New approaches to the AGC non-conforming load problem

Northern Indiana Public Service Company (NIPSCO) has many nonconforming loads that are a challenge for their current automatic generation control (AGC) software. This paper presents two techniques that address the nonconforming load problem for AGC. One technique used a neural network algorithm for pattern recognition of controllable signals, and the other technique is based on the detection of a controllable signal in the presence of a noisy random load using a random signal probability model. Both algorithms were tested with actual field load data via a dispatcher training simulator that utilized a generic system model