Neuroevolution of a Hybrid Power Plant Simulator

Ever increasing energy demands are driving the development of high-efficiency power generation technologies such as direct-fired fuel cell turbine hybrid systems. Due to lack of an accurate system model, high nonlinearities and high coupling between system parameters, traditional control strategies are often inadequate. To resolve this problem, learning based controllers trained using neuroevolution are currently being developed. In order for the neuroevolution of these controllers to be computationally tractable, a computationally efficient simulator of the plant is required. Despite the availability of real-time sensor data from a physical plant, supervised learning techniques such as backpropagation are deficient as minute errors at each step tend to propagate over time. In this paper, we implement a neuroevolutionary method in conjunction with backpropagation to ameliorate this problem. Furthermore, a novelty search method is implemented which is shown to diversify our neural network based-simulator, making it more robust to local optima. Results show that our simulator is able to achieve an overall average error of 0.39% and a maximum error of 1.26% for any state variable averaged over the time-domain simulation of the hybrid power plant.