Performance evaluation of PID and Fuzzy Logic controllers for residential ORC-based cogeneration systems

Abstract

Cogeneration systems have been identified as a way to reduce primary energy consumption, especially the ones using fossil fuels. Although this technology has already reached maturity on a medium-to-large scale, it is not very well explored on a micro-scale, especially at a residential level where the market is currently filled with carbon-fueled boilers and combi-boilers. For implementation at a residential level, the organic Rankine cycle has been mentioned as a suitable technology because of its simplicity and thermal/ electrical efficiencies. Despite its performance, one of the most important aspects of these applications is the response time, which is mainly connected to the system’s control. Due to its influence, this study aims to provide insights for developing flexible control strategies and analyze two different control methodologies, a standard PID and a Fuzzy Logic controller, considering the operating conditions of an experimental ORC-based micro-cogeneration test rig.Before being implemented in the test rig, both control methodologies were optimized and tested in a virtual environment in order to avoid the exposure of the test rig to unsafe operating conditions. While both controllers show a reasonable response in operating conditions similar to the ones used in the virtual environment, the Fuzzy Logic approach proved to be more adaptable to changes in the system’s dynamics, which are caused by intrinsic (e.g., unpredicted evaporative phenomena) or external disturbances (e.g., a pressure ratio variation), obtaining an overall better performance. Additionally, the limitations of static PID controllers for complex nonlinear processes, like the ORC, were highlighted by their incapacity to stabilize the system when the organic fluid is diverted from the bypass line to the expansion valve. Conversely, it was concluded that the ability to incorporate expert knowledge and heuristics makes the Fuzzy Logic controller well-suited for ORC systems with uncertain, unstable, and fluctuating behavior.