Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

Abstract

This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

Matrix modelling and optimisation calculation method for large‐scale integrated We‐Energy

“…Based on the above mechanism, the optimisation result is likely to discharge all the energy in the energy storage device at the end of the period to ensure the maximum profits, but in actual operation, this situation will affect the optimisation result of the next period. To avoid this problem, the optimisation problem usually adds constraints that each storage device stores the same amount of energy at the beginning and end of the day, so as not to interfere with the schedule of next day [22].…”

“…In addition, several optimisation and control algorithms are also proposed for the energy system. Reference [22] proposed a robust model predictive controller to operate an automatic voltage regulator and the strategy can handle the uncertainty issue of the regulator parameters. The charging balance of batteries is accomplished by utilising a genetic algorithm‐based proportional integral controller with an adaptive neuro‐fuzzy inference system in Ref.…”

Total Frequency Spread: A New Metric to Assess the Switching Frequency Spread of FCS-MPC

“…Equation (1) gives the value of the average switching frequency, fsw , over a determined period equaling MT s , but this quantity does not measure the variability of the switching frequency in FCS-MPC.…”

“…Model Predictive Control (MPC) is a growing field in electrical engineering. Its applications vary widely, from power system control to system management, including wind and solar farms, High-Voltage Direct Current (HVDC) systems, Variable-Speed Drives (VSD), microgrid power management, and multi-level converters [1][2][3][4][5][6][7].…”

Performance Assessment of Open-Loop and Closed-Loop Generation Rate Constraint Models for Optimal LFC of the Three-Area Reheat Thermal System

“…Furthermore, the literature survey discloses the wide implementation of traditional PI/PID and fractional order (FO) controllers for LFC study with different soft computing algorithms such as the seagull optimization algorithm (SOA) (Harideep et al, 2021), artificial field algorithm (AEFA) (Kalyan and Rao, 2020b), flower pollination algorithm (FPA) (Madasu et al, 2018), moth flame algorithm (MFA) (Lal and Barisal, 2019), ant-lion optimizer (ALO) (Pradhan et al, 2020), differential evolution (DE) (Kalyan and Suresh, 2021b), chemical reaction optimizer (CRO), mine blast optimizer (MBO), gravitational search algorithm (GSA) (Sahu et al, 2015), Harris Hawks optimizer (HHO) (Yousri et al, 2020), grasshopper optimizer (GHO) (Nosratabadi et al, 2019), volley ball algorithm (VBA) FIGURE 2 | Model of the three-area RTPS system (Morsali et al, 2014). Frontiers in Energy Research | www.frontiersin.org (Prakash et al, 2019), sine-cosine algorithm (SCA) (Tasnin and Saikia, 2018), water cycle algorithm (WCA) (Goud et al, 2021), teaching-learning-based (TLBO) optimizer (Sahu et al, 2016b), multi-verse optimizer (MVO) (Kumar and Hote, 2018), symbiotic asymptotic search (SAS) (Nayak et al, 2018) algorithm, slap swarm optimizer (SSO) (Sariki and Shankar, 2021), gray wolf optimizer (GWO) (Kalyan, 2021a), and hybrid (H) algorithms like DE-AEFA (Kalyan and Rao, 2020c), HAEFA (Sai , whale optimization (WOA) algorithm (Elsisi, 2020), lightening search algorithm (LSA) (Elsisi and Abdelfattah, 2020), and arithmetic optimization algorithm (AOA) (Elsisi et al, 2021). are extensively reported.…”