Association Rules for Clustering Algorithms for Data Mining of Temporal Power Ramp Balance

Abstract

Power ramp estimation is utmost importance for wind power plants which will be the focus of this paper. Power ramps are caused by intermittent supply of wind power generation. This is an important problem in the power system that needs to keep the load and generation at balance at all times while any unbalance leads to price volatility, grid security issues that can create power stability problems that leads to financial losses. In this study, K-means clustering and association rules of apriori algorithm are implemented to analyze and predict wind power ramp occurrences based on 10 minutes temporal SCADA data of power from records of Ayyildiz wind farm. Power ramps are computed from this data. Five wind turbines with no dissimilarity measure in space were clustered based on temporal data. The power ramp data are analyzed by the K-means algorithm for calculation of their cluster means and cluster labels. Association rules of data mining algorithm were employed to analyze temporal ramp occurrences between wind turbines. Each turbine impact on the other turbines were analyzed as different transactions at each time step. Operational rules based on these transactions are discovered by an apriori association rule algorithm for operation room decision making. Discovery of association rules from an apriori algorithm can help with decision making for power system operator.