Maximum power tracking control for a wind energy conversion system based on a quasi‐ARX neural network model

In high-voltage equipment insulation, multiple partial discharge (PD) sources may exist at the same time. Therefore, it is important to identify PDs from different PD sources under noisy condition in insulations, with the highest accuracy. Although many studies on classifying different PD types in insulation have been performed, some signal processing methods have not been used in the past for this application. Thus, in this work, Cepstrum analysis on PD signals combined with artificial neural network (ANN) is proposed to classify the PD types from different PD sources simultaneously under noisy condition. Measurement data from different sources of artificial PD signals were recorded from insulation materials. Feature extractions were performed on the recorded signals, including Cepstrum analysis, discrete wavelet transform, discrete Fourier transform, and wavelet packet transform for comparison between the different methods. The features extracted were used to train the ANN. To investigate the classification accuracy under noisy signals, the remaining data were corrupted with artificial noise. The noisy data were classified using the ANN, which had been trained by noise-free PD signals. It is found that Cepstrum-ANN yields the highest classification accuracy for noisy PD signals than the other methods tested.

Section: Artificial Neural Networkmentioning

confidence: 99%

Classification of multiple partial discharge sources in dielectric insulation material using Cepstrum analysis–artificial neural network

Illias

Altamimi

Mokhtar

et al. 2016

Research on individual pitch control below rated wind speed

Han

Zhou

2016

The structure of the modern wind turbine is becoming larger and more complex, with the wind rotor exceeding hundreds of meters in diameter. The blade shear force is also becoming increasingly serious below the rated wind speed, which leads to structure fatigue loads and instability of the generator power. For improving the dynamic performance of large wind turbines, it was proposed that individual pitch control (IPC) method was operated below the rated wind speed. In this paper, we analyze the relationship between the aerodynamic characteristics of blades and the nonlinear time-varying pitch control system based on wind shear and the tower shadow effect. The combination of IPC and torque control is used to optimize the control mode of the wind turbine. By fine-tuning the pitch angle, the unbalanced force on the wind rotor was relieved to achieve the purpose of mitigating fatigue loads. Finally, our experimental results prove the validity of the proposed IPC method below the rated wind speed by showing that it can improve power quality and reduce fatigue loads of the key components without reducing the generator output power.

Wind Speed Modeling under Quasi‐Linear Autoregressive Neural Network Model for Prediction of Production Power

Abu Jami'in,

Munadhif,

et al. 2024

Accurate wind speed modeling is beneficial for the design of wind energy conversion systems. Models of wind speed are used to assess the adequacy and dependability of a power supply. However, precise wind speed modeling is challenging due to the sporadic availability of wind speed. In this note, we propose a wind speed model with an autoregressive (AR) structure. A hybrid model is developed under linear and nonlinear parts based on a quasi‐linear autoregressive exogenous neural network (Q‐ARX‐NN). The model's structure is composed of a regression vector and its coefficients. The coefficients are divided into linear and nonlinear coefficients. A set of linear coefficients is identified under the algorithm of least square error (LSE), and a set of nonlinear coefficients is modeled by using a neural network to refine the residual error of the nonlinear part. Some artificial neural network (ANN) models can be set as nonlinear part sub‐models to sharpen the model's accuracy. The proposed model is tested for wind speed modeling to estimate wind energy production. Various nonlinear parts of the sub‐model are tested, such as neural networks, radial basis function networks, and ANN networks. Moreover, we evaluate the effects of the order of the model by varying hidden and output nodes, which can be summarized as the number of coefficients of the regression vector. Using specific wind turbine performance data, prediction models estimate production power. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.