Modelling and performance assessment of a standalone hybrid wind-diesel-superconducting magnetic energy storage system using four-quadrant operation of superconducting magnetic energy storage

Abstract

In this article, a comprehensive model for power quality assessment of a standalone wind-diesel-superconducting magnetic energy storage system is developed using firing angle control scheme of superconducting magnetic energy storage unit. A four-quadrant operation of superconducting magnetic energy storage unit is proposed where firing angle of superconducting magnetic energy storage converter is controlled depending upon active and reactive power demand of wind-diesel system, under various disturbances. Superconducting magnetic energy storage is designed as a controllable current source capable of reducing both frequency and voltage deviations simultaneously. For continuous control of superconducting magnetic energy storage unit, a regulating variable is formulated which forces the superconducting magnetic energy storage current to return to its nominal value after handling a disturbance in order to handle a new disturbance. Frequency of the system is estimated using the concept of centre of inertia. System performance is assessed for disturbances of higher magnitudes. Complete modelling is carried out in MATLAB/Simulink environment, and simulation results demonstrate that the scheme gives expected results under load disturbance and wind perturbations.

Keywords

Power quality improvement wind-diesel-superconducting magnetic energy storage system controllable current source four-quadrant operation regulating variable centre of inertia wind perturbation load disturbance MATLAB/Simulink

Introduction

Renewable energy sources are widely being used because of their outstanding advantages over conventional energy sources. Among all the renewable energy sources, wind energy conversion system (WECS) is the most popular and fast growing. WECS-based power systems are most suitable for habitations which are not grid connected or where power is supplied from diesel-generating units. The integration of wind power system with the already existing diesel-generating system increases the reliability of the power system and reduces the cost of fuel (Das et al., 1999; Kusakana and Vermaak, 2014). Integrating a WECS with a diesel-generating system is, however, a challenging task. Since the inertia is low, any disturbance in the system will cause electromechanical oscillations and deteriorates the power quality (Salih et al., 2014; Sheikh et al., 2009). Power quality problem can, however, be solved by incorporating a superconducting magnetic energy storage (SMES) unit as buffer storage. Advantage of SMES is that it is fast acting with almost zero self-discharge. The coil is kept at a very low temperature with the help of cryogenic coolers, so the ohmic losses are close to zero during the operation. System of this type can charge and discharge very fast and has the ability to absorb or deliver high quantities of power. Another important aspect of SMES is its long life and it has millions of charge and discharge cycles (Iqbal et al., 2009; Moschakis et al., 2005; Nielsen and Molinas, 2010).

SMES being a fast-acting device is capable of absorbing/releasing both active and reactive power by properly controlling the firing angles of the power conditioning system (Hsu, 2002). Due to change in wind speed and load disturbance, any variation in active and reactive power will interact with network, provoking frequency, and voltage deviation (Ali et al., 2005). SMES has been used in wind-based power system for stability enhancement with no emphasis on frequency and voltage deviation control. In the work by Hemeida (2010), frequency control of a two-area power system using fuzzy-based controller for an SMES unit is proposed but voltage control is neglected. In the work by Zargar et al. (2017a), a simple model for a generalised energy storage system is presented for simultaneous voltage and frequency control in a wind-diesel power system. Although energy storage system is modelled as a controllable current source, yet details related to switching control scheme are not considered for power conditioning system. In this article, an SMES unit is used for controlling both frequency and voltage deviations. Four-quadrant operational control for simultaneous active (P) and reactive (Q) power exchange of SMES unit with the wind-diesel system is developed. A detailed model of SMES unit along with its power conditioning system is described where SMES is modelled as a controllable current source which injects both active and reactive components of current into the system by suitably controlling the firing angles of the SMES converters. Also, a suitable regulating variable is chosen to ensure continuous control. Network model is developed using MATLAB function available in the Simulink library and is integrated with the machine and SMES model along with their control systems to form a compact model. System considered comprises two synchronous generators of equal rating with automatic voltage regulator (AVR) and speed governor, eight squirrel cage induction machines driven by wind turbines are represented by a single equivalent induction machine, loads are represented as admittances, and capacitor bank used for reactive power compensation of induction machine are modelled by equivalent admittances. Block diagram of the system is shown in Figure 1. SMES is installed at bus number 4 where load is connected. Modelling of wind-diesel system, network modelling, and SMES are discussed. Simulation results demonstrate the potency of the proposed scheme.

Figure 1.

Block diagram of hybrid wind-diesel-SMES system.

Modelling of wind-diesel system

Modelling of wind turbine model

Mode of operation of induction machine depends upon the sign of torque and slip, that is, negative for generator and positive for motor. Swing equation which represents the mechanical model is given by equation (1)

\frac{d}{d t} (ω_{A}) = \frac{1}{2 H_{A}} (T_{m} - T_{A e})

(1)

Modelling of induction generator

Induction machine is modelled by a voltage source E′ behind stator resistance r_s and transient reactance X′. Stator equations are expressed in synchronously rotating reference frames in a matrix form in equation (2) and rotor equations are given by equations (3) and (4)

[\begin{matrix} V_{D 3} \\ V_{Q 3} \end{matrix}] = [\begin{matrix} {E^{'}}_{D 3} \\ {E^{'}}_{Q 3} \end{matrix}] + [\begin{matrix} - r_{s 3} & {X^{'}}_{3} \\ - {X^{'}}_{3} & - r_{s 3} \end{matrix}] [\begin{matrix} I_{D 3} \\ I_{Q 3} \end{matrix}]

(2)

\frac{d}{d t} ({E^{'}}_{D 3}) = \frac{1}{{T^{'}}_{O}} [- {E^{'}}_{D 3} - (X_{3} - {X^{'}}_{3}) I_{Q 3}] + S ω_{O} {E^{'}}_{Q 3}

(3)

\frac{d}{d t} ({E^{'}}_{Q 3}) = \frac{1}{{T^{'}}_{O}} [- {E^{'}}_{Q 3} - (X_{3} - {X^{'}}_{3}) I_{D 3}] - S ω_{O} {E^{'}}_{D 3}

(4)

Torque developed by the induction machine is given equation (5)

T_{A e} = {E^{'}}_{D 3} I_{D 3} + {E^{'}}_{Q 3} I_{Q 3}

(5)

Voltage, current, active, and reactive power output are given by equations (6)–(8)

V_{t} = \sqrt{V_{D}^{2} + V_{Q}^{2}} and I_{t} = \sqrt{I_{D}^{2} + I_{Q}^{2}}

(6)

P_{A e} = V_{D} {I^{'}}_{D} + V_{Q} {I^{'}}_{Q}

(7)

Q_{A e} = - V_{D} {I^{'}}_{Q} + V_{Q} {I^{'}}_{D}

(8)

The parameters of a single equivalent machine are then given by the following relations

H_{e q} = N H_{A}, r_{s_{e q}} = \frac{r_{S 3}}{N}, {X^{'}}_{e q} = \frac{X^{'}}{N}, X_{e q} = \frac{X}{N}

N is the number of induction machines used, which is 10 in this article.

Diesel engine and speed governor

Diesel engine is modelled by a first-order transfer function model with time constant T_D (Stavrakakis and Kariniotakis, 1995), and IEEE type 1 AVR is used for both the synchronous generators (Stavrakakis and Kariniotakis, 1995).

Modelling of synchronous generator

Synchronous generator is modelled using equations (9)–(12)

[\begin{matrix} V_{d} \\ V_{q} \end{matrix}] = [\begin{matrix} {E^{″}}_{d} \\ {E^{″}}_{q} \end{matrix}] - [\begin{matrix} r_{s} & - {X^{″}}_{q} \\ {X^{″}}_{d} & r_{s} \end{matrix}] [\begin{matrix} I_{d} \\ I_{q} \end{matrix}]

(9)

\frac{d}{d t} ({E^{″}}_{d}) = - \frac{1}{{T^{″}}_{q o}} [{E^{″}}_{d} - (X_{q} - {X^{″}}_{q}) I_{q}]

(10)

\frac{d}{d t} ({E^{'}}_{q}) = \frac{1}{{T^{'}}_{d o}} [E_{f d} - \frac{X_{d} - {X^{″}}_{d}}{{X^{'}}_{d} - {X^{″}}_{d}} {E^{'}}_{q} + \frac{X_{d} - {X^{'}}_{d}}{{X^{'}}_{d} - {X^{″}}_{d}} {E^{″}}_{q}]

(11)

\frac{d}{d t} ({E^{″}}_{q}) = - \frac{1}{{T^{″}}_{d O}} [{E^{″}}_{q} - {E^{'}}_{q} + ({X^{'}}_{d} - {X^{″}}_{d}) I_{d}]

(12)

Load, capacitor bank, and transmission line modelling

When active power (P_L), reactive power (Q_L), and voltage (V_t) is known; load can be modelled as admittance given as follows

Y_{L} = \frac{P_{L} - j Q_{L}}{{| V_{t} |}^{2}}

(13)

Capacitors for reactive power compensation and transmission line parameters such as resistance per unit length, capacitance per unit length, and inductance per unit length are converted into admittances and are incorporated into the network admittance matrix (Serdar et al., 2015).

Four-quadrant operation of SMES

Currents supplied by SMES unit (I_DSMES and I_QSMES) are calculated using the active and reactive powers supplied by SMES as shown in equation (14) (Hiyama et al., 2005)

[\begin{matrix} I_{D S M E S} \\ I_{Q S M E S} \end{matrix}] = \frac{1}{V_{D}^{2} + V_{Q}^{2}} [\begin{matrix} V_{D} & V_{Q} \\ V_{Q} & - V_{D} \end{matrix}] [\begin{matrix} P_{S M E S} \\ Q_{S M E S} \end{matrix}]

(14)

V_D and V_Q are the direct and quadrature axis voltages of the bus, respectively, where SMES is connected.

P_SMES and Q_SMES are the active and reactive powers delivered/absorbed by SMES and are used to calculate the currents injected by SMES as shown in Figure 2.

Figure 2.

Current injected by ESS.

P_dem and Q_dem shown in Figure 2 are proportional to the deviation in frequency (∆ω) and voltage (∆V), respectively (Ngamroo et al., 2009) and are given by equations (15) and (16)

P_{d e m} = K_{1} \times Δ ω

(15)

Q_{d e m} = K_{2} \times Δ V

(16)

P_dem and Q_dem are actually the active and reactive power demands of wind-diesel system which are used to calculate the firing angles of SMES converter. In this article, two sets of gate turn-off thyristors (GTOs) are used as shown in Figure 3, where ∆-Y and ∆-∆ transformers make a 12-pulse arrangement. Direct current (DC) ripple voltage is reduced because the high side voltage and current in Y-∆/∆-Y transformers lead the low side voltage by 30° for positive sequence, and the high side voltage and current lag the low side by 30° for negative sequence. In Figure 3, P₁, Q₁ and P₂, Q₂ are the active and reactive powers of converters 1 and 2, respectively, and are related to P_dem and Q_dem by the following equations

P_{d e m} = P_{1} + P_{2}

(17)

Q_{d e m} = Q_{1} + Q_{2}

(18)

E_{d} = E_{d 1} + E_{d 2}

(19)

Figure 3.

SMES with two converters.

Since two sets of GTO converters are used, the possible regions of firing angles of the converter are from 0° to 360°. At first, the region where the (P_dem, Q_dem) point lies is located using Figure 4. Next, the calculation of firing angles α₁ and α₂ is done using equations (20)–(23). In case P_dem = Q_dem = 0, infinite number of the combinations of α₁ and α₂ exist and one of the solution is α₁ = 90° and α₂ = 270°. Using equations (20)–(23), four-quadrant operation of SMES is achieved. Figure 4 shows the control domains of GTO converters where the control domains are determined as follows:

Area I – use equations (20) and (23) with lower side complex sign.

Area II – use equations (20) and (22) with upper side complex sign.

Area III – use equations (20) and (23) with upper side complex sign.

Area IV – use equations (21) and (23) with upper side complex sign

α_{1} = \cos^{- 1} (\frac{1}{2 E_{d c} I_{d c}} [P_{d e m} \pm Q_{d e m} \sqrt{\frac{4 E_{d c}^{2} I_{d c}^{2} - (P_{d e m}^{2} + Q_{d e m}^{2})}{P_{d e m}^{2} + Q_{d e m}^{2}}}])

(20)

α_{1} = 360 - \cos^{- 1} (\frac{1}{2 E_{d c} I_{d c}} [P_{d e m} \pm Q_{d e m} \sqrt{\frac{4 E_{d c}^{2} I_{d c}^{2} - (P_{d e m}^{2} + Q_{d e m}^{2})}{P_{d e m}^{2} + Q_{d e m}^{2}}}])

(21)

α_{2} = \cos^{- 1} (\frac{1}{2 E_{d c} I_{d c}} [P_{d e m} \pm Q_{d e m} \sqrt{\frac{4 E_{d c}^{2} I_{d c}^{2} - (P_{d e m}^{2} + Q_{d e m}^{2})}{P_{d e m}^{2} + Q_{d e m}^{2}}}])

(22)

α_{2} = 360 - \cos^{- 1} (\frac{1}{2 E_{d c} I_{d c}} [P_{d e m} \pm Q_{d e m} \sqrt{\frac{4 E_{d c}^{2} I_{d c}^{2} - (P_{d e m}^{2} + Q_{d e m}^{2})}{P_{d e m}^{2} + Q_{d e m}^{2}}}])

(23)

Figure 4.

Power control domain of GTO converters.

Converter output voltage, SMES current, active, and reactive power supplied to SMES are calculated using the following equations (Ali et al., 2005)

E_{d} = E_{d c} (\cos α_{1} + \cos α_{2})

(24)

I_{d c} = \frac{1}{L} \int_{0}^{t} E_{d} d τ + I_{d 0}

(25)

P_{S M E S} = E_{d c} I_{d c} (\cos α_{1} + \cos α_{2})

(26)

Q_{S M E S} = E_{d c} I_{d c} (\sin α_{1} + \sin α_{2})

(27)

Simulink model for calculating P_SMES and Q_SMES is shown in Figure 5. I_DSMES and I_QSMES obtained from equation (14) are injected at bus number 4 as expressed by equation (28)

V_{4} = [I_{4} - (Y_{r e d (4, 1)} \times V_{1} + Y_{r e d (4, 2)} \times V_{2} + Y_{r e d (4, 3)} \times V_{3})] (\frac{1}{Y_{r e d (4, 4)}})

(28)

where

I_{4} = I_{D S M E S} + j I_{Q S M E S}

(29)

V_{4} = V_{D} + j V_{Q}

(30)

Figure 5.

Simulink model for SMES active and reactive power.

In order to achieve continuous control of active power, a regulating variable y(k) is formed which forces the SMES current to return to its nominal value after handling a disturbance so that the SMES unit remains ready to tackle a new disturbance. The regulating variable ‘y’ is chosen to be a function of frequency deviation and SMES current deviation given by expression equation (31)

y (k) = P_{d e m} + k_{c} (I_{d o} - I_{d c})

(31)

where the active power demand P_dem is proportional to frequency deviation, I_do is the nominal current of the SMES unit. The second term on the right-hand side of equation (31) is introduced to force the SMES coil current to return to its nominal value after dealing with a disturbance. The value of constant k_c is properly chosen. The large value of k_c will make SMES unit ineffective in reducing the frequency deviations. On the other hand, very small value of k_c will not allow the SMES coil current to return to its nominal value (Zargar et al., 2017b).

The frequency of the system is estimated using the concept of centre of inertia, where frequency computation is carried out using rotor speed and inertia constant of the connected synchronous generators in the system using equation (32), where G represents a synchronous generator set, ω_j is rotor speed and H_j is inertia constant (Mufti et al., 2017; Ortega and Milano, 2016)

ω_{C O I} = \frac{\sum_{j \in G} H_{j} ω_{j}}{\sum_{j \in G} H_{j}}

(32)

System base is assumed to be 400 kVA and frequency is 50 Hz. The data pertaining to SMES unit, equivalent induction generator, various line, buses, synchronous generators, IEEE type 1 AVRs, and diesel engines are given in Tables 1 to 7.

Table 1.

SMES data.

E_dc	I_do	L
85 V	800 A	0.264 mH

Table 2.

Induction generator data in p.u.

r_s ₃	H_A	X	X′	${T^{'}}_{0}$	N
0.0916	0.06216	7.441	1.4318	0.2584	8

Table 3.

Line data.

Bus	Bus	R (p.u.)	X (p.u.)
1	5	0	0.04
2	5	0	0.04
5	6	0.00029	0.000504
6	3	0.000725	0.12126
6	4	0.000725	0.00126

Table 4.

Bus data.

Bus no.	Vol.	kW	kVar	Gen. kW	Gen. kVar	Inj. kVar
1	1	0	0	0	0	0
2	0.9995	0	0	112	0	0
3	1	0	0	36	−59.77	44.83
4	1	260	182	0	0	0
5	1	0	0	0	0	0
6	1	0	0	0	0	0

Table 5.

Synchronous generator data in p.u.

H	r_s	${X^{'}}_{d}$	X_d	${X^{″}}_{d}$	X_q	${X^{″}}_{q}$	${T^{″}}_{d o}$	${T^{'}}_{d o}$	${T^{″}}_{q o}$	D
1.35	0.176	0.293	3.47	0.2	2.08	0.3	0.015	1.18	1.182	−0.0225

Table 6.

Automatic voltage regulator data in p.u.

K_A	K_E	K_F	T_A	T_E	T_F	K_R	T_R	S_E
270	1	0.048	0.1	0.65	0.95	1	0.05	0

Table 7.

Speed governor and diesel engine data in p.u.

R	K_I	K_D	T	T_D
0.085333	3.1125	1	0.1	0.4

As shown in Figure 1, SMES unit is installed at load bus, that is, bus number 4. To reduce the computational time, order of the bus admittance matrix is reduced from 6 to 4, resulting in a reduced bus admittance matrix (Y_red). Initially, the system is assumed to be in steady state with frequency and voltage deviations equal to zero. In this article, two types of disturbances are considered:

Load disturbance;

Wind perturbations.

In the first case, study system is subjected to a load disturbance (both active and reactive) at times t = 0 s, t = 20 s, and t = 40 s. At time t = 0 s, load is reduced by 10%; at time t = 20 s, load is increased to its rated value; and at time t = 40 s, load is again reduced by 10%. Reduced bus admittance matrices corresponding to load changes are shown as Y_red₂ and Y_red₁

\begin{matrix} Y_{r e d 2} = 0.527 - 23.967 j 0.527 + 1.032 j 0.122 + 0.243 j - 1.176 + 22.691 j \\ 0.527 + 1.032 j 0.527 - 23.967 j 0.122 + 0.243 j - 1.176 + 22.691 j \\ 0.122 + 0.243 j 0.122 + 0.243 j 0.091 - 8.049 j - 0.335 + 7.677 j \\ - 1.176 + 22.691 j - 1.176 + 22.691 j - 0.335 + 7.677 j 3.353 - 53.525 j \end{matrix}

\begin{matrix} Y_{r e d 1} = 0.527 - 23.967 i 0.527 + 1.032 i 0.122 + 0.243 i - 1.176 + 22.691 i \\ 0.527 + 1.032 i 0.527 - 23.967 i 0.122 + 0.243 i - 1.176 + 22.691 i \\ 0.122 + 0.243 i 0.122 + 0.243 i 0.091 - 8.049 i - 0.335 + 7.677 i \\ - 1.176 + 22.691 i - 1.176 + 22.691 i - 0.335 + 7.677 i 3.287 - 53.478 i \end{matrix}

Pre-disturbance load of the system is assumed to be 260 kW and 182 kVAr. Frequency and voltage deviations because of load change are shown in Figures 6 to 9.

Figure 6.

Frequency response of system under load disturbance.

Figure 7.

Voltage at bus number 1 under load disturbance.

Figure 8.

Voltage at bus number 3 under load disturbance.

Figure 9.

Voltage at load bus under load disturbance.

Variation of SMES current (I_dc) and currents injected by SMES unit (I_DSMES, I_QSMES) are shown in Figures 10 and 11.

Figure 10.

SMES current under load disturbance.

Figure 11.

Currents injected by SMES under load disturbance.

In the second case, study system is exposed to a continuous wind perturbation of the form shown in Figure 12. Frequency and voltage deviations are shown in Figures 13 to 16.

Figure 12.

Wind turbine torque under wind gust.

Figure 13.

Frequency response of system under wind perturbation.

Figure 14.

Voltage at bus number 1 under wind perturbation.

Figure 15.

Voltage at bus number 3 under wind perturbation.

Figure 16.

Voltage at load bus under wind perturbation.

Currents injected by SMES unit into the system are shown in Figure 17.

Figure 17.

Currents injected by SMES under wind perturbation.

Results in these figures show the following:

Peak frequency and voltage deviations are reduced considerably both under load disturbance and wind perturbation. These figures also show that oscillations in frequency and voltages are damped out quickly by incorporation of SMES unit.

In case of wind perturbation, there is a significant improvement in voltage at load bus as compared to induction generator bus. It is because SMES unit is installed at load bus.

Figure 10 shows that the SMES current returns to its nominal value after handling a disturbance because of the regulating variable y(k). However, in case of wind perturbation, it is not possible because wind torque is continuously changing.

For time t = 0 s to t = 20 s, load is decreased by 10% which results in the rise in frequency and thus SMES unit should charge which is evident by the increase in SMES current during this time period. Between 20 and 40 s, load is increased to its rated value and thus SMES is discharging which is evident by the decrease in SMES current.

Figures 11 and 17 show the currents injected by SMES unit into the wind-diesel system.

Conclusion

A comprehensive MATLAB/Simulink model of a hybrid wind-diesel-SMES system is developed. SMES is modelled as a current source capable enough to keep frequency and voltage deviations to minimum. In case of load disturbance, frequency deviation is reduced by almost 37%, and for wind perturbation, frequency deviation is reduced by 82%. Apart from active and reactive power balance, continuous control is also achieved by SMES unit. The performance of the compact model comprising machines, load, and SMES is tested for both wind and load disturbance. Comparison of the simulation results obtained for the system with and without SMES demonstrates that improved power quality results are obtained by incorporation of SMES unit.

Footnotes

Appendix 1 Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Ali

Murata

Tamura

(2005) Stabilization of power system including wind generator by fuzzy logic-controlled superconducting magnetic energy storage. In: Proceedings of the international conference on power electronics and drives systems, Kuala Lumpur, Malaysia, 28 November–1 December, pp. 1611–1616. New York: IEEE.

Das

Aditya

Kothari

(1999) Dynamics of diesel and wind turbine generators on an isolated power system. International Journal of Electrical Power & Energy Systems 21: 183–189.

Hemeida

(2010) A fuzzy logic controlled superconducting magnetic energy storage, SMES frequency stabilizer. Electric Power Systems Research 80: 651–656.

Hiyama

Tsukawaki

Kawakita

(2005) Real time transient stability simulator of large scale multi-machine power system in Matlab/Simulink environment. In: Presented at the international conference on power systems transients (IPST’05), Montreal, QC, Canada, 19–23 June, paper no. IPST05-105, pp. 1–6.

Hsu

(2002) Enhancement of transient stability of an industrial cogeneration system with superconducting magnetic energy storage unit. IEEE Transactions on Energy Conversion 17: 445–452.

Iqbal

Mufti

Lone

et al . (2009) Intelligently controlled superconducting magnetic energy storage for improved load frequency control. International Journal of Power and Energy Systems 29: 241–254.

Kusakana

Vermaak

(2014) Hybrid diesel generator/renewable energy system performance modeling. Renewable Energy 67: 97–102.

Moschakis

Kladas

Hatziargyriou

(2005) A voltage source converter model for exchanging active and reactive power with a distribution network. Journal of Materials Processing Technology 161: 128–135.

Mufti

Zargar

Lone

(2017) Predictive controlled SMES for frequency control of hybrid wind-diesel standalone system. In: Proceedings of the international conference on computing, communication and automation (ICCCA), Greater Noida, India, 29–30 May, pp. 1–6. New York: IEEE.

10.

Ngamroo

Cuk Supriyadi

Dechanupaprittha

et al . (2009) Stabilization of tie-line power oscillations by robust SMES in interconnected power system with large wind farms. In: Proceedings of the 2009 transmission and distribution conference and exposition: Asia and Pacific, Seoul, South Korea, 26–30 October, pp. 1–4. New York: IEEE.

11.

Nielsen

Molinas

(2010) Superconducting Magnetic Energy Storage (SMES) in power systems with renewable energy sources. In: Proceedings of the 2010 IEEE international symposium on industrial electronics, Bari, 4–7 July, pp. 2487–2492. New York: IEEE.

12.

Ortega

Milano

(2016) Comparison of bus frequency estimators for power system transient stability analysis. In: Proceedings of the 2016 IEEE international conference on power system technology (POWERCON), Wollongong, NSW, Australia, 28 September–1 October, pp. 1–6. New York: IEEE.

13.

Salih

Lachowic

Bass

et al . (2014) Superconducting Magnetic Energy Storage unit for increasing stability of a wind power generation system. In: Proceedings of the 2014 Australasian universities power engineering conference, Perth, WA, Australia, 28 September–1 October, pp. 1–6. New York: IEEE.

14.

Serdar

Zeynelgil

Demiroren

(2015) A didactic procedure for transient stability simulation of a multi-machine power system utilizing SIMULINK. International Journal of Electrical Engineering Education 53: 54–71.

15.

Sheikh

MRI

Muyeen

Takahashi

et al . (2009) Smoothing control of wind generator output fluctuations by using superconducting Magnetic Energy storage unit. In: Proceedings of the international conference on electrical machines and systems, Tokyo, Japan, 15–18 November, pp. 1–6. New York: IEEE.

16.

Stavrakakis

Kariniotakis

(1995) A general simulation algorithm for the accurate assessment of isolated diesel-wind turbines systems interaction. Part II: Implementation of the algorithm and case-studies with induction generators. IEEE Transactions on Energy Conversion 10(3): 584–590.

17.

Zargar

Lone

Mufti

(2017a) MATLAB/Simulink-based modelling and performance assessment of wind–diesel energy storage system. Wind Engineering. Epub ahead of print 19 October. DOI: 10.1177/0309524X17736481.

18.

Zargar

Mufti

Lone

(2017b) Adaptive predictive control of a small capacity SMES unit for improved frequency control of a wind-diesel power system. IET Renewable Power Generation 11: 1832–1840.