با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
A novel control approach based on hybrid Fuzzy Logic and Seeker Optimization for optimal energy management between micro-sources and supercapacitor in an islanded Microgrid
یک روش کنترل جدید مبتنی بر منطق فازی ترکیبی و بهینه سازی جستجوگر برای مدیریت بهینه انرژی بین منابع کوچک و ابر رسانا در یک میکروگرید جزیره ای-2020
This work presents a novel control technique for proportional power sharing among parallel VSCs connected to an islanded Microgrid in a distributed generation system consisting of Photovoltaic (PV) and Solid Oxide Fuel Cell (SOFC) as two micro-sources. For tracking the maximum solar energy, a Seeker Optimized Fuzzy based Dynamic PI (FSOA-DPI) controller is implemented for the Modified Perturb and Observe MPPT method. Again, for the optimum cost management of the Microgrid system, DPI controller based decentralized Virtual Impedance Drooping (VID) technique is implemented for suitable load sharing between the two hybrid micro-sources. An Energy Storage System (ESS) regulated by FSOA-DPI controller is also proposed for this Microgrid system to ensure the better transient and sub-transient stability during fault occurrence. The dynamic response and stability of the system with proposed method is compared and contrasted with conventional PI controller based method during load sharing for ensuring the robust control under conditions of nonlinear load and faults. The harmonic analysis has been carried out by Fast Fourier Transform (FFT) and the values indicate that the Total Harmonic Distortion (THD) is well within the prescribed IEEE standard limits. Validation and justification of the improvements achieved by the proposed controller are realized using Matlab/Simulink environment.
Keywords: Microgrid | Distributed generation (DG) | Hybrid micro-source | Photovoltaic (PV) | Solid Oxide Fuel Cell (SOFC) | Virtual Impedance Drooping | Seeker Optimization Approach (SOA)
Comfort evaluation of seasonally and daily used residential load insmart buildings for hottest areas via predictive mean vote method
ارزیابی راحتی ساختمانهای بار مسکونی فصلی و روزانه برای گرمترین مناطق با استفاده از روش پیش بینی میانگین رای گیری-2020
tIn this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mam-dani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonallyused appliances are considered for the analysis of the efficient energy management through these con-trollers. Energy management is performed using the two Demand Side Management (DSM) strategies:load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of thedaily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances,i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are appliedin this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of thehottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occu-pancy, price, decision control variables, priority and length of operation times of the appliances, whereasthe output parameters are: energy consumption, cost and thermal and appliance usage comfort. More-over, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger’spredictive mean vote method. The comfort is further investigated by incorporating the renewable energysources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllersas compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energyconsumption and cost of the proposed scenario.
Keywords:Energy management | Thermal comfort | Appliance usage comfort | Fuzzy logic | Fuzzy inference systems
Application of a fuzzy-logic based model for risk assessment in additive manufacturing r&d projects
استفاده از یک مدل مبتنی بر منطق فازی برای ارزیابی ریسک در پروژه های تحقیق و توسعه افزودنی-2020
Experts from industry and academics have highlighted Additive Manufacturing (AM) as a technology that is revolutionizing manufacturing. AM is a process that consists of creating a three-dimensional object by incorporating layers of a material such as metal or polymer. This research studies risks associated with AM R&D Project Management. A significant set of risks with a potential negative impact on project objectives in terms of scope, schedule, cost and quality are identified through an extensive literature review. These risks are assessed through a survey answered by ninety academics and professionals with noteworthy sector expertise. This process is made by the measurement of two parameters: likelihood of occurrence and impact on project objectives. According to the responses of the experts, the level of relevance of each risk is calculated, innovatively, through a fuzzy logic-based model, specifically developed for this study, implemented in MATLAB Fuzzy Logic Toolbox. The results of this study show that the risks “Defects occurring during the manufacturing process”, “Defective design”, “Poor communication in the project team” and “Insufficient financing” are determined as the most critical in AM R&D Project Management. The proposed model is presented as a powerful new tool for organizations and academics, to prioritize the risks that are more critical to develop appropriate response strategies to achieve the success of their projects.
Keywords: Additive Manufacturing | 3D printing | Risk Assessment | Project Management | Fuzzy Logic
Prediction of greenhouse gas emissions from Ontario’s solid waste landfills using fuzzy logic based model
پیش بینی انتشار گازهای گلخانه ای از محل های دفع زباله جامد انتاریو با استفاده از مدل مبتنی بر منطق فازی-2020
In this study, multi-criteria assessment technique is used to predict the methane generation from large municipal solid waste landfills in Ontario, Canada. Although a number of properties determine the gas generation from landfills, these parameters are linked with empirical relationships making it difficult to generate precise information concerning gas production. Moreover, available landfill data involve sources of uncertainty and are mostly insufficient. To fully characterize the chemistry of reaction and predict gas generation volumes from landfills, a fuzzy-based model is proposed having seven input parameters. Parameters were identified in a linguistic form and linked by 19 IF-THEN statements. When compared to measured values, results of the fuzzy based model showed good prediction of landfill gas generation rates. Also, when compared to other first order decay and second order decay models like LandGEM, the fuzzy based model showed better results. When plotting the LandGEM and Fuzzy model values to the actual measured data, the fuzzy model resulted in a better fit to actual data than the LandGEM model with a coefficient of determination R2 of 0.951 for fuzzy model versus 0.804 for LandGEM model. The results show how multi-criteria assessment technique can be used in modelling of complicated processes that take place within the landfills and somehow accurately predicting the landfill gas generation rate under different operating conditions
Keywords: Municipal solid waste | Landfill gas | Life-cycle assessment | Waste to energy | Greenhouse gas emissions | Fuzzy model
Fuzzy logic interpretation of quadratic networks
تفسیر منطق فازی از شبکه های درجه دوم-2020
Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed arti- ficial quadratic neural networks consisting of quadratic neurons in potentially many layers. In cellular level, a quadratic function is used to replace the inner product in a traditional neuron, and then under- goes a nonlinear activation. With a single quadratic neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and quadratic counterparts can represent each other and fuzzy logic operations are naturally implemented in quadratic neural networks, it is plau- sible to explain how a deep neural network works with a quadratic network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual quadratic neu- rons, and then perform statistical/information-theoretic analyses of exemplary quadratic neural networks.
Keywords: Machine learning | Artificial neural network (ANN) | Quadratic network | Fuzzy logic
Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy
سیستم قدرت هیبریدی سلول سوختی مبتنی بر باتری ساختار متغیر و استراتژی مدیریت انرژی منطق فازی افزایشی آن-2020
A hybrid power system consists of a fuel cell and an energy storage device like a battery and/or a supercapacitor possessing high energy and power density that beneficially drives electric vehicle motor. The structures of the fuel cell-based power system are complicated and costly, and in energy management strategies (EMSs), the fuel cell’s characteristics are usually neglected. In this study, a variable structure battery (VSB) scheme is proposed to enhance the hybrid power system, and an incremental fuzzy logic method is developed by considering the efficiency and power change rate of fuel cell to balance the power system load. The principle of VSB is firstly introduced and validated by discharge and charge experiments. Subsequently, parameters matching of the fuel cell hybrid power system according to the proposed VSB are designed and modeled. To protect the fuel cell as well as ensure the efficiency, a fuzzy logic EMS is formulated via setting the fuel cell operating in a high efficiency and generating an incremental power output within the affordable power slope. The comparison between a traditional deterministic rules-based EMS and the designed fuzzy logic was implemented by numerical simulation in three different operation conditions: NEDC, UDDS, and user-defined driving cycle. The results indicated that the incremental fuzzy logic EMS smoothed the fuel cell power and kept the high efficiency. The proposed VSB and incremental fuzzy logic EMS may have a potential application in fuel cell vehicles.
Keywords: Fuel cell vehicles | Hybrid fuel cell/battery power | system | Variable structure battery (VSB) | Incremental fuzzy logic energy | management | Maintain efficiency and lifespan
The impact of big data on firm performance in hotel industry
تأثیر داده های بزرگ بر عملکرد شرکت در صنعت هتلداری-2020
Big data has increasingly appeared as a frontier of opportunity in enhancing firm performance. However, it still is in early stages of introduction and many enterprises are still un-decisive in its adoption. The aim of this study is to propose a theoretical model based on integration of Human-Organization-Technology fit and Technology- Organization-Environment frameworks to identify the key factors affecting big data adoption and its consequent impact on the firm performance. The significant factors are gained from the literature and the research model is developed. Data was collected from top managers and/or owners of SMEs hotels in Malaysia using online survey questionnaire. Structural Equation Modelling (SEM) is used to assess the developed model and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) technique is used to prioritize adoption factors based on their importance levels. The results showed that relative advantage, management support, IT expertise, and external pressure are the most important factors in the technological, organizational, human, and environmental dimensions. The results further revealed that technology is the most important influential dimension. The outcomes of this study can assist the policy makers, businesses and governments to make well-informed decisions in adopting big data.
Keywords: Firm performance | Big data | Hotel industry | Fuzzy logic | Structural equation modelling
Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition
استراتژی هوشمند مدیریت انرژی سیستم ذخیره انرژی هیبریدی برای وسایل نقلیه الکتریکی مبتنی بر شناخت الگوی رانندگی-2020
To achieve optimal power distribution of hybrid energy storage system composed of batteries and supercapacitors in electric vehicles, an adaptive wavelet transform-fuzzy logic control energy management strategy based on driving pattern recognition (DPR) is proposed in view of the fact that driving cycle greatly affects the performance of EMS. The DPR uses cluster analysis to classify driving cycles into different patterns according to the features extracted from the historical driving data sampling window and utilizes pattern recognition to identify real-time driving patterns. After recognition results are obtained, an adaptive wavelet transform is employed to allocate the high frequency components of power demand to supercapacitor which contains transient power and rapid variations, while the low frequency components are distributed to battery accordingly. The use of fuzzy logic control is to maintain the SOC of supercapacitor within desired level. The simulation results indicate that the proposed control strategy can effectively decrease the maximum charge/discharge current of battery by 58.2%, and improve the battery lifetime by 6.16% and the vehicle endurance range by 11.06% compared with conventional control strategies. Further demonstrate the advantage of hybrid energy storage system and the presented energy management strategy.
Keywords: Hybrid energy storage system | Driving patterns recognition | Transient power | Adaptive wavelet transform | Fuzzy logic control
Consumers’ sensitivities and preferences modelling and integration in a decentralised two levels energy supervisor
حساسیت ها و ترجیحات مصرف کنندگان مدل سازی و ادغام در یک ناظر غیر متمرکز انرژی در دو سطح-2020
To address the new challenges arising from the higher penetration of renewable energy in electrical grid, Demand Response (DR) aims to involve the residential consumers in the grid equilibrium. Ensuring benefits for both utility and users requires the consumers sensitivities to be understood and then included in the Energy Management System (EMS). For this purpose, the cost is the predominant and most often only factor taken into account in the literature, although in the residential sector other concerns influencing electricity consumption behaviour have been observed. This paper presents a two levels EMS applied to a neighbourhood of consumers mathematically modelled at the level of their appliances and incorporating 5 consumers profiles along three sensitivities: cost, environment and appliances shifting comfort. The first level is a day ahead supervision based on a multi-agent optimisation lead by a central aggregator but performed locally by the household using Dynamic Programming (DP), thus ensuring privacy protection for the stakeholders. The second level is a real time supervision using the same decentralised structure and based on fuzzy logic. Both levels are evaluated in this paper, with a focus on the balance between grid and consumers objectives.
Keywords: Demand response | Energy management | Game Theory | Fuzzy Logic | Decentralised load management | Consumers profiles
A knowledge-based expert system to assess power plant project cost overrun risks
یک سیستم خبره مبتنی بر دانش برای ارزیابی هزینه ریسک بیش ازحد پروژه نیروگاهی-2019
Preventing cost overruns of such infrastructure projects as power plants is a global project management problem. The existing risk assessment methods/models have limitations to address the complicated na- ture of these projects, incorporate the probabilistic causal relationships of the risks and probabilistic data for risk assessment, by taking into account the domain experts’ judgments, subjectivity, and un- certainty involved in their judgments in the decision making process. A knowledge-based expert system is presented to address this issue, using a fuzzy canonical model (FCM) that integrates the fuzzy group decision-making approach (FGDMA) and the Canonical model ( i.e. a modified Bayesian belief network model) . The FCM overcomes: (a) the subjectivity and uncertainty involved in domain experts’ judgment, (b) sig- nificantly reduces the time and effort needed for the domain experts in eliciting conditional probabilities of the risks involved in complex risk networks, and (c) reduces the model development tasks, which also reduces the computational load on the model. This approach advances the applications of fuzzy-Bayesian models for cost overrun risks assessment in a complex and uncertain project environment by addressing the major constraints associated with such models. A case study demonstrates and tests the application of the model for cost overrun risk assessment in the construction and commissioning phase of a power plant project, confirming its ability to pinpoint the most critical risks involved ̶ in this case, the complex- ity of the lifting and rigging heavy equipment, inadequate work inspection and testing plan, inadequate site/soil investigation, unavailability of the resources in the local market, and the contractor’s poor plan- ning and scheduling.
Keywords: Cost overruns | Risk assessment | Power plant projects | Fuzzy logic | Canonical model