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Kirill Kulikov
Kirill Kulikov

Electrical Machines By Ashfaq 31


Assistant Professor email: [email protected]0222-2771351 Ext. Cell : 03332667848 B.E.(EL) [MUET]M.E In progress [MUET]Research Interest:Power system protection , Electrical machines , Electrical power distribution and utilization , Harmonics.




electrical machines by ashfaq 31



Another problem that effect the power production from HPP is the failure of machines employed to assist in power production. The turbine, generator, transformers, pipe lines, switch gears each of the element which are utilized to help in the power production can fail due to various regions like electrical or mechanical stress, over load, lifetime etc.


Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.


Many papers related to electricity forecasting are based on energy consumption. As exposed by Alipour et al. [25] renewable energy resources have an uncertainty of electric power generation, which can lead to problems in the electrical power systems. As presented by Kazemzadeh et al. [26] load forecast, it is one of the main base studies for the planning and operation of the expansion of the electric power system. In [27], an evaluation of the forecast is made for a project up to 2030, many models are covered in this work in order to show that there is a better performance depending on the algorithm.


In addition to electricity price and load forecasting, the use of artificial intelligence is powerful to assess the development of possible failures in the electrical system [29]. As presented by Stefenon et al. [30], the use of the wavelet transform reduces signal noise, improving the analysis of chaotic time signals. The results using the wavelet group method of data handling proved to be superior to well-consolidated algorithms as LSTM and adaptive neuro fuzzy inference system. Additionally, pre-processing techniques of time series are robust approaches that are widely used to de-noise the raw signal, and then enhance the forecasting accuracy, as approached in da Silva et al. [31].


Focused on the analysis of the electrical system about electricity generation in Brazil, many works evaluated the generation capacity concerning a hydroelectric source, as presented in Brito et al. [32] and Fredo et al. [33], or as a hydrothermal problem as discussed by Finardi et al. [34] and van Ackooij et al. [35]. Moreover, according to Silveira Gontijo and Azevedo Costa [36] in Brazil, there is a predominance of hydroelectric generation (73%), which makes the analysis of hydroelectric energy price forecasting an important field of study.


Using an intramolecular single-electron transfer process, we show how computing inside a quantum system can be performed using the time evolution driven by the preparation of the system in a nonstationary state. The molecule Hamiltonian is separated in three parts: the input, calculation, and output parts. Two optimization procedures are described in order to design an efficient monoelectronics level structure for molecular logic gates. An XOR gate and a half-adder using six electronic quantum levels are presented in a prospect to integrate a full logic gate inside a single molecule without forcing the molecule to have the shape of an electrical circuit. We foresee the merger of molecular electronics with quantum computation at the nanoscale. 350c69d7ab


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