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Enhancing Power Enterprise Inspection and Supervision: A LoRA-Based Lightweight LLM Framework Integrating Retrieval-Augmented Generation and Prompt Engineering 认领 引用
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作者 Jianfeng Liu Yongjiao Yang +4 位作者 Kangyi Yang Changhua Hu Zijia Xu Qingguo Shi Yi Su 《Computers, Materials & Continua》 SCIE EI 2026年第8期2256-2275,共20页
Power enterprise inspection and supervision require greater intelligence,efficiency,and standardization;however,existing approaches are limited by inefficient knowledge retrieval,inaccurate issue identification,and in... Power enterprise inspection and supervision require greater intelligence,efficiency,and standardization;however,existing approaches are limited by inefficient knowledge retrieval,inaccurate issue identification,and insufficient support for standardized reporting and rectification tracking.This study proposes a lightweight,domain-adaptive large language model(LLM)framework based on Low-Rank Adaptation(LoRA),integrating Retrieval-Augmented Generation(RAG)and structured prompt engineering to enable evidence-grounded inspection tasks.The framework achieves parameter-efficient adaptation through low-rank decomposition and constructs a domain-specific multimodal knowledge base,enhancing output traceability,consistency,and task generalization.A key contribution is the introduction of a Sensitive Information Control Gate,which enforces role-based access control and automated redaction,ensuring secure and compliant generation in regulated environments while preserving traceability.Experimental results demonstrate that the proposed method achieves improved performance over the base model and demonstrates competitive effectiveness under the evaluated conditions,supported by statistical analysis(paired t-test,p<0.01,bootstrap 95%confidence intervals),while maintaining high parameter efficiency with only 0.4%–0.5%trainable parameters. 展开更多
关键词 Large language models LoRA fine-tuning retrieval-augmented generation prompt engineering inspection and supervision power enterprise governance
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Chronological Passage Assembly for Retrieval-Augmented Generation in Narrative Question Answering 认领 引用
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作者 Byeongjeong Kim Jeonghyun Park +1 位作者 Joonho Yang Hwanhee Lee 《Computers, Materials & Continua》 SCIE EI 2026年第9期2167-2187,共21页
Long-context question answering over narrative documents remains challenging because many questions require reconstructing event sequences while preserving local contextual flow under limited context budgets.Existing ... Long-context question answering over narrative documents remains challenging because many questions require reconstructing event sequences while preserving local contextual flow under limited context budgets.Existing retrieval-augmented generation(RAG)methods typically retrieve document snippets independently,which can fragment narratives and harm temporal dependencies.We propose ChronoRAG,a retrieval framework for narrative question answering that first converts sequential document chunks into concise relation descriptions and then retrieves relevant units together with their adjacent chronological context.This design preserves retrieval precision while providing the generator with coherent local narrative structure.Experiments on NarrativeQA and GutenQA show that ChronoRAG improves performance on NarrativeQA and remains competitive on GutenQA,with particularly strong gains on questions that require chronology-sensitive context.These results suggest that explicitly modeling local event order is a useful retrieval signal for narrative question answering. 展开更多
关键词 Retrieval-augmented generation narrative question answering long-context reasoning temporal reasoning knowledge graphs
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Nursing Retrieval-Augmented Generation:Retrieval augmented generation for nursing question answering with large language models 认领 引用
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作者 Liping Xiong Qiqiao Zeng +1 位作者 Weixiang Luo Ronghui Liu 《International Journal of Nursing Sciences》 CSCD 2025年第6期516-523,I0001,共8页
Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Me... Objective:This study aimed to develop a Nursing Retrieval-Augmented Generation(NurRAG)system based on large language models(LLMs)and to evaluate its accuracy and clinical applicability in nursing question answering.Methods:A multidisciplinary team consisting of nursing experts,artificial intelligence researchers,and information engineers collaboratively designed the NurRAG framework following the principles of retrieval-augmented generation.The system included four functional modules:1)construction of a nursing knowledge base through document normalization,embedding,and vector indexing;2)nursing question filtering using a supervised classifier;3)semantic retrieval and re-ranking for evidence selection;and 4)evidence-conditioned language model generation to produce citation-based nursing answers.The system was securely deployed on hospital intranet servers using Docker containers.Performance evaluation was conducted with 1,000 expert-verified nursing question–answer pairs.Semantic fidelity was assessed using Recall Oriented Understudy for Gisting Evaluation–Longest Common Subsequence(ROUGE-L),and clinical correctness was measured using Accuracy.Results:The NurRAG system achieved significant improvements in both semantic fidelity and answer accuracy compared with conventional large language models.For ChatGLM2-6B,ROUGE-L increased from(30.73±1.48)%to(64.27±0.27)%,and accuracy increased from(49.08±0.92)%to(75.83±0.35)%.For LLaMA2-7B,ROUGE-L increased from(28.76±0.89)%to(60.33±0.21)%,and accuracy increased from(43.27±0.83)%to(73.29±0.33)%.All differences were statistically significant(P<0.001).A quantitative case analysis further demonstrated that NurRAG effectively reduced hallucinated outputs and generated evidence-based,guideline-concordant nursing responses.Conclusion:The NurRAG system integrates domain-specific retrieval with LLMs generation to provide accurate,reliable,and traceable evidence-based nursing answers.The findings demonstrate the system’s feasibility and potential to improve the accuracy of clinical knowledge access,support evidence-based nursing decision-making,and promote the safe application of artificial intelligence in nursing practice. 展开更多
关键词 Evidence-based nursing Large language models Nursing knowledge base Question-answering system Retrieval-augmented generation
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A Dynamic Knowledge Base Updating Mechanism-Based Retrieval-Augmented Generation Framework for Intelligent Question-and-Answer Systems 认领 引用 被引量：2
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作者 Yu Li 《Journal of Computer and Communications》 2025年第1期41-58,共18页
In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilizati... In the context of power generation companies, vast amounts of specialized data and expert knowledge have been accumulated. However, challenges such as data silos and fragmented knowledge hinder the effective utilization of this information. This study proposes a novel framework for intelligent Question-and-Answer (Q&A) systems based on Retrieval-Augmented Generation (RAG) to address these issues. The system efficiently acquires domain-specific knowledge by leveraging external databases, including Relational Databases (RDBs) and graph databases, without additional fine-tuning for Large Language Models (LLMs). Crucially, the framework integrates a Dynamic Knowledge Base Updating Mechanism (DKBUM) and a Weighted Context-Aware Similarity (WCAS) method to enhance retrieval accuracy and mitigate inherent limitations of LLMs, such as hallucinations and lack of specialization. Additionally, the proposed DKBUM dynamically adjusts knowledge weights within the database, ensuring that the most recent and relevant information is utilized, while WCAS refines the alignment between queries and knowledge items by enhanced context understanding. Experimental validation demonstrates that the system can generate timely, accurate, and context-sensitive responses, making it a robust solution for managing complex business logic in specialized industries. 展开更多
关键词 Retrieval-Augmented Generation Question-and-Answer Large Language Models Dynamic Knowledge Base Updating Mechanism Weighted Context-Aware Similarity
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The Semantic Design Space of Retrieval-Augmented Recommender Systems:A Systematic Review of LLM-Based Approaches 认领 引用
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作者 Minhyeok Choi Imran Ahsan +2 位作者 Hyunwook Yu Taeyoung Choe Mucheol Kim 《Computers, Materials & Continua》 SCIE EI 2026年第7期77-119,共43页
Large language models(LLMs)are increasingly integrated into recommender systems to support semantic reasoning,natural language understanding,and user-adaptive personalization.However,their reliance on static parametri... Large language models(LLMs)are increasingly integrated into recommender systems to support semantic reasoning,natural language understanding,and user-adaptive personalization.However,their reliance on static parametric knowledge and fixed representations limits robustness in dynamic environments,particularly under long-tail and cold-start conditions.Retrieval-augmented architectures have emerged to address these limitations by grounding LLMs in external,non-parametric knowledge sources.This systematic literature review synthesizes 138 peer-reviewed studies published between 2023 and 2025 in conferences and journals,focusing on retrieval-augmented and LLM-enhanced recommendation.We analyze these works through a three-dimensional framework covering:(i)domain application,(ii)semantic feature and representation design,and(iii)algorithmic strategies for retrieval and personalization.The review shows that current research is concentrated in general recommendation and information retrieval,that similarityetrieval,user-item interaction,and textual content signals dominate semantic modeling,and that LLM and BERT-style encoders form the primary representation backbones,while graph-based,multimodal,and hybrid approaches remain comparatively underexplored.Algorithmically,most systems adopt generic LLM-centric modeling with limited use of retrieval optimization,reinforcement learning,or structure-aware strategies,and only sporadic attention to explicit cold-start,hallucination,and robustness treatment.By mapping co-occurrence patterns between domains,semantic features,representation choices,and strategy families,this review identifies concrete gaps and transfer opportunities for future work on retrieval-augmented recommendation and provides a structured reference for designing more context-aware,explainable,and data-efficient LLM-based recommender systems. 展开更多
关键词 Large language models(LLMs) recommender system retrieval-augmented generation(RAG) semantic features
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Retrieval-Augmented Large Language Model for AWS Cloud Threat Detection and Modelling:Cloudtrail Mitre ATT&CK Mapping 认领 引用
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作者 Goodness Adediran Kenny Awuson-David Yussuf Ahmed 《Computers, Materials & Continua》 SCIE EI 2026年第5期2307-2331,共25页
Amazon Web Services(AWS)Cloud Trail auditing service provides detailed records of operational and security events,enabling cloud administrators to monitor user activity and manage compliance.Although signaturebased th... Amazon Web Services(AWS)Cloud Trail auditing service provides detailed records of operational and security events,enabling cloud administrators to monitor user activity and manage compliance.Although signaturebased threat detection methods have been enhanced with machine learning and Large Language Models(LLMs),these approaches remain limited in addressing emerging threats.This study evaluates a two-step Retrieval Augmented Generation(RAG)approach using Gemini 2.5 Pro to enhance threat detection accuracy and contextual relevance.The RAG system integrates external cybersecurity knowledge sources including the MITRE ATT&CK framework,AWS Threat Technique Catalogue,and threat reports to overcome limitations of static pre-trained LLMs.We constructed an evaluation dataset of 200 unique CloudTrail events(122 malicious,78 benign)using the Stratus Red Team adversary emulation framework,covering 9 MITRE ATT&CK techniques across 8 tactics.Events were sampled from 1724 total events using stratified sampling.Ground truth labels were created through systematic expert annotation with 90%inter-annotator agreement.The RAG-enabled model achieved estimated 78%accuracy,85%precision,and 79%F1-score,representing 70.5%accuracy improvement and 76.4%F1-score improvement over baseline Gemini 2.5 Pro(46%accuracy,45%F1-score).Performance are based on evaluation results on 200-event dataset.Cost-latency analysis revealed processing time of 4.1 s and cost of$0.00376 per event,comparable to commercial SIEM solutions while providing superior MITRE ATT&CK attribution.The findings demonstrate that RAG substantially enhances context-aware threat detection,providing actionable insights for cloud security operations. 展开更多
关键词 Retrieval-augmented generation Amazon web services LLM cloud service provider threat detection threat modelling MITRE ATT&CK RAG-enabled model RAG-enabled LLM system
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From Documents to Decisions:Enterprise-Grade LLM Systems for Zero-Hallucination,Attributed Generation,and Regulatory Alignment 认领 引用
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作者 Yenjou Wang Chihtan Cheng Jia-Wei Chang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期270-324,共55页
As large language models(LLMs)become increasingly integrated into enterprise decision-making processes,structural pressures such as version drift,cross-source evidence integration,and regulatory accountability have sh... As large language models(LLMs)become increasingly integrated into enterprise decision-making processes,structural pressures such as version drift,cross-source evidence integration,and regulatory accountability have shifted the primary challenge from isolated generative performance to system-level consistency,traceability,and governability.This paper systematically reviews key technological developments relevant to enterprise requirements,including document perception,retrieval-augmented generation(RAG),hybrid RAG-KG architectures,fine-grained attribution evaluation,and multi-agent coordination.The analysis demonstrates that the main obstacle to enterprise LLM adoption is not model capability,but rather the structural gap between fragmented technical modules and the need for high-reliability decision-making.In response,a risk-controlled data flywheel architecture is proposed that integrates perception,reasoning,verification,and governance layers.By converting reasoning outputs into observable risk signals and feeding them back into retrieval and structural components,this architecture establishes a continuous improvement loop.This approach provides a systematic deployment blueprint for enterprise-grade LLM systems,emphasizing traceability,accountability,and sustainable optimization in high-risk and long-term operational contexts. 展开更多
关键词 Large language models(LLMs) retrieval-augmented generation(RAG) knowledge graph(KG) optical character recognition(OCR) enterprise AI systems risk-controlled architecture governance and compliance attribution and faithfulness multi-agent systems data flywheel
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A Comprehensive Review of Sizing and Allocation of Distributed Power Generation:Optimization Techniques,Global Insights,and Smart Grid Implications 认领 引用 被引量：1
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作者 Abdullrahman A.Al-Shamma’a Hassan M.Hussein Farh +4 位作者 Ridwan Taiwo Al-Wesabi Ibrahim Abdulrhman Alshaabani Saad Mekhilef Mohamed A.Mohamed 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第11期1303-1347,共45页
Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive ... Optimal sizing and allocation of distributed generators(DGs)have become essential computational challenges in improving the performance,efficiency,and reliability of electrical distribution networks.Despite extensive research,existing approaches often face algorithmic limitations such as slow convergence,premature stagnation in local minima,or suboptimal accuracy in determining optimal DG placement and capacity.This study presents a comprehensive scientometric and systematic review of global research focused on computer-based modelling and algorithmic optimization for renewable DG sizing and placement.It integrates both quantitative and qualitative analyses of the scholarly landscape,mapping influential research domains,co-authorship structures,the articles’citation networks,keyword clusters,and international collaboration patterns.Moreover,the study classifies and evaluates the most prominent objective functions,key computational models and optimization algorithms,DG technologies,and strategic approaches employed in the field.The findings reveal that advanced algorithmic frameworks substantially enhance network stability,minimize real power losses,and improve voltage profiles under various operational constraints.This review serves as a foundational resource for researchers and practitioners,highlighting emerging algorithmic trends,modelling innovations,and data-driven methodologies that can guide future development of intelligent,optimization-based DG integration strategies in smart distribution systems. 展开更多
关键词 Systematic and scientometric global trends distributed generation sizing and allocation multiobjectives modelling and algorithmic optimization
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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking 认领 引用 被引量：1
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作者 Shu-Chuan Chu Libin Fu +2 位作者 Jeng-Shyang Pan Xingsi Xue Min Liu 《Computers, Materials & Continua》 SCIE EI 2025年第5期3135-3163,共29页
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm... Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency. 展开更多
关键词 Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework
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Design strategy of advanced generation breeding population of Pinus tabuliformis based on genetic variation and inbreeding level 认领 引用 被引量：1
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作者 Chengcheng Zhou Fan Sun +2 位作者 Zhiyuan Jiao Yousry A.El-Kassaby Wei Li 《Forest Ecosystems》 SCIE CAS CSCD 2025年第4期607-618,共12页
The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement.The relationship between genetic variation level within Pinus tabuliformis breeding... The level of genetic variation within a breeding population affects the effectiveness of selection strategies for genetic improvement.The relationship between genetic variation level within Pinus tabuliformis breeding populations and selection strategies or selection effectiveness is not fully investigated.Here,we compared the selection effectiveness of combined and individual direct selection strategies using half-and full-sib families produced from advanced-generation P.tabuliformis seed orchard as our test populations.Our results revealed that,within half-sib families,average diameter at breast height(DBH),tree height,and volume growth of superior individuals selected by the direct selection strategy were higher by 7.72%,18.56%,and 31.01%,respectively,than those selected by the combined selection strategy.Furthermore,significant differences(P<0.01)were observed between the two strategies in terms of the expected genetic gains for average tree height and volume.In contrast,within full-sib families,the differences in tree average DBH,height,and volume between the two selection strategies were relatively minor with increase of 0.17%,2.73%,and 2.21%,respectively,and no significant differences were found in the average expected genetic gains for the studied traits.Half-sib families exhibited greater phenotypic and genetic variation,resulting in improved selection efficiency with the direct selection strategy but also introduced a level of inbreeding risk.Based on genetic distance estimates using molecular markers,our comparative seed orchard design analysis showed that the Improved Adaptive Genetic Programming Algorithm(IAPGA)reduced the average inbreeding coefficient by 14.36% and 14.73% compared to sequential and random designs,respectively.In conclusion,the combination of the direct selection strategy with IAPGA seed orchard design aimed at minimizing inbreeding offered an efficient approach for establishing advanced-generation P.tabuliformis seed orchards. 展开更多
关键词 Breeding population Parental selection strategies Advanced generation seed orchard Pinus tabuliformis Seed orchard design Improved Adaptive Genetic Programming Algorithm
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Large Language Models for Effective Detection of Algorithmically Generated Domains:A Comprehensive Review 认领 引用 被引量：1
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作者 Hamed Alqahtani Gulshan Kumar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期1439-1479,共41页
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me... Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems. 展开更多
关键词 Adversarial domains cyber threat detection domain generation algorithms large language models machine learning security
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Improving Clinical Support through Retrieval-Augmented Generation Powered Virtual Health Assistants 认领 引用
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作者 Biju Baburajan Anandavally 《Journal of Computer and Communications》 2024年第11期86-94,共9页
This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with p... This article examines the implementation of a virtual health assistant powered by Retrieval-Augmented Generation (RAG) and GPT-4, aimed at enhancing clinical support through personalized, real-time interactions with patients. The system is hypothesized to improve healthcare accessibility, operational efficiency, and patient outcomes by automating routine tasks and delivering accurate health information. The assistant leverages natural language processing and real-time data retrieval models to respond to patient inquiries, schedule appointments, provide medication reminders, assist with symptom triage, and answer insurance-related questions. By integrating RAG-based virtual care, the system reduces the burden on healthcare specialists and helps mitigate healthcare disparities, particularly in rural areas where traditional care is limited. Although the initial scope of testing did not validate all potential benefits, the results demonstrated high patient satisfaction and strong response accuracy, both critical for systems of this nature. These findings underscore the transformative potential of AI-driven virtual health assistants in enhancing patient engagement, streamlining operational workflows, and improving healthcare accessibility, ultimately contributing to better outcomes and more cost-effective care delivery. 展开更多
关键词 Retrieval-Augmented Generation (RAG) GPT-4 Healthcare Assistants Artificial Intelligence
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Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm 认领 引用 被引量：10
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作者 K.Jagatheesan B.Anand +3 位作者 Sourav Samanta Nilanjan Dey Amira S.Ashour Valentina E.Balas 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2019年第2期503-515,共13页
Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system ... Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller. 展开更多
关键词 Automatic generation control(AGC) firefly algorithm genetic algorithm(GA) particle swarm optimization(PSO) proportional-integral-derivative(PID) controller
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Optimising PID Controllers for Multi-Area Automatic Generation Control With Improved NSGA-Ⅱ 认领 引用
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作者 Yang Yang Yuchao Gao +1 位作者 Shangce Gao Jinran Wu 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2025年第4期1135-1147,共13页
Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonli... Modern automated generation control(AGC)is increasingly complex,requiring precise frequency control for stability and operational accuracy.Traditional PID controller optimisation methods often struggle to handle nonlinearities and meet robustness requirements across diverse operational scenarios.This paper introduces an enhanced strategy using a multi-objective optimisation framework and a modified non-dominated sorting genetic algorithm Ⅱ(SNSGA).The proposed model optimises the PID controller by minimising key performance metrics:integration time squared error(ITSE),integration time absolute error(ITAE),and rate of change of deviation(J).This approach balances convergence rate,overshoot,and oscillation dynamics effectively.A fuzzy-based method is employed to select the most suitable solution from the Pareto set.The comparative analysis demonstrates that the SNSGA-based approach offers superior tuning capabilities over traditional NSGA-Ⅱ and other advanced control methods.In a two-area thermal power system without reheat,the SNSGA significantly reduces settling times for frequency deviations:2.94s for Δf1 and 4.98s for Δf2,marking improvements of 31.6%and 13.4%over NSGA-Ⅱ,respectively. 展开更多
关键词 automatic generation control load frequency control multi-objective optimization nondominated sorting genetic algorithmⅡ PID controller
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On the Generation Algorithm of VRS Virtual Observations 认领 引用 被引量：3
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作者 WEI Erhu CHAI Hua +1 位作者 LIU Jingnan AN Zhiguo 《Geo-Spatial Information Science》 EI 2007年第2期91-95,共5页
In the past few years, network RTK positioning technology, especially the VRS technology, has been widely used in some parts of China and in many countries around the world. The principle of the VRS technology is disc... In the past few years, network RTK positioning technology, especially the VRS technology, has been widely used in some parts of China and in many countries around the world. The principle of the VRS technology is discussed with corresponding formula deduction, and detailed descriptions and applications of VRS corrections and virtual observations generation algorithm are given. 展开更多
关键词 network-RTK MRS corrections generation algorithm virtual observation
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Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm 认领 引用 被引量：3
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作者 Wei XIE Chang-ming JI +1 位作者 Zi-jun YANG Xiao-xing ZHANG 《Water Science and Engineering》 EI CAS 2012年第1期46-58,共13页
Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity... Power generation dispatching is a large complex system problem with multi-dimensional and nonlinear characteristics. A mathematical model was established based on the principle of reservoir operation. A large quantity of optimal scheduling processes were obtained by calculating the daily runoff process within three typical years, and a large number of simulated daily runoff processes were obtained using the progressive optimality algorithm （POA） in combination with the genetic algorithm （GA）. After analyzing the optimal scheduling processes, the corresponding scheduling rules were determined, and the practical formulas were obtained. These rules can make full use of the rolling runoff forecast and carry out the rolling scheduling. Compared with the optimized results, the maximum relative difference of the annual power generation obtained by the scheduling rules is no more than 1%. The effectiveness and practical applicability of the scheduling rules are demonstrated by a case study. This study provides a new perspective for formulating the rules of power generation dispatching. 展开更多
关键词 scheduling rule short-time power generation dispatching, hybrid algorithm cascade hydropower station
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Improving PID Controller Performance in Nonlinear Oscillatory Automatic Generation Control Systems Using a Multi-objective Marine Predator Algorithm with Enhanced Diversity 认领 引用 被引量：1
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作者 Yang Yang Yuchao Gao +2 位作者 Jinran Wu Zhe Ding Shangrui Zhao 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第5期2497-2514,共18页
Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy... Power systems are pivotal in providing sustainable energy across various sectors.However,optimizing their performance to meet modern demands remains a significant challenge.This paper introduces an innovative strategy to improve the opti-mization of PID controllers within nonlinear oscillatory Automatic Generation Control(AGC)systems,essential for the stability of power systems.Our approach aims to reduce the integrated time squared error,the integrated time absolute error,and the rate of change in deviation,facilitating faster convergence,diminished overshoot,and decreased oscillations.By incorporating the spiral model from the Whale Optimization Algorithm(WOA)into the Multi-Objective Marine Predator Algorithm(MOMPA),our method effectively broadens the diversity of solution sets and finely tunes the balance between exploration and exploitation strategies.Furthermore,the QQSMOMPA framework integrates quasi-oppositional learning and Q-learning to overcome local optima,thereby generating optimal Pareto solutions.When applied to nonlinear AGC systems featuring governor dead zones,the PID controllers optimized by QQSMOMPA not only achieve 14%reduction in the frequency settling time but also exhibit robustness against uncertainties in load disturbance inputs. 展开更多
关键词 Multi-objective optimization Automatic generation control PID controller Multi-objective marine predator algorithm Whale optimization algorithm
Optimal Scheduling Method of Cogeneration System with Heat Storage Device Based on Memetic Algorithm 认领 引用 被引量：2
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作者 Haibo Li YibaoWang +2 位作者 Xinfu Pang Wei Liu Xu Zhang 《Energy Engineering》 EI 2023年第2期317-343,共27页
Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To imp... Electric-heat coupling characteristics of a cogeneration system and the operating mode of fixing electricity with heat are the main reasons for wind abandonment during the heating season in the Three North area.To improve the wind-power absorption capacity and operating economy of the system,the structure of the system is improved by adding a heat storage device and an electric boiler.First,aiming at the minimum operating cost of the system,the optimal scheduling model of the cogeneration system,including a heat storage device and electric boiler,is constructed.Second,according to the characteristics of the problem,a cultural gene algorithm program is compiled to simulate the calculation example.Finally,through the system improvement,the comparison between the conditions before and after and the simulation solutions of similar algorithms prove the effectiveness of the proposed scheme.The simulation results show that adding the heat storage device and electric boiler to the scheduling optimization process not only improves the wind power consumption capacity of the cogeneration system but also reduces the operating cost of the system by significantly reducing the coal consumption of the unit and improving the economy of the system operation.The cultural gene algorithm framework has both the global evolution process of the population and the local search for the characteristics of the problem,which has a better optimization effect on the solution. 展开更多
关键词 Combined heat and power generation heat storage device memetic algorithm simulated annealing wind abandonment
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Solving open vehicle problem with time window by hybrid column generation algorithm 认领 引用 被引量：1
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作者 YU Naikang QIAN Bin +2 位作者 HU Rong CHEN Yuwang WANG Ling 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2022年第4期997-1009,共13页
This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the ... This paper addresses the open vehicle routing problem with time window(OVRPTW), where each vehicle does not need to return to the depot after completing the delivery task.The optimization objective is to minimize the total distance. This problem exists widely in real-life logistics distribution process.We propose a hybrid column generation algorithm(HCGA) for the OVRPTW, embedding both exact algorithm and metaheuristic. In HCGA, a label setting algorithm and an intelligent algorithm are designed to select columns from small and large subproblems, respectively. Moreover, a branch strategy is devised to generate the final feasible solution for the OVRPTW. The computational results show that the proposed algorithm has faster speed and can obtain the approximate optimal solution of the problem with 100 customers in a reasonable time. 展开更多
关键词 open vehicle routing problem with time window(OVRPTW) hybrid column generation algorithm(HCGA) mixed integer programming label setting algorithm
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ALGORITHMS FOR TETRAHEDRAL NETWORK(TEN) GENERATION 认领 引用 被引量：11
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作者 LI Qingquan LI Deren 《Geo-Spatial Information Science》 2000年第1期11-16,共6页
The Tetrahedral Network(TEN) is a powerful 3-D vector structure in GIS, which has a lot of advantages such as simple structure, fast topological relation processing and rapid visualization. The difficulty of TEN appli... The Tetrahedral Network(TEN) is a powerful 3-D vector structure in GIS, which has a lot of advantages such as simple structure, fast topological relation processing and rapid visualization. The difficulty of TEN application is automatic creating data structure. Although a raster algorithm has been introduced by some authors, the problems in accuracy, memory requirement, speed and integrity are still existent. In this paper, the raster algorithm is completed and a vector algorithm is presented after a 3-D data model and structure of TEN have been introducted. Finally, experiment, conclusion and future work are discussed. 展开更多
关键词 3-D GIS tetrahedral network(TEN) generation algorithm
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