# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 1 Introduction
Despite the advantages of methods such as MPC and sliding mode control, which require understanding part of the system model and its uncertainties, these methods are explored. PI control, due to its simplicity, is widely used across various control systems, yet it encounters significant difficulties in parameter design when dealing with complex dynamic characteristics of microgrids. Moreover, although methods based on neural networks and deep learning excel in handling large-scale data, they generally require extensive training data and a complex training process, demanding high system hardware configurations. Therefore, the development of a simple online data-based secondary control method for microgrids is particularly crucial. Current transformers (CTs) and voltage transformers (VTs) serve as crucial links between measurement devices, such as data acquisition systems and phasor measurement units, and the power system. They convert high voltage and large current values into reduced voltage and smaller current signals suitable for measurement within monitoring and control systems. However, under certain conditions, these transformers may exhibit saturation. Saturation can be caused by transient voltage or current spikes, electrical faults, and line overloads, among other factors [28]. Adverse weather conditions and operating environments can also accelerate the aging of transformers, thereby increasing the risk of saturation. Once saturation occurs, the output of the transformers will deviate from the actual measurements. If the effects of transformer saturation are not adequately addressed, it could adversely affect the online monitoring, control, and protection strategies of microgrids [29]. In [30], researchers explored a single integrator multi-agent system with heterogeneous output saturation and proposed a new consensus control protocol. In [31], the issues of actuator saturation and matching uncertainty in discrete-time singular systems were studied. In [32], an event-triggered consistency controller was designed to address output saturation issues. The aforementioned studies considering sensor saturation are all model-based. However, the actual equipment in microgrids is mostly heterogeneous, and the parameters of the lines vary greatly, making it impossible to obtain an accurate microgrid model as in laboratory settings. Furthermore, sensor saturation in AC microgrids has not been considered, and designing a model-free voltage secondary control algorithm for AC microgrids under sensor saturation conditions is urgently needed. Based on the discussions above, the paper introduces a distributed adaptive iterative learning secondary voltage control approach that utilizes input-output data and takes into account the impacts of sensor saturation and disturbances. A control strategy that integrates iterative learning control (ILC) with dynamic linearization techniques has been proposed for secondary control of microgrids. This strategy divides the continuous control tasks of the AC microgrid into multiple periods and utilizes a data-driven iterative learning mechanism. By leveraging historical control data, it optimizes the current control performance. This method does not rely on an accurate system model. Instead, it utilizes input and output data to construct a dynamic linearization model for the microgrid. Through iterative learning, this method greatly improves the control's effectiveness and precision. The proposed DAILC strategy requires only input and output data for execution. Moreover, this paper also proves the convergence of the control algorithm. Simulation experiments demonstrate that, even with sensor saturation and external disturbances in islanded microgrids, the proposed strategy successfully returns the voltage of DREGUs to predefined reference values.