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 modern power infrastructure has evolved from small isolated circuits into an expansive interconnected system that is dependant on multiple supporting infrastructures. One of the most significant and potentially detrimental supporting infrastructures to interact with power systems is telecommunications. Currently, there are no established analytic techniques that enable system operators or planners to analyze the interactions that occur at the seam between these two infrastructures. This paper presents a method for analyzing and assessing events that occur at the interface of the power and telecommunications infrastructures

Assessment of interactions between power and telecommunications infrastructures

Bilateral contracts are important risk-hedging instruments constituting a major component in the portfolios held by many electric power market participants. However, bilateral contract negotiation is a complicated process because it involves risk management, strategic bargaining, and multi-market participation. This study analyzes a financial bilateral contract negotiation process between a generating company and a load-serving entity in a wholesale electric power market with congestion managed by locational marginal pricing. Nash bargaining theory is used to model a Pareto-efficient settlement point. The model predicts negotiation results under varied conditions and identifies circumstances in which the two parties might fail to reach an agreement. Both analysis and simulation are used to gain insight regarding how relative risk aversion and biased price estimates influence negotiated outcomes. These results should provide useful guidance to market participants in their bilateral contract negotiation processes.

Financial Bilateral Contract Negotiation in Wholesale Electric Power Markets Using Nash Bargaining Theory

Fast long-term dynamic simulation of power converters is discussed. Because of switching actions, the converter can operate in different configurations and, therefore, it can be represented by a state-space time-varying linear system. This paper presents a new method for calculating switching time from an accurate and explicit form of the switching condition, hence the step-by-step search of the switching instant can be greatly simplified. In the proposed algorithm, a converter is partitioned into subsystems, which are simulated in a decoupled manner and coordinated through data exchange. >

A fast-decoupled method for time-domain simulation of power converters

In this study, a new method that handles multiple hypotheses is presented for fault diagnosis using sequence of event recorders (SERs). To quantify the certainty of hypotheses, a method to calculate their credibility is provided. The proposed techniques are integrated in a generalized alarm analysis module (GAAM) and have been tested with numerous scenarios from the Italian power system.

Multiple hypotheses and their credibility in on-line fault diagnosis

Black start (BS) capability assessment is important for system planners to prepare the power system restoration (PSR) plan. To achieve a faster restoration process, installing new BS generators can be beneficial in accelerating system restoration. While additional BS capability does not automatically benefit the restoration process, power systems have to update the PSR plan and quantify the benefit based on appropriate criteria. In this paper, a decision support tool, developed in an EPRI project, using Generic Restoration Milestones (GRMs)-based strategy is utilized to provide a quantitative way for assessing the optimal installation location and amount of BS capability. Based on the proposed criteria, the benefit from additional BS capability is quantified in terms of system restoration time. The IEEE Reliability Test System (RTS) 24-bus test system is used for validation of the proposed strategy, using the System Restoration Navigator (SRN) restoration tool. It is shown that power systems can benefit from new BS generators to reduce the restoration time. However, there is a threshold beyond which system restoration time cannot be further reduced from additional BS capability. Economic considerations should be taken into account when assessing additional BS capabilities.

Chen-Ching Liu

Papers

Assessment of interactions between power and telecommunications infrastructures

Financial Bilateral Contract Negotiation in Wholesale Electric Power Markets Using Nash Bargaining Theory

A fast-decoupled method for time-domain simulation of power converters

Multiple hypotheses and their credibility in on-line fault diagnosis

Black start capability assessment in power system restoration