An effective VAR planning to improve dynamic voltage profile of distribution networks with distributed wind generation

This paper proposes an effective VAR planning based on reactive power margin for the enhancement of dynamic voltage stability in distribution networks with distributed wind generation. The analysis is carried over a distribution test system representative of the Kumamoto area in Japan. The detailed mathematical modeling of the system is also presented. Firstly, this paper provides simulation results showing the effects of composite load on voltage dynamics in the distribution network through an accurate time-domain analysis. Then, a cost-effective combination of shunt capacitor bank and distribution static synchronous compensator (D-STATCOM) is selected to ensure fast voltage recovery after a sudden disturbance. The analysis shows that the proposed approach can reduce the size of compensating devices, which in turn, reduces the cost. It also reduces power loss of the system.

Dynamic load variation and stability analysis in distribution networks with distributed generators

In this paper, the modeling of the distribution network is done in a different way where the distributed generator and dynamic loads are considered. Based on this modeling, this paper presents an analysis to investigate the dynamic and static load variation effect on the distribution network. Graphical interface industry software is used to conduct all the aspects of model implementation and carry out the extensive simulation studies. Here also focuses on the worst case scenario and the different fault effect on the generator. Finally, this paper presents the voltage profile for different penetration with different network configurations.

A dynamic time warped clustering technique for discrete event simulation-based system analysis

Abstract This paper introduces a novel approach for discrete event simulation output analysis. The approach combines dynamic time warping and clustering to enable the identification of system behaviours contributing to overall system performance, by linking the clustering cases to specific causal events within the system. Simulation model event logs have been analysed to group entity flows based on the path taken and travel time through the system. The proposed approach is investigated for a discrete event simulation of an international airport baggage handling system. Results show that the method is able to automatically identify key factors that influence the overall dwell time of system entities, such as bags that fail primary screening. The novel analysis methodology provides insight into system performance, beyond that achievable through traditional analysis techniques. This technique also has potential application to agent-based modelling paradigms and also business event logs traditionally studied using process mining techniques.

Nonlinear large deformation dynamic analysis of electroactive polymer actuators

Electroactive polymers have attracted considerable attention in recent years due to their sensing and actuating properties which make them a material of choice for a wide range of applications including sensors, biomimetic robots, and biomedical micro devices. This paper presents an effective modeling strategy for nonlinear large deformation (small strains and moderate rotations) dynamic analysis of polymer actuators. Considering that the complicated electro-chemo-mechanical dynamics of these actuators is a drawback for their application in functional devices, establishing a mathematical model which can effectively predict the actuator's dynamic behavior can be of paramount importance. To effectively predict the actuator's dynamic behavior, a comprehensive mathematical model is proposed correlating the input voltage and the output bending displacement of polymer actuators. The proposed model, which is based on the rigid finite element (RFE) method, consists of two parts, namely electrical and mechanical models. The former is comprised of a ladder network of discrete resistive-capacitive components similar to the network used to model transmission lines, while the latter describes the actuator as a system of rigid links connected by spring-damping elements (sdes). Both electrical and mechanical components are validated through experimental results.

Managerial entrenchment and firm value: a dynamic perspective

We examine the impact of managerial entrenchment on firm value using a dynamic model with firm fixed effects. To estimate the model, we employ the long-difference technique, which is shown by our simulation to deliver the least biased estimates. Based on a large sample of U.S. companies, we document a significantly negative and causal effect of managerial entrenchment on firm value after taking into account omitted variables, reverse causality, and highly persistent endogenous variables. Additional analysis suggests that the causality running from managerial entrenchment to firm value is more pronounced than that for reverse causality.

A predictive framework for modeling healthcare data with evolving clinical interventions

Medical interventions critically determine clinical outcomes. But prediction models either ignore interventions or dilute impact by building a single prediction rule by amalgamating interventions with other features. One rule across all interventions may not capture differential effects. Also, interventions change with time as innovations are made, requiring prediction models to evolve over time. To address these gaps, we propose a prediction framework that explicitly models interventions by extracting a set of latent intervention groups through a Hierarchical Dirichlet Process (HDP) mixture. Data are split in temporal windows and for each window, a separate distribution over the intervention groups is learnt. This ensures that the model evolves with changing interventions. The outcome is modeled as conditional, on both the latent grouping and the patients' condition, through a Bayesian logistic regression. Learning distributions for each time-window result in an over-complex model when interventions do not change in every time-window. We show that by replacing HDP with a dynamic HDP prior, a more compact set of distributions can be learnt. Experiments performed on two hospital datasets demonstrate the superiority of our framework over many existing clinical and traditional prediction frameworks.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

Time cumulative complexity modeling and analysis for space-based networks

In this paper, the notion of the cumulative time varying graph (C-TVG) is proposed to model the high dynamics and relationships between ordered static graph sequences for space-based information networks (SBINs). In order to improve the performance of management and control of the SBIN, the complexity and social properties of the SBIN's high dynamic topology during a period of time is investigated based on the proposed C-TVG. Moreover, a cumulative topology generation algorithm is designed to establish the topology evolution of the SBIN, which supports the C-TVG based complexity analysis and reduces network congestions and collisions resulting from traditional link establishment mechanisms between satellites. Simulations test the social properties of the SBIN cumulative topology generated through the proposed C-TVG algorithm. Results indicate that through the C-TVG based analysis, more complexity properties of the SBIN can be revealed than the topology analysis without time cumulation. In addition, the application of attack on the SBIN is simulated, and results indicate the validity and effectiveness of the proposed C-TVG and C-TVG based complexity analysis for the SBIN.

Control-oriented modeling of a polymeric soft robot

This article describes an efficient control-oriented model of a soft robot made of electroactive polymers. The proposed soft robot is constructed from two flexible links and has a multiphysics dynamic model consisting of both an electrochemical and electromechanical model. The electrochemical model is based on a distributed RC line approach, and the electromechanical model, considering the continuum vibration of the robot, is derived based on Hamilton's principle. The governing equation of the soft robot is solved by means of the Rayleigh-Ritz-Meirovitch substructure synthesis method, and the Laplace operator is used to obtain the transfer function of the soft robot as a 2 by 2 multiple-input multiple-output system.