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Optimizing energy Dynamics: A comprehensive analysis of hybrid energy storage systems integrating battery banks and supercapacitors

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ENERGY CONVERSION AND MANAGEMENT 2024年 312卷

作者： Guven, Aykut Fatih Abdelaziz, Almoataz Y. Samy, Mohamed Mahmoud Barakat, Shimaa Yalova Univ Engn Fac Dept Elect & Elect Engn Yalova Turkiye Ain Shams Univ Fac Engn Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt Beni Suef Univ Dept Elect Engn Bani Suwayf Egypt

This study investigates the optimization of a grid-connected hybrid energy system integrating photovoltaic (PV) and wind turbine (WT) components alongside battery and supercapacitor storage. The research addresses the critical need for efficient energy storage solutions in renewable energy integration. Six optimization algorithms-AGTO, ARO, BOA, CGO, PFA, and TSO-are evaluated for their efficacy in determining optimal system configurations. The system's adaptability to dynamic scenarios is examined through comprehensive sensitivity analyses, shedding light on its robustness. The findings reveal that the CGO algorithm outperforms others by achieving optimal solutions with fewer iterations, highlighting its efficiency. Key conclusions include the identification of an optimal configuration comprising a 589.58 kW PV system, 664 kW WT, a 675-kW supercapacitor, and a 1000 kWh battery bank. This configuration achieves an 80 % renewable energy fraction (REF), reduces the annual system cost (ACS) to $603,537.8522, and maintains a competitive levelized cost of electricity (LCOE) at $0.2380 per kWh. The research underscores the significance of integrated energy storage solutions in optimizing hybrid energy configurations, offering insights crucial for advancing sustainable energy initiatives. The study contributes valuable insights to the scientific community, paving the way for more efficient and resilient renewable energy systems.

关键词： Hybrid renewable energy system Hybrid energy storage system Supercapacitor optimization algorithms Sensitivity Analysis Sustainable energy

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Review of Modeling, Modulation, and Control Strategies for the Dual-Active-Bridge DC/DC Converter

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ENERGIES 2023年第18期16卷 6646页

作者： He, Jiayang Chen, Yangyu Lin, Jiongtao Chen, Jianghui Cheng, Li Wang, Yu Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Zhongkai Univ Agr & Engn Coll Automat Guangzhou 510225 Peoples R China

This paper provides a comprehensive review of the existing research on the Dual Active Bridge (DAB) DC-DC converter, focusing on modeling methods, modulation strategies, optimization algorithms, and control methods. A comparative analysis of selected methods along with guidelines to assist engineers and researchers in their study of DAB is also presented. Firstly, a comprehensive review of modulation strategies for DAB is provided, ranging from classical phase-shift modulation to the popular asymmetric duty modulation. The intrinsic relationships among different modulation methods are summarized, and a comparison is made based on the difficulty of control and DAB operating characteristics. Secondly, the various modeling methods for DAB are described, including reduced-order modeling, generalized state-space averaging modeling, and discrete-time modeling methods. A comparison is made based on the suitability for different application scenarios, providing recommendations for the adoption of different modeling methods. Furthermore, a survey of optimization algorithms for modulation methods is presented, including classical algorithms, swarm intelligence optimization, and reinforcement learning algorithms. A number of criteria are proposed for different algorithms, and an analysis of the unresolved challenges and future prospects is provided. Finally, the advanced control methods for DAB are summarized based on control effectiveness and applicability. The article concludes with a summary and an outlook on future research directions is also provided.

关键词： dual active bridge (DAB) modeling methods modulation strategies optimization algorithms advanced control methods

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An improved grey wolf optimizer algorithm for identification and location of gas emission

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JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES 2023年第1期82卷

作者： Liu, Yizhe Jiang, Yu Zhang, Xin Pan, Yong Wang, Jun Nanjing Tech Univ Coll Safety Sci & Engn Jiangsu Key Lab Hazardous Chem Safety & Control Nanjing 211816 Peoples R China

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identifi-cation and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.

关键词： Source term estimation (STE) Swarm optimization method Hazardous gases leakage Grey wolf optimizer (GWO) optimization algorithms Differential evolutionary (DE)

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A Fuzzy Gravitational Search Algorithm to Design Optimal IIR Filters

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ENERGIES 2018年第4期11卷 736页

作者： Pelusi, Danilo Mascella, Raffaele Tallini, Luca Univ Teramo Fac Commun Sci I-64100 Teramo Italy

The goodness of Infinite Impulse Response (IIR) digital filters design depends on pass band ripple, stop band ripple and transition band values. The main problem is defining a suitable error fitness function that depends on these parameters. This fitness function can be optimized by search algorithms such as evolutionary algorithms. This paper proposes an intelligent algorithm for the design of optimal 8th order IIR filters. The main contribution is the design of Fuzzy Inference Systems able to tune key parameters of a revisited version of the Gravitational Search Algorithm (GSA). In this way, a Fuzzy Gravitational Search Algorithm (FGSA) is designed. The optimization performances of FGSA are compared with those of Differential Evolution (DE) and GSA. The results show that FGSA is the algorithm that gives the best compromise between goodness, robustness and convergence rate for the design of 8th order IIR filters. Moreover, FGSA assures a good stability of the designed filters.

关键词： optimization algorithms IIR filters gravitational search algorithm fuzzy systems

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Hierarchical prosody modeling for Mandarin spontaneous speech

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JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2019年第4期145卷 2576-2596页

作者： Lin, Cheng-Hsien You, Chung-Long Chiang, Chen-Yu Wang, Yih-Ru Chen, Sin-Horng Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 30010 Taiwan Natl Taipei Univ Dept Commun Engn New Taipei 23741 Taiwan

In this paper, a hierarchical prosody model (HPM)-based method for Mandarin spontaneous speech is proposed. First, an HPM is designed for describing relations among acoustic features of utterances, linguistic features of texts, and prosodic tags representing the underlying hierarchical prosodic structures of utterances. Subsequently, a sequential optimization algorithm is employed to train the HPM based on a large conversational speech corpus, the Mandarin Conversational Dialogue Corpus (MCDC), which features orthographic transcriptions and prosodic event annotations. In this unsupervised training method, all utterances of the MCDC are labeled with two types of prosodic tags, namely, break and prosodic states, automatically and simultaneously. After training, the HPM parameters are examined to identify critical prosodic properties of Mandarin spontaneous speech, which are then compared with their counterparts in the read-speech HPM. The prosodic tags on the studied utterances enable mapping of various prosodic events onto the hierarchical prosodic structures of the utterances. Prosodic analyses of some disfluent events are conducted using the prosodic tags affixed to the MCDC. Finally, an application of the HPM to assist in Mandarin spontaneous-speech recognition is discussed. Significant relative error rate reductions of 9.0%, 9.2%, 15.6%, and 7.3% are obtained for base-syllable, character, tone, and word recognition, respectively. (C) 2019 Acoustical Society of America.

关键词： Prosody Speech perception Energy model Cognitive science Speech communication optimization algorithms Decision theory Phonetics Speech recognition Pitch

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Improved Population Intelligence Algorithm and BP Neural Network for Network Security Posture Prediction

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2023年第1期2023卷

作者： Li, Yueying Wu, Feng Xinyang Agr & Forestry Univ Coll Informat Engn Xinyang 464000 Peoples R China

To address the problems of low prediction accuracy and slow convergence of the network security posture prediction model, a population intelligence optimization algorithm is proposed to improve the network security posture prediction model of the BP neural network. First, the adaptive adjustment of the two parameters with the increase of iterations is achieved by improving the inertia weights and learning factors in the particle swarm optimization (PSO) algorithm so that the PSO has a large search range and high speed at the initial stage and a strong and stable convergence capability at the later stage. Secondly, to address the problem that PSO is prone to fall into a local optimum, the genetic operator is embedded into the operation process of the particle swarm algorithm, and the excellent global optimization performance of the genetic algorithm is used to open up the spatial vision of the particle population, revive the stagnant particles, accelerate the update amplitude of the algorithm, and achieve the purpose of improving the premature problem. Finally, the improved algorithm is combined with the BP neural network to optimize the BP neural network and applied to the network security posture assessment. The experimental comparison of different optimization algorithms is applied, and the results show that the network security posture prediction method of this model has the smallest error, the highest accuracy, and the fastest convergence, and can effectively predict future changes in network security posture.

关键词： optimization algorithms

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Improving the Quality of Pareto Optimal Solutions in Water Distribution Network Design

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JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2017年第8期143卷 04017036-04017036页

作者： Choi, Young Hwan Jung, Donghwi Lee, Ho Min Yoo, Do Guen Kim, Joong Hoon Korea Univ Sch Civil Environm & Architectural Engn Seoul 136713 South Korea Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 136713 South Korea

This paper proposes five methods for improving the quality of Pareto optimal solutions of multiobjective optimal water distribution network (WDN) design problems: (1) three warm initial solution methods, (2) the postoptimization method, and (3) the guided-search method. The five methods were demonstrated through resilience-based design of the Hanoi network. The guided-search method, considering the reasonable range of decision variables, was identified as the best method with respect to the nondomination and diversity of the obtained Pareto solutions. Then, the effect of considering known initial solutions (e.g., least-cost solutions obtained from single-objective optimal design) on the final Pareto solution quality was investigated using the guided-search method. Finally, the guided-search method was compared with five multiobjective optimization algorithms widely used in the WDN research community through the resilience-based design of well-known benchmark WDNs (i.e., two-loop, Hanoi, Balerma, and P-city). (C) 2017 American Society of Civil Engineers.

关键词： Search methods Water distribution networks optimal solution Civil engineers American Society of Civil Engineers (ASCE) optimization algorithms

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Meta-model structural monitoring with cutting-edge AAE-VMD fusion alongside optimized machine learning methods

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年

作者： Hassani, Sahar Univ New South Wales Ctr Infrastruct Engn & Safety Sch Civil & Environm Engn Sydney NSW 2052 Australia

Transfer learning significantly enhances machine learning by leveraging knowledge from one dataset to boost performance on another, which is particularly beneficial when labelled data ares limited or costly. Sensor fusion, an essential technique in structural health monitoring, improves the effectiveness of transfer learning by combining data from multiple sensors to extract more informative features. This article proposes a novel meta-model structural monitoring using a new data fusion method named the adversarial autoencoder (AAE)-variational mode decomposition (VMD) algorithm, which integrates optimization algorithms, transfer learning, and machine learning techniques for damage detection tasks. The proposed meta-model combines transfer learning with an optimized machine learning framework for training and employs pretrained models for testing across diverse datasets. First, the tensors are fused using the AAE approach into 300 data points, reducing noise and outliers, performing dimensionality reduction, and enriching the training and test datasets. Then, a preprocessing step involves decomposing and denoising tensors using the VMD algorithm, followed by selecting the most informative intrinsic mode functions (IMFs) based on their variance. These selected IMFs are concatenated, and 13 statistical features are extracted from them and used as inputs for machine learning models. Various optimization algorithms are employed to fine-tune the hyperparameters of the machine learning models for optimal classification results. Validation of this meta-model utilizes the dataset collected from a steel grandstand structure, available in benchmark dataset format. For decomposed fused tensors, both particle swarm optimization and covariance matrix adaptation evolution strategy emerge as equally effective optimization techniques for K-nearest neighbours (KNN), consistently achieving the highest mean test accuracy and F1 score of 99.5%. Conversely, KNN stands out as a robust an

关键词： Structural health monitoring data fusion noise reduction method machine learning transfer learning optimization algorithms

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P-FARFAR2: A multithreaded greedy approach to sampling low-energy RNA structures in Rosetta FARFAR2

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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2023年第1期104卷 107878页

作者： Youmbi, Franklin Ingrid Kamga Tchendji, Vianney Kengne Djamegni, Clementin Tayou Univ Dschang Dept Math & Comp Sci POB 67 Dschang Cameroon Univ Dschang Dept Comp Engn POB 134 Bandjoun Cameroon

RNA (ribonucleic acid) structure prediction finds many applications in health science and drug discovery due to its importance in several life regulatory processes. But despite significant advances in the close field of protein prediction, RNA 3D structure still poses a tremendous challenge to predict, especially for large sequences. In this regard, the approach unfolded by Rosetta FARFAR2 (Fragment Assembly of RNA with Full-Atom Refinement, version 2) has shown promising results, but the algorithm is non-deterministic by nature. In this paper, we develop P-FARFAR2: a parallel enhancement of FARFAR2 that increases its ability to assemble low-energy structures via multithreaded exploration of random configurations in a greedy manner. This strategy, appearing in the literature under the term "parallel mechanism", is made viable through two measures: first, the synchronization window is coarsened to several Monte Carlo cycles;second, all but one of the threads are differentiated as auxiliary and set to perform a weakened version of the problem. Following empirical analysis on a diverse range of RNA structures, we report achieving statistical significance in lowering the energy levels of ensuing samples. And consequently, despite the moderate-to-weak correlation between energy levels and prediction accuracy, this achievement happens to propagate to accuracy measurements.

关键词： RNA structure prediction Fragment assembly Parallel computing Multicore CPU Greedy algorithms optimization algorithms

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Training algorithm matters for the performance of neural network potential: A case study of Adam and the Kalman filter optimizers

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JOURNAL OF CHEMICAL PHYSICS 2021年第20期155卷 204108-204108页

作者： Shao, Yunqi Dietrich, Florian M. Nettelblad, Carl Zhang, Chao Uppsala Univ Angstrom Lab Dept Chem Lagerhyddsvagen 1POB 538 S-75121 Uppsala Sweden Uppsala Univ Sci Life Lab Dept Informat Technol Div Sci Comp Lagerhyddsvagen 2POB 337 S-75105 Uppsala Sweden

One hidden yet important issue for developing neural network potentials (NNPs) is the choice of training algorithm. In this article, we compare the performance of two popular training algorithms, the adaptive moment estimation algorithm (Adam) and the extended Kalman filter algorithm (EKF), using the Behler-Parrinello neural network and two publicly accessible datasets of liquid water [Morawietz et al., Proc. Natl. Acad. Sci. U. S. A. 113, 8368-8373, (2016) and Cheng et al., Proc. Natl. Acad. Sci. U. S. A. 116, 1110-1115, (2019)]. This is achieved by implementing EKF in TensorFlow. It is found that NNPs trained with EKF are more transferable and less sensitive to the value of the learning rate, as compared to Adam. In both cases, error metrics of the validation set do not always serve as a good indicator for the actual performance of NNPs. Instead, we show that their performance correlates well with a Fisher information based similarity measure. (C) 2021 Author(s).

关键词： Topics: Interatomic potentials Molecular dynamics optimization algorithms Artificial neural networks Kalman filter Machine learning.

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