Robust control techniques for DFIG driven WECS with improved efficiency

Wind energy has emerged as one of the fastest growing renewable energy sources since mid-80‘s due to its low cost and environmentally friendly nature compared to conventional fossil fuel based power generation. Current technologies for the design and implementation of wind energy conversion systems (WECSs) include induction generator and synchronous generator based units. The doubly fed induction generator (DFIG) is chosen in this thesis because of its economic operation, ability to regulate in sub-synchronous or super-synchronous speed and decoupled control of active and reactive powers. Among the major challenges of wind energy conversion system, extraction of maximum power from intermittent generation and supervision on nonlinear system dynamics of DFIG-WECS are of critical importance. Maximization of the power produced by wind turbine is possible by optimizing tip-speed ratio (TSR), turbine rotor speed or torque and blade angle. The literature reports that a vast number of investigations have been conducted on the maximum power point tracking (MPPT) of wind turbines. Among the reported MPPT control algorithms, the hill climb search (HCS) method is typically preferred because of its simple implementation and turbine parameter-independent scheme. Since the conventional HCS algorithm has few drawbacks such as power fluctuation and speed-efficiency trade-off, a new adaptive step size based HCS controller is developed in this thesis to mitigate its deficiencies by incorporating wind speed measurement in the controller. In addition, a common practice of using linear state-feedback controllers is prevalent in speed and current control of DFIG-based WECS. Traditional feedback linearization controllers are sensitive to system parameter variations and disturbances on grid-connected WECS, which demands advanced control techniques for stable and efficient performance considering the nonlinear system dynamics. An adaptive backstepping based nonlinear control (ABNC) scheme with iron-loss minimization algorithm for RSC control of DFIG is developed in this research work to obtain improved dynamic performance and reduced power loss. The performance of the proposed controller is tested and compared with the benchmark tuned proportional-integral (PI) controller under different operating conditions including variable wind speed, grid voltage disturbance and parameter uncertainties. Test results demonstrate that the proposed method exhibits excellent performance on the rotor side and grid side converter control. In addition, the compliance with the modern grid-code requirements is achieved by featuring a novel controller with disturbance rejection mechanism. In order to reduce the dependency on system‘s mathematical model, a low computational adaptive network fuzzy interference system (ANFIS) based neuro-fuzzy logic controller (NFC) scheme is developed for DFIG based WECS. The performance of the proposed NFC based DFIG-WECS is tested in simulation to regulate both grid and rotor side converters under normal and voltage dip conditions. Furthermore, a new optimization technique known as grey wolf optimization (GWO) is also designed to regulate the battery power for DFIG driven wind energy system operating in standalone mode. In order to verify the effectiveness of the proposed control schemes, simulation models are designed using Matlab/Simulink. The proposed model for MPPT and nonlinear control of grid-connected mode and GWO based power control of standalone DFIG-WECS has been successfully implemented in the real-time environment using DSP controller board DS1104 for a laboratory 480 VA DFIG. The comparison among different controllers suggests that each control technique has its own specialty in wind power control application with specific merits and shortcomings. However, the PI controller provides fast convergence, the ANFIS based NFC controller has better adaptability under grid disturbances and ABNC has moderate performance. Overall, the thesis provides a detailed overview of different robust control techniques for DFIG driven WECS in grid-connected and standalone operation mode with practical implementation

A novel differential-based protection scheme for intertie zone of large-scale centralized DFIG wind farms

Doubly Fed Induction Generator (DFIG) wind farms as a reliable source of renewable energy have been increasingly integrated to power grid in the last two decades. Distance protection relay has continuously been the most common protection scheme implemented for wind farm intertie zone, however, nowadays with the enormous penetration of large-scale DFIG wind farms, these relays are no longer reliable, due to their incapability of providing accurate impedance measurement during internal and external faults, thus, causing maloperation, false tripping or delayed operation. In this study, a differential-based protective relay scheme is developed in Matlab/Simulink in order to provide reliable protection for wind farm intertie zone. Also, an aggregated model of a large-scale centralized wind farm has been designed to examine the performance of the proposed protection technique by imposing numerous internal and external faults at different locations. The results proved that differential-based protection relays (DBPR) are able to provide reliable, efficient and robust protection for the intertie zone of wind farms. Because, the differential relays provide high sensitivity, swift operation, immunity to power swings, and also inherently being a unit protection-based scheme that is extremely advantageous compared to distance relays. Moreover, unlike distance relays DBPRs do not require to cope with “underreach” and “overreach” characteristics, resulting in no false tripping during external faults

Genetic algorithm based optimization of overcurrent relay coordination for improved protection of DFIG operated wind farms

Rigorous protection of wind power plants is an immensely momentous aspect in electrical power protection engineering which must be contemplated thoroughly during designing the wind plants to afford a proper protection for power components in case of fault occurrence. The most commodious protection apparatus are overcurrent relays (OCRs) which are responsible for protecting power systems from impending faults. These relays are set and coordinated with each other by applying IEEE or IEC standards methods, however, their operation times are relatively long and the coordination between these relays are critical. The other common problem in wind farm protection systems is when a fault occurs in a plant, several OCRs operate instead of a designated relay to that particular fault location. This undesirable action can result in unnecessary power loss and disconnection of healthy feeders out of the plant which is extremely dire. Therefore, this research proposes a novel genetic algorithm (GA) based optimization for proper coordination of OCRs to improve their functions for protection of wind farms. GA optimization technique has some advantages over other intelligent algorithms including high accuracy, fast response and most importantly achieving optimal solutions for nonlinear characteristics of OCRs. In this work the improvement in protection of wind farm is achieved through optimizing the relay settings, reducing their operation time, time setting multiplier of each relay, improving the coordination between relays after implementation of IEC 60255-151:2009 standard. It is found that the new approach achieves significant improvement in operation of OCRs at the wind farm and drastically reduces the accumulative operation time of the relays

Grey wolf optimization based improved protection of wind power generation systems

Proper design of wind farm power system protection is an immensely challenging task for electrical power protection engineers which must be accomplished thoroughly to provide an adequate protection for power apparatus in case of fault incidence. Overcurrent relays (OCRs) are the most crucial protection tools for wind farms which are responsible for protecting power systems from faults. These relays need to be properly coordinated with each other and their settings function should be according to IEEE or IEC standards. During a fault occurrence in the wind farm especially, in the intertie section, several OCRs operate instead of a designated relay to that particular fault location, which would cause unnecessary power loss and disconnection of healthy feeders out of the wind farm that makes the situation tremendously ominous. Thus, this research proposes a novel grey wolf optimizer (GWO) based optimization technique for proper coordination of OCRs to gain improved protection of wind farms. GWO have ample advantages compared to other intelligent algorithms including, fast response, high accuracy and most notably attaining optimal solutions for nonlinear characteristics of OCRs. In this work the improvement in protection of wind farm is realized through optimizing the relay settings, reducing their operation time and time setting multiplier of each relay, improving the coordination between relays after implementation of IEC 60255-151:2009 standard. The results show that the new approach is able to achieve significant improvement in operation of OCRs at the wind farm and diminish the total operation time of the relays significantly

Genetic algorithm-based optimization of overcurrent relay Coordination for improved protection of DFIG operated wind farms

Rigorous protection of wind power plants is a critical aspect of the electrical power protection engineering. A proper protection scheme must be planned thoroughly while designing the wind plants to provide safeguarding for the power components in case of fault occurrence. One of the conventional protection apparatus is overcurrent relay (OCR), which is responsible for protecting power systems from impending faults. However, the operation time of OCRs is relatively long and accurate coordination between these relays is convoluted. Moreover, when a fault occurs in wind farm-based power system, several OCRs operate instead of a designated relay to that particular fault location, which could result in unnecessary power loss and disconnection of healthy feeders out of the plant. Therefore, this article proposes a novel genetic algorithm (GA)-based optimization technique for proper coordination of the OCRs in order to provide improved protection of the wind farms. The GA optimization technique has several advantages over other intelligent algorithms, such as high accuracy, fast response, and most importantly, it is capable of achieving optimal solutions considering nonlinear characteristics of OCRs. In this article, the improvement in protection of wind farm is achieved through optimizing the relay settings, reducing their operation time, time setting multiplier of each relay, improving the coordination between relays after implementation of IEC 60255-151:2009 standard. The developed algorithm is tested in simulation for a wind farm model under various fault conditions at random buses and the results are compared with the conventional nonlinear optimization method. It is found that the new approach achieves significant improvement in the operation of OCRs for the wind farm and drastically reduces the accumulative operation time of the relays

Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected DFIG-WECS

This paper presents an adaptive neuro-fuzzy controller (NFC)to deal with grid voltage dip conditions for grid-connected operation of doubly fed induction generator (DFIG)driven wind energy conversion system (WECS). Due to the partial scale power converters, wind turbines based on DFIG are very sensitive to grid disturbances. Current saturation at the rotor side converter (RSC)and overvoltage at the dc-link are the major concerns of DFIG driven WECS during grid-voltage fluctuation. In synchronous reference frame, an oscillatory stator flux appears during voltage dip and it is difficult to suppress with conventional proportional-integral (PI)controllers considering nonlinear system dynamics. Therefore, an adaptive-network fuzzy inference system based NFC is proposed in this paper to handle the system uncertainties and minimize the effect of grid voltage fluctuations. During normal operation, the proposed controller aims to regulate the currents as demanded by the reference real and reactive power. Under voltage dip condition, the controllers adjust the positive sequence d-q axis current components both at the grid and rotor sides by supplying required reactive power to the grid. The negative sequence reference currents at rotor end actuate the demagnetization effect of minimizing the impact of voltage dips. The simulation results exhibit the proposed NFC performance through its robust control over the rotor side currents and bus voltage during both the voltage dip and normal operation

A novel hybrid machine learning classifier-based digital differential protection scheme for intertie zone of large-scale centralized DFIG-based wind farms

The protection of intertie zone between wind farm and grid line is critical for stable and safe operation of both the grid line and wind farm in the event of fault within or outside the intertie zone. As a reliable source of renewable energy doubly fed induction generator (DFIG)-based wind farms have been increasingly integrated to the power grid over the last two decades. Nowadays with the enormous penetration of large-scale DFIG wind farms, the commonly used distance relays are no longer reliable due to their incapability of providing accurate impedance measurement during internal and external faults. Thus, it results in maloperation, false tripping, and/or delayed operation. Therefore, in this article a digital differential-based protective relay (DBPR) scheme is designed and developed to provide reliable protection for wind farm intertie zone. Additionally, a new Bayesian-based optimized support vector machine (SVM), as a supervised machine learning classifier approach, is developed to take into account both the dynamic behaviors of wind speed and the current measured by the current transformers. Thus, the proposed hybrid SVM-DBPR scheme can distinguish among the normal operation, internal and external faults correctly that helps to avoid any false tripping. In a laboratory environment the proposed DBPR is implemented in realtime using FPGA DE2-115 board equipped with Cyclone IV-E device (EP4CE115F29C7). It is found from both simulation and experimental results that the proposed hybrid SVM-DBPR is able to provide reliable, efficient, and robust protection for the intertie zone of wind farms with 97.5% accuracy rate