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International Journal of Electrical and Electronics Engineering Research (IJEEER) ISSN 2250-155X Vol. 3, Issue 4, Oct 2013, 129-138 Š TJPRC Pvt. Ltd.

FUZZY LOGIC CONTROLLER BASED UPQC FOR POWER QUALITY MITIGATION IN GRID CONNECTED WIND ENERGY CONVERSION SYSTEM S. DILEEP KUMAR VARMA1 & K. SRUJANA DEVI2 1

Associate Professor, Department of EEE, SVECW, Bhimavaram, Andhra Pradesh, India

2

PG Student, M.Tech Power Electronics, Department of EEE, SVECW, Bhimavaram, Andhra Pradesh, India

ABSTRACT Wind energy generation and its penetration with electric grid are increasing worldwide. Wind active power is always fluctuating due to intermittent nature of wind causing power quality problems and stability problems. This paper mainly focuses on grid connected wind energy conversion system for power quality improvement using efficient control scheme of unified power quality conditioner (UPQC) based on SRF theory and instantaneous power theory is presented to control active power output of the wind turbine, reactive power flow from grid side and regulation of the Voltage at PCC of the wind farm terminals. This paper also proposes the application of a fuzzy controller based UPQC for controlling the DC capacitor voltage under steady and transient conditions and compared with conventional PI Controller using MATLAB/SIMULINK environment. Simulation results show that the effectiveness of the proposed compensation strategy for the enhancement of power quality and wind farm stability

KEYWORDS: Power Quality, UPQC, Wind Energy, PI Controller, Fuzzy Logic Controller, Weak Grid, Synchronous Reference Frame Theory (SRF)

INTRODUCTION Utilization of renewable energy sources increases rather than conventional energy sources because of conventional sources are limited and pollute the environment. Wind energy is the fastest growing and most promising renewable energy source due to economically viable. Wind turbine generation systems supply real power variations into the upstream grid. These power deviations cause voltage variations with consequences for the electric power system and the customers. By increasing use of power electronics in wind turbine generation systems introduces voltage and current harmonics into the power system. It can cause problems with voltage stability and transient stability [1, 2]. Use of squirrel-cage induction machine in wind generation is widely accepted. The squirrel-cage induction machine is Simple, reliable, lightweight, and requires very little maintenance. The induction generator is connected to the utility at constant frequency. SCIG absorbs reactive power which leads to the variation of wind farm terminal voltage. In order to diminish the voltage fluctuation and improve the power quality UPQC is connected at the point of common coupling. UPQC the combination of series and shunt active filters fulfilled the objectives of both voltage and reactive power support by properly injected the voltage in series with the source voltage and injecting the reactive current which cancels the harmonic current due to sudden changes of loads. Operation is based on the generation of three phase voltages, using either voltage source type inverter or current source type inverter. VSI converter is favored because of lower DC link losses and faster response in the system than CSI [5]. The sharing of active power between converters is managed through the common DC link. Fuzzy logic controller is fast response compared to other controllers. Performance of DC link using fuzzy logic controller is improved and compared with PI controller.


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WEAK GRID Weak grid is defined as voltage level is not as constant. Grid can be characterized by voltage level, total power capability and short circuit capacity. Weak grid determined by ratio between short circuit power and rated wind farm power [1]. (1) Values of r < 10 considered as â&#x20AC;&#x153;weak gridâ&#x20AC;? connection. Problem of weak grids in connection with wind energy is voltage level and fluctuations. To rectify these problems mainly two control strategies preferred. One is Grid reinforcement increases the capability of the grid by increasing the cross section of the cables by erecting a new line parallel to the existing line for some part of the distance. Another control strategy is slightly more advanced is to continuously control the power output of the wind turbine in such way that the voltage limit is not exceeded [2]. This can be done by measuring the voltage and controlling it at the point of common coupling.

POWER QUALITY According to IEEE standard power quality is defined as concept of powering and grounding the sensitive equipment which is suitable to the operation of technique. Power quality equal to voltage quality because of power is rate of energy delivery and is proportional to the product of the voltage and current.

POWER QUALITY PROBLEMS Power Outages Power outages are total interruptions of electrical supply. Ice storms and lightening are the causes of power outages. Sags Sag is a decrease to between 0.1and 0.9 PU in RMS voltage or current at power frequency for durations from 0.4 cycles to 1min. Examples system faults and energization of heavy loads. Swells A swell is increased to between 1.1 to 1.8 PU in RMS voltage or current at power frequency for durations from 0.4 cycles to 1 min. Examples switching off heavy loads, energizing a large capacitor banks. Voltage Fluctuations Voltage fluctuations are changes or swings in the steady-state voltage above or below the designated input range. Causes of voltage fluctuations are large equipment start-up or shut down, unexpected change in load. Transients Transients commonly called surges are sub-cycle disturbances of very short duration that vary greatly in magnitude. Causes are lightening, start up and shut down the equipment, welding equipment [7].

DESCRIPTION AND MODELLING OF SYSTEM Figure 1 explains the power system under consideration in this study. The wind farm is composed by 36 wind


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Fuzzy Logic Controller Based UPQC for Power Quality Mitigation in Grid Connected Wind Energy Conversion System

turbines using squirrel cage induction generators, adding up to 21.6MW electric power. And is connected to the power grid via 630 KVA 0.69/33KV transformer. Short circuit power considered as 120 MVA. This system is taken from a real case [5].

Figure 1: Study Case Power System Model of Wind Turbine The power that can be obtained from a wind (2) Power that can be extract from a wind turbine (3) (4) Where ρ is air density, R is radius of the swept area, υ the wind speed and

the power coefficient and in terms of

speed ratio (λ) and pitch angle (β). (5) For the considered turbines (600KW) the values are R = 3.12m, ρ = 1.225 kg/m3. Fixed Speed Induction Generator The stator winding is connected directly to the grid and the rotor is driven by wind turbine and is transmitted to the grid by the stator winding. The pitch angle is controlled to limit the generator output power to its nominal value for high wind speeds. The reactive power is absorbed by the induction generator is provided by grid.

Figure 2: Connection of Wind Turbine to the Induction Generator


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CONTROL STRATEGY OF UPQC

Figure 3: Block Diagram of UPQC UPQC series converter is used to compensate the voltage disturbances coming from the grid and cannot spread to the wind farm. Shunt converter of UPQC is used to filter the active and reactive power pulsations generated by the wind farm and cannot spread to the grid. Sharing of active power between converters is managed through common DC link. Control Strategy of Series Converter

Figure 4: Extraction of Unit Vector Templates and Reference Voltages

Figure 5: Control Strategy of Series Converter Control Strategy is based on extraction of unit vector templates and reference voltages from the three phase distorted wind farm terminal voltage are sensed at point of common coupling (PCC). These unit vector templates are equal to pure sinusoidal voltages generated with proper phase delay [10]. Distorted wind farm terminal voltage sensed at PCC contains both fundamental component and distorted component. To get unit input voltage vector multiplied by gain

. Where

, sensed voltage is

equal to the peak amplitude fundamental input voltage. These unit voltage vectors

are given to the phased locked loop which generates two quadrature unit vectors (

,

(6)


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Fuzzy Logic Controller Based UPQC for Power Quality Mitigation in Grid Connected Wind Energy Conversion System

Multiplying the peak amplitude of fundamental input voltage with unit vector templates of equation (6) gives the reference voltage signals (7) Reference voltage signals are compared with the instantaneous voltage sensed at PCC. Error generated is then given to the PWM generator to generate the required gate signals for series converter; in such a way that it compensates the voltage variations at PCC to maintain the nominal value of wind farm terminal voltage by injecting voltage is in phase with the PCC voltage. Control Strategy of Shunt Converter Control strategy of Shunt converter controller based on “instantaneous power theory” is shown in figure 6. Mean values of active and reactive power are obtained by low-pass filtering, and the bandwidth of such filters is chosen so that the power fluctuation components selected for compensation. Shunt converter controller generates both voltage commands and

based on power fluctuations ∆P and ∆Q respectively. Such deviations are calculated subtracting

the mean power from the instantaneous power measured in PCC.

Also contains the control action for DC-bus

voltage loop.

Figure 6: Control Strategy of Shunt Converter Control voltage commands transform to phase voltages by using Inverse Park’s transformation is then given to the PWM generator to generate the required gate signals for shunt converter. Powers

and

are calculated in

the rotating reference frame, as follows: (8)

(9) Ignoring PCC voltage variation, these equations can be written as follows: (10) (11) VSI model proposed leading to linear relationship between the generated power and the controller voltages. Resultant equations are:


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(12) (13)

FUZZY LOGIC CONTROLLER (FLC) Fuzzy set theory exhibits immense potential for effective solving of the uncertainty in the problem. It is an outstanding mathematical tool to handle the uncertainty arising due to vagueness. Fuzzy logic control is divided into fuzzification, inference and defuzzification as shown in figure 7.

Figure 7: Fuzzy Controller Block Diagram Fuzzification Input variable transformed to the linguistic variables depends on the control variables such as error and change of error Error Calculation Error is calculated from the difference between actual value and reference value. Change of Error Change of error is the difference between the variation of error at current sampling and its previous sampling. C (k) = E (k)-E (k-1)

(14)

The input values of the fuzzy logic controller are connected to the output values by if-then rules. If-then rule is defined as â&#x20AC;&#x153;If (error is negative large and change of error is negative large) then output is negative large. Fuzzy rules are shown in Linguistic variable table 1. Knowledge Base Knowledge base includes the fuzzy membership functions defined for each control variables and the necessary rules that specify the control goals using linguistic variables. Table 1: Linguistic Variable Table E(k) CE(k) NL NM NS Z PS PM PL

NL

NM

NS

Z

PS

PM

PL

NL NL NL NL NM NS Z

NL NM NM NM NS Z PS

NL NM NS NS Z PS PM

NL NM NS Z PS PM PL

NM NS Z PS PS PM PL

NS Z PS PM PM PM PL

Z PS PM PL PL PL PL


Fuzzy Logic Controller Based UPQC for Power Quality Mitigation in Grid Connected Wind Energy Conversion System

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Inference Mechanism It should capable of simulating human decision making and influencing the control actions based on fuzzy logic. In the inference mechanism rules are based by the user on the basis of these rules output of fuzzy logic controller is controlled. Defuzzification In the defuzzification process, the controller output represents as linguistic labels by fuzzy set are converted to the analog signals.”Weighted Average” method which is the special case of”Mamdani Model” is selected for the defuzzification process.

SIMULATION RESULTS AND DISCUSSIONS Power system model illustrated in figure 1 was implemented without and with UPQC using Matlab/Simulink software. Numerical solutions performed to voltage regulation problems due to sudden load connection. Power system with UPQC was conducted with the following chronology. Simulation starts with the series converter at t = 0.0’’ and the DC-bus voltage controllers in operation. Real and reactive power control loops are enabled at t = 3.0” Load is connected at t = 6.0’’, Load is disconnected at t = 10 .0’’. Comparison of Wind Energy Generation System Connected to the Grid without and with UPQC

Figure 8: Wind Energy Generation System Connected to Grid without UPQC

Figure 9: Wind Energy Generation System Connected to Grid with UPQC

Figure 10: Active and Reactive Power Demand at Grid Side without UPQC


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Figure 11: Active and Reactive Power Demand at Gird Side with UPQC

Figure 12: PCC Voltage without UPQC

Figure 13: PCC Voltage with UPQC

Figure 14: Wind Farm Terminal Voltage without UPQC

Figure 15: Wind Farm Terminal Voltage with UPQC

Figure 16: Shunt and Series Active Power


Fuzzy Logic Controller Based UPQC for Power Quality Mitigation in Grid Connected Wind Energy Conversion System

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Figure 17: Series Injected Voltage at “a” Phase Compared to figure 10, in figure 11. Active and reactive power pulsations are reduced to the great extent at t = 3.0’’ because the P and Q controllers come into action. Sudden connection of load is performed at t = 6.0’’. So voltage dip occurs from 270 kV to 250 kV at wind farm terminal voltage as shown in figure 14. By using UPQC, Series converter injecting voltage at PCC as shown in figure 17 in response to sudden change of load and maintain the wind farm terminal voltage remains constant value as 270kV as shown in figure 15. Comparison of DC – Link Voltage of UPQC Based PI and Fuzzy Logic Controller

Figure 18: DC - Link Voltage of UPQC Using PI Controller

Figure 19: DC – Link Voltage of UPQC Using FLC By using UPQC based PI controller in DC link having fluctuations in DC link voltage as shown in figure 18. Fuzzy logic controller scheme is fast dynamic response compare to PI controller. By implementing fuzzy logic controller regulating the DC link voltage effectively and maintain as a constant value 4000V as shown in figure 19.

CONCLUSIONS In this paper wind energy conversion system connected to grid without and with UPQC has been implemented. The simulation results have shown that the active, reactive power and wind farm PCC voltage have been effectively regulated with UPQC using MATLAB/SIMULINK environment and simulation results have been compared with and without UPQC. Simulations are also conceded out to verify the performance of the proposed UPQC based fuzzy logic controller and compared with conventional PI controller and the results shown that the proposed UPQC based fuzzy logic


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controller has high accuracy of regulating the DC-voltage reference, fast dynamic response and strong robustness to load parameters variation.

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