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INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING & International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) IJEET Volume 4, Issue 4, July-August (2013), pp. 108-117 © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2013): 5.5028 (Calculated by GISI) ©IAEME www.jifactor.com WIND ENERGY CONVERSION SYSTEMS USING FUZZY CONTROLLED STATCOM FOR POWER QUALITY IMPROVEMENT 1 S.MUNISEKHAR, 2O.HEMAKESAVULU, 3Dr.M.PADMALALITHA 1 PG Student, Dept of EEE, AITS, Rajampet, India 2 Associate Professor, Dept of EEE, AITS, Rajampet, India 3 Professor & Head of the Dept, Dept of EEE, AITS, Rajampet, India ABSTRACT This paper investigates the power quality issues due to installation of wind energy conversion system(WECS) with the distribution system.When the wind energy conversion system is connected to distribution system the power quality issues like variation of voltage,current and harmonics at source side and load side will be penerated into the distribution system. To mitigate the harmonics produced at source side and load side, a fuzzy controlled static compensator(F-STATCOM) is connected at point of common coupling. The F-Statcom controller and STATCOM for distribution system connected wind energy generating(WGS) to mitigate power quality issues is simulated by MATLAB/SIMULINK software. Keywords: Fuzzystatcom (F-STATCOM), Powerquality (PQ), Wind Generating System (WGS), Wind Energy Conversion System(WECS). I. INTRODUCTION The generation of wind energy has been increasing rapidly and has become cost competitive with other means of generation. The power generated from wind turbine is always fluctuating due to environmental conditions. The wind power generated from wind turbine is expected to be a promising alternative energy source which can bring new challenges[1]. The kinetic energy of the wind is being absorbed by the rotor which constitutes blades which are mechanically coupled to the alternator. There are three types of alternator technologies to interface with wind turbine. 1. Conventional wound rotor or squirrel cage induction machines. These are supplemented by capacitors to supply reactive power needs. 2. Doubly fed wound rotor induction machines which employ power converters to control the rotor current to provide reactive power support and control. 3. Non-power frequency generation that requires an inverter or converter interface. The issue of power quality is of great importance to the wind energy conversions systems. The main power quality that arises when wind turbine connected to distribution system are sustained 108 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME interruptions, voltage regulation, harmonics and voltage sags. The power quality issues that arise when wind turbine is connected to distribution system is minimized by fuzzy controlled statcom. The fuzzy controlled statcom works with a set of fuzzy rules which are implemented in the matlab program. The way to represent inexact data and knowledge, closer to humanlike thinking, is to use fuzzy rules instead of exact rules when representing knowledge. Fuzzy systems are rule-based expert systems based on fuzzy rules and fuzzy inference. Fuzzy rules represent in a straightforward way "commonsense" knowledge and skills, or knowledge that is vague, or contradictory. This knowledge might have come from many different sources. Commonsense knowledge may have been acquired from long-term experience, from the experience of many people, over many years. The FUZZY-STATCOM control scheme for Distribution line connected wind energy generation for power quality improvement has following objectives. • Sustained interruptions are minimized in the distribution system. • Reactive power support only from F-STATCOM to wind Generator and Load. • Minimization of Harmonics at source and load sides i.e reduction in THD values. II. FUZZY-STATIC COMPENSATOR (STATCOM) A. Principle of STATCOM A STATCOM is a voltage source converter (VSC), with the voltage source behind a reactor. The voltage source is created from a DC capacitor and therefore a STATCOM has very little active power storage. However, its active power capacity can be increased if a suitable energy storage device is connected across the DC capacitor. The reactive power at the terminals of the STATCOM depends on the amplitude of the voltage source. For example, if the terminal voltage of the VSC is higher than the AC voltage at the point of connection, the STATCOM generates reactive current; on the other hand, when the amplitude of the voltage source is lower than the AC voltage, it absorbs reactive power. The time respone of a STATCOM is shorter than that of an SVC, mainly due to the fast switching times provided by the IGBTs of the voltage source converter[3]. The STATCOM also provides better reactive power support at low AC voltages than an SVC, since the reactive power from a STATCOM decreases linearly with the AC voltage. Reactive power can be altered by modifying the voltage amplitude of the VSC. For this purpose, a transformer with a turns-ratio of 1:1 or a reactor is assumed. In addition, constant distribution voltage is assumed. STATCOMs have the ability to address transient events at a faster rate and with better performance at lower voltages than a Static Voltage Compensator (SVC). The maximum compensation current in a STATCOM is independent of the system voltage. A STATCOM provides dynamic voltage control and power oscillation damping, and improves the system’s transient stability. By controlling the phase angle, the flow of current between the converter and the ac system are controlled[5]. Figure 1: STATCOM connected to distribution line 109 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME B. Fuzzy logic controller In a fuzzy logic controller (FLC), the dynamic behaviour of a fuzzy system is characterised by a set of linguistic description rules based on expert knowledge. The expert knowledge is usually of the form IF (a set of conditions are satisfied) THEN (a set of consequences can be inferred). Since the antecedents and the consequents of these IF-THEN rules are associated with fuzzy concepts (Linguistic terms), there are often called fuzzy conditional statements. A fuzzy control rule is a fuzzy conditional statement in which the antecedent is a condition in its application domain and the consequent is a control action for the system under control. Basically, fuzzy control rules provide a convenient way for expressing control policy and domain knowledge. Furthermore several linguistic variables might be involved in the antecedents and the conclusion of these rules. When this is the case the system will be referred as multiple input multiple output fuzzy systems. Figure 2: Fuzzy logic controller III. DISTRIBUTION CO-ORDINATION RULE The Distribution quality characteristics and limits are given for references that the customer and the utility may expect. A. Voltage Rise (u): The voltage rise at the point of common coupling can be approximated as a function of maximum apparent power Smax of the turbine, the grid impedances R and X at the point of common coupling and the phase angle ∅ given in Eq.1 u = Smax(Rcos φ– Xsinφ )/U2 (1) Where u = Voltage Rise Smax = Maximum Apparent power U = Nominal Voltage of the grid Φ = Phase difference The limiting voltage rise value is < 2%. B. Voltage Dips (d): The voltage dips is due to startup of wind turbine and it causes a sudden reduction of voltage. It is the relative % voltage change due to switching operation of wind turbine. The decrease of nominal voltage change is given in Eq. 2. 110 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME d=KuSn/Sk (2) Where, d is relative voltage change. Sn is rated apparent power. Sk is short circuit apparent power. Ku is sudden voltage reduction factor. The acceptance voltage dips limiting value is ≤ 3%. C. Flicker: The long term flicker is given by Plt = C ( k) Sn/Sk (3) Where, Plt = long term flicker. C ( k) =flicker coefficient calculated from Rayleigh distribution of the wind speed . The Limiting Value for flicker coefficient is about ≤ 0.4. D. Harmonics: The harmonic distortion is assessed for variable speed turbine with an electronic power converter at the point of common connection. The total harmonic voltage distortion of voltage is given as in Eq. 4 V (4) Where, Vn is the nth harmonic voltage. V1 is the fundamental frequency(50) Hz. The THD limit for 132 KV is 3%. THD of current ITHD is given as in Eq. 5 (5) Where, In is the nth harmonic current. I1 is the fundamental frequency (50) Hz. The THD of current and limit for 132 KV is <2.5%. IV. WIND ENERGY GENERATING SYSTEM A. Wind Turbine Generating System Wind generations are based on constant speed topologies with pitch control turbine. The induction generator is used in the proposed scheme because of its simplicity, it does not require a separate field circuit, it can accept constant and variable loads, and has natural protection against short circuit. The available power of wind energy system is presented as below. The kinetic energy in air of mass “m” moving with speed V is given by the following in SI units: Kinetic Energy = 1/2. m . V2 joules. The power in moving air is the flow rate of kinetic energy per second wind Power = ½. (ρA V3). 111 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME Figure 3: wind energy conversion system connected to distribution system cut‐in wind speed (in the order of 3‐5 m/s) and nominal wind speed or rated wind speed: wind speed at which the nominal power of the turbine is reached (between 11 m/s and 16 m/s). cut‐out wind speed : When the wind speed becomes very high, the energy contained in the airflow and the structural loads on the turbine become too high and the turbine is taken out of operation. Depending on whether the wind turbine is optimized for low or high wind speeds, (between 17 and 30 m/s). B. Generator Model equations Vds = - Rsids - ωs ψqs + D(ψds) (6) Vqs = - Rsiqs + ωs ψds + D(ψqs) (7) Vdr =0= - Rridr -s ωs ψqr + D(ψdr) (8) Vqr =0= - Rriqr +s ωs ψdr + D(ψqr) (9) Where D=d/dt. All quantities are in per unit. Indices d and q indicate the direct and quadrature axis components and s and r indicate stator and rotor quantities. The d‐q reference frame is rotating at the synchronous speed with the q‐axis leading the d‐axis by 90°. C. Torque Balance Equation Te = ψds iqs – ψqs ids (10) D(ωm) = 1/2Hm(Tm – Te) (11) Where D=d/dt. Hm = mechanical inertia constant. Tm = Mechanical torque. Te = Electromagnetic torque. V. TOPOLOGY FOR POWER QUALITY IMPROVEMENT The F-STATCOM based current control voltage source inverter injects the current into the grid in such a way that the source current are harmonic free and their phase-angle with respect to source voltage has a desired value. The injected current will cancel out the reactive part and harmonic part of the load and induction generator current, thus it improves the power factor and the power quality. To accomplish these goals, the grid voltages are sensed and are synchronized in generating the current command for the inverter. The proposed distribution connected system in Fig. 4 consists of wind energy generation system and battery energy storage system with F-STATCOM. 112 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME Figure 4: Line Diagram for Proposed system (with FUZZY STATCOM) VI. SIMULATION CIRCUITS AND RESULTS A. System Performance The Simulink model library includes the model of Conventional Source, Asynchronous Generator, FUZZY STATCOM, Non-Linear Load, Inverter, Distribution Voltage, Line Series Inductance and others that has been constructed for simulation. The simulation parameter values for the given system are given in Table 1. S. No Parameters Rating 1 Source voltage 3-phase 11KV 50Hz 2 Asynchronous generator 3.35KVA, 415V 3 Distribution line R=2ohms,L=0.1H 4 Non Linear Load Diode Bridge Table 1: System parameters The STATCOM is designed by IGBT’S as shown in the figure 5.The gating pulses to the IGBT is given through fuzzy logic controller. A capacitor of 100 micro farads is connected in parallel to the circuit and it acts as storage element or battery in the circuit. The reactive power is fed to the power system network by the capacitor. Figure 5: Simulink model of Statcom Simulation Circuit 113 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME B. Control Scheme The control scheme approach is based on injecting the currents into the distribution system using ―Fuzzy controller. Using such technique, the controller keeps the control system variable between boundaries of hysteresis area and gives correct switching signals for STATCOM operation. The control system scheme for generating the switching signals to the F-STATCOM is shown in Fig. 6. Figure 6: Simulink model of Fuzzy Statcom Control Circuit The output of Load Voltage , Load Current, Source current and Source Voltage without fuzzy Logic Controller is shown in figure 7 as shown below. The percentage of harmonics are more in the output waveforms and the waveforms are distorted in their position. Figure 7: Output Waveforms of load voltage, load current, Source current and Source voltage Without F-Statcom The output of Load Voltage ,Load Current, Source current and Source Voltage with fuzzy Logic Controller is shown in figure 8. as shown below. The percentage of harmonics are less in the output waveforms. 114 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME Figure 8: Output Waveforms of load voltage,load current,Source current and Source voltage With F-Statcom The output waveforms of stator current, Rotor current, Electromagnetic torque and speed in RPM are shown in the figure 9. Figure 9: Output waveforms of Generator and Wind Turbine The Total Harmonic distortion i.e THD analysis of Load current and load voltage without and with Fuzzy Stacom are shown in fig10 ,11,12 &13. Figure 10: FFT Analysis (Load Current) Without F-STATCOM 115 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME Figure 11: FFT Analysis (Load Current) With F-STATCOM Figure 12: FFT Analysis (Load Voltage) Without F-STATC0M Figure 13: FFT Analysis (Load Voltage) With F-STATCOM Table 2: Comparison Results S.No. Parameters Without With F-STATCOM (THD %) F-STATCOM (THD %) 1 Source voltage 20.00 0.00 2 Source Current 27.55 0.00 3 Load voltage 43.49 0.07 4 Load Current 27.55 0.00 VII. CONCLUSION The paper presents the FUZZY STATCOM-based control scheme for power quality improvement in Distribution line connected wind generating system and with non linear load. The power quality issues and its consequences on the consumer and electric utility are presented. The operation of the control system developed for the FUZZY STATCOM in MATLAB/SIMULINK for maintaining the power quality is simulated. It has a capability to cancel out the harmonic parts of the load current. It maintains the source voltage and current in-phase and support the reactive power demand for the wind generator and load at PCC in the Distribution system, thus it gives an opportunity to enhance the utilization factor of Distribution line. The integrated wind generation and STATCOM with FUZZY Controller have shown the outstanding performance. Thus the proposed scheme in the Distribution connected system fulfills the power quality norms as per the IEC standard 61400-21[8]. 116 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME REFERENCES [1] K. S. Hook, Y. Liu, and S. Atcitty, “Mitigation of the wind generation integration related power quality issues by energy storage,” EPQU J., vol. XII, no. 2, 2006. [2] R. Vibin, K. Malarvizhi (2012), “ Power Flow Control Scheme for wind Energy Conversion System using FACTS Controller”International Journal of Modern Engineering Research (IJMER) www.ijmer.com Vol.2, Issue.3, pp: 644-648 ISSN: 2249-6645. [3] R.Meinski, R.Pawelek and I.Wasiak, ―Shunt Compensation For Power Quality Improvement Using a STATCOM controller Modelling and Simulation‖, IEEE Proce, Volume 151, No. 2, March 2004. [4] C. Han, A. Q. Huang, M. Baran, S. Bhattacharya, and W. Litzenberger, STATCOM impact study on the integration of a large wind farm into a weak loop power system,‖ IEEE Trans. Energy Conv., vol. 23, no. 1, pp. 226–232, Mar. 2008. [5] J. Manel, “Power electronic system for grid integration of renewable energy source: A survey,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1002–1014, 2006, Carrasco. [6] S. Heier, ―Grid Integration of Wind Energy Conversions‖. Hoboken, NJ: Wiley, 2007, pp. 256–259. [7] A. Sannino, “Global power systems for sustainable development,” in IEEE General Meeting, Denver, CO, Jun. 2004. [8] S. W. Mohod and M. V. Aware, “Grid power quality with variable speed wind energy conversion,” in Proc. IEEE Int. Conf. Power Electronic Drives and Energy System (PEDES), Delhi, Dec. 2006. [9] C. C. Lee, "Fuzzy Logic in Control Systems: Fuzzy Logic Controller-Part I", IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, No. 2, pp. 404-418, 1990. [10] R. R. Yager and D. P. Filev, "Essentials of Fuzzy ModelingControl", JohnWiley&Sons, 1994. [11] B.Sivaprasad, O.Felix, K.Suresh, G.Pradeep Kumar Reddy and E.Mahesh, “A New Control Methods for Offshore Grid Connected Wind Energy Conversion System Using Doubly Fed- Induction Generator and Z-Source Inverter”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 305 - 323, ISSN Print : 0976-6545, ISSN Online: 0976-6553. [12] I.Arul, Dr.M.Karthikeyan, Dr.N.Krishnan and Dr. N.Albert Singh, “Neuro Fuzzy Based Maximum Power Tracking Control Algorithm in Wind Energy Conversion Systems”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 2, 2012, pp. 285 - 293, ISSN Print : 0976-6545, ISSN Online: 0976-6553. [13] Laith O. Maheemed, Prof. D.S. Bankar and Dr. D.B. Talange, “Power Quality Improvement of Wind Energy Conversion System using Unified Power Quality Conditioner”, International Journal of Electrical Engineering & Technology (IJEET), Volume 3, Issue 1, 2012, pp. 288 - 302, ISSN Print : 0976-6545, ISSN Online: 0976-6553. 117

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