# A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation

^{1}

^{2}

^{*}

## Abstract

**:**

^{2}with an efficiency of 99.3%.

## 1. Introduction

_{OC}) and short circuit current (I

_{SC}) are MPPT techniques based on the linearity between the voltage at MPP (V

_{MP}) and V

_{OC}and between the current at MPP (I

_{MP}) and I

_{SC}[14,15]. The open-circuit voltage (OCV) technique uses the linear relationship between open-circuit voltage (V

_{OC}) and the voltage at MPP (V

_{MP}) of the PV module. On the other hand, the short circuit current technique (SCC) uses the linear relationship between I

_{SC}and I

_{MP}of the PV module. The PV module is periodically isolated from the system to measure I

_{SC}and V

_{OC}, which causes a loss of generation. Moreover, tracking of the MPP at any irradiance level may not be accurate [16]. In order to overcome these issues, the Perturbation and Observation (P&O) method achieves the MPP by applying a series of slight constant modifications to the reference voltage or current to obtain a tolerance error in the power ΔP [17]. The perturbations cause oscillation around the MPP and power losses. In addition, for a sudden change in the weather conditions, P&O could fail to track the MPP [18]. A newly formulated P&O was presented in [19], but the method is based on measured solar irradiance, which is difficult and expensive to obtain. Another approach for tracking MPP is the Incremental Conductance (Inc. Cond). The idea of Inc. Cond is to increment or decrement the reference voltage guided by the power derivative to the voltage (dP/dV). The MPP is reached when (dP/dV) is equal to zero [20]. Inc. Cond has less oscillation, fast-tracking, and better performance under a sudden change in the weather conditions than the P&O method [21]. A modified Inc. Cond was presented in [22] to improve the tracking under fast variation in solar irradiance. However, the output power oscillates around the MPP, and the efficiency does not exceed 96.4%.

_{MP}and I

_{MP}. The adaptive block assumes pre-knowledge of solar irradiance and PV panel temperature. The challenge in this approach is measuring irradiance and temperature under weather conditions changes.

_{PV}, V

_{PV}) estimate the maximum power point (I

_{MP}, V

_{MP}), the short circuit current (I

_{SC}), and the open-circuit voltage (V

_{OC}). This estimation eliminates the need for solar irradiance sensors, in contrast to existing studies that utilize solar irradiance, where it is assumed that irradiance information is available through dedicated sensors. In addition, the suggested method does not fluctuate around MPP and efficiently establishes a stable state. Thus, avoiding two of the major drawbacks of similar MPPT methods, which rely on estimating the system parameters without a priori data. Namely, oscillations around MPP and poor performance under fast variations in solar irradiance.

## 2. Modeling PV Panel

_{P}is the loss caused by a minor leakage current flowing in a parallel route (high value in the range of kΩ). R

_{S}denotes the losses, including metal grid, contact, and current collecting bus losses. A diode is a semiconductor device having a pn junction that produces a cross-current [34,35,36]. The equations for a solar cell’s output current:

_{PV}denotes output current, I

_{ph}denotes photovoltaic current without loss, dependent on irradiance and solar cell temperature, and I

_{P}denotes current leakage in parallel resistance. I

_{d}denotes the current through the diode and can be expressed as:

_{o}, the electron charge is q, and the Boltzmann constant is k. T denotes the pn junction temperature.

_{S}is extremely small, and R

_{P}is significant; for example, the ‘manufacturer’s curve for the MSX60 indicates that R

_{S}is 8 mΩ. Then by ignoring the two internal resistances, (3) is reduced further to (4).

_{SC}) and the open-circuit voltage (V

_{OC}) [9,35].

_{MP}, V

_{MP}) then could be expressed by (4) and (6) as:

_{OC}, I

_{SC}, I

_{MP,}and V

_{MP}, the output voltage V

_{PV}is given as:

## 3. The PV I–V Characteristics

_{rr}) and temperature (T) highlights three significant points: short-circuit (0, I

_{SC}), maximum power point (MPP) (V

_{MP}, I

_{MP}), and open-circuit (V

_{OC}, 0). At MPP, the current I

_{MP}has a significant variation as the solar irradiance varies and a minor variation as the temperature changes. Moreover, at MPP, the voltage V

_{MP}has a considerable variation as the temperature changes and a slight variation as the solar irradiance varies [8]. The following equations show the variation of current and voltage values (I

_{SC}, V

_{OC}, I

_{MP}, and V

_{MP}) in PV modules as a function of irradiance and temperature [31,35], where STC denotes the standard test conditions (1000 W/m

^{2}and 25 °C).

_{SC}):

_{OC}):

_{MP}):

_{MP}):

_{rr}is the irradiance (W/m

^{2}), ΔI

_{rr}is I

_{rr}− I

_{rr_STC}, T is the modules’ temperature (°C), ΔT is T − T

_{STC}, and b represents the effect of the solar irradiance on the voltage, and it is around 0.0005 [34]. Theoretical and simulation verification of (10)–(13) were presented in [34] based on measured solar irradiance and temperature.

## 4. Description of the PV System

## 5. The Proposed Maximum Power Point Tracking (MPPT) Algorithm

_{SC}, V

_{OC}, I

_{MP}, V

_{MP}). The adaptive block in the proposed method would estimate the irradiance at the PV module’s surface based on the PV module’s measured output current and voltage. As a result, the boost converter controller uses the predicted optimal operating point (I

_{MP_es}, V

_{MP_es}) as a reference point.

_{PV}) over the PV voltage (V

_{PV}) represents a conductance:

_{PV}and V

_{PV}is not unique, i.e., the same conductance may be achieved at multiple levels of I

_{PV}and V

_{PV}. This is illustrated in Figure 4, where the conductance lines are shown on the PV I–V characteristic curves. For instance, points A, B, C, D, and E have the same conductance of G1 at different irradiance levels. The conductance, however, changes uniquely with I

_{PV}and V

_{PV}at constant solar irradiance. This may be expressed in terms of the normalized current (α):

_{PV}= 3 A, it might be K, L, or M, at solar irradiances of 1000 W/m

^{2}, 900 W/m

^{2}, and 800 W/m

^{2}, respectively. The conductance G, on the other hand, may vary for a given α. The conductance from Figure 5 might be (G

_{a}, G

_{b}, or G

_{c}) at α = K, for example; nevertheless, only one of these is the proper conductance. The normalized current varies as a result of the adaptive computation and thus provides information about I

_{SC}. On the other hand, the adaptive block determines solar irradiance (10). The perturbation might be accomplished by creating a new quantity, the estimated conductance (G

_{es}), based on the estimated solar irradiance:

_{SC}(15) and, as a result, the I–V characteristics parameters, including the reference to the boost converter controller (I

_{MP}), may be calculated. A variable step-change in perturbation of α is employed in this work. When the operating point is far from MPP, there is a considerable change in α. However, when the operating point is near MPP, there is a slight disturbance in α. The proposed perturbation value Δα(n) is proportional to the difference in estimated and measured conductance ΔG(n), where the assumption is that Δα(n) = ΔG(n). Equation (18) is used to update the value of α(n). When the tolerance error in (17) is reached, α(n) will not be modified. The algorithm will compute the estimated irradiance and circuit parameters in this situation. As a result, the boost converter uses the estimated optimal operating point (I

_{MP}, V

_{MP}) as a reference point to run the PV panel at the MPP. The flowchart in Figure 7 illustrates the proposed MPPT, considering only the impact of the solar irradiance, that is ΔT = 0.

_{MP}and I

_{SC}[16,37]. Moreover, the circuit parameters at the STC are defined based on the PV module. Then the algorithm detects the average output voltage V

_{PV}(n) and average current, I

_{PV}(n), of the PV panel, where n denotes the current sample. The estimated I

_{SC}(n) is then calculated based on (15). By estimating the I

_{SC}(n), one could compute the solar irradiance, I

_{rr}(n), from (10). Thus, the estimated V

_{OC}(n), I

_{MP}(n), and V

_{MP}(n) could be determined by (11), (12), and (13), respectively. Moreover, the estimated conductance, G

_{es}(n), could be determined based on (16), and the measured conductance, G(n), could be calculated based on (14). If the difference error between G

_{es}(n) and G(n) is less than the tolerance error ∈ (17), the estimated parameters I

_{SC}(n), V

_{OC}(n), I

_{MP}(n), and V

_{MP}(n) are equal to the circuit parameters of the PV panel and the estimated I

_{rr}(n) is equal to the solar irradiance. Otherwise, α(n) is updated based on (18) to achieve the tolerance error.

## 6. Simulation Test and Results

^{2}and 25 °C). The load is modeled using a variable resistor. Table 2 shows the boost converter parameters. The inductor and capacitor are chosen to guarantee Continuous Conduction Mode (CCM) operation. The low pass filter filters the measured data with a cut-off frequency f

_{c}= 1 kHz. The PI controller is designed with a cross-over frequency of f

_{x}= 2 kHz and a phase margin of PM = 70° [38]. The resulting PI controller parameters are K

_{P}= 0.4624 and K

_{I}= 2523.6. The proposed algorithm, summarized in the flowchart in Figure 7, is verified and represented by the adaptive calculation block.

^{2}, 900 W/m

^{2}, 800 W/m

^{2}, 700 W/m

^{2}, and 600 W/m

^{2}). Figure 8 shows the actual I–V curve and the estimated curve at T = 25 °C, where the actual represents the MATLAB-provided module curve. The proposed technique successfully calculates the solar irradiance and the circuit parameters at different solar irradiance. Table 3 compares the actual I–V curve and the estimated I–V curve, where the subscript (es) represents the estimated parameters. At solar irradiance of 600 W/m

^{2}, in the worst-case scenario, the relative error (19) in the estimated solar irradiance is 2.64%, and the efficiency of the MPPT to extract the maximum power is 99.3%. The algorithm with the adaptive block is retested to estimate the solar irradiances at T = 4 °C; the proposed technique successfully calculates the solar irradiance, as summarized in Table 4, with a maximum relative error of 1.58% at 1000 W/m

^{2}.

^{2}while the estimated solar irradiance is 907.3 W/m

^{2}. Then at t = 0.1 s, a step-change in the actual soar irradiance from 900 W/m

^{2}to 700 W/m

^{2}is applied to the PV panel. The proposed technique successfully estimates solar irradiance with accuracy (19) of 2.18%. At t = 0.2 s, another step-change in the actual soar irradiance from 700 W/m

^{2}to 1000 W/m

^{2}is applied on the PV panel, and the proposed technique successfully estimates the solar irradiance. Moreover, the MP delivered to the load is 192.34 W (actual: 192.40 W), 150.15 W (actual: 150.70 W), and 212.86 W (actual: 221.86 W), at solar irradiance of 900 W/m

^{2}, 700 W/m

^{2}, and 1000 W/m

^{2}, respectively. At t = 0.3 s, the load is changed to R = 9.764 Ω. The proposed technique continues tracking the MP at 212.86 W. When the load is decreased again to R = 10.82 Ω at t = 0.4 s, the MP delivered to the load still 212.86 W. An additional decrement in the load is applied at t = 0.5 s and the MP delivered to the load does not change. In addition, the proposed technique accurately tracks the MP in a fast manner (<1 ms) under varying irradiance, as illustrated in Figure 9. Finally, at t = 0.6 s, a step change in the temperature from 25 to 45 °C is applied, and the proposed technique continues tracking the MP at 194.8 W.

_{o}), the load current (I

_{o}), the inductor current (I

_{L}), the PV voltage (V

_{PV}), and the PV currents (I

_{PV}). Initially, at solar irradiance 900 W/m

^{2}and load R = 10.82 Ω, the load voltage and current are 45.11 V and 4.17 A, respectively. The PV Voltage and current are 28.88 V and 6.66 A, respectively, and the system operates at MPP. Following the solar irradiance, step down to 700 W/m

^{2}at t = 0.1 s, and the PV voltage and PV current drop, as desired, to 28.61 V and 5.248 A, respectively. The output voltage reaches the steady-state 39.82 V in 8 ms. In terms of the settling time, similar behavior can be observed when the solar irradiance is stepped up back to 1000 W/m

^{2}at t = 0.2 s. After t = 0.2 s, the solar irradiance is kept constant. The PV operates at the MPP with 212.86 W. A load step is applied at t = 0.3 s, and the load voltage and current drop to 45.08 V and 4.616 A, respectively, in 8 ms. In terms of settling time, similar behavior can be observed when the load is stepped back at t = 0.4 s. Finally, a step change in the temperature from 25 to 45 °C is applied at t = 0.4 s; the system operates at MPP.

## 7. Conclusions

_{MP}, V

_{MP}). The proposed MPPT is non-searchable; therefore, the output power does not oscillate around the maximum power. The algorithm performance was verified using MATLAB/Simulink. The ability of the algorithm to estimate the solar irradiance and the PV I–V curve circuit parameters were examined first, where it achieved 2.7% accuracy in estimating the solar irradiance. Then, the algorithm tracking the maximum power point under variable solar irradiance and the variable load was verified, where it was able to extract 99.3% of the PV power. Finally, the solar system dynamics were tested under sudden solar irradiance and load changes, and the system successfully tracks the maximum power.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Dehedkar, M.N.; Murkute, S.V. Optimization of PV system using distributed MPPT control. In Proceedings of the 2018 International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 23–24 November 2018; pp. 216–220. [Google Scholar]
- Dincer, I. Environmental impacts of energy. Energy Policy
**1999**, 27, 845–854. [Google Scholar] [CrossRef] - Koutroulis, E.; Blaabjerg, F. Overview of maximum power point tracking techniques for photovoltaic energy production systems. Electr. Power Compon. Syst.
**2015**, 43, 1329–1351. [Google Scholar] [CrossRef] - Liu, Y.; Tang, Y.; Shi, J.; Shi, X.; Deng, J.; Gong, K. Application of small-sized SMES in an EV charging station with DC bus and PV system. IEEE Trans. Appl. Supercond.
**2014**, 25, 5700406. [Google Scholar] [CrossRef] - Kumar, R.; Singh, S.K. Solar photovoltaic modeling and simulation: As a renewable energy solution. Energy Rep.
**2018**, 4, 701–712. [Google Scholar] - Motahhir, S.; El Hammoumi, A.; El Ghzizal, A. The most used MPPT algorithms: Review and the suitable low-cost embedded board for each algorithm. J. Clean. Prod.
**2020**, 246, 118983. [Google Scholar] [CrossRef] - Mao, M.; Cui, L.; Zhang, Q.; Guo, K.; Zhou, L.; Huang, H. Classification and summarization of solar photovoltaic MPPT techniques: A review based on traditional and intelligent control strategies. Energy Rep.
**2020**, 6, 1312–1327. [Google Scholar] [CrossRef] - Bendib, B.; Belmili, H.; Krim, F. A survey of the most used MPPT methods: Conventional and advanced algorithms applied for photovoltaic systems. Renew. Sustain. Energy Rev.
**2015**, 45, 637–648. [Google Scholar] [CrossRef] - Malkawi, A.; Lopes, L. A novel seamless control algorithm for a single-stage photovoltaic interface employing DC bus signaling. Int. J. Electr. Power Energy Syst.
**2019**, 113, 90–103. [Google Scholar] [CrossRef] - Moreira, H.S.; Oliveira, T.P.; Dos Reis, M.V.G.; Guerreiro, J.F.; Villalva, M.G.; De Siqueira, T.G. Modeling and simulation of photovoltaic systems under non-uniform conditions. In Proceedings of the 2017 IEEE 8th International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Florianopolis, Brazil, 17–20 April 2017; pp. 1–6. [Google Scholar]
- Wang, Y.; Li, Y.; Ruan, X. High-accuracy and fast-speed MPPT methods for PV string under partially shaded conditions. IEEE Trans. Ind. Electron.
**2015**, 63, 235–245. [Google Scholar] [CrossRef] - Spertino, F.; Ahmad, J.; Di Leo, P.; Ciocia, A. A method for obtaining the IV curve of photovoltaic arrays from module voltages and its applications for MPP tracking. Sol. Energy
**2016**, 139, 489–505. [Google Scholar] [CrossRef] - Ashouri-Zadeh, A.; Toulabi, M.; Dobakhshari, A.S.; Taghipour-Broujeni, S.; Ranjbar, A.M. A novel technique to extract the maximum power of photovoltaic array in partial shading conditions. Int. J. Electr. Power Energy Syst.
**2018**, 101, 500–512. [Google Scholar] [CrossRef] - Ahmad, J. A fractional open circuit voltage based maximum power point tracker for photovoltaic arrays. In Proceedings of the 2010 2nd International Conference on Software Technology and Engineering, San Juan, PR, USA, 3–5 October 2010; Volume 1, p. V1-247. [Google Scholar]
- Noguchi, T.; Togashi, S.; Nakamoto, R. Short-current pulse-based maximum-power-point tracking method for multiple photovoltaic-and-converter module system. IEEE Trans. Ind. Electron.
**2002**, 49, 217–223. [Google Scholar] [CrossRef] - Sher, H.A.; Murtaza, A.F.; Noman, A.; Addoweesh, K.E.; Al-Haddad, K.; Chiaberge, M. A new sensorless hybrid MPPT algorithm based on fractional short-circuit current measurement and P&O MPPT. IEEE Trans. Sustain. Energy
**2015**, 6, 1426–1434. [Google Scholar] - Nedumgatt, J.J.; Jayakrishnan, K.B.; Umashankar, S.; Vijayakumar, D.; Kothari, D.P. Perturb and observe MPPT algorithm for solar PV systems-modeling and simulation. In Proceedings of the 2011 Annual IEEE India Conference, Hyderabad, India, 16–18 December 2011; pp. 1–6. [Google Scholar]
- Ishaque, K.; Salam, Z.; Lauss, G. The performance of perturb and observe and incremental conductance maximum power point tracking method under dynamic weather conditions. Appl. Energy
**2014**, 119, 228–236. [Google Scholar] [CrossRef] - Abdel-Salam, M.; El-Mohandes, M.T.; El-Ghazaly, M. An Efficient Tracking of MPP in PV Systems Using a Newly-Formulated P&O-MPPT Method Under Varying Irradiation Levels. J. Electr. Eng. Technol.
**2020**, 15, 501–513. [Google Scholar] [CrossRef] - Sera, D.; Mathe, L.; Kerekes, T.; Spataru, S.V.; Teodorescu, R. On the perturb-and-observe and incremental conductance MPPT methods for PV systems. IEEE J. Photovolt.
**2013**, 3, 1070–1078. [Google Scholar] [CrossRef] - Rezk, H.; Eltamaly, A.M. A comprehensive comparison of different MPPT techniques for photovoltaic systems. Sol. Energy
**2015**, 112, 1–11. [Google Scholar] [CrossRef] - Belkaid, A.; Colak, I.; Isik, O. Photovoltaic maximum power point tracking under fast varying of solar radiation. Appl. Energy
**2016**, 179, 523–530. [Google Scholar] [CrossRef] - Yilmaz, U.; Kircay, A.; Borekci, S. PV system fuzzy logic MPPT method and PI control as a charge controller. Renew. Sustain. Energy Rev.
**2018**, 81, 994–1001. [Google Scholar] [CrossRef] - Al-Majidi, S.D.; Abbod, M.F.; Al-Raweshidy, H.S. A novel maximum power point tracking technique based on fuzzy logic for photovoltaic systems. Int. J. Hydrogen Energy
**2018**, 43, 14158–14171. [Google Scholar] [CrossRef] - Rizzo, S.A.; Scelba, G. ANN based MPPT method for rapidly variable shading conditions. Appl. Energy
**2015**, 145, 124–132. [Google Scholar] [CrossRef] - Lasheen, M.; Rahman, A.K.A.; Abdel-Salam, M.; Ookawara, S. Performance enhancement of constant voltage based MPPT for photovoltaic applications using genetic algorithm. Energy Procedia
**2016**, 100, 217–222. [Google Scholar] [CrossRef] [Green Version] - Tajuddin, M.F.N.; Ayob, S.M.; Salam, Z.; Saad, M.S. Evolutionary based maximum power point tracking technique using differential evolution algorithm. Energy Build.
**2013**, 67, 245–252. [Google Scholar] [CrossRef] - Liu, Y.-H.; Huang, S.-C.; Huang, J.-W.; Liang, W.-C. A particle swarm optimization-based maximum power point tracking algorithm for PV systems operating under partially shaded conditions. IEEE Trans. Energy Convers.
**2012**, 27, 1027–1035. [Google Scholar] [CrossRef] - Blange, R.; Mahanta, C.; Gogoi, A.K. MPPT of solar photovoltaic cell using perturb & observe and fuzzy logic controller algorithm for buck-boost DC-DC converter. In Proceedings of the 2015 International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE), Shillong, India, 12–13 June 2015; pp. 1–5. [Google Scholar]
- Radjai, T.; Gaubert, J.P.; Rahmani, L.; Mekhilef, S. Experimental verification of P&O MPPT algorithm with direct control based on Fuzzy logic control using CUK converter. Int. Trans. Electr. Energy Syst.
**2015**, 25, 3492–3508. [Google Scholar] - Radjai, T.; Rahmani, L.; Mekhilef, S.; Gaubert, J.P. Implementation of a modified incremental conductance MPPT algorithm with direct control based on a fuzzy duty cycle change estimator using dSPACE. Sol. Energy
**2014**, 110, 325–337. [Google Scholar] [CrossRef] - Study of Characteristics of Single and Double Diode Electrical Equivalent Circuit Models of Solar PV Module|IEEE Conference Publication|IEEE Xplore. Available online: https://ieeexplore.ieee.org/abstract/document/7503362?casa_token=UATpLRGRfV4AAAAA:EVz6Gd35RDU6E6z5O5idPZW3UnX0Nkh74522Sl4yj239Z2C-F8sPOfJBIym9Oodo5CpFA7_vtGgB (accessed on 2 April 2022).
- Farahani, M.; Shamsi-nejad, M.A.; Najafi, H.R. Design and construction of a digital solar array simulator with fast dynamics and high performance. Sol. Energy
**2020**, 196, 319–326. [Google Scholar] [CrossRef] - Yilmaz, U.; Turksoy, O.; Teke, A. Improved MPPT method to increase accuracy and speed in photovoltaic systems under variable atmospheric conditions. Int. J. Electr. Power Energy Syst.
**2019**, 113, 634–651. [Google Scholar] [CrossRef] - Mutoh, N.; Ohno, M.; Inoue, T. A Method for MPPT Control While Searching for Parameters Corresponding to Weather Conditions for PV Generation Systems. IEEE Trans. Ind. Electron.
**2006**, 53, 1055–1065. [Google Scholar] [CrossRef] - Bellia, H.; Youcef, R.; Fatima, M. A detailed modeling of photovoltaic module using MATLAB. NRIAG J. Astron. Geophys.
**2014**, 3, 53–61. [Google Scholar] [CrossRef] [Green Version] - Kim, J.-W.; Choi, H.-S.; Cho, B.H. A novel droop method for converter parallel operation. IEEE Trans. Power Electron.
**2002**, 17, 25–32. [Google Scholar] - Dokić, B.L.; Blanuša, B. Power Electronics: Converters and Regulators; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar]

**Figure 9.**Actual solar irradiance vs. estimated solar irradiance and the power delivered to the load.

**Figure 10.**Simulation results of the dynamics for the proposed system under step changes in solar irradiance and load.

Parameter | Value |
---|---|

Maximum Power (P_{MP}) | 212.86 W |

Voltage at MP (V_{MP}) | 29 V |

Current at MP (I_{MP}) | 7.34 A |

Open Circuit Voltage (V_{OC}) | 36.3 V |

Short Circuit Current (I_{SC}) | 8 A |

Temperature Coefficient of I_{SC} | 0.027%/°C |

Temperature Coefficient of Power | −0.23107%/°C |

Parameter | Value |
---|---|

Switching frequency (f_{S}) | 20 kHz |

Inductor | 1.5 mH |

Capacitor | 400 $\mathsf{\mu}$F |

I_{rr}(W/m ^{2}) | I_{r}_{_}_{es}(W/m ^{2}) | Relative Error | I_{SC}(A) | I_{SC}_{_}_{es}(A) | V_{OC}(V) | V_{OC}_{_}_{es}(V) | I_{MP}(A) | I_{MP}_{_}_{es}(A) | V_{MP}(V) | V_{MP}_{_}_{es}(V) | P_{MP}(W) | P_{MP}_{_}_{es}(W) | Efficiency |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1000 | 1000 | 0 | 8.00 | 8.00 | 36.30 | 36.30 | 7.34 | 7.34 | 29.00 | 29.00 | 212.86 | 212.86 | 100% |

900 | 907.3 | 0.8% | 7.20 | 7.26 | 36.06 | 36.15 | 6.65 | 6.66 | 28.91 | 28.88 | 192.40 | 192.34 | 99.96% |

800 | 812.3 | 1.51% | 6.40 | 6.49 | 35.88 | 36.00 | 5.88 | 5.96 | 29.21 | 28.76 | 171.70 | 171.4 | 99.8% |

700 | 715.3 | 2.14% | 5.60 | 5.72 | 35.74 | 35.80 | 5.15 | 5.25 | 29.28 | 28.6 | 150.70 | 150.15 | 99.6% |

600 | 616.3 | 2.64% | 4.80 | 4.93 | 35.46 | 35.60 | 4.42 | 4.52 | 29.27 | 28.43 | 129.50 | 128.60 | 99.3% |

I_{rr}(W/m ^{2}) | I_{r_es}(W/m ^{2}) | Accuracy | I_{SC}(A) | I_{SC_es}(A) | V_{OC}(V) | V_{OC_es}(V) | I_{MP}(A) | I_{MP_es}(A) | V_{MP}(V) | V_{MP_es}(V) | P_{MP}(W) | P_{MP_es}(W) | Efficiency |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|

1000 | 984.4 | 1.58% | 8.19 | 7.72 | 33.69 | 33.21 | 7.39 | 7.34 | 26.50 | 26.53 | 195.80 | 194.80 | 99.49% |

900 | 900 | 0 | 7.37 | 7.06 | 33.50 | 33.09 | 6.68 | 6.74 | 26.51 | 26.43 | 177.10 | 178.30 | 99.32% |

800 | 805.8 | 0.72% | 6.55 | 6.32 | 33.32 | 32.94 | 5.96 | 6.03 | 26.51 | 26.31 | 158.00 | 158.80 | 99.50% |

700 | 708.9 | 1.26% | 5.73 | 5.56 | 33.11 | 32.77 | 5.23 | 5.30 | 26.52 | 26.18 | 138.70 | 139.00 | 99.78% |

600 | 610.2 | 1.67% | 4.91 | 4.74 | 32.85 | 32.55 | 4.5 | 4.57 | 26.47 | 26.01 | 119.10 | 118.90 | 99.83% |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Malkawi, A.M.A.; Odat, A.; Bashaireh, A.
A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation. *Sustainability* **2022**, *14*, 7699.
https://doi.org/10.3390/su14137699

**AMA Style**

Malkawi AMA, Odat A, Bashaireh A.
A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation. *Sustainability*. 2022; 14(13):7699.
https://doi.org/10.3390/su14137699

**Chicago/Turabian Style**

Malkawi, Ahmad M. A., Abdallah Odat, and Ahmad Bashaireh.
2022. "A Novel PV Maximum Power Point Tracking Based on Solar Irradiance and Circuit Parameters Estimation" *Sustainability* 14, no. 13: 7699.
https://doi.org/10.3390/su14137699