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A novel hybrid optimized incremental relevance vector machine and filtering technique for state of charge estimation of lithium-ion batteries

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JOURNAL OF ENERGY STORAGE 2024年 90卷

作者： Wang, Chao Zhang, Xin Yun, Xiang Fan, Xingming Guilin Univ Elect Technol Dept Elect Engn & Automat Guilin 541004 Peoples R China

Despite not requiring a comprehensive understanding of the intricate electrochemical reactions within the battery, the data-driven approach faces challenges regarding state of charge (SOC) fluctuations during abrupt changes in battery current. Currently, research on data-driven filtering methods predominantly focuses on neural network-based algorithms, with limited exploration of the relevance vector machine (RVM) algorithm that possesses advantages in handling small sample sizes, high sparsity, and probabilistic distributions. Drawing inspiration from this premise, the present manuscript introduces an innovative fusion algorithm, founded upon the framework of the adaptive Kalman filtering (AKF) algorithm, which intricately melds the optimized incremental RVM (OIRVM) with the Coulomb counting methodology, called Coulomb_AKF_OIRVM (CFR) algorithm. Experimental findings suggest that the CFR algorithm displays exceptional generalization capacity across different driving cycles and temperatures, exhibiting high estimation accuracy and robustness. The average root mean square error, determination coefficient, mean absolute error and maximum absolute error of the public datasets and experimental datasets are 0.25 %, 0.02 %, 0.20 %, and 0.85 %, respectively.

关键词： Optimized incremental relevance vector machine whale optimization algorithm Adaptive Kalman filter SOC estimation Experimental test

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Forecasting of the Short-Term Electricity Load Based on WOA-BILSTM

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2023年第11期37卷 2359018-2359018页

作者： Zhao, Huaxin Zhou, Zhenliu Zhang, Pizhen Shenyang Inst Engn Elect Engn Coll Shenyang Peoples R China Shenyang Key Lab Energy Internet Intelligent Perce Shenyang Peoples R China Shenyang Inst Engn Informat Coll Shenyang Peoples R China

One of the biggest difficulties, which China's power infrastructure is now experiencing, is the capacity to estimate demand of short-term electricity. It can accurately estimate changes in the total power load in particular locations. Accurate forecasting results, which also serve as a dependable guide for power system operation, may increase the flexibility and resource usage of the contemporary power market. Power load characteristics are impacted by several things. This study presents an estimate method for short-term demand of electricity based on WOA-BILSTM in order to comprehensively evaluate the time series features presented in the power load data and improve the accuracy of power load forecasting. After extracting features for the variables that influence power load, this method uses the bidirectional long-term and short-term memory (BILSTM) neural network layer for bidirectional time series feature learning. Using the local electricity load data from Quanzhou from 2018 as the data set, the prediction model is constructed by screening the multi-dimensional input parameters and selecting the feature vectors with good association as the input carefully. By comparing the outcomes of three popular load forecasting models, including LSTM network, BILSTM network, and WOA-BILSTM, it is demonstrated that the WOA-BILSTM neural network method is more accurate and effective than others two. By building an optimal combination model, this technique could increase the accuracy of power load data's short-term predictions. At the same time, it also reduces the time of personnel debugging.

关键词： Short-term power load forecasting machine learning whale optimization algorithm neural network with dual long-short-term memory

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Optimal tuning procedure for fopid controller of integrated industrial processes with deadtime

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2022年第1期70卷

作者： Anuja, R. Sivarani, T. S. Nisha, M. Germin Arunachala Coll Engn Women Nagercoil Tamil Nadu India St Xaviers Catholic Coll Engn Nagercoil Tamil Nadu India

Industrial processes such as batch distillation columns, supply chain, level control etc. integrate dead times in the wake of the transportation times associated with energy, mass and information. The dead time, the cause for the rise in loop variability, also results from the process time and accumulation of time lags. These delays make the system control poor in its asymptotic stability, i.e. its lack of self-regulating savvy. The haste of the controller's reaction to disturbances and congruence with the design specifications are largely influenced by the dead time;hence it exhorts a heed. This article is aimed at answering the following question: "How can a fractional order proportional integral derivative controller (FOPIDC) be tuned to become a perfect dead time compensator apposite to the dead time integrated industrial process?" The traditional feedback controllers and their tuning methods do not offer adequate resiliency for the controller to combat out the dead time. The whale optimization algorithm (WOA), which is a nascent (2016 developed) swarm-based meta-heuristic algorithm impersonating the hunting maneuver of a humpback whale, is employed in this paper for tuning the FOPIDC. A comprehensive study is performed and the design is corroborated in the MATLAB/Simulink platform using the FOMCON toolbox. The triumph of the WOA tuning is demonstrated through the critical result comparison of WOA tuning with Bat and particle swarm optimization (PSO) algorithm-based tuning methods. Bode plot based stability analysis and the time domain specification based transient analysis are the main study methodologies used.

关键词： industrial process integrated with dead time tuning of FOPID whale optimization algorithm

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Multiple Unmanned Aerial Vehicle Autonomous Path Planning algorithm Based on whale-Inspired Deep Q-Network

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DRONES 2023年第9期7卷

作者： Wang, Wenshan Zhang, Guoyin Da, Qingan Lu, Dan Zhao, Yingnan Li, Sizhao Lang, Dapeng Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China

In emergency rescue missions, rescue teams can use UAVs and efficient path planning strategies to provide flexible rescue services for trapped people, which can improve rescue efficiency and reduce personnel risks. However, since the task environment of UAVs is usually complex, uncertain, and communication-limited, traditional path planning methods may not be able to meet practical needs. In this paper, we introduce a whale optimization algorithm into a deep Q-network and propose a path planning algorithm based on a whale-inspired deep Q-network, which enables UAVs to search for targets faster and safer in uncertain and complex environments. In particular, we first transform the UAV path planning problem into a Markov decision process. Then, we design a comprehensive reward function considering the three factors of path length, obstacle avoidance, and energy consumption. Next, we use the main framework of the deep Q-network to approximate the Q-value function by training a deep neural network. During the training phase, the whale optimization algorithm is introduced for path exploration to generate a richer action decision experience. Finally, experiments show that the proposed algorithm can enable the UAV to autonomously plan a collision-free feasible path in an uncertain environment. And compared with classic reinforcement learning algorithms, the proposed algorithm has a better performance in learning efficiency, path planning success rate, and path length.

关键词： deep reinforcement learning deep Q-network whale optimization algorithm multi-UAV path planning

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Conformable fractional accumulation in triangular fuzzy sequences grey nonlinear model for tertiary industry gross output forecast

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APPLIED MATHEMATICAL MODELLING 2024年 136卷

作者： Cao, Zhenxiu Zeng, Xiangyan He, Fangli Guilin Univ Elect Technol GUET Sch Math & Comp Sci Ctr Appl Math Guangxi Guangxi Coll & Univ Key Lab Data Anal & Computat Gulin 541004 Peoples R China

The prediction of the tertiary industry's output value is essential for enhancing the market economic system and providing guidance for the government in formulating relevant economic policies. The tertiary industry encompasses various sectors such as finance and transportation, and its total output value exhibits uncertain and nonlinear trends. Triangular fuzzy number sequences contain more comprehensive information than exact number sequences. Therefore, a nonlinear grey Bernoulli model with conformable fractional accumulation for triangular fuzzy sequences is proposed. Firstly, the conformable fractional accumulation generating operation is utilized to mitigate the volatility tendency of the original sequences. Additionally, a time power function term is introduced to capture the power exponential trend. Furthermore, an improved whale optimization algorithm incorporating opposition-based learning strategy and adaptive control parameters is employed to optimize the model parameters, which has been verified through algorithm comparison experiments. Three existing grey models are used as competing models to validate the prediction accuracy of our proposed model. Finally, we utilize this proposed model to predict the tertiary industry's output values in both Yangtze and Pearl River Deltas for 2024-2026.

关键词： Nonlinear Grey Bernoulli model Triangular fuzzy number sequences whale optimization algorithm Time power function term Conformable fractional accumulation generating operator

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A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas

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SENSORS 2024年第7期24卷 2340页

作者： Wang, Yonggang Li, Zhida Zhang, Nannan Shenyang Agr Univ Coll Informat & Elect Engn Shenyang 110866 Peoples R China

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas's oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm-long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm-long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization-least squares support vector machine (PSO-LSSVM), and particle swarm optimization-long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.

关键词： soft sensor model oxygen content in flue gas long short-term memory whale optimization algorithm

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Topology Design of an Offshore Wind Farm with Multiple Types of Wind Turbines in a Circular Layout

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ENERGIES 2020年第3期13卷 556页

作者： Tao, Siyu Feijoo, Andres Zhou, Jiemin Zheng, Gang Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Univ Vigo Dept Enxeneria Elect Campus Lagoas Vigo 36310 Spain Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing 210016 Peoples R China

The advances in the manufacturing industry make it possible to install wind turbines (WTs) with large capacities in offshore wind farms (OWFs) in deep water areas far away from the coast where there are the best wind resources. This paper proposes a novel method for OWF optimal planning in deep water areas with a circular boundary. A three-dimensional model of the planning area's seabed is established in a cylindrical coordinate. Two kinds of WTs with capacities of 4 and 8 MW respectively are supposed to be mixed-installed in that area. Baseline cases are analyzed and compared to verify the superiority of a circular layout pattern and the necessity of a non-uniform installation. Based on the establishment of the optimization model and a realistic wind condition, a novel heuristic algorithm, i.e., the whale optimization algorithm (WOA), is applied to solve the problem to obtain the type selection and coordinates of WTs simultaneously. Finally, the feasibility and advantages of the proposed scheme are identified and discussed according to the simulation results.

关键词： circular pattern multiple types of wind turbines offshore wind farm three-dimensional seabed whale optimization algorithm

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Hybrid random forest-based models for predicting shear strength of structural surfaces based on surface morphology parameters and metaheuristic algorithms

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CONSTRUCTION AND BUILDING MATERIALS 2023年 409卷

作者： Zhou, Jian Yang, Peixi Li, Chuanqi Du, Kun Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Grenoble Alpes Univ Lab 3SR CNRS UMR 5521 F-38000 Grenoble France

The prediction of shear strength between soil-structure interactions is of great significance to the stability of geotechnical engineering. In this study, 480 morphological data with seven morphological parameters (deviation of the root mean square value of the profile (Pq), skewness of the height distribution in the profile (Psk), kurtosis of the height distribution of the profiles (Pku), average width of outline elements (PSm), root mean square slope of the profile (Pdq), material ratio of the profile(Pmr), number of peaks (Ppc)) were selected to generate a comprehensive database for predicting the peak interface efficiency (IEp) considering three different soil particle sizes (0.35 mm, 0.53 mm, and 0.80 mm). Three random forest (RF) models optimized using dragonfly algorithm (DA-RF), sparrow search algorithm optimized random forest (SSA-RF), and whale optimization algorithm (WOA-RF) were generated to predict IEp. and compared the predictive performance with extreme learning machine (ELM), support vector regression with radial basis function kernel (SVR-RBF) and initial RF models. The mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination (R2) were used to evaluate the performance of all models. The results showed that the WOA-RF model has achieved the best performance by resulting in MAE of (0.0145, 0.0181, 0.0179 and 0.0210, 0.0273, 0.0216), MAPE of (1.9866, 2.6417, 2.5310 and 2.8924, 4.0294, 3.0816), and RMSE of (0 0178, 0.0237,0.0224 and 0.0252, 0.0362, 0.0276), R2 (0.9473, 0.9262, 0.9352 and 0.9404, 0.8433, 0.9313) in the training and testing phases. The results of significance analysis indicated that Pdq and Pq have more importance than other parameters for predicting IEp.

关键词： Peak interface efficiency Morphological parameters Dragonfly algorithm Sparrow Search algorithm whale optimization algorithm

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An Adaptive Parameterized Domain Mapping Method and Its Application in Wheel-Rail Coupled Fault Diagnosis for Rail Vehicles

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SENSORS 2023年第12期23卷 5486-5486页

作者： Xu, Zihang Yang, Jianwei Yao, Dechen Wang, Jinhai Wei, Minghui Beijing Univ Civil Engn & Architecture Beijing Key Lab Performance Guarantee Urban Rail T Beijing 100044 Peoples R China Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 100044 Peoples R China

The rapid development of cities in recent years has increased the operational pressure of rail vehicles, and due to the characteristics of rail vehicles, including harsh operating environment, frequent starting and braking, resulting in rails and wheels being prone to rail corrugation, polygons, flat scars and other faults. These faults are coupled in actual operation, leading to the deterioration of the wheel-rail contact relationship and causing harm to driving safety. Hence, the accurate detection of wheel-rail coupled faults will improve the safety of rail vehicles' operation. The dynamic modeling of rail vehicles is carried out to establish the character models of wheel-rail faults including rail corrugation, polygonization and flat scars to explore the coupling relationship and characteristics under variable speed conditions and to obtain the vertical acceleration of the axle box. An APDM time-frequency analysis method is proposed in this paper based on the PDMF adopting Renyi entropy as the evaluation index and employing a WOA to optimize the parameter set. The number of iterations of the WOA adopted in this paper is decreased by 26% and 23%, respectively, compared with PSO and SSA, which means that the WOA performs at faster convergence speed and with a more accurate Renyi entropy value. Additionally, TFR obtained using APDM realizes the localization and extraction of the coupled fault characteristics under rail vehicles' variable speed working conditions with higher energy concentration and stronger noise resistance corresponding to prominent ability of fault diagnosis. Finally, the effectiveness of the proposed method is verified using simulation and experimental results that prove the engineering application value of the proposed method.

关键词： rail vehicles parameterized domain mapping wheel-rail coupled fault fault diagnosis whale optimization algorithm

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Evaluating the performance of nature inspired algorithms using 52-bar steel truss subjected to dynamic load

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Materials Today: Proceedings 2021年 38卷 2464-2470页

作者： Saiteja Sistla J.S. Kalyana Rama BITS Pilani-Hyderabad Campus Hyderabad 500078 India Vignana Bharathi Institute of Technology Hyderabad 501301 India

Linear programing problem revolutionized during the world war since it has helped the army to minimize the costs and increase the efficiency in battlefield. Since then optimization techniques have gained popularity in various fields like science & technology, biology, mathematics etc. Nature inspired algorithms mimic the nature’s behavior in order to achieve certain optimization objectives which can achieve productive results to complex problems. Classical algorithms like Genetic algorithm, Particle swarm optimization etc. are widely known but the accuracy of the solution for complex engineering problems is less. Performance evaluation of latest nature inspired algorithms i.e. Moth flame optimizer, Salp Swarm optimizer and whale optimizer is carried out in the present study. These algorithms are proposed recently and they possess salient features like better convergence rate, avoiding the local optimum and robustness, which is the motivation behind choosing these algorithms. A 52-bar steel truss has been chosen for the present study to assess the performance of the chosen optimization techniques. The behavior of steel truss subjected to two different ground motions is also assessed using the three optimization techniques. A comparative study is done to assess the performance of the chosen techniques. MATLAB is adopted for the simulation of chosen problem statement. Based on the results it is observed that Mouth Flame Optimizer has better performance in terms of accuracy, convergence rate and computational time and is suggested for various types of mechanical and structural problems involving 52-bar trusses.

关键词： Moth flame optimizer Salp Swarm algorithm whale optimization algorithm MATLAB Steel truss

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