On the contribution of wind farms in automatic generation control: Review and new control approach

© 2018 by the authors. Wind farms can contribute to ancillary services to the power system, by advancing and adopting new control techniques in existing, and also in new, wind turbine generator systems. One of the most important aspects of ancillary service related to wind farms is frequency regulation, which is partitioned into inertial response, primary control, and supplementary control or automatic generation control (AGC). The contribution of wind farms for the first two is well addressed in literature; however, the AGC and its associated controls require more attention. In this paper, in the first step, the contribution of wind farms in supplementary/load frequency control of AGC is overviewed. As second step, a fractional order proportional-integral-differential (FOPID) controller is proposed to control the governor speed of wind turbine to contribute to the AGC. The performance of FOPID controller is compared with classic proportional-integral-differential (PID) controller, to demonstrate the efficacy of the proposed control method in the frequency regulation of a two-area power system. Furthermore, the effect of penetration level of wind farms on the load frequency control is analyzed

Partitioning approach for large wind farms: active power control for optimizing power reserve

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Nowadays, large wind farms are expected to guarantee stability of the electrical grid contributing with ancillary services, such as frequency support. To this end, wind farm controllers must set the power generation of each turbine to compensate generation and demand imbalances. With the aim of optimizing primary frequency support, this paper proposes a partitioning approach to split large wind farms into several disjoint subsets of turbines according to the wake propagations through the wind farm. The partitioning problem is solved as a mixed-integer multi-objective optimization problem stated to maximize the strength of the coupling among the turbines due to the wake effect. Thus, no additional information sharing related to the wake propagations needs to be considered between the subsets. Different control tasks are assigned to the local controller of each subset, such that the total power generated meets the power demanded by the grid while the power reserve for enhancing primary frequency support is maximized. Finally, as an application of the proposed model, a decentralized wind farm control strategy is designed and compared with a centralized approach.Peer ReviewedPostprint (author's final draft

Prädiktive Regelung und Finite-Set-Beobachter für Windgeneratoren mit variabler Drehgeschwindigkeit

This dissertation presents several model predictive control (MPC) techniques and finite-position-set observers (FPSOs) for permanent-magnet synchronous generators and doubly-fed induction generators in variable-speed wind turbines. The proposed FPSOs are novel ones and based on the concept of finite-control-set MPC. Then, the problems of the MPC techniques like sensitivity to variations of the model parameters and others are investigated and solved in this work.Die vorliegende Dissertation stellt mehrere unterschiedliche Verfahren der modellprädiktiven Regelung (MPC) und so genannte Finite-Position-Set-Beobachter (FPSO) sowohl für Synchrongeneratoren mit Permanentmagneterregung als auch für doppelt gespeiste Asynchrongeneratoren in Windkraftanlagen mit variabler Drehzahl vor und untersucht diese. Für die Beobachter (FPSO) wird ein neuartiger Ansatz vorgestellt, der auf dem Konzept der Finite-Control-Set-MPC basiert. Außerdem werden typische Eigenschaften der MPC wie beispielsweise die Anfälligkeit gegenüber Parameterschwankungen untersucht und kompensiert

THE STABILITY ANALYSIS FOR WIND TURBINES WITH DOUBLY FED INDUCTION GENERATORS

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability. This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis. Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different wind generator technologies, and the challenges in high wind penetration conditions. Secondly, the small signal stability analysis model of wind turbines with DFIG is developed, including detailed rotor/grid side converter models, and the interface with the power grid. Thirdly, Particle swarm optimization (PSO) is selected to off-line calculate the optimal parameters of DFIG PI gains to maximize the damping ratios of system eigenvalues in different wind speeds. Based on the historical data, the artificial neural networks (ANNs) are designed, trained, and have the ability to quickly forecast the optimal parameters. The ANN controllers are designed to dynamically adjust PI gains online. Finally, system studies have been provided for a single machine connected to an infinite bus system (SMIB), a single machine connected to a weak grid (SMWG), and a multi machine system (MMS), respectively. A detailed analysis for MMS with different wind penetration levels has been shown according to grid code. Moreover, voltage stability improvement and grid loss reduction in IEEE 34-bus distribution system, including WT-DFIG under unbalanced heavy loading conditions, are investigated. The simulation results show the algorithm can greatly reduce low frequency oscillations and improve transient performance of DFIGs system. It realizes off-line optimization of MMS, online forecasts the optimal PI gains, and adaptively adjusts PI gains. The results also provide some useful conclusions and explorations for wind generation design, operations, and connection to the power grid. Advisors: Sohrab Asgarpoor and Wei Qia

THE STABILITY ANALYSIS FOR WIND TURBINES WITH DOUBLY FED INDUCTION GENERATORS

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability. This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis. Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different wind generator technologies, and the challenges in high wind penetration conditions. Secondly, the small signal stability analysis model of wind turbines with DFIG is developed, including detailed rotor/grid side converter models, and the interface with the power grid. Thirdly, Particle swarm optimization (PSO) is selected to off-line calculate the optimal parameters of DFIG PI gains to maximize the damping ratios of system eigenvalues in different wind speeds. Based on the historical data, the artificial neural networks (ANNs) are designed, trained, and have the ability to quickly forecast the optimal parameters. The ANN controllers are designed to dynamically adjust PI gains online. Finally, system studies have been provided for a single machine connected to an infinite bus system (SMIB), a single machine connected to a weak grid (SMWG), and a multi machine system (MMS), respectively. A detailed analysis for MMS with different wind penetration levels has been shown according to grid code. Moreover, voltage stability improvement and grid loss reduction in IEEE 34-bus distribution system, including WT-DFIG under unbalanced heavy loading conditions, are investigated. The simulation results show the algorithm can greatly reduce low frequency oscillations and improve transient performance of DFIGs system. It realizes off-line optimization of MMS, online forecasts the optimal PI gains, and adaptively adjusts PI gains. The results also provide some useful conclusions and explorations for wind generation design, operations, and connection to the power grid. Advisors: Sohrab Asgarpoor and Wei Qia

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

Wind Farms and Flexible Loads Contribution in Automatic Generation Control: An Extensive Review and Simulation

With the increasing integration of wind energy sources into conventional power systems, the demand for reserve power has risen due to associated forecasting errors. Consequently, developing innovative operating strategies for automatic generation control (AGC) has become crucial. These strategies ensure a real-time balance between load and generation while minimizing the reliance on operating reserves from conventional power plant units. Wind farms exhibit a strong interest in participating in AGC operations, especially when AGC is organized into different regulation areas encompassing various generation units. Further, the integration of flexible loads, such as electric vehicles and thermostatically controlled loads, is considered indispensable in modern power systems, which can have the capability to offer ancillary services to the grid through the AGC systems. This study initially presents the fundamental concepts of wind power plants and flexible load units, highlighting their significant contribution to load frequency control (LFC) as an important aspect of AGC. Subsequently, a real-time dynamic dispatch strategy for the AGC model is proposed, integrating reserve power from wind farms and flexible load units. For simulations, a future Pakistan power system model is developed using Dig SILENT Power Factory software (2020 SP3), and the obtained results are presented. The results demonstrate that wind farms and flexible loads can effectively contribute to power-balancing operations. However, given its cost-effectiveness, wind power should be operated at maximum capacity and only be utilized when there is a need to reduce power generation. Additionally, integrating reserves from these sources ensures power system security, reduces dependence on conventional sources, and enhances economic efficiency

Impact of communication delay on distributed load frequency control (dis-LFC) in multi-area power system (MAPS)

In this paper, impact of communication delay on distributed load frequency control (dis-LFC) of multi-area interconnected power system (MAIPS) is investigated. Load frequency control (LFC), as one of ancillary services, is aimed at maintaining system frequency and inter-area tie-line power close to the scheduled values, by load reference set-point manipulation and consideration of the system constraints. Centralized LFC (cen-LFC) requires inherent communication bandwidth limitations, stability and computational complexity, as such, it is not a good technique for the control of large-scale and geographically wide power systems. To decrease the system dimensionality and increase performance efficiency, distributed and decentralized control techniques are adopted. In distributed LFC (dis-LFC) of MAIPS, each control area (CA) is equipped with a local controller and are made to exchange their control actions by communication with controllers in the neighboring areas. The delay in this communication can affect the performance of the LFC scheme and in a worst case deteriorates power system stability. To investigate the impact of this delay, model predictive controller (MPC) is employed in the presence of constraints and external disturbances to serve as LFC tracking control. The scheme discretizes the system and solves an on-line optimization at each time sample. The system is subjected to communication delay between the CAs, and the response to the step load perturbation with and without the delay. Time-based simulations were used on a three-area MAIPS in MATLAB/SIMULINK environment to verify the investigations. The overshoot and settling time in the results reveals deterioration of the control performance with delay. Also, the dis-LFC led to zero steady states errors for frequency deviations and enhanced the MAIPS’ performance. With this achievement, MPC proved its constraints handling capability, online rolling optimization and ability to predict future behavior of systems

Islanded Wind Energy Management System Based on Neural Networks

Wind power, as the main renewable energy source, is increasingly deployed and connected into electrical networks thanks to the development of wind energy conversion technologies. This dissertation is focusing on research related to wind power system include grid-connected/islanded wind power systems operation and control design, wind power quality, wind power prediction technologies, and its applications in microgrids. The doubly fed induction generator (DFIG) wind turbine is popular in the wind industry and thus has been researched in this Dissertation. In order to investigate reasons of harmonic generation in wind power systems, a DFIG wind turbine is modeled by using general vector representation of voltage, current and magnetic flux in the presence of harmonics. In this Dissertation, a method of short term wind power prediction for a wind power plant is developed by training neural networks in Matlab software based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data. Based on the above research work, a microgrid with high wind energy penetration has been designed and simulated by using Matlab/Simulink. Besides wind energy, this microgrid system is operated with assistance of a diesel generator. A three-layer energy management system (EMS) is designed and applied in this microgrid system, which is to realize microgrid islanded operation under different wind conditions. Simulation results show that the EMS can ensure stable operation of the microgrid under varying wind speed situations