Electrical insulation is an integral part of power components. The aging of electrical insulation is an undeniable fact that limits the operational lifetime of power components. Apart from regular aging, abnormal stresses and the development of defects are real threats because of their contribution in accelerating the aging rate and thereby leading to a premature failure of the power components. Over the decades, various studies have been carried out to understand the aging behavior of electrical insulation mainly considering electrical and thermal stresses. Similarly, a number of mathematical (aging) models have been developed based on the theoretical and experimental investigations and evidences. However, a dependable formulation of the models that can provide more practical estimation of the insulation degradation profile has not been achieved yet. This paper presents a comprehensive review of the aging models considering single and multistress conditions. Further, the paper discusses possible challenges and barricades averting the conventional models to achieve a suitable accuracy. Finally, suggestions are provided that can be considered to improve the modeling approaches and their performance.
Already installed cables are aging and the cable network is growing rapidly. Improved condition monitoring methods are required for greater visibility of insulation defects in the cable networks. One of the critical challenges for continuous monitoring is the large amount of partial discharge (PD) data that poses constraints on the diagnostic capabilities. This paper presents the performance comparison of two data acquisition techniques based on phase resolved partial discharge (PRPD) and pulse acquisition (PA). The major contribution of this work is to provide an in-depth understanding of these techniques considering the perspective of randomness of the PD mechanism and improvements in the reliability of diagnostics. Experimental study is performed on the medium voltage (MV) cables in the laboratory environment. It has been observed that PRPD based acquisition not only requires a significantly larger amount of data but is also susceptible to losing the important information especially when multiple PD sources are being investigated. On the other hand, the PA technique presents improved performance for PD diagnosis. Furthermore, the use of the PA technique enables the efficient practical implementation of the continuous PD monitoring by reducing the amount of data that is acquired by extracting useful signals and discarding the silent data intervals.
During the transition towards a low carbon energy system, and as a result of the increasing penetration of renewable energy generation, ancillary services play an important role in ensuring the security and economics of power system operation. An integrated energy service provider (IESP), with the energy coupling and storage devices inside, can flexibly participate in electric energy markets and ancillary service markets. In this paper, a mixed-integer optimization problem is formulated to determine the optimal operating strategy for the IESP with ancillary service provision. First, the mathematical model of a regional integrated energy system (RIES) is established, including energy coupling devices and energy storage devices. Then, an optimization model for the IESP operation is formulated with the objective of maximizing daily operation profit, and the corresponding optimal capacity of ancillary service provision from the IESP is attained. Finally, case studies of an RIES are carried out to demonstrate the feasibility and effectiveness of the proposed method, and the impacts of the IESP with ancillary service provision on operational characteristics and economic benefits are analyzed.
The partial discharge (PD) measurement is an important method used in determining the condition of medium- and high-voltage cable insulation. Considering the propagation velocity of PD signals in power cables is necessary for determining the location of PD defects. However, the determination of velocity is not straightforward due to propagation-related attenuation and dispersion, which distorts the PD pulse waveform. This introduces a degree of uncertainty into the pulse velocity as well as the PD source locations determined based on that velocity, which is usually considered to be of constant value in PD analysis. This paper investigates the accuracy of the pulse propagation velocity measurement in power cables. Tests were performed on a medium voltage power cable in a laboratory setting using two sets of PD-specific measurement equipment: a high-frequency current transformer (HFCT) and an IEC 60270-compliant conventional measurement system. The propagation velocities and their statistical variability were determined using both devices to assess the uncertainty of the propagation velocity measurement. The results indicate that the measured velocity is slightly higher in the case of the HFCT and that the 50% peak threshold value should be used rather than the peak value of PD sensor response waveforms to increase the precision of velocity measurements.
The reliable and economical operation of modern power systems is increasingly complex due to the integration of diverse energy sources and dynamic load patterns. A critical challenge is maintaining the balance between electricity supply and demand within various operational constraints. This study addresses the economic scheduling of generation units using a Mixed Integer Programming (MIP) optimization model. Key constraints considered include reserve requirements, ramp rate limits, and minimum up/down time. Simulations are performed across multiple scenarios, including systems with spinning reserves, responsive demand, renewable energy integration, and energy storage systems. For each scenario, the optimal mix of generation resources is determined to meet a 24 h load forecast while minimizing operating costs. The results show that incorporating demand responsiveness and renewable resources enhances the economic efficiency, reliability, and flexibility of the power system.