25-08-2017, 09:32 PM
Power-Management Strategies for a Grid-Connected PV-FC Hybrid System By Using Fuzzy Logic Controller Project Report
Power-Management Strategies.pdf (Size: 571.14 KB / Downloads: 73)
ABSTRACT
This paper presents a method of maximum power point tracking, MPPT using adaptive fuzzy logic control for grid
connected photovoltaic system. The system composed of photovoltaic module, boost converter and the fuzzy logic
controller. The maximum power point tracking control is based on adaptive fuzzy logic to control ON/OFF time of
MOSFET switch of boost converter. The complete simulation results using Simulink software for the whole system
containing the PV array, boost converter, and fuzzy controller were presented. The control strategy for the boost converter
and the whole system is carried out by using field programmable gate array (FPGA). The FPGA used is a Spartan 3E from
Xilinx. The code of the control system is shown in VHDL language.
Introduction
Renewable energy is currently widely used. One of these resources is solar energy. The photovoltaic (PV) array normally
uses a maximum power point tracking (MPPT) technique to continuously deliver the highest power to the load when there
are variations in irradiation and temperature. The disadvantage of PV energy is that the PV output power depends on weather
conditions and cell temperature, making it an uncontrollable source. Furthermore, it is not available during the night. In order
to overcome these inherent drawbacks, alternative sources, such as PEMFC, should be installed in the hybrid system. By
changing the FC output power, the hybrid source output becomes controllable. However, PEMFC, in its turn, works only at a
high efficiency within a specific power range [1], [2]. The hybrid system can either be connected to the main grid or work
autonomously with respect to the grid-connected mode or islanded mode, respectively. In the grid-connected mode, the
hybrid source is connected to the main grid at the point of common coupling (PCC) to deliver power to the load. When load
demand changes, the power supplied by the main grid and hybrid system must be properly changed. The power delivered
from the main grid and PV array as well as PEMFC must be coordinated to meet load demand. Generally the hybrid source
has two control modes: 1) unit-power control (UPC) mode and feeder-flow control (FFC) mode. In the UPC mode,
variations of load demand are compensated by the main grid because the hybrid source output is regulated to reference
power. Therefore, the reference value of the hybrid source output must be determined. In the FFC mode, the feeder flow is
regulated to a constant, the extra load demand is picked up by the hybrid source, and hence, the feeder reference power must
be known. Here Fuzzy logic or fuzzy set theory is a new method of controlling the MPPT is implemented in obtaining the
peak power point. It has the advantage of being robust, fast in response. Fuzzy controller operates in two basic modes coarse
and fine modes. The proposed fuzzy operating strategy is to coordinate the two control modes and determine the reference
values of the fuzzy control so that all constraints are satisfied. This operating strategy will minimize the number of operating
mode changes, improve performance of the system operation, and enhance system stability.
SYSTEM DESCRIPTION
The photovoltaic [3], [4] and the PEMFC [5], [6] are modelled as nonlinear voltage sources. These sources are connected to
dc–dc converters which are coupled at the dc side of a dc/ac inverter.
The dc/dc connected to the PV array works as an MPPT controller. Many MPPT algorithms have been proposed in the
literature, such as incremental conductance (INC), constant voltage (CV), and perturbation and observation (P&O). The
P&O method has been widely used because of its simple feedback structure and fewer measured parameters [7]. The P&O
algorithm with power feedback control [8]–[10] is shown in Fig. 2.As PV voltage and current are determined, the power is
calculated. At the maximum power point, the derivative dp/dv is equal to zero. The maximum power point can be achieved
by changing the reference voltage by the amount of
vref
Fuzzy Controller
Fuzzy logic or fuzzy set theory is a new method of controlling the MPPT in obtaining the peak power point. It has the
advantage of being robust, fast in response . Fuzzy controller operates in two basic modes coarse and fine modes. Input
variables of fuzzy controller are
dp dI ph ph / (where
Pph is PV array output power and I ph is PV array output current) and
Change of it. These variables are expressed in terms of linguistic variables or labels such as PB (positive big), PS (positive
small), ZE (zero), NS (negative small), NB (positive big) using basic fuzzy subset. There are three stages in this control
algorithm, namely fuzzification, inference method and defuzzification. An error function (E) and a change of error ( Δ E) are
created during fuzzification. These variables are then compared to a set of pre-designed valuesduring inference method, in
order to determine the appropriate response. Defuzzification is for converting the fuzzy subset of control form inference back
to values. The E and Δ E function is compared to the graph a & b to obtain a variable NB or ZE, then this parameter will be
used to locate the respective the output function (dD) from the fuzzy rule table.
Fuzzification
Membership function‟s value are assigned to the linguistic variables, using seven
fuzzy subsets : NB (Negative Big), NM(Negative Medium), NS (Negative small), ZE
(Zero), PS (Positive small), PM (Positive Medium), and PB (Positive Big). The partition
of fuzzy subsets and the shape of membership function which can adapt shape up to
appropriate system are shown in Fig.16. The value of error (e) and change of error (de)
are normalized by input scaling factor βe and βde. In this system input scaling has
designed between –1 to 1
Inference Method
The composition operation by which a control output can be generated. Several
composition methods such as MAX-MIN and MAX-DOT have been proposed in the
literature. The commonly used method is MAX-MIN (AND connection) as we used in our search . The output membership
function of each rule is given by the MIN (minimum)operator, MAX(maximum) operator. Table 2 shows the rule table for fuzzy logic controller.
(3) Defuzzification: As the plant usually required a nonfuzzy value of control, a defuzzification stage is needed.
Defuzzificaion for this system is the height method. The height method is both very simple and very fast method.
Conclusion
The method of maximum power point tracking, MPPT using adaptive control for grid connected
photovoltaic system is presented for better performance of the whole system .The maximum
power point tracking control is based on adaptive fuzzy logic to control ON/OFF time of
MOSFET switch of boost converter. The complete simulation results are shown by using Simulink software for the whole
system containing the PV array, boost converter, and fuzzy controller are also shown above.