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2-D analytical magnetic model for optimal design of an outer rotor permanent magnet synchronous machine

IET ELECTRIC POWER APPLICATIONS 2023年第1期17卷 1-13页

作者： Vahaj, Amir Abbas Rahideh, Akbar Faradonbeh, Vahid Zamani Salehi, Ali Reza Ghaffari, Alireza Shahnazari, Mostafa Lubin, Thierry Shiraz Univ Technol Dept Elect & Elect Engn Shiraz *** Iran Yazd Univ Elect Engn Dept Yazd Iran Univ Lorraine Grp Rech Energie Elect Nancy Nancy France

The objective of this paper is to investigate the optimal design of a 120 W outer-rotor permanent magnet synchronous machine for air-conditioning applications to reduce the electricity consumption and machine costs. To this end, a 2-D analytical magnetic model, combined with a teaching-learning based optimisation algorithm is employed. The objectives are to simultaneously maximise the machine efficiency and minimise the material cost. Constraints such as the required output torque, predefined machine volume, maximum allowable torque ripple and unbalanced magnetic force and limitations to avoid iron core magnetic saturation are imposed simultaneously. To find the flux density in iron parts, a new method based on the subdomain technique has been presented. To evaluate the analytical model in terms of accuracy and speed, the results of the 2-D analytical approach are compared with those of the 2-D and 3-D linear/non-linear finite element method. Finally, a prototype of the optimised machine is fabricated and the superiority of the presented approach is demonstrated.

关键词： optimisation algorithm optimisation techniques optimal design synchronous machines nonlinear finite element method permanent magnet machines Air conditioning Finite element analysis optimisation 2-d analytical magnetic model air conditioning air-conditioning applications rotors iron core magnetic saturation maximum allowable torque ripple finite element analysis Synchronous machines outer rotor permanent magnet synchronous machine

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Distributed energy resource placement considering hosting capacity by combining teaching-learning-based and honey-bee-mating optimisation algorithms

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107953-107953页

作者： Taheri, Seyed Iman Salles, Mauricio B. C. Nassif, Alexandre B. Univ Sao Paulo Lab Adv Elect Grids LGrid Escola Politecn Sao Paulo Brazil ATCO Edmonton AB T5J 2V6 Canada

An emerging challenge of most electric utilities is to expand hosting capacity constraints and maximise the interconnection of distributed energy resources (DER). DERs have proliferated in distribution systems in most jurisdictions, and this proliferation has not occurred in an orderly fashion. Reports of degradation in power quality and operations are common. With the continuing adoption of DERs, it is imperative to develop methods to unlock the potential of DERs in a way that they are used to support system needs, rather than degrading them. This concept's advantages are varied in different power system scenarios based on the considered models for owning and operating DER. With this objective in mind, this paper proposes a new algorithm to identify the optimal locations for new DERs. The proposed method is a novel combination of teaching-learning-based optimisation (TLBO) and honeybee-mating optimisation (HBMO) algorithms, and it capitalises on both techniques' benefits. A major novelty of this method is that it fully accounts for current hosting capacity and all existing DERs and incorporates these parameters in the optimisation algorithm. The results reveal that adding fuzzy clustering to the multiobjective process improves the DER placement problem. The DERs considered in this optimisation problem are fuel cell units, and the objective function includes cost, losses, and voltage deviation. The proposed technique is adopted in the IEEE 70-bus-radial test system, and its performance is compared with those of other optimisation algorithms. The results reveal the proposed algorithm's superiority in accuracy and computational speed when reaching the optimal solution. As a case in point, the calculation time of the proposed algorithm is about %9 and %22 faster than TLBO and HBMO, respectively. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Energy management Distribution system Distributed generation optimisation algorithm Energy planning

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Land Surface Model Calibration Using Satellite Remote Sensing Data

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SENSORS 2023年第4期23卷 1848-1848页

作者： Khaki, Mehdi Univ Newcastle Sch Engn Callaghan NSW 2308 Australia

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer-earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model's parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations' uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.

关键词： model calibration satellite remote sensing optimisation algorithm terrestrial water storage soil moisture

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Optimal design and cost analysis of single-axis tracking photovoltaic power plants

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RENEWABLE ENERGY 2023年第1期211卷 626-646页

作者： Barbon, A. Carreira-Fontao, V. Bayon, L. Silva, C. A. Univ Oviedo Dept Elect Engn Oviedo Spain Univ Oviedo Polytech Sch Engn Gijon Oviedo Spain Univ Oviedo Dept Math Oviedo Spain Univ Lisbon Ctr Innovat Technol & Policy Res IN Inst Super Tecn Lisbon Portugal

The increasing penetration of photovoltaic technology in the electricity market requires the development of a methodology that facilitates the optimisation of photovoltaic plants with single-axis trackers. This paper presents an optimisation methodology that takes into account the most important design variables of single-axis photovoltaic plants, including irregular land shape, size and configuration of the mounting system, row spacing, and operating periods (for backtracking mode, limited range of motion, and normal tracking mode). Equations for the determination of the optimal row spacing and operating periods have been developed and is presented in detail. A packing algorithm that takes into account the irregular land shape and the possible configurations of the mounting systems is also presented. The objective function is the total area of the photovoltaic field and the optimisation is performed by a packing algorithm. As the economic aspect of energy generation also plays a key role in decision-making, the levelised cost of energy has been used to assess the economic viability of the optimal layout of the mounting systems. The results show that the proposed methodology and packing algorithm are able to optimise the photovoltaic plant with single-axis solar tracking and provide reliable results after a reasonable computation time. The methodology was demonstrated in detail for a Spanish photovoltaic plant (Granjera photovoltaic power plant), including the optimal layout of the mounting systems and the cost analysis for this layout. The optimal layout of the mounting systems could increase the amount of energy captured by 91.18% in relation to the current of Granjera photovoltaic power plant. The mounting system configuration used in the optimal layout is the one with the best levelised cost of energy efficiency, 1.09. The presented optimisation methodology can be utilised to facilitate the optimal design of commercial photovoltaic plants with single-axis tracke

关键词： Single-axis tracker optimisation algorithm Normal tracking mode Backtracking mode Energy gain Analysis cost

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Preserving sensitive data with deep learning assisted sanitisation process

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2023年第4期35卷 589-616页

作者： Mohana, S. Shyamala, C. Rani, E. Shapna Ambika, M. Saranathan Coll Engn Dept Comp Sci & Engn Trichy Tamil Nadu India K Ramakrishnan Coll Technol Dept Comp Sci & Engn Trichy India Sastra Deemed Univ Sch Comp Dept Comp Sci & Engn Trichy Tamil Nadu India Saranathan Coll Engn Dept Comp Sci & Engn Trichy 620012 Tamil Nadu India

This work introduces a novel privacy preservation scheme. In large databases, the data sanitisation process preserves the stored sensitive data safely from unauthorised access and users by hiding it. Moreover, the statistical features are extracted. Further, the normalised data and features are processed under the data sanitisation process. For the sanitisation process, the optimal key is produced by utilising the Deep Belief Network (DBN) with Chaotic Map-adopted Poor and Rich optimisation (CMPRO) model. It is the modified version of the classical PRO algorithm. As a novelty, chaotic map and cycle crossover operation is included in the CMPRO algorithm. Privacy, modification degree, data preservation ratio, and hiding failure are considered as the objectives for the key generation process. Then, the data restoration process restores or recovers the sanitised data, and it is the reverse process. Then, the outcomes of the adopted scheme are analysed over the traditional systems based on certain measures. Especially, the sanitisation effectiveness of the proposed approach for data 1 in test case 2 and it is 54.56%, 51.82%, 47.94%, 49.59%, 18.17%, 43.32%, 47.03%, 47.03%, 55.79%, 21.84%, 47.33%, and 32.13% better than the existing CNN+CMPRO, RNN+CMPRO, LSTM+CMPRO, BiLSTM+CMPRO, DBN+PRO, DBN+SSA, DBN+SMO, DBN+LA, DBN+SSO, DBN+J-SSO, DBN+BS-WOA, and DBN+R-GDA schemes.

关键词： Privacy preservation data sanitisation key generation data restoration optimisation algorithm

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Comparative analysis between traditional and emerging technologies: economic and viability evaluation in a real case scenario

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MATERIALS FOR RENEWABLE AND SUSTAINABLE ENERGY 2023年第1期12卷 1-22页

作者： Bernardo, Catarina Pinho Correia Valerio Lameirinhas, Ricardo Marques A. Torres, Joao Paulo Neto Baptista, Antonio Univ Lisbon Elect & Comp Engn Dept Inst Super Tecn Av Rovisco Pais P-1049001 Lisbon Portugal Inst Telecomunicacoes Av Rovisco PaisTorre Norte10 Piso P-1049001 Lisbon Portugal Acad Mil CINAMIL Av Conde Castro GuimaraesAmadora P-2720113 Lisbon Portugal

This research work aims to study photovoltaic systems that generate energy for self-consumption using different traditional technologies, such as silicon, and emerging technologies, like nanowires and quantum. The photovoltaic system without batteries was implemented in a residential property in three different places, in Portugal. According to Portuguese Law, the sale of surplus energy to the grid is possible but the respective value for its selling is not defined. To evaluate the project viability, two different analyses are considered: with and without the sale of surplus energy to the grid. Results show that if there is no sale of excess energy produced to the grid, the project is not economically viable considering the four different technologies. Otherwise, using traditional technologies, the project is economically viable, presenting a payback time lower than 10 years. This shows that the introduction of nanostructures in solar cells is not yet a good solution in the application of solar systems namely with the current law. Furthermore, independently of the used technology, the current Portuguese law seems to difficult the investment return, which should not be the way to encourage the use of renewable sources.

关键词： Economic analysis optimisation algorithm Nanostructures Photovoltaic technology Solar energy Sustainability

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Resilience-based post-earthquake recovery strategies for substation systems

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INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION 2023年 96卷

作者： Liu, Xiao Xie, Qiang Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China

Electrical substation systems require significant manpower and material resources for repair after an earthquake;thus, repair efficiency directly affects economic loss. To quantify the functional recovery process of substation systems, this study proposes a seismic resilience assessment frame- work based on the functional characteristics of substation systems. Reliability models of the equipment and systems were constructed to characterise the structural connection features and power transmission paths of substation systems, and the system functional state matrix was ob- tained through a seismic simulation. Moreover, factors influencing functional recovery and repair path control parameters were proposed to determine efficient post -earthquake recovery strate- gies. The Monte Carlo method was used to construct a post -earthquake recovery strategy analysis framework for a substation system. By analysing a typical 220 kV substation using static -based repair strategies, we obtained the functional recovery process and seismic resilience level. Fur- thermore, based on the actual functional requirements, a dynamic recovery strategy analysis method was proposed based on an improved genetic algorithm. Notably, optimal recovery strate- gies for different earthquake intensities and recovery priorities were obtained through iterative reproduction analysis.

关键词： Substation system Functional state model Seismic resilience framework Post-earthquake recovery strategy optimisation algorithm

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An evaluation model for an optimal decarbonisation process in the built environment

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BUILT ENVIRONMENT PROJECT AND ASSET MANAGEMENT 2025年第1期15卷 51-66页

作者： Sica, Francesco Tajani, Francesco Cerullo, Giuseppe Univ Roma La Sapienza Fac Architecture Dept Architecture & Design Rome Italy

PurposeThe goal is to deliver a decision-support framework to both public and private entities engaged in energy retrofit investments in the property market. Design/methodology/approachThe evaluation algorithm that is being offered takes an innovative approach to financial and economic analysis. Its foundation is a market-driven/cost-driven method, drawing logic from operational research and goal programming. FindingsThe algorithm is tested to a real estate portfolio yielding an optimal asset retrofitting schedule. The ranking list is determined by taking into consideration a variety of parameters, including investment costs and total CO2 emissions from energy retrofit initiatives. The Carbon Risk Real Estate Monitor's (CRREM) emission targets for 2030 are employed as a reference point in the process of creating a ranking list of the assets that compose the real estate portfolio under examination. Practical implicationsThe evaluation algorithm will allow to determine, in a real estate portfolio, a priority list of assets to be enhanced. This is accomplished by taking into account the client's financial resources, the overall cost of the intervention programmes for each asset, and the effects that each asset would have on the environment and the energy once the suggested retrofit programme is put into place. Originality/valueThe study proposes a methodological approach that seeks to balance the optimisation of energy performance, the reduction of environmental effect, the promotion of social well-being and economic sustainability in the context of managing the current property sector.

关键词： Decarbonisation Real estate Economic evaluation MCDA optimisation algorithm CRREM

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Resilience-based optimisation framework for post-earthquake restoration of power systems

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 257卷

作者： Xie, Qiang Liu, Xiao Wu, Siyuan Tongji Univ Coll Civil Engn Shanghai 200092 Peoples R China

Power systems suffer significant damage during earthquakes and recover slowly. To assess the seismic resilience and optimise the post-earthquake recovery of power systems, this paper proposes a multi-model framework for assessing power systems' performance. The seismic intensity attenuation model was determined based on seismic characteristics. A method for evaluating the operational state of power-system components was developed using seismic vulnerability curves. An operational network model of power systems was created using undirected weighted graphs and adjacency matrices. The power-transmission efficiency, reliability, and power-load capacity of the network model were considered to propose functional metrics for quantitatively assessing the working state of the system. Subsequently, a post-earthquake recovery optimisation framework for power systems was proposed by establishing functional time-varying parameters, curves, and resilience metrics. A four-stage process for analysing recovery-path optimisation was explicitly defined based on seismic resilience metrics., the recovery characteristics and the optimal recovery path were identified by analysing the recovery optimisation of a typical power-network test system. Finally, the seismic retrofitting effects and economic benefits of infrastructure were evaluated, and key components of seismic reinforcement were identified.

关键词： Power system System functional model Post-earthquake recovery path optimisation algorithm Seismic resilience improvement

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Prediction of carbon nanotubes reinforced interphase properties in fuzzy fibre reinforced polymer via inverse analysis and optimisation

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COMPUTATIONAL MATERIALS SCIENCE 2024年 231卷

作者： Pawlik, Marzena Le, Huirong Wood, Paul Lu, Yiling Univ Derby Coll Sci & Engn Sch Comp & Engn Markeaton St Derby DE22 3AW England Tsinghua Univ Future Lab Beijing Peoples R China Univ Derby Inst Innovat Sustainable Engn Quaker Way Derby DE1 3HD England

This paper presents the parameter identification procedure of carbon nanotubes (CNTs) reinforced interphase in fuzzy fibre reinforced polymer (FFRP). The procedure was completed with ANSYS Workbench 19.2 software by combining Mechanical ADPL and Goal-Driven optimisation. Firstly, a three-phase representative volume element (RVE) containing carbon fibre, CNTs reinforced interphase and epoxy resin was developed as a collection of Mechanical APDL commands. This RVE model was simulated to evaluate the elastic constants of FFRP lamina. CNTs reinforced interphase was characterised by transversely isotropic model. Interphase prop-erties were parametrised and became input parameters in the Goal-Driven optimisation. FFRP lamina elastic constants were set as the output parameters. Multi-objective Genetic algorithm (MOGA) was used to identify the interphase properties, so that the output FFRP lamina elastic constants match the objective and constraints. The optimisation algorithm converged after 585 evaluations. Five potential candidate point, which met required objectives and constraints, were found. The identified interphase properties agreed well with the literature (an average percentage error of around 2%). This inverse procedure shows the potential to identify the interphase properties in nano-engineered composites, which are extremely difficult to measure experimentally.

关键词： Fuzzy fibre reinforced polymer Nano-reinforced interphase Inverse problem Parameters' identification optimisation algorithm

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