CAREER: Open-Loop Discrete-Event Control in Electric Power Systems

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9983653RovnyakThe goal of this research is to develop and validate patternrecognition methods such as Decision Trees (DTs) and Neural Networks (NNs)for electric power system wide-area open-loop discrete-event control. Theproposed controls will provide Independent Service Operators (ISOs) andpresent-day utilities with tools necessary to achieve the cost reductiongoals of deregulation without sacrificing reliability. The proposedresearch will investigate the selection of contingencies to simulate forthe training data, algorithms for converting simulation data into input-output pairs that make up the training sets, and measures of performanceused to evaluate the classifier operation. The PI and graduate studentswill collaborate with researchers at Cornell University and the BonnevillePower Administration and will test the proposed methods against otherschemes reported in the literature for out-of-step relaying, generatortripping, active load modulation, HVDC fast power changes, and reactivepower switching. Students in EE 588, Advanced Topics in Power Systems,will train and test NNs on data from the research. Undergraduate studentsin EE 479, Automatic Control Systems Laboratory, will design feedbackcontrols for the simple power system model that is analyzed in thisproposal. The PI will engage all students in active learning throughfrequent and extensive exercises that lead students through the discoveryprocess of problem solving instead of just rewarding the memorization oftechniques. The PI will distribute detailed, annotated solutions to allhomework problems on the due date. These teaching methods have been foundby the PI to be successful with a broad spectrum of students, includingwomen, minorities and people with disabilities.***