# Application of the Residue Method in Steam Superheater Fault Detection
## 1 Introduction
proposes a single-shaft combined cycle power plant (CCPP) fault isolation scheme [5]. This scheme involves temporarily replacing the load control with the output power of the CCPP as a feedback signal to its load control system in the case of pulsating load mutation. This approach ensures the unit operation's fuel quantity control and stability. Another interesting approach is the machine learning integration proposed in [6] by [PERSON], [PERSON] et al. in their paper. They propose developing an intelligent tube leak detection system to increase efficiency and reliability in modern power plants. Their idea of e-maintenance based on multivariate algorithms has also been introduced for intelligent fault detection and diagnosis in power plants. However, these multivariate algorithms depend heavily on the number of input process variables. To address this, the paper proposes a machine learning-based model integrated with an optimal sensor selection scheme to analyze boiler water wall tube leakage. The proposed model was validated using a real steam power plants (SPP) test case and showed improved accuracy compared to similar models. There are various issues that can arise with sensors in large power plants, which can have a significant impact on their performance. While numerous scientific studies examine and suggest various solutions to these issues [7; 8; 9; 10], this paper will look at other faults that can occur within the system. Over the years, the analytical methods employed for detecting flaws have undergone significant transformations, with various models borrowed from related engineering disciplines. The need to rapidly address diverse boundary circumstances that arise during complex installations has necessitated the search for adaptable and flexible solutions to management and control issues. To this end, numerous works have been developed, integrating everything from hybrid parameter control techniques to machine learning algorithms [11; 12; 13]. Furthermore, the techniques presented in this article are also noteworthy, as they have been included in view of the outcomes attained through employing the algorithm. The reliability, safety, and efficiency of technical processes in power plants are crucial for their optimal functioning. Achieving these objectives requires advanced control, monitoring, fault detection, and diagnosis methods. Early detection of process and equipment faults is essential to ensure equipment's safe operation, prevent damage to power systems, avoid accidental stops, and enhance technological operations' economic efficiency. This paper presents an alternative approach to detect and isolate faults in steam boilers and superheater systems. The proposed method combines mathematical models of power plants with the residual generator method and provides fast solutions in case of failures at different points of steam superheaters. The significant advantage of this structure is that it enables online monitoring capabilities, allowing timely intervention and ensuring optimal power plant performance. Moreover, the proposed method detects and localizes faults based on real-time information from measured process variables, making it easy to implement and providing several benefits, including detecting errors in airflow control modules, highlighting steam temperature measurement problems, detecting accuracy issues on steam flow measurements, and displaying the controller output. The method can be extended to other cases of faults, making it a versatile solution for power plant operators to detect equipment and process faults.