Abstract: [Objectives]　Under the national strategic goals of carbon peak and carbon neutrality, coal-fired power units frequently participate in deep peak-shaving operations at low loads, during which uneven flow distribution and combustion bias in the boiler heating surfaces cause thermal deviations, thereby increasing the risk of tube bursts due to water wall overheating. Conventional wall temperature monitoring methods, based on the enthalpy change of the working fluid, exhibit hysteresis and have difficulty in differentiating between inherent temperature distribution patterns and abnormal operational deviations. To address this, a thermal deviation discrimination method based on historical data and Euclidean distance is proposed for a 660 MW ultra-supercritical opposed-firing boiler.[Methods]　Through analyzing the wall temperature of the main heating surfaces of the boiler, the operating load range is divided into intervals and a benchmark temperature database is constructed. Characteristic temperatures are extracted to eliminate the influence of overall temperature fluctuations. Furthermore, a thermal deviation index is established to quantify real-time deviations, and the method is applied in practice.[Results]　Significant temperature differences exist among the tube panels of the heating surfaces, and this is especially apparent in the high-temperature reheater. The fluctuations in wall temperature consist of two parts: one is the inherent deviation arising from the system’s own characteristics, and the other is the fluctuating deviation brought about by adjustments in the operating mode.[Conclusions]　The proposed method successfully identifies both the inherent deviations of the equipment and the operational fluctuating deviations, and it provides a real-time discrimination basis for boiler safety early-warning systems, ensuring operational safety under deep peak-shaving conditions.

Key words: coal-fired power generation, ultra-supercritical boiler, deep peak-shaving, wall temperature analysis, Euclidean distance, thermal deviation