首页|New Findings on Machine Learning Described by Investigators at University of Manitoba (Short-term Voltage Instability Prediction Using Pre-identified Voltage Templates and Machine Learning Classifiers)
New Findings on Machine Learning Described by Investigators at University of Manitoba (Short-term Voltage Instability Prediction Using Pre-identified Voltage Templates and Machine Learning Classifiers)
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Investigators discuss new findings in Machine Learning. According to news originating from Winnipeg, Canada, by NewsRx correspondents, research stated, "System operators use different procedures to detect disturbances which can lead to short-term voltage instability. These schemes are designed using the domain knowledge and generally specific for the corresponding system." Our news journalists obtained a quote from the research from the University of Manitoba, "The design procedure of such schemes is heuristic and cannot be directly applied to any other power system. This paper proposes a data-driven approach to predict short-term voltage stability using set of post-disturbance voltage templates and a machine learning-based classifier. In this scheme, a set of voltage templates corresponding to stable/unstable events is identified for selected buses using a suitably generated training data set. The proximity of real-time voltage trajectory to each of the template is computed and input to a trained machine learning classifier to predict the short-term voltage stability status. The proposed approach is validated using IEEE Nordic test system. The investigations show that this method provides accurate short-term voltage stability predictions."
WinnipegCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversity of Manitoba