Advanced Direct Power Control for Grid-connected Distribution Generation System Based on Fuzzy Logic and Artificial Neural Networks Techniques

Abstract

This paper proposes an improvement of the direct power control (DPC) scheme of a grid connected three phase voltage source inverter based on artificial neural networks (ANN) and fuzzy logic (FL) techniques for the renewable energy applications. This advanced control strategy is based on two intelligent operations, the first one is the replacement of the conventional switching table of a three phase voltage source inverter (VSI) by a selector based on artificial neural networks approach, and the second one is the replacement of the hysteresis comparators by fuzzy logic controllers for the instantaneous active and reactive power errors. These operations enable to reduce the power ripples, the harmonic disturbances and increase the response time period of the system. Finally, the simulation results were obtained by Matlab/Simulink environment, under a unity power factor (UPF). These results verify the transient performances, the validity and the efficiency of the proposed DPC scheme.

Fuzzy Logic-Based Direct Power Control Method for PV Inverter of Grid-Tied AC Microgrid without Phase-Locked Loop

“…Another FLC-based power control method was proposed by Omar et al to control the output power of grid-connected PV-VSI [27]. Jamma et al proposed an FLC and ANN combined DPC for controlling the VSI output power of a grid-tied PV system [28]. For a grid-tied PV system VSI, a control method based on FLC and the Levenberg-Marquardt optimization method was proposed by Islam et.al.…”

“…Teekaraman et al developed an FLC-based current control method for a grid-tied Z-source VSI [32]. In all these studies [26][27][28][29][30][31][32], even though FLC was considered when designing the feedback controller, all the control methods were based on dq CCS where Park Transformation was used for abc to dq transformation, and PLL was implemented to extract the voltage angle. As mentioned earlier, due to the use of the PLL system, the control methods performance deteriorated, and most of the control methods consisted of two control loops.…”

Transient response improvement of direct current using supplementary control based on ANFIS for rectifier in HVDC

“…Fuzzy control is developed to design of wind-PV combined-generator model [32], to regulate induction motor speed combined by proportional-integral control [33], and fuzzy type-2 to control permanent magnet synchronous machine through digital-signal processing [34]. The neural network-fuzzy model is applied to advance direct power control for grid-connected to distributed generator [35]. An adaptive neuro-fuzzy inference system (ANFIS) has been designed to regulate converter [36] and inverter [37] of HVDC-line, and to control voltage collapse in PS [38].…”

Improved control strategy of DFIG-based wind turbines using direct torque and direct power control techniques

“…The proposed structure of fuzzy direct power controlIn this control scheme, the hysteresis controllers are replaced by fuzzy logic controllers (FLC) however, it uses the same concept as DPC for the selection of the switching vector. The inputs to the FLC are the errors of real and reactive power ( , ) and their variations ( , ) and the output of the FLC is the actual voltage vector applied to the GSC93,6 . inference method used is based on Mamdani min-max rule.…”