The low voltage cables in the nuclear power plant have the same importance as veins have in the human body. Since, the cables inside the containment are under a number of environmental stress, out which the thermal and radiation stresses are important and become more effective with the presence of oxygen. These stresses cause the degradation of the insulation and hence may lead to insulation failure. To study the behavior of the cables under the nuclear power plant environmental stress, the cables are subjected to accelerated aging tests in laboratories which helps in determining their withstand capacity and degradation behavior in the harsh thermal-radiation environment. In this paper, the accelerated aging tests conditions are discussed with the focus on the role of certain uncertainty factors which effect the aging markers during the condition monitoring of the insulation material of cable after the accelerated aging tests. This review has been put into a framework to better understand the aging process in the low voltage nuclear power plant cables. In the conclusion part, some future directions in the field of the thermal-radiation multi-stress accelerated aging tests and condition monitoring techniques are also identified.

This paper discusses the effect of thermal stress on the electrical properties, conduction and polarization, of low-voltage CSPE/XLPE-based power cables used in a nuclear power plant. The cables were subjected to an accelerated thermal stress at 120°C for an equivalent service period of 20, 40, 60 and 80 years. The capacitance, tanδ, resistivity at different frequencies and extended voltage response were the methods used for the investigation. A significant variation in the values of tanδ at low frequency, i.e., at 100 Hz, was observed. For the extended voltage response measurement, the decay voltage slope increased, showing the increase in the conduction particles inside the insulation material. While the decrease in return voltage slope showed that the slow polarization processes occurring at very low frequencies were decreased with aging. A strong correlation between the tanδ and decay voltage slope was also observed, which increased as the insulation aging increased. The results showed that the methods could be used for the in situ measurement of conduction and polarization phenomenon in the low-voltage cables, and hence helping in the assessment of the condition of cable insulation.

In a nuclear power plant environment, low-voltage cables experience different stresses during their service life which challenge their integrity. A non-destructive and reliable condition monitoring technique is desired to determine the state of these low-voltage cables during service and for the life extension of nuclear power plants. Hence, in this research work, an EPR/CSPE-based low-voltage cable was exposed to γ-rays for five different absorbed doses. The overall behavior of the cable under stress was characterized by frequency and time domain electrical measurements (capacitance, tan δ, and Extended Voltage Response) and a mechanical measurement (elongation at break). Significant variations in the electrical parameters were observed, as was a decline in the elongation at break values. A strong correlation between the measurement methods was observed, showing the ability of the electrical methods to be adopted as a non-destructive condition monitoring technique.

In this paper, the effect of radiation stress on the overall degradation of jacket and insulation of low voltage unshielded power cable used in nuclear power plants is discussed. The XLPE insulated and CSPE jacketed cable samples were exposed to a total dose of 400 kGy γ-irradiation with 0.5 kGy/h dose-rate. Dielectric spectroscopy; complex permittivity, and extended voltage response; namely slopes of decay and return voltages were used to study the effect of the irradiation on the cable with an aim to establish the application of these techniques for a non-destructive condition monitoring of the unshielded cables. Shore D hardness was also used to investigate the mechanical characteristic of the cable. It was observed that the radiation stress affected the electrical and mechanical properties of the cable polymeric components i.e. insulation and jacket. The imaginary part of permittivity at low frequency; 100 Hz, the slope of return voltage, and hardness showed a significant increase with absorbed dose showing the effect of irradiation on the jacket and insulating material, as well. A strong correlation was observed between the imaginary part of permittivity at 100 Hz, slopes of return voltage, and hardness. The results show the dielectric measurement techniques are potential non-destructive condition monitoring methods for the unshielded low voltage nuclear power plant power cables for on-site diagnosis.

Islanded microgrids, powered by renewable energy sources, offer a sustainable electricity solution for remote areas. However, maintaining frequency stability in these systems remains a challenge due to the intermittent nature of renewables. This research proposes an approach to enhance microgrid stability by integrating a green hydrogen energy storage system (GHESS) and employing advanced AI-based control strategies. The GHESS plays a pivotal role in storing excess renewable energy as hydrogen and then converting it back to electricity when needed, reducing reliance on traditional backup generators. To optimize microgrid performance, a hybrid single-neuron PID (SNPID) controller, augmented by machine learning techniques, is developed and compared against conventional proportional, integral, and derivative PID and fuzzy self-tuning PID (FSTPID) controllers. The system’s performance was evaluated using four realistic scenarios. In all cases, the SNPID controller significantly outperformed the existing options. It achieved a 58% reduction in frequency fluctuations compared to the fuzzy self-tuning PID (FSTPID) controller and an impressive 87% reduction compared to the traditional PID controller. The Simulation results underscore the SNPID controller’s exceptional performance in frequency stability, emphasizing the transformative potential of AI for microgrid management.