A Novel Technique for Load Frequency Control of Multi-Area Power Systems

Abstract

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability.

Designing of 2‐degree of freedom load frequency controller for power system using novel improved pole clustering and genetic method of reduced‐order modelling

“…The process to obtain MDI involves the calculation of the relative distance of each pole from the origin. This is achieved after partial fraction of the higher-order system of Equation (5). Then the MDI of each pole is calculated from the residue associated with that pole as given in Equations ( 6) and ( 7) and then arranging the MDIs obtained from each pole in descending order.…”

“…Hence, the system could not respond to the disturbances and nonlinearities occurring in the power system. 5,6 So, the conventional PI and PID controllers were combined with the other methods to work in real time, but resulted in complex and complicated structures. Hence, the adaptive controllers were designed to resolve the LFC problem.…”

“…As the fuzzy controller has constant gain, these controllers are not able to work properly for the power systems employing uncertain and nonlinear behavior. 13 In addition, the model predictive control 2 employed the rolling optimization technique to overcome the complexity and uncertainty in the power system. But due to the predictive nature of the technique, it requires the large online computing and hence a high-performing computer space to deal with large power systems.…”

“…In fact, LFC problem is basically dependent on the small disturbance in the load, hence a disturbance rejection controller can provide a better elimination of LFC problem. Hence, two-degree-of-freedom (2-DOF) controllers 13,20,21 are able to get more efficient control on the load disturbance. From the literature, it is observed that in power systems, the fluctuations in the values of parameters of governor, turbine, reservoir etc.…”

Decentralized Robust Disturbance-Observer Based LFC of Interconnected Systems

“…The pioneer works on LFC were proportional-integral (PI) and proportional-integral-derivative (PID) controllers [4], which were tuned and improved by using soft computing optimization techniques, including heuristic [5], fuzzy [6], [7], and neural network [8]. However, as the size and intricacy of modern AC power applications are enlarged, the tuning of conventional PID controllers turns into an obstacle factor to design a proper controller for nonlinear and complicated systems.…”