This paper is available online at www.praiseworthyprize.org International Review of Automatic Control (I.RE.A.CO.), Vol. 11, N. 3 ISSN 1974-6059 May 2018 Copyright © 2018 The Authors. Published by Praise Worthy Prize S.r.l. This article is open access published under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/ ) Available online by May 31th, 2018 https://doi.org/10.15866/ireaco.v11i3.13849 143 143 Fuzzy-IP Controller for Voltage Regulation in a Stand-Alone Microgrid System Seno D. Panjaitan 1 , Bomo W. Sanjaya 2 , Rudi Kurnianto 3 Abstract – This paper presents a method that combines fuzzy inference system and Integral- Proportional named Fuzzy-IP controller to regulate an independent microgrid voltage with a distributed energy resource unit. The unit employed photovoltaic (PV) array with DC voltage converted to three-phase AC voltage. The control design was carried out through modeling and simulation using Matlab software environment. Transfer function with the 2×2 structure using system identification has estimated the non-linear plant model. Two controllers transformed the a- b-c to the d-q axis coordinates of voltage to simplify linear control design. An oscillator is applied to set frequency according to the recommendation. The results show that the compensated case- study system using Fuzzy-IP with disturbance tracked the setpoint excellently and it regulated the voltage properly including frequency control using internal oscillator. The paper presents performance superiority of the proposed method over PI control by comparing the transient response, the mean squared error, and the root mean squared error. Copyright © 2018 The Authors. Published by Praise Worthy Prize S.r.l.. This article is open access published under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/ ). Keywords: Smart Grid, Microgrid, Energy Control, Distributed Generator, Distributed Energy Resource Nomenclature V abc Three-phase Load Voltage r 1 / r 2 Reference of d-axis / q-axis coordinates y 1 / y 2 Output of d-axis / q-axis coordinates u 1 / u 2 Input related to d-axis / q-axis coordinates D 1 / D 2 Disturbance to d-axis / y-axis coordinates Gp Transfer function of the entire plant Gp 11 Transfer function between y 1 and u 1 Gp 12 Transfer function of the coupling between y 2 and u 1 Gp 21 Transfer function of the coupling between y 1 and u 2 Gp 22 Transfer function between y 2 and u 2 e Error which is the difference between reference input and actual output, e.g. e 1 (for d-axis) = y 1 -r 1 , e 2 (for q-axis) = y 2 -r 2 de Differential error or error change X i,1 fuzzy sets of e (input variable) where i is the rules number X i,2 fuzzy sets of de (input variable), where i is the rules number X i,3 Fuzzy set of K Ki (output variable), where i is the rules number () A z  Aggregated output Membership Function (MF) a fuzzy set A z COA Aggregation result on fuzzy inference system based on method Centroid of Area K i Integral gain of PI controller where Ki min and Ki max are respectively the minimum and maximum value of K i K Ki Coefficient generated by the fuzzy system for Ki max - Ki min Kp Proportional gain of PI Controller Gc(s) Transfer function of the controller pu Per unit dimensionless value (maximum 1) I. Introduction Electrical engineering capacity from renewable sources is increasing steadily and it is bringing a new paradigm of a distribution system that faces new challenges in adopting new control mechanism and framework to integrate many kinds of available energy sources with different characteristics. The renewable sources offer the potential of a sustainable and an environmental-friendly power generation, but the technologies still present challenges due to their intermittent characteristics. Efforts to apply control engineering in the conversion, distribution process and conservation have been attracting much attention to recent researches due to the present and uprising energy and environmental problems. The new topology and design of generation and distribution systems from renewable sources develop continuously. It is essential to understand the distinctive characteristics of each element of the system and the interaction between them, in order