International Journal of Energy Engineering 2013, 3(2): 74-96 DOI: 10.5923/j.ijee.20130302.05 A GA-ANFIS Self Regulating Scheme for Induction Motor Filter Compensation Ahmed M. Othman 1,* , Adel M. Sharaf 2 1 Electrical Power & Machine Department, Zagazig University, Zagazig, 44519, Egypt 2 Department of Electrical and Computer Engineering, School of Science and Engineering, Habib University, Karachi, Pakistan Abstract The paper presents a Novel Green Plug-Filter Compensation Scheme developed by the Second Author and controlled by an integrated Genetics algorithm (GA) with Adaptive Neuro-Fuzzy Inference System (ANFIS) controller developed by the First Author for gain adjusting of a PID tri-loop stage control scheme applied on single phase Induction motor. A Tri Loop dynamic error controlled technique is used to reduce inrush current conditions, improve energy utilization, ensure soft starting, reduce inrush current as well as effectively ensure motor dynamic speed tacking.The proposed technique is used to adjust the feeding of PWM switching of GP-FC by finding the optimal control gain settings that dynamically minimize the global dynamic error. Digital simulations are provided to validate the effectiveness of this device in improving the power quality and system stability. Keywords Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Green Plug-Modulated Filter Compensator, Genetic Algorithm (GA), Green Power Filter, Tri-loop Dynamic Control 1. Introduction A novel Green Plug-Modulated Filter Compensator (GP-FC) is used as green energy efficient plug compensation scheme. The Simple modulated filter/Capacitor compensation scheme, developed by the Second Author and controlled by an integrated Genetics algorithm (GA) with Adaptive Neuro-Fuzzy Inference System (ANFIS) controller developed by the First Author, is a member of a family of Energy efficient, Soft Starting Switched/Modulated FACTS based Compensation Devices for single phase and three phase motorized, inrush and nonlinear loads. Active and Reactive powers have direct impact on the energy efficiency, efficient utilization, power factor, power quality and voltage profile of the system. There are many techniques to supply and compensate for load reactive power requirements needed by nonlinear/inrush/motorized type loads. So fast control action is needed. By building on technologies developed for FACTS and LC Switched Compensators and high-power CSI and VSI-electronic converters and drives, it is offered a number of advantages in control of power systems, including speed and accuracy of the controlled response. Advanced control and improved semiconductor switching of these devices have provided distinguished solutions for power quality enhancement[1]-[5]. * Corresponding author: ahmed_othman80@yahoo.com (Ahmed M. Othman) Published online at http://journal.sapub.org/ijee Copyright © 2013 Scientific & Academic Publishing. All Rights Reserved The functions of the Energy Efficient, soft starting and reactive compensation green plug scheme can be power factor correction, power quality enhancement, efficient utilization, dynamic voltage control, inrush current reduction and dynamic speed reference tracking. GP-FC may be used in AC power system for various applications, from controlling reactive power to the system to improving voltage regulation and power factor by reducing transient/inrush content in voltage and current supplied to the dynamic nonlinear/inrush type motorized load. Beside the power demand requirements, some contingencies and negative sequence /ripple content are among other factors that are crucial when it happens to power quality issue[6],[7]. The most important point is to find the optimal dynamic self- regulating switching patterns for the GP-FC devices, this selection can be adapted by AI trends. In recent years, AI theory applications have received increasing attention in various areas of power systems such as operation, planning, and control. The effect of different controllers as conventional and adaptive AI controllers can be compared to conclude the most effective controller. The main benefit in using the genetics algorithm is the ability of GA to reach the optimal solutions, which guarantees that if the GA is well self designed and trained that will lead to the most achieved level from the desired performance for the system under any problem space. About the Adaptive Neuro-Fuzzy Inference (ANFIS), we can first state that ANFIS is a merging system between the neural network system and fuzzy logic system. Therefore, it has the resultant benefits of both systems. The fuzzy system is a very efficient tool in the controlling actions and the