Power–frequency balance with superconducting magnetic energy storage using optimized intelligent controller

Abstract

This paper presents an optimized design of the fuzzy logic controller (FLC) for the two-area interconnected power system to regulate the frequency deviation and power deviations when subjected to change in load. The developed power system model apart from having conventional sources also has a superconducting magnetic energy storage (SMES) unit which is capable of storing electric energy and releases it as per system requirements. SMES is used to support short-term active power in order to maintain power-frequency balance in the system under test. This two-area power system considered comprises reheat thermal and hydro units of the same capacity in each area. In the proposed control scheme, optimization of the FLC is carried out in four different steps: the first step is for optimization of the range of input and output variables, the second one for membership functions, the third and fourth steps for rule base and rule weight optimization, respectively. Genetic algorithm (GA) is used for the proposed step-by-step optimization process. Comparing the other methods available in literature, the proposed method is found more effective in achieving optimal results. Simulation results are also tabulated as a comparative performance in view of the main performance indices, i. e. settling time and peak undershoot.

Adaptive-Width Generalized Correntropy Diffusion Algorithm for Robust Control Strategy of Microgrid Autonomous Operation

“…Significant efforts have been undertaken in recent years to build the perfect MG controller to provide a stable system. When the system is believed to be linear, PI-Cs employ a dq converter to modify the converter's output voltage [10], and they are adjusted using simple processes like the Ziegler-Nichols approach [11]. The PI-C, on the other hand, delivers saturated outcomes, lowering system durability through a more significant phase lag.…”

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“…Fit coots (1, i) = F obj (P 0Scoot (𝑖)), i = 1 to N coots (17) where Ncoots is the number of Coot's followers =Npop -Nleaders Gbest score and Gbest pos identify the best global coots score and its position, respectively as seen in eq. (18).…”

“…Figs. 6 (a, b, c) depict the Set the COOT's parameters N pop and IT max Initialization of COOT's followers and leaders positions randomly using ( 15) and ( 16), respectively Calculate the fitness of each COOT's follower using (17) and update the best position and the corresponding best score using (18) Calculate the fitness of each COOT's leader using (19) and update the best position and the corresponding best score using (20) for l = 2:IT max Generate random values using (21)…”

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