Ensembles of climate change models for risk assessment of nuclear power plants

Climate change affects technical Systems, Structures and Infrastructures (SSIs), changing the environmental context for which SSI were originally designed. In order to prevent any risk growth beyond acceptable levels, the climate change effects must be accounted for into risk assessment models. Climate models can provide future climate data, such as air temperature and pressure. However, the reliability of climate models is a major concern due to the uncertainty in the temperature and pressure future projections. In this work, we consider five climate change models (individually unable to accurately provide historical recorded temperatures and, thus, also future projections), and ensemble their projections for integration in a probabilistic safety assessment, conditional on climate projections. As case study, we consider the Passive Containment Cooling System (PCCS) of two AP1000 Nuclear Power Plants (NPPs). Results provided by the different ensembles are compared. Finally, a risk-based classification approach is performed to identify critical future temperatures, which may lead to PCCS risks beyond acceptable levels

Fuel cell characteristic curve approximation using the Bezier curve technique

Accurate modelling of the fuel cell characteristics curve is essential for the simulation analysis, control management, performance evaluation, and fault detection of fuel cell power systems. However, the big challenge in fuel cell modelling is the multi-variable complexity of the characteristic curves. In this paper, we propose the implementation of a computer graphic technique called Bezier curve to approximate the characteristics curves of the fuel cell. Four different case studies are examined as follows: Ballard Systems, Horizon H-12Wstack, NedStackPS6, and 250Wproton exchange membrane fuel cells (PEMFC). The main objective is to minimize the absolute errors between experimental and calculated data by using the control points of the Bernstein-Bezier function and de Casteljau's algorithm. The application of this technique entails subdividing the fuel cell curve to some segments, where each segment is approximated by a Bezier curve so that the approximation error is minimized. Further, the performance and accuracy of the proposed techniques are compared with recent results obtained by different metaheuristic algorithms and analytical methods. The comparison is carried out in terms of various statistical error indicators, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Errors (MBE), and Autocorrelation Function (ACF). The results obtained by the Bezier curve technique show an excellent agreement with experimental data and are more accurate than those obtained by other comparative techniques

Bootstrapped ensemble of artificial neural networks technique for quantifying uncertainty in prediction of wind energy production

The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction

An ensemble of echo state networks for predicting the energy production of wind plants

The electricity grid relies on a mixture of conventional (e.g. oil and gas) and renewable (e.g. wind, solar, and geothermal) energy sources. Ensuring the reliability of electric power distribution becomes a fundamental and complex issue due to the stochasticity of the production from renewable sources and the fluctuating behaviour of energy market demand. The necessity to integrate and mitigate these two uncertainty sources requires to tackle the problem of the unit commitment. It is, therefore, fundamental the capability of predicting electrical power output from plants with intermittent energy sources. We propose an approach to predict wind energy production based on an ensemble of Echo State Networks (ESNs) trained with different sets of historical data. A novel Local Fusion (LF) strategy is employed to aggregate the predictions of the individual ESN models. The proposed approach is applied to the prediction of the energy production of a wind plant located in Italy. The obtained results show that the proposed ensemble provides more accurate predictions than a single ESN model and an ensemble approach of literature

Fault prognostics by an ensemble of Echo State Networks in presence of event based measurements

Fault prognostics aims at predicting the degradation of equipment for estimating the Remaining Useful Life (RUL). Traditional data-driven fault prognostic approaches face the challenge of dealing with incomplete and noisy data collected at irregular time steps, e.g. in correspondence of the occurrence of triggering events in the system. Since the values of all the signals are missing at the same time and the number of missing data largely exceeds the number of triggering events, missing data reconstruction approaches are difficult to apply. In this context, the objective of the present work is to develop a one-step method, which directly receives in input the event-based measurement and produces in output the system RUL with the associated uncertainty. Two strategies based on the use of ensembles of Echo State Networks (ESNs), properly adapted to deal with data collected at irregular time steps, have been proposed to this aim. A synthetic and a real-world case study are used to show their effectiveness and their superior performance with respect to state-of-the-art prognostic methods

Fault prognostics in presence of event-based measurements

In practice, fault prognostics has often touched with incomplete and noisy data collected at irregular time steps, e.g. in correspondence of the occurrence of triggering events in the system. Under these conditions, we investigate the possibility of predicting the Remaining Useful Life (RUL) of industrial systems using a properly tailored Echo State Network. A synthetic case study is used to show the effectiveness of the developed ESN-based methods and its superior performance with respect to traditional feedforward neural networks

Analytical optimization of photovoltaic output with Lagrange Multiplier Method

This paper proposes a non-iterative and direct optimization method for the optimization of the output characteristics of single and double diode of cell model, including series and shunt resistances. The proposed method is based on the Lagrange Multiplier Method (LMM). Typically, the proposed method is used under necessary conditions of the optimization, and it is adopted to optimize power outputs as objective functions of different solar cell technologies and Photovoltaic (PV) modules. The objective functions are formulated to determine the greatest rectangle inside I(V) characteristics and the efficiency frontier. Furthermore, by an analytical mathematical resolution we obtain the optimal current and the formula of efficiency frontier. The efficiency frontier for single diode model is a polynomial equation of second order, and fourth order in the case of double diodes model. A realistic numerical example for different technologies via computer simulations are presented to verify the performance of the proposed optimization method

A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

We present a novel method for concept drift detection, based on: 1) the development and continuous updating of online sequential extreme learning machines (OS-ELMs) and 2) the quantification of how much the updated models are modified by the newly collected data. The proposed method is verified on two synthetic case studies regarding different types of concept drift and is applied to two public real-world data sets and a real problem of predicting energy production from a wind plant. The results show the superiority of the proposed method with respect to alternative state-of-the-art concept drift detection methods. Furthermore, updating the prediction model when the concept drift has been detected is shown to allow improving the overall accuracy of the energy prediction model and, at the same time, minimizing the number of model updatings

Supporting Maintenance Decision with Empirical Models Based on Fleet-Wide Data

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Effect of hydrogen sulfide content on the combustion characteristics of biogas fuel in homogenous charge compression ignition engines

The use of biogas fuel in homogeneous charge compression ignition (HCCI) engines has been promoted recently due to the environmental advantages. However, hydrogen sulfide (H2S) forms a non-environment friendly content and biogas impurity that its removal is associated with high costs. In this study, the effect of using biogas fuel in the HCCI engines under different operating conditions is investigated to shed light on the best scenarios of biogas combustion, even with the presence of H2S contents. A modified reaction mechanism is introduced by considering the reactions of H2S with other species in the air-fuel mixture. The new chemical kinetics model, and a multi-zone combustion approach, have been validated against experimental data from the literature and then used to simulate the HCCI combustion of biogas fuel. It was obtained that having up to 3.8% content of H2S in the intake charge can enhance the rate of heat release and combustion pressure. However, it had no significant effect on the in-cylinder temperature profile. Higher rates of H2S contents led to produce higher rates of sulfur oxides (SOx) emissions. Increasing the equivalence ratio (up to 0.50) results in a higher in-cylinder temperature and pressure as well as a higher SOx and nitrogen oxides (NOx) emissions. Also, the HCCI combustion is influenced by the intake temperature and pressure, where increasing those parameters leads to an increase in the in-cylinder temperatures and pressures, as well as the NOx emissions. However, SOx emissions are affected only when the intake temperature reaches over 400K and the intake pressure is over 1.5bar