Maximum power point tracking (MPPT) for photovoltaic (PV) arrays is essential to optimize conversion efficiency under variable and nonuniform irradiance conditions. Unfortunately, conventional MPPT algorithms such as perturb and observe (P&O), incremental conductance, and current sweep method need to iterate command current or voltage and frequently operate power converters with associated losses. Under partial overcast conditions, tracking the real MPP in multipeak
Globally, installed photovoltaic (PV) capacity has passed 100 GW while the US is projected to install 24 GW by 2015 [
Many MPPT algorithms have been proposed to achieve best energy harvesting efficiency under different operating conditions. Several well-known MPPT algorithms, such as hill climbing, perturb and observe (P&O), incremental conductance, ripple correlation control, and d
Whichever MPPT algorithm is utilized, two distinct actions are engaged in the MPP search process. The first action is to calculate the next-step command current or voltage at the current step; the second action is to set this current or voltage commanding at the DC-DC converter. In all the algorithms considered above, the two actions are linked to execute together for each searching step. Therefore, the total execution time of the current or voltage switching operation is dominated by and depends on the total execution time of the search algorithms during numerous iterations. Consequently, considerable time and energy are unnecessarily lost in the search while the MPP is directly available in real-time but idling pending search update. Also, the MPP of PV panels depends on the
To address the issues discussed above, a novel ultrafast maximum power point setting scheme based on model parameter identification is proposed. The scheme addresses the real-time availability of MPP and also the drawback in the conventional MPPT algorithms for PV arrays under both uniform and nonuniform insolation. In this scheme, an analytical model parameter identification approach is presented to identify the insolation parameter for each PV panel using their measured voltage and current. Based on real-time estimated insolation parameters for each solar panel, an overall
The main contribution and difference between the proposed MPPT scheme and other MPPT algorithms is that the proposed MPPT algorithm is executed in the controller for virtually constructed
The remainder of this paper is organized as follows. In Section
A typical MPPT based PV DC-DC power conversion circuit and P&O MPPT algorithm diagram are shown in Figure
P&O MPPT DC-DC power conversion.
Converter operation is engaged in the iterative search process, resulting in power being unnecessarily lost in this procedure. Furthermore, variations of voltage or current are discontinuous and the associated steps may be very large. Consequently, these discontinuous and large-step current or voltage switching actions will reduce the mean time to failure of components such as IGBT or MOSFET due to increased stress, with associated switching losses, and increased ripple current in the converter circuits.
Assuming that the
Generally, we can classify the PV panel parameters into two sets. One set is temperature and irradiance; the other is
In this section, the ultrafast MPP setting scheme is presented in two parts. Firstly, the insolation parameter identification based on algebraic equations solving is proposed. Secondly, the real-time
The proposed ultrafast MPP setting scheme.
If a PV panel/cell unit experiences partial shading, dust, water, aging, or any adverse conditions, the
As previously stated, the general numerical model of a PV cell can be depicted in Figure
PV cell circuit model.
The circuit mathematical model can be denoted as below:
Based on sampling data, the model parameter identification procedure could be described as shown in Figure
Flowchart for model parameter identification.
Furthermore, because the PV cell model is a nonlinear algebraic formulation without dynamics, the parameter identification problem can be actually defined as an algebraic equation solving problem. Therefore, the solving problem can be expressed as below:
In order to solve this nonlinear algebraic equation to obtain the unknown variable ins, many of the methods used in optimization solvers are proposed. For the sake of generality and easy implementation for general micro-computers, a trust-region dogleg algorithm is employed in order to solve the PV equations with unknown insolation parameter.
The flowchart trust-region dogleg algorithm is presented in Figure
Flowchart for trust-region dogleg algorithm.
Given a nonlinear function set with
In order to obtain the solution, a Newton’s method for linear system is firstly used to define the search direction. The search step
So, the key problem here is to compute step
By using a dogleg strategy, step
After the parameter insolation for each PV unit is solved from the PV cell equations, an overall
In order to get a full picture of the overall
Based on the
The converter operation in the complete MPPT process can be described as in Figure
The proposed converter operation diagram.
In order to validate the proposed MPP setting scheme, PV arrays with uniform insolation, nonuniform insolation, and time-varying insolation are tested. Corresponding results of three issues such as insolation parameter identification, generated real-time
The PV panel parameters used in our work are listed below:
In order to test the accuracy of parameter identification and simulate the measurement scenarios of the real converter controller, band-limited white noise is injected into the measured signals to test the identification parameter’s accuracy. A sine waveform variation of insolation, which varies from 495 w/m2 to 505 w/m2, is set for a PV panel model. The comparison result between real insolation and identification insolation is shown in Figure
Comparison between real insolation and identification insolation.
In order to validate the effectiveness of real-time
Figure
Generated real-time curves.
Figure
MPP tracking comparison under uniform and invariable insolation.
Figure
MPP tracking under nonuniform and variable insolation.
Based on the validation result presented above, it can be concluded that the proposed MPP scheming has significant advantages. It can obtain the insolation parameter for each PV panel or unit but without the need to deploy additional insolation sensors, which is useful for monitoring large-scale solar arrays. It only requires the power converter to perform two power operations during one MPPT tracking period. So it can reduce the unnecessary power losses in the conventional MPPT process. It is ultrafast to monitor and track the MPP variation due to insolation changes. As a result, the proposed scheme has large potential to apply extreme adverse PV harvesting applications in the Middle East PV industry.
In order to improve the MPP tracking efficiency of PV harvesting, a novel MPP setting scheme based on model parameter identification is studied in this paper. Unlike previous MPPT methods, the proposed scheme can separate the MPP search from the power converter operation by identifying the key ambient condition parameter such as insolation and rebuilding the virtual
The author declares that there is no conflict of interests regarding the publication of this paper.
The author highly appreciates the reviewer’s comments and Drs. Said A. Mansour and Antonio P. Sanfilippo from QEERI, Professor Paul Stewart at the University of Derby, UK, and Drs. Wenlong Ming and Yu Zeng at the University of Sheffield, UK, for their constructive comments on the research work.