Design and Performance Analysis of Walrus Optimization Algorithm (WaOA)‐Based Cascade Controller for Load Frequency Control of a Multi‐Area Power System With Renewable Sources

• Published in: International journal of numerical modelling Vol. 38; no. 2
• Main Authors: Hussain, Jahanzeab, Zou, Runmin, Wu, Zhenlong, Pathak, Pawan Kumar, Akhtar, Samina
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.03.2025; Wiley Subscription Services, Inc
• Subjects: Algorithms; Cascade control; cascade PPD‐PI controller; Control systems; Controllers; Conversion; Effectiveness; Electronic load governors; Error analysis; Frequency control; load frequency control; Optimization; Optimization algorithms; Parameter uncertainty; Photovoltaic cells; Power management; Power sources; power system; Proportional integral derivative; Random loads; renewable energy; Renewable energy sources; Sensitivity analysis; walrus optimization algorithm
• ISSN: 0894-3370; 1099-1204
• DOI: 10.1002/jnm.70046

ABSTRACT One of the key challenges in interconnected power systems is developing an effective control strategy to mitigate frequency and power deviations caused by the intermittency of renewable energy sources (RESs) and varying load demands. This research introduces an innovative cascade control strategy featuring a PPD controller followed by a PI controller (PPD‐PI) for load frequency control (LFC) in a two‐area power system with photovoltaic (PV), wind, and thermal reheat power sources. The walrus optimization algorithm (WaOA) is employed to fine‐tune the parameters of both the PIDn and PPD‐PI controllers, with the goal of minimizing the integral time absolute error (ITAE). The study first applies the WaOA‐tuned PID with filter (PIDn) controller to showcase WaOA's effectiveness in LFC, achieving the lowest objective function value of 0.3862, surpassing MFO (0.3921) and GA (0.4127). The robustness of the WaOA‐tuned PPD‐PI controller is then evaluated under various conditions, including step load disturbances, random load patterns, and parameter uncertainties. The proposed controller achieves significant improvements, with a 36.8% reduction in ITAE compared to the second‐best CGO‐tuned PIDn‐PI controller in Case 2, and a 54.45% reduction in ITAE compared to the second‐best COA‐tuned PDn‐PI controller in Case 3. To further highlight the advantages of the proposed scheme, the analysis also includes nonlinearities such as governor dead band (GDB), boiler dynamics (BD), and generation rate constraints (GRC), along with sensitivity analysis and stability testing under a ±25%$$ \pm 25\% $$ change in system parameters. The results strongly demonstrate the superior performance of the WaOA‐optimized PPD‐PI controller over existing methods.