Recently, the wind generation systems are attracting attention as a clean and safe renewable energy source. Induction machines have many advantageous characteristics such as high robustness, reliability and low cost. Therefore, induction...
moreRecently, the wind generation systems are attracting attention as a clean and safe renewable energy source. Induction machines have many advantageous characteristics such as high robustness, reliability and low cost. Therefore, induction machines are used in high-performance applications, which require independent torque and flux control. The induction machines may be used as a motor or a generator. Self-excited induction generators (SEIG) are good candidates for wind-power electricity generation especially in remote areas, because they do not need an external power supplies to produce the excitation magnetic fields in [1–3]. The excitation can be provided by a capacitor bank connected to the stator windings of the induction generator. Magnetizing inductance is the main factor for voltage build-up of the IG. The minimum and maximum values of capacitance required for self-excitation have been analyzed previously in [4–7].
The three phase current regulated pulse-width modulation (CRPWM) AC/DC/AC converters have been increasingly used for wind energy system applications. Their attractive features include: regulated DC-link voltage, low harmonic distortion of the induction generator currents and controllable power factor and efficiency in [8–9]. The current regulation of a SEIG in the synchronous frame has the advantages of fast dynamic current response, good accuracy, constant switching frequency and less sensitivity to parameter variations. In wind generation systems, a variable speed generation system is more attractive than a fixed speed one because of the improvement in the wind energy production. In a variable speed system, wind turbine can be operated to produce its maximum power at every wind speed by adjusting the shaft speed optimally. In order to achieve the maximum power point tracking (MPPT) control, some control schemes have been studied. For example, a search-based or perturbation-based strategy in [10–11], a fuzzy- logic based control in [12], a wind speed-estimation-based algorithm has been applied. Since the squirrel-cage IGs have robust construction, lower initial, run-time and maintenance cost, squirrel-cage IGs are suitable for grid-connected as well as isolated power sources in small hydroelectric and wind-energy applications. Therefore an IG system using radial basis function network (RBFN) was proposed to yield maximum power output through the DC-link power control in [13-14].
In the past several years, much research has been carried out in neural network control. It has proven that an artificial neural network can approximate a wide range of nonlinear functions to any desired degree of accuracy under certain conditions. In the conventional gradient descent method of weight adaptation, the sensitivity of the controlled system is required in the on-line training process. However, it is difficult to acquire sensitivity information for unknown or highly non-linear dynamics. Wavelets have been combined with the neural network to create wavelet–neural–networks (WNNs). It combine the capability of artificial neural networks for learning from process and the capability of wavelet decomposition for identification and control of dynamic systems. The training algorithms for WNN typically converge in a smaller number of iterations than the conventional neural networks. Unlike the sigmoid functions used in the conventional neural networks, the second layer of WNN is a wavelet form, in which the translation and dilation parameters are included. Thus, WNN has been proved to be better than the other neural networks in that the structure can provide more potential to enrich the mapping relationship between inputs and outputs in [15-23].
Particle swarm optimization (PSO), first introduced by Kennedy and Eberhart in [24], is one of the modern heuristic algorithm. It was developed through simulation of a simplified social system and has been found to be robust in solving continuous nonlinear optimization problem in [25-29]. The PSO technique can generate a high quality solution within shorter calculation time and stable convergence characteristics than other stochastic methods in [30-34]. Much research is still in progress for proving the potential of the PSO in solving complex dynamical systems.
The recent evolution of power-electronics technologies has aided the advancement of variable-speed wind-turbine generation systems in [35–39]. In spite of the additional cost of power electronics and control circuits, the total energy captured in a variable-speed wind-turbine system is more than the conventional one. Thus, the variable-speed wind-turbine system has lower life-cycle cost. Moreover, the PWM converters not only can be used as a variable capacitor but also can supply the needed reactive power to load and to minimize the harmonic current and imbalance in the generator current. On the other hand, the variable speed wind turbine driven SEIG systems display highly resonant, nonlinear, and time-varying dynamics subject to wind turbulence and operating temperature of the SEIG. Furthermore, there is an appreciable amount of fluctuation in the magnitude and frequency of the generator terminal voltage owing to a varying rotor speed governed by the wind velocity and the pulsating input torque from the wind turbine. The phenomena of fluctuation are objectionable to some sensitive loads. Therefore, the employment of PWM converters with advanced control methodologies to control the wind turbine driven SEIG systems is necessary in [36–38]. In addition, for the research of wind energy conversion systems, the developments of wind turbine emulators are also necessary in [43, 44]. However, the fuzzy logic controller, the sliding-mode controller, and the PI controller adopted in [40–48] may not guarantee the robustness when parameter variations or external disturbance occurred in the control system in practical applications due to the lack of online learning ability.
This Chapter is organized as follows. Section 2 presents the variable speed wind generation system description. In this section the analysis of the wind turbine is carried out and the maximum power point tracking analysis is also introduced. In Section 3, the dynamic model of the self-excited induction generator is introduced to analyze all its characteristics. Section 4 provides the indirect field-orientation control (IFOC) dynamics for the IG (torque, slip angular frequency and voltage commands) which are derived from the dynamic model of SEIG. The d-q axes current control according to the IG rotor speed gives the maximum mechanical power from the wind turbine and the losses of the IG are minimized. In Section 5, the dynamic equations of the CRPWM converter in the synchronous reference frame are carried out based on the IFOC dynamics of the IG. The dynamic equations of the grid-side CRPWM voltage source inverter connected to the grid are given in Section 6. By using vector control technique, the currents of the CRPWM inverter are controlled with very high bandwidth. The vector control approach is used, with a reference frame oriented along the grid voltage vector position. This allows independent control of the active and reactive power. Section 7 considers the design procedures for the PID voltage controller of the IG-side CRPWM voltage source converter, the PID active power and reactive power controllers for the grid-side CRPWM inverter. In Section 8, an intelligent maximization hybrid control system based on the WNN with IPSO is proposed in order to control the DC-link voltage of the IG-side CRPWM voltage source converter, active power and reactive power of the grid-side CRPWM voltage source inverter effectively based on the MPPT from the wind driven SEIG system. Finally, to testify the design of the proposed hybrid control system and MPPT control scheme, the variable speed wind generation system is simulated in Section 9. The dynamic performance of the system has been studied under different wind velocities. The simulation results are provided to demonstrate the effectiveness of the proposed hybrid control for variable speed wind generation system.
