A Microgrid Design Case Study : Synchrophasor Placement and Development of a Protection Laboratory for the Oregon State University - Corvallis Campus

In the past two years, Oregon State University (OSU) Corvallis campus suffered several power outages. The estimated cost of one of those outages was close to a million dollars worth of equipment and impact on research projects. Meanwhile, the campus grid operates with severely aging power assets. The reliability of campus grid operations impact students, faculty and staff and is becoming a general concern. The university, as many other institutions that have critical loads within particularly weak power system areas, is seeking for a sustainable solution in order to cope with these reliability issues. Moreover, due to the fast speed of power system upgrades, integration of renewable energy resources, increasing demands for grid reliability, and aging and retiring infrastructure, there is a growing set of operational and hands-on requirements for the power system students who graduate from the college. Based on the feedback from job interviews and the local industry, utilities are more willing to hire students that familiar with both hardware and software operation of modern power system. Motivated by the challenges described above, we propose two complementary research efforts: a microgrid synchrophasor placement approach, and the design of a power system protection laboratory. OSU campus grid is supported by the main grid with several feeders. There is an advanced cogeneration facility, and two large photovoltaic arrays inside the campus and owned by OSU. Also, many buildings are equipped with smart meters and energy management systems. Therefore, the campus grid has the potential to operate itself as a microgrid. Before applying microgrids technology to the OSU campus grid, the topic of optimization of synchrophasors placement will be an important initial step. In addition, for a microprocessor-based relay, which is widely used in a typical microgrid, the principle and function are the main contents for the new power system curriculum. It is also including the coordination between multiple relays in both a radial system and a looped system by using a software that widely used in industry area. By using the relay testing system, an advanced microprocessor-based relay, which is commonly used in industry, will be tested as well

A Survey of FPGA Optimization Methods for Data Center Energy Efficiency

This article provides a survey of academic literature about field programmable gate array (FPGA) and their utilization for energy efficiency acceleration in data centers. The goal is to critically present the existing FPGA energy optimization techniques and discuss how they can be applied to such systems. To do so, the article explores current energy trends and their projection to the future with particular attention to the requirements set out by the European Code of Conduct for Data Center Energy Efficiency. The article then proposes a complete analysis of over ten years of research in energy optimization techniques, classifying them by purpose, method of application, and impacts on the sources of consumption. Finally, we conclude with the challenges and possible innovations we expect for this sector.Comment: Accepted for publication in IEEE Transactions on Sustainable Computin

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

Decoupled voltage sensitivity analysis for cluster-oriented smart grid operations

The power systems of today use “smart grids” to improve grid operations and energy efficiency. State-of-the-art technologies and advanced control mechanisms have meant the renewable energy sources (RESs) can now be increasingly integrated into power grids. The grid integration of the RESs makes power generation more sustainable but concurrently causes bidirectional energy flow. This can lead to imbalances between phases. Instability during grid operations is consequently concerned, such as overcurrent in power lines and over/under bus voltages. In the power systems, distribution grids are especially affected, since they were not originally designed to handle power generation. The traditional grid operation, which is a centralised architecture, is therefore impractical for smart grids. Accordingly, an active distribution network is required. In this thesis, an impedance network model and a method for decoupled voltage sensitivity analysis are proposed. Their key contribution to the academic community in the field of smart grids is to enable distributed steady-state analysis based on a clustering power systems approach (CPSA), resulting in decentralised active operations in distributed areas of the smart grids. The voltage sensitivity analysis proposed in this thesis examines the response of voltage magnitude and angle in relation to bus current in sequence systems, active power, and reactive power. The results from the analysis therefore indicate that there are impacts between buses in term of the voltage magnitudes, which can be further used for power management and voltage regulation. The proposed analysis method is derived from a mathematical description of complex bus voltage, based on the proposed impedance model. It requires only measurement data gathered from the phasor measurement unit, without the information from grid topology. The required measurement data consist of bus voltages, bus currents, and the line currents of the connecting line between the distributed areas. As the foundation of the proposed method, first, the impedance model for each distributed area is determined from the measurement data. Only bus impedances between buses of concern are produced in this step. The impedance model is further used together with the measured voltage of the concerned bus in the sensitivity analysis. The proposed analysis method is devised to deal with both balanced and unbalanced grid conditions. The accuracy of the proposed analysis method was verified by simulations in three case studies. The results from the first two case studies demonstrated the accurate voltage sensitivity analysis in all selected grid cases under the balanced and unbalanced grid conditions, including the case of the measurement errors up to the maximum of 1% total vector error. Use of the outcome from voltage sensitivity analysis for regulating voltage profile was then examined in the third case study. Once verification was achieved, the proposed analysis method enabled decoupled voltage sensitivity analysis by using only the measurement data. This makes the proposed method suitable for further use in smart grids. Further research is recommended, which should give consideration to possible additional measurement errors, dynamic characteristics of the power grid, and the implementation of the proposed method

Survivability modeling for cyber-physical systems subject to data corruption

Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii