Mathematical morphology algorithm for smart micro-grid deployment in distributed power generation

Abstract

The uncertainties associated with renewable Smart Grid (SG) and distribution networks are a common factor that every researcher must contend with. The renewable energy system has thousands of source components, and all are located at different distances from each other. With time, some sources may become inefficient, or any natural calamity may damage the source, causing power line faults or requiring scheduled maintenance for better service. Some of these faults are slow to develop and some are high impact. Though the systems have a safety component to suppress the issue, it affects power generation and conducts smart grid miscalculations. A smart microgrid should have a smart fault location detection (FLD) and fault suppression (FS) unit to overcome any unnecessary burdens and uncertainties. This research modelled a Dynamic Phasor Solution (DPS) based SG system combined with mathematical morphology (MM) algorithmic fault detection for an efficient mechanism to overcome uncertainties. The Fault Location Detection (FLD) process is centred around the wavelet trigger signal and the mathematical morphology (MM) algorithm. In the method, the wavelet trigger signal, caused by the equivalent current or voltage for a short time, travels to both terminals of the line to identify whether the fault occurs in the short branch using a mathematical morphology algorithm. The modelled SG system is divided into several segmental short branches for the FLD system. The DPS controller will control the required power output accumulated from the sources and power reserve unit. A combined Human Machine Interface (HMI) for SG performance and FLD monitoring process is shown as a smart approach. For performance validation, the combined process of a Smart Micro Grid (SMG) system is modelled on the MATLAB simulation platform. Where the observation has been made for performance testing of the proposed controller through multiple simulated test case scenarios. Moreover, the simulation showed the proposed MM-DPS combined control method performance is 1.02% better than the MPPT control method in the case of power-saving and quality, which offers a practicable alternative for existing schemes.