Thrust allocation is one of the key technologies in dynamic positioning (DP) systems. Accurately allocating the thrust and angle of each thruster will achieve the desired force and moment of the ships, which is crucia...
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Thrust allocation is one of the key technologies in dynamic positioning (DP) systems. Accurately allocating the thrust and angle of each thruster will achieve the desired force and moment of the ships, which is crucial for improving the positioning accuracy and positioning performance of marine ships. This paper develops an improved non-dominated sorting genetic algorithm (NSGA-II) to handle thrust allocation problem of dynamic positioning (DP) system in rough sea conditions. Firstly, the multi-objective optimisation model is built considering the thrust prohibited area, output angle, output thrust, angle change rate and thrust change rate as the constraints, and considering the power consumption, thrust error and thruster wear, and singular structure penalty term as the optimisation objectives simultaneously. Then, an improved NSGA-II algorithm is proposed to optimise the selection process of Pareto-optimal solution. Finally, multiple simulation results show that improved NSGA-II has better optimisation performance compared with basic NSGA-II. All of the comparisons could fully be conducted to demonstrate the effectiveness and advantages of the proposed ***: DP: Dynamic Positioning;NSGA-II: non-dominatedsortinggeneticalgorithm;SQP: Sequential Quadratic Programming;GA: geneticalgorithm;PSO: Particle Swarm Optimisation;ABC: Artificial Bee Colony;AHABC: Adaptive Hybrid Artificial Bee Colony
Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-object...
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Improving the utilization efficiency of renewable energy sources (RES) is an important task for the development of an integrated energy system (IES). To address this challenge, this paper proposes a novel multi-objective interval optimization framework for the energy hub (EH) planning problem from the perspective of the source load synergy, while considering the impacts of both supply- and demand-side uncertainties. For this aim, based on an in-depth analysis of the adjustable characteristics of various loads in EH and their effect on RES absorption, an interval model is first established to describe the responsiveness of users' load demand to real-time energy price variations and its associated uncertainties. In view of the natural contradiction between the system's economic and environmental benefits, a multi-objective interval optimization model for the EH planning problem is developed, wherein the minimization of the system's economic costs and the maximization of the RES utilization rate are considered as the dual objectives to be optimized simultaneously. Moreover, this study takes into account the uncertainties of RES availability and demand-side behaviors by using interval numbers and properly considering their impacts in the context of long-term planning. According to the features of the proposed model, the interval order relation and possible degree method are jointly used to transform the model into a deterministic optimization problem first, and then an improvednon-dominant sortinggeneticalgorithm is used to derive the optimal solution to the problem. The results show that the proposed method can effectively improve the economy of EH and the utilization efficiency of RES and flexibly meet different planning requirements, giving better engineering practicability.
The capabilities of four population based algorithms including multi-objective particle swarm optimization (MOPSO), improved non-dominated sorting genetic algorithm (NSGA-II), improved strength Pareto evolutionary alg...
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ISBN:
(纸本)9781538647691
The capabilities of four population based algorithms including multi-objective particle swarm optimization (MOPSO), improved non-dominated sorting genetic algorithm (NSGA-II), improved strength Pareto evolutionary algorithm (SPEA2) and modified Pareto envelop-based selection algorithm (PESA2) to acquire the solution for the multi-objective optimal power flow (Mo-OPF) problem are compared in this paper. For the Mo-OPF problem solution, the non-dominated solution sets are created by Pareto optimal method. The best compromise solution among different solutions sets is chosen with the help of a fuzzy based decision mechanism. These operations are carried out on a standard IEEE 30-bus six-generator system subjected to system constraints and power flow balance. The load flow calculation is conducted with the help of iterative method. Total cost minimization of generation and total generation emission minimization are observed as desired function for optimal power flow (OPF) problem. In this paper, above algorithms solve Mo-OPF problem on the basis of operational feasibility, efficient operation, operational speed and best optimal solution for the given objectives.
Water-sediment regulation (WSR) is an effective non-engineering measure to alleviate the problem of suspended river and bring benefit to flood control security in sediment-laden river. However, WSR may decrease the so...
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Water-sediment regulation (WSR) is an effective non-engineering measure to alleviate the problem of suspended river and bring benefit to flood control security in sediment-laden river. However, WSR may decrease the socioeconomic benefit of reservoirs, for example, reduction of hydropower production and water supply. In order to satisfy the practical requirement of WSR and other utilization objectives, this paper presents a multi-objective operation model for a cascade reservoirs simultaneously considering the maximization water volume for WSR and power generation and water supply, as well as various complex constraints. Then, the non-dominatedsortinggeneticalgorithm (NSGA-II) is improved to solve the aforementioned model and key control indicators of WSR are analyzed. Meanwhile, a sediment transport model has been introduced to quantify the effect of WSR. The models are applied to the cascade reservoirs in the Upper Yellow River. The following conclusion can be drawn from results (1) Pareto fronts of the model solution demonstrate a strong competition between WSR and water supply, water supply and power generation, a low sensitivity between WSR and power generation;(2) the ability of WSR in Upper Yellow River is 6 times in 24 years, which means the frequency of WSR is four years averagely;(3) 233.77 million tons of sediments are transported by long-term WSR in the Ningxia-Inner Mongolia reaches, account for 19.10% of sediment deposition;(4) the risk-free conditions of LYX and LJX reservoirs' water volume for WSR are 137.42 x 10(8) m(8) and 41.08 x 10(8) m(8), respectively, which could be used as a reference in actual operation. The research results have an important practical significance and application for sediment control and governance of suspended river, and the multi-objective operation model of WSR proposed in this study can be effectively and suitably used in sediment regulation with similar conditions.
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