Adaptive Soft Sensor Modeling for Coal-Fired Power Plants Based on a Tracking Structured State-Space Model

Le résumé fourni par la source

Accurate and continuous measurement of key process variables in modern coal-fired power plants is critical, yet challenging due to ”concept drift” induced by flexible operating regimes. Frequent start-ups, shutdowns, and rapid load changes introduce complex, variable, and unknown operating conditions, which degrade the performance and adaptability of conventional soft-sensing instruments. To address this measurement problem, this paper proposes a novel soft-sensing model with high accuracy and robust tracking capabilities: the Tracking Structured State Space Sequence Model (TS4). The TS4 architecture is composed of three key innovations for superior measurement performance. First, a Probabilistic Structured State Space Sequence (PS4) model is developed to establish a highly accurate baseline measurement under stable conditions and provide probabilistic uncertainty quantification. Second, a lightweight Probabilistic Kernel Recursive Least Squares (PKRLS) algorithm with a forgetting factor is derived, enabling the soft sensor to rapidly adapt to new operating conditions through online updates. Finally, a dynamic posterior weight updating strategy is designed to intelligently fuse the deep representation of PS4 with the online tracking ability of PKRLS, creating a single, robust measurement model. The proposed soft sensor was validated on operational data from a large-scale coal-fired power plant for the task of power generation measurement. Compared to various baseline models, the TS4-based instrument demonstrates significantly improved measurement accuracy and superior capability in tracking dynamic operating conditions, proving its value for enabling intelligent, safe, and efficient plant operations.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.