Predicting Cascading Failures in Power Grids using Machine Learning Algorithms

Although there has been notable progress in modeling cascading failures in power grids, few works included using machine learning algorithms. In this paper, cascading failures that lead to massive blackouts in power grids are predicted and classified into no, small, and large cascades using machine learning algorithms. Cascading-failure data is generated using a cascading failure simulator framework developed earlier. The data set includes the power grid operating parameters such as loading level, level of load shedding, the capacity of the failed lines, and the topological parameters such as edge betweenness centrality and the average shortest distance for numerous combinations of two transmission line failures as features. Then several machine learning algorithms are used to classify cascading failures. Further, linear regression is used to predict the number of failed transmission lines and the amount of load shedding during a cascade based on initial feature values. This data-driven technique can be used to generate cascading failure data set for any real-world power grids and hence, power-grid engineers can use this approach for cascade data generation and hence predicting vulnerabilities and enhancing robustness of the grid

Online identification of cascading events in power systems with renewable generation using machine learning

This PhD project deals with the Modelling of Cascading Events in Power Systems and their Online Identification with Machine Learning, considering the integration of Renewable Energy Sources. Cascading events involve highly complex dynamic phenomena and in some cases can pose significant challenges to the stability and reliability of power grids, leading even to blackouts. The intermittent nature of renewable generation introduces additional complexities, as the system dynamic behavior following a contingency becomes more unpredictable. Consequently, there is an increasing need for cascading event identification methods that can effectively handle these emerging challenges and ensure secure network operation. Machine Learning methods can extract complex relationships from power system data, by capturing the underlying dynamics, offering a promising tool for the accurate and timely identification of the online system state. In addition, due to the extensive installation of Phasor Measurement Units in modern power systems, it is possible to acquire measurement data related to electrical system variables in close-to-real time. The thesis first delves into the understanding of cascading events appearance, as defined by the discrete action of protection devices, using detailed dynamic simulations and considering uncertainties associated with network operating conditions, contingencies and renewable generation. To address the online nature of the problem, supervised machine learning methods that utilize measurement data are developed. Different contemporary machine learning approaches are investigated, to identify the most suitable techniques for the detection of the appearance of cascading events, formulated as a binary classification problem, and the prediction of the reason of the upcoming cascading event, formulated as a multi-class classification problem. Furthermore, this thesis explores the challenges associated with the application of machine learning models on power system data, such as the online inference time, class imbalance, practical considerations related to measurement data and investigates techniques for model explainability to enhance the trustworthiness of the developed models. The contributions of this thesis lie in the development of machine learning-based techniques for online identification of cascading events in power systems, enabling more proactive and efficient situational awareness. These insights have the potential to significantly enhance the resilience and stability of power grids, minimizing the risk of large-scale blackouts and improving the overall reliability of the system. Georgios Nakas is sponsored through Engineering & Physical Sciences Research Council (EPSRC) Research Excellence Award (REA) and is supervised by Dr. Panagiotis Papadopoulos and Professor Graeme Burt.This PhD project deals with the Modelling of Cascading Events in Power Systems and their Online Identification with Machine Learning, considering the integration of Renewable Energy Sources. Cascading events involve highly complex dynamic phenomena and in some cases can pose significant challenges to the stability and reliability of power grids, leading even to blackouts. The intermittent nature of renewable generation introduces additional complexities, as the system dynamic behavior following a contingency becomes more unpredictable. Consequently, there is an increasing need for cascading event identification methods that can effectively handle these emerging challenges and ensure secure network operation. Machine Learning methods can extract complex relationships from power system data, by capturing the underlying dynamics, offering a promising tool for the accurate and timely identification of the online system state. In addition, due to the extensive installation of Phasor Measurement Units in modern power systems, it is possible to acquire measurement data related to electrical system variables in close-to-real time. The thesis first delves into the understanding of cascading events appearance, as defined by the discrete action of protection devices, using detailed dynamic simulations and considering uncertainties associated with network operating conditions, contingencies and renewable generation. To address the online nature of the problem, supervised machine learning methods that utilize measurement data are developed. Different contemporary machine learning approaches are investigated, to identify the most suitable techniques for the detection of the appearance of cascading events, formulated as a binary classification problem, and the prediction of the reason of the upcoming cascading event, formulated as a multi-class classification problem. Furthermore, this thesis explores the challenges associated with the application of machine learning models on power system data, such as the online inference time, class imbalance, practical considerations related to measurement data and investigates techniques for model explainability to enhance the trustworthiness of the developed models. The contributions of this thesis lie in the development of machine learning-based techniques for online identification of cascading events in power systems, enabling more proactive and efficient situational awareness. These insights have the potential to significantly enhance the resilience and stability of power grids, minimizing the risk of large-scale blackouts and improving the overall reliability of the system. Georgios Nakas is sponsored through Engineering & Physical Sciences Research Council (EPSRC) Research Excellence Award (REA) and is supervised by Dr. Panagiotis Papadopoulos and Professor Graeme Burt

On machine learning-based techniques for future sustainable and resilient energy systems

Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified

Design, monitoring and performance evaluation of high capacity optical networks

Premi Extraordinari de Doctorat, promoció 2018-2019. Àmbit de les TICInternet traffic is expected to keep increasing exponentially due to the emergence of a vast number of innovative online services and applications. Optical networks, which are the cornerstone of the underlying Internet infrastructure, have been continuously evolving to carry the ever-increasing traffic in a more flexible, cost-effective, and intelligent way. Having these three targets in mind, this PhD thesis focuses on two general areas for the performance improvement and the evolution of optical networks: i) introducing further cognition to the optical layer, and ii) introducing new networking solutions revolutionizing the optical transport infrastructure. In the first part, we present novel failure detection and identification solutions in the optical layer utilizing the optical spectrum traces captured by cost-effective coarse-granular Optical Spectrum Analyzers (OSA). We demonstrate the effectiveness of the developed solutions for detecting and identifying filter-related failures in the context of Spectrum-Switched Optical Networks (SSON), as well as transmitter-related laser failures in Filter-less Optical Networks (FON). In addition, at the subsystem level we propose an Autonomic Transmission Agent (ATA), which triggers local or remote transceiver reconfiguration by predicting Bit-Error-Rate (BER) degradation by monitoring State-of-Polarization (SOP) data obtained by coherent receivers. I have developed solutions to push further the performance of the currently deployed optical networks through reducing the margins and introducing intelligence to better manage their resources. However, it is expected that the spectral efficiency of the current standard Single-Mode Fiber (SMF) based optical network approaches the Shannon capacity limits in the near future, and therefore, a new paradigm is required to keep with the pace of the current huge traffic increase. In this regard, Space Division Multiplexing (SDM) is proposed as the ultimate solution to address the looming capacity crunch with a reduced cost-per-bit delivered to the end-users. I devote the second part of this thesis to investigate different flavors of SDM based optical networks with the aim of finding the best compromise for the realization of a spectrally and spatially flexible optical network. SDM-based optical networks can be deployed over various types of transmission media. Additionally, due to the extra dimension (i.e., space) introduced in SDM networks, optical switching nodes can support wavelength granularity, space granularity, or a combination of both. In this thesis, we evaluate the impact of various spectral and spatial switching granularities on the performance of SDM-based optical networks serving different profiles of traffic with the aim of understanding the impact of switching constraints on the overall network performance. In this regard, we consider two different generations of wavelength selective switches (WSS) to reflect the technology limitations on the performance of SDM networks. In addition, we present different designs of colorless direction-less, and Colorless Directionless Contention-less (CDC) Reconfigurable Optical Add/Drop Multiplexers (ROADM) realizing SDM switching schemes and compare their performance in terms of complexity and implementation cost. Furthermore, with the aim of revealing the benefits and drawbacks of SDM networks over different types of transmission media, we preset a QoT-aware network planning toolbox and perform comparative performance analysis among SDM network based on various types of transmission media. We also analyze the power consumption of Multiple-Input Multiple-Output (MIMO) Digital Signal Processing (DSP) units of transceivers operating over three different types of transmission media. The results obtained in the second part of the thesis provide a comprehensive outlook to different realizations of SDM-based optical networks and showcases the benefits and drawbacks of different SDM realizations.Se espera que el tráfico de Internet siga aumentando exponencialmente debido a la continua aparición de gran cantidad de aplicaciones innovadoras. Las redes ópticas, que son la piedra angular de la infraestructura de Internet, han evolucionado continuamente para transportar el tráfico cada vez mayor de una manera más flexible, rentable e inteligente. Teniendo en cuenta estos tres objetivos, esta tesis doctoral se centra en dos áreas cruciales para la mejora del rendimiento y la evolución de las redes ópticas: i) introducción de funcionalidades cognitivas en la capa óptica, y ii) introducción de nuevas estructuras de red que revolucionarán el transporte óptico. En la primera parte, se presentan soluciones novedosas de detección e identificación de fallos en la capa óptica que utilizan trazas de espectro óptico obtenidas mediante analizadores de espectros ópticos (OSA) de baja resolución (y por tanto de coste reducido). Se demuestra la efectividad de las soluciones desarrolladas para detectar e identificar fallos derivados del filtrado imperfecto en las redes ópticas de conmutación de espectro (SSON), así como fallos relacionados con el láser transmisor en redes ópticas sin filtro (FON). Además, a nivel de subsistema, se propone un Agente de Transmisión Autónomo (ATA), que activa la reconfiguración del transceptor local o remoto al predecir la degradación de la Tasa de Error por Bits (BER), monitorizando el Estado de Polarización (SOP) de la señal recibida en un receptor coherente. Se han desarrollado soluciones para incrementar el rendimiento de las redes ópticas mediante la reducción de los márgenes y la introducción de inteligencia en la administración de los recursos de la red. Sin embargo, se espera que la eficiencia espectral de las redes ópticas basadas en fibras monomodo (SMF) se acerque al límite de capacidad de Shannon en un futuro próximo, y por tanto, se requiere un nuevo paradigma que permita mantener el crecimiento necesario para soportar el futuro aumento del tráfico. En este sentido, se propone el Multiplexado por División Espacial (SDM) como la solución que permita la continua reducción del coste por bit transmitido ante ése esperado crecimiento del tráfico. En la segunda parte de esta tesis se investigan diferentes tipos de redes ópticas basadas en SDM con el objetivo de encontrar soluciones para la realización de redes ópticas espectral y espacialmente flexibles. Las redes ópticas basadas en SDM se pueden implementar utilizando diversos tipos de medios de transmisión. Además, debido a la dimensión adicional (el espacio) introducida en las redes SDM, los nodos de conmutación óptica pueden conmutar longitudes de onda, fibras o una combinación de ambas. Se evalúa el impacto de la conmutación espectral y espacial en el rendimiento de las redes SDM bajo diferentes perfiles de tráfico ofrecido, con el objetivo de comprender el impacto de las restricciones de conmutación en el rendimiento de la red. En este sentido, se consideran dos generaciones diferentes de conmutadores selectivos de longitud de onda (WSS) para reflejar las limitaciones de la tecnología en el rendimiento de las redes SDM. Además, se presentan diferentes diseños de ROADM, independientes de la longitud de onda, de la dirección, y sin contención (CDC) utilizados para la conmutación SDM, y se compara su rendimiento en términos de complejidad y coste. Además, con el objetivo de cuantificar los beneficios e inconvenientes de las redes SDM, se ha generado una herramienta de planificación de red que prevé la QoT usando diferentes tipos de fibras. También se analiza el consumo de energía de las unidades DSP de los transceptores MIMO operando en redes SDM con tres tipos diferentes de medios de transmisión. Los resultados obtenidos en esta segunda parte de la tesis proporcionan una perspectiva integral de las redes SDM y muestran los beneficios e inconvenientes de sus diferentes implementacionesAward-winningPostprint (published version

Enhancing Grid Reliability With Phasor Measurement Units

Over the last decades, great efforts and investments have been made to increase the integration level of renewable energy resources in power grids. The New York State has set the goal to achieve 70% renewable generations by 2030, and realize carbon neutrality by 2040 eventually. However, the increased level of uncertainty brought about by renewables makes it more challenging to maintain stable and robust power grid operation. In addition to renewable energy resources, the ever-increasing number of electric vehicles and active loads have further increased the uncertainties in power systems. All these factors challenge the way the power grids are operated, and thus ask for new solutions to maintain stable and reliable grids. To meet the emerging requirements, advanced metering infrastructures are being integrated into power grids that transform traditional grids into \u27\u27 smart grids . One example is the widely deployed phasor measurement units (PMUs), which enable generating time-synchronized measurements with high sampling frequency, and pave a new path to realize real-time monitoring and control in power grids. However,the massive data generated by PMUs raises the questions of how to efficiently utilize the obtained measurements to understand and control the present system. Additionally, to meet the communication requirements between the advanced meters, the connectivity of the cyber layer has become more sophisticated, and thus is exposed to more cyber-attacks than before. Therefore, to enhance the grid reliability with PMUs, robust and efficient grid monitoring and control methods are required. This dissertation focuses on three important aspects of improving grid reliability with PMUs: (1) power system event detection; (2) impact assessment regarding both steady-state and transient stability; and (3) impact mitigation. In this dissertation, a comprehensive introduction of PMUs in the wide-area monitoring system, and comparisons with the existing supervisory control and data acquisition (SCADA) systems are presented first. Next, a data-driven event detection method is developed for efficient event detection with PMU measurements. A text mining approach is utilized to extract event oscillation patterns and determine event types. To ensure the integrity of the received data, the developed detection method is further designed to identify the fake events, and thus is robust against cyber-threat. Once a real event is detected, it is critical to promptly understand the consequences of the event in both steady and dynamic states. Sometimes, a single system event, e.g., a transmission line fault, may cause subsequent failures that lead to a cascading failure in the grid. In the worst case, these failures can result in large-scale blackouts. To assess the risk of an event in steady state, a probabilistic cascading failure model is developed. With the real-time phasor measurements, the failure probability of each system component at a specific operating condition can be predicted. In terms of the dynamic state, a failure of a system component may cause generators to lose synchronism, which will damage the power plant and lead to a blackout. To predict the transient stability after an event, a predictive online transient stability assessment (TSA) tool is developed in this dissertation. With only one sample of the PMU voltage measurements, the status of the transient stability can be predicted within cycles. In addition to the impact detection and assessment, it is also critical to identify proper mitigations to alleviate the failures. In this dissertation, a data-driven model predictive control strategy is developed. As a parameter-based system model is vulnerable to topology errors, a data-driven model is developed to mimic the grid behavior. Rather than utilizing the system parameters to construct the grid model, the data-driven model only leverages the received phasor measurements to determine proper corrective actions. Furthermore, to be robust against cyber-attacks, a check-point protocol, where past stored trustworthy data can be used to amend the attacked data, is utilized. The overall objective of this dissertation is to efficiently utilize advanced PMUs to detect, assess, and mitigate system failure, and help improve grid reliability

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

Time-Series Embedded Feature Selection Using Deep Learning: Data Mining Electronic Health Records for Novel Biomarkers

As health information technologies continue to advance, routine collection and digitisation of patient health records in the form of electronic health records present as an ideal opportunity for data-mining and exploratory analysis of biomarkers and risk factors indicative of a potentially diverse domain of patient outcomes. Patient records have continually become more widely available through various initiatives enabling open access whilst maintaining critical patient privacy. In spite of such progress, health records remain not widely adopted within the current clinical statistical analysis domain due to challenging issues derived from such “big data”.Deep learning based temporal modelling approaches present an ideal solution to health record challenges through automated self-optimisation of representation learning, able to man-ageably compose the high-dimensional domain of patient records into data representations able to model complex data associations. Such representations can serve to condense and reduce dimensionality to emphasise feature sparsity and importance through novel embedded feature selection approaches. Accordingly, application towards patient records enable complex mod-elling and analysis of the full domain of clinical features to select biomarkers of predictive relevance.Firstly, we propose a novel entropy regularised neural network ensemble able to highlight risk factors associated with hospitalisation risk of individuals with dementia. The application of which, was able to reduce a large domain of unique medical events to a small set of relevant risk factors able to maintain hospitalisation discrimination.Following on, we continue our work on ensemble architecture approaches with a novel cas-cading LSTM ensembles to predict severe sepsis onset within critical patients in an ICU critical care centre. We demonstrate state-of-the-art performance capabilities able to outperform that of current related literature.Finally, we propose a novel embedded feature selection application dubbed 1D convolu-tion feature selection using sparsity regularisation. Said methodology was evaluated on both domains of dementia and sepsis prediction objectives to highlight model capability and generalisability. We further report a selection of potential biomarkers for the aforementioned case study objectives highlighting clinical relevance and potential novelty value for future clinical analysis.Accordingly, we demonstrate the effective capability of embedded feature selection ap-proaches through the application of temporal based deep learning architectures in the discovery of effective biomarkers across a variety of challenging clinical applications