Research on designated calibration method of fault sensor in photovoltaic thermal heat pump system based on fault detection and virtual calibration

Abstract

With the increasingly serious global energy problem, clean energy sources such as solar energy have become the mainstream focus of development. Among these, solar Photovoltaic thermal (PVT) heat pump systems have promising prospects. However, incorrect data transmitted by sensors can significantly impact the operation and control of the entire heat pump system, leading to reduced efficiency. Given the specific nature of PVT heat pump systems, their internal sensors are prone to errors during operation. To address these challenges, the Autoencoder Virtual in-situ calibration (AE-VIC) is applied to PVT heat pump systems. Preliminary studies have shown that this method can effectively reduce systematic and random errors in sensors. However, the current AE-VIC method faces certain issues, including unclear calibration targets and inefficient calibration of multiple sensors simultaneously, making it difficult to implement in practical systems. In order to overcome the limitations, fault detection is integrated with AE-VIC. By combining the feature extraction capability of Autoencoder with a Softmax classifier, sensors with faults can be identified before the overall calibration process, making the calibration objective of the AE-VIC more targeted. Following fault detection, inputs of the AE model are optimized using the mRMR algorithm for the identified faulty sensors. This optimization alleviates the difficulty of calibration. Through validation with actual system, the improved method effectively diagnoses and locates faulty sensors, and subsequently calibrates them. After calibration, the sensor system error can be reduced by over 95%. The improved calibration method surpasses the original AE-VIC method in terms of both time and accuracy.