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Wind Power Prediction Based on PSO-SVR and Grey Combination Model

IEEE ACCESS 2019年 7卷 136254-136267页

作者： Zhang, Yi Sun, Hexu Guo, Yingjun Hebei Univ Technol Sch Artificial Intelligence Tianjin 300130 Peoples R China Hebei Univ Sci & Technol Sch Informat Sci & Engn Shijiazhuang 050018 Hebei Peoples R China

As a kind of green, clean and renewable energy, wind power generation has been widely utilized in various countries in the world. With the rapid development of wind energy, it is also facing prominent problems. Because wind power generation is intermittent, unstable and stochastic, it has caused serious difficulties for power grid dispatch. At present, the important method to solve this problem is to predict wind speed and wind power. Grey model is suitable for uncertain systems with poor information and needs less operation data, so it can be used for wind speed and wind power prediction. However, the traditional grey system model has the disadvantage of low prediction accuracy. Therefore, firstly the GM (1,1) for wind speed prediction is improved by background value optimization in this paper. In order to comprehensively reveal the inherent uncertainty of wind speed random series, the fractional order grey system models with different orders are constructed. Secondly, in order to overcome the shortcoming of single grey model, each grey model is effectively united, and a combination prediction model based on neural network is presented. The two NWP outputs, i.e. ECMWF and GRAPES-MESO, have been added to the prediction model for reducing the uncertainty. The structure parameters of the neural network are optimized by trial and error. Thirdly, the support vector regression model is established to fit the scatter operation data of wind speed-power, and the parameters of the model are optimized by the particle swarm algorithm. Then the power prediction value is obtained by the fitted wind speed-power relationship and the corresponding result of the grey combination model for wind speed prediction. Finally, wind speed and wind power are predicted based on the actual operation data. In addition, the prediction model based on ARIMA is also constructed as a benchmark model. The results show that the proposed grey combination model improves the prediction accuracy.

关键词： Predictive models Wind power generation Wind speed Data models Computational modeling Support vector machines Prediction algorithms Grey model background value support vector regression fractional order particle swarm algorithm wind speed prediction wind power prediction combination model

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Comprehensive Evaluation Index System of Distribution Network for Distributed Photovoltaic Access

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Xiao, Jinxing Ye, Ying Wang, Fa Shen, Jieshi Gao, Feng State Grid Shanghai Municipal Elect Power Co Shanghai Peoples R China State Grid Shanghai Jinshan Elect Power Supply Co Shanghai Peoples R China

Affected by the light intensity and multiple adjacent stations, the output power of photovoltaic power stations presents the characteristics of randomness and correlation, which puts forward new requirements for the safe operation and planning evaluation of the distribution network. First, in order to accurately describe the randomness and fluctuation of photovoltaic output, a Gaussian mixture model based on an improved optimal particle swarm optimization algorithm is proposed, and the joint probability density function of multiple photovoltaic outputs is solved. Then, the power flow equation is linearized, the linear expressions of bus voltage and line power flow are derived, and the joint probability distributions of multi-node voltage and multi-line power flow are obtained, respectively. Based on this, the reliability index and voltage quality index in the presence of the distributed renewable energy were constructed. Finally, the novel power grid planning evaluation index is tested in a real system in China, the combined weight is determined by the analytic hierarchy process, and the comprehensive evaluation results are obtained. The simulation results prove the feasibility of the evaluation index system.

关键词： particle swarm algorithm Gaussian mixture model voltage distribution index analytic hierarchy process comprehensive evaluation index system

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Pressure Drop Prediction of Crude Oil Pipeline Based on PSO-BP Neural Network

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ENERGIES 2022年第16期15卷 5880页

作者： Wei, Lixin Zhang, Yu Ji, Lili Ye, Lin Zhu, Xuanchen Fu, Jin Northeast Petr Univ Key Lab Enhanced Oil Recovery Minist Educ Daqing 163318 Peoples R China Tianjin Branch CNOOC China Ltd Bohai Petr Inst Tianjin 300452 Peoples R China

Pipeline transportation of crude oil has great advantages over traditional oil transmission methods, in terms of economic and environmental protection. The main costs in the oilfield surface system are the fuel costs for heating the crude oil during transportation and the electricity costs for the pumping units. In the northeast of China, where winter temperatures are extremely low and the oil has a high freezing point and high viscosity, higher temperatures, and pressures are required to transport crude oil. With machine learning widely used in many industries and achieving better results, the digital management of oil pipelines has stored a large amount of production and operation data, which has laid the foundation for the research of oil pipeline process calculation using machine learning methods. In this paper, a crude oil pressure drop calculation of an oil pipeline in Northeast China is carried out based on a neural network. For pipeline pressure drop calculation, the back propagation neural network (BP) pressure drop calculation model and particle swarm optimization for back propagation neuron network (PSO-BP) pressure drop calculation model are established. Two models were used to calculate and compare the measured data, and the average absolute error of the PSO-BP model was the smallest, which was 0.015%. Compared with the BP model, the average relative error is reduced by 13.16%. Therefore, The PSO-BP pressure drop calculation model has high accuracy and is of practical significance for predicting pipeline pressure drop.

关键词： neural network particle swarm algorithm pressure drop hot oil pipeline

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Isogeometric shape optimization for widening band gaps of periodic composite plates

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EUROPEAN JOURNAL OF MECHANICS A-SOLIDS 2024年 103卷

作者： Yin, Shuohui Huang, Jiahui Zou, Zhihui Bui, Tinh Quoc Cong, Yu Yu, Tiantang Zhang, Gongye Xiangtan Univ Sch Mech Engn & Mech Xiangtan 411105 Hunan Peoples R China Univ Texas Austin Oden Inst Computat Engn & Sci Austin TX USA Duy Tan Univ Duy Tan Res Inst Computat Engn DTRICE 6 Tran Nhat Duat1 Dist Ho Chi Minh City Vietnam Duy Tan Univ Fac Civil Engn Da Nang City 550000 Vietnam Univ Evry Univ Paris Saclay LMEE F-91020 Evry France Hohai Univ Dept Engn Mech Nanjing 210098 Peoples R China Southeast Univ Sch Civil Engn Jiangsu Key Lab Engn Mech Nanjing 210096 Jiangsu Peoples R China

This paper presents an isogeometric shape optimization methodology to widen the band gap of periodic com-posite plates by smoothly optimizing its thickness profile. To save the computational cost, two NURBS surfaces are used in the isogeometric optimization method. While a fine NURBS surface (analysis mesh) is used for modeling the band gap of periodic composite plates within the isogeometric analysis (IGA) framework, a coarser NURBS design surface, i.e., the design mesh, is employed to describe the periodic plate thickness profile exactly and the position of control points are taken as optimal thickness variables. The particle swarm optimization (PSO) algorithm is adopted to solve the constrained dynamic maximization problems. On the other hand, the level set method and triangular domain integration technique are employed to describe the two-phase material interface of periodic composite plates. Numerical examples are provided to validate the accuracy and reliability of the present method. The numerical results showed that the present method can provide an accurate analysis and smooth plate thickness profile in the optimization process. The band gap of periodic composite plates is maximized effectively by the design of the plate thickness profile using the present optimization approach.

关键词： Shape optimization Isogeometric analysis Band gap particle swarm algorithm Mindlin plate

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Collaborative optimization of energy-efficient train schedule and train circulation plan for urban rail

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ENERGY 2023年第PartA期263卷

作者： Zhou, Wenliang Huang, Yu Deng, Lianbo Qin, Jin Cent South Univ Sch Traff & Transportat Engn Changsha 410000 Hunan Peoples R China

It is of great practical significance to save train traction energy for reducing the operation cost of urban rail transit. The energy-efficient train scheduling without combining with train circulation planning may inadver-tently increase the other cost of rolling stocks, and finally lead to an increment of the total operation cost. This paper studies the integrated problem of energy-efficient train scheduling and train circulation planning for urban rail, and aims to reduce the total operation cost of rolling stocks including energy consumption. Its main chal-lenge is to simultaneously solve three subproblems, namely the saving of train's traction energy in each rail section, the utilizing of regenerative braking energy and the optimizing of train circulation plan. We construct an optimization model to simultaneously optimize schedule and train circulation plan. Based on the designing of a strategy to create the train circulation plan for each train schedule, an efficient particle swarm algorithm is formed to solve our proposed model. The numerical experiments based on Guangzhou Metro Line 9 of China illustrate that the collaborative optimization can reduce the total operation cost of trains by 4.48% compared with the initial train schedule.

关键词： Urban rail transit Energy -efficient train scheduling Train circulation planning Train tracking strategy Regenerative braking energy particle swarm algorithm

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Distributed Shared Energy Storage Double-Layer Optimal Configuration for Source-Grid Co-Optimization

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PROCESSES 2023年第7期11卷 2194页

作者： Yang, Meng Zhang, Yihan Liu, Junhui Yin, Shuo Chen, Xing She, Lihui Fu, Zhixin Liu, Haoming State Grid Henan Econ Res Inst Zhengzhou 450052 Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China

Shared energy storage is an energy storage business application model that integrates traditional energy storage technology with the sharing economy model. Under the moderate scale of investment in energy storage, every effort should be made to maximize the benefits of each main body. In this regard, this paper proposes a distributed shared energy storage double-layer optimal allocation method oriented to source-grid cooperative optimization. First, considering the regulation needs of the power side and the grid side, a distributed shared energy storage operation model is proposed. Second, a distributed shared energy storage double-layer planning model is constructed, with the lowest cost of the distributed shared energy storage system as the upper-layer objective, and the lowest daily integrated operation cost of the distribution grid-distributed new energy stations as the lower-layer objective. Third, a double-layer iterative particle swarm algorithm combined with tide calculation is used to solve the distributed shared energy storage configuration and distribution grid-distributed new energy stations' economic operation problem. Finally, a comparative analysis of four scenarios verifies that configuring distributed shared energy storage can increase the new energy consumption rate to 100% and reduce the net load peak-valley difference by 61%. Meanwhile, distributed shared energy storage operators have realized positive returns.

关键词： distributed shared energy storage double-layer optimal new energy consumption net load peak-to-valley difference particle swarm algorithm

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Prediction of the Electricity Generation of a 60-kW Photovoltaic System with Intelligent Models ANFIS and Optimized ANFIS-PSO

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ENERGIES 2023年第16期16卷 6050-6050页

作者： Lara-Cerecedo, Luis O. Hinojosa, Jesus F. Pitalua-Diaz, Nun Matsumoto, Yasuhiro Gonzalez-Angeles, Alvaro Univ Sonora Dept Ingn Quim & Met Blvd Luis Encinas & Rosales S NCol Ctr Hermosillo 83000 Mexico Univ Sonora Dept Ingn Ind Blvd Luis Encinas & Rosales S NCol Ctr Hermosillo 83000 Mexico Ctr Invest & Estudios Avanzados IPN Dept Ingn Electr San Pedro ZacatencoAve Inst Politecn Nacl 2508 Ciudad de Mexico 07360 Mexico Univ Autonoma Baja Calif Fac Ingn Blvd Benito Juarez S N Mexicali 21280 BC Mexico

The development and constant improvement of accurate predictive models of electricity generation from photovoltaic systems provide valuable planning tools for designers, producers, and self-consumers. In this research, an adaptive neuro-fuzzy inference model (ANFIS) was developed, which is an intelligent hybrid model that integrates the ability to learn by itself provided by neural networks and the function of language expression, how fuzzy logic infers, and an ANFIS model optimized by the particle swarm algorithm, both with a predictive capacity of about eight months. The models were developed using the Matlab (R) software and trained with four input variables (solar radiation, module temperature, ambient temperature, and wind speed) and the electrical power generated from a photovoltaic (PV) system as the output variable. The models' predictions were compared with the experimental data of the system and evaluated with rigorous statistical metrics, obtaining results of RMSE = 1.79 kW, RMSPE = 3.075, MAE = 0.864 kW, and MAPE = 1.47% for ANFIS, and RMSE = 0.754 kW, RMSPE = 1.29, MAE = 0.325 kW, and MAPE = 0.556% for ANFIS-PSO, respectively. The evaluations indicate that both models have good predictive capacity. However, the PSO integration into the hybrid model allows for improving the predictive capability of the behavior of the photovoltaic system, which provides a better planning tool.

关键词： photovoltaic systems intelligent models ANFIS particle swarm algorithm ambiental variables electrical energy prediction

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Optimization of Water Distribution Network Design for Resisting Cascading Failures

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IEEE ACCESS 2020年 8卷 128856-128865页

作者： Shuang, Qing Huang, Chao Ran Wang, Jun Beijing Jiaotong Univ Sch Econ & Management Dept Construct Management Beijing 100044 Peoples R China Mississippi State Univ Dept Civil & Environm Engn Starkville MS 39762 USA

Water distribution networks (WDNs) is crucial to ensure social operations and economic activities. However, WDNs are highly sensitive and vulnerable to disasters. The aim of this study is to mitigate the catastrophic consequences of cascading failures in WDNs. A flow-based WDN cascading failure model is built. The extended multi-objective particle swarm optimization model is developed to resist cascading failures and improve resilience. This model takes pipe diameter as the decision variable to minimize cost and maximize pressure deficit. Water balance, pressure, and standard pipe diameter are the constraints. The classical optimal scenario (COS) and the cascading failure scenario (CFS) are simulated. The model is applied to a small and medium-sized benchmarked WDN. Results show that the extended PSO can find the optimal solution on the benchmarked WDN. The Pareto fronts are obtained. Compare to the Pareto fronts between COS and CFS, the pressure deficit under CFS is significantly reduced, and the cost is reduced while the same pressure deficit increased. Different tolerance parameters are tested. The small network is not sensitive to the tolerance parameter, but the medium-sized network is sensitive. The model evaluates a variety of conflicting goals, which help designers and water managers resist cascading failures in WDNs.

关键词： Water distribution networks cascading failure pressure deficit multi-objective optimization model particle swarm algorithm

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Energy Management Strategy Considering Fuel Economy and Life of Fuel Cell for Fuel Cell Electric Vehicles

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JOURNAL OF ENERGY ENGINEERING 2023年第1期149卷 04022054-04022054页

作者： Gu, Hao Yin, Bifeng Yu, Yingxiao Sun, Yan Jiangsu Univ Sch Automot & Traff Engn Xuefu Rd 301 Zhenjiang 212013 Peoples R China

The optimization of energy management strategy (EMS) for fuel cell electric vehicles (FCEV) is mainly applied to reduce fuel consumption. However, serious power fluctuations affect the durability of the fuel cell, which is still a challenge for the FCEV. In order to optimize the fuel cell's service life and economy, this paper proposes an EMS scheme based on fuzzy control. In the proposed EMS, the first layer uses the power following strategy to address the output power distribution issue between the battery and the fuel cell. In order to limit the rapid shift in fuel cell power, the second layer employs a fuzzy control method. Considering the variety of constraints, such as fuel cell power fluctuation and hydrogen consumption, the particle swarm optimization algorithm is used to optimize the membership function of the fuzzy controller. The performance and the operating characteristics of the fuel cells are then evaluated using a test bench. Afterward, the proposed EMS is verified by AMEsim joint simulation. The optimized strategy increased the driving range by 4.7% and 5.9% in world light vehicle test cycle (WLTC) and new European driving cycle (NEDC) conditions, respectively. It also reduces the fuel cell degradation by 21.4% and 19.8%.

关键词： Fuel cell electric vehicle (FCEV) Energy management strategy (EMS) Power follow strategy Fuzzy control strategy particle swarm algorithm

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Strength reduction strategy for rock slope stability using the variation principle based on the Hoek-Brown failure criterion

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2023年第8期82卷 297-297页

作者： Hu, Shihong Li, Liang Zhao, Lianheng Zuo, Shi Huang, Dongliang Cent South Univ Sch Civil Engn Changsha 410075 Hunan Peoples R China Cent South Univ Key Lab Heavy Haul Railway Engn Struct Minist Educ Changsha 410075 Hunan Peoples R China Cent South Univ Hunan Prov Key Lab Disaster Prevent & Mitigat Rail Changsha 410075 Hunan Peoples R China

Rotational failure may exhibit in homogenous rock slopes, and the critical sliding surface of a rock slope based on the rotational failure mechanism is not a single log-spiral under nonlinear failure criterion. This work proposed an advanced nonlinear analysis method for estimating the seismic stability of homogenous slope in rock masses which is governed by the generalized Hoek-Brown failure criterion. According to the virtual power principle, the critical slope height is obtained using particle swarm algorithm. Strength reduction technique is further introduced to explore the reduction strategy of the strength parameters of the Hoek-Brown failure criterion. The outcomes indicate that the factor of safety obtained by simultaneously reducing the unconfined compressive strength & sigma;(ci) and material parameter m(i) is in good agreement with the results of other methods. In addition, two cases are re-analyzed to illustrate the applicability of the proposed method, and the maximum discrepancy with existing results is about 8%. The effects of strength parameters, slope angle, and seismic quasi-static coefficients on the slope stability factor and critical sliding surface are analyzed, which shows that the seismic load and the Hoek-Brown parameters have a significant effect on the slope stability factor. There is no need to assume the expression of the potential sliding surface, which can provide theoretical support and a useful reference for the nonlinear analysis of slope stability.

关键词： Rock slope stability Hoek-Brown failure criterion Factor of safety Upper bound variation analysis Strength reduction strategy particle swarm algorithm

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