A transition from manual to Intelligent Automated power system operation -A Indicative Review

This paper reviews the transition of the power system operation from the traditional manual mode of power system operations to the level where automation using Internet of Things (IOT) and intelligence using Artificial Intelligence (AI) is implemented. To make the review paper brief only indicative papers are chosen to cover multiple power system operation based implementation. Care is taken there is lesser repeatation of similar technology or application be reviewed. The indicative review is to take only a representative literature to bypass scrutinizing multiple literatures with similar objectives and methods. A brief review of the slow transition from the traditional to the intelligent automated way of carrying out power system operations like the energy audit, load forecasting, fault detection, power quality control, smart grid technology, islanding detection, energy management etc is discussed .The Mechanical Engineering Perspective on the basis of applications would be noticed in the paper although the energy management and power delivery concepts are electrical

Advanced Power Loss Modeling and Model-Based Control of Three-Phase Induction Motor Drive Systems

Three-phase induction motor (IM) drive systems are the most important workhorses of many industries worldwide. This dissertation addresses improved modeling of three-phase IM drives and model-based control algorithms for the purpose of designing better IM drive systems. Enhancements of efficiency, availability, as well as performance of IMs, such as maximum torque-per-ampere capability, power density, and torque rating, are of major interest. An advanced power loss model of three-phase IM drives is proposed and comprehensively validated at different speed, load torque, flux and input voltage conditions. This model includes a core-loss model of three-phase IMs, a model of machine mechanical and stray losses, and a model of power electronic losses in inverters. The drive loss model shows more than 90% accuracy and is used to design system-level loss minimization control of a motor drive system, which is integrated with the conventional volts-per-hertz control and indirect field-oriented control as case studies. The designed loss minimization control leads to more than 13% loss reduction than using rated flux for the testing motor drive under certain conditions. The proposed core-loss model is also used to design an improved model-based maximum torque-per-ampere control of IMs by considering core losses. Significant increase of torque-per-ampere capability could be possible for high-speed IMs. A simple model-based time-domain fault diagnosis method of four major IM faults is provided; it is nonintrusive, fast, and has excellent fault sensitivity and robustness to noise and harmonics. A fault-tolerant control scheme for sensor failures in closed-loop IM drives is also studied, where a multi-controller drive is proposed and uses different controllers with minimum hand-off transients when switching between controllers. A finite element analysis model of medium-voltage IMs is explored, where electromagnetic and thermal analyses are co-simulated. The torque rating and power density of the simulated machine could be increased by 14% with proper change of stator winding insulation material. The outcome of this dissertation is an advanced three-phase IM drive that is enhanced using model-based loss minimization control, fault detection and diagnosis of machine faults, fault-tolerant control under sensor failures, and performance-enhancement suggestions

Energy efficiency improvement of a squirrel-cage induction motor through the control strategy

Energy efficiency optimization of electric machines is an important research field and is part of the objectives of several international projects such as the European Commission Climate and Energy package which has set itself a 20% energy savings target by 2020, and was extended for 2030 with higher targets. Therefore, this thesis proposes an efficiency optimization method of the Induction Machine (IM) through the variation of the control parameters. To achieve this goal, the flux in the airgap is modified according to an optimal flux table computed off-line for all possible operating points. The flux table is calculated with the best possible accuracy through an improved dynamic model of the IM, developed in these works. The latter avoids the main drawback of the classic dynamic model, by considering the effect of core losses. The core loss model established by Bertotti is used. It depends on the frequency and the amplitude of the magnetic field. The losses are then represented by a variable resistor, continuously evaluated according to the operating point. The established optimal flux table is a function of the operating conditions in terms of torque and speed. Indeed, the results show that the flux can be optimized for torque values less than about half the rated torque, and that this threshold is influenced by the speed. The proposed optimization method is simulated, then tested for the scalar control and the field-oriented control, in order to show the genericity of the proposed approach. The validation is carried on an experimental test bench for two 5.5 kW induction motors of different efficiency standards (IE2 and IE3). The results obtained show the reduction of the losses in the motor, thus an improvement of the overall efficiency while preserving a satisfactory dynamic behavior. Consequently, the optimization of the energy efficiency is validated for the two control structures and for the two studied motors. In addition to the validation of the simulation results, the proposed approach is compared to existing methods to assess its effectivenes

Estudio del efecto de la distorsión armónica de tensión sobre la eficiencia y la potencia del motor trifásico de inducción mediante modelos eléctricos y térmicos

En este trabajo se presenta un procedimiento para cuantificar la eficiencia y la potencia del motor trifásico de inducción totalmente cerrado refrigerado por ventilador TEFC del orden de 3 HP alimentado por tensiones con componentes armónicas, a partir del análisis de sus pérdidas eléctricas y temperatura de operación. La distorsión armónica de tensión abunda en los sistemas de eléctricos y en la literatura consultada se reportan pocos métodos para determinar la eficiencia en esas condiciones y que involucren también el estudio de las variaciones eléctricas y térmicas en la máquina. El procedimiento se fundamenta en modelos eléctricos y térmicos de parámetros concentrados en estado estacionario, independientes entre sí pero relacionados a través de las pérdidas del motor. Este método permite estimar la eficiencia del motor en cualquier punto de operación. Los parámetros de los circuitos equivalentes eléctrico y térmico se determinan a partir de pruebas experimentales estándar y aplicando un algoritmo de forraje bacterial (AFB) respectivamente. Las ecuaciones de operación del circuito eléctrico se resuelven aplicando el principio de superposición y los resultados del mismo son utilizados como variables de entrada para la solución del circuito térmico. Se aplicó este procedimiento a un motor de inducción de 3 HP estimando, mediante simulaciones de los circuitos equivalentes propuestos, sus curvas de eficiencia para cuatro estados de carga, igual que su variación de temperatura de operación en estado estacionario en condiciones de alimentación no sinusoidales. Con base en las variaciones térmicas de la máquina respecto a su operación nominal se propuso un factor de desclasificación de la potencia que garantice su vida útil para operaciones bajo las condiciones de alimentación simuladas. El método se validó satisfactoriamente en condiciones de laboratorio, para una alimentación que incluye armónicos de tensión. El error obtenido durante la evaluación fue inferior al 2 % excepto, tal como sucede en otros métodos reportados en la literatura, para baja carga. El proyecto se presenta entonces en diez capítulos ordenados así: En el capítulo uno se presenta el estado del arte del tema de la investigación, los métodos utilizados en la literatura para abordar la problemática y los resultados obtenidos. Se analizan los aspectos a mejorar de estos antecedentes. El capítulo dos presenta el problema de investigación; a partir de un planteamiento fundamentado en los antecedentes y una revisión bibliográfica, se formula la pregunta de investigación que guía este proyecto. En el capítulo tres se presenta la justificación del proyecto y del proceso de resolver el problema planteado. El capítulo cuatro plantea los objetivos a alcanzar en este proyecto: el objetivo general y los cinco objetivos específicos que se cumplen para conseguirlo. En el quinto capítulo se describe el modelo eléctrico de parámetros concentrados a utilizar para la estimación de la eficiencia del motor en condiciones no sinusoidales. Se parte del uso del circuito eléctrico equivalente convencional de la máquina y se propone una modificación del mismo para su operación ante formas de onda no sinusoidales. Son entonces determinados los parámetros del circuito equivalente con armónicos a partir de pruebas recomendadas por la norma IEEE 112-2004 y de un conjunto de expresiones teóricas. El sexto capítulo presenta una deducción del circuito térmico equivalente como modelo de parámetros concentrados a utilizar para la estimación de la temperatura de operación del motor de inducción en condiciones no sinusoidales. Para ello se presentan los conceptos básicos de transferencia de calor y de circuitos térmicos y se plantea el circuito a utilizar. Para determinar los parámetros del circuito térmico se utiliza un algoritmo de forraje bacterial AFB cuyos principios de operación y algoritmia general son explicados. Con base en las pérdidas nominales del motor de inducción calculadas a partir del circuito eléctrico equivalente y el AFB son obtenidos los parámetros del circuito térmico. En el capítulo siete son simulados los circuitos equivalentes eléctrico y térmico para estimar las curvas de eficiencia y el incremento de temperatura del motor de inducción alimentado por tensiones con componentes armónicas. Los resultados permiten proponer la potencia de salida en condiciones de armónicos así como un factor de desclasificación de la potencia de la máquina en estas condiciones. El capítulo ocho presenta el procedimiento de laboratorio para la validación experimental de los modelos simulados. Se utiliza para ellos un freno de histéresis que simula una carga variables y se alimenta el motor con un sistema de tensiones con componentes armónicas. Se mide la potencia de entrada, de salida y la temperatura de operación. Con ello se encuentran niveles de error satisfactorios entre las estimaciones y las mediciones. Finalmente en el capítulo nueve se presentan las conclusiones y en el diez las recomendaciones para trabajos futurosProyecto de grado (Magíster en Ingeniera)-- Universidad Autónoma de Occidente, 2015MaestríaMagíster en Ingenierí

Energy Management

Forecasts point to a huge increase in energy demand over the next 25 years, with a direct and immediate impact on the exhaustion of fossil fuels, the increase in pollution levels and the global warming that will have significant consequences for all sectors of society. Irrespective of the likelihood of these predictions or what researchers in different scientific disciplines may believe or publicly say about how critical the energy situation may be on a world level, it is without doubt one of the great debates that has stirred up public interest in modern times. We should probably already be thinking about the design of a worldwide strategic plan for energy management across the planet. It would include measures to raise awareness, educate the different actors involved, develop policies, provide resources, prioritise actions and establish contingency plans. This process is complex and depends on political, social, economic and technological factors that are hard to take into account simultaneously. Then, before such a plan is formulated, studies such as those described in this book can serve to illustrate what Information and Communication Technologies have to offer in this sphere and, with luck, to create a reference to encourage investigators in the pursuit of new and better solutions

Deep Learning based Prediction of Clogging Occurrences during Lignocellulosic Biomass Feeding in Screw Conveyors

Over the last decades, there have been substantial government and private sector investments to establish a commercial biorefining industry that uses lignocellulosic biomass as feedstock to produce fuels, chemicals, and other products. However, several biorefining plants experienced material conveyance problems due to the variability and complexity of the biomass feedstock. While the problems were reported in most conveyance unit operations in the biorefining plants, screw conveyors merit special attention because they are the most common conveyors used in biomass conveyance and typically function as the last conveyance unit connected to the conversion reactors. Thus, their operating status affects the plant production rate. Therefore, detecting emerging clogging events and, ultimately, proactively adjusting operating conditions to avoid downtime is crucial to improving overall plant economics. One promising solution is the development of sensor systems to detect clogging to support automated decision-making and process control. In this study, two deep learning based algorithms are developed to detect an imminent clogging event based on the current signature and vibration signals extracted from the sensors connected to the benchtop screw conveyor system. The study focuses on three biomass materials (switchgrass, loblolly pine, and hybrid poplar) and is designed around three research objectives. The first research objective examines the relationship between the occurrence of clogging in a screw conveyor and the current and vibration signals on the different feedstocks to establish the presence of clogging event fingerprint that could be exploited in automated decision-making and process-control. The second research objective applies two deep learning algorithms to the current and vibration signals to detect the imminent occurrence of clogging and its severity for decision making with an optimization procedure. The third objective examines the robustness of the optimized deep learning algorithm to detection imminent clogging events when feedstock properties (size distribution and moisture contents) vary. In the long-term, the early clogging detection methodology developed in this study could be leveraged to develop smart process controls for biomass conveyance

Modelling and Control of Switched Reluctance Machines

Today, switched reluctance machines (SRMs) play an increasingly important role in various sectors due to advantages such as robustness, simplicity of construction, low cost, insensitivity to high temperatures, and high fault tolerance. They are frequently used in fields such as aeronautics, electric and hybrid vehicles, and wind power generation. This book is a comprehensive resource on the design, modeling, and control of SRMs with methods that demonstrate their good performance as motors and generators

Contribuitions and developments on nonintrusive load monitoring

Energy efficiency is a key subject in our present world agenda, not only because of greenhouse gas emissions, which contribute to global warming, but also because of possible supply interruptions. In Brazil, energy wastage in the residential market is estimated to be around 15%. Previous studies have indicated that the most savings were achieved with specific appliance, electricity consumption feedback, which caused behavioral changes and encouraged consumers to pursue energy conservation. Nonintrusive Load Monitoring (NILM) is a relatively new term. It aims to disaggregate global consumption at an appliance level, using only a single point of measurement. Various methods have been suggested to infer when appliances are turned on and off, using the analysis of current and voltage aggregated waveforms. Within this context, we aim to provide a methodology for NILM to determine which sets of electrical features and feature extraction rates, obtained from aggregated household data, are essential to preserve equivalent levels of accuracy; thus reducing the amount of data that needs to be transferred to, and stored on, cloud servers. As an addendum to this thesis, a Brazilian appliance dataset, sampled from real appliances, was developed for future NILM developments and research. Beyond that, a low-cost NILM smart meter was developed to encourage consumers to change their habits to more sustainable methods.Eficiência energética é um assunto essencial na agenda mundial. No Brasil, o desperdício de energia no setor residencial é estimado em 15%. Estudos indicaram que maiores ganhos em eficiência são conseguidos quando o usuário recebe as informações de consumo detalhadas por cada aparelho, provocando mudanças comportamentais e incentivando os consumidores na conservação de energia. Monitoramento não intrusivo de cargas (NILM da sigla em inglês) é um termo relativamente novo. A sua finalidade é inferir o consumo de um ambiente até observar os consumos individualizados de cada equipamento utilizando-se de apenas um único ponto de medição. Métodos sofisticados têm sido propostos para inferir quando os aparelhos são ligados e desligados em um ambiente. Dentro deste contexto, este trabalho apresenta uma metodologia para a definição de um conjunto mínimo de características elétricas e sua taxa de extração que reduz a quantidade de dados a serem transmitidos e armazenados em servidores de processamento de dados, preservando níveis equivalentes de acurácia. São utilizadas diferentes técnicas de aprendizado de máquina visando à caracterização e solução do problema. Como adendo ao trabalho, apresenta-se um banco de dados de eletrodomésticos brasileiros, com amostras de equipamentos nacionais para desenvolvimentos futuros em NILM, além de um medidor inteligente de baixo custo para desagregação de cargas, visando tornar o consumo de energia mais sustentável

Modelling and Control of Switched Reluctance Machines

Today, switched reluctance machines (SRMs) play an increasingly important role in various sectors due to advantages such as robustness, simplicity of construction, low cost, insensitivity to high temperatures, and high fault tolerance. They are frequently used in fields such as aeronautics, electric and hybrid vehicles, and wind power generation. This book is a comprehensive resource on the design, modeling, and control of SRMs with methods that demonstrate their good performance as motors and generators