Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

To solve the distributed hybrid flow shop scheduling problem(DHFS)in raw glass manufacturing systems,we investigated an improved hyperplane assisted evolutionary algorithm(IhpaEA).Two objectives are simultaneously considered,namely,the maximum completion time and the total energy consumptions.Firstly,each solution is encoded by a three-dimensional vector,i.e.,factory assignment,scheduling,and machine assignment.Subsequently,an efficient initialization strategy embeds two heuristics are developed,which can increase the diversity of the population.Then,to improve the global search abilities,a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions.Furthermore,a local search heuristic based on three parts encoding is embedded to enhance the searching performance.To enhance the local search abilities,the cooperation of the search operator is designed to obtain better non-dominated solutions.Finally,the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms.The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

Geographically distributed hybrid testing & collaboration between geotechnical centrifuge and structures laboratories

Distributed Hybrid Testing（DHT） is an experimental technique designed to capitalise on advances in modern networking infrastructure to overcome traditional laboratory capacity limitations. By coupling the heterogeneous test apparatus and computational resources of geographically distributed laboratories, DHT provides the means to take on complex, multi-disciplinary challenges with new forms of communication and collaboration. To introduce the opportunity and practicability afforded by DHT, here an exemplar multi-site test is addressed in which a dedicated fibre network and suite of custom software is used to connect the geotechnical centrifuge at the University of Cambridge with a variety of structural dynamics loading apparatus at the University of Oxford and the University of Bristol. While centrifuge time-scaling prevents real-time rates of loading in this test, such experiments may be used to gain valuable insights into physical phenomena, test procedure and accuracy. These and other related experiments have led to the development of the real-time DHT technique and the creation of a flexible framework that aims to facilitate future distributed tests within the UK and beyond. As a further example, a real-time DHT experiment between structural labs using this framework for testing across the Internet is also presented.

Two-Stage Adaptive Memetic Algorithm with Surprisingly Popular Mechanism for Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

This paper considers the impact of setup time in production scheduling and proposes energy-aware distributed hybrid flow shop scheduling problem with sequence-dependent setup time(EADHFSP-ST)that simultaneously optimizes the makespan and the energy consumption.We develop a mixed integer linear programming model to describe this problem and present a two-stage adaptive memetic algorithm(TAMA)with a surprisingly popular mechanism.First,a hybrid initialization strategy is designed based on the two optimization objectives to ensure the convergence and diversity of solutions.Second,multiple population co-evolutionary approaches are proposed for global search to escape from traditional cross-randomization and to balance exploration and exploitation.Third,considering that the memetic algorithm(MA)framework is less efficient due to the randomness in the selection of local search operators,TAMA is proposed to balance the local and global searches.The first stage accumulates more experience for updating the surprisingly popular algorithm(SPA)model to guide the second stage operator selection and ensures population convergence.The second stage gets rid of local optimization and designs an elite archive to ensure population diversity.Fourth,five problem-specific operators are designed,and non-critical path deceleration and right-shift strategies are designed for energy efficiency.Finally,to evaluate the performance of the proposed algorithm,multiple experiments are performed on a benchmark with 45 instances.The experimental results show that the proposed TAMA can solve the problem effectively.

Decomposition-Based Multi-Objective Optimization for Energy-Aware Distributed Hybrid Flow Shop Scheduling with Multiprocessor Tasks

This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks(EADHFSPMT)by considering two objectives simultaneously,i.e.,makespan and total energy consumption.It consists of three sub-problems,i.e.,job assignment between factories,job sequence in each factory,and machine allocation for each job.We present a mixed inter linear programming model and propose a Novel MultiObjective Evolutionary Algorithm based on Decomposition(NMOEA/D).We specially design a decoding scheme according to the characteristics of the EADHFSPMT.To initialize a population with certain diversity,four different rules are utilized.Moreover,a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors.To enhance the quality of solutions,two local intensification operators are implemented according to the problem characteristics.In addition,a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence,which can adaptively modify weight vectors according to the distribution of the non-dominated front.Extensive computational experiments are carried out by using a number of benchmark instances,which demonstrate the effectiveness of the above special designs.The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.

Resource abstraction and data placement for distributed hybrid memory pool

Emerging byte-addressable non-volatile memory(NVM)technologies offer higher density and lower cost than DRAM,at the expense of lower performance and limited write endurance.There have been many studies on hybrid NVM/DRAM memory management in a single physical server.However,it is still an open problem on how to manage hybrid memories efficiently in a distributed environment.This paper proposes Alloy,a memory resource abstraction and data placement strategy for an RDMA-enabled distributed hybrid memory pool(DHMP).Alloy provides simple APIs for applications to utilize DRAM or NVM resource in the DHMP,without being aware of the hardware details of the DHMP.We propose a hotness-aware data placement scheme,which combines hot data migration,data replication and write merging together to improve application performance and reduce the cost of DRAM.We evaluate Alloy with several micro-benchmark workloads and public benchmark workloads.Experimental results show that Alloy can significantly reduce the DRAM usage in the DHMP by up to 95%,while reducing the total memory access time by up to 57%compared with the state-of-the-art approaches.

Power Quality Improvement Using ANN Controller For Hybrid Power Distribution Systems

In this work,an Artificial Neural Network(ANN)based technique is suggested for classifying the faults which occur in hybrid power distribution systems.Power,which is generated by the solar and wind energy-based hybrid system,is given to the grid at the Point of Common Coupling(PCC).A boost converter along with perturb and observe(P&O)algorithm is utilized in this system to obtain a constant link voltage.In contrast,the link voltage of the wind energy conversion system(WECS)is retained with the assistance of a Proportional Integral(PI)controller.The grid synchronization is tainted with the assis-tance of the d-q theory.For the analysis of faults like islanding,line-ground,and line-line fault,the ANN is utilized.The voltage signal is observed at the PCC,and the Discrete Wavelet Transform(DWT)is employed to obtain different features.Based on the collected features,the ANN classifies the faults in an effi-cient manner.The simulation is done in MATLAB and the results are also validated through the hardware implementation.Detailed fault analysis is carried out and the results are compared with the existing techniques.Finally,the Total harmonic distortion(THD)is lessened by 4.3%by using the proposed methodology.

A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine

The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.

Research on power electronic transformer applied in AC/DC hybrid distribution networks

The AC/DC hybrid distribution network is one of the trends in distribution network development, which poses great challenges to the traditional distribution transformer. In this paper, a new topology suitable for AC/DC hybrid distribution network is put forward according to the demands of power grid, with advantages of accepting DG and DC loads, while clearing DC fault by blocking the clamping double sub-module(CDSM) of input stage. Then, this paper shows the typical structure of AC/DC distribution network that is hand in hand. Based on the new topology, this paper designs the control and modulation strategies of each stage, where the outer loop controller of input stage is emphasized for its twocontrol mode. At last, the rationality of new topology and the validity of control strategies are verified by the steady and dynamic state simulation. At the same time, the simulation results highlight the role of PET in energy regulation.

E-Bayesian estimation for competing risk model under progressively hybrid censoring

This paper considers the Bayesian and expected Bayesian（E-Bayesian） estimations of the parameter and reliability function for competing risk model from Gompertz distribution under Type-I progressively hybrid censoring scheme（PHCS）. The estimations are obtained based on Gamma conjugate prior for the parameter under squared error（SE） and Linex loss functions. The simulation results are provided for the comparison purpose and one data set is analyzed.

Hybrid orthogonal and non-orthogonal pilot distribution based channel estimation in massive MIMO system

How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.

Low complexity hybrid power distribution combined with subcarrier allocation algorithm for OFDMA

To improve the total throughput of the uplink orthogonal frequency division multiple access system,a low complexity hybrid power distribution（HPD） combined with subcarrier allocation scheme is proposed.For the fairness mechanism for the subcarrier,the inter-cell interference is first analyzed to calculate the capacity of the multi-cell.The user selects the subcarrier with the largest channel gain.Based on the above subcarrier allocation scheme,a new kind of HPD scheme is proposed,which adopts the waterfilling-power-distributed scheme and the equal-power-distributed scheme in the cell-boundary and the cellcenter,respectively.Simulation results show that compared with the waterfilling-power-distributed scheme in the whole cell,the proposed HPD scheme decreases the system complexity significantly,meanwhile its capacity is 2% higher than that of the equal-powerdistributed scheme over the same subcarrier allocation.

Intelligent Optimization Under Multiple Factories: Hybrid Flow Shop Scheduling Problem with Blocking Constraints Using an Advanced Iterated Greedy Algorithm

The distributed hybrid flow shop scheduling problem(DHFSP),which integrates distributed manufacturing models with parallel machines,has gained significant attention.However,in actual scheduling,some adjacent machines do not have buffers between them,resulting in blocking.This paper focuses on addressing the DHFSP with blocking constraints(DBHFSP)based on the actual production conditions.To solve DBHFSP,we construct a mixed integer linear programming(MILP)model for DBHFSP and validate its correctness using the Gurobi solver.Then,an advanced iterated greedy(AIG)algorithm is designed to minimize the makespan,in which we modify the Nawaz,Enscore,and Ham(NEH)heuristic to solve blocking constraints.To balance the global and local search capabilities of AIG,two effective inter-factory neighborhood search strategies and a swap-based local search strategy are designed.Additionally,each factory is mutually independent,and the movement within one factory does not affect the others.In view of this,we specifically designed a memory-based decoding method for insertion operations to reduce the computation time of the objective.Finally,two shaking strategies are incorporated into the algorithm to mitigate premature convergence.Five advanced algorithms are used to conduct comparative experiments with AIG on 80 test instances,and experimental results illustrate that the makespan and the relative percentage increase(RPI)obtained by AIG are 1.0%and 86.1%,respectively,better than the comparative algorithms.

Control strategy of hybrid fuel cell/battery distributed generation system for grid-connected operation

This paper presents a control strategy of a hybrid fuel cell/battery distributed generation (HDG) system in distribution systems. The overall structure of the HDG system is given, dynamic models for the solid oxide fuel cell (SOFC) power plant, battery bank and its power electronic interfacing are briefly described, and controller design methodologies for the power conditioning units and fuel cell to control the power flow from the hybrid power plant to the utility grid are presented. To distribute the power between the fuel cell power plant and the battery energy storage, a neuro-fuzzy controller has been developed. Also, for controlling the active and reactive power independently in distribution systems, the current control strategy based on two fuzzy logic controllers has been presented. A Matlab/Simulink simulation model is developed for the HDG system by combining the individual component models and their controllers. Simulation results show the overall system performance including load-following and power management of the HDG system.

Two-stage Optimal Dispatching of AC/DC Hybrid Active Distribution Systems Considering Network Flexibility

The increasing flexibility of active distribution systems(ADSs)coupled with the high penetration of renewable distributed generators(RDGs)leads to the increase of the complexity.It is of practical significance to achieve the largest amount of RDG penetration in ADSs and maintain the optimal operation.This study establishes an alternating current(AC)/direct current(DC)hybrid ADS model that considers the dynamic thermal rating,soft open point,and distribution network reconfiguration(DNR).Moreover,it transforms the optimal dispatching into a second-order cone programming problem.Considering the different control time scales of dispatchable resources,the following two-stage dispatching framework is proposed.d dispatch uses hourly input data with the goal(1)The day-ahea of minimizing the grid loss and RDG dropout.It obtains the optimal 24-hour schedule to determine the dispatching plans for DNR and the energy storage system.(2)The intraday dispatch uses 15-min input data for 1-hour rolling-plan dispatch but only executes the first 15 min of dispatching.To eliminate error between the actual operation and dispatching plan,the first 15 min is divided into three 5-min step-by-step executions.The goal of each step is to trace the tie-line power of the intraday rolling-plan dispatch to the greatest extent at the minimum cost.The measured data are used as feedback input for the rolling-plan dispatch after each step is executed.A case study shows that the comprehensive cooperative ADS model can release the line capacity,reduce losses,and improve the penetration rate of RDGs.Further,the two-stage dispatching framework can handle source-load fluctuations and enhance system stability.

Battery Storage Configuration of AC/DC Hybrid Distribution Networks

The upscaling requirements of energy transition highlight the urgent need for ramping up renewables and boosting system efficiencies.However,the stochastic nature of excessive renewable energy resources has challenged stable and efficient operation of the power system.Battery energy storage systems(BESSs)have been identified as critical to mitigate random fluctuations,unnecessary green energy curtailment and load shedding with rapid response and flexible connection.On the other hand,an AC/DC hybrid distribution system can offer merged benefits in both AC and DC subsystems without additional losses during AC/DC power conversion.Therefore,configuring BESSs on an AC/DC distribution system is wellpositioned to meet challenges brought by carbon reductions in an efficient way.A bi-level optimization model of BESS capacity allocation for AC/DC hybrid distribution systems,considering the flexibility of voltage source converters(VSCs)and power conversion systems(PCSs),has been established in this paper to address the techno-economic issues that hindered wide implementation.The large-scale nonlinear programming problem has been solved utilizing a genetic algorithm combined with second-order cone programming.Rationality and effectiveness of the model have been verified by setting different scenarios through case studies.Simulation results have demonstrated the coordinated operation of BESS and AC/DC hybrid systems can effectively suppress voltage fluctuations and improve the cost-benefit of BESSs from a life cycle angle.

SYNTHETIC STRESSLIFE TESTING OF MECHATRONIC PRODUCTS UNDER VARIABLE STRESS SPECTRUM

Mechatronic products usually endure the variable stress spectrum when they operate in certain operational condition and environmental condition, which obey the Weibull distribution. In accordance with the features of mechatronic product, this paper analyzes the failure mode, its corresponding sensitive stress and the design principles of life testing profiles. Based on the above analyses, this paper presents a synthetic stress life testing method based on the hybrid Weibull distribution and its statistical method under variable stress spectrum to evaluate the reliability and life indices of mechatronic products. Because the mechatronic products have many characteristics such as high price, long life and small testing samples, the synthetic stress life testing method under variable load spectrum can simulate the real various spectra, decrease the life testing time and reduce the testing samples. So it is effective to carry out the life testing to mechatronic products. The application results of hydraulic pumps indicate that this method can easily handle the experimental data under variable amplitude spectrum, obtain the high precision parameters point estimation and confidence interval estimation and reduce the testing cost greatly.

RSOA-basRSOA-based distributed access long reach hybrid WDM-TDM PON with OADMs

A distributed access scheme using optical add/drop multiplexers （OADMs） for long reach hybrid wavelength division multiplexing and time division multiplexing passive optical networks （WDM-TDM PONs） is proposed and demonstrated. Colorless operations are implemented by using commercially available reflective semiconductor optical amplifiers （RSOAs） at both the center office （CO） and the customer side. Four 1.25-Gb/s channels are successfully transmitted over 80-km single-mode fiber with four OADMs. The dynamic input power range of the RSOA is also investigated. Compared with traditional access schemes, the proposed scheme could cover the area along the feed fiber with no blind zone. The experimental results show that it could be an ideal solution for the next generation access networks.

Fuzzy Logic-based Management of Hybrid Distribution Transformers Using LoRa Technology

Hybrid distribution transformers(HDTs)have better performance than traditional distribution transformers in improving power quality through reducing harmonics,unbalance,voltage fluctuations and low power factors in future smart power distribution systems.In order to increase the service life and reliability of hybrid distribution transformers,this paper proposes a remote management system using LoRa technology based on fuzzy logic.HDT based on a fuzzy logic judgment system(FLJS)replaces the Boolean logic with fuzzy logic and several power quality problems including power factors,load-side current harmonics and voltage unbalance are considered,as well as grid-side voltage deviation and unbalance.This management system can dynamically adjust the working states of HDT according to the output results of the FLJS to reduce the use time of power electronic devices.Due to the application of LoRa,this management system can remotely adjust the parameters of the FLJS in real time for different distribution network nodes to avoid frequent switching of HDT working states.In addition,it is able to remotely monitor the real-time working states and fault states of HDT to reduce recovery time and maintenance costs in case of HDT failure.Finally,simulation and experimental results are presented to verify the effectiveness of the proposed management system for HDT.

Reverse Droop Control-based Smooth Transfer Strategy for Interface Converters in Hybrid AC/DC Distribution Networks

Hybrid AC/DC distribution networks are promising candidates for future applications due to their rapid advancement in power electronics technology.They use interface converters(IFCs)to link DC and AC distribution networks.However,the networks possess drawbacks with AC voltage and frequency offsets when transferring from grid-tied to islanding modes.To address these problems,this paper proposes a simple but effective strategy based on the reverse droop method.Initially,the power balance equation of the distribution system is derived,which reveals that the cause of voltage and frequency offsets is the mismatch between the IFC output power and the rated load power.Then,the reverse droop control is introduced into the IFC controller.By using a voltage-active power/frequency-reactive power(U-P/f-Q)reverse droop loop,the IFC output power enables adaptive tracking of the rated load power.Therefore,the AC voltage offset and frequency offset are suppressed during the transfer process of operational modes.In addition,the universal parameter design method is discussed based on the stability limitations of the control system and the voltage quality requirements of AC critical loads.Finally,simulation and experimental results clearly validate the proposed control strategy and parameter design method.