Control Method for Power Quality Compensation Based on Levenberg-Marquardt Optimized BP Neural Networks

2006 ◽  
Author(s):  
Zhou Ming ◽  
Wan Jian-Ru ◽  
Wei Zhi-Qiang ◽  
Cui Jian
Keyword(s):  
Neural Networks ◽  
Power Quality ◽  
Control Method ◽  
Bp Neural Networks ◽  
Levenberg Marquardt

scholarly journals Levenberg-Marquardt based MLP for Detection and Classification of Power Quality Disturbances

2019 ◽  
Vol 8 (2) ◽  
pp. 155-161
Keyword(s):  
Neural Networks ◽  
Power Quality ◽  
Multilayer Perceptron ◽  
Hardware Implementation ◽  
Mean Square ◽  
Three Phase ◽  
Proposal A ◽  
Levenberg Marquardt ◽  
Power Quality Disturbances

In recent years, power quality (PQ) has become an important issue for utilities and users. In order to improve PQ, a method for detecting and classifying power quality disturbances (PQDs) is proposed. Hence in addition to identifying the disturbance signals, the proposed method is able to determine its type when occurring. This approach is based on Multilayer perceptron and Levenberg-Marquardt training rule. It is inspired by the desire to take advantage of the parallelism inherent to neural networks in view of hardware implementation using reconfigurable chips. The inputs of these networks are the samples obtained on the power grid in various conditions. The proposed method is tested for sags and swells. To classify the disturbances, the neural architectures have been generalized and configured according to the number and type of disturbances to be treated. To validate and test the proposal, a grid model was built with a three-phase fault generator under Matlab / Simulink R2017a. After comparing the results with those obtained by certain methods in the literature, the proposal proves to be an efficient and reliable tool for monitoring PQ. In fact it has the smallest mean square error and a highperformance with precision of 96%.

scholarly journals Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Control Method ◽  
Joint Space ◽  
Robot Kinematics ◽  
Rbf Neural Networks ◽  
Task Space ◽  
Actuator Dynamics ◽  
Rigid Link ◽  
On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

scholarly journals Improving Power Quality by a Four-Wire Shunt Active Power Filter: A Case Study

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1951
Author(s):  
Mihaela Popescu ◽  
Alexandru Bitoleanu ◽  
Mihaita Linca ◽  
Constantin Vlad Suru
Keyword(s):  
Power Quality ◽  
Quality Indicators ◽  
Reactive Power ◽  
Control Method ◽  
Active Power Filter ◽  
Active Power ◽  
Current Control ◽  
Shunt Active Power Filter ◽  
Power Filter ◽  
Unbalanced Load

This paper presents the use of a three-phase four-wire shunt active power filter to improve the power quality in the Department of Industrial Electronics of a large enterprise from Romania. The specificity is given by the predominant existence of single-phase consumers (such as personal computers, printers, lighting and AC equipment). In order to identify the power quality indicators and ways to improve them, an A-class analyzer was used to record the electrical quantities and energy parameters in the point of common coupling (PCC) with the nonlinear loads for 27 h. The analysis shows that, in order to improve the power quality in PCC, three goals must be achieved: the compensation of the distortion power, the compensation of the reactive power and the compensation of the load unbalance. By using the conceived three-leg shunt active power filter, controlled through the indirect current control method in an original variant, the power quality at the supply side is very much improved. In the proposed control algorithm, the prescribed active current is obtained as a sum of the loss current provided by the DC voltage and the equivalent active current of the unbalanced load. The performance associated with each objective of the compensation is presented and analyzed. The results show that all the power quality indicators meet the specific standards and regulations and prove the validity of the proposed solution.

Decoupling Control for Bearingless Synchronous Reluctance Motor Based on Neural Networks Inverse

2012 ◽  
Vol 150 ◽  
pp. 30-35
Author(s):  
Ze Bin Yang ◽  
Huang Qiu Zhu ◽  
Xiao Dong Sun ◽  
Tao Zhang
Keyword(s):  
Neural Networks ◽  
Control Method ◽  
Decoupling Control ◽  
Performance Simulation ◽  
Inverse Control ◽  
Linear Control Theory ◽  
Synchronous Reluctance Motor ◽  
Reluctance Motor ◽  
Static Performance ◽  
Simulation And Experiment

A novel decoupling control method based on neural networks inverse system is presented in this paper for a bearingless synchronous reluctance motor (BSRM) possessing the characteristics of multi-input-multi-output, nonlinearity, and strong coupling. The dynamic mathematical models are built, which are verified to be invertible. A controller based on neural network inverse is designed, which decouples the original nonlinear system to two linear position subsystems and an angular velocity subsystem. Furthermore, the linear control theory is applied to closed-loop synthesis to meet the desired performance. Simulation and experiment results show that the presented neural networks inverse control strategy can realize the dynamic decoupling of BSRM, and that the control system has fine dynamic and static performance.

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