D.A. Pierce

Bio: D.A. Pierce is an academic researcher from University of Washington. The author has contributed to research in topics: Computer Applications & Intelligent decision support system. The author has an hindex of 1, co-authored 1 publications receiving 24 citations.

Intelligent system applications to power systems

Abstract: The authors describe how the use of intelligent systems (IS) is an opportunity to add new dimensions to the field of computer applications in power systems. A number of practical power system problems require logic reasoning, heuristic search, perception, and/or the ability to handle uncertainties; IS tools can be part of their solution. The authors describe how AI-related application areas, such as expert systems, fuzzy logic, neural networks and genetic algorithms, can all be applied in such intelligent systems.

Artificial Intelligence and Advanced Mathematical Tools for Power Quality Applications: A Survey

Abstract: Increasing interest in power quality has evolved over the past decade. This paper surveys the literature for current applications of advanced artificial intelligence techniques in power quality (PQ). Applications of some advanced mathematical tools in general, and wavelet transform in particular, in power quality are also reviewed. An extensive collection of literature covering applications of fuzzy logic, expert systems, neural networks, and genetic algorithms in power quality is included. Literature exposing the use of wavelets in power quality analysis as well as data compression is also cited.

Industrial Agents as a Key Enabler for Realizing Industrial Cyber-Physical Systems: Multiagent Systems Entering Industry 4.0

Abstract: Industrial agents (IAs) [1] are multiagentbased systems (MASs) [2] that, for many years, have been advocated as a promising and realistic solution for an emerging set of industrial challenges. In the past, MASs fell into the scope of enterprise agility [3]-[8], and now, more than ever, pertain to the industrial digital transformation and sustainability spheres. MAS technology is being applied to several industrial applications in the cyber-physical system (CPS) context, namely, in smart production, smart electric grids, smart logistics, and smart health care [9]. To understand the future potential of IAs, one must first have a sufficiently concise view of the past and present efforts, i.e., understand their early applications and current directions. Such a view is necessary because, over the last decades, the concept of the IA has proven to be a bit of a moving target, adjusting to the needs, visions, and technologies of each era.

A Static State Estimation Approach Including Bad Data Detection and Identification in Power Systems

Abstract: This paper presents an efficient method for power system static state estimation along with a statistical technique of bad data detection and identification. In the estimation process, the exponential function is utilized to modify the variances of measurements in anticipation of maintaining the estimation performance under the bad data scenario. Besides, with the aid of the proposed gap statistic method, those bad data can be effectively detected and identified from the set of raw measurements. To validate the effectiveness of the proposed approach, this method has been tested under different simulated scenarios. Test results help confirm the feasibility of the method for the applications considered.

AI in Power Systems and Energy Markets

Abstract: This Guest Editors' Introduction identifies an opportunity for the cross-fertilization between power systems and energy markets researchers and new developments of AI. The articles selected for this special issue provide the state-of-the-art information about research being conducted using AI in power systems and energy markets.