Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

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The CMS experiment at the CERN LHC

An Introduction to MultiAgent Systems.

The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

The objective of the North-East Pacific Time-Series Undersea Networked Experiment (NEPTUNE) program is to construct an underwater cabled observatory on the floor of the Pacific Ocean. While the goal is to provide as much power as possible to users of the system, the system conditions need to be monitored on line in order to determine whether or not any corrective action is needed. Due to the unique nature of the underwater observatory system and load characteristics, an analytical tool is needed to calculate the optimal operating condition that serves the maximum number of loads while satisfying all the constraints. In this paper, an optimization based load management algorithm is presented. The method takes into account all system constraints as well as priorities of the loads.

Optimization Based Load Management for the NEPTUNE Power System

When a major outage occurs on a distribution system due to extreme events, microgrids, distributed generators, and other local resources can be used to restore critical loads and enhance resiliency. This paper proposes a decision-making method to determine the optimal restoration strategy coordinating multiple sources to serve critical loads after blackouts. The critical load restoration problem is solved by a two-stage method with the first stage deciding the post-restoration topology and the second stage determining the set of loads to be restored and the outputs of sources. In the second stage, the problem is formulated as a mixed-integer semidefinite program. The objective is maximizing the number of loads restored, weighted by their priority. The unbalanced three-phase power flow constraint and operational constraints are considered. An iterative algorithm is proposed to deal with integer variables and can attain the global optimum of the critical load restoration problem by solving a few semidefinite programs under two conditions. The effectiveness of the proposed method is validated by numerical simulation with the modified IEEE 13-node test feeder and the modified IEEE 123-node test feeder under plenty of scenarios. The results indicate that the optimal restoration strategy can be determined efficiently in most scenarios.

Coordinating Multiple Sources for Service Restoration to Enhance Resilience of Distribution Systems

In this study, rules and verification techniques are proposed to handle scenarios in which substations do not have Sequence of Events Recorders (SERs). The proposed techniques enable a Generalized Alarm Analysis Module (GAAM) with the software capability to cope with incomplete informa-tion. This paper also describes an extension of the `basic context representation' technique to incorporate the scenarios resulting from incomplete or missing information. Using the modified basic context techniques, GAAM was tested for a wide range of scenarios covering data problems and I or mal-functioning devices. This paper presents the test results using actual scenarios that occurred in the Italian power system. Keywords: Fault Diagnosis, Verification, Validation, Logic Based System, Missing SER, Missing Message

Logic and validation techniques for handling of missing information in fault diagnosis

Power system contingency screening is aimed at detecting potential threats, such as tripped transmission lines, to support secure operation of power systems. The growing penetration of intermittent-renewable resources into the power system challenges the traditional paradigm of contingency screening. One particular concern is that different renewable-generation profiles may lead to changing severe contingencies. This paper presents a method for power system severe contingency screening considering renewable energy. By running the hourly economic dispatch (ED), network congestion patterns under different renewable-generation profiles are determined. Next, spectral clustering is used to determine the most severe or highly congested multiple-line transmission cutset. Additionally, boundaries in the renewable-generation profile space leading to different severe transmission cutsets are investigated. Moreover, cutset-based contingency management through redispatch of conventional generators is implemented to reduce the congestion of transmission lines within the cutset. Simulation results based on the IEEE standard 14-bus system show that the proposed method is promising.

Chen-Ching Liu

Papers

Optimization Based Load Management for the NEPTUNE Power System

Coordinating Multiple Sources for Service Restoration to Enhance Resilience of Distribution Systems

Logic and validation techniques for handling of missing information in fault diagnosis

Power system severe contingency screening considering renewable energy

Closure to Discussion on “Optimal Generator Start-Up Strategy for Bulk Power System Restoration”