Voltage Regulation using Probabilistic and Fuzzy Controlled Dynamic Voltage Restorer for Oil and Gas Industry

Abstract

In a power distribution system, faults occurring can cause voltage sag that can affect critical loads connected in the power network which can cause serious effects in the oil and gas industry. The objective of this paper is to design and implement an efficient and economical dynamic voltage restorer (DVR) to compensate for voltage sag conditions in the oil and gas industry. Due to the complexity and sensitivity of loads, a short voltage sag duration can still cause severe power quality problems to the entire system. Dynamic Voltage Restorer (DVR) is a static series compensating type custom power device. The overall efficiency of the DVR largely relies on the effectiveness of the control strategy governing the switching of the inverters. It can be said that the heart of the DVR control strategy is the derivation of reference currents. This paper deals with the extraction of reference current values using a controller based on a combination of probabilistic and fuzzy set theory. The basis of the proposed controller is that Gaussian Mixture Model (GMM) which is a probabilistic approach can be translated to an additive fuzzy interface system i.e. Generalized Fuzzy Model (GFM). The proposed controller (GMM-GFM) initially optimizes the membership functions using GMM and the final output is calculated using GFM in a single iteration i.e. with no recursions. In the control scheme two control loops are used: a feed-forward loop that uses the Proportional and Integral (PI) controller and the feedback loop uses GMM-GFM based controller. The controller is implemented and respective simulations are performed in the MATLAB SIMULINK environment for three-phase, three-wire distribution system with various issues. A comparative analysis is then done amongst all the three controllers which are based on the T-S, ML, and GMM-GFM modes respectively. The simulation results of this comparison rank the DVR with the GMM-GFM controller first, followed by the fuzzy logic Mamdani model and then with the fuzzy logic T-S model.