Chen-Ching Liu

Bio: Chen-Ching Liu is an academic researcher from Virginia Tech. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 57, co-authored 269 publications receiving 12126 citations. Previous affiliations of Chen-Ching Liu include Washington State University & Purdue University.

Learning to recognize vulnerable patterns due to undesirable Zone-3 relay operations

Abstract: Undesirable zone 3 relay operations caused by unexpected loading conditions can contribute to the cascaded events, leading to catastrophic outages. Identifying the basic patterns of zone 3 relay operations in advance is an effective way to help prevent cascaded events. The postcontingency impedances seen by zone 3 relays can be calculated on line in a steady state security assessment framework. However, their accuracy is inadequate compared with the postcontingency apparent impedance obtained from off-line time domain dynamic simulations. This paper proposes a fuzzy inference system (FIS) to correct discrepancies between the postcontingency apparent impedances obtained from the results of steady state security assessment and the corresponding values obtained by time-domain simulations. The postcontingency apparent impedances obtained from the results of steady state security assessment can be corrected on line using correction terms provided by the FIS. The dynamic model of a 200-bus system is used to validate the performance of the proposed FIS. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Bounding the computation time of forward-chaining rule-based systems

Abstract: For real-time applications of expert systems, success depends on the computational efficiency of the implementation. In this study, we propose an analytical method for evaluating the processing time of forward-chaining rule-based systems. An upper bound based on this system model is developed. If the upper bound stays within the time available for planning the operational or control task, the expert system would be able to complete the rule-processing in time. To compute the upper bound, the worst case working memory element sets are obtained for each functional step of the matching procedure. The worst case time for rule selection in the conflict resolution step is also derived. The maximal number of firings for each rule is considered in order to arrive at a bound for total processing time. Numerical examples are presented which point out the importance of rule and data structures in the efficient implementation of rule-based systems.

Parameter estimation algorithm for performance fine tuning of switching DC power supplies

Abstract: A parameter estimation algorithm to aid power converter designers in fine-tuning the performance of switching DC power supplies is described. This algorithm identifies the optimal parameter values which satisfy the design specifications. The estimation algorithm is incorporated with a fast time-domain circuit simulator to form a user-friendly design environment. Numerical results are provided. The estimation algorithm is general and can be applied to other types of converter design. >

A Bilateral Transactive Energy Framework for Electric Power Distribution Systems

Abstract: The power grid is being transformed from a system with centralized fossil-fuel-based generation and passive customers into the one with a large-scale deployment of distributed energy resources (DERs) and proactive customers. At the distribution level, centralized and decentralized market constructs have been proposed; however, these methods are not well suited for the coordination of a large number of DERs and responsive loads. In this paper, a bilateral Transactive Energy coordination framework is developed to manage the grid participants’ trading activities. The proposed method consists of long-term bilateral transactions, an optimal social welfare based real-time adjustment stage, and a settlement phase. The IEEE 123 node distribution feeder is used to validate the proposed method. The results indicate the effectiveness of the proposed mechanism and the approach to achieve optimal bilateral transactions.

Topology error identification for the NEPTUNE power system using an artificial neural network

Abstract: The goal of the North Eastern Pacific Time-Series Undersea Networked Experiment (NEPTUNE) is to construct a cabled observatory on the floor of the Pacific Ocean, encompassing the Juan de Fuca Tectonic Plate. The power system associated with the proposed observatory is unlike conventional terrestrial power systems in many ways due to the unique operating conditions of cabled observatories. The unique operating conditions of the system require hardware and software applications that are not found in terrestrial power systems. This paper builds upon earlier work and describes a method for topology error identification in the NEPTUNE system that utilizes an artificial neural network (ANN) to determine single contingency topology errors.

Machine learning

Abstract: 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.).

The CMS experiment at the CERN LHC

Abstract: 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.

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

Abstract: 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.