D.A. Pierce

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.

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.

Abstract: GAMMEU1 constitutes an integrated approach that covers the different elements related to the asset management of power transformers in the environment of a utility. GAMMEU harmonizes and interrelates all the relevant subsystems of the asset management that normally are studied as individual entities and not as a system. Concretely, GAMMEU consists of a platform for data integration, an intelligent system for detection and diagnosis of failures, a failure rate estimation model, a module of reliability analysis and an optimisation model for maintenance scheduling. In this work, a brief description of the elements of GAMMEU is presented and the implementation of the intelligent system for detection and diagnosis as well as the failure rate estimation model is exemplified using data of measurements performed in real power transformers. A robust anomaly detection module using prediction models based on artificial intelligence techniques was developed for top oil temperature monitoring and the use of decision trees as classifiers for the assessment of FRA2 measurements is also illustrated. For failure rate estimation, the use of a model based on hidden Markov chains is presented using data of dissolved gas analysis tests. The experience obtained from the implementation of part of the modules of GAMMEU using real data has demonstrated its feasibility.

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.

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.

Abstract: Load shedding is a crucial issue in power systems especially under restructured electricity environment. Market-driven load shedding in reregulated power systems associated with security as well as reliability is investigated in this paper. A technoeconomic multi-objective function is introduced to reveal an optimal load shedding scheme considering maximum social welfare. The proposed optimization problem includes maximum GENCO s and loads' profits as well as maximum loadability limit under normal and contingency conditions. Particle swarm optimization (PSO) as a heuristic optimization technique, is utilized to find an optimal load shedding scheme. In a market-driven structure, generators offer their bidding blocks while the dispatchable loads will bid their price-responsive demands. An independent system operator (ISO) derives a market clearing price (MCP) while rescheduling the amount of generating power in both pre-contingency and post-contingency conditions. The proposed methodology is developed on a 3-bus system and then is applied to a modified IEEE 30-bus test system. The obtained results show the effectiveness of the proposed methodology in implementing the optimal load shedding satisfying social welfare by maintaining voltage stability margin (VSM) through technoeconomic analyses.