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)

Pitch Control of Wind Turbine through PID, Fuzzy and adaptive Fuzzy-PID controllers

As the penetration of the wind energy into the electrical power grid is extensively increased, the influence of the wind turbine systems on the frequency and voltage stability becomes more and more significant. Wind turbine rotor bears different types of loads; aerodynamic loads, gravitational loads and centrifugal loads. These loads cause fatigue and vibration in blades, which cause degradation to the rotor blades. These loads can be overcome and the amount of collected power can be controlled using a good pitch controller (PC) which will tune the attack angle of a wind turbine rotor blade into or out of the wind. Each blade is exposed to different loads due to the variation of the wind speed across the rotor blades. For this reason, individual electric drives can be used in future to control the pitch of the blades in a process called Individual Pitch Control. In this thesis work, an enhanced pitch angle control strategy based on fuzzy logic control is proposed to cope with the nonlinear characteristics of wind turbine as well as to reduce the loads on the blades. A mathematical model of wind turbine (pitch control system) is developed and is tested with three controllers -PID, Fuzzy, and Adaptive Fuzzy-PID. After comparing all the three proposed strategies, the simulation results show that the Adaptive Fuzzy-PID controller has the best performance as it regulates the pitch system as well as the disturbances and uncertain factors associated with the system

Relative Rate Observer Self-Tuning of Fuzzy PID Virtual Inertia Control for An Islanded microgrid

Expanding the usage of renewable energy in islanded microgrids leads to a reduction in its total inertia. Low inertia microgrids have difficulties in voltage and frequency control. That affected saving its stability and preventing a blackout. To improve low inertia islanded microgrids\u27 dynamic response and save their stability, this paper presented relative rate observer self-tuning fuzzy PID (RROSTF-PID) based on virtual inertia control (VIC) for an islanded microgrid with a high renewable energy sources (RESs) contribution. RROSTF-PID based on VIC\u27s success in showing remarkable improvement in the microgrid\u27s dynamic response and enhancement of its stability. Moreover, it handles different contingency conditions successfully by giving the desired frequency support. Ant colony optimization (ACO) technique is used to find the optimal values of the RROSTF-PID based on VIC parameters. Furthermore, using MATLAB TM/Simulink, RROSTF-PID based on VIC response is compared to Optimal Fuzzy PID (OF-PID) based VIC, Fuzzy PID (F-PID) based VIC, PID-based VIC, conventional VIC responses, and the microgrid without VIC response under different operation conditions

Optimal control of wind energy conversion systems with doubly-fed induction generators

Wind energy conversion systems (WECSs) have become the interesting topic over recent years for the renewable electrical power source. They are a more environmentally friendly and sustainable resource in comparison with the fossil energy resource. The WECS using a doubly-fed induction generator (DFIG) to convert mechanical power into electrical power has a significant advantage. This WECS requires a smaller power converter in comparison with a squirrel cage induction generator. Efficiency of the DFIG-WECS can be improved by a suitable control system to maximise the output power from WECS. A maximum power point tracking (MPPT) controller such as tip-speed ratio (TSR)control and power signal feedback (PSF) control is use to maximise mechanical power from wind turbine and a model-based loss minimisation control (MBLC) is used to minimise electrical losses of the generator. However, MPPT and MBLC require the parameters of the wind turbine and the generator for generating the control laws like optimal generator speed reference and d-axis rotor current reference. The Efficiencies of the MPPT and MBLC algorithms deteriorate when wind turbine and generator parameters change from prior knowledge. The field oriented control for a DFIG in the WECS is extended by introducing a novel control layer generating online optimal generator speed reference and d-axis rotor current reference in order to maximise power produced from the WECS under wind turbine and DFIG parameter uncertainties, which is proposed. The single input rule modules (SIRMs) connected fuzzy inference model is applied to the control algorithm for optimal power control for variable-speed fixed-pitch wind turbine in the whole wind speed range by generating an online optimal speed reference to achieve optimal power under wind turbine parameter uncertainties. The proposed control combines a hybrid maximum power point tracking (MPPT) controller, a constant rotational speed controller for below-rated wind speed and a limited-power active stall regulation by rotational speed control for above-rated wind speed. The three methods are appropriately organised via the fuzzy controller based SIRMs connected fuzzy inference model to smooth transition control among the three methods. The online parameter estimation by using Kalman filter is applied to enhance model-based loss minimisation control (MBLC). The d-axis rotor current reference of the proposed MBLC can adapt to the accurate determination of the condition of minimum electrical losses of the DFIG when the parameters of the DFIG are uncertain. The proposed control algorithm has been verified by numerical simulations in Matlab/Simulink and it has been demonstrated that the energy generated for typical wind speed profiles is greater than that of a traditional control algorithm based on PSF MPPT and MBLC

Risk-based DC security assessment for future DC-independent system operator

—The use of multi-terminal HVDC to integrate wind power coming from the North Sea opens de door for a new transmission system model, the DC-Independent System Operator (DC-ISO). DC-ISO will face highly stressed and varying conditions that requires new risk assessment tools to ensure security of supply. This paper proposes a novel risk-based static security assessment methodology named risk-based DC security assessment (RB-DCSA). It combines a probabilistic approach to include uncertainties and a fuzzy inference system to quantify the systemic and individual component risk associated with operational scenarios considering uncertainties. The proposed methodology is illustrated using a multi-terminal HVDC system where the variability of wind speed at the offshore wind is included

Entropy and Exergy in Renewable Energy

Lovelock identified Newcomen’s atmospheric steam engine as the start of Anthropocene with these words: “…there have been two previous decisive events in the history of our planet. The first was … when photosynthetic bacteria first appeared [conversing sunlight to usable energy]. The second was in 1712 when Newcomen created an efficient machine that converted the sunlight locked in coal directly into work.” This book is about the necessity of energy transition toward renewables that convert sunlight diurnally, thus a sustainable Anthropocene. Such an energy transition is equally momentous as that of the kick start of the second Industrial Revolution in 1712. Such an energy transition requires “it takes a village” collective effort of mankind; the book is a small part of the collective endeavor