Diagnostic Applications for Micro-Synchrophasor Measurements

This report articulates and justifies the preliminary selection of diagnostic applications for data from micro-synchrophasors (µPMUs) in electric power distribution systems that will be further studied and developed within the scope of the three-year ARPA-e award titled Micro-synchrophasors for Distribution Systems

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Stochastic management framework of distribution network systems featuring large-scale variable renewable energy sources and flexibility options

The concerns surrounding climate change, energy supply security and the growing demand are forcing changes in the way distribution network systems are planned and operated, especially considering the need to accommodate large-scale integration of variable renewable energy sources (vRESs). An increased level of vRESs creates technical challenges in the system, bringing a huge concern for distribution system operators who are given the mandate to keep the integrity and stability of the system, as well as the quality of power delivered to end-users. Hence, existing electric energy systems need to go through an eminent transformation process so that current limitations are significantly alleviated or even avoided, leading to the so-called smart grids paradigm. For distribution networks, new and emerging flexibility options pertaining to the generation, demand and network sides need to be deployed for these systems to accommodate large quantities of variable energy sources, ensuring an optimal operation. Therefore, the management of different flexibility options needs to be carefully handled, minimizing the sideeffects such as increasing costs, worsening voltage profile and overall system performance. From this perspective, it is necessary to understand how a distribution network can be optimally operated when featuring large-scale vRESs. Because of the variability and uncertainty pertinent to these technologies, new methodologies and computational tools need to be developed to deal with the ensuing challenges. To this end, it is necessary to explore emerging and existing flexibility options that need to be deployed in distribution networks so that the uncertainty and variability of vRESs are effectively managed, leading to the real-time balancing of demand and supply. This thesis presents an extensive analysis of the main technologies that can provide flexibility to the electric energy systems. Their individual or collective contributions to the optimal operation of distribution systems featuring large-scale vRESs are thoroughly investigated. This is accomplished by taking into account the stochastic nature of intermittent power sources and other sources of uncertainty. In addition, this work encompasses a detailed operational analysis of distribution systems from the context of creating a sustainable energy future. The roles of different flexibility options are analyzed in such a way that a major percentage of load is met by variable RESs, while maintaining the reliability, stability and efficiency of the system. Therefore, new methodologies and computational tools are developed in a stochastic programming framework so as to model the inherent variability and uncertainty of wind and solar power generation. The developed models are of integer-mixed linear programming type, ensuring tractability and optimality.As mudanças climáticas, a crescente procura por energia e a segurança de abastecimento estão a modificar a operação e o planeamento das redes de distribuição, especialmente pela necessidade de integração em larga escala de fontes de energia renováveis. O aumento desses recursos energéticos sustentáveis gera enormes desafios a nível técnico no sistema, atendendo a que o operador do sistema de distribuição tem o dever de manter a integridade e a estabilidade da rede, bem como a qualidade de energia entregue aos consumidores. Portanto, os sistemas de energia elétrica existentes devem passar por um eminente processo de transformação para que as limitações atuais sejam devidamente atenuadas ou mesmo evitadas, esperando-se assim chegar ao paradigma das redes elétricas inteligentes. Para as redes de distribuição acomodarem fontes variáveis de energia renovável, novas e emergentes opções de flexibilidade, que dizem respeito à geração, carga e à própria rede, precisam de ser desenvolvidas e consideradas na operação ótima da rede de distribuição. Assim, a gestão das opções de flexibilidade deve ser cuidadosamente efetuada para minimizar os efeitos secundários como o aumento dos custos, agravamento do perfil de tensão e o desempenho geral do sistema. Desta perspetiva, é necessário entender como uma rede de distribuição pode operar de forma ótima quando se expõe a uma integração em larga escala de fontes variáveis de energia renovável. Devido à variabilidade e incerteza associadas a estas tecnologias, novas metodologias e ferramentas computacionais devem ser desenvolvidas para lidar com os desafios subsequentes. Desta forma, as opções de flexibilidade existentes e emergentes devem ser implantadas para gerir a incerteza e variabilidade das fontes de energia renovável, mantendo o necessário balanço entre carga e geração. Nesta tese é feita uma análise extensiva das principais tecnologias que podem providenciar flexibilidade aos sistemas de energia elétrica, e as suas contribuições para a operação ótima dos sistemas de distribuição, tendo em consideração a natureza estocástica dos recursos energéticos intermitentes e outras fontes de incerteza. Adicionalmente, este trabalho contém investigação detalhada sobre como o sistema pode ser otimamente gerido tendo em conta estas tecnologias de forma a que a uma maior percentagem de carga seja fornecida por fontes variáveis de energia renovável, mantendo a fiabilidade, estabilidade e eficiência do sistema. Por esse motivo, novas metodologias e ferramentas computacionais usando programação estocástica são desenvolvidas para modelizar a variabilidade e incerteza inerente à geração eólica e solar. A convergência para uma solução ótima é garantida usando programação linear inteira-mista para formular o problema

Parameter estimation of electric power transformers using Coyote Optimization Algorithm with experimental verification

In this work, the Coyote Optimization Algorithm (COA) is implemented for estimating the parameters of single and three-phase power transformers. The estimation process is employed on the basis of the manufacturer's operation reports. The COA is assessed with the aid of the deviation between the actual and the estimated parameters as the main objective function. Further, the COA is compared with well-known optimization algorithms i.e. particle swarm and Jaya optimization algorithms. Moreover, experimental verifications are carried out on 4 kVA, 380/380 V, three-phase transformer and 1 kVA, 230/230 V, single-phase transformer. The obtained results prove the effectiveness and capability of the proposed COA. According to the obtained results, COA has the ability and stability to identify the accurate optimal parameters in case of both single phase and three phase transformers; thus accurate performance of the transformers is achieved. The estimated parameters using COA lead to the highest closeness to the experimental measured parameters that realizes the best agreements between the estimated parameters and the actual parameters compared with other optimization algorithms

PhyNetLab: An IoT-Based Warehouse Testbed

Future warehouses will be made of modular embedded entities with communication ability and energy aware operation attached to the traditional materials handling and warehousing objects. This advancement is mainly to fulfill the flexibility and scalability needs of the emerging warehouses. However, it leads to a new layer of complexity during development and evaluation of such systems due to the multidisciplinarity in logistics, embedded systems, and wireless communications. Although each discipline provides theoretical approaches and simulations for these tasks, many issues are often discovered in a real deployment of the full system. In this paper we introduce PhyNetLab as a real scale warehouse testbed made of cyber physical objects (PhyNodes) developed for this type of application. The presented platform provides a possibility to check the industrial requirement of an IoT-based warehouse in addition to the typical wireless sensor networks tests. We describe the hardware and software components of the nodes in addition to the overall structure of the testbed. Finally, we will demonstrate the advantages of the testbed by evaluating the performance of the ETSI compliant radio channel access procedure for an IoT warehouse

Smart Grid Technologies in Europe: An Overview

The old electricity network infrastructure has proven to be inadequate, with respect to modern challenges such as alternative energy sources, electricity demand and energy saving policies. Moreover, Information and Communication Technologies (ICT) seem to have reached an adequate level of reliability and flexibility in order to support a new concept of electricity network—the smart grid. In this work, we will analyse the state-of-the-art of smart grids, in their technical, management, security, and optimization aspects. We will also provide a brief overview of the regulatory aspects involved in the development of a smart grid, mainly from the viewpoint of the European Unio

Soft Infrastructure in Smart Sustainable Cities: A Literature Review

Learning from the cases in Indonesia, the proliferation of advanced technologies has engendered a burgeoning interest in smart city promotion as a dominant developmental theme, and this has an association heavily with physical infrastructure development, while there are other things that need to be thought about.  The methodology entails the scholarly works, procurement of data, classification of data, and integration of resultant discoveries.  The objective of this article is to furnish a thorough and intricate comprehension of the soft infrastructure that upholds crucial infrastructure systems. Qualitative assessments scrutinize outcomes within multiple frameworks to gauge the efficacy of the supple infrastructure in promoting resilience.  As a result, the occurrence of the theme of soft infrastructure in smart sustainable cities poses a novel challenge to continuously enhance their skills and expertise. The soft infrastructure in smart sustainable cities addresses business-spatial, cultural-political, and humane-innovation issues. Such resources can effectively address integrated regional challenges and well-conceived planning for cities