###### Abstract
These standards provide guidelines for power-generating companies, consumers, manufacturers, and national power system organizations on the acceptable limits of PQ disturbances. Table 2 shows the limits of PQ disturbances according to IEC 61000, EN 50160, and IEEE 519-2022 [44; 45]. The thresholds of the PQ indices are the reference magnitudes against which the adjustment of voltages and currents are made. After analyzing the information provided above, it is important to understand the significance of utilizing appropriate control systems, such as FCSs, to improve power quality. To achieve an effective and efficient controller, one must have a sound knowledge of the structure and limitations of FCSs, as well as explore their possibilities. Although FCSs have been studied and analyzed in various engineering fields, such as refrigeration, and hydraulic and pneumatic systems [46; 47; 48; 49], only a few authors have focused exclusively and intensively on PQ improvement in power systems in recent years. These studies emphasize the usefulness of fuzzy logic and explore different aspects of control systems and power systems. In the field of microgrids, a study [50] was conducted to examine the effectiveness of fuzzy control. The study concludes that a simple FLC with only one input can provide comparable results to a traditional PI (proportional-integral) controller, but it is easier to adjust. Another study [10] classified the different types of UPQCs, along with their corresponding control strategies, to eliminate or reduce PQ issues. The authors of this study mention that fuzzy logic controllers in association with other artificial intelligence \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Issue**} & \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Limits**} & \multirow{2}{*}{**Standard/Observations**} \\ \cline{3-4} \cline{5-5}  & & **LV** & & **MV** \\ \hline Frequency variation & \(\Delta f_{\text{\textbackslash{s}}}\)1  & \multirow{2}{*}{10-95\% of fundamental} & \multirow{2}{*}{10-95\% of seconds} & \multirow{2}{*}{5\%} & \multirow{2}{*}{15\%} \\  & & & & & \\ \cline{5-5}  & & & & & \\ \cline{5-5}  & & & & & \\ \cline{5-5}  & & & & & \\ \cline{5-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Power quality limits. techniques have the capability to improve PQ. Similarly, in a review [51], voltage variation compensators are examined, and the authors conclude that employing fuzzy logic in the control of these compensators can improve PQ. Devices such as active power filters are also reviewed in [10; 52; 53], and different control methods are discussed. One significant conclusion from these studies is that a PI controller in association with fuzzy logic provides better results than a conventional PI controller in PQ improvement. Upon reviewing the available literature, it is evident that there are still unanswered questions regarding the use of fuzzy control for improving power quality. These questions can only be addressed by systematically analyzing the literature. Thus, the main objective of this work is to provide a comprehensive review of the applications of fuzzy logic control in power quality issues, with a focus on FCSs' effectiveness and efficiency. The review covers information on fuzzy-based control systems, input and output values, fuzzy numbers, inference rules, decisions, and defuzzification methods, as well as the implementation of fuzzy logic. The work focuses on control systems that contain fuzzy logic controllers in their simple (type-1 fuzzy) and advanced (type-2 fuzzy) forms, or in association with other conventional techniques, such as PI or hysteresis control, and novel techniques like genetic algorithm optimization. In the research, ANFIS was not considered; only control strategies that maintain the classic structure and logic-based functioning of an FLC were considered. The Section 2 of the work details the methodology employed to obtain the articles used in the review and the criteria to assess the literature.