Abstract
This paper proposes a new population-based hybrid particle swarm optimized-gravitational search algorithm (PSO-GSA) for tuning the parameters of the proportional-integral-derivative (PID) controller of a two-area interconnected dynamic power system with the presence of nonlinearities such as generator rate constraints (GRC) and governor dead-band (GDB). The tuning of controller parameters such as Kp, Ki, and Kd are obtained by minimizing the objective function formulated using the steady-state performance indices like Integral absolute error (IAE) of tie-line power and frequency deviation of interconnected system. To test the robustness of the propounded controller, the system is studied with system uncertainties, such as change in load demand, synchronizing power coefficient and inertia constant. The two-area interconnected power system (TAIPS) is modeled and simulated using Matlab/Simulink. The results exhibit that the steady-state and transient performance indices such as IAE, settling time, and control effort are impressively enhanced by an amount of 87.65%, 15.39%, and 91.17% in area-1 and 86.46%, 41.35%, and 91.04% in area-2, respectively, by the proposed method compared to other techniques presented. The minimum control effort of PSO-GSA-tuned PID controller depicts the robust performance of the controller compared to other non-meta-heuristic and meta-heuristic methods presented. The proffered method is also validated using the hardware-in-the-loop (HIL) real-time digital simulation to study the effectiveness of the controller.
Highlights
Automatic load frequency control (ALFC) plays a significant role in modern power systems to exchange scheduled power between the interconnected areas through tie-lines with minimum steady-state errors of frequency deviation and tie-line power variations
To study the effectiveness of the proposed hybrid particle swarm optimized-gravitational search algorithm (PSO-gravitational search algorithm (GSA))-tuned PID controller compared to other controllers, the system was analyzed with various disturbances such as change in load, variation, synchronizing power coefficient, and inertia constant because of the following reasons: The power system is highly non-linear pertaining to system uncertainties such as generator outage, line outage, load outage, and so on
The results indicate that the frequency and tie-line power deviation converge to zero faster through the propounded particle swarm optimization (PSO)-GSA-tuned PID controller than the PID, PSO–PID, and GSA–PID controllers
Summary
Automatic load frequency control (ALFC) plays a significant role in modern power systems to exchange scheduled power between the interconnected areas through tie-lines with minimum steady-state errors of frequency deviation and tie-line power variations. Many researchers have investigated conventional classical controllers such as proportional–integral (PI), proportional–integral–derivative (PID), proportional–derivative (PD), integral (I), integral–derivative (ID), and integral–double-derivative (IDD) [1,2] for ALFC application. The investigation of these controllers reveals that the design model majorly depends on the mathematical modeling and their performances are not superior for dynamic behavior of the system like nonlinearities of generator rate constraint (GRC) and governor dead-band (GDB) [3]. The increase in the complexity of advanced power systems requires a simple control scheme for ALFC application using intelligent control method [10]
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