ArticleScientific reports2025
Genetic algorithm type 2 fuzzy logic controller of microgrid system with a fractional-order technique.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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6 citing papers in PubMed.
- A Self-Tuning Minimal-Rule Fuzzy Logic Controller for High-Performance Induction Motor Drives.Sensors (Basel, Switzerland) · 2026Article
- Secure event-triggered control of nonlinear networked systems via interval type-2 fuzzy observer design.Scientific reports · 2026Article
- Comparative evaluation and architectural enhancement of a genetic algorithm-tuned fuzzy logic battery control in microgrid energy management.Scientific reports · 2026Article
- Optimized battery energy management using an improved type-2 fuzzy logic approach.Scientific reports · 2026Article
- A bi-objective robust fuzzy programming model to configure a microgrid-based cement supply chain.Scientific reports · 2025Article
- Management of power in single rotor wind turbine systems using fuzzy controller based on fractional order error approaches.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a hybrid approach that combines a genetic algorithm (GA)-optimized type-2 fuzzy logic controller (T2FLC) with a fractional-order technique for enhanced control of a microgrid system. The T2FLC approach is employed to handle the inherent uncertainties in the microgrid due to fluctuating renewable energy inputs and varying loads. The GA optimizes the parameters of the designed FO-T2FLC approach, ensuring optimal performance under different operational conditions. This developed strategy is a modification and development of the traditional approach, as it is characterized by rapid dynamic response, high durability, distinctive performance, ease of application, and inexpensive. Also, this designed strategy does not depend on the mathematical model of the studied system, which gives satisfactory results if the system parameters change. The microgrid system on the direct current side features a photovoltaic array with battery storage. In contrast, the alternating current section comprises a multi-functional voltage source inverter integrated with a shunt active power filter. This setup delivers energy to the connected loads and the network. To manage the system effectively; traditional power control methods (direct power control and space vector modulation) are used for the alternating current section. Additionally, the proposed regulator control the direct current bus voltage loop, regulate the reactive and active power loops of the network, and compensate for the total harmonic distortion in the source streams. It also injects the required active power into the network to enhance the competence of the power network. In this work, the efficiency of the proposed FO-T2FLC-GA approach is verified using MATLAB, comparing it to the T2FLC-GA approach and some existing strategies such as third-order sliding mode control. The results obtained highlight the effectiveness and strength of the FO-T2FLC-GA approach in improving power quality and reducing the total harmonic distortion value, as it reduces the total harmonic distortion value of the current by percentages estimated at 80%, 33.87%, and 32.50% in all cases. The FO-T2FLC-GA approach also reduces the steady-state error, undershoot, fluctuations, and overshoot of direct current link voltage compared to the T2FLC-GA approach by percentages estimated at 1.54%, 33.04%, 25%, and 33.04%, respectively. Compared with other works, the proposed approach improves the response time, overshoot, and ripples of direct current link voltage by 59.38%, 50%, and 75%, respectively, compared to the third-order sliding mode control approach. These results could make the designed FO-T2FLC-GA approach a prominent solution in the future in other industrial applications such as propulsion and traction.
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