In-situ Data Analytics In Cyber-Physical Systems

Cyber-Physical System (CPS) is an engineered system in which sensing, networking, and computing are tightly coupled with the control of the physical entities. To enable security, scalability and resiliency, new data analytics methodologies are required for computing, monitoring and optimization in CPS. This work investigates the data analytics related challenges in CPS through two study cases: Smart Grid and Seismic Imaging System. For smart grid, this work provides a complete solution for system management based on novel in-situ data analytics designs. We first propose methodologies for two important tasks of power system monitoring: grid topology change and power-line outage detection. To address the issue of low measurement redundancy in topology identification, particularly in the low-level distribution network, we develop a maximum a posterior based mechanism, which is capable of embedding prior information on the breakers status to enhance the identification accuracy. In power-line outage detection, existing approaches suer from high computational complexity and security issues raised from centralized implementation. Instead, this work presents a distributed data analytics framework, which carries out in-network processing and invokes low computational complexity, requiring only simple matrix-vector multiplications. To complete the system functionality, we also propose a new power grid restoration strategy involving data analytics for topology reconfiguration and resource planning after faults or changes. In seismic imaging system, we develop several innovative in-situ seismic imaging schemes in which each sensor node computes the tomography based on its partial information and through gossip with local neighbors. The seismic data are generated in a distributed fashion originally. Dierent from the conventional approach involving data collection and then processing in order, our proposed in-situ data computing methodology is much more ecient. The underlying mechanisms avoid the bottleneck problem on bandwidth since all the data are processed distributed in nature and only limited decisional information is communicated. Furthermore, the proposed algorithms can deliver quicker insights than the state-of-arts in seismic imaging. Hence they are more promising solutions for real-time in-situ data analytics, which is highly demanded in disaster monitoring related applications. Through extensive experiments, we demonstrate that the proposed data computing methods are able to achieve near-optimal high quality seismic tomography, retain low communication cost, and provide real-time seismic data analytics

COMPRESSIVE SENSING-BASED METHODOLOGIES FOR SMART GRID MONITORING

Modern distribution networks, commonly known as Smart Grids, will be characterized by strictly requirements in terms of reliability and efficiency of the power supply. This will require a high empowerment in the management of the distribution, and transmission, networks by the system operators. Problems such as the identification of the prevailing harmonic sources and the fault location are characterized by criticality which must be appropriately taken into account, in order to fully exploit the capabilities of the Smart Grids. The analysis of both phenomena requires an appropriate monitoring of the networks, which are currently characterized by the availability of a limited number of measurements. This increase the complexity of the analysis of distribution networks, and the necessity of developing ad-hoc algorithms and solutions aimed at supporting the system operators while managing the networks. In this thesis, Compressive Sensing-based algorithms for detecting the main harmonic polluting sources, and for identifying the location of faults occurring in distribution systems have been presented. With reference to the identification of the main harmonic sources, two algorithms have been proposed: one for detailed analysis, with reference to a specific harmonic order, and one for more general analysis, which allows to investigate multiple harmonic orders simultaneously. The performed tests have proved how both methodologies are robust with respect to the measurement uncertainties, underlying the different capabilities of the two methods. Contrarily, the performance of the fault location algorithms are more influenced by the higher uncertainties in measuring the dynamic signals involved during the fault. The analysis performed considering the proper uncertainty scenarios have underlined how the use of modern devices for branch current measurements allow to increase the performance of the fault location algorithms; providing additional information which are useful for locating the fault

Situational awareness in low-observable distribution grid - exploiting sparsity and multi-timescale data

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringBalasubramaniam NatarajanThe power distribution grid is typically unobservable due to a lack of real-time measurements. While deploying more sensors can alleviate this issue, it also presents new challenges related to data aggregation and the underlying communication infrastructure. Limited real-time measurements hinders the distribution system state estimation (DSSE). DSSE involves estimation of the system states (i.e., voltage magnitude and voltage angle) based on available measurements and system model information. To cope with the unobservability issue, sparsity-based DSSE approaches allow us to recover system state information from a small number of measurements, provided the states of the distribution system exhibit sparsity. However, these approaches perform poorly in the presence of outliers in measurements and errors in system model information. In this dissertation, we first develop robust formulations of sparsity-based DSSE to deal with uncertainties in the system model and measurement data in a low-observable distribution grid. We also combine the advantages of two sparsity-based DSSE approaches to estimate grid states with high fidelity in low observability regions. In practical distribution systems, information from field sensors and meters are unevenly sampled at different time scales and could be lost during the transmission process. It is critical to effectively aggregate these information sources for DSSE as well as other tasks related to situational awareness. To address this challenge, the second part of this dissertation proposes a Bayesian framework for multi-timescale data aggregation and matrix completion-based state estimation. Specifically, the multi-scale time-series data aggregated from heterogeneous sources are reconciled using a multitask Gaussian process. The resulting consistent time-series alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints for accurate state estimation low-observable distribution system. We also develop a computationally efficient recursive Gaussian process approach that is capable of handling batch-wise or real-time measurements while leveraging the network connectivity information of the grid. To further enhance the scalability and accuracy, we develop neural network-based approaches (latent neural ordinary differential equation approach and stochastic neural differential equation with recurrent neural network approach) to aggregate irregular time-series data in the distribution grid. The stochastic neural differential equation and recurrent neural network also allows us to quantify the uncertainty in a holistic manner. Simulation results on the different IEEE unbalanced test systems illustrate the high fidelity of the Bayesian and neural network-based methods in aggregating multi-timescale measurements. Lastly, we develop phase, and outage awareness approaches for power distribution grid. In this regard, we first design a graph signal processing approach that identifies the phase labels in the presence of limited measurements and incorrect phase labeling. The second approach proposes a novel outage detector for identifying all outages in a reconfigurable distribution network. Simulation results on standard IEEE test systems reveal the potential of these methods to improve situational awareness

Algorithms to Improve Performance of Wide Area Measurement Systems of Electric Power Systems

Power system operation has become increasingly complex due to high load growth and increasing market pressure. The occurrence of major blackouts in many power systems around the world has necessitated the use of synchrophasor based Wide Area Measurement Systems (WAMS) for grid monitoring. Synchrophasor technology is comparatively new in the area of power systems. Phasor measurement units (PMUs) and phasor data concentrators (PDCs) are new to the substations and control centers. Even though PMUs have been installed in many power grids, the number of installed PMUs is still low with respect to the number of buses or lines. Currently, WAMS systems face many challenges. This thesis is an attempt towards solving some of the technical problems faced by the WAMS systems. This thesis addresses four problems related to synchrophasor estimation, synchrophasor quality detection, synchrophasor communication and synchrophasor application. In the first part, a synchrophasor estimation algorithm has been proposed. The proposed algorithm is simple, requires lesser computations, and satisfies all the steady state and dynamic performance criteria of the IEEE Standard C37.118.1-2011 and also suitable for protection applications. The proposed algorithm performs satisfactorily during system faults and it has lower response time during larger disturbances. In the second part, areas of synchrophasor communication which can be improved by applying compressive sampling (CS) are identified. It is shown that CS can reduce bandwidth requirements for WAMS networks. It is also shown that CS can successfully reconstruct system dynamics at higher rates using synchrophasors reported at sub-Nyquist rate. Many synchrophasor applications are not designed to use fault/switching transient synchrophasors. In this thesis, an algorithm has been proposed to detect fault/switching transient synchrophasors. The proposed algorithm works satisfactorily during smaller and larger step changes, oscillations and missing data. Fault transient synchrophasors are not usable in WAMS applications as they represent a combination of fault and no-fault scenario. In the fourth part, two algorithms have been proposed to extract fault synchrophasor from fault transient synchrophasor in PDC. The proposed algorithms extract fault synchrophasors accurately in presence of noise, off-nominal frequencies, harmonics, and frequency estimation errors

Impulsive noise cancellation and channel estimation in power line communication systems

Power line communication (PLC) is considered as the most viable enabler of the smart grid. PLC exploits the power line infrastructure for data transmission and provides an economical communication backbone to support the requirements of smart grid applications. Though PLC brings a lot of benefits to the smart grid implementation, impairments such as frequency selective attenuation of the high-frequency communication signal, the presence of impulsive noise (IN) and the narrowband interference (NBI) from closely operating wireless communication systems, make the power line a hostile environament for reliable data transmission. Hence, the main objective of this dissertation is to design signal processing algorithms that are specifically tailored to overcome the inevitable impairments in the power line environment. First, we propose a novel IN mitigation scheme for PLC systems. The proposed scheme actively estimates the locations of IN samples and eliminates the effect of IN only from the contaminated samples of the received signal. By doing so, the typical problem encountered while mitigating the IN is avoided by using passive IN power suppression algorithms, where samples besides the ones containing the IN are also affected creating additional distortion in the received signal. Apart from the IN, the PLC transmission is also impaired by NBI. Exploiting the duality of the problem where the IN is impulsive in the time domain and the NBI is impulsive in the frequency domain, an extended IN mitigation algorithm is proposed in order to accurately estimate and effectively cancel both impairments from the received signal. The numerical validation of the proposed schemes shows improved BER performance of PLC systems in the presence of IN and NBI. Secondly, we pay attention to the problem of channel estimation in the power line environment. The presence of IN makes channel estimation challenging for PLC systems. To accurately estimate the channel, two maximumlikelihood (ML) channel estimators for PLC systems are proposed in this thesis. Both ML estimators exploit the estimated IN samples to determine the channel coefficients. Among the proposed channel estimators, one treats the estimated IN as a deterministic quantity, and the other assumes that the estimated IN is a random quantity. The performance of both estimators is analyzed and numerically evaluated to show the superiority of the proposed estimators in comparison to conventional channel estimation strategies in the presence of IN. Furthermore, between the two proposed estimators, the one that is based on the random approach outperforms the deterministic one in all typical PLC scenarios. However, the deterministic approach based estimator can perform consistent channel estimation regardless of the IN behavior with less computational effort and becomes an efficient channel estimation strategy in situations where high computational complexity cannot be afforded. Finally, we propose two ML algorithms to perform a precise IN support detection. The proposed algorithms perform a greedy search of the samples in the received signal that are contaminated by IN. To design such algorithms, statistics defined for deterministic and random ML channel estimators are exploited and two multiple hypothesis tests are built according to Bonferroni and Benjamini and Hochberg design criteria. Among the proposed estimators, the random ML-based approach outperforms the deterministic ML-based approach while detecting the IN support in typical power line environment. Hence, this thesis studies the power line environment for reliable data transmission to support smart grid. The proposed signal processing schemes are robust and allow PLC systems to effectively overcome the major impairments in an active electrical network.The efficient mitigation of IN and NBI and accurate estimation of channel enhances the applicability of PLC to support critical applications that are envisioned for the future electrical power grid.La comunicación a través de líneas de transmisión eléctricas (PLC) se considera uno de los habilitadores principales de la red eléctrica inteligente (smart grid). PLC explota la infraestructura de la red eléctrica para la transmisión de datos y proporciona una red troncal de comunicación económica para poder cumplir con los requisitos de las aplicaciones para smart grids. Si bien la tecnología PLC aporta muchos beneficios a la implementación de la smart grid, los impedimentos, como la atenuación selectiva en frecuencia de la señal de comunicación, la presencia de ruido impulsivo (IN) y las interferencias de banda estrecha (NBI) de los sistemas de comunicación inalámbrica de operación cercana, hacen que la red eléctrica sea un entorno hostil para la transmisión fiable de datos. En este contexto, el objetivo principal de esta tesis es diseñar algoritmos de procesado de señal que estén específicamente diseñados para superar los impedimentos inevitables en el entorno de la red eléctrica como son IN y NBI. Primeramente, proponemos un nuevo esquema de mitigación de IN en sistemas PLC. El esquema propuesto estima activamente las ubicaciones de las muestras de IN y elimina el efecto de IN solo en las muestras contaminadas de la señal recibida. Al hacerlo, el problema típico que se encuentra al mitigar el IN con técnicas tradicionales (donde también se ven afectadas otras muestras que contienen la IN, creando una distorsión adicional en la señal recibida) se puede evitar con la consiguiente mejora del rendimiento. Aparte de IN, los sistemas PLC también se ven afectados por el NBI. Aprovechando la dualidad del problema (el IN es impulsivo en el dominio del tiempo y el NBI es impulsivo en el dominio de la frecuencia), se propone un algoritmo de mitigación de IN ampliado para estimar con precisión y cancelar efectivamente ambas degradaciones de la señal recibida. La validación numérica de los esquemas propuestos muestra un mejor rendimiento en términos de tasa de error de bit (BER) en sistemas PLC con presencia de IN y NBI. En segundo lugar, prestamos atención al problema de la estimación de canal en entornos PLC. La presencia de IN hace que la estimación de canal sea un desafío para los sistemas PLC futuros. En esta tesis, se proponen dos estimadores de canal para sistemas PLC de máxima verosimilitud (ML) para sistemas PLC. Ambos estimadores ML explotan las muestras IN estimadas para determinar los coeficientes del canal. Entre los estimadores de canal propuestos, uno trata la IN estimada como una cantidad determinista, y la otra asume que la IN estimada es una cantidad aleatoria. El rendimiento de ambos estimadores se analiza y se evalúa numéricamente para mostrar la superioridad de los estimadores propuestos en comparación con las estrategias de estimación de canales convencionales en presencia de IN. Además, entre los dos estimadores propuestos, el que se basa en el enfoque aleatorio supera el determinista en escenarios PLC típicos. Sin embargo, el estimador basado en el enfoque determinista puede llevar a cabo una estimación de canal consistente independientemente del comportamiento de la IN con menos esfuerzo computacional y se convierte en una estrategia de estimación de canal eficiente en situaciones donde no es posible disponer de una alta complejidad computacionalPostprint (published version

Synchronized measurement data conditioning and real-time applications

Phasor measurement units (PMU), measuring voltage and current phasor with synchronized timestamps, is the fundamental component in wide-area monitoring systems (WAMS) and reveals complex dynamic behaviors of large power systems. The synchronized measurements collected from power grid may degrade due to many factors and impacts of the distorted synchronized measurement data are significant to WAMS. This dissertation focus on developing and improving applications with distorted synchronized measurements from power grid. The contributions of this dissertation are summarized below. In Chapter 2, synchronized frequency measurements of 13 power grids over the world, including both mainland and island systems, are retrieved from Frequency Monitoring Network (FNET/GridEye) and the statistical analysis of the typical power grids are presented. The probability functions of the power grid frequency based on the measurements are calculated and categorized. Developments of generation trip/load shedding and line outage events detection and localization based on high-density PMU measurements are investigated in Chapters 3 and 4 respectively. Four different types of abnormal synchronized measurements are identified from the PMU measurements of a power grid. The impacts of the abnormal synchronized measurements on generation trip/load shedding events detection and localization are evaluated. A line outage localization method based on power flow measurements is proposed to improve the accuracy of line outage events location estimation. A deep learning model is developed to detect abnormal synchronized measurements in Chapter 5. The performance of the model is evaluated with abnormal synchronized measurements from a power grid under normal operation status. Some types of abnormal synchronized measurements in the testing cases are recently observed and reported. An extensive study of hyper-parameters in the model is conducted and evaluation metrics of the model performance are presented. A non-contact synchronized measurements study using electric field strength is investigated in Chapter 6. The theoretical foundation and equation derivations are presented. The calculation process for a single circuit AC transmission line and a double circuit AC transmission line are derived. The derived method is implemented with Matlab and tested in simulation cases