Data Mining via Association Rules for Power Ramps Detected by Clustering or Optimization

Power ramp estimation has wide ranging implications for wind power plants and power systems which will be the focus of this paper. Power ramps are large swings in power generation within a short time window. This is an important problem in the power system that needs to maintain the load and generation at balance at all times. Any unbalance in the power system leads to price volatility, grid security issues that can create power stability problems that leads to financial losses. In addition, power ramps decrease the lifetime of turbine and increase the operation and maintenance expenses. In this study, power ramps are detected by data mining and optimization. For detection and prediction of power ramps, data mining K means clustering approach and optimisation scoring function approach are implemented [1]. Finally association rules of data mining algorithm is employed to analyze temporal ramp occurrences between wind turbines for both clustering and optimization approaches. Each turbine impact on the other turbines are 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 will serve the power system operator for decision making.