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:

A PMU-based method using Maximal Lyapunov Exponent (MLE) to determine the stability of a system following a disturbance has been established in previous research. This paper proposes a new method to determine the proper time window of MLE in an on-line environment. Spectral analysis is applied to the oscillation waveforms to calculate the variable window size of MLE. Two MLEs are calculated using the same window size but at different initial times. The consistency of the two MLEs indicates that sufficient information has been included in the first time window to characterize the system dynamics. Otherwise, the window size needs to be adapted for the operating condition. This method increases the accuracy of prediction given by MLE. A case study using a 200-bus system is presented to validate the feasibility of the proposed method.

Lyapunov exponents over variable window sizes for prediction of rotor angle stability

............................................................................................................................................ iii Acknowledgments ............................................................................................................................... v Acronyms and Abbreviations ...........................................................................................................vii 1.0 Introduction ................................................................................................................................ 1 1.1 Traditional Mitigation ........................................................................................................ 1 1.2 Distribution Resiliency ....................................................................................................... 1 2.0 Microgrids as a Resiliency Resource .......................................................................................... 3 2.1 Previous Work .................................................................................................................... 3 2.2 Asset Feasibility ................................................................................................................. 3 2.2.1 Dynamic considerations .......................................................................................... 4 2.2.2 Distribution line and transformer in-rush ................................................................ 7 2.2.3 Resilience-Oriented Service Restoration and Reconfiguration ............................. 11 2.2.4 Nomograms ........................................................................................................... 19 2.3 Consolidated approach ..................................................................................................... 22 3.0 Distribution-level State Estimation and Parameter Estimation ................................................ 25 3.1 Background and Introduction ........................................................................................... 25 3.2 Data and Measurements in Distribution Systems ............................................................. 28 3.3 Distribution System State Estimation............................................................................... 29 3.3.1 Weighted Least Squares Formulation for Distribution System State Estimation . 29 3.3.2 Simulation Studies ................................................................................................. 34 3.3.3 Case 6: IEEE 8500 Node Test System .................................................................. 38 3.4 Distribution System Parameter Estimation ...................................................................... 39 3.4.1 Methods ................................................................................................................. 40 3.4.2 Simulation Studies ................................................................................................. 42 3.5 Conclusions and Future Work .......................................................................................... 47 4.0 Conclusions .............................................................................................................................. 48

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Improving Distribution Resiliency with Microgrids and State and Parameter Estimation

The day-ahead electricity market is modeled as a multi-agent system with interacting agents including supplier agents, load-serving entities, and a market operator. Simulation of the market clearing results under the scenario in which agents have learning capabilities is compared with the scenario where agents report true marginal costs. It is shown that, with Q-learning, electricity suppliers are making more profits compared to the scenario without learning due to strategic gaming. As a result, the LMP at each bus is substantially higher. Related work can be accessed at: http://www2.econ.iastate.edu/tesfatsi/AMESMarketHome.htm Annotated pointers to related work can be accessed here: http://www2.econ.iastate.edu/tesfatsi/aelect.htm

Modeling of Suppliers Learning Behaviors in an Electricity Market Environment

To meet the needs of the increasing number and importance of energy interchanges in new competitive power system environments, a method for multi-objective optimization is proposed which uses a goal decision network (GDN) to model the knowledge used to analyze interchange contracts A new search algorithm, guided by negotiation operators and the knowledge in the GDN, is developed to identify optimal solutions and the tradeoffs between problem objectives The algorithm is used to optimize an energy-purchase contract with respect to the objectives of reliability, cost savings, and the survival rate of fish migrating through a hydro-electric system

Interchange contract decision making in electric power systems

A distributed underfrequency load shedding (UFLS) scheme is proposed as a last resort to mitigate the frequency decline. In the agent-based scheme, Paxos and average-consensus protocols are utilized by agents to reach an agreement. The monitoring, estimation, and distribution steps are presented in detail for implementation of the multi-agent system (MAS). Simulation results are provided to validate the performance of the proposed agent-based UFLS scheme, which is being implemented as a demonstrative application of the RIAPS (Resilient Information Architecture Platform for the Smart grid).

Chen-Ching Liu

Papers

Lyapunov exponents over variable window sizes for prediction of rotor angle stability

Improving Distribution Resiliency with Microgrids and State and Parameter Estimation

Modeling of Suppliers Learning Behaviors in an Electricity Market Environment

Interchange contract decision making in electric power systems

Agent-based distributed underfrequency load shedding