Optimal Generation Control of Interconnected Power System Including DFIG-Based Wind Turbine

Abstract

The impact of wind power with higher size of penetration becomes significant for frequency control problem due to its inconsistent nature and lack of frequency support from the wind turbine units. The frequency can be managed using enhanced control of inertia and droop characteristics. However, at very low and high speeds, the adjustment of pitch angle of wind turbine is necessary so that the aerodynamic power produced remains within the designed limits. This paper discloses the design of proportional integral (PI) control algorithm to adjust pitch angle of doubly fed induction generator (DFIG)-based wind turbine (WT) integrated with two-area-interconnected power system. Here, a heuristic gravitational search algorithm (GSA) is employed to optimize the gains of PI controllers used for pitch angle adjustment and governor speed control of wind turbine, frequency regulation, and tie-line power flow control. The influence of optimal gains by GSA is compared with genetic algorithm (GA) and ant colony optimization (ACO) approaches. Finally, the effectiveness of GSA-optimized controllers is demonstrated in terms of reduced settling time, overshoot, eigenvalues, and oscillations. The competency of proposed controller is also studied by considering the variation of wind power source from 10% to 40% in the power system and also the real-time physical constraints like generation rate, time delay, and governor deadband.

Hybridized gravitational search algorithm tuned sliding mode controller design for load frequency control system with doubly fed induction generator wind turbine

“…The performance of 2‐area thermal system including DFIG wind turbine is also evaluated in the presence of physical constraints, namely, GRC, governor deadband, and time delay during signal processing in the system. The settling time and peak undershoots of the dynamic responses for optimal sliding mode–controlled wind thermal interconnected power system given in Table reveals the superiority of DOGSA‐tuned SMC over the GSA‐optimized PI‐based controllers reported in a previous study …”

“…It is apparent from the analysis shown in Figure to Figure and Table that the DOGSA‐optimized SMC minimizes the deviations in frequency and tie‐line power effectively as compared to GSA in terms of settling time and peak undershoot and eliminates the chattering problem in SMC. The feedback gains, switching vector, speed, pitch angle, and steam turbine controller gains obtained using DOGSA for objective function J 2 are as follows: …”

“…The transfer function of thermal nonreheat turbine G tk ( s ), its governor G gk ( s ), and power system load are , , and , respectively. The complete transfer function model of power system model 2 including thermal and wind turbines and system parameters are taken from a previous study . The frequency of the wind‐integrated power system is managed using enhanced control of inertia and droop characteristics.…”

“…The design of sliding mode controller requires optimal tuning of its parameters. In order to study and establish DOGSA technique, the following objective functions for power system model 1 and power system model 2 respectively are considered in the present work: where objective function J 1 minimizes the deviations in frequency ( Δf k ) and tie‐line power ( ΔP tiek ) of the k th control area along with the chattering in control input ( Δu k ), which is resolved by minimizing the variations in the control signal by considering it in the objective function. The objective function J 2 is employed to minimize the area control error of k th area and deviation in wind speed ( e ).…”

“…The decoupling of wind turbines from the power network because of the power electronic converters limits their capacity to support frequency control in the system. The enhanced inertia, droop, and pitch control methods are generally used to resolve LFC problem in the presence of wind turbines . In, the authors control the output of doubly fed induction generator (DFIG) wind turbine through a PID controller optimized through fuzzy and Ziegler‐Nichol's methods.…”