Optimal Model Predictive and Linear Quadratic Gaussian Control for Frequency Stability of Power Systems Considering Wind Energy

Abstract

This work presents a new robust control technique which combines a model predictive control (MPC) and linear quadratic gaussian (LQG) approach to support the frequency stability of modern power systems. Moreover, the constraints of the proposed robust controller (MPC-LQG) are fine-tuned based on a new technique titled Chimp optimization algorithm (ChOA). The effectiveness of the proposed robust controller is tested and verified through a multi-area power system (i.e., single-area and two-area power systems). Each area contains a thermal power plant as a conventional generation source considering physical constraints (i.e. generation rate constraint, and governor dead band) in addition to a wind power plant as a renewable resource. The superiority of the proposed robust controller is confirmed by contrasting its performance to that of other controllers which were used in load frequency control studies (e.g., conventional integral and MPC). Also, the ChOA's ingenuity is verified over several other powerful optimization techniques; particle swarm optimization, gray wolf optimization, and ant lion optimizer). The simulation outcomes reveal the effectiveness as well as the robustness of the proposed MPC-LQG controller based on the ChOA under different operating conditions considering different load disturbances and several penetration levels of the wind power.

A Comprehensive Survey on the Current Trends in Improvising the Renewable Energy Incorporated Global Power System Market

“…Rather than time consuming traditional and heuristic optimization technique for MPC controllers, a new speeder Chimp optimization algorithm (ChOA) achieves optimal solution within short span of time. The new robust control approach binds the MPC and linear quadratic Gaussian (LQG) for frequency stability [142].…”

Robust Control of Frequency Variations for a Multi-Area Power System in Smart Grid Using a Newly Wild Horse Optimized Combination of PIDD2 and PD Controllers

“…For numerous power system architectures, the problem of frequency stability has been addressed. Researchers in [4,5] investigated LFC for one-area systems, whereas [6][7][8] investigated a multi-area system with nonlinearities, while [9,10] examined a deregulated power system. Several control methods have been used to solve the problem of load frequency control in power systems.…”

“…These algorithms, however, face several difficulties, including slumps, deathtraps in local minimums, the demand for several iterations, and reliance on initial conditions for selecting the optimal settings. As a result, scholars overcame these obstacles by improving meta-heuristic optimization methods, such as the grey wolf optimizer [33], particle swarm optimization [35], ant lion optimization [36], chimp optimization algorithm [5], teaching-learning-based optimization [37], moth-flame optimization [11], equilibrium optimization [38], and atom search optimization [39]. Substantial emphasis has been placed on the use of various optimization techniques to assist them in tackling technical difficulties, particularly the load frequency control issue.…”

Fast hierarchical coordinated controller for distributed battery energy storage systems to mitigate voltage and frequency deviations

“…Bellman's principle yields a solution to minimise error and effort by optimising control actions that modify the dynamics of the power network by reducing the deviations of the state variables (x) [33][34][35]. Thus, the quadratic cost function 𝐽 𝑐 is proposed to balance the aggressive regulation of x with the cost of control actions u.…”