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.

/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:

Underfrequency load shedding (UFLS) is critical for mitigation of large disturbances on a power grid. There are centralized and distributed UFLS methods. In this paper, the proposed distributed methodology uses a multi-agent system (MAS). The framework and classification of agents are presented. Steps for monitoring, estimation, and distributed computation are developed, implemented, and tested in a testbed environment. A comparison has been performed to show that the performance and capability of load shedding can be enhanced by the multi-agent scheme.

Distributed underfrequency load shedding using a multi-agent system

Automatic switching is the protective scheme installed on subtransmission lines to maintain service to distribution substations along each line after faults. At present, control engineers design the automatic switching for the new/modified subtransmission lines using a manual, time-consuming process. Not only is this process laborious, but many solutions are possible. The designer needs to select the one which yields maximum service restoration subject to constraints based on priority of loads and probable fault locations. This paper presents a new knowledge-based approach to design of automatic switching. This methodology uses model-based reasoning to incorporate information of the substation loads, segment lengths, fault probabilities, and protective devices as well as the relative priorities of the substations. This model is used in conjunction with design constraints, analytical criteria, and heuristic rules to guide the designer through the process of identifying the placement of automatic switches, the functions to be used, and the time setting(s) for each. Further, this approach can explain the detailed reasoning process and provide evaluation information in a causal/temporal network format. >

An integrated model- and rule-based approach to design of automatic switching for subtransmission lines

Hierarchical state estimation (HSE) is often deployed to evaluate the states of an interconnected power system from telemetered measurements. By HSE, each low-level control center (LCC) takes charge of the estimation of its internal states, whereas a trusted high-level control center (HCC) assumes the coordination of boundary states. However, a trusted HCC may not always exist in practice; a cloud server can take the role of an HCC in case no such facility is available. Since it is prohibited to release sensitive power grid data to untrustworthy cloud environments, considerations need to be given to avoid breaches of LCCs’ privacy when outsourcing the coordination tasks to the cloud server. To this end, this article proposes a privacy-preserving HSE framework, which rearranges the regular HSE procedure to integrate a degree-2 variant of the Thresholded Paillier Cryptosystem (D2TPC). Attributed to D2TPC, computations by the cloud-based HCC can be conducted entirely in the ciphertext space. Even if the HCC and some LCCs conspire together to share the information they have, the privacy of non-conspiring LCCs is still assured. Experiments on various scales of test systems demonstrate a high level of accuracy, efficiency, and scalability of the proposed framework.

Privacy-Preserving Hierarchical State Estimation in Untrustworthy Cloud Environments

The recent movement towards an open, competitive market environment introduced new optimization problems such as market clearing mechanism, bidding decision and Available Transfer Capability (ATC) calculation. These optimization problems are characterized by the complexity of power systems and the uncertainties in the electricity market. Accurate evaluation of the transfer capability of a transmission system is required to maximize the utilization of the existing transmission systems in a competitive market environment. The transfer capability of the transmission networks can be limited by various system constraints such as thermal, voltage and stability limits. The ability to incorporate such limits into the optimization problem is a challenge in the ATC calculation from an engineering point of view. In the competitive market environment, a power supplier needs to find an optimal strategy that maximizes its own profits under various uncertainties such as electricity prices and load. On the other hand, an efficient market clearing mechanism is needed to increase the social welfare, i.e. the sum of the consumers’ and producers’ surplus. The need to maximize the social welfare subject to system operational constraints is also a major challenge from a societal point of view. This paper presents new optimization techniques motivated by the competitive electricity market environment. Numerical simulation results are presented to demonstrate the performance of the proposed optimization techniques.

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Optimization techniques for Available Transfer Capability (ATC) and market calculations

This is a summary of the presentation in the panel session, ldquoAgent-Based Test Beds for Restructured Electricity Marketsrdquo for the 2008 IEEE PES General Meeting. The multi-agent system concepts have been applied to power engineering in the areas of protection, defense system, diagnosis, control, distribution, etc. For electricity markets, multi-agent models have been used for analysis of gaming, learning, and decision support. In this presentation, the panelist will provide a review of the state of the art. For the future, it is believed that a platform for the evaluation of multi-agent systems is critically needed in order to gain further insights into the performance of suppliers, load serving entities, market operators and market rules. Simulation examples of the agent learning behavior using the Java Agent DEvelopment Framework (JADE) platform will be provided.

Chen-Ching Liu

Papers

Distributed underfrequency load shedding using a multi-agent system

An integrated model- and rule-based approach to design of automatic switching for subtransmission lines

Privacy-Preserving Hierarchical State Estimation in Untrustworthy Cloud Environments

Optimization techniques for Available Transfer Capability (ATC) and market calculations

Multi-agent systems and electricity markets: State-of-the-art and the future