Optimal Tuning of Fractional Order Controllers for Dual Active Bridge-Based DC Microgrid Including Voltage Stability Assessment

Abstract

In this article, three evolutionary search algorithms: particle swarm optimization (PSO), simulated annealing (SA) and genetic algorithms (GA), have been employed to determine the optimal parameter values of the fractional-order (FO)-PI controllers implemented in the dual active bridge-based (DAB) DC microgrid. The optimum strategy to obtain the parameters of these FO-PI controllers is still a major challenge for many power systems applications. The FO-PI controllers implemented in the DAB are used to control the DC link voltage to the desired value and limit the current flowing through the converter. Accordingly, the investigated control system has six parameters to be tuned simultaneously; Kp1, Ki1, λ1 for FO-PI voltage controller and Kp2, Ki2, λ2 for FO-PI current controller. Crucially, this tuning optimization process has been developed to enhance the voltage stability of a DC microgrid. By observing the frequency-domain analysis of the closed-loop and the results of the subsequent time-domain simulations, it has been demonstrated that the evolutionary algorithms have provided optimal controller gains, which ensures the voltage stability of the DC microgrid. The main contribution of the article can be considered in the successful application of evolutionary search algorithms to tune the parameters of FO-based dual loop controllers of a DC microgrid scheme whose power conditioner is a DAB topology.

ANFIS-based FOPID controller to enhanced stability of cascaded converter for electric vehicle (EV) charger

“…Depending upon selection of parameter values from (λ-μ) plane (7) can behave as P, PI, and PD controller. (λ-μ) plane is represented in figure 6 [15].…”

“…In contrast, the FOPID represents an extended and more generalized form of the PID, incorporating fractional derivative-integral calculus. This controller is suitable for non-linear systems, enhances the robustness and flexibility of the system, and offers several advantages like reduced oscillation, and overshoot, improved response time, and insensitivity to disturbing effects [15]. The problem with this controller is that it has five parameters.…”

Genetic-Algorithm-Driven Parameter Optimization of Three Representative DAB Controllers for Voltage Stability

“…Each individual is represented as a chromosome, and the chromosome is composed of genes. Genes can be meaningful numerical values, symbols, or binary codes [21]. Figure 7 shows the flowchart of GA, while Algorithm 1 presents the pseudocode of GA, and its basic steps include the following.…”

“…Opting for other heuristic algorithms might require more practice and case validation to ascertain their performance and applicability [20]. Azab and Serrano-Fontova [21] further elaborate that in microgrid controllers, the most stable performance occurs when parameters are adjusted using GA, while the use of PSO for parameter tuning tends to yield less stability. GA achieves the best dynamic response, whereas PSO results in the least dynamic response.…”

Analysis of Voltage Control Strategies for DC Microgrid with Multiple Types of Energy Storage Systems

“…The strategy proposed in this paper can effectively accelerate the bus stability time and reduce its fluctuation, but the selection of energy storage unit is still based on the battery energy storage. Reference [11] proposed the use of three evolutionary search algorithms, particle swarm optimization (PSO), simulated annealing (SA) and genetic algorithms (GA), to determine the optimal parameter values of the fractional-order (FO)-PI controllers implemented in dual-active bridge-based (DAB) DC microgrids. The aim of this was to find the optimal parameters for these controllers in terms of voltage stability.…”