Fault-tolerant optimal control of a building HVAC system

Abstract

This article presents the development and application of a fault-tolerant control technology, its online implementation, and results from several tests conducted for a large-sized HVAC system. By integrating model-based model predictive control and data-driven fault detection and diagnosis algorithms, the technology automatically adapts the HVAC control laws to a set of subsystem faults and can therefore reach and maintain the largest energy consumption reduction levels that are achievable at any point throughout a building lifecycle. The model predictive control algorithm generates optimal set-points that minimize energy consumption for the HVAC actuator loops while meeting equipment operational constraints and occupant thermal comfort constraints. The fault detection and diagnosis algorithm uses probabilistic graphical models to detect and classify in real time potential faults of the HVAC actuators based on data from multiple sensors. The fault-tolerant control system is realized by executing the two algorithms on the same platform, within the same framework, and by using the fault detection and diagnosis algorithm's output to continuously update the model predictive control algorithm constraints. The proposed integrated technology is executed at the supervisory level in a hierarchical control architecture as an extension of a baseline building management system. The performance and limitations of the fault detection and diagnosis, model predictive control, and fault-tolerant control algorithms are illustrated and discussed using measurement data recorded from multiple tests.