Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation

Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke\u27s transformation is used to transform the sequence networks into the αβ stationary coordinate frame, which leads to system model formulation. A novel fault tolerant extended Kalman filter based real-time estimation framework is proposed for smart grid synchronization with noisy bad data measurements. Computer simulation studies have demonstrated that the proposed fault tolerant extended Kalman filter (FTEKF) provides more accurate voltage synchronization results than the extended Kalman filter (EKF). The proposed approach has been implemented with dSPACE DS1103 and National Instruments CompactRIO hardware platforms. Computer simulation and hardware instrumentation results have shown the potential applications of FTEKF in smart grid synchronization

Optimized meter placement in low voltage grids based on asymmetric state estimation

Alongside the ongoing energy system transition towards sustainability new challenges for low voltage grids arise. New technologies connected to those subordinate grids are less predictable, especially decentralized solar plants. Larger loads and a possible reversed power flow lead to increasingly unknown states and can evoke violations of power quality. This paper presents a method to determine an optimized meter placement in low voltage grids using an asymmetric state estimation in order to achieve a cost-efficient monitoring. First, the utilized state estimation method is introduced as well as the usage and parameterization of pseudo measurement values are discussed. Furthermore, a new approach for an optimized meter placement is presented and simulation results for exemplary grids and corresponding power flow data are shown. Subsequent discussions focus on the quality of results subject to the amount as well as the specific positioning of meters placed

Stima dello stato nelle reti di distribuzione

La stima dello stato di un sistema elettrico rappresenta un processo di stima di un gruppo di variabili di stato, in un preciso istante di tempo, che lo descrivano in modo univoco partendo da un certo numero di misure tenendo conto delle incertezze ad esse correlate. Conoscere lo stato di un sistema è fondamentale per una gestione efficiente, in sicurezza e nel rispetto dei vincoli tecnici, delle reti elettriche. La tesi ha come obiettivo la procedura di stima dello stato mediante il metodo dei minimi quadrati ed in particolare alla sua applicazione a reti di distribuzione dell’energia elettrica. La stima dello stato di reti di distribuzione pone problemi particolari essenzialmente legati al gran numero di carichi, e quindi di nodi, in genere non monitorati in tempo reale, e alla lunghezza limitata delle linee, specie in reti urbane. Il gran numero di nodi e l’assenza di misure in numero sufficiente a rendere la rete osservabile e La tesi analizza tali aspetti mediante applicazione di un algoritmo di stima dello stato, disponibile nella libreria Matpower di Matlab, a due reti test di diverse caratteristiche. La prima è una rete test IEEE disponibile in letteratura, caratterizzata da lunghi feeder rurali a tensione nominale 4,16 kV, ed una porzione della rete di distribuzione AMAIE di Sanremo, caratterizzata da feeder urbani in cavo, a tensione nominale 15 kV. Le principali difficoltà riscontrate nell’esecuzione di calcoli di lod flow e stima dello stato della rete reale sono risultati legati alla complessità della topologia della rete, al gran numero di nodi utenti finali e alla presenza di misure distribuite solo in un determinato porzione della rete, dalla presenza di un gran numero di misure in bassa tensione ed alla necessità di identificare la rete a partire dalla sua descrizione contenuta in un file avente un formato particolare (hdc)

Multi-Agent Distributed Optimization and Estimation over Lossy Networks

Nowadays, optimization is a pervasive tool, employed in a lot different fields. Due to its flexibility, it can be used to solve many diverse problems, some of which do not seem to require an optimization framework. As so, the research on this topic is always active and copious. Another very interesting and current investigation field involves multi-agent systems, that is, systems composed by a lot of (possibly different) agents. The research on cyber-physical systems, believed to be one of the challenges of the 21st century, is very extensive, and comprises very complex systems like smart cities and smart power-grids, but also much more simple ones, like wireless sensor networks or camera networks. In a multi-agent context, the optimization framework is extensively used. As a consequence, optimization in multi-agent systems is an attractive topic to investigate. The contents of this thesis focus on distributed optimization within a multi-agent scenario, i.e., optimization performed by a set of peers, among which there is no leader. Accordingly, when these agents have to perform a task, formulated as an optimization problem, they have to collaborate to solve it, all using the same kind of update rule. Collaboration clearly implies the need of messages exchange among the agents, and the focus of the thesis is on the criticalities related to the communication step. In particular, no reliability of this step is assumed, meaning that the packets exchanged between two agents can sometime be lost. Also, the sought-for solution does not have to employ an acknowledge protocol, that is, when an agent has to send a packet, it just sends it and goes on with its computation, without waiting for a confirmation that the receiver has actually received the packet. Almost all works in the existing literature deal with packet losses employing an acknowledge (ACK) system; the effort in this thesis is to avoid the use of an ACK system, since it can slow down the communication step. However, this choice of averting the use of ACKs makes the development of optimization algorithms, and especially their convergence proof, more involved. Apart from robustness to packet losses, the algorithms developed in this dissertation are also asynchronous, that is, the agents do not need to be synchronized to perform the update and communication steps. Three types of optimization problems are analyzed in the thesis. The first one is the patrolling problem for camera networks. The algorithm developed to solve this problem has a restricted applicability, since it is very task-dependent. The other two problems are more general, because both concern the minimization of the sum of cost functions, one for each agent in the system. In the first case, the form of the local cost functions is particular: these, in fact, are locally coupled, in the sense that the cost function of an agent depends on the variables of the agent itself and on those of its direct neighbors. The sought-for algorithm has to satisfy two properties (apart from asynchronicity and robustness to packet losses): the requirement of asking a single communication exchange per iteration (which also reduces the need of synchronicity) and the requirement that the communication among agents is only between direct neighbors. In the second case, the local functions depend all on the same variables. The analysis first focuses on the special case of local quadratic cost functions and their strong relationship with the consensus problem. Besides the development of a robust and asynchronous algorithm for the average consensus problem, a comparison among algorithms to solve the minimization of the sum of quadratic cost functions is carried out. Finally, the distributed minimization of the sum of more general local cost functions is tackled, leading to the development of a robust version of the Newton-Raphson consensus. The theoretical tools employed in the thesis to prove convergence of the algorithms mainly rely on Lyapunov theory and the separation of scales theory