A hybrid SCSO-QNN approach based load frequency control of three area power system with renewable sources using FOPID controller

ABSTRACT

This research proposes a hybrid SCSO-QNN method for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method. The proposed method’s primary goal is to overcome load disturbances and attain the intended output of the interconnected system. The SCSO generates controller parameters tuned by the FOPID controller, providing a significant improvement in performance, while the QNN is employed to forecast the optimal control signal of the converter. Reducing steady-state errors and improving system stability are areas where the FOPID performs better than a traditional integer-order PID controller. Moreover, SCSO-QNN optimization technique exhibits excellent convergence properties, resulting in improved control performance. The proposed method ensures system frequency control by controlling frequency deviation and power fluctuations in the connect line during load disturbances. The proposed method is executed on the MATLAB platform and contrasted with current techniques. The proposed technique outperforms all current techniques, including ABC-FOPID, Genetic Algorithm, and Artificial Bee Colony PID. The proposed method Area control error is 1.8%, which is less than other existing methods.

GRAPHICAL ABSTRACT

This research proposes a hybrid SCSO-QNN approach for designing a FOPID controller in a three-area power system to regulate load frequency. The proposed hybrid approach combines quantum neural networks with sand cat swarm optimization, and it is commonly named as the SCSO-QNN method.

KEYWORDS:

• Load frequency control (LFC)
• fractional order PID
• wind turbine
• thermal power plant
• controller
• frequency

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23080477.2024.2355747.

Data availability statement

This paper does not fall under the data sharing policy because no new data were generated for this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

There is no research included in this article that any of the authors conducted with human subjects.

Additional information

Funding

No specific grant was obtained for this study from public, commercial, or charitable funding agencies.