Gravitational-Search Algorithm for Optimal Controllers Design of Doubly-fed Induction Generator

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)

Optimal Design of PID Controller for Doubly-Fed Induction Generator-Based Wave Energy Conversion System Using Multi-Objective Particle Swarm Optimization

This paper presents the complete modeling and simulation of Wave Energy Conversion System (WECS) driven doubly-fed induction generator with a closed-loop vector control system. Two Pulse Width Modulated voltage source (PWM) converters for both rotor- and stator-side converters have been connected back to back between the rotor terminals and utility grid via common dc link. The closed-loop vector control system is normally controlled by a set of PID controllers which have an important influence on the system dynamic performance. This paper presents a Multi-objective optimal PID controller design of a doubly-fed induction generator (DFIG) wave energy system connected to the electrical grid using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). PSO and GA are used to optimize the controller parameters of both the rotor and grid-side converters to improve the transient operation of the DFIG wave energy system under a fault condition as compared with the conventional methods to design PID controllers

A novel approach to frequency support in a wind integrated power system

This paper discusses the impact of wind penetration on frequency control of a thermal dominated system considering Generation Rate Constraints (GRC) and dead band non-linearities. The hidden inertia emulation and coordinated operation of conventional power generation systems with wind energy can effectively alleviate the frequency excursions during sudden load disturbances. Conventional energy storage device like Flywheel Energy Storage (FES) system can be used in conjunction with wind integrated power system to overcome the intermittent nature of power generation. Thyristor Controlled Series Compensator (TCSC) is found to be effective in damping low frequency oscillations in weak tie-lines and supplement the frequency regulation. A stochastic population based evolutionary computation technique - Particle Swarm Optimization (PSO) is used to tune the controller gains. A strategy comprising inertia control, coordinated operation of conventional generation units with wind energy and TCSC-FES has been proposed to enhance the frequency regulation which is effective in controlling low frequency oscillations as established by the simulation results

Control Studies of DFIG based Wind Power Systems

Wind energy as an outstanding and competitive form of renewable energy, has been growing fast worldwide in recent years because of its importance to reduce the pollutant emission generated by conventional thermal power plants and the rising prices and the unstable supplies of fossil-fuel. However, in the development of wind energy, there are still many ongoing challenges. An important challenge is the need of voltage control to maintain the terminal voltage of a wind plant to make it a PV bus like conventional generators with excitation control. In the literature with PI controllers used, the parameters of PI controllers need to be tuned as a tradeoff or compromise among various operating conditions. In this work, a new voltage control approach is presented. In the proposed approach, the PI control gains are dynamically adjusted based on the dynamic, continuous sensitivity which essentially indicates the dynamic relationship between the change of control gains and the desired output voltage. Hence, this control approach does not require any good estimation or tuning of fixed control gains because it has the self-learning mechanism via the dynamic sensitivity. This also gives the plug-and-play feature of DFIG controllers to make it promising in utility practices. Another key challenge in power regulation of wind energy is the control design in wind energy conversion system (WECS) to realize the tradeoff between the energy cost and control performance subject to stochastic wind speeds. In this work, the chance constraints are considered to address the control inputs and system outputs, as opposed to deterministic constraints in the literature, where the chance constraints include the stochastic behavior of the wind speed fluctuation. Two different control problems are considered here: The first one assumes the wind speed disturbance’s distribution is Gaussian; the second one assumes the disturbance is norm bounded, and the problem is formulated as a min-max optimization problem which has not been considered in the literature. Both problems are formulated as semi-definite program (SDP) optimization problems that can be solved efficiently with existing software tools. And simulation results are provided to demonstrate the validity of the proposed method