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.

Reliability Modeling Considerations for Emerging Cyber-Physical Power Systems Prepared by Task Force on Reliability Consideration for Emerging Cyber-Physical Energy Systems under IEEE PES Reliability, Risk and Probability Applications Subcommittee

Abstract: Power system operation considering an increasingly complex cyber infrastructure may be one of the key factors of the next generation power systems. The effective operation of a power system in a massively deployed cyber network environment will be affected by cyber network reliability. Therefore, it is vital not only to understand the operation of a cyber network and its reliability, but also it is critical to integrate the interdependency of cyber and power systems into power system planning and operations. This requires a three-layer approach to reliability modeling and evaluation. The cyber and power layers are interconnected by the information layer. The objective of this paper is to define the three-layer model and report a generalized framework for combined reliability modeling.

Cyber-vulnerability of power grid monitoring and control systems

Abstract: In this paper, a methodology is proposed for the evaluation of the impact of cyber attacks on the power grid. This is a systematical approach to evaluate the vulnerabilities of SCADA system at three levels, i.e., system, scenario, and access points. The impact of potential intrusion is evaluated based on the power flow solution. The cause-effect on the proposed method determines the likelihood of the consequence, which can be evaluated based on a substation outage. An IEEE 30 bus system is used to build a test case for the proposed method.

Determining current sharing criterion for parallel operation of power converters in multi-module bus systems

Abstract: A DC model for a computer backplane power distribution system with multiple sources and loads is proposed. Criteria are derived to determine the allowable range of variation in DC output voltage for a power module operating in a multiple-source, multiple-load system with a backplane interconnect. These criteria impose constraints on output voltage as a consequence of load voltage specifications, and of current-sharing accuracy required of the sources. An algorithm for implementing the solution of these criteria is presented. Computational results for two examples of power distribution systems using this algorithm are presented. The advantage of the system model is that a complete description is provided for a generalized power bus system, which may include ground and power planes of arbitrary geometry. The advantages of the algorithm are that the constraints determined for source voltage are valid under all combinations of load variation and source voltage variation. >

A PMU-based risk assessment framework for power control systems

Abstract: Cyber threats are serious concerns for power systems. This paper proposes a risk assessment framework to enhance the resilience of power systems against cyber attacks. The Duality Element Relative Fuzzy Evaluation Method (DERFEM) is used to evaluate quantitatively security vulnerabilities within cyber systems of power systems; the Attack Graph is employed to identify intrusion scenarios that exploit multiple vulnerabilities; an System Stability Monitoring and Response System (SSMARS) is developed to monitor the impact of intrusion scenarios on power system dynamics in real time. SSMARS is a novel PMU application for the Smart Grid. It is designed be implemented in a RTO/ISO control center to assess and enhance power system security on line. SSMARS calculates the Conditional Lyapunov Exponents (CLEs) in real time based on the Phasor Measurement Unit (PMU) data. Power system stability is predicted through the values of CLEs. Control actions based on CLEs are suggested if power system instability is likely to happen. The effectiveness of SSMARS is illustrated with the IEEE 39 bus system model.

Cybersecurity of SCADA Systems: Vulnerability assessment and mitigation

Abstract: There has been a growing concern over the cyber security of Supervisory Control and Data Acquisition (SCADA) Systems due to the fast increasing connectivity of power grids with the information and communication systems. Although energy control centers are normally highly secured, the cyber-power network is large and complex. Potential intrusions can be launched from many different places in many different ways. Therefore, the complexity of the overall cyber-physical system vulnerability assessment and mitigation is very high.

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.