Research on reliability index of a large communication network with domain partition and interconnection

In order to indicate the performances of a large-scale communication network with domain partition and interconnection today, a kind of reliability index weighed by normalized capacity is defined. Based on the route rules of network with domain partition and interconnection, the interconnection indexes among the nodes within the domain and among the domains are given from several aspects. It is expatiated on that the index can thoroughly represent the effect on the reliability index of the objective factor and the subjective measures of the designer, which obeys the route rules of a network with domain partition and interconnection. It is discussed that the defined index is rational and compatible with the traditional index.

Partition coefficient prediction of Baker's yeast invertase in aqueous two phase systems using hybrid group method data handling neural network

A hybrid GMDH neural network model has been developed in order to predict the partition coefficients of invertase from Baker's yeast. ATPS experiments were carried out changing the molar average mass of PEG(1500–6000 Da), p H(4.0–7.0), percentage of PEG(10.0–20.0 w/w), percentage of MgSO_4(8.0–16.0 w/w), percentage of the cell homogenate(10.0–20.0 w/w) and the percentage of MnSO_4(0–5.0 w/w) added as cosolute. The network evaluation was carried out comparing the partition coefficients obtained from the hybrid GMDH neural network with the experimental data using different statistical metrics. The hybrid GMDH neural network model showed better fitting(AARD = 32.752%) as well as good generalization capacity of the partition coefficients of the ATPS than the original GMDH network approach and a BPANN model. Therefore hybrid GMDH neural network model appears as a powerful tool for predicting partition coefficients during downstream processing of biomolecules.

Community-detection-based Approaches for Distribution Network Partition

A rational partition is the key prerequisite for the application of distributed algorithms in distribution networks.This paper proposes community-detection-based approaches to a distribution network partition,including a non-overlapping partition and a border-node partitioning method.First,a novel electrical distance is defined to quantify the coupling relationships between buses and it is further used as the edge weight in a transformed equivalent graph.Then,a vertex/link partition community detection approach is applied to over-partition the network into high intra-cohesive and low inter-coupled subregions.Following this,a greedy algorithm and a tabu search method are combined to merge these subregions into target numbers according to the scale similarity principle.The proposed approaches take the influence of three-phase imbalance into consideration and they are decoupled from the power flow.Finally,the approaches are tested on an IEEE 123-bus distribution system and the results verify the effectiveness and the credibility of our proposed methods.

Consensus Clustering for Bi-objective Power Network Partition

Partitioning a complex power network into a number of sub-zones can help realize a divide-and-conquer’management structure for the whole system,such as voltage and reactive power control,coherency identification,power system restoration,etc.Extensive partitioning methods have been proposed by defining various distances,applying different clustering methods,or formulating varying optimization models for one specific objective.However,a power network partition may serve two or more objectives,where a trade-off among these objectives is required.This paper proposes a novel weighted consensus clustering-based approach for bi-objective power network partition.By varying the weights of different partitions for different objectives,Pareto improvement can be explored based on the node-based and subset-based consensus clustering methods.Case studies on the IEEE 300-bus test system are conducted to verify the effectiveness and superiority of our proposed method.

Real-time electromagnetic transient simulation algorithm for integrated power systems based on network level and component level parallelism

The analysis and simulation of power system are becoming more and more challenging as the complexity of system topology and components has been increased. In this paper, a hybrid parallel algorithm is proposed for the real-time electromagnetic transient simulation （EMTS） of integrated power systems containing multiphase machines. The proposed algorithm is com- posed of a novel network partition method called component level parallelization and the Multi-Area Thevenin Equivalent （MATE） method, which extends the flexibility of the network partition in parallel simulation. Moreover, several methods are developed to enhance the efficiency of the communication and computation. Power systems with up to 410 single-phase elec- trical nodes and 336 switches are simulated in a time step of 50 ~ts to validate the proposed algorithm.

A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.

Optimal Network Partition and Edge Server Placement for Distributed State Estimation

This paper investigates network partition and edge server placement problem to exploit the benefit of edge computing for distributed state estimation.A constrained many-objective optimization problem is formulated to minimize the cost of edge server deployment,operation,and maintenance,avoid the difference in the partition sizes,reduce the level of coupling between connected partitions,and maximize the inner cohesion of each partition.Capacities of edge server are constrained against underload and overload.To efficiently solve the problem,an improved non-dominated sorting genetic algorithm III(NSGA-III)is developed,with a specifically designed directed mutation operator based on topological characteristics of the partitions to accelerate convergence.Case study validates that the proposed formulations effectively characterize the practical concerns and reveal their trade-offs,and the improved algorithm outperforms existing representative ones for large-scale networks in converging to a near-optimal solution.The optimized result contributes significantly to real-time distributed state estimation.

Special issue on future network:software-defined networking

Computer networks have to support an everincreasing array of applications,ranging from cloud computing in datacenters to Internet access for users.In order to meet the various demands,a large number of network devices running different protocols are designed and deployed in networks.

Large-scale OPF Based on Voltage Grading and Network Partition

In this paper,a model of a large-scale optimal power flow(OPF)under voltage grading and network partition and its algorithm is presented.Based on the principles of open loop operations,the node injecting current method is used to divide the large-scale power grid into voltage grading and district dividing structures.The power network is further divided into a high-voltage main network and several subnets according to voltage levels of 220 kV.The subnets are connected by means of boundary nodes,and the partition model is solved using the improved approximate Newton direction method,which achieves complete dynamic decoupling simply by exchanging boundary variables between the main network and the subnets.A largescale power grid thus is decomposed into many subnets,making the solution of the problem simpler and faster while helping to protect the information of individual subnets.The system is tested for correctness and effectiveness of the proposed model,and the results obtained are matched in real-time.Finally,the algorithm is seen to have good convergence while improving calculation speed.