Development and Evaluation of an Arduino-Based Data Logging System Integrated with Microsoft Excel for Monitoring On-Grid Photovoltaic Systems

This paper presents the development and evaluation of an Arduino-based data logging system integrated with Microsoft Excel for monitoring on-grid photovoltaic (PV) systems. The system combines open-source hardware and software to enable real-time data acquisition, logging, and analysis of key performance metrics such as solar irradiance, temperature, voltage, and current levels. Leveraging the versatility of Arduino microcontrollers and the accessibility ofMicrosoft Excel, the proposed system offers a cost-effective and user-friendly solution for PV system monitoring. The integration of the MS Data Streamer add-in for Excel facilitates seamless data logging and visualization, empowering PV system owners, researchers, and practitioners with actionable insights for optimizing system performance and contributing to a sustainable energy future. Experimental validation of the system demonstrates its effectiveness in accurately measuring and logging sensor data, highlighting its potential for widespread adoption in renewable energy monitoring applications.

Introduction

The widespread adoption of renewable energy sources, particularly photovoltaic (PV) systems, has led to a growing interest in monitoring and optimizing their performance. On-grid PV systems, which are connected to the utility grid, play a significant role in meeting energy demands while reducing carbon emissions and dependency on fossil fuels. However, ensuring the efficient operation of these systems requires continuous monitoring of various parameters such as solar irradiance, temperature, voltage, and current levels.

The motivation for this research stems from the need for cost-effective and accessible monitoring solutions for on-grid PV setups. While commercial monitoring systems are available, they often come with high costs and limited flexibility in terms of customization and data analysis. Hence, there is a demand for open-source and DIY solutions that leverage affordable hardware platforms such as Arduino for data acquisition and logging.

The objective of this study is to design, implement, and evaluate an Arduino-based data logging system for monitoring an on-grid PV setup. The system integrates a range of sensors to collect data on environmental conditions, solar irradiance, voltage, and current levels. The collected data is logged to a Microsoft Excel spreadsheet in real time, enabling comprehensive analysis of the PV system’s performance over time.

By developing a cost-effective and accessible monitoring solution, this research aims to empower PV system owners, researchers, and practitioners with the tools to optimize energy generation, identify performance issues, and make informed decisions regarding system maintenance and upgrades. Furthermore, the open-source nature of the proposed system encourages collaboration, innovation, and knowledge sharing within the renewable energy community.

In the subsequent sections of this paper, we will delve into the hardware design, software implementation, experimental setup, results, analysis, and discussion, providing a comprehensive overview of the Arduino-based data logging system for on-grid PV monitoring.

Literature Review

Monitoring the performance of photovoltaic (PV) systems is essential for maximizing energy generation, ensuring reliability, and optimizing return on investment. A review of existing literature reveals several approaches and technologies used for PV monitoring, ranging from commercial monitoring systems to DIY solutions based on open-source platforms like Arduino.

Hakim et al. [1] proposed a low-cost data acquisition system for monitoring solar PV power plants. Their study emphasizes the importance of protecting solar panels from moisture and damage to maintain efficiency. Commercial solar panels typically achieve around 15% efficiency, with rare exceptions exceeding 20%. The use of solar panels contributes to clean energy generation, mitigating the effects of climate change.

Shompa & Hoque [2] developed an IoT-based solar power monitoring and data logger system. This system utilizes four sensors to measure temperature, humidity, voltage, and current, storing data in EEP ROM memory and uploading it to the Thingspeak cloud server for analysis. Real-time monitoring allows technicians to identify issues with battery charging promptly.

Effendi et al. [3] designed a data logger for PV analysis, integrating current and voltage sensors to measure parameters affecting PV efficiency. The device stores parameter data, enabling automatic data collection over time. Results show a maximum output of 20,016 W, indicating the suitability of the data logger for measuring PV performance in various conditions.

Rehman & Iqbal [4] presented an ultra-low powered data logger for off-grid PV systems. Utilizing deep-sleep mode and ESP32-S2 microcontroller, the logger records major system parameters, offering both manual and automatic monitoring modes. Real-time data can be accessed via a local web portal, demonstrating energy consumption of 7.33 mWh in deep-sleep mode.

Pachauri et al. [5] proposed a data acquisition system for PV characterization under partial shading conditions. Analog sensors integrated with Arduino quantify and store real-time performance data, enabling analysis of parameters such as voltage and power at the global maximum power point (GMPP). Results show improved fill factor and minimize power loss under partial shading.

Franklin et al. [6] developed a cost-effective data acquisition system to evaluate the performance of solar PVT systems. Using ATMEL microcontrollers, the system measured parameters such as PV panel surface temperature, inlet and outlet air temperatures, current, and voltage. Results demonstrate acceptable system performance over a three-week testing period.

Sesa & Mahmuddin [7] designed a real-time monitoring system for off-grid PV systems, comprising hardware and software subsystems. An electronic data logger system records data on an SD card, while software developed in Visual Basic facilitates real-time monitoring and data recording. The system enables automatic monitoring of PV results and data analysis.

Ramaprabha & AI [8] introduced a low-cost PV panel characterization kit based on Arduino UNO and Excel. The kit monitors PV characteristics and load connected via maximum power point tracker, enabling real-time measurement of voltage, current, and power. Simulation and hardware validation demonstrate the kit’s effectiveness.

Ali & Mohamad [9] developed a portable wireless data acquisition system for PV solar systems with real-time clock functionality. The system, based on Arduino microcontrollers and NRF24L01 wireless transceivers, aggregates data presented in Excel using the PLX-DAQ data acquisition Excel Macro. Results confirm successful data transmission and monitoring.

Djafer & Ahmed Abdelouarith [10] created a real-time acquisition system for solar panels using Arduino Uno, sensors, and SD card storage. The system tracks voltage, current, and temperature changes, with data accessible for monitoring and analysis via Excel. Real-time monitoring enables prompt identification of system performance issues.

Chidolue & Iqbal [11] introduced a monitoring and data logging system for solar-powered oil well pumping. The system, based on Arduino boards and low-cost sensors, monitors battery bank voltage, PV current, AC converter output, and oil well control. Data presented in Excel using PLX-DAQ enables real-time monitoring and analysis.

These studies collectively underscore the importance of reliable data acquisition and monitoring systems for optimizing the performance and efficiency of solar PV systems. While existing research has made significant strides in this area, there remains a need for further advancements in cost-effectiveness, scalability, and integration with emerging technologies to address evolving challenges in solar energy monitoring and management.

Despite the growing interest in Arduino-based PV monitoring, none in literature used MS Excel and Arduino for data logging and testing a PV system. This can be done since these tools are commonly available.

The present research aims to address this gap by designing, implementing, and evaluating an Arduino-based data logging system for monitoring an on-grid PV setup, with a specific focus on MS Excel data logging using the MS Data Streamer add-in. By conducting thorough experiments and analysis, this study seeks to assess the effectiveness, limitations, and potential applications of Excel-based data logging in the context of PV system monitoring and management.

Methodology

The overall block diagram is illustrated in Fig. 1. The block diagram illustrates the proposed data logging setup for a PV system using Arduino and Microsoft Excel. A PV array is connected to the inverter. The AC load is connected to the 220 V grid as well as to the inverter. The inverter converts the DC electricity from the PV array into AC electricity compatible with household appliances or the power grid. The currents and voltage are measured using current and voltage sensors connected between the PV array and the inverter as well as load and grid. These sensors measure the electrical parameters of the PV system. An Arduino is the main part of the data logging system. It can read analog signals from the voltage and current sensors. The Arduino is connected with two other sensors that measure the temperature and humidity and solar irradiance. Furthermore, the Arduino is connected to a Windows Computer via USB cable. The computer contains MS Excel that acts as the data storage and analysis platform. The Arduino program transmits the collected voltage and current data (and calculated values like power, irradiance, temperature, etc.) to Excel at regular intervals.

Fig. 1. Block diagram of the proposed methodology.

Hardware Design

The hardware design of the data logging system for monitoring an on-grid photovoltaic (PV) setup encompasses the selection and integration of various components to facilitate real-time data acquisition and logging. The key components include Arduino Uno microcontroller, sensors for measuring environmental parameters and PV system performance metrics, and necessary interfacing modules.

Arduino Uno Microcontroller

The Arduino Uno, as illustrated in Fig. 2 serves as the central processing unit for the data logging system, responsible for interfacing with sensors, collecting data, and communicating with external devices such as Microsoft Excel for data logging. The Arduino Uno offers an accessible and versatile platform with ample digital and analog input/output pins, making it suitable for integrating multiple sensors and peripherals. Arduino can be connected to several sensors to measure and control the process.

Fig. 2. Arduino UNO utilized in this study.

Sensors

Several types of sensors are integrated into the data logging system to monitor environmental conditions and PV system performance metrics. These sensors include:

Light Dependent Resistor (LDR) Module

Measures solar irradiance levels to assess the intensity of sunlight falling on the PV panels. The LDR module is shown in Fig. 3.

Fig. 3. LDR module.

Temperature and Humidity Sensor (DHT22)

Provides data on ambient temperature and humidity, which influence the efficiency and operation of PV panels. Fig. 4 illustrates the sensor utilized in this study.

Fig. 4. Temperature and humidity sensor (DHT22).

DC Voltage Sensor

Measures the voltage output of the PV panels to assess their performance and ensure optimal operation. Fig. 5 illustrates the sensor utilized for DC voltage measurement.

Fig. 5. DC voltage sensor.

Current Sensors (HW-872A/B/C Modules)

Monitor the current flowing through the PV system to evaluate power generation and detect anomalies such as overloading or underperformance. The current sensor is shown in Fig. 6.

Fig. 6. Current sensor.

Real-Time Clock (RTC) Module

Provides accurate timekeeping functionality for timestamping data logs and synchronizing data acquisition processes. Fig. 7 shows the RTC module used for timestamping.

Fig. 7. Real time clock module for timestamping.

Circuit Design

The hardware connections are established according to the pin configurations of the Arduino Uno and the respective sensors. Analog sensors such as the LDR module, DC voltage sensor, and current sensors are connected to the analog input pins (A0–A5) of the Arduino Uno. Digital sensors like the DHT22 temperature and humidity sensor are connected to the digital pins (2 and above) of the Arduino Uno Fig. 8 shows the block diagram of the proposed study.

Fig. 8. Overall block diagram of the proposed study. Here, the red and black lines illustrate the power flow while blue dotted lines show the connection of sensors with Arduino.

Software Implementation

The software implementation of the data logging system for monitoring an on-grid photovoltaic (PV) setup involves programming the Arduino Uno microcontroller to interface with sensors, collect data, and communicate with Microsoft Excel for real-time data logging. Additionally, the MS Data Streamer add-in for Excel is configured to receive and log data transmitted by the Arduino Uno.

Arduino Programming

The Arduino programming environment (IDE) is utilized to develop the firmware for the Arduino Uno microcontroller.

The firmware includes code to initialize sensors, read sensor data, format data packets, and establish serial communication with Microsoft Excel via USB.

Sensor Integration

Each sensor is interfaced with the Arduino Uno using appropriate libraries and communication protocols. For analog sensors such as the LDR module, DC voltage sensor, and current sensors, analogRead () function is used to read sensor values from the corresponding analog input pins. Digital sensors like the DHT22 temperature and humidity sensor require specific libraries for communication and data retrieval.

Serial Communication

Serial communication is established between the Arduino Uno and Microsoft Excel using the USB connection. The Arduino Uno acts as a serial device, transmitting data packets containing sensor readings and timestamps to Excel. The Serial.print() function is used to send formatted data strings over the serial port.

Microsoft Excel Configuration

In Microsoft Excel, the MS Data Streamer add-in is installed and configured to receive data from the Arduino Uno. The Data Streamer tab is activated, and the appropriate data format (e.g., CSV) is selected for parsing incoming data packets. The serial port corresponding to the Arduino Uno is specified in the Data Streamer settings. The interface is shown in Fig. 9.

Fig. 9. MS Excel data streamer interface.

Data Logging and Visualization

As data packets are received from the Arduino Uno, the MS Data Streamer add-in automatically logs the data to designated cells in the Excel spreadsheet. Real-time visualization of sensor data is facilitated using Excel’s charting and graphing capabilities, allowing users to monitor PV system performance metrics such as solar irradiance, temperature, voltage, and current levels.

Experimental Setup

The experimental setup for evaluating the Arduino-based data logging system for monitoring an on-grid photovoltaic (PV) setup involves configuring the hardware components, establishing connections, and conducting real-world measurements under controlled conditions. Fig. 10 shows the solar panel used for experimental setup while the experimental setup incorporating Arduino along with associated sensors is shown in Fig. 11.

Fig. 10. A 30-watt solar panel is placed outside the room on the rooftop.

Fig. 11. Experimental setup.

Hardware Configuration

The hardware components including the Arduino Uno microcontroller, sensors, and interfacing modules are assembled according to the circuit design specifications. The sensors are positioned near the PV panels and environmental conditions to ensure accurate data acquisition.

Sensor Calibration

Prior to data collection, the sensors are calibrated to ensure accuracy and consistency in measurements. Calibration procedures involve adjusting sensor parameters, compensating for environmental factors, and validating sensor readings against known reference values.

Software Setup

The firmware for the Arduino Uno is uploaded, and the serial communication settings are configured to establish communication with Microsoft Excel via USB. The MS Data Streamer add-in is activated in Excel, and the appropriate data format (i.e., CSV) is selected for parsing incoming data packets.

Data Collection Procedure

Data collection is initiated, and the Arduino-based data logging system begins acquiring sensor readings in real time. The system logs data to designated cells in the Excel spreadsheet, allowing for simultaneous visualization and analysis of multiple parameters. Fig. 12 shows the collection of data utilizing MS Excel.

Fig. 12. Realtime data logging in MS Excel, here 11 channels were used to log all the parameters.

During data collection, environmental conditions such as solar irradiance, ambient temperature, and humidity are monitored to provide context for PV system performance. Weather conditions, time of day, and seasonal variations are documented to assess their impact on data quality and system operation. Data is collected at regular intervals of every 10 minutes to track diurnal patterns, transient events, and long-term trends in energy generation and environmental conditions. The data was collected over 3 weeks and illustrated in Table I.

Table I. Data Collected From Current and Voltage Sensors with Timestamping

Time	I	T	V (PV)	I (PV)	V (AC)	I (Inv)	I (Grid)	P (DC)	P (Inv)	P (Grid)
10:00	337.9	12.1	9.54	0.726	215	0.032	0.022	7.48	6.73	4.90
10:10	329.4	12.2	9.31	0.732	214	0.032	0.022	7.36	6.62	4.99
10:20	335.6	12.6	9.50	0.756	210	0.034	0.020	7.90	7.27	4.47
10:30	362.4	13.1	10.2	0.786	226	0.036	0.018	8.97	8.25	4.12
10:40	404.2	13.8	11.5	0.828	214	0.045	0.009	10.2	9.26	2.19
10:50	404	14.3	11.5	0.858	226	0.044	0.010	10.8	9.90	2.36
11:00	401.4	15.2	11.5	0.912	221	0.048	0.007	11.3	10.06	1.54
11:10	418.8	15.3	12.0	0.918	222	0.050	0.004	12.2	11.14	1.06
11:20	431.5	15.4	12.3	0.924	220	0.052	0.002	12.4	11.10	0.55
11:30	440.2	15.5	12.6	0.93	212	0.055	0.00	12.6	11.30	−0.20
11:40	440.1	16.1	12.6	0.966	218	0.056	0.00	13.4	12.25	−0.35
11:50	465.2	16.4	13.4	0.984	225	0.059	0.00	14.5	13.21	−0.90
12:00	479.1	16.4	13.8	0.984	215	0.063	0.00	14.9	13.45	−1.91
12:10	498.2	16.3	14.3	0.978	224	0.063	0.00	15.5	13.87	−1.79

Results

The results obtained from the experimental setup provide valuable insights into the performance and effectiveness of the Arduino-based data logging system for monitoring an on-grid photovoltaic (PV) setup. The analysis of the collected data enables us to assess the system’s capability to accurately measure and log various parameters, detect anomalies, and provide actionable insights for optimizing PV system performance.

Sensor Data Analysis

The sensor data collected by the data logging system is analyzed to evaluate the performance of individual sensors and their correlation with PV system performance metrics. Key parameters such as solar irradiance, temperature, humidity, voltage, and current levels are examined for diurnal patterns, seasonal variations, and transient events.

PV System Performance Metrics

PV system performance metrics such as energy generation, efficiency, and reliability are derived from the collected sensor data. Analysis of voltage and current levels enables the calculation of power output, power efficiency, and capacity factor, providing insights into the overall performance of the PV system under different environmental conditions.

Data Visualization

Data visualization techniques such as charts, graphs, and histograms are employed to visualize sensor data and PV system performance metrics in Microsoft Excel. Real-time plotting of sensor data enables users to monitor trends, identify patterns, and make informed decisions regarding system maintenance and optimization. Fig. 13 shows the irradiance logged for 20 days. Similarly, Fig. 14 shows the temperature logged for the same period. Figs. 15 and 16 illustrate the current measured from the inverter and power output, respectively. Fig. 17 shows the power provided by PV and grid during day and night sessions for the first 5 days.

Fig. 13. Irradiance logged during the experimental setup.

Fig. 14. Temperature measured during the experimental setup recordings.

Fig. 15. Inverter current measured using current sensor.

Fig. 16. Output power calculated using inverter voltage and inverter current.

Fig. 17. Power provided by PV and grid during day and night sessions for first 5 days.

Discussion

The findings from the experimental setup reveal the effectiveness of the Arduino-based data logging system in monitoring on-grid PV setups. The system demonstrates accurate measurement and logging of various parameters, enabling real-time analysis and visualization of PV system performance metrics.

The strengths of the Arduino-based data-logging system lie in its versatility, ease of use, and compatibility with Microsoft Excel for data logging and analysis. However, limitations such as sensor accuracy, calibration requirements, and potential data transmission issues may impact the system’s reliability and robustness in long-term monitoring applications.

The findings from this research have implications for PV system owners, researchers, and practitioners involved in renewable energy monitoring and management. The Arduino-based data logging system offers a cost-effective and accessible solution for real-time monitoring of PV system performance, enabling informed decision-making and optimization strategies.

Future research endeavors may focus on addressing the limitations of the Arduino-based data logging system, such as reducing power losses in Arduino and sensors, using low-power computers.

Conclusion

The research presented in this paper aimed to design, implement, and evaluate an Arduino-based data logging system for monitoring an on-grid photovoltaic (PV) setup. Through a combination of hardware design, software implementation, and experimental validation, the effectiveness and feasibility of the proposed system were assessed in real-world conditions. The findings from the experimental setup demonstrated the capability of the Arduino-based data logging system to accurately measure and log various parameters critical to PV system performance, including solar irradiance, temperature, voltage, and current levels. The integration of Microsoft Excel with the MS Data Streamer add-in facilitated real-time data logging and visualization, enabling users to monitor trends, detect anomalies, and make informed decisions regarding system optimization and maintenance. While the Arduino-based data logging system offers several advantages such as affordability, versatility, and accessibility, it is not without limitations. Challenges related to sensor accuracy, calibration, and data transmission may impact the system’s reliability and robustness in long-term monitoring applications. Further research and development efforts are warranted to address these limitations and enhance the system’s performance and scalability.