In this study, three kinds of radical scavenger Chimassorb 944, Tinuvin 622, and Tinuvin 770 are used to suppress the growth of electrical trees in cross‐linked polyethylene (XLPE) with transient superimposed voltage under the temperature gradient. The tree morphology, tree length, accumulated damage, and time to breakdown are used to investigate the effect of radical scavenger on the electrical treeing process. It is found that under the temperature gradient caused by the temperature rise on the high voltage side, only Tinuvin 622 can always suppress the electrical tree as the temperature gradient rises. Under the temperature gradient caused by the temperature rise on the ground side, the three radical scavengers can all suppress the electrical tree. The breakdown of electrical tree exhibits the strong DC polarity dependence. Meanwhile, energy levels of these three radical scavengers are calculated through quantum chemistry, and the results indicate that radical scavengers have greater electron affinity, smaller ionisation energy, and smaller energy gap than XLPE. According to the surface potential decay test results at 60°C, it is found that all three radical scavengers can introduce deep traps. The different performances of radical scavengers under different temperature gradients and voltages are determined by the trap distribution characteristics, the molecular structure and chemical reaction of the additives themselves. It is concluded that Tinuvin 622 has potential for use in high voltage direct current XLPE cable application.

With the expansion of power grids and the growth of renewable energy generation, more uncertainties of topology (e.g. components outages) and injection (e.g. renewable energy outputs variations) need to be analyzed. As a result, large numbers of system states have to be evaluated by optimal power flow (OPF) and this brings a significant challenge to the efficiency of reliability assessment. To address this, a Lagrange-multiplier-based reliability assessment method (LM-T&I) is proposed to accelerate the evaluation of system states. The Lagrange-multiplier-based function is constructed to obtain the optimal load shedding of topology changes and injection variations, avoiding time-consuming OPF computations. Moreover, combined with the impact-increment-based state enumeration method (IISE), the computational efficiency can be further improved. Case studies are conducted on the RTS-79 and IEEE 118-bus systems. The scalability of the LM-T&I method is verified on the practical Brazilian system. The results demonstrate that the LM-T&I method has a superior performance on the reliability assessment compared with traditional methods.

With the integration of multiple types of loads and renewable generations, the number of system states significantly grows. As a result, running optimal power flow (OPF) to analyze a myriad of system states is challenging and this seriously restricts the efficiency of the state enumeration method. To address that, this paper proposes a Lagrange Multiplier based State Enumeration (LMSE) approach to accelerate the analysis without loss of accuracy. The core idea is to directly obtain the optimal load shedding of contingency states by Lagrange multiplier-based functions, rather than the time-consuming OPF algorithms. This approach can also be conveniently integrated with the impact-increment method and the clustering technique for further efficiency enhancement. Case studies are performed on the RTS-79 and IEEE 118-bus systems considering multiple types of loads, photovoltaics (PVs), and wind turbines (WTs). Results indicate that the proposed method can significantly reduce the computing time without compromising the calculation accuracy.

This paper proposes an incremental-variable-based state enumeration method (IVSE) for power system operation risk assessment and develops the risk indices considering the safety margin. Firstly, the traditional risk indices are modified to take into account the safety margin, which is defined as the virtual distance from the operational limits to the current normal operating point. Those indices can provide more potential risk information to operators for reasonable decision-making. Thereafter, an incremental-variable-based state enumeration is developed by utilizing the relationship between high order contingencies and low order contingencies. The proposed method is more efficient and accurate than traditional methods, which makes it more suitable for online application. Finally, case studies are performed on the IEEE-118 bus system. And results verify the accuracy and efficiency of the proposed method in various situations. In addition, it also can diagnose the weak points of the system, which can assist operators in online decision making.

The increasing penetration of intermittent renewable generators and the uncertainty of loads have brought significant challenges to the power system reliability assessment, as large numbers of optimal power flow (OPF) tasks need to be repetitively solved considering these uncertainties. To deal with that, this paper proposes a state-similarity-based (SS) approach to replace the computationally demanding AC OPF. It is observed that the active constraints related to the least load curtailment typically remain consistent across varying system states. Following this, the active constraints are used to represent the state similarity, and then nonlinear equations are derived to replace the original AC OPF problems. Thereafter, an alternating iterative approach is developed to obtain the minimal load curtailment instead of calculating the time-consuming optimization. An optimality criterion is developed to exclude the majority of mismatched solutions, thereby minimizing potential inaccuracies in the reliability assessment. Case studies conducted on the RTS-79, IEEE 118-bus, and Brazilian systems demonstrate that the proposed methods can significantly enhance computational efficiency with minimal errors. In some instances, these methods can outperform traditional reliability assessment methods by over 10 times.

This paper proposes an impact increments-based state enumeration (IISE) reliability assessment approach, specially designed for transmission systems. Firstly, the reliability index calculation formula of the traditional state enumeration technique is transformed into an impact increments-based formation. With the derived formula, the calculation of state probability is simplified and the weight of low order contingency states is increased. Moreover, it is proved in this paper that impact increments of mutually independent high order contingency states, whose outage components are with remote distances, can be eliminated. Due to this fact, the calculation of impacts of most high order contingencies can be reduced without decreasing the precision of reliability indices. When applied to electrical power transmission systems, load variations can also be considered by replacing impact increments by their expectations under various load levels. Thus, annual reliability indices can be obtained by the proposed method. Case studies are performed on the IEEE-118 bus system and a 1354-bus portion of European transmission system from PEGASE project. Results indicate that annual reliability indices can be efficiently obtained with IISE method. Comparing with the traditional state enumeration and Monte Carlo simulation methods, the proposed method is more precise and efficient in various kinds of transmission systems.