Fuzzy-Swarm Controller for Speed-Governor of Synchronous Generator

The main objective of this work is to propose an intelligent controller to enhance the performance of hydraulic turbine speed-governor of a Synchronous Generator (SG) during different loading conditions. The proposed mathematical model of the SG is connected to different loads in two ways. First, each load is connected individually and second, the SG loads change during the operation to ensure the robustness of controller for wide load variations. Two types of controllers are used. The first controller is the Proportional-Integral (PI) based on Particle Swarm Optimization (PSO) technique to obtain optimal gains. The second controller is Fuzzy PD+I with gains and Membership Functions (MFs) tuned by PSO technique. The results show the improvement of PI-PSO performance on conventional PI controller; also show the improvement in the performance of Fuzzy PD+I using PSO technique on PI-PSO

Fuzzy-Swarm Controller for Automatic Voltage Regulator of Synchronous Generator

The main objective of this work is to propose Artificial Intelligence (AI) controller to enhance the performance of Automatic Voltage Regulator (AVR) of a Synchronous Generator (SG) during different loading conditions. The proposed mathematical model of the SG with saturation nonlinearities is connected to different loads in two ways. The first each load is connected individually and the second the SG loads change during the operation to ensure the robustness of controller for wide load variations. Two types of controllers are used. The first controller is the Proportional-Integral (PI) based on Particle Swarm Optimization (PSO) technique to obtain optimal gains. The second controller is Fuzzy PD+I with gains and Membership Functions (MFs) tuned by PSO technique. The results show the improvement of PI-PSO performance on conventional PI controller; also show the improvement in the performance of Fuzzy PD+I using PSO technique on PI-PSO. The simulation of SG is performed using MATLAB program version 7.10.0.499 (R2010a)

Sensored speed control of brushless DC motor based salp swarm algorithm

This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time

Аutomаtіϲ generаtіon control bаѕeԁ wһale орtimіzatіon algorithm

In the designing and operation of interconnected power systems, automatic-generation-control (AGC) represent an important topic. AGC is responsible for maintaining the balance between generation side and load side via controlling the frequency and active power interchange. A new metaheuristic strategy is proposed in this work for optimal controller tuning in AGC system. Ԝһale Орtimіzatіоn Αlgorithm(WOA) is proposed for optimal tuning of reset integral controller. T he proposed strategy is used for optimal AGC in two-areas interconnected-power system. The proposed tuning strategy is compared with other new metaheuristic optimization strategy termed as Harmony Search (HS). The two-area interconnected power system are simulated based MATLAB-toolbox. From results obtained, it is obvious that, the system transient and steady-state behavior are enhanced greatly under the same conditions. This is due to the use of the proposed optimization technique.  The proposed technique has an advanced and superior feature like, local optimum avoiding, fast convergence ability, and lower search agents and iteration are required. All mentioned features, make this strategy optimal for various optimization problems