Affine projection algorithm based adaptive control scheme for operation of variable-speed wind generator

This study presents a novel adaptive control scheme for variable-speed wind turbine (VSWT) driven permanent magnet synchronous generator (PMSG) to ensure its operation under different operating conditions. The adaptive control scheme is based on the affine projection algorithm (APA) which provides a faster convergence and less computational complexity than the least-mean-square algorithm. The proposed adaptive controller is used to control both the generator-side converter and the grid-side inverter without giving additional tuning efforts. Each vector control scheme for the converter/inverter has four APA-based adaptive proportional-integral (PI) controllers. Detailed modelling and the control strategies of the system under study are demonstrated. Real wind speed data extracted from Hokkaido island, Japan is used in this study. The dynamic characteristics of a grid-connected VSWT-PMSG are investigated in details to ensure the proposed controller operation under different operating conditions. The effectiveness of the proposed adaptive controller is compared with that obtained using optimised PI controllers by Taguchi method. The validity of the adaptive vector control scheme is verified by the simulation results which are performed using PSCAD/EMTDC environment

Speed control of grid-connected switched reluctance generator driven by variable speed wind turbine using adaptive neural network controller

In wind energy conversion system, variable speed operation is becoming popular nowadays, where conventional synchronous generators, permanent magnet synchronous generators, and doubly fed induction generators are commercially used as wind generators. Along with the existing and classical solutions of the aforementioned machines used in wind power applications, the switched reluctance generator (SRG) can also be considered as a wind generator due to its inherent characteristics such as simple construction, robustness, low manufacturing cost, etc. This paper presents a novel speed control of switched reluctance generator by using adaptive neural network (ANN) controller. The SRG is driven by variable speed wind turbine and it is connected to the grid through an asymmetric half bridge converter, DC-link, and DC-AC inverter system. Speed control is very important for variable speed operation of SRG to ensure maximum power delivery to the grid for any particular wind speed. Detailed modeling and control strategies of SRG as well as other individual components including wind turbine, converter, and inverter systems are presented. The effectiveness of the proposed system is verified with simulation results using the real wind speed data measured at Hokkaido Island, Japan. The dynamic simulation study is carried out using PSCAD/EMTDC

Design optimization of controller parameters used in variable speed wind energy conversion system by genetic algorithms

This paper presents an optimum design procedure for the controller used in the frequency converter of a variable speed wind turbine (VSWT) driven permanent magnet synchronous generator (PMSG) by using genetic algorithms (GAs) and response surface methodology (RSM). The cascaded control is frequently used in the control of the frequency converter using the proportional plus integral (PI) controllers. The setting of the parameters of the PI controller used in a large system is cumbersome, especially in an electrical power system, which is difficult to be expressed by a mathematical model or transfer function. This study attempts to optimally design the parameters of the PI controllers used in the frequency converter of a variable speed wind energy conversion system (WECS). The effectiveness of the designed parameters using GAs-RSM is then compared with that obtained using a generalized reduced gradient (GRG) algorithm considering both symmetrical and unsymmetrical faults. The permanent fault condition due to unsuccessful reclosing of circuit breakers is considered as well. It represents another salient feature of this study. It is found that fault-ride-through of VSWT-PMSG can be improved considerably using the parameters of its frequency converter obtained from GAs-RSM

Particle swarm optimization-based superconducting magnetic energy storage for low-voltage ride-through capability enhancement in wind energy conversion system

This article presents a novel application of the particle swarm optimization technique to optimally design all the proportional-integral controllers required to control both the real and reactive powers of the superconducting magnetic energy storage unit for enhancing the low-voltage ride-through capability of a grid-connected wind farm. The control strategy of the superconducting magnetic energy storage system is based on a sinusoidal pulse-width modulation voltage source converter and proportional-integral-controlled DC-DC converter. Control of the voltage source converter depends on the cascaded proportional-integral control scheme. All proportional-integral controllers in the superconducting magnetic energy storage system are optimally designed by the particle swarm optimization technique. The statistical response surface methodology is used to build the mathematical model of the voltage responses at the point of common coupling in terms of the proportional-integral controller parameters. The effectiveness of the proportional-integral-controlled superconducting magnetic energy storage optimized by the proposed particle swarm optimization technique is then compared to that optimized by a genetic algorithm technique, taking into consideration symmetrical and unsymmetrical fault conditions. A two-mass drive train model is used for the wind turbine generator system because of its large influence on the fault analyses. The systemic design approach is demonstrated in determining the controller parameters of the superconducting magnetic energy storage unit, and its effectiveness is validated in augmenting the low-voltage ride-through of a grid-connected wind farm

A Taguchi approach for optimum design of proportional-integral controllers in cascaded control scheme

This study aims at resolving a specific problem in the area of renewable energy, energy storage systems, variable-speed drives, and flexible AC transmission system (FACTS) devices used in the electric power systems. The use of power conversion system (PCS) is very common in the aforementioned areas. A cascaded control scheme based on four proportional-integral (PI) controllers is widely used in the control of the PCS unit. Setting the parameters of the four cascaded PI controllers simultaneously is cumbersome, especially when the application is in the area of electrical power system which is difficult to express using a mathematical model or transfer function due to its nonlinear properties. This paper presents an optimum design procedure for the cascaded controller of the PCS unit using Taguchi method. To apply the design parameters obtained from Taguchi method, a renewable energy application is chosen where a variable-speed wind turbine generator system is connected to the power grid through two back-to-back PCS units. The effectiveness of the designed parameters using Taguchi method is then compared with that obtained using response surface methodology and genetic algorithm (RSM-GA) method under the grid fault condition

Wind generator stability enhancement by using an adaptive artificial neural network-controlled superconducting magnetic energy storage

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) to enhance the transient stability of a grid-connected wind generator system. The control strategy of the SMES unit is developed based on cascaded control scheme of a voltage source converter and a two-quadrant DC-DC chopper using insulated gate bipolar transistors (IGBTs). The proposed controller is used to control the duty cycle of the DC-DC chopper. Detailed modeling and control strategies of the system are presented. The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of a conventional proportional-integral (PI)-controlled SMES. The validity of the proposed system is verified with the simulation results which are performed using the standard dynamic power system simulator PSCAD/EMTDC

Adaptive control strategy for low voltage ride through capability enhancement of a grid-connected switched reluctance wind generator

This paper presents the application of an adaptive control strategy to enhance the low voltage ride through (LVRT) capability of a grid-connected switched reluctance wind generator. In this study, the switched reluctance generator (SRG) is driven by a variable-speed wind turbine and connected to the grid through an asymmetric half bridge inverter, DC-link, and DC-AC inverter system. The adaptive proportional-integral (PI) controllers are used to control the power electronic circuits. The Widrow-Hoff adaptation algorithm is used in this study. The Widrow-Hoff delta rule can be used to adapt the PI controllers' parameters. The detailed modelling and control strategies of the overall system are presented. The effectiveness of the proposed control scheme is verified under a severe symmetrical grid fault condition. The validity of the proposed system is verified by the simulation results, which are carried out using PSCAD/EMTDC

Transient stability enhancement of wind farms connected to a multi-machine power system by using an adaptive ANN-controlled SMES

This paper presents a novel adaptive artificial neural network (ANN)-controlled superconducting magnetic energy storage (SMES) system to enhance the transient stability of wind farms connected to a multi-machine power system during network disturbances. The control strategy of SMES depends mainly on a sinusoidal pulse width modulation (PWM) voltage source converter (VSC) and an adaptive ANN-controlled DC-DC converter using insulated gate bipolar transistors (IGBTs). The effectiveness of the proposed adaptive ANN-controlled SMES is then compared with that of proportional-integral (PI)-controlled SMES optimized by response surface methodology and genetic algorithm (RSM-GA) considering both of symmetrical and unsymmetrical faults. For realistic responses, real wind speed data and two-mass drive train model of wind turbine generator system is considered in the analyses. The validity of the proposed system is verified by the simulation results which are performed using the laboratory standard dynamic power system simulator PSCAD/EMTDC. Notably, the proposed adaptive ANN-controlled SMES enhances the transient stability of wind farms connected to a multi-machine power system

Gravitational search algorithm-based photovoltaic array reconfiguration for partial shading losses reduction

The operation of a photovoltaic (PV) array under partial shading (PS) conditions represents a great challenge in the PV systems. The PS of a PV array causes a reduction of the generated power of such array and increases the thermal losses inside the shaded modules. This paper presents the gravitational search algorithm (GSA) to optimally fully reconfigure the PV array with the purpose of reducing the PS losses. The single diode PV model is used to model the PV module. The GSA code is built using MATLAB environment. The target of the optimized problem is to minimize the irradiance level mismatch index. The reconfigurable PV array is modelled using MATLAB/SIMULINK environment. The validity of the GSA-based reconfigurable PV array is verified by the simulation results. The effectiveness of proposed PV array is evaluated by comparing its results with that of other PV array configurations under different PS and PV modules conditions

Dynamic performance enhancement of a grid-connected wind farm using doubly fed induction machine-based flywheel energy storage system

This paper presents the dynamic performance enhancement of a wind farm connected to an IEEE-39 bus New England test system using doubly-fed induction machine (DFIM)-based flywheel energy storage system (FESS). The variable wind speed causes fluctuations in the output power of the wind farms. The use of FESS smoothes the power of the wind farm and improves the dynamic response of the system during fluctuating wind speeds. A DFIM-based FESS is proposed in this study which works on an effective control technique. The cascaded black-box optimization technique based proportional-integral (PI) control strategy is implemented on the FESS. The PI controllers are used to control the insulated gate bipolar transistor (IGBT) based rotor side converter (RSC) and the grid side converter (GSC) of the DFIM. The PI controller In-depth modeling and control strategy of the system under study is presented. The effectiveness of the proposed system is tested under real-time wind speed data. The validity of the system is verified by the simulation results which are carried out using PSCAD/EMTDC