In a paper published in the journal Sensors, researchers explored the development of an affordable wireless energy meter using the Espressif System Platform 32 (ESP32) microcontroller for power quality monitoring in smart grid applications. This approach reduces production costs and energy consumption and improves accuracy.
Study: Affordable Wireless Energy Meter for Accurate Power Quality Monitoring in Smart Grids. Image credit: urbans /Shutterstock
The integrated wireless connectivity allows data transmission to remote monitoring systems, and the smart meter's compatibility with the Internet of Things (IoT) and artificial intelligence (AI) applications adds versatility. This innovation offers an efficient and flexible solution for accurate energy monitoring in complex electric systems and smart grid environments.
Background
The global trend of escalating energy consumption necessitates continual investment in expanding the energy matrix and refining production and distribution processes. Over the past five decades, global energy consumption has surged from 43 terawatt-hours (TWh) in 1965 to over 165 TWh in 2022, marking a nearly 400% increase. Energy sources categorically fall into renewable and non-renewable types, with fossil fuels like oil, natural gas, and coal dominating due to their accessibility, reliability, and cost-effectiveness. However, these non-renewable sources are also prime contributors to greenhouse gas emissions and air pollution.
Related work
Prior studies have highlighted the need to enhance energy transmission, resulting in the emergence of smart meters within Smart Grids (SGs). These meters improve energy distribution through real-time monitoring, rapid failure detection, outage recovery, and insights into consumption for savings. Their key objective is to boost energy efficiency, reduce waste, and support sustainable energy demand growth.
While specialized integrated circuits offer benefits over traditional options, complexity, and cost limit their use in cost-sensitive projects. Yet, microcontroller advancements enable circuit integration, making smart meters more affordable and versatile. This push for economic smart meters aids widespread adoption, nurturing energy awareness and efficiency. Additionally, the growing adoption of smart meters drives the evolution of power grids into intelligent systems, enabling seamless integration of renewable energy and progress toward a sustainable energy future.
Proposed methodology
This study introduces voltage and current conditioning circuits for incorporation into a smart meter system. The voltage conditioning circuit converts electric network voltage into microcontroller inputs, utilizing resistors for voltage reduction, ensuring linearity and cost-effectiveness. Similarly, the current conditioning circuit converts current transformer inputs into voltage signals. A zero-crossing circuit, employing a comparator, identifies sine wave zero crossings, crucial for frequency and phase angle calculations.
The microcontroller ESP32 is utilized for its processing power and connectivity, featuring dual-core 32-bit processing, clock speeds up to 240 MHz and ample storage. Equipped with 12-bit Successive Approximation Register Analog-to-Digital Converters (SAR ADCs), the ESP32 facilitates measurements on 18 channels, operating efficiently in sleep mode while maintaining low power consumption. ADC resolution is adjustable to map input voltages from 0 to 3.3 Vdc based on attenuation filters and recommended ranges, ensuring accurate measurements within the ADC's linearity range. Calibration is imperative, typically employing a single reference value for project consistency.
The Easy Electronic Design Automation (EasyEDA) STD v6.5.34 software was chosen for its online schematic creation and collaboration capabilities. It offers 2D and 3D visualization tools and easy project-to-manufacture integration. The developed hardware provides comprehensive electrical parameter measurement and monitoring at a lower cost than comparable products. This accessibility benefits various users, including companies and residential consumers aiming to control energy consumption and enhance efficiency.
To design the power input stages effectively, track width selection is crucial. Institute for Printed Circuits (IPC) -2221A, the foundational Printed Circuit Board (PCB) design standard, offers track sizing guidelines, especially for accommodating higher currents, like the 5A, in the current conditioning circuit's power stage.
Ensuring data reliability in measurement tools requires a rigorous calibration process. The method involves a two-stage approach: controlled environment analysis and real-load data collection. The process corrects errors in the ADC input of the ESP32 microcontroller, enhancing precision. Voltage and current were varied using the CW500 power quality analyzer, and data was processed to generate polynomial regression equations. The calibration's success was confirmed by low absolute errors and effective fit comparisons, validating the quality of the data collected by the smart meter.
Experimental results and analysis
After bench calibration, the CW500 meter was employed to assess data reliability. This precision tool monitors power system anomalies, records harmonics up to the 50th order, and measures both single-phase and three-phase systems. A field test spanning 14 days evaluated voltage, current, frequency, power, power factor, and harmonics. Results showed strong agreement between the smart meter and CW500 for voltage, with occasional current outliers due to synchronization issues.
The smart meter accurately measured frequency, power factor, and harmonics up to the 50th order. A comparison with Fluke 1770 series power quality analyzer, a reference product in the market, revealed the smart meter's competitive advantages, including lower cost and an array of essential features.
Conclusion
In summary, the cost-effective smart meter's analysis validates its accuracy and reliability in power quality measurement, benefiting energy providers and consumers through real-time monitoring and analysis of various parameters. Despite synchronization challenges, the meter shows low relative errors, offering affordability at a cost of $82.77 or $34.77 without transformers. Future prospects involve AI integration and remote data collection for continuous monitoring and effective energy management in diverse contexts.
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