Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Detecting faults in electrical power grids is of paramount importance, either from the electricity operator and consumer viewpoints. Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e.g., cables and related insulation, transformers, breakers and so on). In real-world smart grid systems, usually, additional information that are related to the operational status of the grid itself are collected such as meteorological information. Designing a suitable recognition (discrimination) model of faults in a real-world smart grid system is hence a challenging task. This follows from the heterogeneity of the information that actually determine a typical fault condition. The second point is that, for synthesizing a recognition model, in practice only the conditions of observed faults are usually meaningful. Therefore, a suitable recognition model should be synthesized by making use of the observed fault conditions only. In this paper, we deal with the problem of modeling and recognizing faults in a real-world smart grid system, which supplies the entire city of Rome, Italy. Recognition of faults is addressed by following a combined approach of multiple dissimilarity measures customization and one-class classification techniques. We provide here an in-depth study related to the available data and to the models synthesized by the proposed one-class classifier. We offer also a comprehensive analysis of the fault recognition results by exploiting a fuzzy set based reliability decision rule

Recent Developments and Challenges on AC Microgrids Fault Detection and Protection Systems–A Review

The protection of AC microgrids (MGs) is an issue of paramount importance to ensure their reliable and safe operation. Designing reliable protection mechanism, however, is not a trivial task, as many practical issues need to be considered. The operation mode of MGs, which can be grid-connected or islanded, employed control strategy and practical limitations of the power electronic converters that are utilized to interface renewable energy sources and the grid, are some of the practical constraints that make fault detection, classification, and coordination in MGs different from legacy grid protection. This article aims to present the state-of-the-art of the latest research and developments, including the challenges and issues in the field of AC MG protection. A broad overview of the available fault detection, fault classification, and fault location techniques for AC MG protection and coordination are presented. Moreover, the available methods are classified, and their advantages and disadvantages are discussed

A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF

The future evolution of technological systems dedicated to improve energy efficiency will strongly depend on effective and reliable Energy Storage Systems, as key components for Smart Grids, microgrids and electric mobility. Besides possible improvements in chemical materials and cells design, the Battery Management System is the most important electronic device that improves the reliability of a battery pack. In fact, a precise State of Charge (SoC) estimation allows the energy flows controller to exploit better the full capacity of each cell. In this paper, we propose an alternative definition for the SoC, explaining the rationales by a mechanical analogy. We introduce a novel cell model, conceived as a series of three electric dipoles, together with a procedure for parameters estimation relying only on voltage measures and a given current profile. The three dipoles represent the quasi-stationary, the dynamics and the istantaneous components of voltage measures. An Extended Kalman Filer (EKF) is adopted as a nonlinear state estimator. Moreover, we propose a multi-cell EKF system based on a round-robin approach to allow the same processing block to keep track of many cells at the same time. Performance tests with a prototype battery pack composed by 18 A123 cells connected in series show encouraging results.Comment: 8 page, 12 figures, 1 tabl

Data Challenges and Data Analytics Solutions for Power Systems

L'abstract è presente nell'allegato / the abstract is in the attachmen

A stacked autoencoder-based convolutional and recurrent deep neural network for detecting cyberattacks in interconnected power control systems

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Advancing Carbon Sequestration through Smart Proxy Modeling: Leveraging Domain Expertise and Machine Learning for Efficient Reservoir Simulation

Geological carbon sequestration (GCS) offers a promising solution to effectively manage extra carbon, mitigating the impact of climate change. This doctoral research introduces a cutting-edge Smart Proxy Modeling-based framework, integrating artificial neural networks (ANNs) and domain expertise, to re-engineer and empower numerical reservoir simulation for efficient modeling of CO2 sequestration and demonstrate predictive conformance and replicative capabilities of smart proxy modeling. Creating well-performing proxy models requires extensive human intervention and trial-and-error processes. Additionally, a large training database is essential to ANN model for complex tasks such as deep saline aquifer CO2 sequestration since it is used as the neural network\u27s input and output data. One major limitation in CCS programs is the lack of real field data due to a lack of field applications and issues with confidentiality. Considering these drawbacks, and due to high-dimensional nonlinearity, heterogeneity, and coupling of multiple physical processes associated with numerical reservoir simulation, novel research to handle these complexities as it allows for the creation of possible CO2 sequestration scenarios that may be used as a training set. This study addresses several types of static and dynamic realistic and practical field-base data augmentation techniques ranging from spatial complexity, spatio-temporal complexity, and heterogeneity of reservoir characteristics. By incorporating domain-expertise-based feature generation, this framework honors precise representation of reservoir overcoming computational challenges associated with numerical reservoir tools. The developed ANN accurately replicated fluid flow behavior, resulting in significant computational savings compared to traditional numerical simulation models. The results showed that all the ML models achieved very good accuracies and high efficiency. The findings revealed that the quality of the path between the focal cell and injection wells emerged as the most crucial factor in both CO2 saturation and pressure estimation models. These insights significantly contribute to our understanding of CO2 plume monitoring, paving the way for breakthroughs in investigating reservoir behavior at a minimal computational cost. The study\u27s commitment to replicating numerical reservoir simulation results underscores the model\u27s potential to contribute valuable insights into the behavior and performance of CO2 sequestration systems, as a complimentary tool to numerical reservoir simulation when there is no measured data available from the field. The transformative nature of this research has vast implications for advancing carbon storage modeling technologies. By addressing the computational limitations of traditional numerical reservoir models and harnessing the synergy between machine learning and domain expertise, this work provides a practical workflow for efficient decision-making in sequestration projects

Dependability analysis and recovery support for smart grids

The increasing scale and complexity of power grids exacerbate concerns about failure propagation. A single contingency, such as outage of a transmission line due to overload or weather-related damage, can cause cascading failures that manifest as blackouts. One objective of smart grids is to reduce the likelihood of cascading failure through the use of power electronics devices that can prevent, isolate, and mitigate the effects of faults. Given that these devices are themselves prone to failure, we seek to quantify the effects of their use on dependability attributes of smart grid. This thesis articulates analytical methods for analyzing two dependability attributes - reliability and survivability - and proposes a recovery strategy that limits service degradation. Reliability captures the probability of system-level failure; Survivability describes degraded operation in the presence of a fault. System condition and service capacity are selected as measures of degradation. Both reliability and survivability are evaluated using N-1 contingency analysis. Importance analysis is used to determine a recovery strategy that maintains the highest survivability in the course of the recovery process. The proposed methods are illustrated by application to the IEEE 9-bus test system, a simple model system that allows for clear articulation of the process. Simulation is used to capture the effect of faults in both physical components of the power grid and the cyber infrastructure that differentiates it as a smart grid --Abstract, page iii

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii