Introducing LQR-fuzzy for a dynamic multi area LFC-DR model

Abstract

It is well known that Load Frequency Control (LFC) model plays a vital role in electric power system design and operation. In the literature, much research works has stated on the advantages and realization of DR (Demand Response), which has proved to be an important part of the future smart grid. In an interconnected power system, if a load demand changes randomly, both frequency and tie line power varies. LFC-DR model is tuned by standard controllers like PI, PD, PID controllers, as they have constant gains. Hence, they are incapable of acquiring desirable dynamic performance for an extensive variety of operating conditions and various load changes. This paper presents the idea of introducing a DR control loop in the traditional Multi area LFC model (called LFC -DR) using LQR- Fuzzy Logic Control. The effect of DR-CDL i.e. (Demand Response Communication Delay Latency) in the design is also considered and is linearized using Padé approximation. Simulation results shows that the addition of DR control loop with proposed controller guarantees stability of the overall closed-loop LFC-DR system which effectively improves the system dynamic performance and is superior over a classical controller at different operating scenarios.

Improved load frequency control performance by tuning parameters of PID controller and BESS using Bat algorithm

“…Ultimately, it regulates load sharing when changes in the load are needed, especially for plants operating on low-cost generation. Therefore, LFC is used to regulate frequency as well as load [19]- [21]. Figure 1 shows a schematic of a LFC of a synchronous generator.…”

Decentralized secondary control for frequency regulation based on fuzzy logic control in islanded microgrid

“…Based on a fuzzy logic controller the objective was to design a robust control for the frequency meanwhile respect the dynamic constraint and treatment time, the structure of the controller is depicted in Figure 4. The use of fuzzy logic controllers in the last decade has been widely increased for power systems and power electronics applications [43], [44] the conventional secondary controller based on PI regulators suffers from many drawbacks due to their design which is tuned on a predefined operating point, any change in the operating conditions outside the operating point leads to the loss of system stability due to the incapability of the PI controller for providing suitable performances, moreover, it has a slow dynamic response which can affect the sensitive loads such as data centers. The fuzzy logic can be used as an intelligent approach to deal with the imperfections of the conventional controller aiming to cover the complex systems with their uncertainties and inaccuracies.…”

Objective functions modification of GA optimized PID controller for brushed DC motor

“…The total value of right hand side of each equation in (2-9) is defined as objective function value. After modifying as in (6)(7)(8)(9), small value of the objective function could not be used as an indicator to evaluate the performance of the objective function as the objective function have additional work in considering the error signal is small simultaneously with considering others performance parameters. The modified objective function did not work solely on integrating the error signal, e(t) only but also considering the performance parameters.…”

“…Genetic Algorithm is widely being used as an optimizer such as in project planning [5], exergoeconomic optimization for geothermal power plant [6], and PID optimization [7]. There is a lot of control method such as conventional PID controller, LQR controller [8], neural network controller [9], fuzzy controller [10][11][12]. Recently there is a lot of intelligent optimization method such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO),Artificial Bee colony, Firefly Algorithm, and Bacterial Foraging (BF) [7,[13][14][15][16][17][18][19][20][21][22][23].…”