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genetic algorithms for the design and optimization of horizontal axis wind turbine (HAWT) blades: A continuous approach or a binary one?

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 44卷

作者： Pourrajabian, Abolfazl Dehghan, Maziar Rahgozar, Saeed Mat & Energy Res Ctr MERC Dept Energy Tehran 141554777 Iran

The continuous and the binary techniques are the main types of genetic algorithms that could be employed for an optimization problem. The study investigates the robustness and accuracy of each technique for the wind turbine blades design and optimization problem. For that purpose, the geometry of the blade was designed for the maximization of the output power, the desirable goal of the blade design. The design variables consist of the distribution of the chord and twist along the blade. Results indicate that the continuous genetic algorithm outperforms the binary one from the standpoint of the accuracy and also the computational time. However, the use of uniform crossover could improve the convergence rate of the binary genetic algorithm. Moreover, the sensitivity analysis of the applied genetic algorithms with respect to the population size and the mutation rate was performed to find the appropriate values for those parameters. The outcomes emphasized that adopting a small number of population size together with a large number of generations could speed up the convergence rate of the problem. Also approved in the study was the efficiency of the so-called "Superblade" operator which improved the convergence rate of the algorithm and resulted in the powerful blades.

关键词： Wind turbine Blade Optimization Continuous genetic algorithm binary genetic algorithm

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System level test selection based on combinatorial dependency matrix

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Journal of Systems Engineering and Electronics 2021年第4期32卷 984-994页

作者： YANG Peng XIE Haoyu QIU Jing Science and Technology on Integrated Logistics Support Laboratory School of Intelligence ScienceNational University of Defense TechnologyChangsha 410073China Unit 91697 of the PLA Qingdao 266000China

Test selection is to select the test set with the least total cost or the least total number from the alternative test set on the premise of meeting the required testability *** existing models and methods are not suitable for system level test *** first problem is the lack of detailed data of the units’fault set and the test set,which makes it impossible to establish a traditional dependency matrix for the system *** second problem is that the system level fault detection rate and the fault isolation rate(referred to as"two rates")are not enough to describe the fault diagnostic ability of the system level *** innovative dependency matrix(called combinatorial dependency matrix)composed of three submatrices is *** first problem is solved by simplifying the submatrix between the units’fault and the test,and the second problem is solved by establishing the system level fault detection rate,the fault isolation rate and the integrated fault detection rate(referred to as"three rates")based on the new *** mathematical model of the system level test selection problem is constructed,and the binary genetic algorithm is applied to solve the problem,which achieves the goal of system level test selection.

关键词： test selection dependency matrix fault detection rate testability prediction binary genetic algorithm

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Adaptive Predictor Subset Selection Strategy for Enhanced Forecasting of Distributed PV Power Generation

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IEEE ACCESS 2019年 7卷 90652-90665页

作者： Eseye, Abinet Tesfaye Lehtonen, Matti Tukia, Toni Uimonen, Semen Millar, R. John Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland

Distributed photovoltaic (PV) solar power plants are playing an increasing role as a power generation resource in the modern electricity grid. However, PVs pose significant challenges to grid planners, operators, owners, investors, aggregators, and other stakeholders. This is due to the high uncertainty of the PV output power, which is caused by its entire dependence on intermittent environmental factors. This has brought a serious problem to the power industry to integrate and manage power grids containing significant penetration of PVs. Thus, an enhanced PV power forecast is very important to operate these power grids efficiently and reliably. Most previous methodologies have focused on predicting the aggregate amount of potential solar power generation at the national or regional scale and ignored the distributed PVs that are installed primarily for local electric supply. Furthermore, a few research groups have carried out predictor selection before training predictive models. This paper proposes an adaptive hybrid predictor subset selection (PSS) strategy to obtain the most relevant and nonredundant predictors for enhanced short-term forecasting of the power output of distributed PVs. In the proposed strategy, the binary genetic algorithm (BGA) is applied for the feature selection process and support vector regression (SVR) is used for measuring the fitness score of the predictors. In order to validate the effectiveness of the proposed strategy, it is applied to actual distributed PVs located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other PSS techniques. The proposed strategy enhances the quality and efficiency of the predictor subset selection, with minimal chosen predictors to achieve enhanced prediction accuracy. It outperforms the other prediction selection methods. Besides, a configuration of an adaptive forecasting model is introduced and the performance tests are presented to further validate the impact of t

关键词： Adaptive model big data binary genetic algorithm distributed PV forecasting fitness evaluation measure predictor subset selection renewable energy smart grid solar energy support vector regression

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Machine Learning Based Integrated Feature Selection Approach for Improved Electricity Demand Forecasting in Decentralized Energy Systems

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IEEE ACCESS 2019年 7卷 91463-91475页

作者： Eseye, Abinet Tesfaye Lehtonen, Matti Tukia, Toni Uimonen, Semen Millar, R. John Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland

Improved performance electricity demand forecast can provide decentralized energy system operators, aggregators, managers, and other stakeholders with essential information for energy resource scheduling, demand response management, and energy market participation. Most previous methodologies have focused on predicting the aggregate amount of electricity demand at national or regional scale and disregarded the electricity demand for small-scale decentralized energy systems (buildings, energy communities, microgrids, local energy internets, etc.), which are emerging in the smart grid context. Furthermore, few research groups have performed attribute selection before training predictive models. This paper proposes a machine learning (ML)-based integrated feature selection approach to obtain the most relevant and nonredundant predictors for accurate short-term electricity demand forecasting in distributed energy systems. In the proposed approach, one of the ML tools - binary genetic algorithm (BGA) is applied for the feature selection process and Gaussian process regression (GPR) is used for measuring the fitness score of the features. In order to validate the effectiveness of the proposed approach, it is applied to various building energy systems located in the Otaniemi area of Espoo, Finland. The findings are compared with those achieved by other feature selection techniques. The proposed approach enhances the quality and efficiency of the predictor selection, with minimal chosen predictors to achieve improved prediction accuracy. It outperforms the other evaluated feature selection methods. Besides, a feedforward artificial neural network (FFANN) model is implemented to evaluate the forecast performance of the selected predictor subset. The model is trained using two-year hourly dataset and tested with another one-year hourly dataset. The obtained results verify that the FFANN forecast model based on the BGA-GPR FS selected training feature subset has achieved an an

关键词： binary genetic algorithm decentralized energy system electricity demand forecasting feature selection feedforward artificial neural network fitness evaluation measure Gaussian process regression machine learning smart grid

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Automatic discrimination between cough and non-cough accelerometry signal artefacts

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2019年第0期52卷 394-402页

作者： Mohammadi, Helia Samadani, Ali-Akbar Steele, Catriona Chau, Tom Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Univ Toronto Inst Biomat & Biomed Engn Toronto ON M5S 3G9 Canada Toronto Rehabil Inst Toronto ON Canada Holland Bloorview Kids Rehabil Hosp Bloorview Res Inst Toronto ON Canada

Cough is the forceful and rapid expulsion of air that clears the airway of foreign material, fluid or mucus, and may be symptomatic of respiratory conditions and swallowing difficulties. The evaluation of cough severity can thus inform appropriate treatment of the underlying issue, but assessment has historically been subjective (e.g., self-report) and episodic. Automatic cough detection has been proposed as a tool for long-term (e.g., nocturnal) monitoring of cough intensity and frequency, but the rejection of non-cough activities remains an elusive challenge. Cervical accelerometry is a novel tool, which measures oropharyngeal and laryngopharyngeal vibrations associated with swallowing, coughing, tongue movements and speech. We propose an automatic cough detection system that discriminates accelerometry signals associated with coughs from those representing swallows, tongue movements and speech. We consider both voluntary and reflexive coughs. Accelerometry signals were represented in term of time-frequency meta features. Using the binary genetic feature selection algorithm and a support vector machine classifier, the proposed system achieved a cough detection accuracy of 99.26 +/- 0.12% when discriminating between voluntary cough and rest accelerometry signals. An accuracy of 90 +/- 13.9% was achieved using elastic net feature selection and support vector machine when classifying involuntary coughs and rest signals. When discriminating between voluntary and involuntary cough versus all other non-cough artefacts, the proposed system achieved accuracies of 90.2 +/- 3.6% and 80.3 +/- 10.5%, respectively. Compared to current cough monitoring systems which require combinations of microphones, accelerometers, and video recorders, the proposed method requires only a single accelerometer. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Accelerometry signal Artificial neural networks Cough Elastic net binary genetic algorithm Principal component analysis Support vector machines

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A discrete shuffled frog optimization algorithm

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ARTIFICIAL INTELLIGENCE REVIEW 2011年第4期36卷 267-284页

作者： Baghmisheh, M. T. Vakil Madani, Katayoun Navarbaf, Alireza Univ Tabriz ICT Res Ctr Fac Elect & Comp Engn Tabriz Iran

The shuffled frog leaping (SFL) optimization algorithm has been successful in solving a wide range of real-valued optimization problems. In this paper we present a discrete version of this algorithm and compare its performance with a SFL algorithm, a binary genetic algorithm (BGA), and a discrete particle swarm optimization (DPSO) algorithm on seven low dimensional and five high dimensional benchmark problems. The obtained results demonstrate that our proposed algorithm, i.e. the DSFL, outperforms the BGA and the DPSO in terms of both success rate and speed. On low dimensional functions and for large values of tolerance the DSFL is slower than the SFL, but their success rates are equal. Part of this slowness could be attributed to the extra bits used for data coding. By increasing number of variables and the required precision of answer, the DSFL performs very well in terms of both speed and success rate. For high dimensional problems, for intrinsically discrete problems, also when the required precision of answer is high, the DSFL is the most efficient method.

关键词： Optimization binary genetic algorithm Discrete shuffled frog algorithm Discrete particle swarm algorithm

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Multistage Optimal PMU Placement Considering Substation Infrastructure

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2018年第6期54卷 6519-6528页

作者： Almasabi, Saleh Mitra, Joydeep Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

Phasor measurement units (PMUs) significantly benefit the operation and control of power systems. They offer precise phasor measurements with a high refresh rate. These measurements are utilized for wide-area measurement system to improve situational awareness and enhance the control infrastructure. In spite of these advantages, the industry has been reluctant to adopt PMU technology, largely due to the high cost of PMUs and the communication infrastructure. However, judicious selection of PMU locations through optimal placement of PMUs (OPP) enables the minimization of installation cost. There have been several approaches to solve the OPP problem most of which assume that the minimum number of PMUs achieves the minimum cost or consider the cost of PMUs without considering the communication infrastructure. This paper presents an OPP approach that considers both the communication infrastructure and the installation cost of PMUs. The proposed approach uses multistage installation where each stage is dependent on the cost set by the utility instead of the number of PMUs. Higher priority buses can be chosen under the proposed approach. An opposition-based elitist binary genetic algorithm is used to solve the OPP problem. The proposed approach is tested on the IEEE reliability test system, and on the IEEE 14-bus, 30-bus, and 118-bus test systems.

关键词： binary genetic algorithm critical buses observability optimal placement phasor measurement units (PMUs) redundancy measurements

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A Demand-Based Structure for the Architecture of Wireless Networks on Chip

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WIRELESS PERSONAL COMMUNICATIONS 2017年第1期96卷 455-473页

作者： Bahrami, Bahareh Jamali, Mohammad Ali Jabraeil Saeidi, Shahram Islamic Azad Univ Khoy Branch Dept Comp Engn Khoy Iran Islamic Azad Univ Shabestar Branch Dept Comp Engn Shabestar Iran Islamic Azad Univ Tabriz Branch Dept Comp Engn Tabriz Iran

Wireless networks on chip (WiNoC) are considered to be a novel approach for designing efficient and scalable systems. The rationale behind this new approach is to reduce power consumption and latency in traditional network-on-chip architecture. Indeed, wireless links (WLs) in WiNoC architecture are shortcuts for the fast data transmission between distant cores. However, the presence of wireless equipment in WiNoC architecture leads to an increase in cost and area. In this paper, with respect to the facility location problem, the researchers attempted to optimally allocate wireless routers (WRs) to the processing elements. The methods of simulated annealing (SA), binary genetic algorithm (BGA) and binary particle swarm optimization (BPSO) were used for optimization under different traffic patterns. The results obtained from the simulations of this study indicate that BGA has higher efficiency than SA and BPSO. Furthermore, the resulting structure has fewer WRs, WLs and relatively desirable performance.

关键词： Wireless network-on-chip Demand-based structure Simulated annealing binary genetic algorithm binary particle swarm optimization

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Identifying the degree of cornstarch adulteration in turmeric powder using optimized convolutional neural network

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第3期18卷 1955-1964页

作者： Mandal, Dipankar Nandi, Debashis Tudu, Bipan Chatterjee, Arpitam Future Inst Engn & Management Dept Elect & Commun Engn Kolkata India Jadavpur Univ Dept Printing Engn Salt Lake CampusBlock LBPlot 8Sect 3 Kolkata 700106 India Jadavpur Univ Dept Instrumentat & Elect Engn Kolkata India

Adulteration in different spices is an emerging challenge in human civilization. It is commonly detected using different analytical and instrumental techniques. Despite good accuracy and precision many of such techniques are limited by their high processing time, skilled manpower requirement, expensive machinery and portability factor. Computer vision methodology driven by powerful convolutional neural network (CNN) architectures can be a possible way to address those limitations. This paper presents a CNN driven computer vision model which can detect cornstarch adulteration in turmeric powder along with the degree of adulteration. The model has been optimized using binary genetic algorithm (BGA) for improved performance and consistency. The experimentations presented in this paper were conducted with an in-house database prepared for 4 levels of adulteration and found to provide about 98% overall accuracy. The less expensive and faster detection capability of the model along with its mobility makes this proposal a promising addition to the existing spice adulteration screening methods.

关键词： Turmeric adulteration computer vision convolutional neural network spice adulteration detection binary genetic algorithm

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Artificial intelligence power controller of fuel cell based DC nanogrid

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RENEWABLE ENERGY FOCUS 2020年 34卷 120-128页

作者： Kumar, Saurabh Krishnasamy, Vijayakumar Neeli, Satyanarayana Kaur, Rajvir MNIT EED Jaipur Rajasthan India IITDM ECED Kancheepuram India SUTD ISTD Singapore Singapore

The expansion of DC nanogrid is projected in rural and remote areas for electrification. Conventional way of powering the rural areas is to extend the central grid to that area. However, extending the network of central grid is a cumbersome process and it involves huge capital cost. Further, with the development of power electronics and evolution of renewable resource, a standalone nanogrid is a simple and feasible solution to power the rural areas. Therefore, a clean and sustainable fuel cell based nanogrid is proposed as a step towards rural electrification. The output voltage of the fuel cell is varying in nature which could damage the voltage sensitive electronic equipment of the house hold. Therefore, boost converter based interfacing unit with an intelligent controller is proposed in this paper to regulate the voltage. The intelligent controller improves the dynamic response of the system and regulates the voltage at the DC distribution bus irrespective of source side or load side disturbances. The proposed intelligent proportional integral and derivative controller parameters are tuned using a binary genetic algorithm and real-coded genetic algorithm by formulating an optimal control problem. The proposed controller is model independent, simple in implementation and gives an excellent dynamic response.A laboratory scaled hardware prototype is developed to validate the performance of proposed controller.

关键词： Boost converter binary genetic algorithm Fuel cell Nanogrid Real-coded genetic algorithm

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