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Detection of faults and DG islanding in PV-Wind DC ring bus microgrid by using optimized VMD based improved broad learning system

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ISA TRANSACTIONS 2022年 131卷 533-551页

作者： Anjaiah, Kanche Dash, P. K. Sahani, Mrutyunjaya Siksha O Anusandhan Deemed Be Univ Bhubaneswar Odisha India

This paper presents a novel approach for the detection and classification of photovoltaic with wind based DC ring bus microgrid DC faults and DG (distributed generation) islanding events. This novel approach consists of adaptive variational mode decomposition (AVMD) and an improved broad learning system (IBLS). Initially, DC fault current signals are captured from the DC bus under different operating conditions and processed through the AVMD to decompose the signals into intrinsic mode functions (IMFs). The VMD is made adaptive by minimizing the objective function of the L-kurtosis index for optimal modal number (K) and penalty factor (Sigma) through the improved whale optimization (IWO) algorithm. From the optimal IMFs, the most significant IMFs are chosen based on the threshold of the L-kurtosis index, and they are passed through statistical features to extract efficient data. Further, the training and testing of this data set is carried out through IBLS for obtaining the accurate detection and discrimination of DC faults. The conventional BLS method is improved through elastic net ridge regression for calculating the weights. The effectiveness of the proposed AVMD based IBLS algorithm is verified by its superiority in terms of relative computation time (RCT), classification accuracy (CA) with the confusion matrix, and their performance indices by comparing with other existing methods under different case studies. Finally, the simplicity and practicability of the proposed work are tested and implemented in the dSPACE 1104 embedded processor.(c) 2022 Published by Elsevier Ltd on behalf of ISA.

关键词： Adaptive variational mode decomposition DC-ring microgrid DG islanding Fault detection Faults classification improved broad learning system improved whale optimization algorithm dSPACE interface

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Explicit aspects extraction in sentiment analysis using optimal rules combination

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 114卷 448-480页

作者： Tubishat, Mohammad Idris, Norisma Abushariah, Mohammad Univ Malaya Fac Comp Sci & Informat Technol Dept Artificial Intelligence Kuala Lumpur 50603 Malaysia Univ Jordan King Abdullah II Sch Informat Technol Comp Informat Syst Dept Amman Jordan

Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. To accomplish the new algorithm, 126 aspect extraction rules are combined to cover both formal and informal texts, because customer reviews are a mix of formal and informal texts. These 126 rules include certain dependency-based rules and pattern-based rules from previous studies, in addition to newly developed rules intended to overcome prior rules' weaknesses. In addition, many aspect extraction rules have remained unexplored by previous studies. However, many of these 126 rules are irrelevant and should be removed. Thus, a prober selection of the included rules is required. Therefore, in this study we also improved the whale optimization algorithm (WOA) to address rules selection problem with an improved algorithm called improved WOA (IWOA). Two major improvements were included into IWOA. The first improvement is the development of a new update equation based on Cauchy mutation to improve WOA population diversity. The second improvement is the development of a new local search algorithm (LSA) to solve WOA local optima. The IWOA algorithm is applied on the full set of rules to select best rules subset and remove low quality rules. Finally, a new pruning algorithm (PA) has been developed to remove incorrect aspects and retain correct aspects. The Results on seven benchmark datasets demonstrate that IWOA+PA outperforms all other state-of-the-art baseline works and most recent works. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Sentiment analysis Dependency-based rules Pattern-based rules improved whale optimization algorithm Explicit aspect extraction

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Research and application of short-term load forecasting based on CEEMDAN-LSTM modeling

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ENERGY REPORTS 2024年 12卷 2144-2155页

作者： Liu, Hongli Li, Zhenyu Li, Chao Shao, Lei Li, Ji Tianjin Univ Technol Tianjin 300000 Peoples R China

Accurate short-term load forecasting plays a crucial role in guiding and regulating the operation of power companies. In this study, we propose a combined prediction model based on Long Short-Term Memory (LSTM) and improved whale optimization algorithm (IWOA) to enhance prediction accuracy and address the limitations of existing single methods. The parameter optimization of the LSTM model requires significant computing resources and time. To tackle this issue, we introduced the whale optimization algorithm (WOA) and improved the algorithm through the roulette method to form a whale optimization algorithm that enhances its global search capability and avoids falling into local optimal solutions. Additionally, in order to handle the nonlinearity and non-smoothness of the input data of the model, we utilized fully integrated Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) in this study to improve the efficiency of model training. By combining these techniques, we constructed a new CEEMDAN-IWOA-LSTM combined prediction model. The simulation results in MATLAB demonstrate that the prediction accuracy of this model reaches 99.05 %, which surpasses other prediction models in all evaluation indexes.

关键词： Power load forecasting Long and short-term memory neural networks improved whale optimization algorithm Adaptive noise-complete ensemble empirical modal decomposition

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Anomaly detection method for TBM construction based on improved VMD-XGBoost-BILSTM combined model

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EARTH SCIENCE INFORMATICS 2023年第4期16卷 4273-4284页

作者： Lu, Zhipeng Shi, Kebin Xinjiang Agr Univ Coll Water Resources & Civil Engn Urumqi 830000 Peoples R China

TBM method construction is an important accompanying construction form vigorously developed in China, and its anomaly detection is an important link to provide a basis for system operation and maintenance decision-making. The abnormal operation state of TBM caused by different surrounding rock geology, jamming or rock burst will directly affect the tunneling speed and ability, and then affect the system safety and construction progress. In this paper, a TBM construction anomaly detection method based on historical tunneling speed and other construction monitoring data is proposed. Firstly, the preprocessing steps based on outlier removal and correlation analysis are used to remove the noise in the original data and select the best features. Secondly, the variational mode decomposition is used to decompose the data into multiple modal components to extract the periodic and aperiodic features of TBM tunneling. Furthermore, an improved VMD-XGBoost-BILSTM combination model is constructed, and the characteristics of combination weighting, attention mechanism and improved whale optimization algorithm are used to realize the normal prediction of accurate and stable tunneling speed. Finally, by comparing with the actual measured values, the set rules are used to judge the anomalies. The experiments are carried out on the actual mining data of YE long-distance water conveyance tunnel in Xinjiang. The results show that the method proposed in this paper improves the RMSE by more than 20% compared with the single BILSTM or XGBoost model. The attention mechanism and IWOA algorithm bring 5.53% and 10.73% results improvement respectively, which can achieve the effect of early warning of different geological information changes.

关键词： TBM construction anomaly detection neural network variational mode decomposition attention mechanism improved whale optimization algorithm

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Research on aerodynamic optimization design method and flow mechanism of a high-subsonic compressor cascade

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2022年第1期16卷 316-334页

作者： Huang, Song Lu, Xingen Han, Ge Zhao, Shengfeng Zhou, Chuangxin Yang, Chengwu Chinese Acad Sci Key Lab Light Duty Gas Turbine IET Beijing Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Innovat Acad Light Duty Gas Turbine Beijing Peoples R China

Designing the high-performance, high-subsonic airfoils suitable for different Reynolds number flow is the major challenge for high-altitude long-endurance drones and ultra-high altitude solar aircraft. To overcome the difficulties, this paper proposed a set of aerodynamic optimization design methods that combine the improved category shape function transformation method, parallel computational fluid dynamics, and an improved whale optimization algorithm. This approach achieves high modeling accuracy, computational efficiency, and strong optimization ability. Verification using single-peak, multi-peak, and fixed-dimensional test functions demonstrates that the improved whale optimization algorithm has significantly enhanced optimization capabilities compared with the conventional whale optimization, particle swarm optimization, and gravity search algorithms. Numerical simulations are used to analyze the internal flow mechanisms before and after airfoil optimization under different Reynolds numbers. The results show that, compared with the original airfoil, the optimized airfoil has front loading characteristics that effectively reduce the adverse pressure gradient of the rear part of suction surface, promoting the transition in this region. The reduced adverse pressure gradient also inhibits the development and growth of laminar separation bubbles and slows down the growth of the boundary layer displacement thickness, thereby reducing wake mixing loss and improving the aerodynamic performance.

关键词： optimization design high-subsonic airfoil improved whale optimization algorithm boundary layer displacement thickness Reynolds number

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Temperature Compensation Model for Monitoring Sensor in Steel Industry Load Management

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INTERNATIONAL JOURNAL OF ENGINEERING AND TECHNOLOGY INNOVATION 2024年第4期14卷 451-462页

作者： Sun, Liyuan Yang, Zeming Pan, Nan Chen, Shilong He, Yaoshen Yang, Junwei Yunnan Power Grid Co Ltd Measurement Ctr Kunming Peoples R China Kunming Univ Sci & Technol Fac Civil Aviat & Aeronaut Kunming Peoples R China Kunming Univ Sci & Technol Fac Transportat Engn Kunming Peoples R China Yunnan Power Grid Co Ltd Yuxi Power Supply Bur Yuxi Peoples R China LongShine Technol Grp Co Ltd Wuxi Peoples R China

The iron ore industry faces increasing electricity demand due to industrialization, making effective management of electricity demand crucial. This study proposes a temperature compensation model using Support Vector Regression (SVR), aiming to enhance the accuracy of sensors in monitoring electricity demand. An experiment is conducted to assess the impact of temperature on sensor measurements, and a modified whale optimization algorithm is employed to correct the sensor outputs. The proposed model is compared with both PSOSVR and unimproved WOA-SVR. Results show that the proposed model significantly improves accuracy, achieving a determination coefficient of 0.7882 and a relative standard deviation of the error square sum of 4.6412%. The results of this study not only enhance power demand management in iron mining but also hold potential applications across various industries.

关键词： power systems temperature compensation SVR improved whale optimization algorithm

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Addressing the contradiction between water supply and demand: a study on multi-objective regional water resources optimization allocation

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ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-29页

作者： Chu, Jingyi Wang, Zhaocai Bao, Xiaoguang Yao, Zhiyuan Cui, Xuefei Shanghai Ocean Univ Aien Inst Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Informat Hucheng Huan Rd 999 Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai 201306 Peoples R China

As a result of economic development, population growth, accelerated urbanization and the frequent occurrence of extreme weather events, the contradiction between the supply and demand for water resources between regions has become increasingly acute. In order to solve the problem of regional water shortage and irrational utilization, the optimal allocation of water resources has become one of the research hotspots in recent years. In this study, firstly a multi-objective integrated allocation model of regional water resources is constructed by introducing social, economic, and environmental objective functions to address the complex uncertainties in the water resources system. Secondly, the standard whale algorithm is optimized and improved by introducing chaotic population initialization, chaotic convergence factor, adaptive L & eacute;vy flight and improved positive cosine mechanism. The model parameters, including the 2025 water resource demand and supply, pollutant discharge content, and unit water supply cost coefficients, are set by consulting the Shanxi Water Resources Bulletin 2022, the Shanxi Provincial Department of Water Resources, and the Report on the Work of the Shanxi Provincial Government 2023. Subsequently, the improved whale algorithm is utilized for the optimization of the predicted water resources for various target years in the future in the lower reaches of the Fen River in Shanxi Province, China. This ultimately yields optimized allocation results independently from both supply and demand sides. The experimental results demonstrate that the framework for water resource optimization using the improved whale algorithm is feasible, providing a reference scheme for regional multi-objective water resource optimization. Finally, the proposed policy recommendations emphasize the necessity of strengthening water diversion planning and management, promoting virtual water and water-saving initiatives, and highlighting water recycling and environmental p

关键词： Multi-objective optimization Optimal allocation of water resources improved whale optimization algorithm Chaotic population initialization Adaptive L & eacute vy Flight The Fen River Basin

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Research on Photovoltaic Power Prediction Based on Multi-model Fusion 4th

Research on Photovoltaic Power Prediction Based on Multi-mod...

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4th International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Chen, Jiaqi Gao, Qiang Ji, Yuehui Xu, Zhao Liu, Junjie Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China Tianjin Key Lab Control Theory & Applicat Complic Tianjin 300384 Peoples R China Tianjin Univ Technol Maritime Coll Tianjin 300384 Peoples R China

ISBN: (纸本)9789819770496;9789819770472;9789819770465

Accurately predicting photovoltaic power generation has a pivotal role in ensuring the safe and stable operation of the power system. This manuscript proposes a multi-network fusion short-term PV power forecasting model. Firstly, the PV power was decomposed into steady-state and unsteady components by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the unsteady components were decomposed twice by variational mode decomposition (VMD). Secondly, the Catboost network is employed to predict steady-state decomposition power, and the WTCN-BiGRU-Attention network optimized by IWOA is employed to predict unsteady component PV power. Finally, the short-term prediction of PV power is realized by integrating the prediction results of each model component. A photovoltaic power plant's measured power generation for the whole year of 2018 is utilized to validate the prediction model, and the results show that the proposed multi-model fusion network with quadratic mode decomposition has a high accuracy for power generation prediction.

关键词： Modal decomposition Photovoltaic Power Prediction improved whale optimization algorithm Multi-model fusion

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A whale optimization of vapor compression refrigeration system 33

A whale optimization of vapor compression refrigeration syst...

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第33届中国控制与决策会议

作者： Dehao Kong Xiaohong Yin Ning Fang School of Automation & Electronic Engineering Qingdao University of Science & Technology Department of Basic Courses Shandong Institute of Commerce and Technology

ISBN: (纸本)9781665431293

In this paper,a model-based optimization strategy for vapor compressor refrigeration system is proposed to reduce system energy *** accurate components' models of the vapor compression refrigeration system are *** improved whale algorithm with fast convergence rate is proposed to solve the optimization problem and obtain the optimal set-points for control *** with the traditional strategy,the energy consumption of vapor compression refrigeration system in the proposed optimization strategy is reduced by 15.28%.Meanwhile,the energy saving potential of the optimal control strategy is more remarkable for low cooling *** proposed optimal control strategy can work well for applications in control and energy efficiency improvement of the existing system.

关键词： vapor compression refrigeration system global optimization improved whale optimization algorithm

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Automatic Modulation Recognition Based on Features Fusion of IQH and AAH

Automatic Modulation Recognition Based on Features Fusion of...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Wan, Pengwu Hui, Xi Chen, Dongrui Kang, Siying Zhang, Xu Xian Univ Posts & Telecommun Xian 710121 Peoples R China China Acad Space Technol Natl Key Lab Sci & Technol Space Microwave Xian 100094 Peoples R China

ISBN: (纸本)9798350378412

With the rapid development and popularization of the Internet of Things (IoT), its extensive applications make networks vulnerable to malicious attacks, leading to various security and privacy issues. Automatic modulation recognition (AMR) technology provides crucial support for the stable operation and security of the IoT. Based on this, this paper proposes a modulation recognition method based on histogram feature fusion. Firstly, signal sequences are transformed into asynchronous in-phase quadrature histogram (IQH) and asynchronous amplitude histogram (AAH) to represent signal features. While IQH exhibits a stronger advantage in certain conditions, AAH provides complementary information that can enhance the robustness and accuracy of the recognition process. Therefore, IQH and AAH are fused through discriminant correlation analysis (DCA) to obtain comprehensive feature vectors. Compared with single-input features, the combination of the two inputs will improve the final recognition accuracy. Secondly, the improved whale optimization algorithm (IWOA) is employed to optimize the radial basis function neural network (RBFNN), where the fused feature vector is utilized as input to recognize the modulation type of the input signal. Finally, seven modulation types are identified in simulation experiments. The experimental results indicate that under the additive white Gaussian noise channel model, a signal-to-noise ratio of 6 dB can achieve a recognition accuracy of 99.52%. Compared with other experimental analyses, this method exhibits high performance and robustness.

关键词： modulation recognition asynchronous in-phase quadrature histogram asynchronous amplitude histogram discriminant correlation analysis improved whale optimization algorithm

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