Enhancing Doubly Fed Induction Generator Low-Voltage Ride-Through Capability Using Dynamic Voltage Restorer with Adaptive Noise Cancellation Technique

Some of the major challenges facing micro-grids (MGs) during their connection with the utility grid are maintaining power system stability and reliability. One term that is frequently discussed in literature is the low-voltage ride-through (LVRT) capability, as it is required by the utility grid to maintain its proper operation and system stability. Furthermore, due to their inherent advantages, doubly fed induction generators (DFIGs) have been widely installed on many wind farms. However, grid voltage dips and distortion have a negative impact on the operation of the DFIG. A dynamic voltage restorer (DVR) is a commonly used device that can enhance the LVRT capability of DFIG compared to shunt capacitors and static synchronous compensator (STATCOM). DVR implements a series compensation during fault conditions by injecting the proper voltage at the point of common coupling (PCC) in order to preserve stable terminal voltage. In this paper, we propose a DVR control method based on the adaptive noise cancelation (ANC) technique to compensate for both voltage variation and harmonic mitigation at DFIG terminals. Additionally, we propose an online control of the DC side voltage of the DVR using pulse width modulation (PWM) rectifier to reduce both the size of the storage element and the solid-state switches of the DVR, aiming to reduce its overall cost. A thorough analysis of the operation and response of the proposed DVR is performed using MATLAB/SIMULINK under different operating conditions of the grid. The simulation results verify the superiority and robustness of the proposed technique to enhance the LVRT capability of the DFIG during system transients and faults.

Short-Term Load Probabilistic Forecasting Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Reconstruction and Salp Swarm Algorithm

Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with sample entropy (SE). The load series is decomposed by ICEEMDAN and is reconstructed into a trend component, periodic component, and random component by comparing with the sample entropy of the original series. Extreme learning machine optimized by salp swarm algorithm (SSA-ELM) is used to predict respectively, and the final prediction value is obtained by superposition of the prediction results of the three components. Then, the prediction error of the training set is divided into four load intervals according to the predicted value, and the kernel probability density is estimated to obtain the error distribution of the training set. Combining the predicted value of the prediction set with the error distribution of the corresponding load interval, the prediction load interval can be obtained. The prediction method is verified by taking the hourly load data of a region in Denmark in 2019 as an example. The final experimental results show that the proposed method has a high prediction accuracy for short-term load forecasting.

Understanding the nonlinear behavior of EEG with advanced machine learning in artifact elimination

Steady-state Visually Evoked Potential (SSVEP) based Electroencephalogram (EEG) signal is utilized in brain-computer interface paradigms, diagnosis of brain diseases, and measurement of the cognitive status of the human brain. However, various artifacts such as the Electrocardiogram (ECG), Electrooculogram (EOG), and Electromyogram (EMG) are present in the raw EEG signal, which adversely affect the EEG-based appliances. In this research, Adaptive Neuro-fuzzy Interface Systems (ANFIS) and Hilbert-Huang Transform (HHT) are primarily employed to remove the artifacts from EEG signals. This work proposes Adaptive Noise Cancellation (ANC) and ANFIS based methods for canceling EEG artifacts. A mathematical model of EEG with the aforementioned artifacts is determined to accomplish the research goal, and then those artifacts are eliminated based on their mathematical characteristics. ANC, ANFIS, and HHT algorithms are simulated on the MATLAB platform, and their performances are also justified by various error estimation criteria using hardware implementation.

Influence of Sampling Frequency Ratio on Mode Mixing Alleviation Performance: A Comparative Study of Four Noise-Assisted Empirical Mode Decomposition Algorithms

Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors.

Evaluating The Performance of Several Data Preprocessing Methods Based On GRU in Forecasting Monthly Runoff Time Series

The optimal planning and management of modern water resources depends highly on reliable and accurate runoff forecasting. Data preprocessing technology can provide new possibilities for improving the accuracy of runoff forecasting, when basic physical relationships cannot be captured using a single prediction model. Yet, few researches evaluated the performances of various data preprocessing technology in predicting monthly runoff time series so far. In order to fill this research gap, this paper investigates the potential of five data preprocessing techniques based on gated recurrent unit network (GRU) model in monthly runoff prediction, namely variational mode decomposition (VMD), wavelet packet decomposition (WPD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), extreme-point symmetric mode decomposition (ESMD), and singular spectrum analysis (SSA). In this study, the original monthly runoff data is first decomposed into a set of subcomponents using five data preprocessing methods; second, each component is predicted by developing an appropriate GRU model; finally, the forecasting results of different two-stage hybrid models are obtained by aggregating of forecast results of the corresponding subcomponents. Four performance metrics are employed as the evaluation benchmarks. The experimental results from two hydropower stations in China show that five data preprocessing techniques can attain satisfying prediction results, while VMD and WPD methods can yield better performance than CEEMDAN, ESMD and SSA in both training and testing periods in terms of four indexes. Indeed, it is significantly important to carefully determine an appropriate data preprocessing method according to the actual characteristics of the study area.