Search

As Canada and the world move rapidly toward increased reliance on wind power generation, self-excited induction generators (SEIGs) will play an important role in distributed wind power generation (DWPG). Understanding the significance and prospects of SEIGs in DWPG, first, this paper elucidates the significance of fault detection (FD) and voltage regulation (VR) in the aforementioned application. A comprehensive analysis of VR and faults on niche industrial 7.5-hp copper-rotor SEIG and conventional 7.5-hp aluminum-rotor SEIG is performed through numerical simulations, and the calculated results are validated through experimental investigations. Second, a twofold Daubechies-wavelet-transform-based module is designed for the following: 1) FD and 2) VR, respectively. A discrete-wavelet-transform-based algorithm is proposed and implemented on a low-cost embedded system to provide an economical solution for the aforementioned issues. Thereafter, the aforementioned schemes are tested, and results are investigated. [ABSTRACT FROM PUBLISHER]

Uncertainty modeling of renewable energy sources, load demand, electricity price, etc. create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting method based on a deep learning concept with microclustering (MC) task is presented. The MC method is structured based on hybrid unsupervised and supervised clustering tasks by K-means and Gaussian support vector machine, respectively. In the proposed method, the input data sequence is clustered by the MC task, and then the forecasting process is employed. By applying the MC, input data in each hour are categorized into different groups, and a distinctive forecasting unit is allocated to each one. In this way, more clusters and forecasting networks are earmarked for the hours with higher fluctuation rates. The bi-directional long short-term memory (B-LSTM), which is one of the newest recurrent artificial neural networks, is proposed as the forecasting unit. The B-LSTM has bidirectional memory—feedforward and feedback loops—that helps us to investigate both previous and future hidden layers data. The optimal number of clusters in each hour is determined based on the Davies–Bouldin index. To evaluate the performance of the proposed method, in this study, three forecasting tasks including the wind speed, load demand, and electricity price are studied in different periods using the Ontario province, Canada, data set. The results are compared with other benchmarking methods to verify the robustness and effectiveness of the proposed method. In fact, the proposed method, which is equipped with the MC technique and B-LSTM networks, significantly promotes the forecasting results, especially in spike points. [ABSTRACT FROM AUTHOR]