ExaGridPF: A Parallel Power Flow Solver for Transmission and Unbalanced Distribution Systems

This paper investigates parallelization strategies for solving power flow problems in both transmission and unbalanced, three-phase distribution systems by developing a scalable power flow solver, ExaGridPF, which is compatible with existing high-performance computing platforms. Newton-Raphson (NR) and Newton-Krylov (NK) algorithms have been implemented to verify the performance improvement over both standard IEEE test cases and synthesized grid topologies. For three-phase, unbalanced system, we adapt the current injection method (CIM) to model the power flow and utilize SuperLU to parallelize the computing load across multiple threads. The experimental results indicate that more than 5 times speedup ratio can be achieved for synthesized large-scale transmission topologies, and significant efficiency improvements are observed over existing methods for the distribution networks

Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks

Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.Comment: Accepted at Thirty-First AAAI Conference on Artificial Intelligence (AAAI17

Back-to-back Converter Control of Grid-connected Wind Turbine to Mitigate Voltage Drop Caused by Faults

Power electronic converters enable wind turbines, operating at variable speed, to generate electricity more efficiently. Among variable speed operating turbine generators, permanent magnetic synchronous generator (PMSG) has got more attentions due to low cost and maintenance requirements. In addition, the converter in a wind turbine with PMSG decouples the turbine from the power grid, which favors them for grid codes. In this paper, the performance of back-to-back (B2B) converter control of a wind turbine system with PMSG is investigated on a faulty grid. The switching strategy of the grid side converter is designed to improve voltage drop caused by the fault in the grid while the maximum available active power of wind turbine system is injected to the grid and the DC link voltage in the converter is regulated. The methodology of the converter control is elaborated in details and its performance on a sample faulty grid is assessed through simulation

Antifragility = Elasticity + Resilience + Machine Learning: Models and Algorithms for Open System Fidelity

We introduce a model of the fidelity of open systems - fidelity being interpreted here as the compliance between corresponding figures of interest in two separate but communicating domains. A special case of fidelity is given by real-timeliness and synchrony, in which the figure of interest is the physical and the system's notion of time. Our model covers two orthogonal aspects of fidelity, the first one focusing on a system's steady state and the second one capturing that system's dynamic and behavioural characteristics. We discuss how the two aspects correspond respectively to elasticity and resilience and we highlight each aspect's qualities and limitations. Finally we sketch the elements of a new model coupling both of the first model's aspects and complementing them with machine learning. Finally, a conjecture is put forward that the new model may represent a first step towards compositional criteria for antifragile systems.Comment: Preliminary version submitted to the 1st International Workshop "From Dependable to Resilient, from Resilient to Antifragile Ambients and Systems" (ANTIFRAGILE 2014), https://sites.google.com/site/resilience2antifragile

Wind Power Integration Control Technology for Sustainable, Stable and Smart Trend: A Review

The key to achieve sustainable development of wind power is integration absorptive, involving the generation, transmission, distribution, operation, scheduling plurality of electric production processes. The paper based on the analyses of the situation of wind power development and grid integration requirements for wind power, summarized wind power integration technologies' development, characteristics, applicability and trends from five aspects, grid mode, control technology, transmission technology, scheduling, and forecasting techniques. And friendly integration, intelligent control, reliable transmission, and accurate prediction would be the major trends of wind power integration, these five aspects interactive and mutually reinforcing would realize common development both grid and wind power, both economic and ecological

Privacy Management Service Contracts as a New Business Opportunity for Operators

Recognizing the importance of privacy management as a business process and a business support process, this paper proposes the use of service level agreements (SLA’s) around privacy features, including qualitative and quantitative ones. Privacy metrics are defined by both parties with boundary values on each qualitative or qualitative feature. Their distribution is relying on stress distributions used in this field. The use of service level agreements also casts privacy management into a business perspective with benefits and costs to either party in a process. This approach is especially relevant for communications operators as brokers between content owners (individuals, businesses) and enterprise applications; in this context, the privacy SLA management would be carried out by the operator, while the terms and conditions of the SLA negotiation reside with the two external parties. This work was carried out as part of the large EU project PRIME www.prime.project.eu.org. on privacy enhancing technologies.Content Owners;Enterprise Business Processes;Managed Service Contracts;Privacy Agreements;Service Level Agreements (SLA's);Telecommunications Operators

Predictive Duty Cycling of Radios and Cameras using Augmented Sensing in Wireless Camera Networks

Energy efficiency dominates practically every aspect of the design of wireless camera networks (WCNs), and duty cycling of radios and cameras is an important tool for achieving high energy efficiencies. However, duty cycling in WCNs is made complex by the camera nodes having to anticipate the arrival of the objects in their field-of-view. What adds to this complexity is the fact that radio duty cycling and camera duty cycling are tightly coupled notions in WCNs. Abstract In this dissertation, we present a predictive framework to provide camera nodes with an ability to anticipate the arrival of an object in the field-of-view of their cameras. This allows a predictive adaption of network parameters simultaneously in multiple layers. Such anticipatory approach is made possible by enabling each camera node in the network to track an object beyond its direct sensing range and to adapt network parameters in multiple layers before the arrival of the object in its sensing range. The proposed framework exploits a single spare bit in the MAC header of the 802.15.4 protocol for creating this beyond-the-sensing-rage capability for the camera nodes. In this manner, our proposed approach for notifying the nodes about the current state of the object location entails no additional communication overhead. Our experimental evaluations based on large-scale simulations as well as an Imote2-based wireless camera network demonstrate that the proposed predictive adaptation approach, while providing comparable application-level performance, significantly reduces energy consumption compared to the approaches addressing only a single layer adaptation or those with reactive adaptation