Optimized Operation Rules for Imbalanced Classes

Jochen L. Cremer, Ioannis Konstantelos, Goran Strbac

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review


Supervised machine learning methods were applied to assess the reliability of the power system. Typically, the reliability boundary that defines the operation rules is learned using a training database consisting of a large number of potential operation states. Many of these operation states are historical observations and these are typically all reliable operation states. However, to learn a classifier that can predict unseen operation states requires unreliable operation states as well. Thus, a statistical model is typically fitted to the historical observations, and then, unreliable operation states are sampled from this model. Still, the share of reliable states may be much larger than the portion of unreliable states. This imbalance in the data results in biasing the learning methods toward predicting reliable states with higher accuracy than unreliable states. However, an unreliable operating state involves (per-definition) a risk of failing system operation. Therefore, a higher accuracy is required in predicting unreliable states rather than in reliable states. This paper focuses on accounting for this bias when learning from imbalanced data. To optimally learn operation rules for an imbalanced training database a novel Optimal Classification Tree (OCT) is applied. We modify the OCT approach to address the corresponding bias that is introduced in an imbalanced training database. Our fully Controllable and Optimal Classification Tree (COCT) approach controls directly in the objective function the class weights of each operation state that is used for training. By using a database from the French transmission grid it is showcased how the proposed COCT method results in fewer missed alarms than the standard approach that is used to learn operation rules.

Original languageEnglish
Title of host publication2019 IEEE Power and Energy Society General Meeting, PESGM 2019
ISBN (Electronic)9781728119816
Publication statusPublished - 2019
Externally publishedYes
Event2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States
Duration: 4 Aug 20198 Aug 2019

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933


Conference2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Abbreviated titlePESGM 2019
Country/TerritoryUnited States
Internet address


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