This brief proposes an approach to estimate static gains of dynamic models for automatic generation control systems in coal-fired power generation units. The main idea is to infer static gains of dynamic models from amplitude changes of model inputs and outputs in finite-time ramp responses. First, input and output time sequences are approximated by a few straight lines as piecewise linear representations. Second, finite-time ramp responses are detected as the data segments where inputs and outputs simultaneously move along straight lines with significant amplitude changes. Third, estimated static gains are obtained by solving linear equations of significant amplitude changes in the ramp responses. One technical challenge of selecting the thresholds of significant amplitude changes is resolved by exploiting a relationship among the amplitude change, noise variance, and signal-to-noise ratio. Comparing with a standard system identification approach, the obtained results from the proposed approach can be validated in a convincing manner: ramp responses being found from historical data samples can be confirmed by checking whether data segments of inputs and outputs are actually in straight lines; estimated static gains can be verified by comparing output amplitude changes in the ramp responses with their estimated values. Numerical and industrial examples are provided to support the proposed approach.

中文翻译：

---

根据历史斜坡响应对自动发电控制系统进行静态增益估计


本简介提出了一种估计燃煤发电机组自动发电控制系统动态模型静态增益的方法。主要思想是根据有限时间斜坡响应中模型输入和输出的幅度变化来推断动态模型的静态增益。首先，输入和输出时间序列由几条直线近似为分段线性表示。其次，有限时间斜坡响应被检测为数据段，其中输入和输出同时沿着具有显着幅度变化的直线移动。第三，通过求解斜坡响应中显着幅度变化的线性方程来获得估计的静态增益。选择显着幅度变化的阈值的一项技术挑战是通过利用幅度变化、噪声方差和信噪比之间的关系来解决的。与标准系统识别方法相比，该方法获得的结果可以以令人信服的方式进行验证：可以通过检查输入和输出的数据段是否实际上处于直线来确认从历史数据样本中找到的斜坡响应；可以通过将斜坡响应中的输出幅度变化与其估计值进行比较来验证估计的静态增益。提供了数值和工业示例来支持所提出的方法。