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THE PURPOSE. A study of the influence of coordinated control of electricity storage devices and distributed generation installations on the operating modes of a network cluster, which is an electrical distribution network connected to the AC railway power supply system through a direct current insert (DCI).METHODS. The studies were carried out using a simulation model implemented in the MATLAB system.RESULTS. The following disturbing influences were considered: turning off the main power supply of the network cluster from the DCI side for 0.5 seconds; three-phase short circuit with a duration of 0.5 s at the end of a 6 kV cable line with a length of two kilometers. The simulation results showed the effectiveness of coordinated control of electricity storage devices and a turbogenerator with self-adjusting predictive controllers. On this basis, the values of overshoot of the rotor speed are reduced and the transient process time is reduced for all parameters in various modes. For example, the transient time for the generator rotor speed in the mode of connecting an additional load is reduced by three times, and for voltage - by two times.CONCLUSION. Based on computer modeling, it is shown that coordinated control of electricity storage devices and a turbogenerator with predictive regulators reduces the amount of rotor speed overshoot to almost zero and reduces the transient process time by almost 90%. It is advisable to conduct further research on the application of the method of coordinated control of active elements used in the power supply system to regulate the mode parameters.

A tree topology is used to construct to construct a Zigbee networks practices by wireless sensor network for data delivery applications. There are 3 types of nodes in zigbee networks; coordinator, router and mobile end devices. Coordinator performs the initialization and maintenance functions in the network. A router is responsible for routing data between the coordinator and mobile end device. In-order to avoid the delivery failures occurs due to node movements and network topology changes, the existing system collect and analyze data about device movement and gives Zigbee node deployment and tree construction framework, which uses three algorithms ZND(zigbee node deployment), ZCD(zigbee coordinator decision) and ZTC(zigbee tree construction). In the proposed system we improve the data delivery by introducing the capacity calculation. If any two nodes have same number of indegree or out-degree, we select the node with maximum capacity. [ABSTRACT FROM AUTHOR]

A measure of distance between two clusterings has important applications, including clustering validation and ensemble clustering. Generally, such distance measure provides navigation through the space of possible clusterings. Mostly used in cluster validation, a normalized clustering distance, a.k.a. agreement measure, compares a given clustering result against the ground-truth clustering. The two widely-used clustering agreement measures are adjusted rand index and normalized mutual information. In this paper, we present a generalized clustering distance from which these two measures can be derived. We then use this generalization to construct new measures specific for comparing (dis)agreement of clusterings in networks, a.k.a. communities. Further, we discuss the difficulty of extending the current, contingency based, formulations to overlapping cases, and present an alternative algebraic formulation for these (dis)agreement measures. Unlike the original measures, the new co-membership based formulation is easily extendable for different cases, including overlapping clusters and clusters of inter-related data. These two extensions are, in particular, important in the context of finding communities in complex networks. [ABSTRACT FROM AUTHOR]

Abstract: The problem of dividing a network into communities is extremely complex and grows very rapidly with the number of nodes and edges that are involved. In order to develop good algorithms to identify optimal community divisions it is extremely beneficial to identify properties that are similar for most networks. We introduce the concept of modularity density, the distribution of modularity values as a function of the number of communities, and find strong indications that the general features of this modularity density are quite similar for different networks. The region of high modularity generally has very low probability density and occurs where the number of communities is small. The properties and shape of the modularity density may give valuable information and aid in the search for efficient algorithms to find community divisions with high modularities. [Copyright &y& Elsevier]