This paper focuses on the environmental and economic optimal operation of microgrid in grid-connected and island modes. Considering the operation constrains of microgrid, the constrained multi-objective optimization p...
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ISBN:
(纸本)9781538629017
This paper focuses on the environmental and economic optimal operation of microgrid in grid-connected and island modes. Considering the operation constrains of microgrid, the constrained multi-objective optimization problem (CMOP) is built with the fuel cost, depreciation expense and emission cost of distributed generators as optimization objectives. The comprehensive learning particle swarm optimization (CLPSO) is applied for the solutions of outputs from distributed generators. An AC/DC hybrid microgrid including photovoltaic, diesel generator, lithium battery, electric vehicle charging points and load is researched. optimization results demonstrate the effectiveness of the proposed models and algorithm.
In this paper, the environment heterogeneous fixed fleet vehicle routing problem with time windows (EHFFVRP-TW) has been proposed. The model added the carbon emission factor and time windows based on the heterogeneous...
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ISBN:
(纸本)9783319410098;9783319410081
In this paper, the environment heterogeneous fixed fleet vehicle routing problem with time windows (EHFFVRP-TW) has been proposed. The model added the carbon emission factor and time windows based on the heterogeneous fixed fleet vehicle routing problem (HFFVRP) where we consider the cost benefit of carbon trading by selling or purchasing the carbon emission rights. comprehensive learning particle swarm optimization (CLPSO) is presented to solve the model, and the performance of CLPSO is estimated by comparing with PSO and GA. We adopt binary encoding way and set 2N dimension of particle to correspond the customer. The first N dimensional coding represents the vehicle number which visited the customer. And the last N dimensional coding correspond to the path order of vehicle. In the experiment, it's demonstrated CLPSO performs best compared with other two algorithm. CLPSO has improved the shortage of premature convergence in PSO and showed the advantages of getting lower cost and better routing.
To solve the problem of predicting water level in front of check gate under different time scales, a different time scale prediction model with a long short term memory (LSTM) neural network based on adaptive inertia ...
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To solve the problem of predicting water level in front of check gate under different time scales, a different time scale prediction model with a long short term memory (LSTM) neural network based on adaptive inertia weight comprehensive learning particle swarm optimization (AIW-CLPSO) is proposed. The AIW and CLPSO are adopted to improve the global optimization ability and convergence velocity of particleswarmoptimization in the proposed model. The model was applied to the water level prediction in front of the Chaohu Lake check gate. The example of the water level prediction in front of the Chaohu Lake check gate shows that the proposed model predicts the trend of water level fluctuation better than LSTM with high accuracy of Nash coefficient up to 0.9851 and root mean square error up to 0.0273 m. The optimized algorithm can obtain the optimal parameters of the LSTM neural network, overcome the limitations of the traditional LSTM neural network in parameter selection and inaccurate prediction, and maintain good prediction results in the predicting water level in front of the check gate at different time *** study can provide important reference for water level prediction, scheduling warning, water resources scheduling decision and intelligent gate control in long distance water transfer projects.
Generation Expansion Planning (GEP) is a problem that comprises multiple contradictory features while planning to construct new generating units. It must be solved by considering cost, reliability and environmental em...
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Generation Expansion Planning (GEP) is a problem that comprises multiple contradictory features while planning to construct new generating units. It must be solved by considering cost, reliability and environmental emission. Hence the mathematical representations have been developed to be accurate, and to improve the understanding of the multifaceted and contradictory aspects of the GEP problem. In this study, the Multi-Objective comprehensive learning particle swarm optimization (MOCLPSO) algorithm is implemented for solving Multi-Objective GEP (MOGEP) problem. The objectives such as minimization of overall cost, decrement of the pollutant emission and enhancement of reliability have been considered by considering the constraints. The real-world MOGEP problem has been solved for seven-year (from the year 2020 to 2027) and fourteen-year (from the year 2020 to 2034) planning span for the utility power system of Tamil Nadu state, India. The problem is solved for four different cases with the consideration of retirement and recuperation of the older generating units. coal, gas, oil, nuclear, hydel, wind, Solar-photovoltaic (SPV) and biomass power plants were considered in this planning study. The results establish the competence of MOCLPSO to produce well-spread Pareto optimal non-dominated solutions of the MOGEP problem.
Frequent pattern mining has attracted much attention and wide applications owing to its simple concept and strategy. It is of the most important task in data mining and knowledge discovery. But usually a large number ...
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Frequent pattern mining has attracted much attention and wide applications owing to its simple concept and strategy. It is of the most important task in data mining and knowledge discovery. But usually a large number of frequent patterns get generated from a large scale of data matrix which is a time consuming affair. So, in order to discretize the data matrix a mathematical concept called fuzzy logic was used. It generalizes the data matrix values in the range of 0 to 1. In the due course of time, an evolutionary algorithm, called particleswarmoptimization (PSO) has also gained much popularity. But due to the premature convergence of PSO, a comprehensivelearning strategy was introduced that used all particles’ best information to update a particle's velocity. It also enabled the diversity of the swarm to be preserved to discourage premature convergence. In this paper, frequent patterns were generated from the fuzzy dataset (data matrix converted into fuzzy data matrix) using the Frequent Pattern (FP) growth algorithm. In order to generate some of the best individual frequent patterns out of the entire set of patterns, the CLPSO algorithm was used with a selection measure called mean squared residue (MSR) score. It was noted that the CLPSO algorithm outperformed the traditional PSO algorithm in the generation of the best individual patterns with a comparatively lower MSR value.
The servo turntable is an essential part of radar antenna, plays an important role for the accuracy and smoothness of tracking. There are several working stations which is different from even running in the antenna se...
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ISBN:
(纸本)9781479984671
The servo turntable is an essential part of radar antenna, plays an important role for the accuracy and smoothness of tracking. There are several working stations which is different from even running in the antenna servo turntable, such as lowspeeding, changing-over, acceleration or deceleration etc. In this paper, a kind of switched model is introduced to describe the relationship between input and output signals in complex working conditions for the turntable. Then the parameters identification of switched system is summarized as a Constrained Multi-Objective Problem (CMOP). Further, the comprehensive learning particle swarm optimization (CLPSO) is applied to the proposed CMOP to obtain a set of appropriate parameters. Finally, the simulation and quality of fitness calculation results demonstrate the precision of the switched model and effectiveness of the identification algorithm.
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