is complex and multi-modal. Moreover, the evaluation of each parameter setting is computationally expensive and thus limits the number of full model evaluations that can be made. To handle this problem, we propose a new hybrid cooperative co-evolution algorithm (HCCA). HCCA consists of a symmetric local search plus Nelder-Mead and a cooperative co-evolution algorithm (CC) with a backtracking strategy for optimising the positions and PTO settings of WECs, respectively. For assessing the effectiveness of the proposed approach five popular Evolutionary Algorithms (EAs), four alternating optimisation methods and two recent hybrid ideas (LS-NM and SLS-NM-B) are compared in four real wave situations (Adelaide, Tasmania, Sydney and Perth) with two wave farm sizes (4 and 16). The experimental study shows that the hybrid cooperative framework performs best in terms of both runtime and quality of obtained solutions. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
4. Layout optimisation of offshore wave energy converters using a novel multi-swarm cooperative algorithm with backtracking strategy: A case study from coasts of Australia.
- Author
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Neshat, Mehdi, Mirjalili, Seyedali, Sergiienko, Nataliia Y., Esmaeilzadeh, Soheil, Amini, Erfan, Heydari, Azim, and Garcia, Davide Astiaso
- Subjects
- *
METAHEURISTIC algorithms , *WAVE energy , *ALGORITHMS , *RENEWABLE energy sources , *ENERGY consumption , *FARM size - Abstract
Wave energy technologies have the potential to play a significant role in the supply of renewable energy worldwide. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this paper, we explore the optimisation of WEC arrays consisting of three-tether buoys. Such arrays can be optimised for total energy output by adjusting the relative positions of buoys in a wave farm. As there are complex hydrodynamic interactions among WECs, the evaluation of each parameter setting is computationally expensive and thus limits the feasible number of full model evaluations that can be made. Furthermore, these WEC interactions make up a non-convex, multi-modal (with multiple local-optima), continuous and constrained optimisation problem. This problem is challenging to solve using optimisation methods. To tackle the challenge of optimising the positions of WECs in a wave farm, we propose a novel multi-swarm cooperative co-evolution algorithm which consists of three meta-heuristics: the multi verse optimiser (MVO) algorithm, the equilibrium optimisation (EO) method, and the moth flame optimisation (MFO) approach with a backtracking strategy, we introduce a fast, effective new surrogate model to speed up the process of optimisation. To assess the effectiveness of our proposed approach, 11 state-of-the-art bio-inspired algorithms and three recent hybrid heuristic techniques were compared in six real wave situations located on the coasts of Australia, with two wave farm sizes (four and nine WECs). The experimental study presented in this paper shows that our hybrid cooperative framework exhibited the best performance in terms of the quality of obtained solutions, computational efficiency, and convergence speed compared with other 14 state-of-the-art meta-heuristics. Furthermore, we found that the power output of the best-found 9-buoy arrangements were higher than that of perpendicular layouts at at 4.15 % , 3.29 % , 3.62 % , 9.2 % , 5.74 % , and 2.43 % for the Perth, Adelaide, Sydney, Tasmania, Brisbane, and Darwin wave sites, respectively. Our investigations reveal that the best-found arrangement at the Tasmania wave site was able to absorb the highest level of wave power relative to the other locations. • A new Multi-swarm Cooperative optimisation framework is proposed to optimise wave farms performance. • A new fast surrogate model is developed to speed up expensive optimisation process of wave farm. • To improve the placement of converters, a symmetrical backtracking search algorithm is proposed. • To handle the infeasible wave converters layout during the optimisation, a new and effective repair function is developed. • The proposed optimiser enhances significantly the total power output at six wave farms compared with previous methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. Exploring Wind Energy Potential as a Driver of Sustainable Development in the Southern Coasts of Iran: The Importance of Wind Speed Statistical Distribution Model.
- Author
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Filom, Siyavash, Radfar, Soheil, Panahi, Roozbeh, Amini, Erfan, and Neshat, Mehdi
- Abstract
Wind energy as a clean and inexhaustible source of renewable energy can be a key element of sustainable development that decreases dependence of countries on fossil fuels. Therefore, implementing accurate and comprehensive feasibility studies in countries with a high level of consumption of traditional energy resources is vital; an approach encouraged and supported by green funds and climate change action. It is also crucial to helping spur economic and sustainable growth of these countries. In this regard, this study aims at accurate evaluation of onshore wind energy potential in seven coastal cities in the south of Iran. Six Probability Distribution Functions (PDFs) were examined over representative stations. It was deduced that the Weibull function, which is the most used PDF in similar studies, was only applicable to one station. Here, Gamma distribution offered the best fit for three stations and for the other ones, Generalized Extreme Value (GEV) performed better. Considering the ranking of six examined PDFs and the simplicity of Gamma, it was identified as the effective function in the southern coasts of Iran bearing in mind the geographic distribution of stations. Moreover, six wind energy converter power curve functions contributed to investigating the capacity factor. It is found that, using only one function could cause under- or over-estimation. Then, stations were classified based on the National Renewable Energy Laboratory system. Last but not least, examining a range of wind energy converters enabled scholars to extend this study into practice and prioritize the development of stations considering budget limits. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
6. New insights into position optimisation of wave energy converters using hybrid local search.
- Author
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Neshat, Mehdi, Alexander, Bradley, Sergiienko, Nataliia Y., and Wagner, Markus
- Subjects
WAVE energy ,ENERGY consumption ,ENERGY futures ,FARM mechanization ,HYBRID electric vehicles ,WORK design - Abstract
Renewable energy will play a pivotal role in meeting future global energy demand. Of current renewable sources, wave energy offers enormous potential for growth. This research investigates the optimisation of the placement of oscillating buoy-type wave energy converters (WECs). This work explores the design of a wave farm consisting of an array of fully submerged three-tether buoys. In a wave farm, buoy positions strongly determine the farm's output. Optimising the buoy positions is a challenging research problem due to complex and extensive interactions (constructive and destructive) between buoys. This research focuses on maximising the power output of the farm through the placement of buoys in a size-constrained environment, and we propose a new hybrid approach mixing local search, using a surrogate power model, and numerical optimisation methods. The proposed hybrid method is compared with other state-of-the-art search methods in five different wave scenarios – one simplified irregular wave model and four real wave regimes. The new hybrid methods outperform well-known previous heuristic methods in terms of both quality of achieved solutions and the convergence-rate of search in all tested wave regimes. The best performing method in real-wave scenarios uses the active set non-linear optimisation method to tune final placements. The effectiveness of this method seems to stem for its capacity to search over a larger area than other compared tuning methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
7. A Parametric Study of Wave Energy Converter Layouts in Real Wave Models.
- Author
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Amini, Erfan, Golbaz, Danial, Amini, Fereidoun, Majidi Nezhad, Meysam, Neshat, Mehdi, and Astiaso Garcia, Davide
- Subjects
WAVE energy ,OCEAN waves ,RENEWABLE energy sources ,FREQUENCY-domain analysis ,OCEAN wave power - Abstract
Ocean wave energy is a broadly accessible renewable energy source; however, it is not fully developed. Further studies on wave energy converter (WEC) technologies are required in order to achieve more commercial developments. In this study, four CETO6 spherical WEC arrangements have been investigated, in which a fully submerged spherical converter is modelled. The numerical model is applied using linear potential theory, frequency-domain analysis, and irregular wave scenario. We investigate a parametric study of the distance influence between WECs and the effect of rotation regarding significant wave direction in each arrangement compared to the pre-defined layout. Moreover, we perform a numerical landscape analysis using a grid search technique to validate the best-found power output of the layout in real wave models of four locations on the southern Australian coast. The results specify the prominent role of the distance between WECs, along with the relative angle of the layout to dominant wave direction, in harnessing more power from the waves. Furthermore, it is observed that a rise in the number of WECs contributed to an increase in the optimum distance between converters. Consequently, the maximum exploited power from each buoy array has been found, indicating the optimum values of the distance between buoys in different real wave scenarios and the relative angle of the designed layout with respect to the dominant in-site wave direction. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
8. A New Bi-Level Optimisation Framework for Optimising a Multi-Mode Wave Energy Converter Design: A Case Study for the Marettimo Island, Mediterranean Sea.
- Author
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Neshat, Mehdi, Sergiienko, Nataliia Y., Amini, Erfan, Majidi Nezhad, Meysam, Astiaso Garcia, Davide, Alexander, Bradley, and Wagner, Markus
- Subjects
- *
WAVE energy , *OCEAN waves , *ROGUE waves , *RENEWABLE energy sources , *ENERGY harvesting , *DIFFERENTIAL evolution - Abstract
To advance commercialisation of ocean wave energy and for the technology to become competitive with other sources of renewable energy, the cost of wave energy harvesting should be significantly reduced. The Mediterranean Sea is a region with a relatively low wave energy potential, but due to the absence of extreme waves, can be considered at the initial stage of the prototype development as a proof of concept. In this study, we focus on the optimisation of a multi-mode wave energy converter inspired by the CETO system to be tested in the west of Sicily, Italy. We develop a computationally efficient spectral-domain model that fully captures the nonlinear dynamics of a wave energy converter (WEC). We consider two different objective functions for the purpose of optimising a WEC: (1) maximise the annual average power output (with no concern for WEC cost), and (2) minimise the levelised cost of energy (LCoE). We develop a new bi-level optimisation framework to simultaneously optimise the WEC geometry, tether angles and power take-off (PTO) parameters. In the upper-level of this bi-level process, all WEC parameters are optimised using a state-of-the-art self-adaptive differential evolution method as a global optimisation technique. At the lower-level, we apply a local downhill search method to optimise the geometry and tether angles settings in two independent steps. We evaluate and compare the performance of the new bi-level optimisation framework with seven well-known evolutionary and swarm optimisation methods using the same computational budget. The simulation results demonstrate that the bi-level method converges faster than other methods to a better configuration in terms of both absorbed power and the levelised cost of energy. The optimisation results confirm that if we focus on minimising the produced energy cost at the given location, the best-found WEC dimension is that of a small WEC with a radius of 5 m and height of 2 m. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
9. A Parametric Study of Wave Energy Converter Layouts in Real Wave Models
- Author
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Erfan Amini, Mehdi Neshat, Davide Astiaso Garcia, Fereidoun Amini, Danial Golbaz, Meysam Majidi Nezhad, Amini, Erfan, Golbaz, Danial, Amini, Fereidoun, Nezhad, Meysam Majidi, Neshat, Mehdi, and Astiaso Garcia, Davide
- Subjects
Wave energy converter ,Control and Optimization ,Computer science ,020209 energy ,Acoustics ,layout assessment ,wave energy conversion ,Energy Engineering and Power Technology ,02 engineering and technology ,lcsh:Technology ,real wave model ,Physics::Geophysics ,Wind wave ,renewable energy ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Engineering (miscellaneous) ,Physics::Atmospheric and Oceanic Physics ,Parametric statistics ,Buoy ,lcsh:T ,Renewable Energy, Sustainability and the Environment ,business.industry ,Power (physics) ,Renewable energy ,020201 artificial intelligence & image processing ,business ,Rotation (mathematics) ,Energy (signal processing) ,Energy (miscellaneous) - Abstract
Ocean wave energy is a broadly accessible renewable energy source; however, it is not fully developed. Further studies on wave energy converter (WEC) technologies are required in order to achieve more commercial developments. In this study, four CETO6 spherical WEC arrangements have been investigated, in which a fully submerged spherical converter is modelled. The numerical model is applied using linear potential theory, frequency-domain analysis, and irregular wave scenario. We investigate a parametric study of the distance influence between WECs and the effect of rotation regarding significant wave direction in each arrangement compared to the pre-defined layout. Moreover, we perform a numerical landscape analysis using a grid search technique to validate the best-found power output of the layout in real wave models of four locations on the southern Australian coast. The results specify the prominent role of the distance between WECs, along with the relative angle of the layout to dominant wave direction, in harnessing more power from the waves. Furthermore, it is observed that a rise in the number of WECs contributed to an increase in the optimum distance between converters. Consequently, the maximum exploited power from each buoy array has been found, indicating the optimum values of the distance between buoys in different real wave scenarios and the relative angle of the designed layout with respect to the dominant in-site wave direction. Refereed/Peer-reviewed
- Published
- 2020
- Full Text
- View/download PDF
10. New insights into position optimisation of wave energy converters using hybrid local search
- Author
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Mehdi Neshat, Nataliia Y. Sergiienko, Bradley Alexander, Markus Wagner, Neshat, Mehdi, Alexander, Bradley, Sergiienko, fNataliia Y., and Wagner, Markus
- Subjects
Mathematical optimization ,hybrid local search ,position optimisation ,General Computer Science ,Buoy ,Heuristic (computer science) ,business.industry ,Computer science ,General Mathematics ,05 social sciences ,050301 education ,02 engineering and technology ,renewable energy ,Renewable energy ,Set (abstract data type) ,Wave model ,Position (vector) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Local search (optimization) ,wave energy converters ,business ,0503 education ,Energy (signal processing) - Abstract
Renewable energy will play a pivotal role in meeting future global energy demand. Of current renewable sources, wave energy offers enormous potential for growth. This research investigates the optimisation of the placement of oscillating buoy-type wave energy converters (WECs). This work explores the design of a wave farm consisting of an array of fully submerged three-tether buoys. In a wave farm, buoy positions strongly determine the farm's output. Optimising the buoy positions is a challenging research problem due to complex and extensive interactions (constructive and destructive) between buoys. This research focuses on maximising the power output of the farm through the placement of buoys in a size-constrained environment, and we propose a new hybrid approach mixing local search, using a surrogate power model, and numerical optimisation methods. The proposed hybrid method is compared with other state-of-the-art search methods in five different wave scenarios - one simplified irregular wave model and four real wave regimes. The new hybrid methods outperform well-known previous heuristic methods in terms of both quality of achieved solutions and the convergence-rate of search in all tested wave regimes. The best performing method in real-wave scenarios uses the active set non-linear optimisation method to tune final placements. The effectiveness of this method seems to stem for its capacity to search over a larger area than other compared tuning methods. Refereed/Peer-reviewed
- Published
- 2020
11. Optimisation of large wave farms using a multi-strategy evolutionary framework
- Author
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Markus Wagner, Mehdi Neshat, Nataliia Y. Sergiienko, Bradley Alexander, Neshat, Mehdi, Alexander, Bradley, Sergiienko, Nataliia Y, Wagner, Markus, and 2020 Genetic and Evolutionary Computation Conference, GECCO 2020 Cancun, Mexico 8-12 July 2020
- Subjects
Signal Processing (eess.SP) ,FOS: Computer and information sciences ,Wave energy converter ,Mathematical optimization ,Computer science ,optimisation ,Population ,Evolutionary algorithm ,0102 computer and information sciences ,02 engineering and technology ,01 natural sciences ,FOS: Electrical engineering, electronic engineering, information engineering ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,Local search (optimization) ,Neural and Evolutionary Computing (cs.NE) ,wave energy converters ,Electrical Engineering and Systems Science - Signal Processing ,evolutionary algorithms ,education ,education.field_of_study ,business.industry ,hybrid multi-strategy evolutionary method ,Computer Science - Neural and Evolutionary Computing ,Renewable energy ,discrete local search ,010201 computation theory & mathematics ,020201 artificial intelligence & image processing ,business ,large wave farm ,Energy (signal processing) - Abstract
Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs). Energy maximisation for large farms is a challenging search problem due to the costly calculations of the hydrodynamic interactions between WECs in a large wave farm and the high dimensionality of the search space. To address this problem, we propose a new hybrid multi-strategy evolutionary framework combining smart initialisation, binary population-based evolutionary algorithm, discrete local search and continuous global optimisation. For assessing the performance of the proposed hybrid method, we compare it with a wide variety of state-of-the-art optimisation approaches, including six continuous evolutionary algorithms, four discrete search techniques and three hybrid optimisation methods. The results show that the proposed method performs considerably better in terms of convergence speed and farm output. Refereed/Peer-reviewed
- Published
- 2020
12. A new bi-level optimisation framework for optimising a multi-modewave energy converter design. A case study for the marettimo island, mediterranean sea
- Author
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Erfan Amini, Meysam Majidi Nezhad, Davide Astiaso Garcia, Mehdi Neshat, Nataliia Y. Sergiienko, Bradley Alexander, Markus Wagner, Neshat, Mehdi, Sergiienko, Nataliia Y, Amini, Erfan, Nezhad, Meysam Majidi, Astiaso Garcia, Davide, Alexander, Bradley, and Wagner, Markus
- Subjects
Wave energy converter ,Control and Optimization ,Computer science ,bi-level optimisation method ,020209 energy ,Energy Engineering and Power Technology ,020101 civil engineering ,02 engineering and technology ,evolutionary algorithms ,renewable energy ,wave energy converter ,geometric parameters ,power take-off ,levelised cost of energy ,lcsh:Technology ,0201 civil engineering ,Mediterranean sea ,Wind wave ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Rogue wave ,Power take-off ,Cost of electricity by source ,Engineering (miscellaneous) ,CETO ,Renewable Energy, Sustainability and the Environment ,business.industry ,lcsh:T ,Renewable energy ,Power (physics) ,business ,Energy harvesting ,Energy (signal processing) ,Energy (miscellaneous) ,Marine engineering - Abstract
To advance commercialisation of ocean wave energy and for the technology to become competitive with other sources of renewable energy, the cost of wave energy harvesting should be significantly reduced. The Mediterranean Sea is a region with a relatively low wave energy potential, but due to the absence of extreme waves, can be considered at the initial stage of the prototype development as a proof of concept. In this study, we focus on the optimisation of a multi-mode wave energy converter inspired by the CETO system to be tested in the west of Sicily, Italy. We develop a computationally efficient spectral-domain model that fully captures the nonlinear dynamics of a wave energy converter (WEC). We consider two different objective functions for the purpose of optimising a WEC: (1) maximise the annual average power output (with no concern for WEC cost), and (2) minimise the levelised cost of energy (LCoE). We develop a new bi-level optimisation framework to simultaneously optimise the WEC geometry, tether angles and power take-off (PTO) parameters. In the upper-level of this bi-level process, all WEC parameters are optimised using a state-of-the-art self-adaptive differential evolution method as a global optimisation technique. At the lower-level, we apply a local downhill search method to optimise the geometry and tether angles settings in two independent steps. We evaluate and compare the performance of the new bi-level optimisation framework with seven well-known evolutionary and swarm optimisation methods using the same computational budget. The simulation results demonstrate that the bi-level method converges faster than other methods to a better configuration in terms of both absorbed power and the levelised cost of energy. The optimisation results confirm that if we focus on minimising the produced energy cost at the given location, the best-found WEC dimension is that of a small WEC with a radius of 5 m and height of 2 m. Refereed/Peer-reviewed
- Published
- 2020
13. A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
- Author
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Markus Wagner, Mehdi Neshat, Bradley Alexander, Neshat, Mehdi, Alexander, Bradley, and Wagner, Markus
- Subjects
FOS: Computer and information sciences ,Information Systems and Management ,position optimisation ,Computer science ,Evolutionary algorithm ,adaptive gray wolf optimiser ,power take off system ,Scale (descriptive set theory) ,02 engineering and technology ,Theoretical Computer Science ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,Local search (optimization) ,Neural and Evolutionary Computing (cs.NE) ,wave energy converters ,Power take-off ,business.industry ,Backtracking ,05 social sciences ,cooperative co-Evolution algorithms ,050301 education ,Computer Science - Neural and Evolutionary Computing ,renewable energy ,Computer Science Applications ,Renewable energy ,Control and Systems Engineering ,020201 artificial intelligence & image processing ,business ,0503 education ,Algorithm ,Software ,Energy (signal processing) - Abstract
Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this work, we explore the optimisation of WEC arrays consisting of a three-tether buoy model called CETO. Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys in farms and also the power-take-off (PTO) parameters for each buoy. The search space for these parameters is complex and multi-modal. Moreover, the evaluation of each parameter setting is computationally expensive -- limiting the number of full model evaluations that can be made. To handle this problem, we propose a new hybrid cooperative co-evolution algorithm (HCCA). HCCA consists of a symmetric local search plus Nelder-Mead and a cooperative co-evolution algorithm (CC) with a backtracking strategy for optimising the positions and PTO settings of WECs, respectively. Moreover, a new adaptive scenario is proposed for tuning grey wolf optimiser (AGWO) hyper-parameter. AGWO participates notably with other applied optimisers in HCCA. For assessing the effectiveness of the proposed approach five popular Evolutionary Algorithms (EAs), four alternating optimisation methods and two modern hybrid ideas (LS-NM and SLS-NM-B) are carefully compared in four real wave situations (Adelaide, Tasmania, Sydney and Perth) with two wave farm sizes (4 and 16). According to the experimental outcomes, the hybrid cooperative framework exhibits better performance in terms of both runtime and quality of obtained solutions., Information Sciences (2020)
- Published
- 2019
14. A Hybrid Evolutionary Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
- Author
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Mehdi Neshat, Nataliia Y. Sergiienko, Markus Wagner, Bradley Alexander, Neshat, Mehdi, Alexander, Bradley, Sergiienko, Nataliia Y, Wagner, Markus, and 2019 Genetic and Evolutionary Computation Conference, GECCO 2019 Prague, Czech Republic 13-17 July 2019
- Subjects
FOS: Computer and information sciences ,Mathematical optimization ,Computer science ,Evolutionary algorithm ,0102 computer and information sciences ,02 engineering and technology ,01 natural sciences ,Power Take Off system ,Wind wave ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,Local search (optimization) ,Neural and Evolutionary Computing (cs.NE) ,evolutionary algorithms ,Wave Energy Converters ,Power take-off ,business.industry ,Heuristic ,Computer Science - Neural and Evolutionary Computing ,position optimization ,renewable energy ,Renewable energy ,010201 computation theory & mathematics ,020201 artificial intelligence & image processing ,business ,Energy (signal processing) - Abstract
Ocean wave energy is a source of renewable energy that has gained much attention for its potential to contribute significantly to meeting the global energy demand. In this research, we investigate the problem of maximising the energy delivered by farms of wave energy converters (WEC's). We consider state-of-the-art fully submerged three-tether converters deployed in arrays. The goal of this work is to use heuristic search to optimise the power output of arrays in a size-constrained environment by configuring WEC locations and the power-take-off (PTO) settings for each WEC. Modelling the complex hydrodynamic interactions in wave farms is expensive, which constrains search to only a few thousand model evaluations. We explore a variety of heuristic approaches including cooperative and hybrid methods. The effectiveness of these approaches is assessed in two real wave scenarios (Sydney and Perth) with farms of two different scales. We find that a combination of symmetric local search with Nelder-Mead Simplex direct search combined with a back-tracking optimization strategy is able to outperform previously defined search techniques by up to 3%. Refereed/Peer-reviewed
- Published
- 2019
15. Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation
- Author
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Ehsan Abbasnejad, Qinfeng Shi, Markus Wagner, Mehdi Neshat, Bradley Alexander, Neshat, Mehdi, Abbasnejad, Ehsan, Shi, Qinfeng, Alexander, Bradley, Wagner, Markus, and 26th International Conference on Neural Information Processing, ICONIP 2019 Sydney, Australia 12-15 December 2019
- Subjects
Gray Wolf Optimiser ,Mathematical optimization ,Wind power ,Computer science ,business.industry ,Backtracking ,local search ,020209 energy ,Evolutionary algorithm ,02 engineering and technology ,renewable energy ,Renewable energy ,Recurrent neural network ,surrogate-based optimisation ,0202 electrical engineering, electronic engineering, information engineering ,Wave farm ,020201 artificial intelligence & image processing ,Local search (optimization) ,Evolutionary Algorithms ,sequential deep learning ,Wave Energy Converters ,business ,Energy (signal processing) - Abstract
Installed renewable energy capacity has expanded massively in recent years. Wave energy, with its high capacity factors, has great potential to complement established sources of solar and wind energy. This study explores the problem of optimising the layout of advanced, three-tether wave energy converters in a size-constrained farm in a numerically modelled ocean environment. Simulating and computing the complicated hydrodynamic interactions in wave farms can be computationally costly, which limits optimisation methods to using just a few thousand evaluations. For dealing with this expensive optimisation problem, an adaptive neuro-surrogate optimisation (ANSO) method is proposed that consists of a surrogate Recurrent Neural Network (RNN) model trained with a very limited number of observations. This model is coupled with a fast meta-heuristic optimiser for adjusting the model’s hyper-parameters. The trained model is applied using a greedy local search with a backtracking optimisation strategy. For evaluating the performance of the proposed approach, some of the more popular and successful Evolutionary Algorithms (EAs) are compared in four real wave scenarios (Sydney, Perth, Adelaide and Tasmania). Experimental results show that the adaptive neuro model is competitive with other optimisation methods in terms of total harnessed power output and faster in terms of total computational costs. Refereed/Peer-reviewed
- Published
- 2019
- Full Text
- View/download PDF
16. A detailed comparison of meta-heuristic methods for optimising wave energy converter placements
- Author
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Markus Wagner, Mehdi Neshat, Bradley Alexander, Yuanzhong Xia, Neshat, Mehdi, Alexander, Bradley, Wagner, Markus, Xia, Yuanzhong, and 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 Kyoto, Japan 15-19 July 2018
- Subjects
Power station ,Computer science ,business.industry ,020209 energy ,Evolutionary algorithm ,position optimization ,02 engineering and technology ,Industrial engineering ,renewable energy ,Renewable energy ,Work (electrical) ,Wave Energy Converter ,Wind wave ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Local search (optimization) ,evolutionary algorithms ,Heuristics ,business ,Energy (signal processing) - Abstract
In order to address environmental concerns and meet growing energy demand the development of green energy technology has expanded tremendously. One of the most promising types of renewable energy is ocean wave energy. While there has been strong research in the development of this technology to date there remain a number of technical hurdles to overcome. This research explores a type of wave energy converter (WEC) called a buoy. This work models a power station as an array of fully submerged three-tether buoys. The target problem of this work is to place buoys in a size-constrained environment to maximise power output. This article improves prior work by using a more detailed model and exploring the search space using a wide variety of search heuristics. We show that a hybrid method of stochastic local search combined with Nelder-Mead Simplex direct search performs better than previous search techniques. Refereed/Peer-reviewed
- Published
- 2018
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