The optimal sensor placement problem involves determining the most effective locations to place a network of sensors across an array of measurable signals, in accordance with a set of specified objectives and constraints, such as cost, performance, and sensitivity to variations in uncertain environments. In advanced power systems, such as in pulverized coal and integrated gasification combined cycle power plants, the placement of sensors on-line within the power generation process can be expensive or technically infeasible due to certain harsh environments. This paper uses advanced modeling techniques to simulate the system's steady state behavior, and to capture the variability in unknown process variables using the accuracy information from a given set of online sensors. This variability and measurement error is analyzed using a technique from information theory to determine the most cost-effective network of on-line sensors by formulating a nonlinear, stochastic binary integer problem. The solution is achieved by using an efficient sampling technique, Better Optimization algorithm for Nonlinear Uncertain Systems. The key contribution of using Fisher information as a metric for observation order is that it generalizes the Gaussian assumption on representing process and measurement variability for systems governed by nonlinear dynamics. (c) 2012 Elsevier Ltd. All rights reserved.