An Embedded Sensor Node for the Surveillance of Power Quality

Abstract

The energy supply of office buildings and smart homes is a key issue in the global energy system. The growing use of microelectronics-based technology achieves new devices for a more comfortable life and wider use of electronic office equipment. On the one hand, these applications incorporate more and more sensitive electronic devices which are potentially affected by any external electrical transient. On the other hand, the existing electrical loads, which generally use electronic power systems (such as different types of battery chargers, ballasts, inverters, switching power supplies, etc.), generate different kinds of transients in their own electrical internal network. Moreover, improvements in the information of the state of the mains alternating current (AC) power line allows risk evaluation of any disturbance caused to permanently connected electronic equipment, such as computers, appliances, home security systems, phones, TVs, etc. For this reason, it is nowadays more important to introduce monitoring solutions into the electrical network to measure the level of power quality so that it can protect itself when necessary. This article describes a small and compact detector using a low-cost microcontroller and a very simple direct acquiring circuit. In addition; it analyzes different methods to implement various power quality (PQ) surveillance algorithms that can be implemented in this proposed minimum hardware platform. Hence; it is possible to achieve cheap and low-power monitoring devices that can become nodes of a wireless sensor network (WSN). The work shows that using a small computational effort; reasonable execution speed; and acceptable reliability; this solution can be used to detect a variety of large disturbance phenomena and spread the respective failure report through a 433 MHz or 2.4 GHz radio transmitter. Therefore, this work can easily be extended to the Internet of Things (IoT) paradigm. Simultaneously, a software application (PulsAC) has been developed to monitor the microcontroller’s real-time progress and detection capability. Moreover, this high-level code (C++ language), allows us to test and debug the different utilized algorithms that will be later run by the microcontroller unit. These tests have been performed with real signals introduced by a function generator and superimposed on the true AC sine wave

Advanced and Complex Energy Systems Monitoring and Control: A Review on Available Technologies and Their Application Criteria

“…KPIs are fundamental to estimate the energy efficiency of a system and are specific for the application to be considered [52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]. Complex KPIs can be formulated as a combination of more KPIs properly taking into account weights for each parameter.…”

Current Status and Future Trends of Power Quality Analysis

“…Energies 2022, 15, x FOR PEER REVIEW One last point would be the instrumentation used to collect data from the real Although many researchers opt for the use of commercial instrumentation (44 p the high cost that this device entails, or the difficulty in programming with compl rithms with them have forced some studies to use instrumentation designed by searchers themselves [28,[71][72][73][74][75][76][77][78][79]. Indeed, [71,[73][74][75][76]79] both focus on explaining i how the design for power quality data collection is carried out.…”

Development of a Test-Bench for Evaluating the Embedded Implementation of the Improved Elephant Herding Optimization Algorithm Applied to Energy-Based Acoustic Localization

“…When considering the application level, debugging frameworks are usually used for debugging, verifying, and classifying some performance or behaviors. Implementation examples can be found regarding embedded wireless sensors [38], web-based sensors [39], Internet of Things devices [40], power quality sensor nodes [41], or cyber-physical automation systems [42]. Application-specific debugging is known for the decrease in development cost and time of faulty stage detection [43].…”