## 1 Introduction  
The random fluctuation characteristics of renewable energy sources lead to sudden load changes in their power generation processing [1]. This load mutation problem affects the generator's regulation and the power grid's stability [2]. Therefore, a stable and reliable power supply is required to smooth the defects in renewable energy sources [3]. The gas turbine in a CCPP has a wide and flexible regulating ability of generation, making it increasingly important to achieve a stable frequency and peaking regulation of the power grid, especially for the potential threats to grid-connected stability of renewable energy [4, 5]. Similarly, it also leads to an increasing number of CCPPs taking on more of these tasks [6]. Numerous factors can lead to the phenomenon of load mutation in CCPPs, some of which are beneficial and some of which are harmful. For example, using the characteristics of rapid and flexible regulation of the CCPP to smooth the sudden load changes caused by renewable energy helps maintain the grid's stability. The consistency between load and generation is an important indicator for evaluating the generation performance of CCPPs. The sudden load changes can affect not only the regulation of the unit, but also the grid's stability [7]. Therefore, studies have focused on predicting the power output of CCPP [8, 9]. [PERSON] et al. [10] developed a GA-based multilayer perceptron (MLP) model with four parameters as inputs for the power output prediction of CCPPs. This idea is also applied to five machine learning (ML) algorithms and provides better prediction performance with a gradient-boosted regression tree (GBRT) [11]. However, such methods are mostly applied to CCPP operating under full-load conditions rather than CCPPs with peaking and frequency regulation tasks. This means that methods are unsuitable for the CCPP with the tasks of peak load and primary frequency regulations and cannot predict the load-mutation fault of the CCPP effectively. More tasks of peak load and primary frequency regulations and more load-mutation faults in the CCPP could occur under means working under off-design conditions. In particular, for a CCPP with a single-shaft arrangement, the sudden failure of some components can easily trigger sudden changes in the power output. These load-mutation faults will affect the power grid's stability and reduce the unit's service life. Fault diagnosis or early warning strategies using different algorithms were used during the monitoring process [12], that is, diagnosis of leaks in different equipment of the heat recovery steam generator (HRSG) [13] and fault tolerance for gas turbine measurement systems [14]. [PERSON] et al. [15] analyzed the impact of various equipment faults on the measurements and performance of CCPP. They proposed a fault diagnosis method with a two-layer feed-forward neural network to classify 11 faults in gas turbines, HRSG, and CCPP generators. [PERSON] et al. [16] discussed different assessment methods for the operational status of the CCPP and a lot of operational data verified the validity of these methods. Although the above diagnostic algorithms have achieved good accuracy in various datasets, considerable noise and diversity of fault categories in the actual environment always weaken their effectiveness [17]. There is also a fault summarized as an electrical interference fault on the generator that will lead to load mutation failure of the CCPP in addition to these common equipment failures. More specifically, there is a large deviation between the actual power value and the measured value of the power output caused by excessive interference from the working environment, aging of components, or disturbance of the power grid. Unlike a common sensor fault, this fault is not an abnormal power signal caused by the fault of the sensor itself but the electrical interference on the generator that then acts on the signal measurement system, resulting in an abnormal disturbance in the power measurement signal [18].