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Adaptive neuro-fuzzy controller for grid voltage dip compensations of grid connected dfig-wecs
(Institute of Electrical and Electronics Engineers Inc., 2019)
Amin I.K.
;
Nasir Uddin M.
;
Hannan M.A.
;
Alam A.H.M.Z.
;
10040907100
;
55663372800
;
7103014445
;
57195185389
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. � 2019 IEEE.
Adaptive Step Size Based Hill-Climb Search Algorithm for MPPT Control of DFIG-WECS With Reduced Power Fluctuation and Improved Tracking Performance
(Taylor and Francis Inc., 2018)
Uddin M.N.
;
Amin I.K.
;
55663372800
;
10040907100
One of the major challenges in harnessing wind energy is to extract maximum power from intermittent generation of wind farms as wind power generation strongly depends on wind speed variation. Among different maximum power point tracking (MPPT) algorithms, traditionally, hill climb search (HCS) method is widely used because of its simple implementation and turbine parameter-independence. However, the conventional HCS algorithm has some drawbacks such as power fluctuation and speed-efficiency tradeoff. Similarly constant tip-speed ratio (TSR) MPPT control method requires precise knowledge of optimal TSR of the turbine. Therefore, in this paper a new adaptive step size based HCS controller is proposed to mitigate the deficiencies of other techniques by incorporating wind speed measurement. The function-based adaptive control scheme evaluates the step size by the variation of the wind speed and extracted power range. The proposed variable step size based HCS-MPPT system is simulated at variable wind speed. A prototype system is also built with a low-power doubly-fed induction generator (DFIG). It is found from the results that the proposed controller reduces the steady-state power fluctuation of the DFIG based wind energy conversion system significantly compared to the conventional HCS-MPPT controller and constant TSR based MPPT controller at variable wind speeds. � 2019, � 2019 Taylor & Francis Group, LLC.
ANFIS based neuro-fuzzy control of dfig for wind power generation in standalone mode
(Institute of Electrical and Electronics Engineers Inc., 2019)
Amin I.K.
;
Nasir Uddin M.
;
Marsadek M.
;
10040907100
;
55663372800
;
26423183000
This paper presents an adaptive neuro-fuzzy controller (NFC)for doubly fed induction generator (DFIG)based wind energy conversion system (WECS)to operate under standalone mode. The NFC is developed based on adaptive-network-based fuzzy inference system (ANFIS)architecture since it has the unique advantage of fast convergence combining the robustness of fuzzy logic and flexibility of neural network algorithm. For the isolated operation of DFIG-WECS, ANFIS is designed for load side converter (LSC)control. The proposed scheme demonstrates improved dynamic performance under variable wind speed and load conditions by maintaining stable output voltage. The supply frequency to the load remains stable by virtue of precise control of LSC while turbine rotation varies with fluctuating wind speed. The flux alignment is ensured by the proportional-integral (PI)control of rotor side converter. The simulation results exhibit the controller's outstanding performance through its robust control over load-voltage and supply frequency under the variation of demand load power and wind speed. � 2019 IEEE.
Genetic algorithm based optimization of overcurrent relay coordination for improved protection of DFIG operated wind farms
(Institute of Electrical and Electronics Engineers Inc., 2018)
Rezaei N.
;
Nasir Uddin M.
;
Khairul Amin I.
;
Lutfi Othman M.
;
Marsadek M.
;
57216077273
;
55663372800
;
10040907100
;
55153333400
;
26423183000
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. � 2018 IEEE
Genetic Algorithm-Based Optimization of Overcurrent Relay Coordination for Improved Protection of DFIG Operated Wind Farms
(Institute of Electrical and Electronics Engineers Inc., 2019)
Rezaei N.
;
Uddin M.N.
;
Amin I.K.
;
Othman M.L.
;
Marsadek M.
;
57216077273
;
55663372800
;
10040907100
;
55153333400
;
26423183000
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. � 1972-2012 IEEE.
Grey Wolf optimization based improved protection of wind power generation systems
(Institute of Electrical and Electronics Engineers Inc., 2018)
Rezaei N.
;
Nasir Uddin M.
;
Khairul Amin I.
;
Lutfi Othman M.
;
Abidin I.Z.
;
57216077273
;
55663372800
;
10040907100
;
55153333400
;
35606640500
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. � 2018 IEEE
Grey Wolf optimization based power management strategy for battery storage of dfig-wecs in standalone operating mode
(Institute of Electrical and Electronics Engineers Inc., 2018)
Nasir Uddin M.
;
Khairul Amin I.
;
Rezaei N.
;
Marsadek M.
;
55663372800
;
10040907100
;
57216077273
;
26423183000
This paper presents a novel grey wolf optimization based automatic power management strategy of a doubly fed induction generator (DFIG) - wind energy conversion system (WECS) operating in standalone mode. In isolated wind power generation system, either the dc-link or the ac load terminal is backed up by energy storage units, such as battery, super capacitor, dc power supply etc. In such cases, efficient power exchange from the supporting power source is very crucial during load fluctuation and intermittent wind speed. In this paper, a unique meta-heuristic algorithm known as grey wolf optimization (GWO) is introduced to ensure the optimized power exchange in a battery supported DFIG operating in standalone (SA) mode. The proposed optimization algorithm is chosen for its simplistic implementation, fast convergence and superior ability to avoid local optima over other conventional optimization techniques. The reference battery power is generated by the designed control unit which regulates the power flow in optimized manner through the bi-directional converter at battery end. Besides, the load-side and rotor-side converter control blocks are designed to stabilize the generated output. The simulation results of the overall system shows rigorous control over output voltage and load frequency under fluctuating wind speed and variable load condition and efficient battery power flow in standalone operating mode. � 2018 IEEE
A Novel Differential-based Protection Scheme for Intertie Zone of Large-Scale Centralized DFIG Wind Farms
(Institute of Electrical and Electronics Engineers Inc., 2019)
Rezaei N.
;
Uddin M.N.
;
Amin I.K.
;
Othman M.L.
;
Marsadek M.
;
57216077273
;
55663372800
;
10040907100
;
55153333400
;
26423183000
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. � 2019 IEEE.
A Novel Hybrid Machine Learning Classifier-Based Digital Differential Protection Scheme for Intertie Zone of Large-Scale Centralized DFIG-Based Wind Farms
(Institute of Electrical and Electronics Engineers Inc., 2020)
Rezaei N.
;
Uddin M.N.
;
Amin I.K.
;
Othman M.L.
;
Marsadek M.B.
;
Hasan M.M.
;
57216077273
;
55663372800
;
10040907100
;
55153333400
;
26423183000
;
57220989460
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. � 1972-2012 IEEE.

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