Scientists Have Come up With a Smart System for Electrical Networks Monitoring
An international team of scientists from Russia, India, Croatia, and Iran investigated the possibilities of intelligent monitoring of electrical networks using wireless sensors and invented a new method for detecting errors and failures in their work. The data obtained can be used to monitor the voltage level, the state of the phase measuring devices in the network and overcome power outages that occur in the event of their failure within a short period. The research is published in the highly-rated scientific journal Energies (Q2).
Using sensors to eliminate data transmission errors
Scientists from South Ural State University conducted a study within the current trend in the electric power industry—intelligent monitoring of electrical networks using wireless sensors. Scientists from the Department of Power Plants, Networks and Power Supply Systems of the Power Engineering Faculty of the SUSU Polytechnic Institute—Doctor of Technical Sciences, Professor Irina Kirpichnikova and Senior Researcher Yuvaraja Teekeraman took part in the study. Later they were joined by scientists Hariprasat Manoharan and Ramya Kuppusami from India, Srete Nikolovski from Croatia, and Hamid Reza Bagai from Iran.
Voltage is a significant factor in the transmission of electricity. Special measuring devices that measure the voltage value and phase angle of all relevant buses are used to control it. However, if there is a failure in the operation of these devices, it will take a lot of time, money, and labor to fix it. To make the network work smart and uninterrupted, you need a special sensor device that monitors the operation of measuring devices and transmits data to a new online monitoring system.
"A review of the existing literature on this topic showed that the issue of network monitoring using phase measuring devices is practically not studied. With such devices, there is a large error in the data they transmit to the network monitoring system. We have proposed a new method for detecting data errors with phase sensors using binary logistic regression. This technology allows you to prevent critical situations in the network, for example, loss of energy. We have taken a new step in the development of smart grids and their monitoring system, " Irina Kirpichnikova emphasized.
The problem of high-quality operation of electrical networks exists in all power systems of the world. Scientists from different parts of the world took part in the study, so we can say that the use of the work results will become the starting point for the further development of the direction of intelligent monitoring of networks in the electric power industry of different countries.
Computer simulation as a tool for optimizing sensor performance
The scientists' work was theoretical, using computer modeling to create an optimization algorithm and binary logistic regression, a special statistical model for predicting probability, based on intelligent wireless sensors.
"Binary Logistic Regression has been developed based on a group of smart wireless sensors. A system of sensors integrated into the points of the phase measuring devices can quickly detect faults and report them to the control center. This technology is much more efficient than existing systems. It works in a wider range of studied networks and consumes less energy, both for transmission systems and energy distribution systems," Irina Kirpichnikova commented.
Scientists have already mapped out directions for future work, and they believe that the developed binary logistic regression system can be extended to large-scale electrical systems to monitor network performance in real-time and to monitor large areas.
The main problem of future work is to locate a group of sensors and separate them in places where blocks of phase measuring devices will be installed.
SUSU is a participant of Project 5-100 aiming to enhance the competitiveness of Russian universities among the world's leading research and academic centers.
More information:
doi.org/10.3390/en13153974
Provided by South Ural State University
