As urban power grids continue to expand, ensuring the safe operation of high-voltage (HV) cables has become increasingly critical. A recent study led by Yuhao Ai from the Hubei Key Laboratory of Power Equipment & System Security for Integrated Energy at Wuhan University has made significant strides in diagnosing a previously overlooked defect in HV cables: the reverse connection between the inner core and the outer shield of coaxial cables.
In urban environments, HV cables, particularly those rated at 110 kV and above, are essential for efficient energy transmission and maintaining the aesthetic integrity of the landscape. However, as these cables operate, electromagnetic induction can create induced voltages that vary based on core currents and cable lengths. “The induced voltage can dramatically affect the stability of the power grid, leading to potential outages and financial losses,” Ai notes, emphasizing the urgency of addressing these issues.
The research highlights that while many studies have focused on defects like open circuits in sheath loops or flooding in cross-bonded link boxes, the reverse connection defect has not received adequate attention. This oversight can lead to significant increases in sheath currents, potentially endangering the operational integrity of the entire power system. To tackle this, Ai and his team conducted a thorough theoretical analysis and developed a simulation model, revealing that sheath current amplitudes vary considerably under different reverse-connection conditions.
Utilizing this data, the researchers constructed a feature vector based on sheath current amplitudes and applied a support vector machine (SVM) algorithm optimized by the artificial rabbits optimization (ARO) method. This innovative approach achieved an impressive diagnostic accuracy rate of 99.35%. “Our findings demonstrate that with the right tools, we can swiftly and accurately diagnose reverse-connection defects, which is crucial for maintaining grid stability,” Ai explains.
The implications of this research are far-reaching for the energy sector. As the reliance on HV cables grows, so does the necessity for advanced diagnostic methods. The ability to quickly identify and rectify faults will not only enhance the reliability of power systems but also mitigate the economic impacts associated with outages. The study’s findings could pave the way for more comprehensive fault identification models in the future, potentially integrating machine learning and deep learning techniques that are currently underutilized in this field.
Published in the journal Sensors, this research represents a significant leap forward in cable fault diagnosis, addressing a critical gap in the current literature and offering practical solutions for energy professionals. As the demand for reliable energy solutions continues to rise, innovations like those introduced by Ai and his team will be instrumental in shaping the future of power transmission and distribution systems.
