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Arresters are widely used in power systems to protect other equipment against overvoltages. However, in some conditions, they can’t operate successfully. One of the disturbances leading to the failure of the riser pole arresters is the ferroresonance overvoltages. In this paper, at first the influence of different transformer simulation models of ATP software on the occurrence of ferroresonance is studied and then the effect of ferroresonance on the riser pole arrester has been scrutinized through the thermal and electrical performance of the arrester in an underground distribution system. The results show that the arrester temperature rises due to energy dissipation in a ferroresonance circumstance, which indeed may result into the explosion of the arrester. Also, applying different models of the transformer in the ATP software and comparing the results, it is shown that the available models do not show the same effect on the arrester.

This study investigates finite-time attitude synchronization and tracking control of multiple rigid bodies under event-triggered control strategy in presence of actuator faults and an external disturbance. The event-triggered implementation technique aims to reduce resource utilization in regard to control effort and communication burden. To achieve this aim, the adaptive sliding mode structure is used, and a novel triggering condition is proposed. In presence of unknown actuator faults and external disturbance, it is shown that the multiple rigid bodies track a time-varying attitude of a virtual leader synchronously in finite time under limited data communication. Moreover, a lower bound on the inter-event times has been derived to ensure that the Zeno behavior is avoided. The effectiveness of the proposed method is validated by numerical simulation along with comparison with another relevant research. [ABSTRACT FROM AUTHOR]

In this paper, the state estimation problem for discrete-time jump Markov systems is considered. A minimax filtering technique, interacting multiple model algorithm based on game theory, is developed for discrete-time stochastic systems. Filter performance improvement in presence of model uncertainties, measurement noise, and unknown steering command of the maneuvering target is illustrated. It is shown that the technique presented in this paper has a better performance in comparison with the traditional Kalman filter with minimum estimation error criterion for the case of worst possible steering command of target. In particular, simulation results illustrate the improved performance of the proposed filter compared to Interacting Multiple Model (IMM), diagonal-matrix-weight IMM (DIMM), and IMM based on H∞ (IMMH) filters. [ABSTRACT FROM AUTHOR]

Wireless network sensing and control systems are becoming increasingly important in many application domains due to advent of nanotechnology. The size of a wireless sensor network can easily reach hundreds or even thousands of sensor nodes. Since these types of networks usually have limited battery resources, power consumption optimization for prolonging system lifetime of such networks have received a great attention by the researchers in this field in recent years. In this paper, a centralized approach for clustering and data transmission mechanism is proposed that optimizes the power consumption and hence lifetime of the network. The mechanism is comprised of two phases. In the first phase, a mechanism based on a centralized cluster head selection that utilizes information such as nodes residual energies and their locations in the network is proposed in order to select the most appropriate candidates as cluster heads. In the second phase, the concept of a 'window size' is introduced where minimization of the number of cluster head changes of a node and consequently maximization of the network lifetime is considered. Simulation results validate that the proposed mechanism does effectively reduce data traffic and therefore increases network lifetime. [ABSTRACT FROM AUTHOR]

Summary: Despite power system operators' efforts, blackouts occur; therefore, power systems should be restored. To attain this, power system restoration is done applying different strategies after a complete blackout. The most famous one is the bottom‐up strategy. Determining the islands is of extreme importance in this strategy. Hence, this paper proposes a new version of multi‐objective populated simulated annealing considering the cohesiveness and the quality of islands as objective functions. The proposed method takes into account miscellaneous constraints such as black‐start availability in each island, load‐generation balance, power flow establishment equations, and the availability of synchronization devices. Simulation results for the IEEE 39 and 118‐bus test systems confirm the effectiveness of the proposed method in determining the islands. [ABSTRACT FROM AUTHOR]