Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation

The pulse-recloser uses pulse testing technology to verify that the line is clear of faults before initiating a reclose operation, which significantly reduces stress on the system components (e.g. substation transformers) and voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are essential to increases the overall utility of the devices, especially when there are numerous devices installed throughout the distribution system. In this paper, field data recorded from several devices were analyzed to identify specific activity and fault locations. An algorithm is developed to screen the data to identify the status of each pole and to tag time windows with a possible pulse event. In the next step, selected time windows are further analyzed and classified using a sparse representation technique by solving an l1-regularized least-square problem. This classification is obtained by comparing the pulse signature with the reference dictionary to find a set that most closely matches the pulse features. This work also sheds additional light on the possibility of fault classification based on the pulse signature. Field data collected from a distribution system are used to verify the effectiveness and reliability of the proposed method.Comment: Accepted in: 19th International Conference on Intelligent System Application to Power Systems (ISAP), San Antonio, TX, 201

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

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise

Decentralized and Fault-Tolerant Control of Power Systems with High Levels of Renewables

Inter-area oscillations have been identified as a major problem faced by most power systems and stability of these oscillations are of vital concern due to the potential for equipment damage and resulting restrictions on available transmission capacity. In recent years, wide-area measurement systems (WAMSs) have been deployed that allow inter-area modes to be observed and identified.Power grids consist of interconnections of many subsystems which may interact with their neighbors and include several sensors and actuator arrays. Modern grids are spatially distributed and centralized strategies are computationally expensive and might be impractical in terms of hardware limitations such as communication speed. Hence, decentralized control strategies are more desirable.Recently, the use of HVDC links, FACTS devices and renewable sources for damping of inter-area oscillations have been discussed in the literature. However, very few such systems have been deployed in practice partly due to the high level of robustness and reliability requirements for any closed loop power system controls. For instance, weather dependent sources such as distributed winds have the ability to provide services only within a narrow range and might not always be available due to weather, maintenance or communication failures.Given this background, the motivation of this work is to ensure power grid resiliency and improve overall grid reliability. The first consideration is the design of optimal decentralized controllers where decisions are based on a subset of total information. The second consideration is to design controllers that incorporate actuator limitations to guarantee the stability and performance of the system. The third consideration is to build robust controllers to ensure resiliency to different actuator failures and availabilities. The fourth consideration is to design distributed, fault-tolerant and cooperative controllers to address above issues at the same time. Finally, stability problem of these controllers with intermittent information transmission is investigated.To validate the feasibility and demonstrate the design principles, a set of comprehensive case studies are conducted based on different power system models including 39-bus New England system and modified Western Electricity Coordinating Council (WECC) system with different operating points, renewable penetration and failures

Learning Multimodal Structures in Computer Vision

A phenomenon or event can be received from various kinds of detectors or under different conditions. Each such acquisition framework is a modality of the phenomenon. Due to the relation between the modalities of multimodal phenomena, a single modality cannot fully describe the event of interest. Since several modalities report on the same event introduces new challenges comparing to the case of exploiting each modality separately. We are interested in designing new algorithmic tools to apply sensor fusion techniques in the particular signal representation of sparse coding which is a favorite methodology in signal processing, machine learning and statistics to represent data. This coding scheme is based on a machine learning technique and has been demonstrated to be capable of representing many modalities like natural images. We will consider situations where we are not only interested in support of the model to be sparse, but also to reflect a-priorily known knowledge about the application in hand. Our goal is to extract a discriminative representation of the multimodal data that leads to easily finding its essential characteristics in the subsequent analysis step, e.g., regression and classification. To be more precise, sparse coding is about representing signals as linear combinations of a small number of bases from a dictionary. The idea is to learn a dictionary that encodes intrinsic properties of the multimodal data in a decomposition coefficient vector that is favorable towards the maximal discriminatory power. We carefully design a multimodal representation framework to learn discriminative feature representations by fully exploiting, the modality-shared which is the information shared by various modalities, and modality-specific which is the information content of each modality individually. Plus, it automatically learns the weights for various feature components in a data-driven scheme. In other words, the physical interpretation of our learning framework is to fully exploit the correlated characteristics of the available modalities, while at the same time leverage the modality-specific character of each modality and change their corresponding weights for different parts of the feature in recognition

Approach to monitoring and diagnostics for medium voltage circuit breakers

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 85-88).The Medium Voltage Electric Industry is a very conservative and risk adverse sector that has undergone very little change in the past 30 years when compared to other technologically dependent activities; this reality is rapidly shifting. The advent of cost-effective and reliable telecommunications, coupled with the drastic price decrease of wireless communication and sensing technologies, are steering the industry towards an information based era that is generically known as smart-grid. With an emphasis on medium voltage circuit breakers, the purpose of this thesis was to identify sensor technology and analytics that will allow electric utilities in North America-primarily the United States-to assess the health of their equipment and utilize this information for maintenance operational decisions. The main areas of research included in this work were the market context for medium voltage circuit breaker Monitoring & Diagnostics solutions, the financial justification for such applications, and the technical merit of multiple sensor technologies and associated analytics. The findings of this research helped support the development of an advanced Monitoring and Diagnostics kit currently deployed at a customer site as part of a pilot demonstration program. The prototype system provides real-time monitoring and trending information for six reactor-switching 15 kV circuit breakers. The completion of this thesis, and successful development of the advanced Monitoring and Diagnostics kit, was the result of the collaborative effort of a small interdisciplinary team assembled to identify smart-grid opportunities in the medium voltage space. This work took place at ABB's Medium Voltage Headquarters in the United States.by Erick Corona.S.M.M.B.A

Reliability and resilience evaluation of distribution automation.

Modern distribution grid utilities are steadily adapting to the concepts of Smart Grids by augmenting distribution grids with Distribution Automation (DA) to enhance visibility and control for the purpose of enhanced system availability. Existing methods to place and evaluate DA overlook the important enhancement it provides to the resilience of a system. Resilience, a much-discussed but poorly defined measure for power systems, represents a system’s ability to withstand and recover from High-Impact Low-Probability (HILP) events such as storms and earthquakes. This thesis argues that exisiting resilience quantification methods do not capture the direct contribution which DA can make to enhance system resilience. It develops a novel model and methodology to analyse distribution grid resilience using the formalisms of Reliability Graphs (RGs), and Stochastic Reward Nets (SRNs). These two models capture the different parts of the complex recovery process which distribution grids perform to recover from faults using DA. There are three novel contributions in this thesis. Firstly, a three-tier hierarchical model which contains an RG is developed to assess the enhancement which DA equipment provides to load point and feeder availability and resilience. Next, and SRN is used to develop a load point (LP) model which incorporates the dependence of feeder assets during the fault isolation phase of the recovery process. Finally, the SRN model is augmented with a phased recovery model to represent the complex recovery process for distribution grids. Utilising these models, the placement of switch automation and fault indicators is evaluated, and the contribution they make to resilience demonstrated. Collectively, these models give a novel means of assessing the the availability, sensitivity and resilience of distribution grids which utilise DA

Smart metering infrastructure for distribution network operation

With the increasing demand for energy throughout the world and the associated environmental problems, the development of a highly efficient and environmentally friendly Smart Grid has become an important objective worldwide. In Great Britain, the Smart Grid has been primarily focused on the distribution networks and smart metering is widely considered as a critical step towards the Smart Grid future. Conventionally, the communications infrastructure at the distribution level is very limited in terms of functionality and availability. There was very limited work to evaluate the impact of the communications performance of smart metering infrastructure on distribution network operation. This research investigated the impact of smart metering applications on communications requirements and the impact of the communications performance of smart metering infrastructure on distribution network operation. A smart metering communications infrastructure was modelled and simulated using OPNET. The impact of smart metering applications on smart metering communications requirements has been investigated. It is shown that individual communications requirements for smart meters are not particularly communications intensive and that infrequent large transactions posed the most significant challenges on the communications infrastructure. As the link speed decreased, large time delays were observed which have direct impact on the functions related to distribution network operations. An evaluation method was then developed to quantify the impact of smart metering communications infrastructure on distribution network operation. The main characteristics of the smart metering communications infrastructure were modelled. The characteristics of load variation were analysed and used to quantify the relationship between the time delay and the measurement error of the power system. The measured data from smart meters was refined to be used by the distribution network operational functions using state estimation and the impact was quantified using optimal power flow. Results show that fast data access is necessary for smart meter data to be used by the voltage control and the power control functions of a distribution network. The potential of using smart metering for outage management was investigated. A topology analysis method was developed which maps the physical plant model of a distribution network to a simplified analytical model. An outage area identification algorithm was developed which uses the information from smart meters and is based on the simplified network model. The outage area identification can act as one of the main functions of an outage management system providing possible outage extent information. The impact of smart meter communications on the outage area identification algorithm was investigated based on the OPNET communications model. Test results showed that smart metering has a potential to support outage management of a power distribution network. Test results showed that the arrival criterion and the smart metering communications infrastructure have a large impact on the performance of the outage area identification