Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

Control of the Automatic Voltage Regulator System with a Novel Stability-based Artificial Intelligence Method

This paper proposes a novel Stability-Based Artificial Intelligence Method for predicting the optimum parameters of the proportional-integral-derivative controller in an automatic voltage regulator system. To implement the stability-based artificial intelligence method, first, parameters which are of great importance for the control of the system are applied to the system randomly, data are collected, and then artificial intelligence studies are carried out. The suggested approach has been applied to the system and compared with other control methods in the literature, namely the improved Kidney Inspired algorithm, Jaya algorithm, Tree Seed algorithm, Water Wave Optimization, and Biography-Based Optimization to test the robustness of the new method. The numerical results indicate that the proposed method significantly outperforms all other methods.

Implementation of a Photovoltaic Inverter with Modified Automatic Voltage Regulator Control Designed to Mitigate Momentary Voltage Dip

The main objective of this research is to propose an active and reactive power injection control in order to mitigate voltage sags. The proposed control strategy works in conjunction with a modified version of an automatic voltage regulator (AVR), where it will act on the active and reactive powers injected by the inverter to reduce the effects of voltage sags. In this way, the control will avoid possible shutdowns and damage to the equipment connected to the grid. The voltage improvement can be perceived for consumers connected to the power system. Modifications in AVR model and parameters are performed to speed up its performance, thus identifying the short-duration voltage variations (SDVV) and, consequently, the control acts to alter the powers, decreasing the active power injection and increasing the reactive power based on inverter capacity during the momentary voltage dip (MVD). Finally, when the fault is cleared, all values return to the pre-fault condition, so that the inverter only operates with active power. A 75 kW three-phase grid-connected photovoltaic system (GCPVS) equipped with the proposed control was inserted in a distribution grid of the city of Palmas, state of Tocantins, Brazil, and all of the computer simulations were performed on the Matlab/Simulink®.

Automatic voltage regulator performance enhancement using a fractional order model predictive controller

In this paper, a new design method for fractional order model predictive control (FO-MPC) is introduced. The proposed FO-MPC is synthesized for the class of linear time invariant system and applied for the control of an automatic voltage regulator (AVR). The main contribution is to use a fractional order system as prediction model, whereas the plant model is considered as an integer order one. The fractional order model is implemented using the singularity function approach. A comparative study is given with the classical MPC scheme. Numerical simulation results on the controlled AVR performances show the efficiency and the superiority of the fractional order MPC.

Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

Switched Fractional Order Model Reference Adaptive Control for an Automatic Voltage Regulator

This paper presents the design and implementation of a Switched Fractional Order Model Reference Adaptive Controller (SFOMRAC) for an Automatic Voltage Regulator (AVR). The fractional orders, adaptive gains and switching times of the controller adaptive laws are tuned offline, using Particle Swarm Optimization (PSO). The functional to be optimized contains not only parameters of the AVR response but also the control energy. The obtained controllers are compared to non switched Integer Order Model Reference Adaptive Controller (IOMRAC) and non switched Fractional Order Model Reference Adaptive Controller (FOMRAC) proposed previously for this process, showing that the SFOMRAC can improve both, the system response and the control energy used.