Structure-Preservation Model Aggregation for Two-Stage Inverters Based Large-Scale Photovoltaic System

The reliability of photovoltaic (PV) generators is strongly affected by the performance of Direct Current/Alternating Current (DC/AC) converters, being the major source of PV underperformance. However, generally, their reliability is not investigated at component level: thus, the present work presents a reliability analysis and the repair activity for the components of full bridge DC/AC converters. In the first part of the paper, a reliability analysis using failure rates from literature is carried out for 132 inverters (AC rated power of 350 kW each) with global AC power of 46 MW in a large scale grid-connected PV plant. Then, in the second part of the work, results from literature are compared with data obtained by analyzing industrial maintenance reports in the years 2015–2017. In conclusion, the yearly energy losses involved in the downtime are quantified, as well as their availability.

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Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

Complex & Intelligent Systems ◽

10.1007/s40747-021-00273-5 ◽

2021 ◽

Author(s):

Lu Chen ◽

Handing Wang ◽

Wenping Ma

Keyword(s):

Large Scale ◽

Original Problem ◽

Optimization Problems ◽

Reference Points ◽

Optimization Strategy ◽

Two Stage ◽

Decision Space ◽

Second Stage ◽

Decision Variables ◽

Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

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Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽

10.3390/en14030625 ◽

2021 ◽

Vol 14 (3) ◽

pp. 625

Author(s):

Xinyu Wu ◽

Rui Guo ◽

Xilong Cheng ◽

Chuntian Cheng

Keyword(s):

Dynamic Programming ◽

Stochastic Dynamic Programming ◽

Large Scale ◽

Simulation Optimization ◽

Optimal Operation ◽

Stochastic Dynamic ◽

Operation Model ◽

Two Stage ◽

Operation Rules ◽

Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

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A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽

10.3390/en12183586 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3586 ◽

Cited By ~ 1

Author(s):

Sizhou Sun ◽

Jingqi Fu ◽

Ang Li

Keyword(s):

Wind Power ◽

Large Scale ◽

Wind Farm ◽

Search Algorithm ◽

Interpolation Method ◽

Least Square ◽

Support Vector ◽

Two Stage ◽

Wind Power Forecasting ◽

Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

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Two-stage robust energy management of a hybrid charging station integrated with the photovoltaic system

International Journal of Hydrogen Energy ◽

10.1016/j.ijhydene.2021.01.127 ◽

2021 ◽

Author(s):

Alireza Akbari-Dibavar ◽

Vahid Sohrabi Tabar ◽

Saeid Ghassem Zadeh ◽

Ramin Nourollahi

Keyword(s):

Energy Management ◽

Photovoltaic System ◽

Two Stage ◽

Charging Station

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Large-Scale Simulation of the Clocking Impact of 2D Combustor Profile on a Two Stage High Pressure Turbine

Volume 5B: Heat Transfer ◽

10.1115/gt2014-25883 ◽

2014 ◽

Author(s):

Thomas E. Dyson ◽

David B. Helmer ◽

James A. Tallman

Keyword(s):

High Pressure ◽

Large Scale ◽

High Pressure Turbine ◽

Cfd Simulations ◽

Two Stage ◽

Substantial Impact ◽

Sliding Mesh ◽

Wall Flow ◽

End Wall ◽

Film Flows

This paper presents sliding-mesh unsteady CFD simulations of high-pressure turbine sections of a modern aviation engine in an extension of previously presented work [1]. The simulation included both the first and second stages of a two-stage high-pressure turbine. Half-wheel domains were used, with source terms representing purge and film flows. The end-wall flow-path cavities were incorporated in the domain to a limited extent. The passage-to-passage variation in thermal predictions was compared for a 1D and 2D turbine inlet boundary condition. Substantial impact was observed on both first and second stage vanes despite the mixing from the first stage blade. Qualitative and quantitative differences in surface temperature distributions were observed due to different ratios between airfoil counts in the two domains.

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Fast two-stage phasing of large-scale sequence data

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2021.08.005 ◽

2021 ◽

Author(s):

Brian L. Browning ◽

Xiaowen Tian ◽

Ying Zhou ◽

Sharon R. Browning

Keyword(s):

Large Scale ◽

Sequence Data ◽

Two Stage ◽

Scale Sequence

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A Novel Approach for Two-Stage UAV Path Planning in Pipeline Network Inspection

Volume 3: Operations, Monitoring, and Maintenance; Materials and Joining ◽

10.1115/ipc2020-9604 ◽

2020 ◽

Author(s):

Rui Qiu ◽

Yongtu Liang

Keyword(s):

Path Planning ◽

Large Scale ◽

Flight Path ◽

Mixed Integer ◽

Optimization Approach ◽

Two Stage ◽

Calculation Time ◽

Pipeline Network ◽

Second Stage ◽

Computational Performance

Abstract Currently, unmanned aerial vehicle (UAV) provides the possibility of comprehensive coverage and multi-dimensional visualization of pipeline monitoring. Encouraged by industry policy, research on UAV path planning in pipeline network inspection has emerged. The difficulties of this issue lie in strict operational requirements, variable flight missions, as well as unified optimization for UAV deployment and real-time path planning. Meanwhile, the intricate structure and large scale of the pipeline network further complicate this issue. At present, there is still room to improve the practicality and applicability of the mathematical model and solution strategy. Aiming at this problem, this paper proposes a novel two-stage optimization approach for UAV path planning in pipeline network inspection. The first stage is conventional pre-flight planning, where the requirement for optimality is higher than calculation time. Therefore, a mixed integer linear programming (MILP) model is established and solved by the commercial solver to obtain the optimal UAV number, take-off location and detailed flight path. The second stage is re-planning during the flight, taking into account frequent pipeline accidents (e.g. leaks and cracks). In this stage, the flight path must be timely rescheduled to identify specific hazardous locations. Thus, the requirement for calculation time is higher than optimality and the genetic algorithm is used for solution to satisfy the timeliness of decision-making. Finally, the proposed method is applied to the UAV inspection of a branched oil and gas transmission pipeline network with 36 nodes and the results are analyzed in detail in terms of computational performance. In the first stage, compared to manpower inspection, the total cost and time of UAV inspection is decreased by 54% and 56% respectively. In the second stage, it takes less than 1 minute to obtain a suboptimal solution, verifying the applicability and superiority of the method.

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