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Probabilistic power flow calculation of power system considering DGs based on improved LHS
Published in Rodolfo Dufo-López, Jaroslaw Krzywanski, Jai Singh, Emerging Developments in the Power and Energy Industry, 2019
Under the IEEE-118 node system, assuming that the probabilistic load flow calculated by the 10000 times MCS algorithm is accurate. The wind farm is injected into node 9, the bus 1 is the balance bus and the node 11-13 is connected to the photovoltaic power station. The parameters of the wind farm are in accordance with the IEEE-14 node system. The random output of the photovoltaic cells is obeyed Betadistribution, and the probability density function is as follows: f(PPV)=τ(α+β)τ(α)+τ(β)PPVPmα−11−PPVPmβ−1
Recent application: Results of adaptive control on multi-axis shaking tables
Published in Edmund Booth, Seismic Design Practice into the Next Century, 1998
The decentralised version of the MCS algorithm reduces the amount of computation required, compared to the fully centralised algorithm. The current Bristol decentralised MCS code has been written in Visual C++ and it runs in Windows on a standard pentium pc. Control of all 6 axes, with a sampling frequency of 1000Hz, is easily achieved.
Active Learning Kriging-Based Reliability for Assessing the Safety of Structures Theory and Application
Published in M.Z. Naser, Leveraging Artificial Intelligence in Engineering, Management, and Safety of Infrastructure, 2023
An executive summary of the discussed learning functions is presented in Table 9.1, for practical purposes. The explained AK-MCS algorithm in Fig. 9.8 and Table 9.1 can be used together to implement the procedure effectively.
Statistical Analysis of Capacities of Battery Energy Storage Based on Economic Assessment of PV/Wind Renewable Energy Sources in Micro-Grid Application
Published in Electric Power Components and Systems, 2023
Based on the sample and repeat numbers, the MCS algorithm generated the power capacity of the BESS as shown in Figure 6(a). From this variation, it was noticed that this power evolves correctly within a well-defined margin. Also, in terms of the same capacity, Figure 6(b) illustrates its equivalence at the cumulative probability density level. For different levels of Cdf, the distribution of the power capacity of the BESS is also depicted in Table 5. Similarly, the energy capacity of the BESS is also generated and it is shown in Figure 6(c). It has been observed that this energy evolves correctly within a well-defined margin centered by the value zero. Then, considering the cumulative probability density and its different levels, Figure 6(d) shows the energy capacity of the BESS. Taking into account the graduation at the level of the Cdf, the distribution of the energy capacity of the BESS is also depicted in Table 5.
Reliability assessment of guyed transmission towers through active learning metamodeling and progressive collapse simulation
Published in Structure and Infrastructure Engineering, 2022
Gabriel Padilha Alves, Leandro F. Fadel Miguel, Rafael Holdorf Lopez, André T. Beck
Then, Kriging data is updated as where one may see that each infill point requires one evaluation of the original limit state function. At this stage, the algorithm returns to step 3, building a new Kriging model using the updated data. One may set a learning threshold, such as That is, if one cannot find a point in whose value of U is below 2, the AK-MCS algorithm is stopped and the current is taken as the solution of the reliability problem.
Machine learning classifier-based dynamic surrogate model for structural reliability analysis
Published in Structure and Infrastructure Engineering, 2023
Guoshao Su, Weizhe Sun, Ying Zhao
The flowchart of the proposed method is shown in Figure 4. The proposed DMLC-MCS algorithm has been validated by a mathematical case (as shown in the Supplementary Material). Compared with some traditional reliability analysis methods, the proposed method has certain advantages in terms of calculation efficiency and calculation accuracy. In addition, the method in this paper is more effective in dealing with small sample problems.