Nonlinear optimization of proportional-integral controller in doubly-fed induction generator using the Gradient Extremum Seeking algorithm

Abstract

This paper deals with the problem of nonlinear optimization of proportional-integral (PI) controller in doubly-fed induction generator (DFIG), modeled by the detailed differential-algebraic equations (DAEs), and connected to a power system, where rest of the power system is represented only by measurements in connection point. The basic functions of PI controllers are secondary regulation of speed-active power and reactive power-voltage of multiple DFIGs connected in the same point (typical situation in wind farms). In the presence of PI controllers, stability of the transient responses depends largely on its parameters (proportional and integral part). To calculate optimal parameters of the PI controllers, an 'Extremum Seeking' (ES) algorithm was used, more precisely, its version that assumes that gradient can be calculated [hence the name 'Gradient Extremum Seeking' (GES)]. Optimization of the PI controller parameters is performed for a list of major disturbance trajectories recorded in historical database. The results obtained using the IEEE 14-bus test system show that PI controller parameters must be carefully chosen. Their effect on transient responses and stability of the power system with major disturbance (short circuit, outage etc.) conditions was additionally studied.

Fuzzy-Energy-Management-Based Intelligent Direct Torque Control for a Battery—Supercapacitor Electric Vehicle

“…The basic steps of the GA algorithm are highlighted in Figure 2, and the GA parameters used are shown in Table 2. To implement DTC with a speed loop, the torque and flux estimator shown above requires only three sensors, ia, ib, and θ (IC is deduced from ia, ib), whereas the classical estimator needs another extra sensor for the DC bus voltage measurement, as already stated in Equation (14).…”

“…During the last decade, several bioinspired algorithms, such as the genetic algorithm [14], particle swarm optimization [15], ant colony optimization [16], and adaptive quantuminspired evolutionary algorithm [17], have been used in several engineering disciplines. These algorithms are employed because of their ability to automatically find optimal and near-optimal solutions in contrast with classical methods, which are tedious and timeconsuming.…”