Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units(RTUs)were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive.Fault detection and diagnosis(FDD)tools can be employed for RTU methods to ensure essential faults are addressed promptly.In this aspect,this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units(ODBNFDC-PRTU)model.The ODBNFDC-PRTU technique considers fault diagnosis as amulti-class classification problem and is handled usingDL models.For fault diagnosis in RTUs,the ODBNFDC-PRTU model exploits the deep belief network(DBN)classification model,which identifies seven distinct types of faults.At the same time,the chicken swarm optimization(CSO)algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process,showing the novelty of the work.To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm,a comprehensive set of simulations are applied.The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30%and TPR of 93.09%.

Optimizing Factory Performance for Unit Cost in Semiconductor Manufacturing

The integrated circuit (IC) manufacturing process is capital intensive and complex. The production process of unit product (or die, as it is commonly referred to) takes several weeks. Semiconductor factories (fabs) continuously attempt to improve their productivity, as measured in output and cycle time (or mean flow time). The conflicting objective of producing maximum units at minimal production cycle time and at the highest quality, as measured by die yield, is discussed in this paper. The inter-related effects are characterized, and a model is proposed to address this multi-objective function. We then show that, with this model, die cost can be optimized for any given operating conditions of a fab. A numerical example is provided to illustrate the practicality of the model and the proposed optimization method.

Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network.However,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and buildings.In this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication.This RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement strategies.It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles.The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments.The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

The development of accurate prediction models continues to be highly beneficial in myriad disciplines. Deep learning models have performed well in stock price prediction and give high accuracy. However, these models are largely affected by the vanishing gradient problem escalated by some activation functions. This study proposes the use of the Vanishing Gradient Resilient Optimized Gated Recurrent Unit (OGRU) model with a scaled mean Approximation Coefficient (AC) time lag which should counter slow convergence, vanishing gradient and large error metrics. This study employed the Rectified Linear Unit (ReLU), Hyperbolic Tangent (Tanh), Sigmoid and Exponential Linear Unit (ELU) activation functions. Real-life datasets including the daily Apple and 5-minute Netflix closing stock prices were used, and they were decomposed using the Stationary Wavelet Transform (SWT). The decomposed series formed a decomposed data model which was compared to an undecomposed data model with similar hyperparameters and different default lags. The Apple daily dataset performed well with a Default_1 lag, using an undecomposed data model and the ReLU, attaining 0.01312, 0.00854 and 3.67 minutes for RMSE, MAE and runtime. The Netflix data performed best with the MeanAC_42 lag, using decomposed data model and the ELU achieving 0.00620, 0.00487 and 3.01 minutes for the same metrics.

Optimal Unit Commitment with Renewable Energy in Regulated and Deregulated Systems

In this paper, the impact of the wind power generation system on the total cost and profit of the system is studied by using the proposed procedure of binary Sine Cosine (BSC) optimization algorithm with optimal priority list (OPL) algorithm. As well, investigate the advantages of system transformation from a regulated system to a deregulated system and the difference in the objective functions of the two systems. The suggested procedure is carried out in two parallel algorithms;The goal of the first algorithm is to reduce the space of searching by using OPL, while the second algorithm adjusts BSC to get the optimal economic dispatch with minimum operation cost of the unit commitment (UCP) problem in the regulated system. But, in the deregulated system, the second algorithm adopts the BSC technique to find the optimal solution to the profit-based unit commitment problem (PBUCP), through the fast of researching the BSC technique. The proposed procedure is applied to IEEE 10-unit test system integrated with the wind generator system. While the second is an actual system in the Egyptian site at Hurghada. The results of this algorithm are compared with previous literature to illustrate the efficiency and capability of this algorithm. Based on the results obtained in the regulated system, the suggested procedure gives better results than the algorithm in previous literature, saves computational efforts, and increases the efficiency of the output power of each unit in the system and lowers the price of kWh. Besides, in the deregulated system the profit is high and the system is more reliable.

Optimal Load Dispatch of Gas Turbine Power Generation Units based on Multiple Population Genetic Algorithm

In this paper, a multiple population genetic algorithm (MPGA) is proposed to solve the problem of optimal load dispatch of gas turbine generation units. By introducing multiple populations on the basis of Standard Genetic Algorithm (SGA), connecting each population through immigrant operator and preserving the best individuals of every generation through elite strategy, MPGA can enhance the efficiency in obtaining the global optimal solution. In this paper, MPGA is applied to optimize the load dispatch of 3×390MW gas turbine units. The results of MPGA calculation are compared with that of equal micro incremental method and AGC instruction. MPGA shows the best performance of optimization under different load conditions. The amount of saved gas consumption in the calculation is up to 2337.45m3N/h, which indicates that the load dispatch optimization of gas turbine units via MPGA approach can be effective.

Optimal Gliding Guidance of Vertical Plane for Standoff Released Guided Bomb Unit

Optimal gliding guidance for a guided bomb unit in the vertical plane is studied based on nonlinear dynamics and kinematics.The guidance law is designed under minimum energy loss index.To avoid the complexity in solving two-point-boundary-value problems,the steady-state solutions of the adjoint states in regular equations are suggested to be used.With these considerations,a quasi-closed,optimal gliding guidance law is obtained.The guidance law is described by the angle of attack in a simple nonlinear equation.An iterative computation method can be easily used to get the optimal angle of attack.The further simplified direct computation algorithm for the optimal angle of attack is also given.The guidance properties are compared with those of maximum lift-to-drag angle of attack control.The simulation results demonstrate that the quasi-closed,optimal gliding guidance law can improve the gliding phase terminal performance with significant increase in the altitude and much little decrease in the speed.

Three-dimensional physical simulation and optimization of water injection of a multi-well fractured-vuggy unit

With complex fractured-vuggy heterogeneous structures, water has to be injected to facilitate oil pro- duction. However, the effect of different water injection modes on oil recovery varies. The limitation of existing numerical simulation methods in representing fractured- vuggy carbonate reservoirs makes numerical simulation difficult to characterize the fluid flow in these reservoirs. In this paper, based on a geological example unit in the Tahe Oilfield, a three-dimensional physical model was designed and constructed to simulate fluid flow in a fractured-vuggy reservoir according to similarity criteria. The model was validated by simulating a bottom water drive reservoir, and then subsequent water injection modes were optimized. These were continuous （constant rate）, intermittent, and pulsed injection of water. Experimental results reveal that due to the unbalanced formation pressure caused by pulsed water injection, the swept volume was expanded and consequently the highest oil recovery increment was achieved. Similar to continuous water injection, intermit- tent injection was influenced by factors including the connectivity of the fractured-vuggy reservoir, well depth, and the injection-production relationship, which led to a relative low oil recovery. This study may provide a constructive guide to field production and for the devel- opment of the commercial numerical models specialized for fractured-vuggy carbonate reservoirs.

On units combination and commitment optimization for electric power production

Electric power system is one of the most important and complex engineering in modern society, supplying main and general power for social production and social life. Meanwhile, since it is a productive system with both high input and output, it has an obvious economic significance to improve its operating efficiency. For an example, an unit is 10 GW scale, if its standard coal consumption can be decreased with 1 g/kW·h, it can save about 5 000 tons standard coal per year. It will be discussed mainly that how to establish optimization model and its numerical algorithm for operating management of the electric power system. The idea on establishing optimization model is how to dispatch work state of units or power plants, so that total cost of fuel consumption for generation is reduced to the minimum. Here the dispatch is to decide which unit or plant to operate, which unit or plant to stop running, how much power should be generated for those operating units or plants at each given time interval.

Coordinated Controller Tuning of a Boiler Turbine Unit with New Binary Particle Swarm Optimization Algorithm

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system.In this paper,a new variant of binary particle swarm optimization （PSO） algorithm,called probability based binary PSO （PBPSO）,is presented to tune the parameters of a coordinated controller.The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO,modified binary PSO,and standard continuous PSO.

Real-time analysis and prediction of shield cutterhead torque using optimized gated recurrent unit neural network

An accurate prediction of earth pressure balance(EPB)shield moving performance is important to ensure the safety tunnel excavation.A hybrid model is developed based on the particle swarm optimization(PSO)and gated recurrent unit(GRU)neural network.PSO is utilized to assign the optimal hyperparameters of GRU neural network.There are mainly four steps:data collection and processing,hybrid model establishment,model performance evaluation and correlation analysis.The developed model provides an alternative to tackle with time-series data of tunnel project.Apart from that,a novel framework about model application is performed to provide guidelines in practice.A tunnel project is utilized to evaluate the performance of proposed hybrid model.Results indicate that geological and construction variables are significant to the model performance.Correlation analysis shows that construction variables(main thrust and foam liquid volume)display the highest correlation with the cutterhead torque(CHT).This work provides a feasible and applicable alternative way to estimate the performance of shield tunneling.

Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic,Wind Turbine,and Battery Systems

Renewable energy sources(RESs)are considered to be reliable and green electric power generation sources.Photovoltaics(PVs)and wind turbines(WTs)are used to provide electricity in remote areas.Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment.The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution.This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization(TLBO)named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost(TAC).The reliability of the system is considered by a maximum allowable loss of power supply probability(LPSPmax)concept.The results obtained from the JLBO algorithm are compared with the original Jaya,TLBO,and genetic algorithms.The JLBO results show superior performance in terms of TAC,and the PV–WT–battery hybrid system is found to be the most economical scenario.This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV–battery and WT–battery systems.

Operation and Optimization of Residue Hydrotreating Unit Using New Catalyst System

This article has summarized the optimized measures relating to the loading of catalyst,and the sixth operating cycle of the residue hydrotreating unit at SINOPEC's Maoming Branch Company,and made a detailed comparison on the impurities removal rate,hydrogen consumption and energy consumption of the sixth operating cycle with those achieved by the previous five cycles.Test results have revealed that the second-generation RHT series novel residue hydrotreating catalysts featured high activity,good stability,and long operating cycle and could remarkably reduce the hydrogen consumption and energy consumption of process unit.The hydrotreated AR product,having low Conradson carbon residue,low sulfur content,low metal content,high content of saturated hydrocarbons,and low content of asphaltenes and resins,is an excellent FCC feed.Judging from their overall property the second-generation RHT series of residue hydrotreating catalysts used in the sixth operating cycle have commanded a leading position among other catalysts used in previous operating cycles.

Evaluation of optimal UPFC allocation for improving transmission capacity

A unified power flow controller(UPFC)combines the advantages of various flexible alternating current transmission system(FACTS)devices into a powerful format.Using a 500 kV power grid,this study evaluates the selection and use of a UPFC to improve transmission capacity.The"UPFC unit capacity control proportionality coefficient"is introduced to quantify the control effect of the UPFC,and an optimal calculation method for the UPFC capacity is presented.Following the proposal of a UPFC site selection process,the data of an existing power grid is used to conduct simulations.The simulation results show that the UPFC has a strong ability to improve transmission capacity,and its use is greatly advantageous.Additionally,by applying the proposed selection method,the control effect and economic benefits of the UPFC can be comprehensively considered during project site selection.These findings have a guiding significance for UPFC site selection in ultra-high voltage power grids.

Optimal design of the separate type heat pipe heat exchanger

Separate type heat pipe heat exchangers are often used for large-scale heat exchanging. The arrangement of such a heat exchanger conveniently allows heat input to and output from the heat exchanger at remote locations. The traditional method of designing an ordinary HPHE (heat pipe heat exchanger) is commonly applied in the separate type exchanger design, but the calculations have to be carried out separately, which makes it very complicated. In this work, the ε-NTU (effectiveness-Number of Transfer Units) method was applied for optimization analysis of single- or multi-level separate type heat pipe heat exchangers. An optimizing formula for single-level separate type heat pipe heat exchangers was obtained. The optimizing principles of effec- tiveness-NTU and heat transfer rate by the equal distribution method for multi-level separate type heat pipe heat exchanger are presented. The design of separate type heat pipe heat exchangers by the optimizing method is more convenient and faster than by the traditional method.

Optimal Selection of Hybrid Renewable Energy System Using Multi-Criteria Decision-Making Algorithms

Several models of multi-criteria decision-making(MCDM)have identified the optimal alternative electrical energy sources to supply certain load in an isolated region in Al-Minya City,Egypt.The load demand consists of water pumping system with a water desalination unit.Various options containing three different power sources:only DG,PV-B system,and hybrid PV-DG-B,two different sizes of reverse osmosis(RO)units;RO-250 and RO-500,two strategies of energy management;load following(LF)and cycle charging(CC),and two sizes of DG;5 and 10 kW were taken into account.Eight attributes,including operating cost,renewable fraction,initial cost,the cost of energy,excess energy,unmet load,breakeven grid extension distance,and the amount of CO_(2),were used during the evaluation process.To estimate these parameters,HOMER®software was employed to perform both the simulation and optimization process.Four different weight estimation methods were considered;no priority of criteria,based on a pairwise comparisons matrix of the criteria,CRITIC-method,and entropy-based method.The main findings(output results)confirmed that the optimal option for the case study was hybrid PV-DG-B with the following specification:5 kW DG,RO-500,and load following control strategy.Under this condition,the annual operating cost and initial costs were$5546 and$161022,respectively,whereas the cost of energy was 0.077$/kWh.The excess energy and unmet loads were 40998 and 2371 kWh,respectively.The breakeven grid extension distance and the amount of CO_(2) were 3.31 km and 5171 kg per year,respectively.Compared with DG only,the amount of CO_(2) has been sharply reduced by 113939 kg per year.

Test Case Prioritization in Unit and Integration Testing:A Shuffled-Frog-Leaping Approach

Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product.Due to resource constraints,when software is subjected to modifications,the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy.One such strategy is test case prioritization(TCP).Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest.Nonetheless,singularity in objective functions and the lack of dissimilitude among the re-ordered test sequences have degraded the cogency of their approaches.Considering such gaps and scenarios when the meteoric and continuous updations in the software make the intensive unit and integration testing process more fragile,this study has introduced a memetics-inspired methodology for TCP.The proposed structure is first embedded with diverse parameters,and then traditional steps of the shuffled-frog-leaping approach(SFLA)are followed to prioritize the test cases at unit and integration levels.On 5 standard test functions,a comparative analysis is conducted between the established algorithms and the proposed approach,where the latter enhances the coverage rate and fault detection of re-ordered test sets.Investigation results related to the mean average percentage of fault detection(APFD)confirmed that the proposed approach exceeds the memetic,basic multi-walk,PSO,and optimized multi-walk by 21.7%,13.99%,12.24%,and 11.51%,respectively.

An Improved United Trajectory Optimization Approach in Boost-phase Considering Attitude Control Ability

This paper covers the trajectory optimization design problem of a class of demonstration flight test vehicles. Different from the traditional "once downward" movement principle of negative attack angle, the "twice downward" lower trajectory is proposed based on a SQP algorithm to meet the requirement for validating thermal protection materials. Furthermore, an important advantage of this presented method, compared to the traditional method, is that both trajectory constraints and attitude control constraints are considered. An engineering example is also given to show the advantage and effectiveness of this method.

镗杆切削力感知单元结构设计与优化

为满足测量镗杆切削力的需求,采用ANSYSWorkbench有限元分析软件对镗杆等效应力与等效应变分析,确定感知单元安装位置。提出两种镗杆感知单元结构,运用响应曲面优化法确定感知单元最佳优化方案,通过计算、分析相关参数,来确定最佳的优化方案,并与优化前的感知单元结构方案进行比较。结果表明:当未超过应变式微力传感器最大等效应变时,均布式感知单元结构中镗杆最大测量值为329N;以附加支撑感知单元最大测量值为1000N为设计目标,获得5种最优结构参数,在实际设计制造中,可根据实际情况从中进行选择。