JASC: Journal of Applied Science and Computations ISSN NO

POSLLC Based PV Fed D-STATCOM for PQ

Improvement in Distribution System using Neuro-

Fuzzy Controller Kasa Sudheer

#1*, Sarabu Kavitha

#2, Panchavati Yashaswini

#3,

Reddycherla Aswini#4,

#Department of EEE, SV College of Engineering, Tirupati.

[email protected], [email protected]

[email protected], [email protected],

[email protected]

Abstract—The Grid system’s Power Quality is mostly affected by the integrated renewable sources and non linear loads

connected. The uncertain renewable source input causes changes in grid parameters like voltage and current. To over

come the adverce affects by non linear loads and renewable energy unit, paper pesents a hybrid cotrol technique.This

technique suppreses the harmonics in load current, reactive power and brings the unity power factor at PCC. The

techique utilizes te Fuzzy logic and neural network for Active Power Filter to obtain the desired control strategy. The

total model was simulated in MATLAB/SIMULINK .

Keywords— Solar Energy System, Power Quality, FACTS, PI, FUZZY, Neuro Controllers, Luo Converters.

I. INTRODUCTION

In this paper, a renewable energy unit (Solar Energy Unit) is integrated to Grid at distribution side for providing

the power demanded by the load. Due to integration of SEU at distribution side, it affects the system power quality

in wide aspects. SEU’s suffers with unavailability of constant input. The connected non linear loads also impose

many effects on the overall system power quality [1-2]. This deterioration of system power quality can be improved

by using effective power switching devices and efficient control technique.

This paper presents a novel technique based on Neural Network and Fuzzy controlled control Scheme for Active

Power Filter (D-STATCOM) to compensate the harmonics and reactive power requirement. The Non Linear Loads

and solar energy unit (SEU) places requirement of reactive power and injects harmonics into the Grid connected

system lines [3]. This diminishes power factor, and causes, current and voltage variations.

The proposed shunt connected D-STATCOM injects the required Reactive power and absorbs the excess reactive

power at the CCL and injects the currents to cancel out the harmonic currents in the line (CCL) [4]. The purpose of

proposed system is to obtain the a) Unit power factor at common connecting line, b) required reactive power

adjustment, c) % THD improvement of current at common connecting line.

In this paper, Section II provides proposed system description and control scheme, Section III provides

supporting simulation results. At last, section IV concludes the Paper.

.

II. PROPOSED SYSTEM

The proposed grid interfaced wind energy unit for providing quality power to the nonlinear loads is presented in

Fig.3. The system has wind energy unit, D-STATCOM with control circuit and nonlinear load. Here, load unit

introducing harmonics and reactive power requirement on the common connecting line. These nonlinearities will be

cancelled out using D-STATCOM with hybrid control technique.

A. PV unit

The solar unit consists of PV cells, Maximum Power Point Tracking unit (MPPT)[5], and DC-DC

converter as power conditioning is required before connected to DC link [6-7]. Here the solar unit is designed with

MPPT which produces maximum output power.

PPV = VPV * IPV (1)

B. DC Link

In the D-STATCOM, capacitor interfaces the inverter and Energy storage unit as shown in Fig. 1

JASC: Journal of Applied Science and Computations

Volume VI, Issue V, May/2019

ISSN NO: 1076-5131

Page No:691

Ponnuru Karthik#5

mailto:%[email protected]

Fig. 1. DC Link for Energy storage.

The source solar/battery unit connected to dc link series with a resistance of Rb. The internal voltage

changes with the charge on the battery. The DC link voltage Vdc is given by eqn.(2).

Vdc = Eb + Ib * Rb (2)

Here Ib is battery current. It is necessary to maintain Vdc to meet the inverter voltage according to eqn (3).

Vdc ≥ Vinv (3)

Here Vinv is 230 V rms, Ma is modulation index 0.9.Final DC link voltage shoulb be maintained at 800V.

C. Positive Output Superlift Luo Converter

The positive output elementary super lift Luo converter is a new series of DC-DC converters possessing

high-voltage transfer gain, high power density; high efficiency, reduced ripple voltage and current . These

converters are widely used in computer peripheral equipment, industrial applications and switch mode power

supply, especially for high voltage-voltage projects. The positive output elementary super lift Luo converter

performs the voltage conversion from positive source voltage to positive load voltage. The gain in this

converter increases in geometric progression, stage by stage. It effectively enhances the voltage transfer gain

in power series. Each circuit has one switch, n inductors, 2n capacitors, and (3n-1) diodes. The conduction

duty ratio is d, switching frequency is f (period T = 1/ f), the load is resistive load R. The input voltage and

current are Vin and Iin, output voltage and current are VO and IO. Assume no power losses during the

conversion process, Vin × Iin = Vo × Io. The voltage transfer gain is G. G = Vo/ Vin. The first three stages

of positive output super-lift converters are shown in Fig.2.

Fig. 1. Positive Output Superlift Luo Converter Modes of Operation



ISSN NO: 1076-5131

Page No:692

D. Proposed Model Operation

The energy storage unit with shunt connected D-STATCOM is connected with non linear load and wind energy unit

at the common connecting line (CCL).The output of D-STATCOM varies according to the control strategy for

maintain the power quality.The control scheme consists Neuro– Fuzzy based controller. The IGBT based D-

STATCOM provides the reactive power requirement to the line caused by non linear loads .The PV unit levels the

DC link voltage. The complete operational scheme is described by the diagram shown in Fig.3.

Fig. 3. Proposed System.

E. Control Scheme

The reactive power requirement and harmonics developed by the nonlinear loads and wind systems will

be cancelled out using D-STATCOM controlled by hybrid Neuro- Fuzzy controller. The utilized control scheme is

clearly described by the Fig.4. In the control scheme , the difference b/w dc line voltage and reference capacitor

voltage is applied to Neuro controller.The effective Neuro controller reshapes the difference signal and applied to

Fuzzy Controller .This improved control signal from Neuro controller is further tuned by Fuzzy Controller. The

combined Neuro-Fuzzy outcome and source voltage unit vectors generates the reference current.

Fig.4 Control Scheme

Fig. 4. Hybrid Neuro Fuzzy Controller.

Now the difference of the source current taken at common connecting line and reference current signal is

applied to hysteresis current controller which limits the current within the hysteresis band (HB = 0.08).Finally the

required firing angles are developed and applied to D-STATCOM, which consists a 3 leg inverter. The controlling

signals for IGBT of inverter are derived from hysteresis controller [8].The phase–a switching function SA of phase

‘a’ is given by eqn. (15)



ISSN NO: 1076-5131

Page No:693

HB < (Ia*- Ia) →SA=0 and

HB > (Ia*- Ia) →SA=1 (15)

The D-STATCOM injects the currents to cancel out the harmonics in the Source currents at CCL. It also

provides the required reactive power to the line and makes the common connecting line power factor as unity. The

inductors are used to limits the harmonics developed from the inverter of D-STATCOM .The energy storage unit

maintains the dc link voltage at constant level.

F. Hybrid Fuzzy-NEURO Controller

This controller is designed to generate effective reference current generation to suppress harmonics at

common connecting line. It utilizes the Neural and Fuzzy capabilities which is presented in Fig. 5..

Fig. 5 Hybrid Fuzzy –NEURO Controller

The fuzzy controller [9-12] has three stages to produce desired control signal. Fuzzification inference

engine, and defuzzfication are the stages .in fuzzfication the crisp variables are modeled into fuzzy linguistic

variables. The selected input and outputs are assigned with triangular membership functions. Here, 7 linguistics

variables are selected HN, MN, SN, ZE, SP, MP and HP. The inference engine processes the input variable

according to rules stored in knowledge base .The rules are shown in Table.1 and membership functions and rules

are shown fig. 6(a) and 6(b)

Fig. 6 (a) Fuzzy Membership functions

Fig. 6 (b) Fuzzy Rules

The o/p variables of inference engine are combined using centroid method to develop the required control pulses, in

defuzzfication block. The adopted rules in the fuzzy knowledge base are shown in Table I.

TABLE I.

FUZZY RULE TABLE

de

e HN MN SN ZE SP MP HP

HN HN HN HN HN MN SN ZE

MN HN HN MN MN SN ZE SP

SN HN MN SN SN ZE SP MP

ZE MN MN SN ZE SP MP HP

SP MN SN ZE SP SP MP HP

MP SN ZE SP MP MP HP HP

HP ZE SP MP HP HP HP HP



ISSN NO: 1076-5131

Page No:694

III. SIMULATION RESULTS

The proposed model is simulated in MATLAB/SIMUNLINK environment. The proposed system modeled in

SIMULINK is shown in Fig.7

Fig.7 SIMULINK model of proposed system

The Fig. 7 clearly shows the pulse generating from the proposed control scheme to maintain the power quality at the CCL. It

also shows the SEU connected at common coupling line (CCL).

A. Steady State Analysis of the System

The harmonics in the grid current are injected by the non linear loads. To suppress these harmonic components in the current

equivalent opposite current has to be injected such that the current is free from harmonics. Fig.8 shows the various currents

developed at CCL.

Fig. 8 System Currents with Hybrid FUZZY- NEURO a) grid current b) load current c) injected Inverter current d) wind current

B. DC Link:

The voltage across the capacitor is maintained at constantly at 800V by the storage unit. The DC link voltage current and are

as shown in Fig. 9.

C. UPF:

The D-STATCOM is connected at T=0.1.Before this time both voltage and current are not in phase. When D-STATCOM is

connected, the voltage and currents are in phase, which leads to unity power factor at Common Coupling Line (CCL) and

desired power quality norms are maintained .The in-phase grid voltage and currents are as shown in Fig.10.Here,at T= 0.1

voltage and currents are in phase due to the D-STATCOM injected reactive power required to the line.



ISSN NO: 1076-5131

Page No:695

Fig. 9 DC link Voltage

Fig .10 Voltage and Current waveforms with and without D-STATCOM

D. Power Quality at CCL:

The power quality is analyzed in three different cases without D-STATCOM, with PI -D-STATCOM and with hybrid

NEURO–Fuzzy controlled D-STATCOM [13-17].

Case1: The % THD of the source current without D-STATCOM are as shown in Fig.11

Fig. 11 %THD of Grid Current without D-STATCOM(9.7%)

Case2: D-STATCOM is connected to the line at T=0.1. After T=0.1 the % THD of the currents with PI controlled D-

STATCOM are as shown in Fig.12

Fig. 12 %THD of Grid Current with PI Controlled D-STATCOM

Case3: The % THD of the currents with Fuzzy –Neuro controlled D-STATCOM are as shown in Fig.13

Fig. 13 %THD of Grid Current with Fuzzy- Neuro controlled D-STATCOM



ISSN NO: 1076-5131

Page No:696

TABLE – III

COMPARISON OF % THD FOR VARIOUS CURRENTS AND POWER FACTOR FOR THE THREE CASES

Parameters Without D-

STATCOM

With pi

based D-

STATCOM

With hybrid

Fuzzy-

NEURO D-

STATCOM

P F 0.86 1 1

%THD

Source

current 9.7 1.7 0.4

From the Table –III it is clear that the Hybrid Fuzzy – NEURO controlled D-STATCOM effectively reduces the Harmonics

at Grid common connecting line from 9.7% to 0.4% THD

IV. CONCLUSIONS

Here the Paper presents hybrid Fuzzy Neuro D-STATCOM based control strategy which improves the power quality of the

renewable source integrated Grid System. The control Strategy keeps the source current in phase with the Source voltage. It also

compensates the Load current harmonics and reactive power at CCL. It also provides the reactive power required by the Wind

unit. Here, the hybrid control strategy improves the %THD of Currents by injecting the currents to cancel out the harmonic

current. The simulated result compares the various currents %THD without D-STATCOM, with PI-D-STATCOM and with

Hybrid Neuro- Fuzzy D-STATCOM control techniques. Acknowledgment

The author would like to thank the management of SV College of Engineering, Tirupati , for providing the opportunity to

carry out this research.

REFERENCES

[1] Dusan Graovac,Vladimir A.Katic,Alfred Rufer , “Power quality problems compensation with universal power quality conditioning system,” IEEE Trans. on Power Delivery, vol. 22, no. 2, pp. 968-97, April 2007

[2] J. H. R. Enslin and P. J. M. Heskes, “Harmonic interaction between a large number of distributed power inverters and the distribution network,”IEEE Trans. Power Electron., vol. 19, no. 6, pp. 1586–1593, Nov. 2004.

[3] P. Jintakosonwit, H. Fujita, H. Akagi, and S. Ogasawara, “Implementation and performance of cooperative control of shunt active filters for harmonic damping throughout a power distribution system,” IEEE Trans. Ind. Appl., vol. 39, no. 2, pp. 556–564, Mar./Apr. 2003

[4] Sudheer Kasa, Sudha Ramasamy, Prabhu Ramanathan, D.P.Kothari "Effective Grid Interfaced Renewable Sources with Power Quality Improvement using Dynamic Active Power Filter", International Journal of Electrical Power & Energy Systems, Elsevier, Vol. 82, pp. 150-160, 2016.

[5] Sudheer Kasa, Sudha Ramasamy “Photovoltaic Fed Dynamic Voltage Restorer with Voltage Disturbance Mitigation Capability Using ANFIS Controller” International Journal of Renewable Energy Research, vol.6, No.3, pp-825-832, 2016.

[6] Sudheer Kasa, Sudha Ramasamy “Mitigating Voltage Imperfections with Photovoltaic fed ANFIS based ZSI-DVR in Three Phase System” International Journal of Renewable Energy Research, Vol.7, No.4, pp-2103-2110, 2017.

[7] Sudheer Kasa, Sudha Ramasamy “Mitigation of Current Harmonics in Renewable Source Integrated Grid Topology with Fuzzy based Dynamic Shunt Active Filter” IPACT, IEEE Conference, pp.1-6, Mar 2017, VIT University.

[8] Sudheer Kasa, Sudha Ramasamy, Prabhu Ramanathan, “Hybrid Fuzzy-ZN PID control based Grid Interfaced distribution level Renewable Energy Source with Power Quality" ICCPCT, IEEE Conference, pp.1-7, Mar 2015, Noorul Islam University.

[9] Y. F. Tang and L. Xu, “Fuzzy logic application for intelligent control of a variable speed drive,” IEEE Trans. Energy Conversion, Vol. 9,No. 4, Dec. 1994.

[10] Reddy, K. Harshavardhana, Sudha Ramasamy, and Prabhu Ramanathan. "Hybrid Adaptive Neuro Fuzzy based speed Controller for Brushless DC Motor." Gazi University Journal of Science 30.1 (2017): 93-110.

[11] Sudheer Kasa, Sudha Ramasamy “Enhancement of Power Quality in Photovoltaic fed Multi Feeder Three Phase System with ANFIS-Unified Multi Converter Controller” Matec Web of Conferences, 2018.

[12] Sudheer Kasa et. al., “Neuro Fuzzy Control Single Stage Single Phase AC-DC Converter for High Power factor” International Journal of Engineering Research & Technology (IJERT) 2013 (ISSN: 2278-0181).

[13] Sudheer Kasa et. al., “A Cascaded H-Bridge and Novel Multilevel Inverter Topology for Induction Motor Drive" in International Journal of Advanced and Innovative Research (IJAIR) - 2013 (ISSN: 2278-7844).

[14] Sudheer Kasa et. al., “Pi Based Power Quality Enhancement of Grid Connected Wind Energy System For Dc – Link Energy Storage System” International Journal of Science Engineering and Advance Technology (IJSEAT), Vol. 2, Issue 10, October – 2014 (ISSN: 2321-6905).

[15] Suresh, P., & Manohar, T. G.: Effective Renewable Source Integration using Unified Power Quality Conditioner with Power Quality Enhancement in Three Phase System. In MATEC Web of Conferences EDP Sciences. Vol. 225, p. 05014 (2018)..



ISSN NO: 1076-5131

Page No:697

[16] Penagaluru, S., & Manohar, T. G. : Review on Renewable Source Integrated Topologies with Power Quality Enhancing Strategies. International Journal of Renewable Energy Research,Vol. 8 No.4, pp.2350-2366, (2018).

[17] Mukhtiar Singh, Chandra, and Rajiv K. Varma, et al “Grid Interconnection of Renewable Energy Sources at the Distribution Level With Power-Quality Improvement Features” IEEE transactions on power delivery, vol. 26, no. 1, Jan. 2011



ISSN NO: 1076-5131

Page No:698

Documents

JASC: Journal of Applied Science and Computations ISSN NO