Hierarchical Control of Microgrid Using IoT and Machine Learning Based Islanding Detection

Due to the increase in penetration of renewable energy sources, the control technique plays a vital role to determine the performance of Microgrid (MG). Recently, the Internet of Things (IoT) and cloud computing has gained significance in solving various industrial problems. Robust and scalable Information Communication Technology (ICT) infrastructure is critical for efficient control of MG. IoT Devices with efficient measurement and control capability can play a key role in the MG environment. In this paper three layers hierarchical control of inverter based MG was developed using cloud-based IoT infrastructure and machine learning (ML) based islanding detection scheme. MG was operated in both island and grid connected mode. In the Primary layer, a voltage frequency (V-F) droop control with virtual impedance control was applied to avoid the disturbances in island mode. Moreover, Active Reactive (P-Q) power control was used for grid connected mode. In the secondary layer voltage and frequency deviations were removed by using the decentralized averaging based method. Voltage and frequency from each distributed generator (DG) were communicated by using a lightweight IoT-based protocol through an edge device (ED). Context-aware policy (CAP) was adopted in ED to optimize traffic flow over a communication network (CN) by comparing the difference in the present and previous data values. In the tertiary layer, a cloud-based ML model was developed using an artificial neural network (ANN) for islanding detection. ANN model was trained by data produced by simulating islanding scenarios in Matlab. Phasor measurement unit (PMU) data was communicated to the cloud for island prediction. The Proposed scheme was implemented on a modified IEEE-13 bus system with four inverter-based distributed generators (DGs) in Matlab, and Microsoft cloud services were used. The successful implementation of MG hierarchical control using an IoT feedback network with less data traffic along with cloud-based islanding detection using machine learning are the main contributions in this work. The whole system achieves stability within 2 seconds of islanding according to IEEE 1547 standards.