1

Abstract— This paper proposes an overall power management

control strategy of a stand-alone power supply system consisting of wind turbine and battery storage system. The overall control strategy consists of two layer structure. The upper layer is overall power management controller that generates the reference signal for the local controller. Based on reference signal, the local controllers control the wind energy conversion system and energy storage system. The wind energy conversion system is controlled in order to achieve optimum power from the changing wind. The energy storage system is controlled by a bi-directional dc-dc controller. The performance of the controller is verified in a simulated wind and load conditions and results are presented.

I. INTRODUCTION n remote and isolated areas, diesel generators are commonly used to provide electricity as grid connection is often neither

available nor economically viable. Diesel generators are popular in remote area power system applications for their reliability, cheap installation, ease of starting, compact power density and portability [1],[2]. However, diesel generators are becoming expensive to run and also need frequent maintenance. Most importantly, they pollute the environment.

Wind energy can be an alternative solution for stand-alone power supply system as they deliver a cost-effective and clean power. However, due to the intermittent nature of the wind, the current power extracted from the wind turbines often does not match with the current load demand. As a result, energy storage systems are essential to provide a continuous and reliable power supply [3]-[5]. However, the main challenge of integrating energy storage system with wind turbine is to develop proper coordination with appropriate control in order to deliver required load demand.

In wind energy conversion system, sophisticated control is required to extract the optimum power and to ensure efficient operation of generator. Optimum power extraction from the wind refers to extracting the necessary power to fulfill the load demand. Different maximum/ optimum power tracking algorithms such as search-based or perturbation-based methods, fuzzy logic based control or wind speed estimation based algorithm [6]-[10] were proposed for both stand-alone and grid connected systems. Optimum power extraction algorithms can be implemented in wind energy conversion stages in different ways. In [6] an unregulated two-level rectifier with a boost or a buck-boost converter is used to

A. M. O. Haruni, M. Negnevitsky, M. E. Haque, and A. Gargoom with centre for renewable energy and power systems (CREPS), University of Tasmania, Tasmania, Australia (e-mail: amharuni@utas.edu.au).

regulate the dc-link voltage or rotor speed. However, this arrangement causes high harmonic distortion which reduces generator efficiency [7]. In [7]-[8], a regulated two-level rectifier is used. This arrangement generates less harmonic distortion compared to the former arrangement and has better power conversion efficiency. In [9], an unregulated rectifier with a current controlled inverter is used to extract the maximum power from wind. However, this arrangement is not suitable for a sand-alone operation as the load-side voltage and frequency control is not addressed.

In energy conversion stage, permanent magnet synchronous generator provides various advantages over conventional generator such as gearless operation, higher efficiency, higher power density, higher power factor, larger power-to-weight ratio, and less maintenance [11]. In addition, interior permanent magnet (IPM) synchronous generators offer higher torque-to-current ratio compared to surface permanent magnet synchronous generators by utilizing the reluctance torque as well as magnetic torque. In addition, they can be operated in a higher speed range for constant power operation by utilizing the flux weakening technique along the d-axis [11].

Recent studies hybrid wind turbine with energy storage system focus on various issues such as size and cost optimization [12]-[14], power management [15]-[17], power quality [18], and reliability [19]. However, only a little attention has been paid to load management in the event of inadequate energy reserve during low wind/solar conditions, which may lead the system into a black-out condition. As a result, load management strategies are essential so that hybrid systems can provide the load demand during this condition.

In this paper, a stand-alone hybrid power supply scheme for isolated communities is proposed using an IPM synchronous generator based variable speed wind turbine, and energy storage systems consisting of battery storage system. The wind energy conversion system is controlled by a controlled rectifier to extracts the optimum power by regulating the rotor speed and to ensure the efficient operation of the IPM type synchronous generator by regulating the d- and q-axis components of stator current. The charging and discharging of battery storage system is controlled by a bi-directional dc-dc converter. A power management system is also proposed which allocates the reference power generation of each sub-system. Moreover, the power management system also manages the load in the event of low wind conditions with inadequate energy storage. The system is implemented in the MATLAB/SIMPOWER environment, tested for various wind and load conditions, and results are presented.

A Novel Power Management Control Strategy for a Renewable Stand-Alone Power System

A. M. O. Haruni, Student Member, IEEE, M. Negnevitsky, Senior Member, IEEE, M. Enamul Haque, Senior Member, IEEE, and A. Gargoom, Member, IEEE

I

978-1-4673-2729-9/12/$31.00 ©2012 IEEE

2

Fig. 1 Structure of proposed hybrid power generation system.

II. PROPOSED SYSTEM OVERVIEW The proposed system consists of a wind energy conversion

system, battery storage and a set of ac loads. The system is shown in Fig. 1.

The wind energy conversion system consists of an IPM synchronous generator based-variable speed wind turbine followed by a PWM rectifier. The battery is connected to the dc link by a bi-directional dc-dc converter. Finally, a set of ac loads is connected to the system via a controlled inverter.

III. WIND ENERGY CONVERSION SYSTEM MODELING AND CONTROL

A. Wind Turbine Model for Optimum Energy Extraction The output power from wind (PW) can be expressed as [20]:

( ) iovpCAwP υυυβλρ >>= ;3,5.0 (1) where, ρ is the air density, A is the rotor swept area, υ is the wind speed, υ0 and υi are cut-in and cut-off wind speed, and CP is the power co-efficient which is a function of speed ratio γ and pitch angle β.

The speed ratio of the wind turbine (γ) can be defined as:

υω

λRr= (2)

The optimum power from the wind turbine can be extracted by controlling the rotor speed (ωr). From (1) and (2), for a particular rotor speed, the output power is proportional to the rotor speed and can be expressed as:

3roptoptWopt KP ω= (3)

where, 3

5.0 ⎟⎠⎞⎜

⎝⎛= optRCAK Poptopt λρ

Fig. 2 demonstrates the power generated by a turbine as a function of the rotor speed for different wind speeds. As an example, for a particular wind speed (v3), the optimum power (PWopt) can be generated by keeping the rotor speed either equal to ω1 or ω4. However, as ω4 is higher than the base rotor speed, the control system has to choose the rotor speed ω1. If the wind speed drops to v2 from v3, the control system sets the rotor speed to ω3 to extract the required power.

B. IPM Synchronous Generator Model From Fig. 3, the voltage equation of the IPM synchronous

generator in the d- and q-axis is expressed as follows [21]:

qiqLdidtd

dLsRdidv ω−+= )( (4)

fdidLqidtd

qLsRqiqv ωψω +++= )( (5)

Fig. 2. The wind turbine characteristic curve.

Fig. 3. a) The d- and b) q- axis circuit of IPM synchronous generator. where, vd and vq are the d- and q- axes components of the stator voltage, Rs is the stator resistant, id and iq are the d- and q- axis components of the stator current, ω is the electrical frequency, and ψf is the flux linkage.

The torque equation of the IPM synchronous generator can be expressed as follows [21]:

})({23

qidiqLdLqifnPgT −+−= ψ (6)

where, Pn, and Tg are the number of pole pair and the generated torque, respectively of IPM synchronous generator.

From (6), the q-axis stator current component (iq) for constant torque can be expressed as a function of the d-axis stator current component (id ):

})({3

2

diqLdLfnPT g

qi −+−=

ψ (7)

The maximum efficiency of the IPM synchronous generator operation can be achieved by minimizing the cupper and core losses. From Fig. 3, the copper (PCu) and the core (PCore) loss for the IPM synchronous generator can be determined as follows [22]:

)22( didisRcuP += (8)

cRqiqLfdidL

coreP}2)(2){(2 ++

=ψω (9)

where Rc is the core loss component. The output power from the generator can be given as:

cRqiqLfdidL

didisRgT

CorePCuPwPoutP

}2)(2){(2)22(

++−+−=

−−=

ψωω

(10)

The optimum value of id can be determined from the output power (Pout) vs d-axis stator current (id) curve based on (6)-(10) as shown in Fig. 4. From Fig. 4, the optimum value of d- axis current component is chosen where the output power of the IPM synchronous generator is maximum. The corresponding value of iq can be obtained from (7).

C. Machine Side Converter Controller Design The machine side converter shown in Fig. 5 consisting of

+ _

Vq

-0.4

-0.2 0

0.2 0.4 0.60.8

1

Turbine speed (pu) 0 0.2 0.4 0.6 0.8 1 1.2 1.4

v1 v2 v3

v4 PWout

Turb

ine

pow

er (p

u)

ω1ω2 ω3 ω4

IPMSG

Inverter controller

Vabc

f*

Wind Turbine Rectifier Load-side Battery LC filter

Battery controller

PL

Vabc*

ωg v

Optimum power extraction

Load

Load

Load

Power management system

vd

_

Rs

Rc

Ld

id ω Lqiq

a) b)

Rs

Rc

Lq

iq ω Ldid

frψω

_ + +

3

Fig. 4. The d-axis current vs output electric power

Fig. 5. Machine side converter controller.

three PI controllers works on principle based on (6) – (10). In the first stage, a PI controller is used to regulate the speed by controlling the torque. In the second stage, two PI controllers are used to regulate the d- and q- axis currents for a specific torque for minimum loss conditions.

IV. BATTERY STORAGE SYSTEM MODELING AND CONTROL The battery voltage (Vb) can be expressed as follows [5]

( )∫++

+=∫

− dtbiBAdtbiQ

QKiRVV bbb exp0

(11)

where V0 is the open circuit voltage, Rb is the internal resistance, ib is the battery current, K is the polarization voltage, Q is the battery capacity, A is the exponential voltage, and B is the exponential capacity.

The battery state of charge (SOC) is an indication of the energy reserve and is expressed as follows [5]:

%1100 ⎟⎟⎠

⎞⎜⎜⎝

⎛ ∫−=

Q

dtbiSOC (12)

The battery controller is a bi-directional dc-dc converter that stabilizes the dc link voltage during sudden wind and load changes is shown in Fig. 10.

V. OUTPUT VOLTAGE AND FREQUENCY CONTROLLER The output voltage and frequency of the system is regulated

by the load side inverter controller. In Fig. 11, the voltage balance across the LC filter is given

as:

[ ] [ ] [ ] [ ]111 abcVabcidtd

fLabcifRabcV ++= (13)

where Rf and Lf are the resistance and inductance of LC filter, respectively, and ia, ib and ic are three phase load currents.

The d- and q-axis components of the load voltage are shown as below:

qfdffddd iLidtdLRivv ω−= +− )( (14)

Fig. 6. The battery charger/discharger controller.

Fig. 7. The load side inverter controller.

dfqffqqiq iLidtdLRivv ω−= +− )( (15)

The voltages vd and vq are compared with their reference values (vd*=1 and vq*=0) and a suitable PWM signal is generated.

VI. ENERGY MANAGEMENT & POWER REGULATION SYSTEM The EMPRS ensures a continuous operation of the hybrid

system by proper co-coordination of the wind turbine, energy storage systems and loads. The EMPRS works in three stages. In the first stage, the EMPRS predicts the wind and load profile for a period of time. In the second stage, based on the wind and load profile and the status of energy reserve, the EMPRS schedules the maximum load that can be supplied by the system. In third stage, the EMPRS determines the operating condition of each sub-system.

A. Wind and Load Prediction An accurate wind and load prediction is a key factor that

ensures a robust performance of the EMPRS. In several studies conducted earlier, it was demonstrated that an accurate forecasting system can be developed for the short-term (up to 15 minutes) forecasting of wind and load conditions [23]-[27]. An integration of wind and load forecasting in the EMPRS will allow to implementing the load curtailment in advance, and thus avoid system blackouts as demonstrated below.

B. Load scheduling Based on the wind and load prediction, the power balance

equation of hybrid system can be as follows: BPLPoutWP ±=_ (16)

From (27), during high wind conditions, the excess power (PEX) is consumed by the battery storage system as follows:

BPLPoutWPExP =−= _ (17)

During low wind conditions, the power deficit (Pdef) from the wind can be provided by the battery system as follows:

∫

Va

Vb

Vc _

ωθ

abc

dq0to

Vqref=0

+

vqi PI

Vdref=1 _ + vdi

PI PWM

θ

RL Filter

Load

Load

Vdc

Va Vb Vc

Va1

Vb1 Vc1

ed

eq

Vdc ibat

PI ei

+_ PI ep +_ PWM

V*dc i*

bat

> AND P*

bat

0

> AND P*

bat

0

Q1

Q2 Vbat

Vdc

PI

id*

+

id

+

Ld X

PI +

iq

ωgiqLd

Lq X

X

+ +

fψ

+_

P W M

_

+

Iq*

from

(7)

_

ωgidLq

+id fψ

Tg* ωg

*

ωg

PI + _

ω To

Rectifier

id*

iq*

d-axis current (pu)

0)()( =iddPd out

ω =1pu

ω =0.9 pu

ω =0.8 pu

0

1

-1 -0.75 0.5 -0.25Out

put

elec

trica

l pow

er (p

u)

0.6

0.8

4

BPoutWPLPPdef =−= _ (18)

The energy balance equation can be obtained by integrating (27):

⎪⎩

⎪⎨⎧

±=

±= ∫∫∫

BELEoutWEorBPLPoutWP

_,_ (19)

where, EW_out is the total energy of wind energy conversion system, , EL is the total energy consumed by the load, EB is battery from the battery.

However, in a real hybrid system operation, (19) is only valid for the following conditions:

• If EW_out> EL, the excess energy is stored in the hybrid system.

• If EW_out< EL, the energy deficit from wind has to be balanced by battery storage unit. In this condition, the battery storage can produce required power as long as the SOC of the battery are available. The system may enter into the black-out condition if the energy reserves are not sufficient to meet the load demand.

The robustness of EMPRS depends on the prediction accuracy. Although, it has been demonstrated that the short-term prediction error can be as less as 1% for normal conditions, the prediction error can increase under sudden wind guests, or sudden changes of big industrial load. As a result, sufficient reserves have to be allocated to offset the prediction error (up to 5%).

Moreover, in reality, an unlikely event of no wind condition may occur and continue for a long time. During this condition the hybrid system mainly relies on energy storage. Thus, energy reserves that can serve only emergency loads have to be preserved. This condition can be defined as ‘emergency condition’ and the controller allows the SOC of battery to reach 20%.

Considering operating practical aspects during low wind conditions, management of energy reserves of the hybrid system is vital. In order to ensure the system operation, the load curtailment is adopted. The load management algorithm is shown in Fig. 8. It is described as follows:

• Calculate the total energy difference (Ed) between the wind (EW_out) and load demand (EL).

• If EW_out>EL , check SOC of the battery. If SOC>55%, no load curtailment is required. If SOC<55% and extra energy from wind is not sufficient to bring the SOC to 55%, the load curtailment is executed.

• If EW_out<EL , check SOC of the battery. If SOC>55% and the battery (EB) have enough reserves to supply the deficit energy, no load curtailment is needed. For other conditions, load curtailment is implemented.

• During emergency condition the system supplies only the emergency loads (Pm). During this condition, the EMPRS allows the SOC of battery to go as low as 20%.

To implement load curtailment, loads are divided according to their priority. The loads such as hospitals, police stations, etc. can be considered as emergency loads. The hybrid system has to fulfil the power demand of these loads at any conditions. On the other hand, some lighting loads, washing machine loads, etc. can be considered as lower priority and can be switched off when required.

Fig. 8. The Load management algorithm.

C. Operation point of each sub-system: The EMPRS generates the operation point based on the wind and load conditions and limitations of each sub-system as described in the following:

Mode 1: Normal Operation In this mode, the wind turbine extracts optimum power. The

battery storage device charges and discharges in order to balance the power offset between wind turbine and load demand.

Mode 2: Load Shedding Operation In this mode, the wind turbine with battery storage system

cannot meet the load demand. As a result, load curtailment operation takes place as discussed in the previous section.

Mode 3: Emergency Operation Emergency condition occurs when the wind turbine is shut

down due to fault or unfavorable wind condition or the power from wind turbine is even less the emergency loads (Pm). During this conditions, the hybrid system supplies only the emergency load demand. In this condition, the battery storage is allowed to discharge up to 20% of its SOC.

VII. SIMULATION SYSTEM OPERATION AND PERFORMANCE

The proposed system shown in Fig. 1 is implemented in Matlab/Simpower environment. The performance of the system is simulated for different wind and load conditions. The parameters of the wind turbine, IPM synchronous generator, and the energy storage system are shown in Table I. Two case studies are presented to justify the performance of the proposed model in different wind and load conditions.

A. Effect of wind and load variation In this section, the performance of the proposed system is

evaluated under wind and load variations assuming low wind speed conditions.

A1. Wind and Load Profile Fig 9 shows the hypothetical wind and load profiles. Wind

speed changes from 10.5 m/sec to 11.1 m/sec at t=10.5 sec; 11.1 m/sec to 11.7 m/sec at t=17.5 sec; and 11.7 m/sec to 10.5 m/sec at t=23 sec. The load changes from 720 watt to 750 watt at t=13.5 sec; from 750 watt to 900 watt at t=17.5 sec; from 900 watt to 800 watt at t=22.5 sec.

YES

SOC > 55%

NO

Ed=ELW_out -EL

Ed > 0

Normal Operation

NO

EFC> Ed+ EB (SOC>55%)

No Load Shedding

YES Load

Shedding Operation

NO

EB> ED

YES

YES NO

ELW_out > Em

Emergency Operation

YES

No Load Shedding

No Load Shedding

NO

SOC > 55%

NO

Load Shedding

Ed> EB (SOC>55%) YES

NO

Load Shedding Operation

5

A2. Machine-side Converter Performance:

Fig. 10 shows the maximum power extraction from the wind by regulating the generator speed. From Fig. 10, it can be seen that the proposed controller is able to extract the maximum power at different wind speed conditions.

Fig. 11 shows the maximum efficiency operation of the IPM synchronous generator. From Fig. 11, it is seen that the controller controls the d-and q-axis stator current in order to maintain a high efficiency operation of the IPM synchronous generator. Fig. 12 (a) shows the converted electrical power and power loss of the IPM synchronous generator. Fig. 12(b) shows the efficiency of the generator. Fig. 12(b) shows that the IPM synchronous generator maintains high operation efficiency.

A3. Load-Side Inverter Performance:

The performance of the load side inverter is shown in the Fig. 13. From Fig. 13, it can be seen that the system voltage and frequency remains almost constant despite wind and load variations.

A4. Bi-Directional Battery Storage Controller Performance:

The performance of the bi-directional converter is shown in the Fig. 14. From Fig. 14, it can be seen that the bi-directional converter controller is able to control the battery storage system and provide the necessary power to the system.

B. Performance of energy management and power regulation system:

The EMPRS performance is evaluated under realistic wind and load conditions. Let us consider the proposed hybrid system operating on an island. The average load demand is about 0.6 MW and the peak load demand is 1 MW. Based on the load demand, the hybrid system is assumed as follows:

• 5 wind turbine each rated 250 KW; • 800kWh storage rated 1000 KW.

A case study is performed under low wind condition when load peak occurs. Let us assume the energy reserve is low and cannot support the system without load curtailment.

The wind speed is shown in Fig. 15(a). A hypothetical wind prediction is assumed with an error of ±5 of wind speed. The corresponding extracted power from the wind turbines is shown in Fig. 15(b).

The load profile is shown in Fig. 16(a). The load is divided into 4 categories. Type LC4 is the load with the highest priority that constitutes about 25% - 30% of the total load. Type LC3 is a high priority load that constitute about 25% - 30% of the total load. Type LC2 is the load with a medium priority that constitutes about 25% - 30% of the total load. Type LC1 has the lowest priority that constitutes the rest of the total load. The load curtailment operation is shown in Fig. 16(b).

Fig. 17(a) and 17(b) reveal the operation battery storage and associated SOC, respectively. From Figs. 15-17, it can be shown that the EMPRS can operate the system without any load curtailment up to time of 2 hours. However, as the SOC become low in the next hour, and the wind turbines alone cannot support the load demand. As a result, the EMPRS

curtails the lowest priority loads. During this hour, the EMPRS storages the excessive wind power during high wind

Fig. 9. Hypothetical a) wind and b) load profile.

Fig. 10.Rotor speed regulation for maximum power extraction.

Fig. 11.Stator current regulation.

5 10 15 20 2510

11

12

m/s

ec

a) Wind Speed

b) Load Profile

650750

850950

Time5 10 15 20 25

Wat

t5 10 15 20 25

0.750.850.95

1.05a) Rotor speed

ωg

ωg*pu

Time5 10 15 20 25

700800

9001000

b) Power extracted from the windW

att

5 10 15 20 25-0.32-0.28

-0.24-0.2

a) d- axis current

Am

p

Id*

Id

-1.6-1.4

-1.2-1

5 10 15 20 25

a) q- axis current

Am

p

Time

Iq*

Iq

5 10 15 20 250

300

600

900

a) Output power from IPMSG (Pout), and power conversion loss (Ploss)

Pout

PlossWat

t

Time

75

85

95

5 10 15 20 25

%

b) Power conversion efficiency (%)

6

Fig. 12.a) Output power from the IPM synchronous generator and b) efficiency of operation.

Fig. 13. System a) voltage and b) frequency.

Fig. 14. Battery storage power regulation.

Fig. 15. Wind condition.

Fig. 16. Load condition.

Fig. 17. Battery power and SOC.

conditions and uses it during low wind conditions. From time of 3 hours to 3.4 hours, the EMPRS curtail both low priority and medium priority loads to match the energy generation with the load demand. From 3.4 hours onwards, an emergency condition occurs when the wind power is too low and the energy reserves for the normal operation condition runs out. During this condition, the EMPRS uses the emergency reserves of the battery to supply power for the loads with the highest priority. Although wind comes back at time of 3.8 hour, the EMPRS continues running the system in the emergency mode until the SOC of the battery exceeds 55%.

VIII. CONCLUSION A novel operation and control strategy for a hybrid power system with the energy storage for a stand-alone operation is proposed. The performance of the proposed control strategy is evaluated under different wind and loading conditions. From the simulation studies, it is revealed that the machine side converter is able to extract the optimum power. The machine side converter is also able to operate the IPM synchronous generator with the maximum efficiency. The battery storage system is controlled successfully by a bi-directional converter. The overall co-ordination of the wind turbine, battery storage system and ac loads is done by developing energy management and power regulation system. The obvious advantage of the EMPRS is that it can prevent the system from black-outs in the event of low wind conditions or inadequate energy reserves.

TABLE I. SIMULATION PARAMETERS

Permament Magnet Synchronous Generator Number of Pole Pairs 4 Rated Speed (rpm) 1260 Rated Power (kw) 1 Stator Resistance (ohm) 5.8 Direct Inductance (mh) 0.0448 Quadrature Inductance (mh) 0.1024 Inertia 0.011

Wind Turbine Rated Power (kW) 1.1 Base Wind Speed (m/s) 12

RC Filter Series Inductance (mh) 13 Shunt Capacitance (micro F) 20

Emergency Storage System Rated Voltage (volt) 48 Rated Current (Amp) 5 Rated Capacity (amp-hour) 5

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49

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Time5 10 15 20 25

b) System frequency

Hz

0.9

1

1.1

5 10 15 20 25

a) System Voltage

Vol

t

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A M Osman Haruni (S’10) received BSc (Electrical and Electronic Engineering) from Bangladesh University of Engineering and Technology in 2003, and M.Eng.Sc degrees from University of Tasmania 2008. He worked as a trainee engineer and then as an executive engineer with Siemens Bangladesh Limited in Power Transmission and Distribution (PTD- MV) department from 2004 to 2006. He is currently working towards his Ph.D. degree at the University of Tasmania, Australia. His research interests include power electronics control for renewable energy technologies, load modelling and its application in power system operation and control, and use of artificial intelligence in power systems.

Michael Negnevitsky (M’95-SM’07) received the B.S.E.E. (Hons.) and Ph.D. degrees from the Byelorussian University of Technology, Minsk, Belarus, in 1978 and 1983, respectively. Currently, he is Chair Professor in Power Engineering and Computational Intelligence and Director of the Centre for Renewable Energy and Power Systems at the University of Tasmania, Hobart, Australia. From 1984 to 1991, he was a Senior Research Fellow and Senior Lecturer in the Department of Electrical Engineering, Byelorussian University of Technology. After arriving in Australia, he was with Monash University, Melbourne, Australia. His interests are power system analysis, power quality, and intelligent systems applications in power systems.

Dr. Negnevitsky is a Chartered Professional Engineer, Fellow of the Institution of Engineers Australia, and Member of CIGRE AP C4 (System Technical Performance), Member of CIGRE AP C6 (Distribution Systems and Dispersed Generation), Australian Technical Committee, and Member of CIGRE Working Group JWG C1/C2/C6.18 (Coping with Limits for Very High Penetrations of Renewable Energy), International Technical Committee.

Md. Enamul Haque (M’1997, SM’2010) graduated in Electrical and Electronic engineering from Rajshahi University of Engineering Technology (Formerly, Bangladesh Institute of Technology (BIT)), Rajshahi, Bangladesh, in 1995. He received the M. Engg. degree in Electrical Engineering from University Technology Malaysia in 1998, and Ph.D. degree in Electrical Engineering from University of New South Wales, Sydney, Australia, in 2002. He has worked as an Assistant Professor for King Saud University, Saudi Arabia and United Arab Emirates University for four years. Dr. Haque is currently working as a Lecturer in renewable energy and power systems in the School of Engineering of University of Tasmania, Australia. His research interests include smart energy systems, control and grid integration of renewable energy sources and energy storage system, micro grid system with hybrid wind/solar/fuel cell systems, power electronics applications in smart-grid, micro-grid and power system applications.

Ameen Gargoom (M’08) received BSc, MSc (with honours) and PhD degrees in 1994, 2001, 2007 respectively, all in electrical power engineering. He worked as a consultant engineer with Al-Emara Co. for Engineering Consultants, Libya, for six years before joining the University of Garyounis in 2001 as an associate lecturer. In 2008, he joined the University of Tasmania as a Research Fellow. Currently he is working as a Lecturer in School of Engineering, University of Tasmania, Australia. His research interests include power electronics control for renewable energy technologies and smart grid systems, new techniques for power quality monitoring and classification, and the application of signals processing techniques in power systems.

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