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Conference

International Symposium on Environmental Friendly Energies and Applications 

About: International Symposium on Environmental Friendly Energies and Applications is an academic conference. The conference publishes majorly in the area(s): Photovoltaic system & Renewable energy. Over the lifetime, 187 publications have been published by the conference receiving 547 citations.

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
01 Sep 2016
TL;DR: In this paper, the state of the art biomass gasification technologies, evaluating advantages and disadvantages, the potential use of the syngas and the application of the biomass, a short overview of the current status of the Biomass gasification in Serbia is provided.
Abstract: The reduction of imported forms of energy, and the conservation of the limited supply of fossil fuels, depends up on the utilization of all other available fuel energy sources. Biomass is a renewable energy source and represents a valid alternative to fossil fuels. The abundance of biomass ranks it as the third energy resource after oil and coal. Moreover, when compared to fossil fuels, biomass fuels possess negligible sulphur concentrations, produce less ash, and generate far less emissions in to the air. In other words, biomass can deliver significant greenhouse gas reductions in electricity, heat and transport fuel supply. The energy in biomass may be realized by different thermochemical technologies of which gasification is most promising alternative routes to convert biomass to power/heat generation and production of transportation fuels and chemical feedstock. This paper deals with the state of the art biomass gasification technologies, evaluating advantages and disadvantages, the potential use of the syngas and the application of the biomass gasification. Also, this paper provides short overview of the current status of the biomass gasification in Serbia.

105 citations

Proceedings ArticleDOI
21 Nov 2016
TL;DR: In this article, the authors present a Grid-Connected Photo Voltaic Virtual Instrumentation System (GCPV-VIS) which is intended to facilitate monitoring and failure detection of a grid-connected photovoltaic plant using statistical methods.
Abstract: This paper presents a design and development of a Grid-Connected Photo Voltaic Virtual Instrumentation System (GCPV-VIS) which is intended to facilitate monitoring and failure detection of a grid-connected photovoltaic plant using statistical methods. The approach has been validated using an experimental database of environment and electrical parameters from a 1.98 kip plant installed at the University of Huddersfield, United Kingdom. There are few instances of statistical tools being deployed in the analysis of PV measured data. The main focus of this research is, therefore, to devise a Virtual Instrument capable of simulating theoretical performances of PV systems and deploying statistical analysis of PV real-time data. The fault detection is based on the comparison between measured and theoretical output power using t-test statistical analysis. The obtained results indicate that the proposed method can detect the faults of the grid-connected PV system, and can be used for continuous monitoring of PV system status.

19 citations

Proceedings ArticleDOI
01 Nov 2014
TL;DR: VSAS (Variable Structure Automatic Systems) control methodology is applied to clarify the rationale behind Maximum Power Point Tracking and get the best optimization algorithm, which has several advantages: simplicity, high convergence speed and is independent on PV array characteristics.
Abstract: VSAS (Variable Structure Automatic Systems) control methodology is applied to clarify the rationale behind Maximum Power Point Tracking and get the best optimization algorithm. Two algorithms are developed the Modified and Enhanced Perturb and Observe Algorithm (MEPO) the Robust Unified Control Algorithm (RUCA). The maximum power is computed online using a very simple algorithm. Compared to the other algorithms like Perturb and Observe (PO), Hill Climbing, Incremental Encoder (InCod) and and SMC approach it is proven more efficient and faster despite using low frequency commutation. The proposed MPPT has several advantages: simplicity, high convergence speed and is independent on PV array characteristics. The obtained results have proven that the MPPT is tracked even under sudden change of irradiation level.

15 citations

Proceedings ArticleDOI
01 Nov 2014
TL;DR: This algorithm allows us to obtain the optimal number of photovoltaic panels, wind turbines and storage units ensuring the minimal global high efficiency system total cost and guaranteeing the permanent availabilty of energy to cover the load energy requirements.
Abstract: This paper proposes a sizing methodology to optimize the configuration of the hybrid energy system. For this we used an approach of automatic fuzzy rule base generation by means of Fuzzy-Adaptive Particle Swarm Optimization (PSO), which changes dynamically the acceleration coefficient rates ensuring the convergence. This algorithm allows us to obtain the optimal number of photovoltaic panels, wind turbines and storage units ensuring the minimal global high efficiency system total cost and guaranteeing the permanent availabilty of energy to cover the load energy requirements. The database of wind speed taken hourly, the solar irradiance and the load data are used to stochastically model the wind turbines, photovoltaic generation and load. The total cost is the objective function and the technical size is considered as a contraint.

14 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: Multiple uses artificial neural networks (ANN) in long-term and short-term forecasting electricity prices and loads and the formation of dynamic trifling for Serbia is presented.
Abstract: In the framework of competitive electricity market, prices and load forecasting has become a real challenge for engineers in electric power systems. The increase of production from renewable sources increased number of factors that affect the cost and power consumption. On the other side, smart grids and the development of computers enable the use of artificial intelligence for solving such problems. This paper presents the multiple uses artificial neural networks (ANN) in long-term and short-term forecasting electricity prices and loads. Databases that are used for training ANN contain hours and thirty minutes data from British and Serbian power system. Data are related to the production of different energy sources, import / export of energy, temperature and load diagram form. Trained ANNs are used to predict energy prices and the formation of dynamic trifling for Serbia. Formed ANN models can be used for real time, on-line prediction of load and electricity price.

14 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
202264
201658
201465