A dynamic multi-objective optimal dispatch model is established with considering both the economic and environmental costs in this paper, of which the dynamic constraints are power balance, load interruption rate, cha...
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ISBN:
(纸本)9781538622124
A dynamic multi-objective optimal dispatch model is established with considering both the economic and environmental costs in this paper, of which the dynamic constraints are power balance, load interruption rate, charge and discharge of energy storage and power output restriction of the internal micro-sources. Then, A multi-objective improved biogeography-basedoptimization (MOIBBO) algorithm is used to solve the proposed model, where the individual fitness is based on ''distance evaluation", and the ''congestion mechanism" is used to deal with the capacity overflow of the Pareto optimal solution in order to maintain the diversity and uniformity of Pareto optimal frontier (POF) distribution. Moreover, a technique of the initial point guidance is implemented in the algorithm to widen the POF, and the optimal compromise solution is determined by applying the fuzzy theory. Example simulation results verify the model rationality and present that the mentioned MOIBBO algorithm can obtain a POF with good distribution characteristics.
The stable and quick restoration is necessary for resilient microgrids. This paper proposes the new concept of grey start and its strategy as a restoration mode for hybrid AC/DC microgrid in semi-black state when the ...
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ISBN:
(纸本)9781728153018
The stable and quick restoration is necessary for resilient microgrids. This paper proposes the new concept of grey start and its strategy as a restoration mode for hybrid AC/DC microgrid in semi-black state when the system blackout occurs. Firstly, an optimal reconstruction model of the whole network is established and solved by biogeography-basedoptimization (BBO) algorithm. Secondly, the classification and evaluation model of distributed generations (DGs) is established by using improved BP artificial neural network (ANN). Thirdly, optimal path planning is realized. In addition, to improve the stability during the process of grey start restoration, the control methods of different DGs after classification are discussed. In comparison with conventional black start restoration mode, the restoration time is shortened, and the fluctuation of bus voltage is suppressed. The feasibility of the proposed grey start strategy is verified by the results of numerical example analysis and MATLAB/Simulink simulation, respectively.
With the increasingly serious problem of environmental pollution and resource scarcity, remanufacturing has become one of the popular research fields to solve these issues. However, the practical information of end-of...
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With the increasingly serious problem of environmental pollution and resource scarcity, remanufacturing has become one of the popular research fields to solve these issues. However, the practical information of end-of-life products is different (e.g. type and degree of damage) because of their various operation conditions, which complicates the reprocessing routes. Therefore, a new remanufacturing system scheduling model is proposed in this study that considers not only the coordination of remanufacturing subsystems but also job-shop-type reprocessing shops related to the diversified reprocessing routes. A hybrid meta-heuristic algorithm combining differential evolution algorithm and biogeography-based optimization algorithm through a new representation scheme is presented to address the model efficiently. Furthermore, the basic algorithms are improved by integrating the self-adaptive parameters, efficient migration and mutation operators, local search strategy, and restart strategy. Simulation experiments are performed to demonstrate the effectiveness and practicality of the proposed method compared with four baseline algorithms.
Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar threats accurately. However, PRI recogniti...
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Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar threats accurately. However, PRI recognition is a complex issue due to missing and spurious pulses, resulting in noisy PRI pattern changes in real environments. To address this problem, this paper proposes a novel approach that recognizes the five common types of PRIM through a four-phase process. In the first phase, a deep convolutional neural network (DCNN) is used as a feature extractor. Then, extreme learning machines (ELMs) are used for real-time recognition of the PRIM patterns in the second phase. In the third phase, we employ the biogeography-based optimizer (BBO) to enhance the network's robustness by optimizing the connection weights and biases. To address the increasing complexity of the model, we introduce an optimized variable-length internet protocol-based BBO (VBBO) in the fourth phase. In this approach (i.e., DCNN-VBBO-ELM), each layer of DCNN is encoded by an IP address into a habitat of VBBO in the same sequence as the DCNN layers. To evaluate the proposed method, we develop a real experimental dataset consisting of five common PRI patterns. Our approach achieves a final accuracy of 97.05%, which is better than other ELM-based benchmark models. Moreover, the proposed model requires only 27 s of training time to process 50,000 training images, confirming its real-time capabilities. In conclusion, our proposed approach improves PRI recognition by leveraging DCNN, ELM, and VBBO, resulting in a more accurate and robust real-time radar PRI classifier.
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