Output power levelling for DFIG wind turbine system using intelligent pitch angle control

Blade pitch angle control, as an indispensable part of wind turbine, plays a part in getting the desired power. In this regard, several pitch angle control methods have been proposed in order to limit aerodynamic power gained from the wind turbine system (WTS) in the high-windspeed regions. In this paper, intelligent control methods are applied to control the blade pitch angle of doubly-fed induction generator (DFIG) WTS. Conventional fuzzy logic and neuro-fuzzyparticle swarm optimization controllers are used to get the appropriate wind power, where fuzzy inference system is based on fuzzy c-means clustering algorithm. It reduces the extra repetitive rules in fuzzy structure which in turn would reduce the complexity in neuro-fuzzy network with maximizing efficiently. In comparing the controllers at any given wind speed, adaptive neuro-fuzzy inference systems controller involving both mechanical power and rotor speed revealed better performance to maintain the aerodynamic power and rotor speed at the rated value. The effectiveness of the proposed method is verified by simulation results for a 9 MW DFIG WTS

Performance investigation of stand-alone induction generator based on STATCOM for wind power application

Self-Excited induction generators (SEIG) display a low voltage and frequency regulation due to variable applied load and input rotation speed. Current work presents a simulation and performance analysis of a three-phase wind-driven, SEIG connect to a three-phase load. In addition, an investigation of the dynamic operation of the induction generator from starting steady state until no-load operation. It is assumed that the input mechanical power is constant where the rotor of the SEIG rotates at a constant speed. The value of the excitation capacitance which is necessary to the operation of the induction generator also computed to ensure a smooth and self-excitation starting. The output voltage of the generator is adjusted by varying the reactive power injected by STATCOM. A 3-phase IGBT voltage source inverter with a fuel cell input supply is connected as STATCOM which is used to compensate for the reduction in the supply voltage and its frequency due to variation occurred in the applied loads. This work includes introducing a neuro-fuzzyy logic controller to enhance the performance of the SEIG by regulation the generated voltage and frequency The dynamic model of SEIG with STATCOM and loads are implemented using MATLAB/SIMULIN

Transient stability enhancement of a gridconnected wind farm using an adaptive neurofuzzy controlled-flywheel energy storage system

With the rapid growth of the wind energy systems in the past years and their interconnection with the existing power system networks, it has become very significant to analyse and enhance the transient stability of the wind energy conversion systems connected to the grid. This study investigates the transient stability enhancement of a grid-connected wind farm using doubly-fed induction machine-based flywheel energy storage system. A cascaded adaptive neuro-fuzzy controller (ANFC) is introduced to control the insulated gate bipolar transistor switches-based frequency converter to enhance the transient stability of the grid-connected wind farm. The performance of the proposed control strategy is analysed under a severe symmetrical fault condition on both a single-machine infinite bus model and the IEEE-39 bus New England test system. The transient performance of the system is investigated by comparing the results of the system using the proposed ANFCs with that of the black-box optimisation technique-based proportional-integral controllers. The validity of the system is verified by the simulation results which are carried out using PSCAD/EMTDC environment

Doubly-fed induction generator used in wind energy

Wound-rotor induction generator has numerous advantages in wind power generation over other generators. One scheme for wound-rotor induction generator is realized when a converter cascade is used between the slip-ring terminals and the utility grid to control the rotor power. This configuration is called the doubly-fed induction generator (DFIG). In this work, a novel induction machine model is developed. This model includes the saturation in the main and leakage flux paths. It shows that the model which considers the saturation effects gives more realistic results. A new technique, which was developed for synchronous machines, was applied to experimentally measure the stator and rotor leakage inductance saturation characteristics on the induction machine. A vector control scheme is developed to control the rotor side voltage-source converter. Vector control allows decoupled or independent control of both active and reactive power of DFIG. These techniques are based on the theory of controlling the B- and q- axes components of voltage or current in different reference frames. In this work, the stator flux oriented rotor current control, with decoupled control of active and reactive power, is adopted. This scheme allows the independent control of the generated active and reactive power as well as the rotor speed to track the maximum wind power point. Conventionally, the controller type used in vector controllers is of the PI type with a fixed proportional and integral gain. In this work, different intelligent schemes by which the controller can change its behavior are proposed. The first scheme is an adaptive gain scheduler which utilizes different characteristics to generate the variation in the proportional and the integral gains. The second scheme is a fuzzy logic gain scheduler and the third is a neuro-fuzzy controller. The transient responses using the above mentioned schemes are compared analytically and experimentally. It has been found that although the fuzzy logic and neuro-fuzzy schemes are more complicated and have many parameters; this complication provides a higher degree of freedom in tuning the controller which is evident in giving much better system performance. Finally, the simulation results were experimentally verified by building the experimental setup and implementing the developed control schemes

Performance Improvement of Hybrid System Based DFIG-Wind/PV/Batteries Connected To DC And AC Grid By Applying Intelligent Control

One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow and providing a unit power factor by imposing a zero reactive power. An Energy Management Algorithm (EMA) is developed to maintain energy balance, meet the DC load demand, mitigate fluctuations caused by weather condition variations (wind speed and solar irradiance), and minimize battery overcharge and deep discharge. To test the proposed hybrid microgrid system operation, the different parts of the system are modeled, the wind turbine associated to the DFIG, the photovoltaic system as well as the battery storage system. Furthermore, the associated power converters with their control strategies are also presented. Global system simulation, using MATLAB/Simulink, is carried out to validate the effectiveness of both EMA and control techniques. The obtained results show significant reduction of active/reactive power ripples and THD by about 64%, 72%, and 50%, respectively. The EMA ability to manage the energy flow, produced and requested by the load. The THD rate of all injected currents is less than 4%, meaning that the proposed controls will increase the used equipments’ life span, minimize their maintenance and then reduce the hybrid power system cost

IMPROVEMENT OF POWER QUALITY OF HYBRID GRID BY NON-LINEAR CONTROLLED DEVICE CONSIDERING TIME DELAYS AND CYBER-ATTACKS

Power Quality is defined as the ability of electrical grid to supply a clean and stable power supply. Steady-state disturbances such as harmonics, faults, voltage sags and swells, etc., deteriorate the power quality of the grid. To ensure constant voltage and frequency to consumers, power quality should be improved and maintained at a desired level. Although several methods are available to improve the power quality in traditional power grids, significant challenges exist in modern power grids, such as non-linearity, time delay and cyber-attacks issues, which need to be considered and solved. This dissertation proposes novel control methods to address the mentioned challenges and thus to improve the power quality of modern hybrid grids.In hybrid grids, the first issue is faults occurring at different points in the system. To overcome this issue, this dissertation proposes non-linear controlled methods like the Fuzzy Logic controlled Thyristor Switched Capacitor (TSC), Adaptive Neuro Fuzzy Inference System (ANFIS) controlled TSC, and Static Non-Linear controlled TSC. The next issue is the time delay introduced in the network due to its complexities and various computations required. This dissertation proposes two new methods such as the Fuzzy Logic Controller and Modified Predictor to minimize adverse effects of time delays on the power quality enhancement. The last and major issue is the cyber-security aspect of the hybrid grid. This research analyzes the effects of cyber-attacks on various components such as the Energy Storage System (ESS), the automatic voltage regulator (AVR) of the synchronous generator, the grid side converter (GSC) of the wind generator, and the voltage source converter (VSC) of Photovoltaic (PV) system, located in a hybrid power grid. Also, this dissertation proposes two new techniques such as a Non-Linear (NL) controller and a Proportional-Integral (PI) controller for mitigating the adverse effects of cyber-attacks on the mentioned devices, and a new detection and mitigation technique based on the voltage threshold for the Supercapacitor Energy System (SES). Simulation results obtained through the MATLAB/Simulink software show the effectiveness of the proposed new control methods for power quality improvement. Also, the proposed methods perform better than conventional methods

Intelligent cascaded adaptive neuro fuzzy interface system controller fed KY converter for hybrid energy based microgrid applications

Purpose. This article proposes a new control strategy for KY (DC-DC voltage step up) converter. The proposed hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Renewable energy sources have recently acquired immense significance as a result of rising demand for electricity, rapid fossil fuel exhaustion and the threat of global warming. However, due to their inherent intermittency, these sources offer low system reliability. So, a hybrid energy system that encompasses wind/photovoltaic/battery is implemented in order to obtain a stable and reliable microgrid. Both solar and wind energy is easily accessible with huge untapped potential and together they account for more than 60 % of yearly net new electricity generation capacity additions around the world. Novelty. A KY converter is adopted here for enhancing the output of the photovoltaic system and its operation is controlled with the help of a cascaded an adaptive neuro fuzzy interface system controller. Originality. Increase of the overall system stability and reliability using hybrid energy system fed KY converter is utilized along with adaptive neuro fuzzy interface system controller. Practical value. A proportional integral controller is used in the doubly fed induction generator based wind energy conversion system for controlling the operation of the pulse width modulation rectifier in order to deliver a controlled DC output voltage. A battery energy storage system, which uses a battery converter to be connected to the DC link, stores the excess power generated from the renewable energy sources. Based on the battery’s state of charge, its charging and discharging operation is controlled using a proportional integral controller. The controlled DC link voltage is fed to the three phase voltage source inverter for effective DC to AC voltage conversion. The inverter is connected to the three phase grid via an LC filter for effective harmonics mitigation. A proportional integral controller is used for achieving effective grid voltage synchronization. Results. The proposed model is simulated using MATLAB/Simulink, and from the obtained outcomes, it is noted that the cascaded adaptive neuro fuzzy interface system controller assisted KY converter is capable of maintaining the stable operation of the microgrid with an excellent efficiency of 93 %.Мета. У цій статті пропонується нова стратегія управління перетворювачем KY (підвищення напруги постійного струму). Пропонована гібридна енергетична система, що живиться перетворювачем KY, використовується разом із контролером системи адаптивного нейро-нечіткого інтерфейсу. Відновлювані джерела енергії останнім часом набули величезного значення внаслідок зростання попиту на електроенергію, швидкого виснаження викопного палива та загрози глобального потепління. Однак через властиву їм уривчастість ці джерела забезпечують низьку надійність системи. Таким чином, гібридна енергетична система, що включає енергію вітру/фотоелектричних елементів/акумулятору, реалізована для отримання стабільної і надійної мікромережі. Як сонячна, так і вітрова енергія доступні з величезним невикористаним потенціалом, і разом вони забезпечують понад 60 % щорічного чистого приросту нових потужностей з виробництва електроенергії в усьому світі. Новизна. Перетворювач KY використовується тут для підвищення вихідної потужності фотоелектричної системи, і його робота керується за допомогою каскадного контролера системи з адаптивним нейро-нечітким інтерфейсом. Оригінальність. Підвищення загальної стабільності та надійності системи за допомогою гібридної енергетичної системи, що живиться перетворювачем KY і використовується разом з контролером системи з адаптивним нейро-нечітким інтерфейсом. Практична цінність. Пропорційний інтегральний контролер використовується в системі перетворення енергії вітру на основі асинхронного генератора з подвійним живленням для управління випрямлячою роботою з широтно-імпульсною модуляцією для забезпечення регульованої вихідної напруги постійного струму. Акумуляторна система накопичення енергії, в якій використовується акумуляторний перетворювач для підключення до кола постійного струму, зберігає надмірну потужність, що виробляється з відновлюваних джерел енергії. Залежно від стану заряду акумулятора, процес його зарядки і розрядки контролюється за допомогою пропорційного інтегрального контролера. Керована напруга кола постійного струму подається на трифазний інвертор джерела напруги для ефективного перетворення постійної напруги змінну. Інвертор підключений до трифазної мережі через LC-фільтр для ефективного придушення гармонік. Пропорційний інтегральний регулятор використовується для досягнення ефективної синхронізації напруги мережі. Результати. Запропонована модель змодельована з використанням MATLAB/Simulink, і з отриманих результатів випливає, що каскадний адаптивний нейро-нечіткий інтерфейс із системним контролером та перетворювачем KY здатний підтримувати стабільну роботу мікромережі з чудовим ККД 93 %