Diseño de D-STATCOM controlado por teoría de componentes simétricos instantáneos (ISCT) para disminuir armónicos y corregir el factor de Potencia en sistemas de distribución eléctrica ante la presencia de cargas no lineales

El presente trabajo académico de investigación propone un método de compensación tipo D-STATCOM (Compensador estático de distribución) es mediante la Teoría de Componentes Simétricos Instantáneos (ISCT). Diseñado para la compensación de sistemas de distribución eléctrica desbalanceados, con el fin de reducir la distorsión armónica y corrección de factor de potencia. Esta teoría (ISCT) descompone las señales de corriente y voltaje para análisis de contenido de armónicos. Se planteó 2 casos de análisis donde se integra cargas no lineales con alta generación de armónicos. En el caso 1 se busca analizar la operación del D STATCOM integrándolo a un sistema trifásico desbalanceado donde se conectará en periodos de tiempo de 0.05[seg]. Los resultados alcanzados fue la disminución de armónicos que se mantiene dentro de los limites mencionados en el documento y la mejora de factor de potencia que oscila entre los 0.85 a 0.97. El caso 2, se analiza un sistema de distribución eléctrica IEEE de 13 barras desbalanceado para demostrar la compensación del D STATCOM. Como resultado de lo antes mencionado existe corrección del factor de potencia de 0.85 a 0.92 además reducción de distorsión de armónicos de voltaje de 24.49% a 5.19% y de corriente de 19.62% a 10.81% con el objetivo de reducir el porcentaje de armónicos y la mejora de factor de potencia.The present academic research work proposes a compensation method type D-STATCOM (Static Distribution Compensator) is by means of the Instantaneous Symmetric Components Theory (ISCT). Designed for the compensation of unbalanced electrical distribution systems, in order to reduce harmonic distortion and power factor correction. This technique (ISCT) decomposes current and voltage signals for harmonic content analysis. Two analysis cases were proposed where non linear loads with high harmonic generation are integrated. In case 1, it is sought to analyze the operation of the D STATCOM by integrating it into an unbalanced three-phase system where it will be connected in periods of time of 0.05 [sec]. The results achieved were the reduction of harmonics that remains within the limits mentioned in the document and the improvement of the power factor that ranges from 0.85 to 0.97. For case 2, an unbalanced IEEE 13- bar unbalanced distribution system was analyzed. As a result, in the case studies there is a power factor correction from 0.85 to 0.92, in addition to a reduction in the distortion of voltage harmonics from 24.49% to 5.19% and the current of the 19.62% to 10.81%. in order to reduce the percentage of harmonics and the power factor improvement

Machine Learning Approach to Islanding Detection for Inverter-Based Distributed Generation

Despite a number of economic and environmental benefits that integration of renewable distributed generation (DG) into the distribution grid brings, there are many technical challenges that arise as well. One of the most important issues concerning DG integration is unintentional islanding. Islanding occurs when DG continues to energize portion of the system while being disconnected from the main grid. Since the island is unregulated, its behavior is unpredictable and voltage, frequency and other power system parameters may have unacceptable levels, which may cause hazardous effect on devices and public. According to the IEEE Standard 1547 DG shall detect any possible islanding conditions and cease to energize the area within 2 sec. In this dissertation work, a new islanding detection method for single phase inverter-based distributed generation is presented. In the first stage of the proposed method, parametric technique called Autoregressive (AR) signal modeling is utilized to extract signal features from voltage and current signals at the Point of Common Coupling (PCC) with the grid. In the second stage, advanced machine learning technique based on Support Vector Machine (SVM) which takes calculated features as inputs is utilized to predict islanding state. The extensive study is performed on the IEEE 13 bus system and feature vectors corresponding to various islanding and non-islanding conditions, such as external grid faults and power system components switching, are used for SVM classifier training and testing. Simulation results show that proposed method is robust to external grid transients and able to accurately discriminate islanding conditions 50ms after the event begins

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

Incremental algorithm for association rule mining under dynamic threshold

Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Object-oriented analysis and design of computational intelligence systems

Machine learning from data, neuro-fuzzy information processing, approximate reasoning and genetic and evolutionary computation are all aspects of computational intelligence (also called soft computing methods). Soft computing methods differ from conventional computing in that they are tolerant of imprecision, uncertainty and partial truths. These characteristics can be exploited to achieve tractability, robustness and low solution costs when the solution to a complex (in machine terms) problem is required. The principal constituents of soft computing include: Neural Networks, Fuzzy Logic and Probabilistic Reasoning Systems. Genetic Algorithms (GAs), Evolutionary Algorithms, Chaos Theory', Complexity Theory and parts of Learning Theory all come under Probabilistic Reasoning Systems. Hybrid systems can be designed incorporating 2 or more aspects of soft computing that are more powerful than any of the components used in a stand alone fashion. A unified framework is needed to implement and manipulate such systems. Such a framework will allow for easy visualisation of the underlying concepts and easy modification of the resulting computer models. In this thesis, an investigation of the major aspects of computational intelligence has been carried out. The main emphasis has been placed on developing an object-oriented framework for architecting computational intelligence systems. Object models for Neural Networks, Fuzzy Logic Systems and Evolutionary Computation systems have been developed. Software has been written in C++ to realise sample implementations of the various systems. Finally, practical applications and the results of using the Neural Networks, Fuzzy Logic systems and Genetic Algorithms developed in solving real world problems are presented. A consistent notation based on the Object Modelling Technique (OMT) is used throughout the thesis to describe the software architectures from which the computer implementation models have been derived

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

Machine Learning based Anomaly Detection for Cybersecurity Monitoring of Critical Infrastructures

openManaging critical infrastructures requires to increasingly rely on Information and Communi- cation Technologies. The last past years showed an incredible increase in the sophistication of attacks. For this reason, it is necessary to develop new algorithms for monitoring these infrastructures. In this scenario, Machine Learning can represent a very useful ally. After a brief introduction on the issue of cybersecurity in Industrial Control Systems and an overview of the state of the art regarding Machine Learning based cybersecurity monitoring, the present work proposes three approaches that target different layers of the control network architecture. The first one focuses on covert channels based on the DNS protocol, which can be used to establish a command and control channel, allowing attackers to send malicious commands. The second one focuses on the field layer of electrical power systems, proposing a physics-based anomaly detection algorithm for Distributed Energy Resources. The third one proposed a first attempt to integrate physical and cyber security systems, in order to face complex threats. All these three approaches are supported by promising results, which gives hope to practical applications in the next future.openXXXIV CICLO - SCIENZE E TECNOLOGIE PER L'INGEGNERIA ELETTRONICA E DELLE TELECOMUNICAZIONI - Elettromagnetismo, elettronica, telecomunicazioniGaggero, GIOVANNI BATTIST