Gas turbine control and load sharing of a shipboard power system

The objective of this research is to design a controller for a gas turbine of an Electric Shipboard Power System (ESPS) and to develop a load sharing strategy for its energy management. A suitable model for the gas turbine is selected and the effects of the dynamics are investigated for the different loads of the ESPS. The gas turbine controller is a Proportional Integral Derivative (PID) controller, whose parameters are tuned using the Particle Swarm Optimization (PSO) technique. The load on the system has three components: a propulsion load, a pulsed load to simulate a high energy weapon system and a power supply load for the remaining loads such as pumps, lighting systems, etc. Load sharing is inevitable when demand exceeds the available power supply. In this case, based on the priorities of the loads and the available power, a strategy is presented to supply power to the most critical loads. To illustrate this, a load allocation algorithm is developed using stateflow diagrams. The potential of this algorithm is demonstrated by two case studies performed using the three loads, with the highest priority assigned to the propulsion load in case 1, and power supply load in case 2. The results of this research can be further extended to real time applications

ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

Distributed collaborative knowledge management for optical network

Network automation has been long time envisioned. In fact, the Telecommunications Management Network (TMN), defined by the International Telecommunication Union (ITU), is a hierarchy of management layers (network element, network, service, and business management), where high-level operational goals propagate from upper to lower layers. The network management architecture has evolved with the development of the Software Defined Networking (SDN) concept that brings programmability to simplify configuration (it breaks down high-level service abstraction into lower-level device abstractions), orchestrates operation, and automatically reacts to changes or events. Besides, the development and deployment of solutions based on Artificial Intelligence (AI) and Machine Learning (ML) for making decisions (control loop) based on the collected monitoring data enables network automation, which targets at reducing operational costs. AI/ML approaches usually require large datasets for training purposes, which are difficult to obtain. The lack of data can be compensated with a collective self-learning approach. In this thesis, we go beyond the aforementioned traditional control loop to achieve an efficient knowledge management (KM) process that enhances network intelligence while bringing down complexity. In this PhD thesis, we propose a general architecture to support KM process based on four main pillars, which enable creating, sharing, assimilating and using knowledge. Next, two alternative strategies based on model inaccuracies and combining model are proposed. To highlight the capacity of KM to adapt to different applications, two use cases are considered to implement KM in a purely centralized and distributed optical network architecture. Along with them, various policies are considered for evaluating KM in data- and model- based strategies. The results target to minimize the amount of data that need to be shared and reduce the convergence error. We apply KM to multilayer networks and propose the PILOT methodology for modeling connectivity services in a sandbox domain. PILOT uses active probes deployed in Central Offices (COs) to obtain real measurements that are used to tune a simulation scenario reproducing the real deployment with high accuracy. A simulator is eventually used to generate large amounts of realistic synthetic data for ML training and validation. We apply KM process also to a more complex network system that consists of several domains, where intra-domain controllers assist a broker plane in estimating accurate inter-domain delay. In addition, the broker identifies and corrects intra-domain model inaccuracies, as well as it computes an accurate compound model. Such models can be used for quality of service (QoS) and accurate end-to-end delay estimations. Finally, we investigate the application on KM in the context of Intent-based Networking (IBN). Knowledge in terms of traffic model and/or traffic perturbation is transferred among agents in a hierarchical architecture. This architecture can support autonomous network operation, like capacity management.La automatización de la red se ha concebido desde hace mucho tiempo. De hecho, la red de gestión de telecomunicaciones (TMN), definida por la Unión Internacional de Telecomunicaciones (ITU), es una jerarquía de capas de gestión (elemento de red, red, servicio y gestión de negocio), donde los objetivos operativos de alto nivel se propagan desde las capas superiores a las inferiores. La arquitectura de gestión de red ha evolucionado con el desarrollo del concepto de redes definidas por software (SDN) que brinda capacidad de programación para simplificar la configuración (descompone la abstracción de servicios de alto nivel en abstracciones de dispositivos de nivel inferior), organiza la operación y reacciona automáticamente a los cambios o eventos. Además, el desarrollo y despliegue de soluciones basadas en inteligencia artificial (IA) y aprendizaje automático (ML) para la toma de decisiones (bucle de control) en base a los datos de monitorización recopilados permite la automatización de la red, que tiene como objetivo reducir costes operativos. AI/ML generalmente requieren un gran conjunto de datos para entrenamiento, los cuales son difíciles de obtener. La falta de datos se puede compensar con un enfoque de autoaprendizaje colectivo. En esta tesis, vamos más allá del bucle de control tradicional antes mencionado para lograr un proceso eficiente de gestión del conocimiento (KM) que mejora la inteligencia de la red al tiempo que reduce la complejidad. En esta tesis doctoral, proponemos una arquitectura general para apoyar el proceso de KM basada en cuatro pilares principales que permiten crear, compartir, asimilar y utilizar el conocimiento. A continuación, se proponen dos estrategias alternativas basadas en inexactitudes del modelo y modelo de combinación. Para resaltar la capacidad de KM para adaptarse a diferentes aplicaciones, se consideran dos casos de uso para implementar KM en una arquitectura de red óptica puramente centralizada y distribuida. Junto a ellos, se consideran diversas políticas para evaluar KM en estrategias basadas en datos y modelos. Los resultados apuntan a minimizar la cantidad de datos que deben compartirse y reducir el error de convergencia. Aplicamos KM a redes multicapa y proponemos la metodología PILOT para modelar servicios de conectividad en un entorno aislado. PILOT utiliza sondas activas desplegadas en centrales de telecomunicación (CO) para obtener medidas reales que se utilizan para ajustar un escenario de simulación que reproducen un despliegue real con alta precisión. Un simulador se utiliza finalmente para generar grandes cantidades de datos sintéticos realistas para el entrenamiento y la validación de ML. Aplicamos el proceso de KM también a un sistema de red más complejo que consta de varios dominios, donde los controladores intra-dominio ayudan a un plano de bróker a estimar el retardo entre dominios de forma precisa. Además, el bróker identifica y corrige las inexactitudes de los modelos intra-dominio, así como también calcula un modelo compuesto preciso. Estos modelos se pueden utilizar para estimar la calidad de servicio (QoS) y el retardo extremo a extremo de forma precisa. Finalmente, investigamos la aplicación en KM en el contexto de red basada en intención (IBN). El conocimiento en términos de modelo de tráfico y/o perturbación del tráfico se transfiere entre agentes en una arquitectura jerárquica. Esta arquitectura puede soportar el funcionamiento autónomo de la red, como la gestión de la capacidad.Postprint (published version

Agent based modeling of power distribution systems

The electric power system is a very vast network and becoming more complex each day. The traditional vertically monopolistic structure has been deregulated and replaced by gencos, transcos and, discos; increasing the power system intricacy. During the past few decades there has been remarkable development in software and hardware technologies for the analysis and design activities in power system planning, operation, and control. However, much still depends on the judgment of human experts. A single fault in power system can lead to multiple faults and can collapse the whole system. Power System needs a more decentralized control mechanism for solving these problems. One novel solution would be Multi-agent Systems. A Multi-agent system is a collection of agents, which perceives the system changes and acts on the system in order to achieve its goals. Recent technology developments in the area of Multi-agent systems making it a viable solution for today\u27s complicated power network.;A Multi-agent system model is developed for fault detection and reconfiguration in this thesis work. These models are developed based on graph theory tree models and mathematical models. A set of objective functions are specified in the mathematical model for the restoration of the network.;The agent platform for the fault detection is developed by Java Agent Development Framework. The restoration algorithm is programmed in MATLAB and applied to the distribution system modeled in the commercial software, Distributed Engineering Workstation and Power World Simulator. The test system in this thesis is, a distribution system developed by Southern California Edison called Circuit of the Future.;The Multi-agent system can detect the fault precisely and reconfigures the circuit using the reconfiguration algorithm. The reconfiguration will happen in a way that it always try to supply all the critical loads in the network. When there are multiple solutions available for reconfiguration, the one with good voltage profile and less power loss is selected as the solution. The algorithm makes use of shunt compensation and priority based load shedding in order to control the voltage across the network. Agents make use of learning to speed up the reconfiguration process

Fault Tolerance for Spacecraft Attitude Management

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83657/1/AIAA-2010-8301-426.pd

Hardware Prototype for a Multi Agent Grid Management System

There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect.;There is great effort in the power industry to incorporate Smart Grid functionalities to existing power systems. Distributed generation and the hardware necessary to interface the existing grid, as well as control algorithms to efficiently couple and operate these systems are being researched and implemented extensively. However, the added complexity of such components results in greater opportunities for failure in a system which is already challenging to protect

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions