Fuzzy Logic Approach in Controlling the Grid Interactive Inverters of Wind Turbines

Transcription

1 Indian Journal of Science and Technology, Vol 7(8), , August 2014 ISSN (Print) : ISSN (Online) : Fuzzy Logic Approach in Controlling the Grid Interactive Inverters of Wind Turbines Maryam Sadeghi * and Majid Gholami Department of Electrical Engineering, Islamic Azad University Islamshahr branch, Islamshahr, Iran; sadeghi@iiau.ac.ir Abstract Due to restrictions on the use of fossil fuels and the need of using the renewable energy resources, and increasing energy demand, it is necessary to use the Distributed Energy Resources (DER) working in a microgrid to end the environmental developing concerns of high investment required for expanding the old current distribution and transition system. In this regard, the moderated fuzzy logic approach is presented for controlling the interactive inverters to integrate the wind turbine to grid using the Permanent Magnet Synchronous Generators (PMSG) to adjust the frequency of wind turbine generator to grid. The proposed interactive solutions is to parallel the electric utility and wind turbine with load and in case of excess of energy generated by wind, it could be exported to grid through the interactive inverter controlled by fuzzy logic controller scheme. Simulation result reveals that the grid current doesn t varied if the wind turbine connect to the grid and it is also in phase with the grid voltage so the unity power factor is expected and the maximum power transferred to load with the minimum total grid current harmonic distortion of 0.02% that is negligible. Keywords: DER, FLC, Microgrid, PMSG, Renewable energy resource, THD 1. Introduction The current world sources of energy including coal, oil, natural gas that are dependent on the fossil fuels which are non renewable and more expensive 1,8. In addition, usage of the current fossil fuels will damage the nature continuously. Wind is one of the renewable energy sources. Wind turbine is found in microgrid to transform the kinetic power of wind to electricity. In this approaches the fuzzy algorithm is used to integrate the wind turbine micro grid into main grid. 2. Microgrid Increasing energy demand and the need to use renewable energy resources, open the new horizontal to use micro-grid to indicate more reliability and surmount the environmental concerns. Moreover in case of the high investment required for renovating and developing the current old traditional transmission and distribution network, using Microgrid seems to be inevitable 2,9. Microgrid presents the best solution to electricity supply and integrates distributed energy resources such as micro turbines, solar photovoltaic, wind and combined heat and power into main grid in a balanced net-zero system. 3. Wind Turbines Wind turbine generator offers as one of the alternative cleanest solutions for generating electricity. Due to its slow rotation with the speed of rev/min, the speed should be increased up to rev/min for directly integrating to grid. This requires a great number of poles for wind turbine. One solution is the using of the Permanent Magnet Synchronous Generators (PMSG) in which the frequency of the induced voltage in the rotor is proportional to the *Author for correspondence

2 Maryam Sadeghi and Majid Gholami number of poles in stator 5. In this case, the wind turbines generators could be connected to the grid through the back to back fully controlled converters with the small pole pitch 3,4. Wind turbine and its connection to grid towards PMGS and back to back converter is shown in Figure 1. The grid interactive inverter brings more enhancements in level of reliability in contrast to tie inverter. It is in parallel with electric utility to supply the local load and if the energy produced by wind turbine is more than the amount of load required, the extra energy could be delivered to grid. This case is achieved by presenting the fully current controlled inverters to regulate the current injected into grid. The advantage of indirect grid connection is that it is possible to run the wind turbine at variable speed. In other word by using the indirect grid connection of wind turbine via the proposed inverter it also be able to store the excess of energy in case of gusts of wind that makes the wind rotor turn faster. 4. Control Strategy At first glance the conventional PID may seems to achieve with an acceptable performance by using a conventional PID controller among the specified operating point, but experiments reveals that poor transient response is almost achieved due to the variation that takes place in dynamic of plant system in time the wind speed varies or any deviation occurred in grid when interactive inverter connected to it. Fuzzy logic controller is nonlinear in nature and use the linguistic terms instead of numerical, so it free designers from determining the precise mathematical model of system and improves the system performance even in the vast majority of variation in system parameters. FLC basic construction is shown in Figure 2. The proposed FLC follow the reference current under the deviation that may have been occurred in conjunction with differences between the output level of inverter and Grid impedance. FLC comprises the fuzzier to map inputs to membership functions, FLC inference engine that use the expert knowledge to make the fuzzy rules to define its outputs by input through the If Then rules and the defuzzifier at last to map the output membership functions to a crisp value. With considering seven membership functions as Negative Large, Negative Medium, Negative small, zero, Positive Small, Positive Medium and Positive Large for each one of inputs (error and change of error), totally 7*7=49 rules are defined for expressing the relations between input/ output ( Figure 3). FLC control loop for making the switching pattern is shown in Figure 4. Deviation between the output current Figure 2. FFLC basic concept. 5. Fuzzy Logic Controller Fuzzy logic was invented first by Professor Lotfi Zadeh in 1965 for unclear or ambiguous notions. It is basically constructed from logic with multi amount to come for a better specification of human behavior and better commentary by describing the intermediate categories between the implications like true and false, black and white rather than the absolute values that have been defined in Boolean logic before 6. It consists of human rules described in sentences to identify the modern control strategy of rule equations which derived from the human experiences. Figure FLC rules. Figure 1. Wind turbine and its connection to grid. Figure 4. FLC current source control loop. Indian Journal of Science and Technology 1197

3 Fuzzy Logic Approach in Controlling the Grid Interactive Inverters of Wind Turbines of inverter and grid current is intended as error, variation between two steps of error is considered for change of error. These two parameters are applied to FLC as inputs. The FLC output comes from the crisp value defined from the defuzzification part to make the switching pattern. 6. Results and Discussion In this approach the current source controller adjust the inverter current to operate as a current source so that the output current of inverter are no sensitive to phase error and output voltage deviation. Figure 5 illustrates the control outline in a simplified diagram. AS the wind turbines with indirect grid connection trough the controllable inverter, runs as the individual Figure 5. Schematic diagram of FLC for interactive inverter controller of wind torbine to integrate to grid. AC microgrid. So in this approach the Distributed energy resource is modeled as a wind turbines or microgrid (Figure 5). At first stage the Phase Lock Loop (PLL) sense the grid current and tracks the phase of its input to match the grid current and voltage for phase and frequency so that the unity power factor be expected. The FLC control the current source. With regard to error between the inverter output current and reference one from the PLL, FLC generate the adaptive switching pattern to fire the IGBT inverters to follow the grid current in minimum deviation. Line transformer is used to prevent the voltage interference between grid and wind turbine 7. LC filter reduces the high frequency harmonic distortions caused by the pulse width modulation. FLC is investigated for adjusting the pulse with modulation pattern for interactive inverters (Figure 6). The input membership functions of error and change of error are shown in Figures 7 and 8, indicating seven functions for each including the boundaries between -1 to 1. The output membership functions are shown in Figure 9. The 49 Fuzzy rules are shown in Figure 10. Figure 11 demonstrates the fuzzy surfaces of error, change of error and output. Fuzzy inputs and error and change of error are demonstrated in Figure 12. The changes of error is from zero Figure 6. FLC for integrating wind turbine to grid simulation in Matlab Indian Journal of Science and Technology

4 Maryam Sadeghi and Majid Gholami Figure 7. Figure 8. Seven membership functions for error. Membership functions for change of error. up to 0.3, the variation of change of error is from mines one to unity and the fuzzy output differs between zero to 0.1. Inverter current and its voltage variation, reference current from PlL and finaly reference current together with thiangle current are shown in Figure 13. Figure 14 depicts the PWM switching pattern for appling to the interactive inverters. Load current and its voltage are shown in Figure 15 which indicate the same phase of current and voltage with the same frequency and unity power factor. The reference current from PLL the grid current and grid voltage are shown in Figure 16. As it shown the grid current and voltage are in phase with each other with the same frequency. Figure 17 demonstrates the grid current and total harmonic distortion that is 0.02%. Figure 9. Seven membership functions for output. Figure 12. Fuzzy input, fuzzy input error, fuzzy input change of error. Figure Fuzzy control roules. Figure 13. Inverter current, inverter voltage, the reference current, and reference current together with triangle current. Figure 11. Fuzzy surface for inputs/output fuzzy set. Figure 14. PWM signals for inverter control. Indian Journal of Science and Technology 1199

5 Fuzzy Logic Approach in Controlling the Grid Interactive Inverters of Wind Turbines Figure 15. Load current, load voltage. integrates to the grid and so the maximum power amount is delivered to the load with the high efficiency. The THD of grid in this case is around 0.02%, which express the adequate robustness of proposed control strategy with the proper performance that could be investigated for any distributed resources in microgrid that may integrate to the main grid. 8. Acknowledgement Authors thank the research deputy of Islamic Azad University, Islamshahr branch for their efforts and their financial support in this research. Figure 16. The sinsodial output current of PLL, grid current, grid voltage. Figure Conclusion Grid current total harmonic distortion. Current source fuzzy logic control strategy is introduced for interactive inverter control to integrate the wind turbine to the grid. The interactive inverter comprises the line frequency transformer together with the LC filter for eliminating the high frequency harmonics comes from the high frequency switching pattern of PWM. FLC and its adaption lows improve the current source controller to achieve the high smooth strategy of control with the minimum THD of grid. Simulation results depict that he grid current is in phase with voltage with the same frequency and unity power factor in time the wind turbine 9. References 1. Twidell J, Weir T. Reneuable energy resources. 2nd ed. London and New York: Taylor & Francis; Salam AA, Mohamed A, Hannan MA. Technical challenges on microgrids. ARPN Journal of Engineering and Applied Sciences. 2008, December; 3(6): Pogaku N, Prodanovic M, Green TC. Modeling, analysis and testing of autonomous operation of an inverter-based microgrid. IEEE Trans Power Electron. 2006; 22(2): Green TC, Prodanovc M. Control of inverter-based microgrids. Elec Power Syst Res. 2007; 77: Mahersi E, Khedher A, Mimoun MF. The wind energy conversion system using PMSG controlled by vector control and SMC strategies. International Journal of Renewable Energy Research. 2013; 3(1): Lee CC. Fuzzy logic in control systems, fuzzy logic controller. IEEE Trans Syst Man Cybern Part I Mar/Apr; 20(2). 7. Sefa I, Altin N. Simulation of fuzzy logic controlled grid interactive inverter. Electronics and Computer Science Journal Hatziargyriou N, Asano H, Iravani R, Marnay C. An Overview of ongoing research, development, and demonstration projects. IEEE Power & Energy Magazine Jul/ Aug; Lopes JAP, Moreira CL, Madureira AG. Defining control strategies for microgrids islanded operationieee Trans Power Syst May; 21(2): Indian Journal of Science and Technology