# A Compact Energy Harvesting System for Outdoor Wireless Sensor Nodes Based on a Low-Cost In Situ Photovoltaic Panel Characterization-Modelling Unit

^{*}

## Abstract

**:**

## 1. Introduction

## 2. In Situ Photovoltaic Panel Characterization

#### 2.1. Variable Load Module

#### 2.2. Control and Acquisition System

#### 2.3. System Software

#### 2.4. Host Control

## 3. ANN-Based Photovoltaic Panel Modelling

## 4. Solar Energy Harvesting System Architecture

_{load}) is powered by the energy stored in the supercap SC, while the inductor L1 is energized from the PV output. When switch S1 is OFF, the output voltage V1 from the PV panel and the energy stored in the inductor load up the supercap voltage (V2), which provides energy directly to the load. Capacitor C

_{in}filters abrupt changes in the output voltage V1 due to variations in the working conditions of PV panel. The component values (Table 3) were estimated using classical techniques [21,22], assuming the system works in continuous conduction mode (CCM) [23].

#### 4.1. Maximum Power Point Tracking

_{mpp}is the voltage at maximum power point transfer, V

_{oc}is the voltage at open circuit (which depends on the incident light and temperature), and k is a constant. The value of k depends on the panel model; its value is typically limited to 0.7–0.8 [25,26,27], and can be experimentally estimated by characterizing the solar panel (see Figure 7). In our case, k = 0.8.

_{oc}), estimating the maximum power transfer voltage V

_{mpp}(Equation (1)), which is compared to the working PV panel voltage V1. When V1 is lower than V

_{mpp}, the switch S1 is turned off, increasing the load impedance, and thus increasing V1. Otherwise, if V1 > V

_{mpp}, the switch is on, reducing the load impedance. To avoid fast and continuous changes in the switch state, the comparator module presents hysteresis.

#### 4.2. Maximum Power Point Tracking Technique Based on ANN Modelling

_{mpp}of the panel from the readings of the sensors of illuminance and temperature; this limits the number of inputs to two. Thus, the PV panel behaviour is modelled by a 2-2-1 MLP with non-linear output function in the hidden layer and linear operation in the output layer. This neural network has been implemented in the same microcontroller used in the panel characterization system (Section 2), a MSP430FR5739 (Texas Instruments, Dallas, TX, USA), which includes a 32 bit hardware multiplier. In this way, the computational resources are shared with the solar panel characterization module. Weight values are represented in 16 bit floating point, and data are normalized in the [−1, +1] range, limiting the training data to the values shown in Table 2. The training data set is generated from the complete ANN panel model previously shown in Section 3, just sweeping the load value for each illuminance-temperature couples (in the valid ranges of the panel location). Therefore, the maximum voltage Voc is obtained for each pair of illuminance—temperature values and, hence, the corresponding V

_{mpp}is straightforwardly derived.

_{mpp}estimation is higher than a set time T (>t), the system updates its value by computing the new V

_{mpp}from the ANN model using current illuminance and temperature readings. Next, if the voltage V1 at the panel output is higher than the V

_{mpp}, the control switch is turn on reducing the input impedance and, hence, reducing the voltage V1. Otherwise, the switch turns off.

#### 4.3. MPPT Comparison

_{mpp}estimation is upgraded every 100 ms (T in Figure 15). Both techniques are executed in parallel over two identical solar panels with the same orientation, seeking for the same working conditions.

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 5.**Panel output voltage values (green) in the characterization process sweeping resistor values in the PV panel load. Rising and descent slope in purple signal indicates the start and end time in the measurement. Time span is below 20 ms (see cursor measures).

**Figure 6.**Real and simulated voltage-to-load curves at three different light and temperature conditions.

**Figure 11.**Multilayer perceptron weight values obtained to modelling the solar panel behavior. Red values correspond to bias weights.

**Figure 13.**(

**a**) Hyperbolic tangent computation using a standard C library tanh(x) function (red) and the piecewise approach (blue) on a microcontroller; (

**b**) Relative error of the polynomial fitting compared to the library function.

**Figure 14.**Computing time measurement for three different input values using (

**a**) C library-based tanh(x) operation and (

**b**) polynomial approach; Times correspond, respectively, to calculate the operations for inputs that meet: abs(x) ≥ 5.3 (${T}_{A}$, ${T}_{A}^{\prime}$); 5.3 > abs(x) > 0.9 (${T}_{B},{T}_{B}^{\prime}$); and abs(x) ≤ 0.9 (${T}_{C},{T}_{C}^{\prime}$).

**Figure 16.**Full solar energy harvesting system based on ANN MPPT and energy storage system (supercapacitor): solar panel (left), control system (centre, on top, includes the characterization system), DC-DC system (centre, bottom) and supercap (right). In this case, only one of the 1.5 F supercaps is connected to the boost system for characterization purposes.

**Figure 17.**Charge curves for a 1.5 F supercap for a VOC algorithm using a reference panel (blue) and the ANN-based solar panel model (yellow). Environmental conditions (irradiance and temperature, average): (

**a**) 850 W/m

^{2}, 35 °C; (

**b**) 1300 W/ m

^{2}, 55 °C; (

**c**) 1120 W/ m

^{2}, 43 °C; (

**d**) 500 W/ m

^{2}, 26 °C.

SEL(1:3) | R_{MUX1} (Ω) | R_{MUX2} (Ω) | R_{MUX3} (Ω) |
---|---|---|---|

000 | 10,000 | 1800 | 62 |

001 | 8200 | 1200 | 33 |

010 | 7500 | 1000 | 16 |

011 | 6200 | 820 | 8.2 |

100 | 5100 | 680 | 3.9 |

101 | 4300 | 430 | 2 |

110 | 3300 | 220 | 1 |

111 | 2200 | 130 | 0.1 |

Parameter | Upper Limit | Lower Limit |
---|---|---|

Light (lux) | 120,000 | 10,000 |

Temperature (°C) | 70 | −10 |

Voltage (V) | 5 | 2.8 |

Panel load (Ω) | 10,000 | 0.1 |

Component | Value |
---|---|

Cin | >25 µF |

L1 | >6.7 mH |

Diode | HSMS-2800 |

Switch | IRFML8422 (MOSFET transistor) |

© 2017 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 (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Antolín, D.; Medrano, N.; Calvo, B.; Martínez, P.A.
A Compact Energy Harvesting System for Outdoor Wireless Sensor Nodes Based on a Low-Cost In Situ Photovoltaic Panel Characterization-Modelling Unit. *Sensors* **2017**, *17*, 1794.
https://doi.org/10.3390/s17081794

**AMA Style**

Antolín D, Medrano N, Calvo B, Martínez PA.
A Compact Energy Harvesting System for Outdoor Wireless Sensor Nodes Based on a Low-Cost In Situ Photovoltaic Panel Characterization-Modelling Unit. *Sensors*. 2017; 17(8):1794.
https://doi.org/10.3390/s17081794

**Chicago/Turabian Style**

Antolín, Diego, Nicolás Medrano, Belén Calvo, and Pedro A. Martínez.
2017. "A Compact Energy Harvesting System for Outdoor Wireless Sensor Nodes Based on a Low-Cost In Situ Photovoltaic Panel Characterization-Modelling Unit" *Sensors* 17, no. 8: 1794.
https://doi.org/10.3390/s17081794