جستجوی مقالات مرتبط با کلیدواژه « self-healing » در نشریات گروه « برق »

This study focuses on achieving a self-healing network with high reliability and minimal losses by discussing the optimal placement of distributed generation (DG) sources in the network using the particle swarm algorithm (PSO). Optimal placement not only reduces losses but also enhances the voltage profile and enhances system reliability. Consequently, the paper explores the concept of a self-healing smart grid. Smart grids possess a crucial attribute known as self-healing, designed to enhance the system’s dependability. The self-healing power grid can detect and rectify disruptions within its infrastructure with minimal or even absent human intervention. This study examines a standard IEEE 69-bus network and presents results demonstrating the network's safety, the prevention of blackouts, the reduction of losses, and thesubstantial enhancement of the voltage profile.

The inevitable emergence of intelligent distribution networks has introduced new features in these networks. According to most experts, self-healing is one of the main abilities of smart distribution networks. This feature increases the reliability and resiliency of networks by reacting fast and restoring the critical loads (CLs) during a fault. Nevertheless, the stochastic nature of the components in a power system imposes significant computational risk in enabling the system to self-heal. In this paper, a mathematical model is introduced for the self-healing operation of networked Microgrids (MGs) to assess the risk in the optimal service restoration (SR) problem. Electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) and their stochastic nature besides the distributed generation units (DGs), the ability to reconfiguration, and demand response program are considered simultaneously. The objective function is designed to maximize the restored loads and minimize the risk. The Conditional Value-at-Risk (CVaR) is used to calculate the risk of the SR as one of the most efficient and famous risk indices. In the general case study and considering $\beta $ equal to the 0, 1, 2, 3, and 4, expected values of SR for the risk-averse problem is 21.2, 20, 19.3, 19.1, and 19\% less than the risk-neutral problem, respectively. The formulation of the problem is mixed-integer linear programming (MILP), and the model is tested in the modified Civanlar test system. The analysis of several case studies has proved the performance of the proposed model and the importance of risk management in the problem.

To realize the self-healing concept of smart grids, an accurate and reliable fault locator is a prerequisite. This paper presents a new fault location method for active power distribution networks which is based on measured voltage sag and use of whale optimization algorithm (WOA). The fault induced voltage sag depends on the fault location and resistance. Therefore, the fault location can be found by investigation of voltage sags recorded throughout the distribution network. However, this approach requires a considerable effort to check all possible fault location and resistance values to find the correct solution. In this paper, an improved version of the WOA is proposed to find the fault location as an optimization problem. This optimization technique employs a number of agents (whales) to search for a bunch of fish in the optimal position, i.e. the fault location and its resistance. The method is applicable to different distribution network configurations. The accuracy of the method is verified by simulation tests on a distribution feeder and comparative analysis with two other deterministic methods reported in the literature. The simulation results indicate that the proposed optimized method gives more accurate and reliable results.</span></span></span></div>

Future mobile communication networks particularly 5G networks require to be efficient, reliable and agile to fulfill the targeted performance requirements. All layers of the network management need to be more intelligent due to the density and complexity anticipated for 5G networks. In this regard, one of the enabling technologies to manage the future mobile communication networks is Self-Organizing Network (SON). Three common types of SON are self-configuration, Self-Healing (SH) and self-optimization. In this paper, a framework is developed to analyze proactive SH by investigating the effect of recovery actions executed in sub-health states. Our proposed framework considers both detection and compensation processes. Learning method is employed to classify the system into several sub-health (faulty) states in detection process.  The system is modeled by Markov Decision Process (MDP) in compensation process in which the equivalent Linear Programing (LP) approach is utilized to find the action or policy that maximizes a given performance metric. Numerical results obtained in several scenarios with different goals demonstrate that the optimized proposed algorithm in compensation process outperforms the algorithm with randomly selected actions.

The advent of DG and SEGs has led to fundamental changes in various fields of power system operation. The current paper is aimed to investigate the reliability of SEGs considering DGRs based on the self-healing concept. Due to the emergence of new uncertainties in the power system resulted from the presence of DGRs, this paper is dedicated to comparing network reliability indices before and after the entry of DGRs and analyzing their effect on improving network reliability. To do so, improving the indices based on customer satisfaction, such as reducing the SAIFI, and SAIDI, is evaluated. More specifically, the improvement of the most important index based on load and energy, namely energy not supplied (ENS), is investigated. To do this, the MCS method is used given the pdf of the samples due to the presence of uncertainty created by the presence of DGRs, demanded load change and network restoration time after the presence of DG. Also, after providing an appropriate model for problem analysis, results of applying this model to the case study system are investigated using reliability indices. Subsequently, in order to improve performance of the system, impacts of the changes of various parameters on the given indices are reported. One of the most important points in this regard is to investigate the impacts of the changes in the system configuration on the results. It is observed that self-healing positively affects the reduction of the electrical energy restoration time as well as the system reliability.