Year of empirical research and efforts brought the world into its current position with modern technical amenities. The Brush Less Direct Current (BLDC) motor for the electric propulsion system is one of the innovations in this modern era. Nowadays, the BLDC motor drives are getting used in all types of automation system, electric vehicles, robotics, drones and in various industrial applications. The PID, FPA, PI, fuzzy logic, adaptive, QFT and PWM are the popular types of a control methods for the BLDC motor system and all of these control methods have their own distinctive functionality. But all the controllers suffer with the BLDC motors for its nonlinear behaviour, parameter variation in load unsettle influences and parametric varieties with high speed or variable speed configuration. To increase BLDC motor control performance a fast, rugged and quick adaptable controller is required. The controller needs to be tested and less rippled than the existing controller. The proposed adaptive PID controller have combined strength of PID-autotuner controller and PID controller for a BLDC motor control system. The PID-autotuner provides the adaptability for self-adjusting the parameters for nonlinearities, load performance and speed variation through steady response and performance accuracy based on frequency-response estimation process. Whether the fast responsive and rugged PID controller is to minimize the PID-autotuner’s slow performance. The combined effect of both controllers, correcting each other by automatically readjusting the parameters for better afford. To verify the performance, MATLAB simulation platform has been used and a benchmark BLDC motor system was developed based on a specific BLDC motor system parameter. For performance comparison the PID and FPA speed controller was developed from reference papers, because of several review papers were mentioned the better performance of them. A brief comparison has been made with the Adaptive-PID controller and targeted PID & FPA controller benchmarking. Where, the proposed controller gave less ripple, less overshoot (>1%) and good load performance then PID and FPA controllers in load variation and different speed condition. The contribution of this research is to design an Adaptive PID controller for BLDC motor system, to increase the adaptable and reliably through performance compare to FPA and PID controller.

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.