Optimal load shedding for microgrids with unlimited DGs

Recent years, increasing trends on electrical supply demand, make us to search for the new alternative in supplying the electrical power. A study in micro grid system with embedded Distribution Generations (DGs) to the system is rapidly increasing. Micro grid system basically is design either operate in islanding mode or interconnect with the main grid system. In any condition, the system must have reliable power supply and operating at low transmission power loss. During the emergency state such as outages of power due to electrical or mechanical faults in the system, it is important for the system to shed any load in order to maintain the system stability and security. In order to reduce the transmission loss, it is very important to calculate best size of the DGs as well as to find the best positions in locating the DG itself.. Analytical Hierarchy Process (AHP) has been applied to find and calculate the load shedding priorities based on decision alternatives which have been made. The main objective of this project is to optimize the load shedding in the micro grid system with unlimited DG’s by applied optimization technique Gravitational Search Algorithm (GSA). The technique is used to optimize the placement and sizing of DGs, as well as to optimal the load shedding. Several load shedding schemes have been proposed and studied in this project such as load shedding with fixed priority index, without priority index and with dynamic priority index. The proposed technique was tested on the IEEE 69 Test Bus Distribution system

An implementation of brain emotional learning based intelligent Controller for AVR system

In this paper, an intelligent controller based on brain emotional learning called BELBIC is applied and optimized by Particle Swarm optimization algorithm. PSO algorithm is used to tuned twelve BELBIC controller parameters in order to improve the time domain parameters such as overshoot percentage (OS%), rise time (tr), settling time (ts) and steady state error (Ess) of the step response for an AVR system in order to minimize value of objective function based on ZLG method. This proposed PSO-BELBIC controller time domain parameters performance is compared with the PSO-PID, IKA-PID and SCA-PID controller. From the simulation, the proposed model free PSO-BELBIC controller was confirm able to provide the best objective function minimization value. This proposed PSO-BELBIC controller also able to provide superior performance to reduce overshoot percentage, steady state error and settling time compared to others controller. However, this proposed controller still have a space to improve its rising time parameter by investigate new formulation of Si and ES for BELBIC controller

A data-driven PID controller for flexible joint manipulator using normalized simultaneous perturbation stochastic approximation

This paper presents a data-driven PID control scheme based on Normalized Simultaneous Perturbation Stochastic Approximation (SPSA). Initially, an unstable convergence of conventional SPSA is illustrated, which motivate us to introduce its improved version. Here, the conventional SPSA is modified by introducing a normalized gradient approximation to update the design variable. To be more specific, each measurement of the objective function from the perturbations is normalized to the maximum objective function measurement at the current iteration. As a result, this improvement is expected to avoid the updated control parameter from producing an unstable control performance. The effectiveness of the normalized SPSA is tested to datadriven PID control scheme of flexible joint plant. The simulation results are presented in terms of the convergence responses and control performances. The outcome of this paper shows that the data-driven controller tuning using the normalized SPSA is able to provide stable and better control performances as compared to the existing modified SPS

A Data-Driven PID Controller For Flexible Joint Manipulator Using Normalized Simultaneous Perturbation Stochastic Approximation

This paper presents a data-driven PID controller based on Normalized Simultaneous Perturbation Stochastic Approximation (SPSA). Initially, an unstable convergence of conventional SPSA is illustrated, which motivate us to introduce its improved version. The unstable convergence always happened in the data-driven controller tuning, when the closed-loop control system became unstable. In the case of flexible joint manipulator, it will exhibit unstable tip angular position with high magnitude of vibration. Here, the conventional SPSA is modified by introducing a normalized gradient approximation to update the design variable. To be more specific, each measurement of the cost function from the perturbations is normalized to the maximum cost function measurement at the current iteration. As a result, this improvement is expected to avoid the updated control parameter from producing an unstable control performance. The effectiveness of the normalized SPSA is tested to the data-driven PID control scheme of a flexible joint plant. The simulation result shows that the data-driven controller tuning using the normalized SPSA is able to provide a stable convergence with 76.68 % improvement in average cost function. Moreover, it also exhibits lower average and best values for both norms of error and input performances as compared to the existing modified SPSA

Development of Smart Autonomous Lawn Mower SALAM

A lawn mower is a machine that uses one or more rotating blades to cut grass to achieve an even height. The cutting height is usually predetermined by the machine’s design, which may result in some variation in grass height

Data-driven PID tuning for liquid slosh-free motion using memory-based SPSA algorithm

This study proposes a data-driven PID tuning for liquid slosh suppression based on enhanced stochastic approximation. In particular, a new version of Simultaneous Perturbation Stochastic Approximation (SPSA) based on memory type function is introduced. Tis memory-based SPSA (M-SPSA) algorithm has a capability to obtain better optimization accuracy than the conventional SPSA, since it is able to keep the best design parameter during the tuning process. The effectiveness of this algorithm is tested to data-drive PID tuning for liquid slosh problem. The achievement of the M-SPSA based algorithm is assessed in terms of trajectory tracking of trolley position, slosh angle reduction and also computation time. The outcome of this study shows that the PID-tuned M-SPSA is able to provide better control performance accuracy than the other variant of SPSA based method