Research on synchronous method for photovoltaic supplied micro-grid based on small-world network model

Considering the problems of potentially generating a disconnected network in the WS small-world network model [Watts and Strogatz, Nature393, 440 (1998)] and of adding edges in the NW small-world network model [Newman and Watts, Phys. Lett. A263, 341 (1999)], we propose a novel small-world network model. First, generate a regular ring lattice of N vertices. Second, randomly rewire each edge of the lattice with probability p. During the random rewiring procedure, keep the edges between the two nearest neighbor vertices, namely, always keep a connected ring. This model need not add edges and can maintain connectivity of the network at all times in the random rewiring procedure. Simulation results show that the novel model has the typical small-world properties which are small characteristic path length and high clustering coefficient. For large N, the model is approximately equal to the WS model. For large N and small p, the model is approximately equal to the WS model or the NW model.

The concept of small world phenomenon has been observed and applied in many types of networks. This paper presents a study of two small-world network models in clustering formation and routing in wireless network, the Watts and Stragtz’s (WS) and the Newman and Watts (NW) models and evaluates their properties in term of average node degree and path length. We tested the small world phenomenon in wireless networks by simulating a wireless mobile ad hoc network with distributed routing protocols. We present the research challenges of bridging the gap between theoretical models and practical wireless network implementation. This study provides insights on how wireless networks behave under small world network models with distributed routing protocols.

The characteristics of the propagation of forest fires under the influence of firebrands and the interaction zone (lx, ly) due to radiation are examined using the model of Small World Network. We analyze the distribution of the connections in a Small World Network and the cluster coefficient that represent the mathematical properties of the network. The used model is a stochastic model for predicting the behavior of wildfires. It is a variant of the social Small World Network, initially proposed by Watts and Strogatz, which allows the creation of more clusters and connections over long distances. This model was successfully applied to the spread of diseases and is characterized by a strong performance in clusters and a Poisson distribution of connections. The Model of Small World Network has also been adapted to study the spread of forest fires where it can include connections beyond nearest neighbors due to radiation from the flames or fire surges induced by firebrands which other propagation models cannot. It has been validated by experimental results of real fires. The main goal of this paper consists to investigate the most robust measures of network topology for a heterogeneous and∕or homogeneous system near the percolation threshold.

The anti-interference characteristics of the neural network have a key impact on its information processing ability in complex environments. Most of the existing research focuses on small-scale networks and simplified models, and there is still a lack of systematic discussion on the influence mechanism of large-scale network expansion and topological complexity. In this study, a large-scale neural network model with different topologies is constructed to explore the influence mechanism of network size and connection complexity on the anti-disturbance characteristics. The optimal synchronization characteristics of complex NW small-world networks under noise interference are revealed, which provides a theoretical reference for the topology design and anti-interference ability improvement of artificial neural networks. Based on the Hodgkin-Huxley neuron dynamics model and Leonid chemical synapse theory, a complex Newman-Watts (NW) small-world network model containing 500 neurons is established for the first time, and the dynamic response characteristics of three topologies of simple ring network, simple NW small-world and complex NW small-world under music noise interference are compared and analyzed. The signal synchronization of the network is quantitatively evaluated by Pearson correlation, and the variation law of the anti-interference performance of different topologies is systematically revealed when the scale of the neural network is expanded from 100 to 500 neurons. The research shows that the expansion of network size and the increase of topological connection complexity can significantly enhance the anti-interference performance of neural network. Among them, the complex NW small-world network performs best in the noise interference environment, and the correlation coefficient increases significantly at the scale of 500 neurons. In this study, the network scale is extended to 500 neurons for the first time. By constructing a complex NW small-world topology, the influence of scale expansion and connection complexity improvement on the network anti-interference performance is systematically quantified, which provides reference simulation data for the simulation research of artificial neural networks.

Simulating wildfire patterns using a small-world network model

The spreading of faults and serious failures in many large-scale engineering systems is today one of the issues of the highest concern. This paper presents a novel method for modeling the fault propagation behaviors from the perspective of complex network theory. By focusing essentially on the topological structure properties of the underlying system network, several principles that are capable of assessing the safety characteristics of the network nodes are proposed. Rather than the probabilistic approach, the fault propagation intensity considering the network statistical information is defined as the weight of the link between the nodes. Subsequently, the critical nodes and the fault propagation paths with high risk are obtained through the qualitative fault propagation analysis. A case study of sym-tetramethyl benzene (STBZ) production system is provided to illustrate the feasibility of the proposed approach, and to verify the efficiency to discover the system vulnerability. Research shows that the safety performance of a complex system is indeed affected by the availability of some critical nodes, which is a helpful method to the safety assessment and failure prevention.

Two characters, scale-free and hierarchical structure, are shown in lots of real systems all over the world. In this article, firstly, we build up a class of network models with hierarchical structure, denoted by N h (t), and prove it to be scale-free. By computing average degrees, we find these network models are all sparse. Secondly, based on their special topological structure, we research analytically average distance of each model belonging to N h (t) and present an iteration computational method of average distance corresponding to every one in N h (t). Finally, we discuss the affect by changing some tunable parameters to average distance in the set N h (t), and compare our hierarchical network models with previous ones, and show some meaning problems for gaining more universal methods of computing average distance in hierarchical networks.

With the widespread application of intelligent systems in major industries, the traditional equipment manufacturing industry has gradually shifted to advanced equipment manufacturing. The innovation network of an industry directly affects the innovation capability of the industry, so the research of innovation network has always been the focus of attention. However, there are few researches on the innovation network of advanced equipment manufacturing industry, so this article has launched an in-depth study on it. First, it analyzes the basic geometric characteristics of the innovation network. Then, based on the small-world network model, an evolution model of the advanced equipment manufacturing innovation network was created, and simulation experiments were performed on the evolution model. The experimental results show that the clustering coefficient and the average distance showed different changes in different stages, but in the end they all gradually stabilized. The clustering coefficient was 0.484, the average distance was 1.854, and the degree of small cosmos was 0.497. This shows that the innovation network evolution model established in this study can reveal the evolution process of the innovation network of advanced equipment manufacturing through simulation experiments.

A small-world network model is proposed to predict wildfire behavior near critical propagation conditions. It includes long-range connections due to flame radiation and a weighting procedure based on times of degradation and combustion of vegetation. Scaling laws are found universal near the propagation transition. For isotropic propagation, the critical behavior of the usual percolation theory is recovered, whereas, for anisotropic conditions due to wind and/or topography, propagation is fractal with a dimension of 1.3 and a critical exponent of the correlation length of 1.7, following the extrapolation for Euclidian systems. The exponent value agrees well with that of directed percolation.

This Paper proposed the concept of industry-university symbiotic networks based on the analysis of the network and symbiotic characteristic of the cooperation of industry, universities and research institutions. The information, material and energy exchange rate, the degree of concentration and the generation and distribution mechanism of the symbiotic energy of the industry-university symbiotic networks were analyzed. We pointed out that increasing the scale of the network, optimizing the symbiotic interface, necessarily breaking and re-bonding the links and promoting the distribution of the symbiotic energy were the most fundamental means of enhancing the ability of industry-university symbiotic networks. At last this paper drew a path map of cultivating the industry-university symbiotic networks.

Evolution prediction method for electric private car ownership considering the decision-making behaviour of consumers

Clean energy vehicles (CEVs) such as EV or PHV should be promoted for the reduction of greenhouse gas emissions in the transportation sector. However, it is important to note that social interactions affect the promotion of CEVs. Therefore, a multiagent simulation system for promoting CEVs has been developed in the present study from the perspective of not only concern for the environment but also for local interaction processes in terms of social conformity. Intention of holding CEV is described with the database of the questionnaire survey about purchasing vehicles. The social network of the artificial society is described as a small-world network model. The time series changes of the numbers of clean energy vehicles and the volume of greenhouse gas emissions are estimated by the proposed multiagent simulation. Finally, it can be concluded that the proposed multiagent simulation with local interaction is useful for promoting the planning of CEVs.

Many complex systems exhibit large fluctuations both across space and over time. These fluctuations have often been linked to the presence of some kind of critical phenomena, where it is well known that the emerging correlation functions in space and time are closely related to each other. Here we test whether the time correlation properties allow systems exhibiting a phase transition to self-tune to their critical point. We describe results in three models: the 2D Ising ferromagnetic model, the 3D Vicsek flocking model and a small-world neuronal network model. We demonstrate that feedback from the autocorrelation function of the order parameter fluctuations shifts the system towards its critical point. Our results rely on universal properties of critical systems and are expected to be relevant to a variety of other settings.

With increasing solar photovoltaic-based power generation, a photovoltaic emulator (PVE) is necessary to experimentally validate new control strategies without the influence of external factors such as irradiance and temperature. However, two significant challenges with PVEs are (i) solving the nonlinear equation of photovoltaic (PV) panel and (ii) oscillations in constant current (voltage) region with voltage (current) control. Thus, in this paper, a PVE with the ability to mimic both uniformly irradiated and partially shaded PV panels is proposed by employing artificial neural network (ANN) and piecewise-linearization technique. Based on the input operating conditions (irradiance, temperature, and partial shading), the ANN breaks the nonlinear I-V curve into piecewise-linear segments and outputs their boundary points. Then, with these boundary points, piecewise-linear equations of the segments relating PVE’s voltage and current are formed. Subsequently, using these piecewise-linear equations, the reference PVE voltage corresponding to PVE’s output current is calculated and given to the PI controller of a synchronous buck converter to mimic a PV panel. Thus, the proposed PVE overcomes the problem of solving nonlinear I-V equation by piecewise linearization which in turn aids an impedance-matching technique to mitigate the aforementioned oscillations. The generation of training data and development of ANN were carried out in MATLAB. Finally, the simulation studies performed in MATLAB/Simulink and hardware experiments validated the steady-state accuracy and the transient response which settled within 10 ms endorsing the real-time application of the proposed PVE.

This study examined the application of network theory to skilled passing behaviour in a team sport by considering small-world and scale-free network models. Using data obtained from a 2006 soccer game in Japan between Japan and Ghana, we counted the number of passes by each player within 5-minute intervals. The structural properties of the passing behaviour, which included a characteristic path length and clustering coefficient, and the degree of distribution were analysed. This showed that the structural property of the passing behaviour represented neither a complete graph nor a random graph; rather, it reflected a small-world or scale-free network. In addition, the probabilities of outgoing and incoming passes reflected links that followed a power-law distribution. Passing behaviour in a soccer match appeared to be similar to behaviour in social networks with smaller vertices in terms of the scale-free property and a self-organising mechanism.