=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper48
|storemode=property
|title=A Fuzzy Model for Controlling an on-Grid LED Lamp with a Battery Bank, Powered by Renewable Energy
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper48.pdf
|volume=Vol-2030
|authors=Maciej Neugebauer,Krzysztof Nalepa,Paweł Pietkiewicz,Wojciech Miąskowski,Piotr Sołowiej
|dblpUrl=https://dblp.org/rec/conf/haicta/NeugebauerNPMS17
}}
==A Fuzzy Model for Controlling an on-Grid LED Lamp with a Battery Bank, Powered by Renewable Energy==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper48.pdf
A Fuzzy Model for Controlling an on-grid LED Lamp
with a Battery Bank, Powered by Renewable Energy
Maciej Neugebauer1, Krzysztof Nalepa, Paweł Pietkiewicz, Wojciech Miąskowski,
Piotr Sołowiej
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, Poland,
1
e-mail: mak@uwm.edu.pl
Abstract. A model for controlling power and energy flow in an outdoor LED
lamp was developed. The lamp was powered by various sources: the power
grid, battery bank, photovoltaic panels and a wind turbine. A set of fuzzy
control rules was developed based on the defined direction of power flow. The
input variables in the control system were battery charge levels, time of day
(night), insolation and wind (power generated by a wind turbine). The direction
of power (electricity) flow was the output variable. Linguistic variables
(distribution of terms) and defuzzification methods were adapted for selected
variables. In the produced fuzzy model, system response spaces were verified
based on the operation of the control system and the adopted assumption. The
resulting fuzzy model adequately meets assumptions and can be used to control
power flow in an outdoor LED lamp.
Keywords: fuzzy logic, outdoor LED lamp, energy storage, on-grid systems,
control system, renewable energy sources
1 Introduction
Fuzzy logic systems can be effectively used to control non-linear processes [1], [2],
[3], [4], including simple control systems in household appliances, as well as more
complex systems for image control, traffic control and metro train control [5], [6].
Fuzzy logic systems for controlling various processes have numerous industrial
applications, including in wind farms [7], [8], [9] and hydraulic control systems of
forging machines [10]. Artificial intelligence and fuzzy logic methods are also
applied in environmental protection [11], [12] and composting [13], [14]. A fuzzy
model of the composting process has been developed [15]. Systems that rely on
fuzzy logic are frequently used in combination with adaptive neuro-fuzzy inference
systems (ANIFS) [16].
Fuzzy control systems have the following characteristics:
• they can be used to describe highly complex non-linear systems, in
particular when conventional (analytical) descriptions are too complex or
impossible;
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• the system/model can be described with the use of natural language
expressions based on “expert” knowledge, and the relationships between
input and output data can be analyzed to facilitate understanding of the
model;
• they can be used to develop hybrid control systems (fuzzy and
conventional);
• similarly to artificial neural networks, they are resistant to incomplete
(imprecise) data sets and can be used for parallel computing.
2 Basic assumptions of power flow control
A fuzzy model of a power flow control system in an outdoor LED lamp was
developed (Fig. 1). The control system was designed based on the following
assumptions:
- the LED lamp operates at night (when it is dark);
- the lamp is powered by a wind turbine when wind conditions are adequate;
- the lamp is powered by the battery bank when wind conditions are not adequate
and when the battery bank is charged;
- the lamp is powered by the grid when wind conditions are not adequate and
when the battery bank is empty;
- the lamp does not operate during the day;
- the battery bank is charged when it is empty and when power is available from
PV panels or the wind turbine;
- when the batter is charged and power is available from PV panels or the wind
turbine, excess electricity is fed to the grid.
The input variables in the control system are: battery charge level, time of day
(night), insolation and wind conditions (power generated by the wind turbine). The
output variable is the direction of power (electricity) flow to the battery bank, the
grid or the LED lamp. Information about the time of day and insolation is provided
by a solar radiation sensor, information about battery charge levels – by the charge
controller, and information about the output of the wind turbine – by a sensor in the
wind turbine generator (Fig. 1).
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Fig. 1. Connection diagram and the measured parameters in the LED lamp, battery bank,
renewable energy sources and the power grid.
3 Fuzzy model
A fuzzy model was developed based on the described assumptions in the
LabVIEW program. The distribution of input variable “Wind” is presented in Figure
2.
Fig. 2. Distribution of fuzzy terms for input variable “wind”.
Twenty-four inference rules were developed (connective: AND (Minimum);
implication: Minimum). Initially, there were 36 rules (4 input variables, 2 two-term
variables and 2 three-term variables), but since “insolation” coupled with “time of
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day” can only assume “low” values, 2x6 rules were eliminated. Selected inference
rules are presented in Table 1.
Table 1. Selected inference rules
No. Rules
1 IF 'Battery charge level' IS 'Empty' AND 'Time of day' IS 'night' AND 'Insolation' IS
'low' AND 'Wind' IS 'weak' THEN 'grid' IS 'FROM' ALSO 'LED Lamp' IS 'ON' ALSO
'Battery charging' IS 'OFF'
2 IF 'Battery charge level' IS 'Empty' AND 'Time of day' IS 'night' AND 'Insolation' IS
'low' AND 'Wind' IS 'medium' THEN 'grid' IS 'NOTHING' ALSO 'LED Lamp' IS 'ON'
ALSO 'Battery charging' IS 'OFF'
3 IF 'Battery charge level' IS 'Empty' AND 'Time of day' IS 'night' AND 'Insolation' IS
'low' AND 'Wind' IS 'strong' THEN 'grid' IS 'NOTHING' ALSO 'LED Lamp' IS 'ON'
ALSO 'Battery charging' IS 'ON'
4 IF 'Battery charge level' IS 'Empty' AND 'Time of day' IS 'day' AND 'Insolation' IS
'low' AND 'Wind' IS 'weak' THEN 'grid' IS 'NOTHING' ALSO 'LED Lamp' IS 'OFF'
ALSO 'Battery charging' IS 'OFF'
5 IF 'Battery charge level' IS 'Empty' AND 'Time of day' IS 'day' AND 'Insolation' IS
'low' AND 'Wind' IS 'medium' THEN 'grid' IS 'NOTHING' ALSO 'LED Lamp' IS
'OFF' ALSO 'Battery charging' IS 'ON'
6 IF 'Battery charge level' IS 'Empty' AND 'Time of day' IS 'day' AND 'Insolation' IS
'low' AND 'Wind' IS 'strong' THEN 'grid' IS 'NOTHING' ALSO 'LED Lamp' IS 'OFF'
ALSO 'Battery charging' IS 'ON'
The defuzzification method was the Center of Maximum. The value of the output
variable was calculated based on the below formula (1):
𝑦1 𝜇1 + 𝑦2 𝜇2 + ⋯ + 𝑦𝑛 𝜇𝑛 (1)
𝑦=
𝜇1 + 𝜇2 + ⋯ + 𝜇𝑛
where:
y – value of the output variable;
yn – input value of function “n”
µn – membership value of function “n” for yn – do
The modeled (control system) response spaces are presented in Figures 3 and 4.
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Fig. 3. Response of the control system – power fed to the grid depending on the time of day
and battery charge level.
Fig 4. Response of the control system – battery charging depending on insolation and battery
charge level.
A detailed analysis of the above figure drawings indicates that at night (when time
of day ranges from 0 to 50) when the battery bank is empty (0 to 30/70), the system
is powered by the grid (Fig. 3), and when the battery charge level is low and
insolation is high, the battery bank is charged (Fig. 4). An analysis of response spaces
indicates that the model well fits the data.
4 Conclusions
The proposed control system has the following advantages:
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• the operation of the power flow control system can be described with linguistic
expressions (input and output variable terms) regardless of the hardware
platform, which facilitates the development of inference rules;
• the operation of the power flow control system can be verified based on
response space diagrams without the need to implement the algorithm in a real
object;
• the power flow control system can be easily modified by introducing changes to
the fuzzy model without modifying the physical system (sensors and switches
in the power controller, etc.). The modifications can be implemented by
entering the new set of fuzzy logic rules into the controller.
The proposed control system operates in accordance with the adopted
assumptions.
Acknowledgments. The presented works were carried out within the framework of
the project: Functional models and studies of the construction of a quasi-autonomous
lighting or signaling point, (Decision of the Minister of Science and Higher
Education No 5119/B/T02/2011/40 from the 4th May 2011)
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