Adaptive Controllers for Enhancement of Stand-Alone Hybrid System Performance

International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 9, No. 3, September 2018, pp. 979~986
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v9.i3.pp979-986  979
Journal homepage: http://iaescore.com/journals/index.php/IJPEDS
Adaptive Controllers for Enhancement of Stand-Alone Hybrid
System Performance
Yew Weng Kean, Agileswari Ramasamy, Shivashankar Sukumar, Marayati Marsadek
Institute of Power Engineering (IPE), Universiti Tenaga Nasional, Selangor, Malaysia
Article Info ABSTRACT
Article history:
Received May 17, 2018
Revised Jul 22, 2018
Accepted Aug 9, 2018
This paper presents a stand-alone hybrid renewable energy system (SHRES)
consisting of solar photovoltaic (PV), wind turbine (WT) and battery energy
storage (BES) in an effort reduce the dependence on fossil fuels. The
renewable energy sources have individual inverters and the PV inverter of
the SHRES is operated using active and reactive power control. The PV
inverter have two main control structures which are active power control and
reactive power control and each contain a proportional integral (PI)
controller. Accurate control of the PV inverter’s active power is essential for
PV curtailment applications. Thus, this paper aims to enhance the
performance of the SHRES in this work by optimizing the performance of
the PV inverter’s active power PI controller parameters through the design of
adaptive controllers. Therefore, an adaptive controller and an optimized
adaptive controller are proposed in this paper. The performances of the
proposed controllers are evaluated by minimizing the objective function
which is the integral of the time weighted absolute error (ITAE) criterion and
this performance is then compared with a controller that is tuned by the
traditional trial and error method. Simulation results showed that the
optimized adaptive controller is better as it recorded an error improvement of
42.59%. The dynamic optimized adaptive controller is more adept at
handling the fast changes of the SHRES operation.
Keyword:
Adaptive controller
Optimization
PI controller
Stand-alone hybrid system
Renewable energy
Copyright © 2018 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Yew Weng Kean,
Institute of Power Engineering (IPE), Universiti Tenaga Nasional,
Jalan Ikram-Uniten, 43000 Kajang, Selangor, Malaysia.
Email: wkyew90@hotmail.com
1. INTRODUCTION
The world is shifting its focus from fossil fuels to renewable energy sources (RES) to cope with the
ever increasing energy demand. However, the output power from RES such as solar photovoltaic (PV) and
wind turbine (WT) is intermittent in nature. In order to overcome this problem, hybrid system consisting of
RES is operated with energy storage system. The stand-alone hybrid renewable energy system (SHRES) in
this work consists of solar photovoltaic (PV), wind turbine (WT) and battery energy storage (BES) that will
be supplying power to AC loads. The distributed generated (DG) sources in the SHRES have individual
inverters which have complex controls involving proportional integral (PI) controllers [1].
PI controller have been used for many decades, and its parameters are commonly found using the
traditional method such as Zigler and Nichols method and trial and error method [2]. These PI controllers
have simple control structures but they are unable to withstand nonlinear and complex systems of the SHRES
without precise tuning [3]. Authors in [4] utilized the linear programming method for the tuning of PID
controller parameters. The authors in [5] highlighted that there is a need to optimize the PI controller’s
parameters in order to overcome the tedious and time-consuming tuning process through the traditional
methods. Therefore the authors applied the artificial bee colony (ABC) method on the PI controllers of a PV
and fuel cell (FC) hybrid system and it provided positive results in both overshoot percentage and steady

 ISSN: 2088-8694
Int J Pow Elec & Dri Syst, Vol. 9, No. 3, September 2018 : 979 – 986
980
state error. Similarly, it was documented in [6] that the control parameters obtained using the genetic
algorithm (GA) is better than the traditional method in terms of peak deviation and settling time. Although
the linear programming, ABC and GA optimization techniques are able to provide better results than the
traditional method, they require significantly high computational time [7]. The fixed gains from these
optimization techniques are also unable to handle fast changes of operation points in a complex system.
Alternatively, the authors in [8] designed an adaptive controller to stabilize the microgrid’s islanded
operation. However, the authors considered an adaptive controller only for the voltage and reactive power
control PI controller. From the literature, it was observed that various ways was used to optimize the PI
controllers to improve its performance. However, limited literature is available on the design of adaptive
controller that focuses solely on optimizing the inverter’s active power control PI controller.
In this work, the PV and WT inverter of the SHRES is operated using active and reactive power
control while the BES is operated using voltage and frequency control. The PV inverter have two main
control structures which are active power control and reactive power control and each contain a proportional
integral (PI) controller with two key parameters namely; proportional gain (Kp) and integral time constant
(Ti). Accurate control of the PV inverter’s active power is essential for PV curtailment applications. Thus,
this paper aims to optimize the active power PI controller parameters of the PV inverter through the design of
adaptive controllers to improve the performance of the SHRES. As a result, an adaptive controller and
optimized adaptive controller are proposed. The performances of the proposed adaptive controllers are
evaluated by minimizing the integral of the time weighted absolute error (ITAE) criterion. The results of the
proposed adaptive controllers are then compared with the result of the controller using the traditional trial and
error method. The paper is organized as follows: Section 2 discusses the overview of the SHRES model and
its control strategy while the proposed methodology for the implementation of the adaptive controllers is
discussed in Section 3. Section 4 presents and discusses the simulation results of the adaptive controllers
using the PSCAD software. Lastly, a conclusion is drawn in Section 5.
2. OVERVIEW OF THE SHRES MODEL
The SHRES in this work consist of three DG sources namely PV, WT and battery energy storage
(BES) and is designated for 10 houses in a rural area in Malaysia. The PV, WT and BES are rated 7.5kW,
3kW and 81kWh respectively and the total load demand per day is 40.5kWh. The DG sources are controlled
individually using an inverter and coupled together in an AC bus with a rating of 400V and 50Hz.The entire
SHRES configuration is shown in Figure 1.
Figure 1. SHRES configuration

Int J Pow Elec & Dri Syst ISSN: 2088-8694 
Adaptive Controllers for Enhancement of Stand-Alone Hybrid System Performance (Yew Weng Kean)
981
2.1. Control Strategy of the Shres
Master-slave control is chosen as the control strategy for this SHRES whereby the master DG
sources will operate in voltage and frequency (VF) control while the slave DG sources will operate in active
and reactive power (PQ) control. The BES inverter is selected as the master DG source while the PV and WT
inverter are selected as the slave DG source for the stand-alone PV, WT and BES hybrid system as shown in
Figure 1.
The objective of the VF control is to provide a reference voltage and frequency for the system [9]
while the objective of the PQ control is to produce active and reactive power for the SHRES according to the
reference given by the central controller [10]. Through elimination of the interaction effect between the direct
axis current and the quadrature axis current in the PQ control, the inverter’s active power and reactive power
can be directly controlled. PI controllers have been used to eliminate the error between the direct and
quadrature axis currents reference and measured values [10].
Accurate control of the PV inverter’s active power is essential for PV curtailment applications.
Initially, the PQ control PI controllers will use the traditional trial and error method to tune its parameters.
However, the PI controller parameters obtained using the trial and error method is not optimal and the
process of tuning the PI controller using the trial and error method is a very tedious and time consuming
process [11]. The fixed gains from the controller tuned by trial and error method are also unable to handle
fast changes of operation points of the SHRES. As a result, the PV inverter active power PI controller’s
parameters have to be optimized to ensure a fast and accurate response. Thus, the PV inverter active power PI
controller with the parameters obtained from the trial and error method is to be replaced with the proposed
adaptive controllers, which are an adaptive controller and an optimized adaptive controller. The entire PQ
control of the PV inverter with the proposed adaptive controllers to replace the active power PI controller is
shown in Figure 2. The proposed methodology for the implementation of the adaptive controllers will be
discussed in the next section.
Figure 2. Proposed adaptive controller in the PV inverter
3. PROPOSED METHODOLOGY FOR THE IMPLEMENTATION OF THE ADAPTIVE
CONTROLLERS
As discussed in the previous section, the adaptive controllers are proposed to replace the active
power PI controller of the PV inverter which was tuned using the trial and error method to optimize the PI
controller parameters. In this work, an adaptive controller and an optimized adaptive controller is proposed
replace the PI controller which was tuned using the trial and error method. The adaptive PI controller is
essentially a PI controller with two additional auxiliary PI controllers aiming to optimize the adaptive PI
controller parameters dynamically. The optimized adaptive controller applies the GA optimization on the
auxiliary PI controller to obtain its optimal parameters. The performance of the controllers are evaluated by

 ISSN: 2088-8694
982
minimizing the integral of the time weighted absolute error (ITAE) criterion. The objective function of ITAE
is shown as:



0
).
|
)
(
|
.
( dt
t
e
t
ITAE
(1)
where e is the error of the direct axis current and t is the time period in hours. The ITAE criterion is an
indication of how close the measured PV inverter output power is able to match the varying PV reference.
Thus, a lower number of the ITAE signify an improved performance of the SHRES as the error between the
measured and reference value is reduced. The proposed adaptive controller is presented in Section 3.1 while
the proposed optimized adaptive controller is presented in Section 3.2.
3.1. Proposed Adaptive Controller
An adaptive controller is adopted from [8] and proposed to replace the PV inverter’s active power PI
controller in the SHRES. The proposed adaptive controller is able to optimize the PI controller parameters
dynamically, allowing the PI controller to respond to fast changes of operation points. As a result, the
proposed adaptive controller is able to overcome the disadvantage of fixed gain PI controller parameters. The
adaptive controller structure consists of an adaptive PI controller with two auxiliary PI controllers. The role
of the auxiliary PI controllers is to optimize the PI controller parameters during operation of the SHRES. One
auxiliary PI controller will control the active power PI controller proportional gain, KpP and another
auxiliary PI controller will control the active power controller integral time constant, TiP. The proposed
auxiliary PI controllers are designed based on the feedback from the error signal and first derivative of the
PV inverter’s output power. Thus, in the adaptive controller, the PI parameters are automatically adjusted
when there is an imbalance between the power measured from the PV inverter’s output and the reference
power given. Additionally, a gain controller is added onto the adaptive controller to decrease the amplitude
of oscillation caused from the fluctuation of PV output power which will result in increased system stability.
The adaptive controller does not require high computational time. However, it does makes the system more
complex and more tedious as the two additional auxiliary PI controllers that are added require tuning as well.
The structure of the adaptive controller is shown in Figure 3.
Figure 3. Adaptive controller structure
3.2. Proposed Optimized Adaptive Controller
In this section, an optimized adaptive controller is proposed where the two auxiliary PI controllers
from the proposed adaptive controller are optimized using GA. The GA is implemented into both the
auxiliary PI controllers of the proposed adaptive controller to form an optimized adaptive controller. The
proposed optimized adaptive controller aims to determine the optimal parameters for the auxiliary PI
controllers while operating a dynamic adaptive PI controller. The optimized adaptive controller is able to
reduce the tedious process of tuning the two auxiliary PI controllers but will require higher computational
time than the proposed adaptive controller.
The GA is implemented in the two auxiliary PI controllers in Figure 3 to determine its optimal
parameters. GA is selected as the optimization method to optimize all four parameters from both of the
auxiliary PI controllers. The four parameters are the proportional gain (KpA1) and integral time constant (TiA1)
of the auxiliary PI controller that controls KpP and the proportional gain (KpA2) and integral time constant

983
(TiA2) of the auxiliary PI controller that controls TiP. The optimal value of the auxiliary PI controller
parameters obtained from GA after 100 iterations is used for the optimized adaptive controller. The structure
of the optimized adaptive controller is shown in Figure 4.
Figure 4. Optimized adaptive controller structure
4. SIMULATION RESULT AND DISCUSSION
The stand-alone PV, WT and BES hybrid system is simulated for 24 hours of load with a varying
PV reference. The simulation is conducted in the PSCAD/EMDTC environment and all the operations of the
PV, WT, BES and its inverters are considered ideal. As discussed earlier, an adaptive controller and an
optimized adaptive controller was proposed to determine the PV inverter active power PI controller
parameters. Thus, the varying PV reference is designed to test the performance of the PV inverter active
power PI controllers. The reference and measured PV inverter output power were recorded while evaluating
its overshoot and steady state error of the PV inverter throughout the entire simulation duration. The
performance of the adaptive controllers are evaluated by minimizing the ITAE criterion in eq. (1). The
improvement of the error of each adaptive controller are compared against the traditional PI controller and
against each other. The comparison of the ITAE criterion obtained from the traditional PI controller and the
adaptive PI controllers are shown in Table 1. The PV output power in the SHRES using the traditional PI
controller and the adaptive PI controllers are shown in Figure 5.
From table 1, the ITAE criterion obtained from the traditional PI controller which was tuned by the
trial and error method is 9.16651. The adaptive controller obtained an improved ITAE criterion of 5.80829
while the optimized adaptive controller obtained the lowest ITAE criterion of 5.26208. Thus, both the
adaptive controller and the optimized adaptive controller are able to enhance the performance of the SHRES
significantly with an error improvement of 36.63% and 42.59% respectively. This was mainly because the
KpP and TiP of the adaptive controllers are dynamic as the parameters are able to self-adjust and converge to
its optimal value based on the error feedback.
From Figure 5(a), we can observe that the PV output power using the traditional PI controller have
recorded large overshoots during the big change of reference PV on the 6th
, 15th
and 21st
hour. This is because
the traditional PI controller uses a fixed gain and is unable to handle fast changes of the operation points. The
steady state error of the PV output power using the traditional PI controller is also high, as the PI controller
parameters are not optimized. Therefore, it is no surprise that this is reflected on the ITAE of the PV output
using the traditional PI controller where it recorded the highest error. From Figure 5(b), the large overshoots
that was seen during the big change of reference PV on the 6th
, 15th
and 21st
hour have been reduced. The
steady state error of the PV output power using the adaptive controller is significantly better as well. This is
because the adaptive PI controller have two additional auxiliary PI controllers that assist in adjusting the PI
controller parameters to its optimal value dynamically.
Lastly, the optimized adaptive controller is proposed to combine the dynamic feature of the adaptive
controller with the precise tuning of the GA. Both auxiliary PI controllers of the adaptive controller are
optimized using the GA optimization method as discussed in Section 3.2 to overcome the tedious process to
tuning the auxiliary PI controllers. In Figure 5(c), the overshoot of the PV output power recorded on the 6th
and the 12th
hour is slightly higher when using the optimized adaptive PI controller than the adaptive PI
controller. On the other hand, the improvement in overshoot was recorded on 9th
, 15th
and 21st
hour and an
overall improvement in steady state error throughout 24 hours was also noticed when optimized adaptive PI
controller was used. Although the computational time is higher, it can be seen clearly that the optimized
adaptive controller provided the best error improvement compared to the traditional PI controller and the

 ISSN: 2088-8694
984
adaptive PI controller. Thus, the optimized adaptive controller is the promising controller to optimize the PI
controller parameters.
Table 1. ITAE of PV Output
Kp Ti ITAE
Error
Improvement
Traditional PI controller 0.03 1.6 9.16651 -
Adaptive PI controller Dynamic dynamic 5.80829 36.63%
Optimized adaptive PI controller Dynamic dynamic 5.26208 42.59%
(a)
(b)

985
(c)
Figure 5. Output power of PV in SHRES with (a) traditional PI controller (b) adaptive PI controller
(c) optimized adaptive PI controller
5. CONCLUSION
In conclusion, the performance of the SHRES has been improved through the implementation of the
adaptive controllers to optimize the active power PI controller parameters. Therefore, an adaptive controller
and an optimized adaptive controller was proposed and designed in this paper. Their results were evaluated
by minimizing the objective function which was the ITAE criterion. Simulation results showed that the
optimized adaptive controller is the promising controller to optimize the PI controller parameters as it
recorded an error improvement of 42.59%. The dynamic optimized adaptive control is more adept at
handling the fast changes of the SHRES operation. Other optimization techniques can be considered to
optimize the adaptive control for future applications.
ACKNOWLEDGEMENTS
The authors would like to thank Universiti Tenaga Nasional (Bold grant project
code:10289176/B/9/2017/49) for supporting the research work.
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[11] S. Pareek, M. Kishnani, and R. Gupta, “Optimal tuning Of PID controller using Meta heuristic algorithms,” in 2014
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BIOGRAPHIES OF AUTHORS
Yew Weng Kean obtained his bachelor degree in Electrical Power Engineering from Universiti
Tenaga Nasional in 2013. He is currently pursuing his Ph.D degree from Universiti Tenaga
Nasional under the sponsorship of Yayasan Tenaga Nasional. Presently, he is also a Research
Assistant in Universiti Tenaga Nasional. His areas of interest are microgrid, smart grid and
renewable energy integration. He is also a Graduate member of the Institution of Engineers
Malaysia (IEM).
Agileswari K. Ramasamy was born in Taiping, Perak and received her B.Sc (Engineering)
degree from Purdue University, USA in 1995 under the sponsorship of Yayasan Tenaga
Nasional. She obtained her MSc. (Control System) from Imperial College, London in 2001 and
PhD in Electrical Engineering from Universiti Tenaga Nasional (UNITEN), under the
sponsorship of UNITEN. She is currently an Associate Professor in the Department of
Electronics Communication Engineering at UNITEN and serving as a Deputy Dean of Research
and Postgraduate for College of Engineering, UNITEN. She is also a Chartered Member of the
Institution of Engineering and Technology (IET). She is currently active in research and
consultancy in control system, power system, power quality, energy efficiency and renewable
energy. She has headed several research projects to date and has successfully published several
indexed journals.
Shivashankar Sukumar obtained his PhD degree from University of Malaya, Malaysia in the year
2017. He is currently working as Post Doctoral Researcher from University Tenega National.
His area of research are renewable energy integration, distribution system automation, and
application of battery storage for low carbon grids.
Dr. Marayati is the Director in the Institute of Power Engineering (IPE) and Senior Lecturer in
the Department of Electric Power at The National Energy University. She received her Bachelor
of Electrical Power and Master in Electrical Engineering in 2002 and 2006 respectively. She
was awarded with PhD in Electrical, Electronics and System Engineering from the National
University of Malaysia in 2011. She has been actively involved in research and consultancies
work related to energy, demand side respond, power system stability and renewable energy.

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