Oppositional Krill Herd Algorithm-Based RLNN Controller for Discrete-Mode AGC in Deregulated Hydrothermal Power System Using SMES

• Published in: Iranian journal of science and technology. Transactions of electrical engineering Vol. 42; no. 3; pp. 309 - 325
• Main Authors: Das, M. K., Bera, P., Sarkar, P. P.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2018; Springer Nature B.V
• Subjects: Algorithms; Automatic control; Controllers; Deregulation; Electrical Engineering; Energy storage; Engineering; Krill; Machine learning; Magnetic energy storage; Neural networks; Proportional integral derivative; Research Paper; Robust control; Sensitivity analysis; Superconducting magnetic energy storage; Oppositional krill herd algorithm; Reinforced learning neural network controller; Automatic generation control
• ISSN: 2228-6179; 2364-1827
• DOI: 10.1007/s40998-018-0062-8

This paper presents the application of oppositional krill herd algorithm (OKHA)-based reinforced learning neural network (RLNN) controller to study the discrete-mode automatic generation control (AGC) problems in the deregulated environment considering superconducting magnetic energy storage (SMES) system for three-area hydrothermal power system. The dynamic responses using OKHA-based RLNN controller for various loading conditions are compared with the proportional–integral–derivative (P–I–D) controllers whose gains are also optimized using OKHA. Area control error (ACE) is used as input to both P–I–D and RLNN controllers, and the weights of neural networks have been adjusted online for RLNN controllers. Sensitivity analyses have been performed to investigate the robustness of the controllers that are subject to change in SMES parameters and loading conditions. Investigation reveals that OKHA-based RLNN controllers give better dynamic performances compared to gains of P–I–D controllers obtained using OKHA considering SMES units for different loading conditions.