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.

/pdf/the-cms-experiment-at-the-cern-lhc-4d2umuzboo.pdf

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.

/pdf/smart-grid-the-new-and-improved-power-grid-a-survey-4jypbbgv3j.pdf

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

Smart Grid - The New and Improved Power Grid:

Regulated electricity utilities are required to provide safe and reliable service to their customers at a reasonable cost. To balance the objectives of reliable service and reasonable cost, utilities build and operate their systems to operate under typical historic conditions. When abnormal events such as major storms or disasters occur, it is not uncommon to have extensive interruptions in service to the end-use customers. Society is becoming less tolerant of extensive interruption in services, and it is not cost effective to harden the existing electrical distribution architecture to ensure 100% reliable power; more utilities are examining the deployment of microgrids as a part of the a coordinated resiliency plan. This paper evaluates the feasibility of microgrids as a resiliency resource under three scenarios: as a local resource, a community resource, and as a black start resource. A method of nomograms is proposed, based on dynamic simulations and evaluations of an operational microgrid, to allow operators to quickly evaluate the feasibility of these difference scenarios in operational conditions.

Evaluating the feasibility to use microgrids as a resiliency resource

This paper is a summary of our research on contract decision making in a competitive electricity market. Bidding decision in a spot market is formulated as a Markov decision process that can be used to determine the price and amount of electricity for a supplier. Pricing in a bilateral market is calculated using the no-arbitrage principle and stochastic optimization. We also propose a method to minimize the uncertainty of profit.

New methods for electric energy contract decision making

As the computational and analytical methods for voltage stability assessment become more mature, voltage control becomes a primary issue. Power systems are large, nonlinear, and dynamic. During the last three decades, the application of differential geometry in the area of nonlinear control has produced significant results for the controllability analysis. This paper is concerned with the controllability problem of power systems using the differential geometric methods. The modeled control devices include the mechanical power input to generators, VAr compensation devices, and tap settings of the on-load tap changers. Conceptually, the main result of this research is the characterization and construction of a complete controllability region, within which a power system can be steered from one state to another by use of piecewise constant controls. The results are obtained for two cases: (1) unbounded piecewise constant controls and (2) bounded piecewise constant controls. A complete controllability region identifies the limitations of the available controls of a power system. The proposed method is believed to be significant since it is a new and systematic approach to the analysis of power system controllability using a nonlinear control system model.

http://ieeexplore.ieee.org/iel5/81/16658/00768826.pdf

Complete controllability of an N-bus dynamic power system model

A knowledge-based system (KBS) which provides an integrated operational planning environment for distribution systems is presented. Four functions are incorporated: remedial control; protective device coordination; loss reduction; and service restoration. A real-time KBS development environment is used, which provides a high level user interface and acceptable processing speed. A systematic method is used to optimize the object-oriented data structure. The KBS has been tested with data from an electric utility. >

IOPADS (intelligent operational planning aid for distribution systems)

Summary

The increasing availability of high-resolution, synchronized measurements from phasor measurement units (PMUs) presents an opportunity to monitor power system dynamics in real time. However, the degree of observability of power system dynamics depends upon the power system states monitored by the PMUs. This paper presents a new algorithm to assess the observability level of rotor angle dynamics, based on PMU locations and monitored states. An observability index is proposed, which can be tracked along the trajectory of system states over time. To overcome the difficulty in calculation of high-order derivatives, an automatic differentiation technique is applied for observability calculation of large-scale systems. The proposed observability algorithms and techniques are validated on a 179-bus system. The proposed method is also helpful in identifying locations where PMUs should be installed for effective observability of nonlinear power system dynamics. This synchrophasor based observability monitoring method serves as a basis for control schemes that require knowledge of power system dynamics. Copyright © 2015 John Wiley & Sons, Ltd.

Chen-Ching Liu

Papers

Evaluating the feasibility to use microgrids as a resiliency resource

New methods for electric energy contract decision making

Complete controllability of an N-bus dynamic power system model

IOPADS (intelligent operational planning aid for distribution systems)

Observability of nonlinear power system dynamics using synchrophasor data