Machine Learning Based Detection of False Data Injection Attacks in Wide Area Monitoring Systems

The Smart Grid (SG) is an upgraded, intelligent, and a more reliable version of the traditional Power Grid due to the integration of information and communication technologies. The operation of the SG requires a dense communication network to link all its components. But such a network renders it prone to cyber attacks jeopardizing the integrity and security of the communicated data between the physical electric grid and the control centers. One of the most prominent components of the SG are Wide Area Monitoring Systems (WAMS). WAMS are a modern platform for grid-wide information, communication, and coordination that play a major role in maintaining the stability of the grid against major disturbances. In this thesis, an anomaly detection framework is proposed to identify False Data Injection (FDI) attacks in WAMS using different Machine Learning (ML) and Deep Learning (DL) techniques, i.e., Deep Autoencoders (DAE), Long-Short Term Memory (LSTM), and One-Class Support Vector Machine (OC-SVM). These algorithms leverage diverse, complex, and high-volume power measurements coming from communications between different components of the grid to detect intelligent FDI attacks. The injected false data is assumed to target several major WAMS monitoring applications, such as Voltage Stability Monitoring (VSM), and Phase Angle Monitoring (PAM). The attack vector is considered to be smartly crafted based on the power system data, so that it can pass the conventional bad data detection schemes and remain stealthy. Due to the lack of realistic attack data, machine learning-based anomaly detection techniques are used to detect FDI attacks. To demonstrate the impact of attacks on the realistic WAMS traffic and to show the effectiveness of the proposed detection framework, a Hardware-In-the-Loop (HIL) co-simulation testbed is developed. The performance of the implemented techniques is compared on the testbed data using different metrics: Accuracy, F1 score, and False Positive Rate (FPR) and False Negative Rate (FNR). The IEEE 9-bus and IEEE 39-bus systems are used as benchmarks to investigate the framework scalability. The experimental results prove the effectiveness of the proposed models in detecting FDI attacks in WAMS

Real-time Prediction of Cascading Failures in Power Systems

Blackouts in power systems cause major financial and societal losses, which necessitate devising better prediction techniques that are specifically tailored to detecting and preventing them. Since blackouts begin as a cascading failure (CF), an early detection of these CFs gives the operators ample time to stop the cascade from propagating into a large-scale blackout. In this thesis, a real-time load-based prediction model for CFs using phasor measurement units (PMUs) is proposed. The proposed model provides load-based predictions; therefore, it has the advantages of being applicable as a controller input and providing the operators with better information about the affected regions. In addition, it can aid in visualizing the effects of the CF on the grid. To extend the functionality and robustness of the proposed model, prediction intervals are incorporated based on the convergence width criterion (CWC) to allow the model to account for the uncertainties of the network, which was not available in previous works. Although this model addresses many issues in previous works, it has limitations in both scalability and capturing of transient behaviours. Hence, a second model based on recurrent neural network (RNN) long short-term memory (LSTM) ensemble is proposed. The RNN-LSTM is added to better capture the dynamics of the power system while also giving faster responses. To accommodate for the scalability of the model, a novel selection criterion for inputs is introduced to minimize the inputs while maintaining a high information entropy. The criteria include distance between buses as per graph theory, centrality of the buses with respect to fault location, and the information entropy of the bus. These criteria are merged using higher statistical moments to reflect the importance of each bus and generate indices that describe the grid with a smaller set of inputs. The results indicate that this model has the potential to provide more meaningful and accurate results than what is available in the previous literature and can be used as part of the integrated remedial action scheme (RAS) system either as a warning tool or a controller input as the accuracy of detecting affected regions reached 99.9% with a maximum delay of 400 ms. Finally, a validation loop extension is introduced to allow the model to self-update in real-time using importance sampling and case-based reasoning to extend the practicality of the model by allowing it to learn from historical data as time progresses

Performance analysis and dynamics of innovative SOFC hybrid systems based on turbocharger-derived machinery

La crescente consapevolezza su temi quali il cambiamento climatico e l\u2019inquinamento atmosferico ha portato a politiche nazionali ed internazionali mirate allo sviluppo di sistemi energetici innovativi e sostenibili. Tra di essi, le fuel cell sono uno dei pi\uf9 promettenti, essendo caratterizzate da alte efficienze e basse emissioni. In particolare, i sistemi ibridi basati sull\u2019integrazione di fuel cell ad alta temperatura con dispositivi derivati da turbocompressori hanno attirato l\u2019attenzione del mondo accademico e dell\u2019industria negli ultimi decenni. Tuttavia, la complessit\ue0, la fragilit\ue0 e l\u2019alto costo di questi impianti ha rallentato il loro sviluppo, e solo poche grandi aziende sono state in grado di realizzare prototipi completi. Le difficolt\ue0 tecniche affrontate dalla comunit\ue0 scientifica hanno messo in luce l\u2019importanza delle simulazioni per progettare, testare, controllare e analizzare i sistemi ibridi a fuel cell. Sulla base di tale esperienza, questa tesi mira ad espandere la attuale conoscenza sui sistemi ibridi a fuel cell a ossidi solidi, ponendo una particolare attenzione su un innovativo sistema turbocompresso di piccola taglia, alimentato con biogas e recentemente introdotto all\u2019interno del progetto europeo Bio-HyPP. Lo scopo principale della tesi \ue8 determinare se questo tipo di sistema possa essere una valida alternativa ai sistemi basati su microturbine a gas, analizzando il suo comportamento in relazione a diversi scenari, sia stazionari, sia transitori. Per fare ci\uf2, \ue8 necessario definire i vincoli operativi del sistema e sviluppare un sistema di controllo in grado di rispettarli, ottimizzando al tempo stesso le prestazioni dell\u2019impianto. Inoltre, l\u2019affidabilit\ue0 dei sistemi ibridi pu\uf2 essere migliorata grazie all\u2019implementazione di strumenti diagnostici e di procedure per prevenire il pompaggio del compressore. La parte finale della tesi \ue8 mirata allo studio di tali strumenti, al loro sviluppo e alla loro integrazione con il sistema di controllo. Tutte le attivit\ue0 presentate in questa tesi sono state svolte facendo affidamento su strumenti di simulazione. Ci\uf2 \ue8 stato possibile grazie alla collaborazione tra il Laboratorio di Matematica Applicata, Simulazione e Modellistica Matematica e il Thermochemical Power Group dell\u2019Universit\ue0 degli Studi di Genova. Dopo aver presentato il layout del sistema a fuel cell con turbocompressore, un dettagliato modello stazionario dell\u2019impianto sviluppato in Matlab\uae-Simulink\uae \ue8 stato utilizzato per progettare una strategia, basata sul controllo di valvole installate sull\u2019impianto, in grado di rispettare tutti i suoi vincoli operativi. Successivamente, \ue8 stata svolta un\u2019analisi di prestazioni in off-design, considerando allo stesso tempo diverse condizioni di carico di potenza e di temperatura ambiente. Tale analisi \ue8 stata utilizzata per confermare l\u2019efficacia della strategia di controllo proposta, e per valutare le capacit\ue0 del sistema con turbocompressore. Successivamente \ue8 stato creato un modello dinamico utilizzando lo strumento TRANSEO, in modo da studiare il comportamento del sistema durante i transitori. Avendo adottato una strategia di controllo basata sulla valvola di cold bypass, \ue8 stata analizzata la risposta del sistema ad una sua apertura a gradino, al fine di progettare un sistema di controllo efficace e reattivo, in grado di mantenere la massima temperatura di cella costante e, allo stesso tempo, di rispettare i vincoli del sistema. Sono stati progettati quattro diversi controllori, che successivamente sono stati testati su due diversi scenari di variazione di carico e confrontati sulla base di vari parametri operativi. La parte finale della tesi ha riguardato lo sviluppo di innovativi strumenti che possano aumentare l\u2019affidabilit\ue0 dei sistemi ibridi a fuel cell a ossidi solidi, in particolare tecniche di prevenzione del pompaggio e sistemi di diagnostica basati su reti Bayesiane. Un modello semplificato del sistema con turbocompressore \ue8 stato sviluppato in TRANSEO e sono state testate diverse tecniche di prevenzione del pompaggio: condizionamento del flusso d\u2019aria, iniezione di acqua, ricircolo e bleed, installazione di un eiettore all\u2019imbocco del compressore. Le soluzioni pi\uf9 efficaci sono state integrate con il controllore del sistema ibrido e sono state testate durante un transitorio per evitare che il punto operativo del compressore si avvicinasse al pompaggio. Infine, grazie ad una collaborazione tra l\u2019Universit\ue0 degli Studi di Genova e la M\ue4lardalens H\uf6gskola di V\ue4ster\ue5s, in Svezia, sono state sviluppate delle reti Bayesiane per la diagnostica di sistemi ibridi a fuel cell a ossidi solidi con microturbina a gas. Questa attivit\ue0 \ue8 stata svolta simulando il sistema su Matlab\uae-Simulink\uae e creando le reti Bayesiane su Hugin Expert. Due sistemi di diagnostica, uno per la microturbina e uno per la fuel cell, sono stati sviluppati e testati in condizioni stazionarie. Il secondo \ue8 stato anche testato in condizioni dinamiche e integrato con il sistema di controllo per prevenire l\u2019usura della cella. In conclusione, questa tesi ha messo in luce il grande potenziale dei sistemi ibridi SOFC-turbocompressore, mostrando la loro alta efficienza in un ampio intervallo di condizioni operative in termini di carico elettrico e temperatura ambiente. La tesi ha anche dimostrato che \ue8 possibile garantire il corretto funzionamento di questi sistemi durante diversi scenari transitori, implementando controllori a cascata progettati per agire sulla valvola di bypass freddo per controllare la massima temperatura della cella. Per quanto riguarda la possibilit\ue0 di migliorare l\u2019affidabilit\ue0 di tali sistemi, le tecniche basate sul ricircolo del compressore sono risultate essere le pi\uf9 efficaci per allontanare il sistema da una condizione di pompaggio. I risultati delle simulazioni mostrano come la loro integrazione con strumenti di monitoraggio possa prevenire diverse situazioni di pericolo. La parte finale della tesi ha mostrato come il deterioramento dei sistemi ibridi a SOFC possa essere limitato grazie a reti Bayesiane, che sono state utilizzate per diagnosticare accuratamente le condizioni di un sistema SOFC-microturbina a gas, ma potrebbero ugualmente essere applicate su impianti con turbocompressore.The growing awareness on climate change and pollution has brought to national and international policies aimed at promoting the development of innovative and environmentally sustainable energy systems. Among these systems, fuel cells are one of the most promising technologies, characterized by high energy conversion efficiencies and low emissions. In particular, hybrid systems based on the integration of a high temperature fuel cell with turbocharger-derived machinery have drawn the interest of academia and industry over the past decades. However, the complexity, fragility and high cost of these plants have slowed down their development, and only a few big companies were able to build complete prototypes. The technological challenges faced by the scientific community have highlighted the importance of simulations to design, test, control and analyse fuel cell hybrid systems. Based on this experience, this thesis wants to expand the current knowledge on solid oxide fuel cell hybrid systems, with a particular focus on an innovative small-scale biofueled turbocharged layout, which was introduced recently within the Bio-HyPP European project. The main goal of this thesis is to determine if this kind of system can be a viable alternative to micro gas turbine-based systems, analysing its steady-state and transient behaviour in various operating conditions. To do this, it is necessary to define the system operative constraints, and to develop a control system capable of ensuring their compliance, while optimizing the plant performance. The possibility of increasing the reliability of solid oxide fuel cell hybrid systems is finally investigated, considering the implementation of surge prevention techniques and diagnostic tools. All these activities strongly relying on simulation tools. This was possible thanks to the collaboration between the Laboratory of Applied Mathematics, Simulation and Mathematical Modelling with the Thermochemical Power Group of the University of Genoa. After introducing the layout of the turbocharged fuel cell system, a detailed steady-state model of the plant is developed in Matlab\uae-Simulink\uae and used to design a strategy, based on the control of valves installed on the plant, able to comply with its many operative constraints. Then, an off-design performance analysis of the system is performed, considering simultaneously various conditions of power load and ambient temperature. This analysis is used to confirm the effectiveness of the proposed control strategy and to assess the capabilities of the turbocharged system. A dynamic model is created using the TRANSEO tool to study the transient behaviour of the system. Having adopted a control strategy based on the cold bypass valve, the response of the system to a valve opening step change is analysed in order to design an effective and responsive control system, able to keep the fuel cell maximum temperature constant while complying with the system constraints. Four different controllers are designed, tested on two different load variation scenarios and compared on the basis of many parameters. The final part of the thesis regards the development of innovative tools aimed at improving the reliability of solid oxide fuel cell hybrid system, in particular surge prevention techniques and Bayesian belief network-based diagnosis systems. A simplified dynamic model of the turbocharged SOFC system is developed in TRANSEO, and various surge prevention techniques are tested on it: intake air conditioning, water spray at compressor inlet, air bleed and recirculation, and installation of an ejector at the compressor intake. The most effective procedures are integrated with the controller of the hybrid system and tested during a transient scenario to prevent the compressor operative point from approaching a surge condition. Bayesian belief networks aimed at diagnosing the status of SOFC hybrid systems are developed thanks to a collaboration between the University of Genoa and the M\ue4lardalens H\uf6gskola of V\ue4ster\ue5s, Sweden. A micro gas turbine \u2013 solid oxide fuel cell system is considered for this study, but the methodology could be easily extended to turbocharged plants. The activity is carried out simulating the system on Matlab\uae-Simulink\uae and designing the Bayesian networks on Hugin Expert. Two different diagnosis systems, one for the turbomachinery and one for the fuel cell stack, are developed and tested on stationary conditions. The second one is also tested during transients and integrated with the control system to prevent degradation of the fuel cells. In conclusion, this thesis highlighted the great potential of turbocharged SOFC hybrid systems, showing high energy conversion efficiencies in a wide operative range in terms of load and ambient conditions. It also showed that the proper operation of the system is possible during various transient scenarios, implementing cascade controllers designed to act on a cold bypass valve to control the SOFC maximum temperature. Regarding the possibility of improving the reliability of these systems, surge prevention techniques based on compressor recirculation appeared as the most effective ones. Simulation results suggest that their integration with a surge precursors detection tool could avoid the occurrence of many potentially dangerous scenarios. The final part of this thesis showed that the durability of SOFC hybrid systems could be further improved thanks to Bayesian belief networks, which were proved to effectively diagnose the status of SOFC-MGT systems but could be applied to turbocharged plants as well

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Combining Synthesis of Cardiorespiratory Signals and Artifacts with Deep Learning for Robust Vital Sign Estimation

Healthcare has been remarkably morphing on the account of Big Data. As Machine Learning (ML) consolidates its place in simpler clinical chores, more complex Deep Learning (DL) algorithms have struggled to keep up, despite their superior capabilities. This is mainly attributed to the need for large amounts of data for training, which the scientific community is unable to satisfy. The number of promising DL algorithms is considerable, although solutions directly targeting the shortage of data lack. Currently, dynamical generative models are the best bet, but focus on single, classical modalities and tend to complicate significantly with the amount of physiological effects they can simulate. This thesis aims at providing and validating a framework, specifically addressing the data deficit in the scope of cardiorespiratory signals. Firstly, a multimodal statistical synthesizer was designed to generate large, annotated artificial signals. By expressing data through coefficients of pre-defined, fitted functions and describing their dependence with Gaussian copulas, inter- and intra-modality associations were learned. Thereafter, new coefficients are sampled to generate artificial, multimodal signals with the original physiological dynamics. Moreover, normal and pathological beats along with artifacts were included by employing Markov models. Secondly, a convolutional neural network (CNN) was conceived with a novel sensor-fusion architecture and trained with synthesized data under real-world experimental conditions to evaluate how its performance is affected. Both the synthesizer and the CNN not only performed at state of the art level but also innovated with multiple types of generated data and detection error improvements, respectively. Cardiorespiratory data augmentation corrected performance drops when not enough data is available, enhanced the CNN’s ability to perform on noisy signals and to carry out new tasks when introduced to, otherwise unavailable, types of data. Ultimately, the framework was successfully validated showing potential to leverage future DL research on Cardiology into clinical standards