Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Electrical power generation from randomly-oriented electrospun nanofibrous nonwoven

In this study, we have demonstrated that randomly-oriented electrospun PVDF nanofiber nonwovens can be used directly as an active layer to generate electrical power with a voltage output as high as 4 volt and current 4 micoramp scales on a small nonwoven piece. This discovery may provide a simple, efficient, cost-effective and flexible solution to self-powering of microelectronics for various purposes.

Combined nonparametric prediction intervals for wind power generation

Prediction intervals (PIs) are a promising tool for quantification of uncertainties associated with point forecasts of wind power. However, construction of PIs using parametric methods is questionable, as forecast errors do not follow a standard distribution. This paper proposes a nonparametric method for construction of reliable PIs for neural network (NN) forecasts. A lower upper bound estimation (LUBE) method is adapted for construction of PIs for wind power generation. A new framework is proposed for synthesizing PIs generated using an ensemble of NN models in the LUBE method. This is done to guard against NN performance instability in generating reliable and informative PIs. A validation set is applied for short listing NNs based on the quality of PIs. Then, PIs constructed using filtered NNs are aggregated to obtain combined PIs. Performance of the proposed method is examined using data sets taken from two wind farms in Australia. Simulation results indicate that the quality of combined PIs is significantly superior to the quality of PIs constructed using NN models ranked and filtered by the validation set.

Prediction intervals for short-term wind farm power generation forecasts

Quantification of uncertainties associated with wind power generation forecasts is essential for optimal management of wind farms and their successful integration into power systems. This paper investigates two neural network-based methods for direct and rapid construction of prediction intervals (PIs) for short-term forecasting of power generation in wind farms. The lower upper bound estimation and bootstrap methods are used to quantify uncertainties associated with forecasts. The effectiveness and efficiency of these two general methods for uncertainty quantification is examined using twenty four month data from a wind farm in Australia. PIs with a confidence level of 90% are constructed for four forecasting horizons: five, ten, fifteen, and thirty minutes. Quantitative measures are applied for objective evaluation and unbiased comparison of PI quality. Demonstrated results indicate that reliable PIs can be constructed in a short time without resorting to complicate computational methods or models. Also quantitative comparison reveals that bootstrap PIs are more suitable for short prediction horizon, and lower upper bound estimation PIs are more appropriate for longer forecasting horizons.

Perspectivas de inserção da energia solar fotovoltaica na geração de energia elétrica no Rio Grande do Norte

The generation of electricity in Brazil is predominantly renewable, with internal hydraulic generation being more than 70% of its energy matrix. The electricity rationing occurred in 2001 due to lack of rain, led the country to increase the participation of alternative energy sources. This need for new sources of energy makes the regional potential to be exploited, which configures the change of generation model from centralized generation to distributed generation. Among the alternative sources of energy, the solar energy is presented as very promising for Brazil, given that most of its territory is located near to the equator line, which implies days with greater number of hours of solar radiation. The state of Rio Grande do Norte (RN) has one of the highest levels of solar irradiation of the Brazilian territory, making it eligible to receive investments for the installation of photovoltaic solar plants. This thesis will present the state-of-the-art in solar photovoltaic power generation and will examine the potential for generation of solar photovoltaic power in Brazil and RN, based on solarimetrics measurements conducted by various institutions and also measurements performed in Natal, the state capital

Time-series-based maximization of distributed wind power generation integration

Energy policies and technological progress in the development of wind turbines have made wind power the fastest growing renewable power source worldwide. The inherent variability of this resource requires special attention when analyzing the impacts of high penetration on the distribution network. A time-series steady-state analysis is proposed that assesses technical issues such as energy export, losses, and short-circuit levels. A multiobjective programming approach based on the nondominated sorting genetic algorithm (NSGA) is applied in order to find configurations that maximize the integration of distributed wind power generation (DWPG) while satisfying voltage and thermal limits. The approach has been applied to a medium voltage distribution network considering hourly demand and wind profiles for part of the U.K. The Pareto optimal solutions obtained highlight the drawbacks of using a single demand and generation scenario, and indicate the importance of appropriate substation voltage settings for maximizing the connection of MPG.

Discrete-time sliding mode control of input-delay systems applied on a power generation system

This paper presents two discrete sliding mode control (SMC) design. The first one is a discrete-time SMC design that doesn't take into account the time-delay. The second one is a discrete-time SMC design, which takes in consideration the time-delay. The proposed techniques aim at the accomplishment simplicity and robustness for an uncertainty class. Simulations results are shown and the effectiveness of the used techniques is analyzed. © 2006 IEEE.

Probabilistic analysis of the distributed power generation in weakly meshed distribution systems

This paper presents an approach for probabilistic analysis of unbalanced three-phase weakly meshed distribution systems considering uncertainty in load demand. In order to achieve high computational efficiency this approach uses both an efficient method for probabilistic analysis and a radial power flow. The probabilistic approach used is the well-known Two-Point Estimate Method. Meanwhile, the compensation-based radial power flow is used in order to extract benefits from the topological characteristics of the distribution systems. The generation model proposed allows modeling either PQ or PV bus on the connection point between the network and the distributed generator. In addition allows control of the generator operating conditions, such as the field current and the power delivery at terminals. Results on test with IEEE 37 bus system is given to illustrate the operation and effectiveness of the proposed approach. A Monte Carlo Simulations method is used to validate the results. © 2011 IEEE.

Thermodynamic analysis and comparison of downdraft gasifiers integrated with gas turbine, spark and compression ignition engines for distributed power generation

The objective of the present article is to assess and compare the performance of electricity generation systems integrated with downdraft biomass gasifiers for distributed power generation. A model for estimating the electric power generation of internal combustion engines and gas turbines powered by syngas was developed. First, the model determines the syngas composition and the lower heating value; and second, these data are used to evaluate power generation in Otto, Diesel, and Brayton cycles. Four synthesis gas compositions were tested for gasification with: air; pure oxygen; 60% oxygen with 40% steam; and 60% air with 40% steam. The results show a maximum power ratio of 0.567 kWh/Nm(3) for the gas turbine system, 0.647 kWh/Nm(3) for the compression ignition engine, and 0.775 kWh/Nm(3) for the spark-ignition engine while running on synthesis gas which was produced using pure oxygen as gasification agent. When these three systems run on synthesis gas produced using atmospheric air as gasification agent, the maximum power ratios were 0.274 kWh/Nm(3) for the gas turbine system, 0.302 kWh/Nm(3) for CIE, and 0.282 kWh/Nm(3) for SIE. The relationship between power output and synthesis gas flow variations is presented as is the dependence of efficiency on compression ratios. Since the maximum attainable power ratio of CIE is higher than that of SIE for gasification with air, more research should be performed on utilization of synthesis gas in CIE. (C) 2014 Elsevier Ltd. All rights reserved.