## 1 Introduction  
With the increasing penetration rate of distributed energy resources (DERs) in the power grid and the increase of controllable load (CL), microgrids, which typically have a form of a small-scale power distribution system that integrates DERs, energy storage devices, loads, converters, communication devices, control and automation systems [1], have become a research hotspot as a link between the distribution network and DER [2; 3; 4]. Some microgrids further include combined cooling heating and power (CCHP). Grid-connected control, island detection, and island stable operation analysis are three key aspects of a microgrid access distribution network. A previous study [5] briefly introduced the American Consortium for Electric Reliability Technology Solution (CERTS) American ElectricPower (AEP) microgrid, General Electric Company (GE) microgrid, Japan's Aichi, Kyoto, and Sendai microgrids, Taiwan's Hsinchu microgrid, Spain's Labein microgrid, the Dutch holiday park microgrid, and the microgrid project of the German MVV (Munich Transport and Tariff Association). DER with a capacity less than 300 KW can be connected to the low voltage 380 V power grid, while DER with a capacity higher than 300 KW must be connected to the medium voltage distribution network. The traditional distribution network can only passively limit the access point and capacity of DERs, at a cost of degraded system stability and deteriorated power quality [1; 6]. Since it is difficult to solve the above problems using the technical methods of monitoring, control, and scheduling of the traditional distribution network, the concept of the active distribution network (ADN) has emerged [7]. The active distribution network, as part of the smart grid, should have functions such as controlling distributed energy, voltage management, controllable load management, demand side management, and protection and fault location of the distribution network [8; 9]. However, the distribution network Supervisory Control And Data Acquisition (SCADA) has limitations, such as a low sampling density, unsynchronized data, and no measurement of phase angle. It cannot complete tasks that require a high data time accuracy, low time delay, or the analysis of phase angle data in the active distribution network, such as DER protection and control, feeder automation, and fault location, etc. WAMS based on synchronous phasor measurement technology is widely used in current transmission networks [10; 11]. WAMS commonly includes phasor measurement unit (PMU), phasor data concentrator (PDC), analysis and application center (AAC), and high-speed communication networks and other components. The PMU is able to perform high-precision acquisition of the real-time operational signals of the power grid (in microseconds) based on the GPS synchronous clock. Synchronized phasors are calculated, which are based on Coordinated Universal Time (CUT) or World Standard Time (UTC). Thus, power signals from different locations can also be compared and analyzed at the same time coordinate system. The output frequency of the PMU is generally between 25-100 HZ (50 Hz grid) or 30-120 Hz (60 Hz grid); in other words, the voltage and current phasors are transmitted every other fundamental wave, every fundamental wave, or half of a fundamental wave. The PMU data is either directly sent to the main station, or collected by the PDC then forwarded to the main station via the communication network. By analyzing the synchronized phasor data measured by the PMU, the main station is able to perform state estimation of the grid, wide-area monitoring protection and control, as well as stability analysis. The PMU in the transmission network WAMS has the advantages of a high sampling rate, high time precision, high-frequency output synchronous phasor data, etc., which can compensate for the shortcomings of the traditional distribution network SCADA in respective aspects.