# Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes

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## Abstract

**:**

## 1. Introduction

- (1)
- A novel solar energy harvesting 3.6 volts battery charger using Pulse Width Modulation (PWM) control technique using MATLAB/Simulink.
- (2)
- A novel solar energy harvesting 3.6 volts battery charger using Perturb & Observation (P&O) type Maximum Power Point Tracking (MPPT) control technique using MATLAB/Simulink.
- (3)
- A novel hardware implementation of a solar battery charger using PWM control technique Solar Panel, DC-DC Buck Converter, and Scientech 2311 WSN trainer kit.
- (4)
- The innovation claim entails the integration of a Commercial WSN trainer Kit (Scientech 2311) with a solar panel and a PWM controlled DC-DC converter, and showing the output on Digital Storage Oscilloscope (DSO).
- (5)
- Another innovation claim made here involves the MATLAB/Simulink based implementation of solar energy harvester system to charge 3.6 volts battery using MATLAB/Simulink. This rechargeable battery is used to provide power to the WSN node.

^{2}) and variations in Inductor (L) and capacitor (C) values to observe the effect on output efficiency. The maximum achieved efficiency is only 85% using theoretical simulation results. In 2009, Ref. [4] proposed the design of a solar-harvesting circuit for battery-less Embedded Systems. In this paper, the simulation results show that by using efficient solar energy harvester circuits, the sensor network lifetime can be increased from a few days to 20–30 years and higher. Section 1 provides an overview of a basic Solar Energy Harvesting System. Section 2 presents the operation of a SEH-WSN Node. Section 3 provides two types of solar energy harvester systems, i.e., pulse width modulation (PWM) controlled and P&O MPPT controlled. Section 4 presents the modeling of the solar cell and solar panels. Section 5 provides modeling of DC-DC Buck converters, and Section 6 provides modeling of maximum power point tracking techniques (MPPTs). The Section 7 provides simulation parameters and Section 8 provides simulation results. In Section 9, Energy harvester systems efficiency calculations are shown, and in Section 10, a hardware experiment is performed for SEH-WSN nodes. Finally, Section 11 provides the conclusion for simulation results and hardware experiment validation.

## 2. Operation of an SEH-WSN Node

## 3. Solar Energy Harvesting System

## 4. Modeling of a Solar PV Panel

_{g}) is incident over a solar cell the electron-hole pair (EHP) is generated. This newly generated EHP contributes to the electric current called a light generated current denoted by (I

_{L}). The ideal theoretical current-voltage (I–V) equation of a solar cell is given as

_{L}= Light generated current by the solar cell, I

_{o}= Reverse Saturation current due to recombination, q = charge of electron (1.6 × 10

^{−19}C), V = open circuit voltage of solar cell, k = Boltzmann’s constant (1.38 × 10

^{−23}J/K), T = Temperature of Solar cell (300 K). The symbol of the solar cell is shown in Figure 3a. The solar cell equivalent circuit model can be represented as shown in Figure 3b. It consists of a light generated current source (I

_{L}), a diode (D) modeled by Shockley equation, and two series and parallel resistances. A MATLAB Simulink model for a solar panel is shown in Figure 3c. In Figure 3b, Kirchhoff’s current law (KCL) can give the characteristic current equation for this equivalent circuit:

_{L}− I

_{D}− I

_{p}

_{p}= current in parallel resistance, I

_{L}= Light generated current, and I

_{D}= diode current.

_{o}= Reverse Saturation current due to recombination, V = open circuit voltage of solar cell, I = solar cell output current, R

_{s}= series resistance, n = diode ideality factor, (1 for ideal, 2 for practical diode), V

_{T}= Thermal voltage (kT/q), k = Boltzmann’s constant (1.38 × 10

^{−23}J/K), T = Temperature of Solar cell (300 K). q = charge of electron (1.6 × 10

^{−19}C). The current in parallel resistance is given as:

_{D}and I

_{p}in current Equation (2), we get the complete IV equation of the equivalent circuit of a single solar Cell, for which related all parameters with output current and voltage are given as [9]:

_{p}= Parallel Resistance and remaining parameters I

_{L}, I

_{o}, q, V, I, R

_{s}

_{,}n, k, T have been already defined in Equation (3). The efficiency (η) of the solar cell is given as:

_{oc}is called Open Circuit Voltage, I

_{sc}is Short Circuit Current, FF is Fill Factor and P

_{in}= incident optical power. The Fill Factor (FF) of a solar cell is given as

_{m}is called maximum current and V

_{m}is the maximum voltage of the solar cell. Practically, there are many types of solar cells, such as monocrystalline silicon solar cell (c-Si), Amorphous Silicon solar cell (a-Si), Polycrystalline solar cell (multi-Si), Thin-film solar cell (TFSC) etc. However, the efficiency of a-Si solar cells is more than all others up to 18% efficiency [10].

#### 4.1. Effect of Solar Radiation (G)

^{2}. From Figure 4a, it is observed that the current in solar panel increased with an increase in the irradiance level [11]. Here, the solar cell current is maximum (6.2 A) for solar irradiance of 1000 W/m

^{2}. The Power-Voltage characteristics of a Solar Panel under different radiations levels is shown in Figure 4b. Here, the harvested power is the maximum (9.8 W) for the highest solar irradiance i.e., 1000 W/m

^{2}.

#### 4.2. Effect of Temperature (T)

## 5. Modeling of DC-DC Converter

_{dc}), an inductor (L), a switch (MOSFET), a diode (D) and a capacitor (C) as shown in Figure 6. When MOSFET switch (S) is closed at time t

_{1}, the input voltage V

_{s}appears across the load resistor. If the MOSFET switch remains OFF for the time t

_{2}, then the voltage across the load resistor is zero. The amplitude of output voltage (V

_{0}) is less than the input voltage V

_{o}. The Duty Cycle (D) can be varied from 0 to 1 by varying time period t

_{1}. The duty cycle of the Buck converter is D = V

_{o}/V

_{in}. The average output voltage of the buck converter is given as:

_{0}is output voltage, V

_{in}is input voltage, t

_{1}= MOSFET switch ON time duration, T = Total Time period, f is the frequency of operation, D is the duty cycle.

_{1}/T is duty cycle, f = chopping frequency.

#### Power Losses in DC-DC Buck Converter

_{L}= Power loss in Inductor (mW), I

_{L}

_{(rms)}= Inductor RMS current, R

_{L}

_{(dc)}= DC resistance of the Inductor.

## 6. Modeling of Maximum Power Point Tracking (MPPT) Technique

_{pv}) and current (I

_{pv}) from the solar panel and calculates the amount of duty cycle (D) to be fed to the MOSFET switch of the DC-DC buck converter. The following algorithms are generally used in photovoltaic applications as [18]:

- Perturbation and Observation (P&O) technique,
- Incremental Conductance (INC) technique and
- Fraction Open Circuit Voltage (OCV).

^{2}). When solar irradiance changes then a change in duty cycle occurs and the solar panel voltage and current changes [19]. The MPPT algorithm senses these changes and adjusts the impedance of the solar panel to the maximum power point. Thus, maximum power (P) can still be extracted from the solar panel even if the irradiance changes. It generates a PWM waveform whose initial duty cycle (D) is 0.7 provided arbitrarily (in the range of 0 to 1) as a seed value during the simulation.

_{in}), the Output voltage (V

_{o}) and duty cycle (D) is given as

_{o}) changes. If the duty cycle (D) is increased the output voltage (V

_{o}) also increases and vice-versa. By changing the duty cycle (D), the impedance of the load resistance (R

_{L}) can be matched with input solar panel impedance for maximum power transfer to the load for optimum performance. The steps in the P&O algorithm are shown by a flowchart and MATLAB codes [17,18,19] are shown in Algorithm 1 respectively.

Algorithm 1. P&O MPPT Algorithm. |

function D = PandO(Vpv,Ipv) persistent Dprev Pprev Vprev if is empty (Dprev) Dprev = 0.7; Vprev = 190; Pprev = 2000; end deltaD = 0.0025; Ppv = Vpv*Ipv; if (Ppv-Pprev) ~= 0 if (Ppv-Pprev) > 0 if (Vpv-Vprev) > 0 D = Dprev - deltaD; else D = Dprev + deltaD; end else if (Vpv-Vprev) > 0 D = Dprev + deltaD; else D = Dprev - deltaD; end end else D = Dprev; end Dprev = D; Vprev = Vpv; Pprev = Ppv; |

## 7. Simulation Experiment Setup

^{2}is incident on the solar panel with a constant temperature of 25-degree Celsius [20]. The Solar panel can extract only this solar energy into 15 mW/cm

^{2}with 15% efficiency [21]. For full irradiance on the simulated solar panel, the output voltage of the solar panel is 6 volts, 500 mA, and 3 watts. Now, this electrical energy from the solar cell is fed to the DC-DC boost converter, which increases the output voltage. The Boost converter output voltage is used to charge the rechargeable battery. The rechargeable battery is used to operate the WSN node. Here, the WSN load is modeled as output with a DC load resistance of 100 ohms. Table 1 shows various simulation parameters i.e. irradiance, temperature, DC-DC converter type, Solar panel current, voltage and power, battery type ad battery voltage, duty cycle, WN load model and power losses.

## 8. Simulation Results

_{B}) and battery voltage (V

_{B}) using PWM controlled and P&O MPPT controlled solar energy harvesting (SEH) system are shown in Figure 10, Figure 11 and Figure 12.

#### Comparison of Battery State of Charge (SoC), Voltage and Current during Charging Using PWM and MPPT Control Techniques

## 9. Energy Harvester Systems Efficiency $\left({\eta}_{sys}\right)$ Calculations

#### 9.1. PWM Efficiency

_{PWM}is 2.5 watts but the rated max. power is 3 watts. Thus PWM efficiency is calculated as 2.5 w/3 w = 83.34%. Furthermore, the buck converter reduces (or regulates) this 2.5 watts power to 650 mW. The buck converter efficiency is defined as output power (P

_{0}) divided by the power losses (P

_{loss}). Mathematically,

_{loss}is the sum of MOSFET switching loss (P

_{sw}) and the Inductor conduction loss (P

_{L}). From the simulation results table, the output power (P

_{0}) is 650 mW and MOSFET switching losses are 5 mW and inductor power loss is 50 mW. Thus buck converter efficiency is calculated as 650 mW/(650 + 55 mW) = 92.19%. The overall harvester efficiency is the average of PWM efficiency and DC-DC buck converter efficiency. Thus

#### 9.2. P&O MPPT Efficiency

_{MPP}) is 2.8 watts and the maximum theoretical power (P

_{m}) is 3 watts. Thus P&O MPPT efficiency is calculated as 2.8 w/3 w = 93.33%. Here, the P

_{loss}also changes due to variations in P&O MPPT of DC-DC buck converter. The P

_{loss}is the sum of MOSFET switching loss (P

_{sw}) and Inductor conduction loss (P

_{L}). From the simulation results table, the output power (P

_{o}) is 1.8 W and MOSFET switching losses are 2 mW and inductor power loss is 20 mW. Thus buck converter efficiency is calculated as 1.8 W/1.8 W+ 22 mW = 98.79%. Finally, the overall energy harvester circuit efficiency (η

_{sys}) is the average of Buck converter efficiency and P&O MPPT efficiency.

_{m}), average buck converter output voltage (V

_{m}), average buck converter output current (I

_{m}), buck converter output power, inductor loss, MOSFET switching loss, and harvester system efficiency (%) are shown. Clearly, from Table 2, the P&O MPPT controlled method gives better results as compared to PWM control in terms of output voltage, current, power, losses and efficiency.

## 10. Hardware Experiment

#### 10.1. Scientech 2311 WSN System

#### 10.2. LM2575 Buck Converter Based Energy Harvesting System

## 11. Conclusions

_{sys}) is calculated by adding Buck converter efficiency and, PWM efficiency and MPPT efficiency. From the comparison of simulation results, as shown in Figure 14 that the MPPT based Solar Energy Harvester system efficiency (96.06%) is better than PWM controlled system efficiency (87.76%) in the MATLAB/SIMULINK simulation. The practical hardware experiment of the SEH-WSN node is used to monitor the room temperature wirelessly using a PWM controlled buck converter. The maximum efficiency of the practical LM2575 based PWM controlled buck converter is 80%, which is less than theoretical simulation results (i.e., 87.76%). In the future, the simulation and hardware experimental work presented in this paper for SEH-WSN systems can be extended to various advanced MPPT algorithms like neural networks, fuzzy logic, and machine learning algorithms.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Block diagram of solar energy harvesting system using PWM and MPPT control. (

**a**) Using PWM control; (

**b**) using MPPT control.

**Figure 3.**Modelling of Solar cell (

**a**) Symbol; (

**b**) Equivalent circuit of Solar Cell; (

**c**) Solar Panel.

**Figure 4.**Solar Panel characteristics with variations in Irradiance level (Watts/m

^{2}). (

**a**) I–V Characteristics; (

**b**) P–V Characteristics.

**Figure 5.**Solar Panel characteristics with variations in Temperature (

^{o}C). (

**a**) I–V Characteristics; (

**b**) P–V Characteristics.

**Figure 8.**MATLAB/SIMULINK model for PWM controlled solar energy harvesting (SEH) system for WSN Node.

**Figure 10.**Simulation results of PWM controlled and P&O MPPT controlled solar energy harvesting (SEH) system for 10 s. (

**a**) Battery SoC, Voltage and Current during Charging using PWM control; (

**b**) Battery SoC, Voltage and Current during Charging using P&O MPPT control.

**Figure 11.**Simulation results of PWM controlled and P&O MPPT controlled solar SEH system for 100 s. (

**a**) Battery SoC, Voltage and Current during Charging using PWM control; (

**b**) Battery SoC, Voltage and Current during Charging using P&O MPPT control.

**Figure 12.**Simulation results of PWM controlled and P&O MPPT controlled solar SEH system for 200 s. (

**a**) Battery SoC, Voltage and Current during Charging using PWM control; (

**b**) Battery SoC, Voltage and Current during Charging using P&O MPPT control.

Parameters | Value | Parameters | Value |
---|---|---|---|

Irradiance (W/m^{2}) | 1000 Watts/m^{2} | Capacitor (C) | 100 uF |

Temperature (T) | 25 degree Celsius | Inductor (L) | 200 uH |

DC-DC Converter | Boost Converter | MOSFET Switching Frequency (f) | 5 KHz |

Max. Solar Panel output voltage (V_{m}) | 6 volts | Initial duty Cycle | 0.5 |

Max. Solar Panel output current (I_{m}) | 500 mA | MOSFET Switching Power Losses (P_{sw}) | 0.5 mW |

Max. Power from Solar Cell (P_{m}) | 3 watts | Switching Voltage Loss (V_{sw}) | 0.2 volts |

Rechargeable Battery Type | NiCd | WSN Load Model | 10-ohm resistor |

Battery Voltage | 3.6 volts | Inductor conduction Power Loss (P_{L}) | 50 mW |

Energy Harvester Parameters | PWM Control | P&O MPPT Control |
---|---|---|

Max. Solar Panel output Power (P_{m}) | 2.5 watts | 2.8 watts |

Average Buck Converter Output Voltage (V_{m}) | 3.3 volts | 3.5 volts |

Average Buck Converter Output Current (I_{m}) | 260 mA | 500 mA |

Buck Converter Output Power | 650 mW | 1.8 watts |

Inductor Loss | 50 mW | 20 mW |

MOSFET Switching Loss | 5 mW | 2 mW |

Harvester System Efficiency (%) | 87.76% | 96.06% |

Hardware Experiment Parameters | Number of Components and Details |
---|---|

Scientech 2311w WSN system: | |

WSN Gateway Node | 1 |

WSN End node | 1 |

Temperature Sensor Module (LM35) | 1 |

Scientech 2311w WSN monitoring software installed on a Laptop PC | 1 |

Energy Harvesting System: | |

Solarcraft Solar Panel | 5 w, 8 V, 0.65 A |

Generic LM2576, 80% efficient, PWM controlled DC-DC Buck Converter | 3.6 V–40 V, 2 A |

Measuring Instruments: | |

Tektronix 200MHz Digital Storage Oscilloscope (DSO) | 1 |

Multimeter | 1 |

Author & Year | Proposed Solar Energy Harvester Model | Irradiance (W/m^{2}) Consider | Temperature (°C) Consider | Inductor and Capacitor Loss Consider | (PWM/MPPT) Consider | Super Capacitor/Battery Consider | Power Consumption of Harvester Circuit Consider | Maximum Efficiency | Model Validation Consider |
---|---|---|---|---|---|---|---|---|---|

Denis Dondi et al. [3], 2008 | Boost Converter with MPPT | Yes (20–1000 W/m^{2}) | No | Yes | MPPT only | Battery | No | 85% | No |

Davide Brunelli et al. [4], 2009 | Boost Converter with MPPT | No | No | No | MPPT only | Both | Yes | 80% | Yes |

Andrea Castagnetti et al. [23], 2012 | Boost Converter | No | No | No | Not Reported | Battery | Yes | Not Reported | yes |

Alex S. Weddell et al. [24], 2012 | Buck-Boost Converter with MPPT | Yes (200–5000 W/m^{2}) | No | No | MPPT | Battery | Yes | Not reported | Yes |

Our Proposed Model | Buck Converter with PWM & MPPT both | Yes | Yes | No | PWM & MPPT both | Both | Yes | 96.06% | Yes |

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**MDPI and ACS Style**

Sharma, H.; Haque, A.; Jaffery, Z.A.
Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes. *J. Sens. Actuator Netw.* **2018**, *7*, 40.
https://doi.org/10.3390/jsan7030040

**AMA Style**

Sharma H, Haque A, Jaffery ZA.
Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes. *Journal of Sensor and Actuator Networks*. 2018; 7(3):40.
https://doi.org/10.3390/jsan7030040

**Chicago/Turabian Style**

Sharma, Himanshu, Ahteshamul Haque, and Zainul Abdin Jaffery.
2018. "Modeling and Optimisation of a Solar Energy Harvesting System for Wireless Sensor Network Nodes" *Journal of Sensor and Actuator Networks* 7, no. 3: 40.
https://doi.org/10.3390/jsan7030040