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Application of DSAPSO algorithm in Distribution Network Reconfiguration with Distributed Generation

Energy Engineering 2024年第1期121卷 187-201页

作者： Caixia Tao Shize Yang Taiguo Li School of Automation and Electrical Engineering Lanzhou Jiaotong UniversityLanzhou730070China

With the current integration of distributed energy resources into the grid,the structure of distribution networks is becoming more *** complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent ***,traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local *** tackle this issue,a more advanced particle swarm optimization algorithm is *** address the varying emphases at different stages of the optimization process,a dynamic strategy is implemented to regulate the social and self-learning *** Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions,thereby mitigating premature convergence in the population optimization *** inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search *** incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions.A fuzzy membership function is employed for selecting the *** analysis is carried out on the restructuring of the distribution network,using the IEEE-33 node system and the IEEE-69 node system as examples,in conjunction with the integration of distributed energy *** findings demonstrate that,in comparison to other intelligent optimization algorithms,the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the ***,it enhances the amplitude of node voltages,thereby improving the stability of distribution network operations and power supply ***,the algorithm exhibits a high level of generality and applicability.

关键词： Reconfiguration of distribution network distributed generation particle swarm optimization algorithm simulated annealing algorithm active network loss

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Optimal Volterra-based nonlinear system identification using arithmetic optimization algorithm assisted with Kalman filter

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EVOLVING SYSTEMS 2023年第1期14卷 117-139页

作者： Singh, Sandeep Ashok, Alaknanda Rawat, Tarun Kumar Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttar Pradesh India Netaji Subhas Univ Technol New Delhi India

This paper presents the handling of nonlinear system identification problem based on Volterra-type nonlinear systems. An efficient arithmetic optimization algorithm (AOA) along with the Kalman filter (KF) is being used for the estimation/identification purpose. The KF is proved to be a good state estimator in estimation theory. It is used to estimate the unknown variables with some given measurements observed over time. However, the performance of KF technique degrades while dealing with real-time state estimation problems. To overcome the problem encountered in KF technique, two steps are followed for nonlinear system identification. The first one involves evaluation of the KF parameters using the AOA algorithm by taking a considerable fitness function. The second step is to estimate the parameters of Volterra model using the KF method utilizing the optimal KF parameters attained in first step. In order to prove the efficiency of the proposed KF assisted AOA algorithm is further tested on various benchmark unknown Volterra models. Simulated results are reported in terms of mean square error (MSE), mean square deviation (MSD), Volterra coefficients estimation error, and fitness percentage. The results are compared with other similar algorithms such as sine cosine algorithm (SCA) assisted KF (SCA-KF), cuckoo search algorithm (CSA) assisted KF (CSA-KF), particle swarm optimization (PSO) assisted KF (PSO-KF) and genetic algorithm (GA) assisted KF (GA-KF). The reported results reveal that AOA-KF algorithm is the right choice for nonlinear system identification problem compared to the SCA-KF, CSA-KF, PSO-KF and GA-KF.

关键词： Nonlinear system identification Volterra model Kalman Filter Arithmetic optimization algorithm Sine cosine algorithm Cuckoo search algorithm particle swarm optimization algorithm Genetic algorithm

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Modelling and forecasting non-renewable energy consumption and carbon dioxide emissions in China using a PSO algorithm-based fractional non-linear grey Bernoulli model

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2023年第26期30卷 69651-69665页

作者： Yang, Jie Wu, Zhiqiang Shanghai Elect Power Univ Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Shanghai 200093 Peoples R China

In China, the consumption of non-renewable energy increases not only in general economic growth but also in large amounts of carbon dioxide (CO2) emissions which cause disasters and catastrophic damages to the environment. To alleviate environmental pressure, it is neccessary to forecast and model the relationship between energy consumption and CO2 emissions. In this study, a fractional non-linear grey Bernoulli (FANGBM(1,1)) model based on particle swarm optimization is proposed to forecast and model non-renewable energy consumption and CO2 emissions in China. Firstly, based on the FANGBM(1,1) model, non-renewable energy consumption in China is predicted. The comparison results of several competitive models show that the FANGBM(1,1) model has the best predictive performance. Then, the relationship between non-renewable energy consumption and CO2 emissions is modeled. On this basis, China's future CO2 emissions are effectively predicted based on the established model. The forecast results show that the growth trend of China's CO2 emissions will continue to grow to 2035, while the prediction results in different scenarios also show that that the different growth rates of renewable energy share lead to different times to peak CO2 emissions. In the end, relevant suggestions are proposed to support China's dual carbon goals.

关键词： Non-renewable energy consumption Carbon dioxide emissions Fractional non-linear grey Bernoulli model particle swarm optimization algorithm

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Research on fault location algorithm of TPSS based on PSOA

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1213页

作者： Jia, Yunqian Shanghai Inst Technol Sch Railway Transport Shanghai Peoples R China

It is extremely important to research traction power supply system (TPSS) protection technology in order to ensure the safe operation of urban rail transit. A TPSS includes rails, return cables, rail potential limiting devices, one-way conducting devices, drainage cabinets, ballast beds, and tunnel structural reinforcements. In urban rail transit, on the basis of the dynamic characteristics of the TPSS, a fault location algorithm based on particle swarm optimization algorithm (PSOA) is developed. An evaluation of multi-point monitoring data is proposed based on fuzzy processing of the average value of polarization potential forward deviation and multi-attribute decision-making. Monitoring points and standard comparison threshold values are determined by the distribution law of stray currents. In conjunction with the actual project, the model is trained using field measured data. Based on the results, TPSSOA is able to achieve optimal discharge current control, reduce network losses and improve power quality. Moreover, the reconstruction results demonstrate the high usability of the proposed method, which will provide guidance to design the TPSS in the future.

关键词： Traction power supply Machine learning particle swarm optimization algorithm Fault location

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Low-Scattering-Sidelobe Phased Array Antenna With Improved Radiation Performance

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第10期71卷 7837-7847页

作者： Li, Peng-Fa Qu, Shi-Wei Yang, Shiwen Jiang, Ming Hu, Jun Univ Elect Sci & Technol China UESTC Sch Elect Sci & Engn Chengdu 611731 Peoples R China

In this article, the formulas to compute phased array scattering fields under oblique incidences are derived as the theoretical basis of scattering sidelobe level (SLL) reduction. Meanwhile, in order to improve antenna radiation performance, the way to compute and evaluate array impedance matching performance is proposed based on an equivalent network model. The accuracy and efficiency of the formulas applied to predict array scattering fields and reflection coefficients are well validated by numerical study. Furthermore, a microstrip phased array antenna with a tapered array shape and optimized element feed networks is designed. A modified particle swarm optimization algorithm is proposed to search the solution of feed networks to reduce monostatic scattering SLL and improve antenna impedance matching simultaneously, where the proposed fast prediction approaches are employed. The optimized phased array antenna features scattering SLL reduction by 8.8 dB in the concerned angular range of 10 degrees-30 degrees and in the operating band of 5.5-7.5 GHz against its conventional counterpart, with improved impedance matching and slightly higher gains. Eventually, the numerical results are well validated in experiments.

关键词： Equivalent network model improved radiation performance particle swarm optimization algorithm phased array scattering prediction scattering sidelobe level (SLL) reduction

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The design and optimization of square cascades by PSOA and GOA to provide fresh fuel for a nuclear power reactor

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SEPARATION SCIENCE AND TECHNOLOGY 2023年第11期58卷 2008-2026页

作者： Mirmohammadi, S. L. Safdari, J. Mallah, M. H. Ezazi, F. Atom Energy Org Nucl Sci & Technol Res Inst Nucl Fuel Cycle Res Sch Tehran Iran Atom Energy Org Nucl Fuel Cycle Res Sch Nucl Sci & Technol Res Inst Tehran 8486 Iran

Due to the higher flexibility of square cascades than tapered cascades, this research focuses on the design and optimization of square cascades to provide the enriched uranium used in the fresh fuel of a power reactor with different enrichment levels. In order to design and optimize square cascades, two computational codes based on the particle swarm optimization algorithm and the grasshopper optimization algorithm have been developed for the design and optimization of square cascades. The results show that by using optimal square cascades, it is possible to directly produce the fresh enriched uranium required for a power reactor at different enrichment levels (4.1%, 3.7%, and 3.3%), and there is no need to dilute the products enriched by natural or depleted uranium, and the mixing unit can be removed from enrichment facilities. Also, the results obtained from both algorithms show that the total number of optimized square cascades and gas centrifuges required for the production of the annual fuel for a power reactor are very close to each other and have a difference of about 0.65-1.24%.

关键词： Square cascades power reactor fuel particle swarm optimization algorithm grasshopper optimization algorithms

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Estimation of Weibull Probability Distribution Parameters with optimization algorithms and Foca Wind Data Application

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GAZI UNIVERSITY JOURNAL OF SCIENCE 2024年第3期37卷 1236-1254页

作者： Kose, Bayram Isikli, Ibrahim Sagbas, Mehmet Izmir Bakircay Univ Elect & Elect Engn Dept TR-35665 Izmir Turkiye

In this study, the scale and shape parameters of the Weibull probability distribution function (***) used in determining the profitability of wind energy projects are estimated using optimization algorithms and the moment method. These parameters are then used to estimate the wind energy potential (WEP) in Foca region of Izmir in Turkey. The values of Weibull parameters obtained using particle swarm optimization (PSO), Sine Cosine algorithm (SCA), Social Group optimization (SGO), and Bat algorithm (BA) were compared with the estimation results of the Moment Method (MM) as reference. Root mean square error (RMSE) and chi-square (chi<^>2) tests were used to compare the parameter estimation methods. The wind speed measurement values of the observation station in Foca were used. As a result of Foca speed data analysis, the annual average wind speed was determined as 6.15 m/s, and the dominant wind direction was found as northeast. Wind speed frequency distributions were compared with the measurement results and calculated with the estimated parameters. When RMSE and chi<^>2 criteria are evaluated together;it can be concluded that each used method behaves similarly for the given parameter estimation problem, with minor variations. As a result, it has been found that the optimization parameters produce very good results in wind speed distribution and potential calculations.

关键词： Weibull distribution parameters particle swarm optimization algorithm Social group optimization algorithm Sine cosine optimization algorithm Bat algorithm

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Multi-criteria optimal sizing and analysis of PV/wind/fuel cell/battery/diesel generator for rural electrification: A case study in Chad

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INTERNATIONAL JOURNAL OF RENEWABLE ENERGY DEVELOPMENT-IJRED 2024年第3期13卷 491-507页

作者： Abdoulaye, Mahamat Adoum Waita, Sebastian Wekesa, Cyrus Wabuge Mwabora, Julius Mwakondo Univ Nairobi Dept Phys POB 30197-00100 Nairobi Kenya Korea Inst Energy Res Jeju Global Res Ctr Distributed Energy Team 200 Haemajihaean Ro Jeju 63357 South Korea Univ Eldoret Sch Engn POB 1125-30100 Eldoret Kenya

. Access to sustainable, clean, affordable, and reliable electricity is crucial for social and economic development, yet Sub-Saharan Africa (SSA) struggles significantly in this context. In CHAD, only 11.3% of the population is able to access electricity, making it one of the least electrified countries in SSA with the lowest clean energy access. In rural areas, electricity access falls to just 1.3%. This research applies and executes a MultiObjective particle swarm optimization (MOPSO) algorithm using MATLAB R2023b to assess the techno-economic, environmental, and social impacts of a hybrid system based on optimal PV/Wind/Battery/Fuel Cell (FC)/Diesel generator (DG) sizing for rural electrification in CHAD. The proposed system's self-sufficiency index (SSSI) and the Annualized System Cost (ASC) were chosen as objective functions to guarantee the economic feasibility of the system, higher self-sufficiency, and lower dependence on external energy sources (DG). The simulation results show that the optimal size of the proposed system supplies the load demand by 100% of the renewable energy sources (RES) fraction, and the optimal capacities of the main components to supply the load demand are: Solar Power (493 KW), Wind Turbine (166 KW), Battery Energy Charge/Discharge (229180 kWh /221300 kWh), Hydrogen tank storage energy (83 874 kWh), Electrolyzer size (202 KW), Fuel cell size (144 KW). The evelized cost of electricity (LCOE) of 0.2982 $/kWh, which is 51.12% lower than the national unit production costs of electricity in rural areas of CHAD (0.61 $/kWh). This LCOE is also the lowest compared to previous works done using HOMER Pro for the country of CHAD. The results also give a levelized cost of hydrogen (LCOH) of 3.8563 US $/kg, lower than for all studies found in the literature for the country of Chad. The proposed system's yearly avoided greenhouse gas (GHG) emission is 374 640 kg. The proposed system will create five (5) new jobs (JCO) and improve the Human Develo

关键词： system particular Avoided greenhouse gas (GHG) social assessment Hybrid energy system Optimal sizing rural electrification particle swarm optimization algorithm zero-carbon electricity

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A novel conformable fractional-order accumulation grey model and its applications in forecasting energy consumption of China

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SCIENTIFIC REPORTS 2024年第1期14卷 1-23页

作者： Chen, Yuzhen Gong, Wenhao Li, Suzhen Guo, Shuangbing Henan Inst Sci & Technol Sch Math Sci Xinxiang 453003 Peoples R China Univ Shanghai Sci & Technol Sch Management Shanghai 200093 Peoples R China

Accurate forecasting of energy consumption demand is crucial to optimize resources and achieve sustainable development goals. However, the energy system is affected by many factors, which are complex and highly uncertain. Therefore, a novel grey model (IBCFGMP (1,1,N)) is proposed, integrating multiple optimization techniques such as background value optimization, initial condition optimization, fractional-order accumulation optimization, and grey action quantity optimization. First, this paper deduces the time response function of the optimization model. The relevant parameters of the model can be found using the particle swarm optimization algorithm. Then, the properties of the model are studied, and it is found that the optimized model have stronger universality and stability. Finally, the model is applied to predict and analyze the energy consumption of China. The prediction results indicate that China's consumption of hydroelectricity, nuclear energy, and coal will be 12.693 exajoules, 5.550 exajoules, and 98.850 exajoules in 2026, respectively. The research results will provide a scientific basis for rationally optimizing resource allocation and realizing the sustainable development of clean energy.

关键词： Grey prediction model Conformable fractional-order particle swarm optimization algorithm Clean energy IBCFGMP (1,1,N) model

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Measurement of gas volume fraction in gas-liquid two-phase flow using arrayed fiber-optic probes combined with the PSO-BP-AdaBoost algorithm

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OPTICAL FIBER TECHNOLOGY 2025年 93卷

作者： Xing, Wenju Gao, Hong Qiao, Xueguang Xian Shiyou Univ Shaanxi Prov Oil & Gas Resource Fiber Opt Detect E Sch Sci Xian 710065 Peoples R China Northwest Univ Sch Phys Xian 710069 Peoples R China

For the measurement of gas volume fraction in natural gas wells, a strategy based on the fusion of arrayed fiberoptic probes (AFOP) and artificial intelligence algorithms is proposed to enhance the precision and efficiency of gas volume fraction monitoring. As a key front-end component for obtaining gas phase information, AFOP determines the optimal structure by analyzing its performance metrics in bubble capture and its interference with fluid flow. A back-end gas volume fraction prediction model was constructed using a machine learning algorithm. The model first uses a particle swarm optimization (PSO) algorithm to enhance the backpropagation (BP) neural network as a weak predictor and then integrates multiple weak predictors through the adaptive boosting (AdaBoost) algorithm to create a strong predictor. The experimental results show that compared with the support vector machine (SVM), BP neural network, and PSO-BP neural network, the PSO-BP-AdaBoost algorithm has advantages in prediction precision, with a maximum relative error of only 0.14 %, providing a more effective solution for research and application in related fields.

关键词： Arrayed fiber-optic probes Gas volume fraction Backpropagation neural network particle swarm optimization algorithm Adaptive boosting algorithm

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