In this paper, a PID controller was used to control the turbinespeed of the steam power plant. Due to the turbine connection of the power plant to the rotor of the generator through the shaft, the turbinespeed is equal to the rotor speed. Therefore, to calculate the turbinespeed, the rotor speed of the generator is measured. In order to control the optimum turbinespeed, the governor's input was set via a PID controller. By designing an optimization problem, the parameters of this controller were calculated using the particle pool algorithm. Three characteristics of Kp, Proportional gain, Ki, integral gain, and Kd were obtained for PID controller. In order to analyze the designed controller behavior, Simulation software was used to simulate a simple power plant state equation. In order to check the behavior of the controller under transitional conditions due to a disturbance or disturbance, 100 seconds after the start of the simulation, a step change was applied at 0.5 ppm in the reference value. The imposed change to the system was 50 seconds later (ie 150 seconds), and the reference value was returned to the initial value of 1 perion. Before the control, the system had severe fluctuations. The designed controller was able to easily rotate the rotor's power output and restore the generator to stable performance. Confirming the results was obtained by simulating the controller's satisfactory performance. The rotor speed variations in transient conditions caused by the change in the reference value from 1 to 1.5 prions and also in the transient state due to the return to
• Hydro process application software In order to guarantee safe, reliable and optimized power plant operations, Alstom Hydro has developed and qualified specific turbinespeed gov- erning systems. With over 100 years experience in the field of turbinespeed regulation, Alstom Hydro proposes cost-effective, reliable, innovative, customized governing solutions to its customers. The governing system is an essential part of the overall plant control system. It provides the
Fig. 3.7 & Fig. 3.8 shows that the studied system is subjected to phase to phase to ground fault occurred at bus 25 and cleared after (0.1s) from 3 sec to 3.1 sec. It can be observed from Fig. 3.7(b) the output active power for each pair of turbine is 3MW, absorbed reactive power for each pair of turbine reaches to 1.47Mvar, turbinespeed for each pair of turbine reaches to 1.005pu. Because of the lack of reactive power support the voltage at bus 25kV now drops to 0.95pu as shown in Fig. 3.7(a). While with STATCOM the output active power for each pair of wind turbine reaches to 3MW, absorbed reactive power for each pair of turbine reaches 1.47Mvar, turbinespeed for each pair of turbine reaches to 1.0045pu as shown in Fig. 3.8(b). With respect bus 25 kV, voltage reaches to 1pu in Fig. 3.8(a), active power reaches to 9MW and absorbed reactive power reaches 2Mvar.
complete wind turbine model such as the AHSE model. Furthermore, the system was tested under turbulent wind conditions with comparative results presented in Fig. 8. Differences in results can be explained by the discussion given in the previous paragraph. As can be observed, the pitch control attempts to keep the wind turbinespeed constant at the rated value whilst maximum optimum power tracking is attempted. However, both constraints cannot be met under these circumstances. Of particular interest is that the DC voltage is virtually unaffected even under these very high transient conditions.
Block diagram of asynchronous electrical system. In this system, the microcomputer accepts inputs such as wind speed and direction, turbinespeed, load requirements, amount of energy in storage, and the voltage and frequency being delivered to the load. The microcomputer sends signals to the turbine to establish proper yaw (direction control) and blade pitch, and to set the brakes in high winds. It sends signals to the generator to change the output voltage, if the generator has a separate yield. It may turn on on critical loads in times of light winds and it may turn on optional loads in strong winds. It may adjust the power conditioner to change the load voltage and frequency. It may also adjust the storage system to optimize its performance.
Bladeless turbine characteristics. The operating characteristic measurements took place in a closed circuit, in which the hydrotechnical potential was created by a centrifugal pump. This arrangement can differ from natural streams in some respects as the usable water gradient usually drops with growing flow rate in natural water courses. The character of the hydraulic potential that the turbine had to handle in the laboratory is simulated by the constant flow curves: Q = 8; 10 and 12 l/s in the relationship of the usable water gradient and the turbinespeed (Figure 5). Another turbine feature (monitored earlier) needs to be mentioned in this respect: the turbine “throttles down”, i.e. reduces the water flow with decreasing speed (Polák et al. 2013).
_____________________________________________________________________________________________________ Abstract - The main objective of Supervisory Control and Data Acquisition (SCADA) is to give a means to the human operator to control and to command a highly automated process. Supervisory control and data acquisition systems (SCADA) are widely used in industries for supervisory control and data acquisition of industrial processes. It has also revolutionized the field of data sciences in a much inevitable manner. The communication between the control centre of SCADA and the remote station takes place by various communication channels such as optical fibre cables, microwave technology and power line carrier communication. The presence of such an efficient system give rise to certain faults results in malfunction of such systems. As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine fault before they reach a catastrophic stage is critical to reduce the operational and maintenance cost due to unnecessary scheduled maintenance. To this end, it is important to be able to perform maintenance before it’s needed. Instead, by performing complex analysis of existing data from turbine’s supervisory control and data acquisition system (SCADA) system, valuable insights into turbine performance can be obtained at a much lower cost. This paper proposes a methodology of fault prediction for wind turbine based on stored SCADA data set using decision tree approach. Fault analysis and diagnosis of faults help in protecting both hardware and software of devices employed in SCADA based systems. On the basis of information received, the SCADA operator is in a position to take important decision related to smooth and faultless generation, transmission and distribution of power. The proposed research provides insight analysis for relationship ships of various attributes like rotor speed, turbinespeed, wind speed, power and output of the dataset and also provides fault analysis using Decision Tree Data Mining approach on the said dataset care of various parameters.
First of all, it seems that the three curves are very similar and peak efficiencies are around 70%. Secondly, the rotational speeds (times solidity) at free-wheeling are very similar for the Atlanta and laboratory turbines, whereas the Denver tur- bine appears to rotate slower than the other two. This is probably due to the ef- fect of non-submergence and aforementioned drag torque created when the arms slice through the water. In contrast, the Denver turbine appears to yet fea- ture a greater peak efficiency and this is most likely because the Denver turbine operated in a housing whereas the other two turbines did not. Apparently, the Denver turbine benefitted from the additional flow acceleration caused near the housing’s sidewalls on the upstroke of the rotor. The effect of the housing on the turbine performance could not have been quantified in the laboratory and in Atlanta due to the limited width of the flume or channel, respectively.
W ind energy is an abundant renewable source of new electrical generation capacity in the world, and it is exploited by converting the kinetic energy of moving air mass into electricity; therefore, it is necessary to introduce tools to make these installations more profitable . Practically, there are two main types of horizontal axis wind turbines: fixed speed and variable speed . In this study, we consider the case of variable speed, due to its great ability in the extraction of energy. In addition to that, variable speed system is more complex and requires an efficient control strategy . Several studies have been devoted to the control of the aeroturbine mechanical as well as the electrical components. This work is devoted to the mechanical part (aeroturbine), with the main objective of designing a controller in order to maximize the energy captured from the wind and minimize the stress on the drive train shafts that takes into consideration the nonlinear nature of the system behavior and the flexibility of the drive-train shaft. The proposed control structure also overcomes the drawbacks of some existing control methods.
Connecting the generator to a resistive load makes it possible to deliver power at any rotational speed off the rated. The unit has two relay protections that measure rotational speed in order to detect faults: the frequency fault protection and the over voltage protection (U/f>). To keep the machine in operation with the dumpload connected, these two relays must be temporarily disabled. On the actual unit the field breaker had a over speed protection (to prevent over voltag- es) this was deactivated in order to keep the field breaker connected, so that the generator would be magnetized at all times.
3. Prakash et al. reported on the effect of loading level and distribution (front, mid and aft) on LP turbine profile losses. The data demonstrated increased suction side separation and high losses as the loading level increases, the loading is moved aft or the Reynolds number decreases. Although secondary loss was not addressed by the authors, they did comment that front loaded blades typically have lower profile losses but higher
This paper presents a nonlinear control of doubly fed induction generator (DFIG) integrated with a wind system. Initially, we propose a model of wind turbine and generator in order to apply the Field Orientation Control (FOC) approach. The Vector Control is still the most commonly used for the control of power and reactive powers, as well as in many industrial systems especially in production of electrical energy. Next, the synthesis of a proportional-integral regulator (PI) is presented. Subsequently, a technical control for wind energy systems is developed. The principle of this control is to direct the flux vector to make the doubly fed induction machine similar control standpoint to a separately excited DC machine. The performance’s system is analyzed and compared with a validation on the environment Matlab / Simulink. Keywords: DFIG, Vector Control, Wind Energy, PI Controller.
Wind energy is one of the most popular types of renewable energy being explored. It has huge potential to fulfil our future power requirements. Wind turbine is a device that is used for converting kinetic energy from the wind into mechanical energy. Wind turbine has high nonlinearity and it is not possible to use a linear model for control design and state estimation. To this extent we introduce Extended Kalman Filter Algorithm. The Extended Kalman Filter (EKF) is based on the nonlinear wind turbine model that includes the rotor speed, tower top displacement and its velocity.
hand, it does not happen for TSR 0.4 and 0.5. It is because the fast turbine rotation makes the air more difficult to enter the gap between the blades to produce the positive moment so that the turbine with the blade number of 22 which has the smallest gap between the blades will have the lowest value of Cm and Cp. The speed vector on TSR 0.5 can be seen in Fig. 11.
VAR wind model outlined above. The system load is anticipated to remain flat over the period from 2010 till 2020 due to the load reduction from energy efficiency measures and increase from electric vehicle charging and heating. The power system inertia constant values are calculated on a hourly basis. The impact of the aggregate inertial response from wind capacity on frequency stability is examined assuming a sudden loss of 1800 MW of generation occurs. Since the response from the wind will depend on the mean wind speed levels prevailing at the time in question, it is important to investigate how this might vary through the day. 24 simulation runs have thus been carried out. Figure 3 shows the frequency minimum (nadir) for each hour through the day following the event with and without inertial frequency support from the wind capacity. It can be seen that the power contribution from the total assumed operational GB wind plant can significantly reduce the extent to which the frequency would fall. Figure 4 shows that the maximum rate of change of frequency (ROCOF) immediately following the transient can be reduced by the wind plant.
The quality of the power output in a wind power system can be improved if adequate controls are incorporated in the system. The fluctuating wind generator output needs to be controlled. This paper deals with the dynamic analysis and control of variable speed wind turbine system for transient studies. A MATLAB model has been developed for simulation and analysis of a wind power system. The simulations were carried out first for an uncontrolled wind generator and then with various commonly used control techniques like blade pitch control, converter/inverter control and the combination of pitch and converter/inverter control. The results are presented and important conclusions have been drawn
ABSTRACT: In the huge connected grid various types of renewable resources are added to extract more power. But due to this addition more number of problems occurs into the grid connection. The problems are voltage fluctuation and harmonic distortion. The very most important renewable source is wind generation system. The grid connected wind turbine produces power fluctuations which may produce flicker. Due to this connection it affects power quality. This paper presents various methodologies which minimized flicker which are produced due to variable speed of wind turbine. This paper overviews the methods which can investigate the flicker emission and mitigation issues.
Wind energy is a source of renewable energy that has been used in some form or another since at least the tenth century A.D. . The original inventor of the wind mill, the predecessor to the wind turbine, is actually greatly debated amongst historians, but the ‘first accepted establishment of the use of windmills’ is dated as ‘the tenth century in Persia’ [4, p7]. The Chinese developed a vertical axis windmill as early as 1219, though it was not very widely adopted. Around the same time, a horizontal axis windmill was developed further west in Europe. This windmill gained popularity very quickly, as David Spera describes in Chapter 1 of his book Wind Turbine Technology, and was much more widely adopted than the Chinese vertical axis wind turbine. Spera explains that “it’s adoption is readily explained by the fact that it was so much more efficient” [4, p14]. He goes on to explain that this increased efficiency was a result of the horizontal axis windmills (inadvertently) using lift rather than drag to extract energy from the wind, which is a much more efficient process. These primitive designs have evolved over time, especially with the advent of the science of aerodynamics at the end of the nineteenth century, into the wind turbines that we are familiar with today.
This paper presents a voltage and frequency control of wind-hydro hybrid system in isolated locations in which one squirrel cage induction generator (SCIG) driven by a variable speed wind turbine and another SCIG driven by a constant power hydro turbine feeding three phase four wire local loads. The system mainly uses a rectifier and a pulse width modulation controlled insulated-gate-bipolar-transistor-based voltage-source converters (VSCs) with a battery energy storage system at their dc link. The main objectives of the rectifier is to convert the ac power generated by the wind system to dc and the control algorithm for the VSC is the control of the magnitude and the frequency of the load voltage. This system has the capability of bidirectional active- and reactive-power flow, by which it controls the magnitude and the frequency of the load voltage. Here pitch angle control is used to achieve maximum power tracking(MPT).In this paper wind turbine, hydro turbine, MPT controller, and a voltage and frequency controller are modeled and simulated in MATLAB using Simulink and Sim Power System set toolboxes, and different aspects of the proposed system are studied for various types of loads, and under varying wind-speed conditions. The performance of the proposed system is presented to demonstrate its capability of MPT, voltage and frequency control (VFC), harmonic elimination, and load balancing.
Several wind turbine controllers have been proposed for the variable-speed operating regime. Reference  defines the control objective as achieving optimal rotational speed tracking while rejecting fast wind speed variations and avoiding significant control efforts that induce undesirable torques and forces on the wind turbine structure. Many control strategies have been proposed in the literature primarily based on linear time-invariant (LTI) models. Classical techniques based on PI or PID controllers [6, 7] have also been used extensively. But they had not given acceptable results