scholarly journals Optimization Model of Key Equipment Maintenance Scheduling for an AC/DC Hybrid Transmission Network Based on Mixed Integer Linear Programming

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 1011 ◽  
Author(s):  
Jie Cai ◽  
Shuyu Guo ◽  
Shuang Liao ◽  
Xing Chen ◽  
Shihong Miao ◽  
...  
Keyword(s):  
Optimization Model ◽  
System Reliability ◽  
Mutual Influence ◽  
Maintenance Scheduling ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Transmission Network ◽  
Equipment Maintenance ◽  
Maintenance Period ◽  
Hybrid Transmission

The unbalanced distribution of resource and consuming centers in China has prompted the AC/DC hybrid transmission technology. The maintenance scheduling of an AC/DC hybrid transmission network is the key technology to ensure its safety and reliability. In this study, the mutual influence mechanism of an AC/DC system in a maintenance period was analyzed in detail. The overhead transmission line and transformer are key equipment within an AC/DC hybrid transmission network, and an optimization model of the key equipment maintenance scheduling was established. The objective of the model was to improve the system reliability during the maintenance scheduling. By considering the constraints of maintenance cost, maintenance resources, and maintenance workload, the maintenance scheduling of overhead transmission lines and transformer branches was obtained. The over-limit situation of power flow and the weakness of the system during the maintenance period was evaluated. The “double-layer substitution method” was adopted to convert the nonlinear constraints into its bilinear formulation such that it could then be solved. The random number sampling method was used to quantify the system reliability, and the commercial optimization software was used to solve the optimized scheduling. Based on the improved IEEE RTS-79 system and the Hubei Province electrical system, the simulation results showed the effectiveness of the proposed method.

scholarly journals Optimization Method of Power Equipment Maintenance Plan Decision-Making Based on Deep Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanhua Yang ◽  
Ligang Yao
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimization Model ◽  
Power Grid ◽  
Optimization Method ◽  
Optimal Decision ◽  
Maintenance Cost ◽  
Equipment Maintenance ◽  
Safety Constraints ◽  
Maintenance Plan

The safe and reliable operation of power grid equipment is the basis for ensuring the safe operation of the power system. At present, the traditional periodical maintenance has exposed the abuses such as deficient maintenance and excess maintenance. Based on a multiagent deep reinforcement learning decision-making optimization algorithm, a method for decision-making and optimization of power grid equipment maintenance plans is proposed. In this paper, an optimization model of power grid equipment maintenance plan that takes into account the reliability and economics of power grid operation is constructed with maintenance constraints and power grid safety constraints as its constraints. The deep distributed recurrent Q-networks multiagent deep reinforcement learning is adopted to solve the optimization model. The deep distributed recurrent Q-networks multiagent deep reinforcement learning uses the high-dimensional feature extraction capabilities of deep learning and decision-making capabilities of reinforcement learning to solve the multiobjective decision-making problem of power grid maintenance planning. Through case analysis, the comparative results show that the proposed algorithm has better optimization and decision-making ability, as well as lower maintenance cost. Accordingly, the algorithm can realize the optimal decision of power grid equipment maintenance plan. The expected value of power shortage and maintenance cost obtained by the proposed method is $71.75$ $MW·H$ and $496000$ $yuan$.

scholarly journals An Optimization Approach Considering User Utility for the PV-Storage Charging Station Planning Process

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 83
Author(s):  
Yingxin Liu ◽  
Houqi Dong ◽  
Shengyan Wang ◽  
Mengxin Lan ◽  
Ming Zeng ◽  
...  
Keyword(s):  
Optimization Model ◽  
Planning Process ◽  
Capacity Allocation ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
Single Level ◽  
Charging Station ◽  
Linearization Methods ◽  
Planning Scheme ◽  
Charging Stations

Based on the comprehensive utilization of energy storage, photovoltaic power generation, and intelligent charging piles, photovoltaic (PV)-storage charging stations can provide green energy for electric vehicles (EVs), which can significantly improve the green level of the transportation industry. However, there are many challenges in the PV-storage charging station planning process, making it theoretically and practically significant to study approaches to planning. This paper promotes a bi-level optimization planning approach for PV-storage charging stations. First, taking PV-storage charging stations and EV users as the upper- and lower-level problems, respectively, during the planning process, a bi-level optimization model for PV-storage charging stations considering user utility is established for capacity allocation and user behavior-based electricity pricing. Second, the model is converted into a single-level mixed-integer linear programming model using the piecewise linear utility function, Karush–Kuhn–Tucker (KKT) conditions, and linearization methods. Finally, to verify the validity of the proposed model and the solution algorithm, a commercial solver is used to solve the optimization model and obtain the planning scheme. The results show that the proposed bi-level optimization model can provide a more economical and reasonable planning scheme than the single-level model, and can reduce the investment cost by 8.84%, operation and maintenance cost by 13.23%, and increase net revenue by 5.11%.

scholarly journals Maintenance Personnel Optimization Model of Vehicle Equipment Based on Support Task

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Weixing Song ◽  
Zhengjun Lei ◽  
Qian Le ◽  
Fengyue Li ◽  
Jingjing Wu
Keyword(s):  
Human Resources ◽  
Prediction Model ◽  
Optimization Model ◽  
Fruit Fly ◽  
Theoretical Research ◽  
Mixed Integer ◽  
Fruit Fly Optimization Algorithm ◽  
Equipment Maintenance ◽  
Allocation Model ◽  
Maintenance Personnel

Vehicle equipment maintenance support tasks have problems such as low maintenance efficiency and unreasonable allocation of maintenance personnel. In order to further strengthen the theoretical research of vehicle equipment maintenance support, an optimization model of vehicle equipment maintenance personnel based on support task is proposed in this paper. Firstly, the maintenance workload model of vehicle equipment is constructed by analyzing the three task sources of vehicle equipment: scheduled maintenance, natural random failure, and combat damage. Then, considering the technical professional level, maintenance efficiency, and other factors of maintenance personnel, two optimization models of maintenance personnel are constructed. In view of the situation where there are enough human resources, the prediction model of the number of personnel with the minimum total number as the goal is constructed to achieve the purpose of saving human resources. Using MATLAB mixed integer nonlinear programming problem (MINP) toolbox to solve the prediction model of the number of personnel, in view of the shortage of maintenance personnel, a maintenance personnel allocation model aiming at minimizing maintenance time is constructed to maximize maintenance efficiency. In order to solve the model, the fruit fly optimization algorithm (FOA) is improved, and the group cooperation is used to update the fruit fly position. The new algorithm not only retains the essential advantages of the FOA but also solves the problem that the algorithm is easy to fall into local extreme value and improves the global optimization ability of the algorithm. Finally, two example simulations verify the effectiveness of the optimization method in this paper and provide a certain theoretical basis for maintenance personnel to optimize decision-making.

The Optimization Analysis of Equipment Maintenance Based on Monte-Carlo Simulation

2011 ◽  
Vol 189-193 ◽  
pp. 424-427 ◽  
Author(s):  
Dai Geng ◽  
Shi Min Zhang ◽  
De Guo Wang ◽  
Jian Gao ◽  
Li Sha Dai
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Distribution Function ◽  
Theoretical Basis ◽  
Economic Benefits ◽  
Maintenance Cost ◽  
Equipment Maintenance ◽  
Maintenance Period ◽  
Optimization Analysis ◽  
Maintenance Decisions

In order to improve the reliability and security in production, effectively checking and maintaining equipment must be put into practice. In this paper, the on-condition maintenance period of the equipment is optimized by Monte-Carlo for the lowest maintenance cost in unit time by expressing the maintenance interval as an exponential function parameterizing Weibull’s distribution function。Finally, the oil centrifugal pump as an example was demonstrated. The results show that our model has the obvious economic benefits. The optimization analysis of equipment maintenance based on Monte-Carlo provides a theoretical basis for optimized detection and maintenance decisions.

Optimizing the Cost and Reliability of Shared Anchors in an Array of Floating Offshore Wind Turbines

2021 ◽  
Author(s):  
Michael Devin ◽  
Bryony DuPont ◽  
Spencer Hallowell ◽  
Sanjay Arwade
Keyword(s):  
Optimization Algorithm ◽  
System Reliability ◽  
Failure Modes ◽  
The United States ◽  
Offshore Wind ◽  
Bayesian Optimization ◽  
Maintenance Cost ◽  
Offshore Wind Turbine ◽  
Trade Offs ◽  
Floating Offshore Wind Turbines

Abstract Commercial floating offshore wind projects are expected to emerge in the United States by the end of this decade. Currently, however, high costs for the technology limit its commercial viability, and a lack of data regarding system reliability heightens project risk. This work presents an optimization algorithm to examine the trade-offs between cost and reliability for a floating offshore wind array that uses shared anchoring. Combining a multivariable genetic algorithm with elements of Bayesian optimization, the optimization algorithm selectively increases anchor strengths to minimize the added costs of failure for a large floating wind farm in the Gulf of Maine under survival load conditions. The algorithm uses an evaluation function that computes the probability of mooring system failure, then calculates the expected maintenance costs of a failure via a Monte Carlo method. A cost sensitivity analysis is also performed to compare results for a range of maintenance cost profiles. The results indicate that virtually all of the farm's anchors are strengthened in the minimum cost solution. Anchor strength is in- creased between 5-35% depending on farm location, with anchor strength nearest the export cable being increased the most. The optimal solutions maintain a failure probability of 1.25%, demonstrating the trade-off point between cost and reliability. System reliability was found to be particularly sensitive to changes in turbine costs and downtime, suggest- ing further research into floating offshore wind turbine failure modes in extreme loading conditions could be particularly impactful in reducing project uncertainty.

Optimization design of drive system for industrial robots based on dynamic performance

2017 ◽  
Vol 44 (6) ◽  
pp. 765-775 ◽  
Author(s):  
LianZheng Ge ◽  
Jian Chen ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Industrial Robot ◽  
Dynamic Performance ◽  
System Optimization ◽  
Drive System ◽  
Industrial Robots ◽  
Mixed Integer ◽  
Light Weight ◽  
Content Type

Purpose The global performance of industrial robots partly depends on the properties of drive system consisting of motor inertia, gearbox inertia, etc. This paper aims to deal with the problem of optimization of global dynamic performance for robotic drive system selected from available components. Design/methodology/approach Considering the performance specifications of drive system, an optimization model whose objective function is composed of working efficiency and natural frequency of robots is proposed. Meanwhile, constraints including the rated and peak torque of motor, lifetime of gearbox and light-weight were taken into account. Furthermore, the mapping relationship between discrete optimal design variables and component properties of drive system were presented. The optimization problem with mixed integer variables was solved by a mixed integer-laplace crossover power mutation algorithm. Findings The optimization results show that our optimization model and methods are applicable, and the performances are also greatly promoted without sacrificing any constraints of drive system. Besides, the model fits the overall performance well with respect to light-weight ratio, safety, cost reduction and others. Practical implications The proposed drive system optimization method has been used for a 4-DOF palletizing robot, which has been largely manufactured in a factory. Originality/value This paper focuses on how the simulation-based optimization can be used for the purpose of generating trade-offs between cost, performance and lifetime when designing robotic drive system. An applicable optimization model and method are proposed to handle the dynamic performance optimization problem of a drive system for industrial robot.

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