Importance of predicting failures in the generation, transmission, and distribution of electricity. By Norma Martinez
The occurrence of faults in distribution networks is the main factor that prevents the adequate supply of electric power. Predicting spots’ accurate and rapid location is essential for reliability, immediate restoration, optimal consumption, and customer satisfaction.
Power grid faults
Power system operators have the primary task of locating faults in the grid. These can cause various problems such as damage to network devices, service interruption, and network instability, reducing network reliability.
The result of grid faults is reflected in financial losses for customers and utilities. Currently, traditional methods of locating faults in the feeders of distribution networks are not effective, especially in those that cover a wide geographical distribution.
Getting a network to cover a large area is costly because of the workforce and devices required for repairs. Consequently, predicting and locating faults automatically and quickly is indispensable.
Causes of faults in the electrical network
The leading causes of faults in electrical distribution networks are lightning, insulation defects, sabotage, tree branches, and animals, which cause a short circuit.
Many faults are transitory and resolved without loss of supply or with a minimum interruption time. On the other hand, persistent defects cause more prolonged interruptions and are determined after the detection and location of the fault.
Predicting network faults
By predicting faults, historical data can be analyzed and extracted to indicate the future absence or current presence of a spot in the system. Thus, pattern recognition methods and machine learning algorithms are of great importance.
There are several methods to measure the distance of faults in transmission networks in the electricity sector. But, they cannot use them to locate spots in distribution networks due to the structural differences between the distribution network compared to the transmission network.
Traditional prediction methods in electricity
There are impedance methods, differential equations, traveling waves, fault indicators and magnetic sensors, protection coordination and current analysis, state estimator, and artificial intelligence methods. However, all of them present some problems.
The main problem of using impedance methods in distribution networks is the multiple response problem. On the other hand, differential methods require connection links with high bandwidth, a high sampling rate, and fast and accurate data synchronization.
Traveling wave methods have a complicated structure and require a high sampling rate. Also, the use of magnetic sensors and fault indicators is not cost-effective due to the size of the distribution network.
The drawback of the methods of coordination of protections and current analysis is interference in the detection of the main fault section. It occurs when the fault resistance is high and affects the existing domain. It affects performance as a result.
The need for much sampling and accurate line parameters and complexity are disadvantages of state estimation methods. Finally, the drawback of intelligent methods is the need for extensive and precise data banks that must be updated when a small change is applied to the network.
Automated power grid
Today, electricity providers have to supply businesses and industries around the world with equipment, systems, and services. In order to provide them with service reliably and efficiently, from their point of generation to the end consumers.
An automated grid is capable of meeting the challenges of the energy transition. It does that by enabling the safe and reliable connection of renewable and distributed energy resources to the grid.
Energy providers need to rely on digital transformation by integrating energy processes and technologies. By fostering the use of the cloud to connect products, controls, software, and service; they will support the management of solutions for the entire lifecycle.
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Power Grid Perspectives
The life of the power grid in homes, buildings, data centers, businesses, and industries will increase in functionality if a digital twin supports the design, construction, operation, and maintenance.
As energy drivers, suppliers are focusing their technological innovations on high-efficiency electricity transmission and storage; generating power with even lower or zero emissions, and reducing CO2 emissions in industrial processes.
Similarly, suppliers are digitalizing energy to decarbonize energy systems. They are starting to offer digital portfolios that boost their customers’ business while protecting them with the best possible security through customized solutions to combat cyber threats.
The introduction of automatic fault prediction and location systems will greatly support organizations in saving time and human resources; it will also improve the system, prevent failures, schedule events, and, finally, lighten the economic factors.
