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Health monitoring of complex power systems require multiple sensors to extract required information from the sensed environment and internal conditions of the systems and its elements. A critical decision, particularly in the context of complex systems, is the number and location of the sensors given a set of technical and non-technical constraints. This paper provides a Bayesian Belief Network (BBN)-based sensor placement optimization methodology for power systems' health monitoring. The approach uses the functional topology of the system, physical models of sensor information, and Bayesian inference techniques along with the constraints. Information metric functions are used for optimized sensor placement based on the value of information that each possible “sensor placement scenario” provides. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (upward propagation); how to infer the health of a subsystem based on knowledge of the health of the main system (downward propagation); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (distributed propagation). Dynamic BBN is used as the engine of projecting the health of the system.