A Power Factor Corrected Bridgeless Buck–Boost Converter-Fed Brushless Direct
Current Motor Drive With Fuzzy Controller
T. VijayaLakshmia, Gopala Venu Madhavb, T. Anil Kumarc, K. Bhadrajid
a PG Scholar, Anurag Group of Institutions (now Anurag University), Hyderabad
b,cAssociate Professor, d Assistant Professor
b,c,dAnurag University, Hyderabad
a vijayalakshmi101097@gmail.com
Article History: Received: 10 January 2021; Revised: 12 February 2021; Accepted: 27 March 2021; Published online: 4 June 2021
Abstract: The main aim of this Paper is a power factor corrected bridgeless buck boost converter fed brushless direct current
motor drive with Fuzzy controller. In this paper A Fuzzy logic execution in adaptable speed control of BLDC engine is done here. A methodology of rate control of the BLDC motor throughmonitoring the dc bus voltage of the (VSI) voltage source inverter is utilized with a solitary voltage sensor. The controller should monitor the varied rates and change the yield speed between the varieties of burden. In comparison to other types of motors, the BLDC has many preferences, but the nonlinearity of the BLDC powertrain attributes is not simple due to use of PI controllers. The Fuzzy Logic Control transforms into an appropriate control to tackle this fundamental problem. A Buck boost type converter is intended to operate in the broken current inductor mode in order to provides the inherent PFC at the power supply. The implementation in MATLAB/Simulink was simulated with the proposed method.
Keywords: BLDC motor, Bridge less buck-boost converter, Fuzzy controller, Power factor correction
1. Introduction
The high efficiency of the DC brushless motors (BLDC) is recommended for many application models on low to mid-powered drives, their large flux density, low maintenance requirements, low problems with EMI, high robustness and a wide range of speed controls[1-2].The engine BLDC consists of a 3-phase stator, centered rotators and a permanent magnet rotor. This engine is also called an electronic switching motor (ECM) as an electronic switch is not mechanical since it is not associated with the motor physically and thus has no EMI problem based on the Hall Effect rotor signal. When driven by a diode bridge rectifier, a BLDC engine has increased driving losses (DBR) [3-4].
Driving loss triggered by the bridge diode's high-forward voltage drop starts weakening the overall device effectiveness. In the bridge rectifier, heat produced will kill each diodes. Higher current handling power or the heat dissipation characteristics must also be used [5]. This raises the size and expense of power supply, which for an effective design is undesirable. The best solution to minimize conduction and switching losses of the
Converter seems to be Bridgeless topology. To date, more than 80% of the controllers are PI (relative and vital) controllers because they are straightforward and easy to operate. The speed controllers are the PI routines and the new controllers are the P controllers for higher commutes. Fuzzy logic [6-7] can be seen as an experimental hypothesis that incorporates several valued reasoning, a probability hypothesis and a fake consciousness to re-establish a human approach to various problems by using an approximate thin king to associate various data sets and to make choices. It is clarified that fuzzy controls are more effective than conventional PI controllers to plant parameter changes and have better reject capabilities. This paper introduces a variable dc link voltage BLDC driven motor drive for enhanced power quality in ac mains with small components and superior control. This paper includes a bridge less buck boostconverter [8-9].
2. Exisitng System
The figure is beneath. Shows VSI-Fed drive BLDC back-boost motor. For continuous inductor current mode, the parameters of the BL Buck-Boost converter are set to correct the inner power factor in the handles (DICM).VSI's dc linked voltage control is used for regulating the speed of the BLDC engine with a buck boost converter. The VSI's low-frequency control of BLDC motors eliminates losses of switching in the VSI.
Fig 1. Circuit diagram BL Buck-Boost converter & BLDC motor with PI controller. 3. Proposed System
Fig.2 shows the proposed VSI BLDC motor with Fuzzy controller BL buck_ boost converter. The following fig.2 shows. The dynamics of the DC brushless motor are monitored and analyzed using the established MATLAB models, For example the inverter part speed, torque, current and voltage. If the machine is troubled, it's a suitable and very resilient fuzzy logic controller system. The proposed VSIfed BLDC motor drive is shown in Figure 2 for the buck _boost converter. The parameters of BL Buck_Boost are set to provide a corrective power factor in the intermittent inductive current mode (DICM). The BL-Buck Boost transformer provides BLDC engine speed with VSI dc power link. This reduces loss of the VSI switching by lower frequency VSI operation for electronic BLDC engine switching.
The BL Buck-Boost converter decision refers to the simplest part number and smallest voltage gadgets in each half's supply voltage cycle, in the proposed Buck-Boost converter method. Taking into account the negative and positive half voltage rotations and the whole exchange time, the Bridge-free Back Boost feature is structured into two parts.
Fig.2 Recommended circuit illustration of BL Buck _ Boost converter & brush less DC motor with fuzzy controller
4. Modelling Of The Porposed System A) PFC CONVERTER
PFC modeling involves a speed regulator, a generator with reference current and a pulse width modulation controller.
Speed Controller: the speed controller is a PI controller with an analog reference voltage matching its speed closely and being the main component of the control system. Where the DC-connected voltage reference voltage is V*dc (k),Vdc (k) will sense DC-connected tension so Ve (k) is computated as a voltage error,
Ve (k) =V*dc (k)-Vdc (k) 1
The PI controller gives the required control signal afterwarddispensation this voltage error. The Ic (k) controller output is defined on kth instant,
IC (k) = Ic (k-1) + Kp {Ve (k) - Ve (k-1)} + KiVe (k) 2 Where Kp and Ki are the PI controller's proportional and total gains.
I*dc = IC (k) uVs 3
Where uV is the voltage template at the input AC power supply, uVs = vd/Vsm; vd = |vs|; vs= Vsm sin ωt 4
The AC mains have a frequency in rad/sec where Vsm is the volume and è lis the amplitude.
PWM Controller: In order for the buck half-bridge converter to produce a current error, the input reference current (idc*) is compared to their sensed current (idc). The recent error was enhanced by the kdc increase in the unipolar shifting mode and compared with the scratch-tooth waveform of fixed frequency (fs) md(t) of the half-bridge PFC-buck convertor to receive switches, as,
If kdc ∆idc> md (t) then SA = 1 else SA = 0 If -kdc ∆idc> md (t) then SB = 1 else SB = 0
If SA, SB, as shown in Fig, is a semi-circle converter with the upper and lower turns. 1 and its values '1' and '0' reflect the PFC converter status on and off.
B) BLDCM DRIVE
PMBLDCM has a VSI and BLDC engine, an electronic switch. Electronic switch: Electronic switch generates the inverter voltage source switching sequence using the Hall-Effect Position Sensor Signals, based on the logic given in table I.
Inverter voltage supply: The analogous circuit of a PMBLDCM fed VSI is presented below. The VSI output to be fed to PMBLDC motor stage 'a' is defined as,
vao = (Vdc/2) for S1 = 1 5 vao = (-Vdc/2) for S2 = 1 6 vao = 0 for S1 = 0, and S2 = 0 7 van = vao – vno 8
Where the voltage of the three phases vao, vbo, vco and vno and the neutral point (n) is shown as 'o' in Fig. in relation to the virtual midpoint in DC connection voltage. 3.2. Van, vbn, and vcn voltages are three-point, and dc is the neutral connecting voltage (n). For the respective VSI IGBTs, S= 1 and 0 are 'on' and 'off' and are similarly treated for other VSI IGBTs i.e. S3- S6. S3.
For two additional steps of the VSI feeding BLDC engine, vco, vbn and vcn are created by similar logic.
Figure 3 : Equivalent Circuit of a VSI fed BLDCM Drive BLDC Engine: The BLDCM is shown in a series of differential equations,
van = Ria + pλa +ean 9 vbn = Rib + pλb +ebn 10 vcn = Ric + pλc +ecn 11
The PMBLDCM motor/phase resistance emfs in each stage are ia, ib, ic as differential operator, 3-phasis emfs, μa, μb and μc, ean, ebn and Ecn.
The flow ties are shown as, λa = Lia - M (ib + ic) 12 λb = Lib - M (ia + ic) 13
λc = Lic - M (ib + ia) 14
M is joint induction of the moteur winding/phase when L is a self-inductance/phase. Therefore, since the PMBLDCM is not neutral,
ia + ib + ic = 0
The voltage from the neutral (n) to the middle of the connector (o) is shown as, vno = {vao +vbo + vco – (ean +ebn +ecn)}/3
The flux linkages are given as,
λa = (L+M) ia, λb = (L+M) ib, λc = (L+M) ic,
These are the dynamic PMBLDC motor model equations. 5. Proposed Fuzzy Logic Controller
Proposed Fuzzy Logic Controller
Fuzzy logic improves traditional device design with the assistance of a design professional. Robust mathematical modeling can be substantially reduced with fuzzy logic in control processes. A human operator is much more effective in controlling a process than a control unit based on current analysis techniques.
FLC has become popular in recent years in implementing fluid-set theory. A fuzzy set is a subset of all sets, the membership function (MF) must be able to produce distinct outputs whenever it is given an input from 0 to 1 with membership values Generally speaking, and afuzzy box is as follows.
A fuzzy set A in Universe U shall be identified by the member function A(x) which is considered to be the actual physically organized and logical element x at interval A measure member status [0, 1] and at that interval
associates A(x) number with each element x of Fuzzy logy. 6 concerns all four of (2) the source code of the fugzztive, (3) the engine inference and (4) the defuzzification. (2) The information base.
Fig...4 Structure of Fuzzy Logic controller 1. Fuzzification:
Maps of the initial semantine to the fluid set that capture other areas of voice. The membership strength of subcategory A is mapped for a given value x. (x). (x). (x). (x). The fuzzification method includes the following steps. Test the effect of the variables.
1. Computers change the new set and map to each of the input variables within the respective discourse universe.
2. It carries out the role of fluctuation that translates input data into acceptable language variables that can be interpreted as fluorescent labels.
2. Knowledge Base (KB):
Knowledge is based on the meanings and rules for regulating how and why the fuzzy rules of the fuse control rules are applied input and output variable.
The system contains a database and language rules base.
1. The database acts as a guide for concepts of language management and excessive data processing.
2. The basic rule is formulated in a variety of language rules to define control priorities and controls for domain experts.
3. Inference Mechanism:
Y = B = B = C. IF X = A = Y.
Two input variable and one power variable for x, y and z, a, b, and c are linguistic values. The x, y and z, a, B and C linguistic values are the core of an FLC which simulates human life, including decision making in furious logic and inference rules.
Fuzzy sets are usually mapped to a pair of outputs, the laws between the two systems. 4. Defuzzification:
It is used for the calculation of numerical values linguistically dependent. The central approach was used in this analysis.
(1) A mapping scale that converts the value range into the value set of the output variable. (2) It is a differentiating approach between fluctuating control activity and control. 6. Simulation Results
The BLDC engine's Simulink model [3]. The closed loop controller is equipped with MATLAB/Simulink [4] and [5] is shown in Fig. A three-phase brushless DC generator. Seventh 1st. Permanent Magnet the EMF trapezoidal trapezoidal synchronous engine is modeled as a Brushless DC Engine.
EXISTING RESULTS
(a)Source voltage (b) Source current (c) DC link voltage (d) actual speed and reference speed (e) torque and reference torque (f) stator currents (g) motor active power
Rated conditions. (a)Source voltage (b) Source current (c) DC link voltage (d) actual speed and reference speed (e) torque and reference torque (f) stator currents (g) motor active power
7. Extension Results
(a)Source voltage (b) Source current (c) DC link voltage (d) actual speed and reference speed (e) torque and reference torque (f) stator currents (g) motor active power
Rated conditions. (a)Source voltage (b) Source current (c) DC link voltage (d) actual speed and reference speed (e) torque and reference torque (f) stator currents (g) motor active power
Fig.12 Source current THD with proposed controller is 2.15% Comparision Table
8. Conclusion
The dynamics of the DC brushless motor (BLDC)are monitored and analyzed using the established MATLAB models, For example the inverter part speed, torque, current and voltage. The suggested fuzzy logic controller system is well suited and highly resilient if the system is troubled. The MATLAB modular model of simulation allows for the successful analysis of dynamic elements, including phase currents, rpm and mechanical torque.
References
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