# An Intelligent Control Strategy for a Highly Reliable Microgrid in Island Mode
## 1 Introduction  
Power electronic inverters play more and more important roles in many research fields like motor-driven machines, microgrids, photovoltaics (PVs) and so on [1; 2; 3; 4; 5]. One of the basic reasons is the increasing incorporation of direct current (DC)-based renewable energy sources (RES) and storage (PVs and batteries) in the grid, for environmental purposes. Another reason is the ascending DC demand in various infrastructures such as data centers, electric vehicle (EV) charging stations, etc. [6; 7; 8]. However, the power switches in the inverter are vulnerable to failure for many reasons. Therefore, enhancing the reliability of power electronic inverters becomes more important and has attracted more attention among researchers [9; 10]. Real-time fault diagnosis technology is of great significance. In model-based methods, the transient switch voltage signals are analyzed to diagnose the fault quickly [11]. In [12], a new fault feature extraction method based on the trend relationship of adjacent fold lines for data curves is proposed, which can be used to extract the fault features and is not affected by asymmetric interference. When a fault occurs, the currents are distorted. These changes can be used to diagnose the fault [13]. In [14], a current vector instantaneous frequency fault diagnosis method is proposed. It can detect the occurrence of a fault in a very short time, but can't locate the fault. Wavelet packet transforms and Fourier transforms are used to extract the main components of the signals which can effectively reduce the interference of noise and more clearly identify the types of failures [15; 16]. Reference [17] uses rough set theory to detect short-circuit faults of single-phase inverters. It expresses fault signals in the form of 0-1, and directly outputs diagnosis results with electronic devices, which simplifies computation. Knowledge-based methods have been the focusof recent research. These methods can diagnose the faults accurately. In [16], an on-line estimator constituted by a neural network is used to observe system states, and then fault detection and diagnosis is realized. Reference [18] applies fuzzy control to inverter fault diagnosis, which enhances the anti-interference ability of the system and improves the diagnosis accuracy. In order to allow circuits to operate smoothly after faults, the fault-tolerance of the inverters needs to be considered. Numerous papers discuss fault tolerant methods for the inverters compromising between costs and reliability. The redundant bridge to keep inverters fault-tolerant operating is usually discussed in many studies [19; 20; 21; 22]. These fault-tolerant inverter circuits can output good voltages under faulty conditions. However, the general cost is higher because more redundant power switches are needed in fault-tolerant inverters. The three-phase four-switch (TPFS) topology structure is widely applied because of the advantages of the reduced power switches, such as inverters [23; 24], active power filters (APF) [25; 26], rectifier [27], and so on. Because of the similarity with the three-phase six-switch (TPSS) structure, the TPFS is proposed as a fault tolerant structure for it [28]. In [29; 30], a fault tolerant inverter with the TPFS structure in a microgrid is proposed, which keeps the system operating after cutting the fault power switch devices from the inverter. Reference [31] proposes a finite states model predictive direct power control method for TPFS bidirectional alternating current (AC)/DC converters under unbalanced grid voltage conditions, which can realize the fault tolerant continuous operation of bidirectional AC/DC converters and eliminate active power or reactive power ripples under unbalanced grid voltage conditions with sinusoidal current waveforms. An intelligent control strategy based on the membership cloud model is proposed and applied in an off-grid microgrid with a reconfigurable inverter in this paper. There are four novel points.