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2021/6/24

New Publication!

Title: Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components

Journal: Energies

Authors: Zhaojun Hao, Francesco di Maio, Enrico Zio

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/1996-1073/14/11/3241/htm

Abstract: Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond to any sudden imbalance of the power grid, due to the variability in power generation by RESs. This raises concerns on the reliability of CPESs traditionally used as base-load facilities, such as Nuclear Power Plants (NPPs), which were not designed for flexible operation, and more so, since traditionally only hardware components aging and stochastic failures have been considered for the reliability assessment, whereas the contribution of the degradation and aging of the cyber components of CPSs has been neglected. In this paper, we propose a multi-state model that integrates the hardware components stochastic failures with the aging of cyber components, and quantify the unreliability of CPES in load-following operations under normal/emergency conditions. To show the application of the reliability assessment model, we consider the case of the Control Rod System (CRS) of a NPP typically used for a base-load energy supply.


2021/6/24

New Publication!

Title: Failure identification in a nuclear passive safety system by Monte Carlo simulation with adaptive Kriging

Journal: Nuclear Engineering and Design

Authors: Puppo, L., Pedroni, N., Bersano, A., Di Maio, F., Bertani, C., Zio, E.

This is an open-access article that can be downloaded from the following link before July 22, 2021: https://authors.elsevier.com/c/1dAUO6j-yo1ve

Abstract: Passive Safety Systems (PSSs) are increasingly employed in advanced Nuclear Power Plants (NPPs). Their safety performance is evaluated through computationally expensive Thermal-Hydraulic (T-H) simulations models and the identification of the operational conditions which lead to unsafe conditions (the so-called Critical failure Regions, CRs) may be challenging.

In the present paper, a computational framework is proposed to identify the CRs of a generic passive Decay Heat Removal (DHR) system of a NPP. A time-demanding Best-Estimate Thermal-Hydraulic (BE-TH) model of the system is used to train a fast-running metamodel embedded within an adaptive sampling technique of literature, namely Adaptive Kriging Monte Carlo Sampling (AK-MCS), so as to provide increased accuracy in proximity of the failure threshold and identify which input values lead the PSS to failure. To the best authors’ knowledge this is the first time that the metamodel-based AK-MCS technique is applied for the identification of the CRs of a PSS of an NPP.

 


2021/6/14

New Publication!

Title: Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production

Journal: Sustainability (Special Issue in Renewable Energy Sources for Electrical Power: Reliability Assessment, Condition Monitoring, Prognostics and Health Management, Production Prediction)

Authors: Sameer Al-Dahidi, Piero Baraldi, Enrico Zio, Lorenzo Montelatici

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/2071-1050/13/11/6417

Abstract: 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.


2021/4/20

NEW PUBLICATION !

Title: A Bayesian framework of inverse uncertainty quantification with principal component analysis and Kriging for the reliability analysis of passive safety systems

Journal: Nuclear Engineering and Design

Authors: Giovanni Roma, Francesco Di Maio, Andrea Bersano, Nicola Pedroni, Cristina Bertani, Fulvio Mascari, Enrico Zio

Free download until June 8th, 2021: https://authors.elsevier.com/c/1cx276j-yo1jX

Abstract: In this work, we propose an Inverse Uncertainty Quantification (IUQ) approach to assigning Probability Density Functions (PDFs) to uncertain input parameters of Thermal-Hydraulic (T-H) models used to assess the reliability of passive safety systems. The approach uses experimental data within a Bayesian framework. The application to a RELAP5-3D model of the PERSEO (In-Pool Energy Removal System for Emergency Operation) facility located at SIET laboratory (Piacenza, Italy) is demonstrated. Principal Component Analysis (PCA) is applied for output dimensionality reduction and Kriging meta-modeling is used to emulate the reduced set of RELAP5-3D code outputs. This is done to decrease the computational cost of the Markov Chain Monte Carlo (MCMC) posterior sampling of the uncertain input parameters, which requires a large number of model simulations.


2021/2/12

NEW PUBLICATION!

Title: Association rules extraction for the identification of functional dependencies in complex technical infrastructures

Journal: Reliability Engineering & System Safety

Authors: Federico Antonello, Piero Baraldi, Ahmed Shokry, Enrico Zio, U. Gentile, L. Serio

Free download: https://authors.elsevier.com/a/1cZf%7E3OQ%7EfTD0i

Abstract: This work proposes a method for identifying functional dependencies among components of complex technical infrastructures using databases of alarm messages. The developed method is based on the representation of the alarm database by a binary matrix, the use of the Apriori algorithm for mining association rules and a new algorithm for identifying groups of functionally dependent components. The effectiveness of the proposed method is shown by means of its application to an artificial case study and a real large-scale database of alarms generated by different supervision systems of the complex technical infrastructure of CERN (European Organization for Nuclear Research).


2021/1/28

NEW PUBLICATION!

Title: Optimal sensor positioning on pressurized equipment based on Value of Information

Journal: Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

Authors: Seyed Mojtaba Hoseyni, Francesco Di Maio, Enrico Zio

Free download: https://journals.sagepub.com/eprint/UV5DRSZSNEB3BC9ZCTWU/full

Abstract: In this work, we apply a simulation-based framework that makes use of the Value of Information (VoI) for identifying the optimal spatial positioning of sensors on pressurized equipment. VoI is a utility-based Figure of Merit (FoM) which quantifies the benefits/losses of acquiring information. Sensors are typically positioned on pressurized equipment in line with specific recommendations based on operational experience, like UNI 11096 in Italy. We show that the recommendations in UNI 11096 are, indeed, justified and that, incidentally, relying on VoI for the optimization of the sensor positioning, one can achieve the same monitoring performance, as measured by VoI, where following UNI 11096, but with a reduced number of sensors. The proposed VoI-based approach can, thus, be used to confirm or revise recommendations coming from operational experience.


2021/1/18

NEW PUBLICATION!

Title: Identification of LOFA precursors in ITER superconducting magnet cryogenic cooling circuit

Journal: Reliability Engineering & System Safety

Authors: V. Destino, R. Bonifetto, F. Di Maio, N. Pedroni, R. Zanino, E. Zio

Free download until March 09, 2021: https://authors.elsevier.com/c/1cR1j_Lf6GyXK-

Abstract: In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at cryogenic temperature by a Superconducting Magnet Cryogenic Cooling Circuit (SMCCC). To guarantee cooling, Loss-Of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, an approach to identify LOFA precursors (i.e., those component failures leading to a LOFA) is presented. The approach is based on a Spectral Clustering (SC) method using the Fuzzy C-Means (FCM) algorithm and is applied to the SMCCC of a single module of the ITER Central Solenoid (CS).


2020/12/9

NEW PUBLICATION

Title: A semi-supervised method for the characterization of degradation of nuclear power plants steam generators

Journal: Progress in Nuclear Energy

Authors: Luca Pinciroli, Piero Baraldi, Ahmed Shokry, Enrico Zio, Redouane Seraoui, Carole Mai

Free download until January 23rd , 2021: https://authors.elsevier.com/c/1cBEaXx6ZPO~l

Abstract: The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.


2020/6/16