Residential voltage dip and swell mitigation using Plug-in Hybrid Electric Vehicle in smart grid

Solutions to remedy the voltage disturbances have been mostly suggested only for industrial customers. However, not much research has been done on the impact of the voltage problems on residential facilities. This paper proposes a new method to reduce the effect of voltage dip and swell in smart grids equipped by communication systems. To reach this purpose, a voltage source inverter and the corresponding control system are employed. The behavior of a power system during voltage dip and swell are analyzed. The results demonstrate reasonable improvement in terms of voltage dip and swell mitigation. All simulations are implemented in MATLAB/Simulink environment.

A phase-locked-loop design for the smooth operation of a hybrid microgrid

A microgrid contains both distributed generators (DGs) and loads and can be viewed by a controllable load by utilities. The DGs can be either inertial synchronous generators or non-inertial converter interfaced. Moreover, some of them can come online or go offline in plug and play fashion. The combination of these various types of operation makes the microgrid control a challenging task, especially when the microgrid operates in an autonomous mode. In this paper, a new phase locked loop (PLL) algorithm is proposed for smooth synchronization of plug and play DGs. A frequency droop for power sharing is used and a pseudo inertia has been introduced to non-inertial DGs in order to match their response with inertial DGs. The proposed strategy is validated through PSCAD simulation studies.

Hybrid multi-channel cooperative spectrum sensing to satisfy channel target

Spectrum sensing of multiple primary user channels is a crucial function in cognitive radio networks. In this paper we propose an optimal, sensing resource allocation algorithm for multi-channel cooperative spectrum sensing. The channel target is implemented as an objective and constraint to ensure a pre-determined number of empty channels are detected for secondary user network operations. Based on primary user traffic parameters, we calculate the minimum number of primary user channels that must be sensed to satisfy the channel target. We implement a hybrid sensing structure by grouping secondary user nodes into clusters and assign each cluster to sense a different primary user channels. We then solve the resource allocation problem to find the optimal sensing configuration and node allocation to minimise sensing duration. Simulation results show that the proposed algorithm requires the shortest sensing duration to achieve the channel target compared to existing studies that require long sensing and cannot guarantee the target.

Beyond actuality : locating an authentic hybridity between heterogeneous media in an installation art practice

Addressing possibilities for authentic combinations of diverse media within an installation setting, this research tested hybrid blends of the physical, digital and temporal to explore liminal space and image. The practice led research reflected on creation of artworks from three perspectives – material, immaterial and hybrid – and in doing so, developed a new methodological structure that extends conventional forms of triangulation. This study explored how physical and digital elements each sought hierarchical presence, yet simultaneously coexisted, thereby extending the visual and conceptual potential of the work. Outcomes demonstrated how utilising and recording transitional processes of hybrid imagery achieved a convergence of diverse, experiential forms. "Hybrid authority" – an authentic convergence of disparate elements – was articulated in the creation and public sharing of processual works and the creation of an innovative framework for hybrid art practice.

An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering

The K-means algorithm is one of the most popular techniques in clustering. Nevertheless, the performance of the K-means algorithm depends highly on initial cluster centers and converges to local minima. This paper proposes a hybrid evolutionary programming based clustering algorithm, called PSO-SA, by combining particle swarm optimization (PSO) and simulated annealing (SA). The basic idea is to search around the global solution by SA and to increase the information exchange among particles using a mutation operator to escape local optima. Three datasets, Iris, Wisconsin Breast Cancer, and Ripley’s Glass, have been considered to show the effectiveness of the proposed clustering algorithm in providing optimal clusters. The simulation results show that the PSO-SA clustering algorithm not only has a better response but also converges more quickly than the K-means, PSO, and SA algorithms.

An efficient hybrid evolutionary optimization algorithm for daily volt/var control at distribution system including DGs

This paper presents a new hybrid evolutionary algorithm based on Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) for daily Volt/Var control in distribution system including Distributed Generators (DGs). Due to the small X/R ratio and radial configuration of distribution systems, DGs have much impact on this problem. Since DGs are independent power producers or private ownership, a price based methodology is proposed as a proper signal to encourage owners of DGs in active power generation. Generally, the daily Volt/Var control is a nonlinear optimization problem. Therefore, an efficient hybrid evolutionary method based on Particle Swarm Optimization and Ant Colony Optimization (ACO), called HPSO, is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. The feasibility of the proposed algorithm is demonstrated and compared with methods based on the original PSO, ACO and GA algorithms on IEEE 34-bus distribution feeder.

A novel hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration with regard to DGs

This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Ant Colony Optimization (ACO) and Simulated Annealing (SA), called ACO-SA, for distribution feeder reconfiguration (DFR) considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. The approach is tested on a real distribution feeder. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving DFR problem.

A hybrid evolutionary algorithm based on ACO and SA for distribution feeder reconfiguration

This paper deals with an efficient hybrid evolutionary optimization algorithm in accordance with combining the ant colony optimization (ACO) and the simulated annealing (SA), so called ACO-SA. The distribution feeder reconfiguration (DFR) is known as one of the most important control schemes in the distribution networks, which can be affected by distributed generations (DGs) for the multi-objective DFR. In such a case, DGs is used to minimize the real power loss, the deviation of nodes voltage and the number of switching operations. The approach is carried out on a real distribution feeder, where the simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for solving the DFR problem.

Distribution harmonic state estimation based on a hybrid PSO and SA algorithm considering parameters uncertainty

This paper presents a new algorithm based on a Hybrid Particle Swarm Optimization (PSO) and Simulated Annealing (SA) called PSO-SA to estimate harmonic state variables in distribution networks. The proposed algorithm performs estimation for both amplitude and phase of each harmonic currents injection by minimizing the error between the measured values from Phasor Measurement Units (PMUs) and the values computed from the estimated parameters during the estimation process. The proposed algorithm can take into account the uncertainty of the harmonic pseudo measurement and the tolerance in the line impedances of the network as well as uncertainty of the Distributed Generators (DGs) such as Wind Turbines (WT). The main feature of proposed PSO-SA algorithm is to reach quickly around the global optimum by PSO with enabling a mutation function and then to find that optimum by SA searching algorithm. Simulation results on IEEE 34 bus radial and a realistic 70-bus radial test networks are presented to demonstrate the speed and accuracy of proposed Distribution Harmonic State Estimation (DHSE) algorithm is extremely effective and efficient in comparison with the conventional algorithms such as Weight Least Square (WLS), Genetic Algorithm (GA), original PSO and Honey Bees Mating Optimization (HBMO) algorithm.

A rule-based hybrid method for anomaly detection in online-social-network graphs

Detecting anomalies in the online social network is a significant task as it assists in revealing the useful and interesting information about the user behavior on the network. This paper proposes a rule-based hybrid method using graph theory, Fuzzy clustering and Fuzzy rules for modeling user relationships inherent in online-social-network and for identifying anomalies. Fuzzy C-Means clustering is used to cluster the data and Fuzzy inference engine is used to generate rules based on the cluster behavior. The proposed method is able to achieve improved accuracy for identifying anomalies in comparison to existing methods.

Shared material on dying

Shared Material on Dying is a trio/solo work commissioned by Jenny Roche and the Dublin Dance Festival in 2008 from choreographer Liz Roche. Touring widely since its creation, it continues to be a rich research environment for the interrogation by Jenny Roche of the dancer’s first-person perspective in choreographic production and performance. The research perspective drawn from the live performance of this iteration was how the exploration of the same dancing moment might be expressed from multiple perspectives by three different dancers and what this might reveal about the dancer’s inner configuring of the performance environment. Erin Manning (2009) describes the unstable and emergent moment before movement materialises as the preacceleration of the movement, when the potentialities of the gesture collapse and stabilize into form. It is this threshold of potentiality that is interesting, the moment before the dance happens when the configuring process of the dancer brings it into being. Cynthia Roses Thema (2007), after neuroscientist Antonio Damasio, writes that embodied experience is mapped as it unfolds and alters from moment to moment in line with a constantly changing internal milieu. As a performer in this piece, I explored the inner terrain of the three performers (myself included) by externalizing these inner states. This research contributed to a paper presentation at the Digital Resources for the Arts and Humanities Conference, UK 2013.

Surface functionalization on the thermal conductivity of graphene–polymer nanocomposites

Exploring thermal transport in graphene-polymer nanocomposite is significant to its applications with better thermal properties. Interfacial thermal conductance between graphene and polymer matrix plays a critical role in the improvement of thermal conductivity of graphene-polymer nanocomposite. Unfortunately, it is still challenging to understand the interfacial thermal transport between graphene nanofiller and polymer matrix at small material length scale. To this end, using non-equilibrium molecular dynamics simulations, we investigate the interfacial thermal conductance of graphene-polyethylene (PE) nanocomposite. The influence of functionalization with hydrocarbon chains on the interfacial thermal conductance of graphene-polymer nanocomposites was studied, taking into account of the effects of model size and thermal conductivity of graphene. An analytical model is also used to calculate the thermal conductivity of nanocomposite. The results are considered to contribute to development of new graphene-polymer nanocomposites with tailored thermal properties.

Structural and material characterisation of insulated rail joints

Insulated rail joints are designed in a similar way to butt jointed steel structural systems, the difference being a purpose made gap between the main rail members to maintain electrical insulation for the proper functioning of the track circuitry at all times of train operation. When loaded wheels pass the gap, they induce an impact loading with the corresponding strains in the railhead edges exceeding the plastic limit significantly, which lead to metal flow across the gap thereby increasing the risk of short circuiting and impeding the proper functioning of the signalling and broken rail identification circuitries, of which the joints are a critical part. The performance of insulated rail joints under the passage of the wheel loading is complex due to the presence of a number of interacting components and hence is not well understood. This paper presents a dynamic wheel-rail contact-impact modelling method for the determination of the impact loading; a brief description of a field experiment to capture strain signatures for validating the predicted impact loading is also presented. The process and the results of the characterisation of the materials from virgin, in-service and damaged insulated rail joints using neutron diffraction method are also discussed.

Supplementary material : large scale read classification for next generation sequencing

Next Generation Sequencing (NGS) has revolutionised molecular biology, resulting in an explosion of data sets and an increasing role in clinical practice. Such applications necessarily require rapid identification of the organism as a prelude to annotation and further analysis. NGS data consist of a substantial number of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. Highly accurate results have been obtained for restricted sets using SVM classifiers, but such methods are difficult to parallelise and success depends on careful attention to feature selection. This work examines the problem at very large scale, using a mix of synthetic and real data with a view to determining the overall structure of the problem and the effectiveness of parallel ensembles of simpler classifiers (principally random forests) in addressing the challenges of large scale genomics.

Smart material interfaces : a vision

In this paper, we introduce a vision called Smart Material Interfaces (SMIs), which takes advantage of the latest generation of engineered materials that has a special property defined “smart”. They are capable of changing their physical properties, such as shape, size and color, and can be controlled by using certain stimuli (light, potential difference, temperature and so on). We describe SMIs in relation to Tangible User Interfaces (TUIs) to convey the usefulness and a better understanding of SMIs.