Quantum Optimization-Based Load Frequency Control in Interconnected Power Systems

Load frequency control (LFC) is an important control in interconnected power system to keep the system frequency and scheduled tie-line power exchange near to their needed values when the load demand varies. Nonlinear dynamics and operational constraints make the controller selection and tuning complicated in a two-area system. In this paper, a quantum inspired optimisation technique is applied to tune a fractional order proportional-integral-derivative (FOPID) controller for a nonlinear interconnected two-area thermal power system. The benchmark is taken as the existing adaptive neuro-fuzzy inference system integrated with the Honey Badger Algorithm (ANFIS-HBA). The suggested technique directly tunes five quantities: proportional gain Kp, integral gain Ki, derivative gain Kd, fractional integral order λ, and fractional derivative order μ. It searches using probabilistic state representations, measurement, and quantum-inspired update operations.