80,822 research outputs found
To develop an efficient variable speed compressor motor system
This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque
is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce
dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy
optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization
control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained
clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
A novel hybrid data-driven approach is developed for forecasting power system
parameters with the goal of increasing the efficiency of short-term forecasting
studies for non-stationary time-series. The proposed approach is based on mode
decomposition and a feature analysis of initial retrospective data using the
Hilbert-Huang transform and machine learning algorithms. The random forests and
gradient boosting trees learning techniques were examined. The decision tree
techniques were used to rank the importance of variables employed in the
forecasting models. The Mean Decrease Gini index is employed as an impurity
function. The resulting hybrid forecasting models employ the radial basis
function neural network and support vector regression. Apart from introduction
and references the paper is organized as follows. The section 2 presents the
background and the review of several approaches for short-term forecasting of
power system parameters. In the third section a hybrid machine learning-based
algorithm using Hilbert-Huang transform is developed for short-term forecasting
of power system parameters. Fourth section describes the decision tree learning
algorithms used for the issue of variables importance. Finally in section six
the experimental results in the following electric power problems are
presented: active power flow forecasting, electricity price forecasting and for
the wind speed and direction forecasting
A new adaptive neural network and heuristics hybrid approach for job-shop scheduling
Copyright @ 2001 Elsevier Science LtdA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.This work is supported by the National Nature Science Foundation (No. 69684005)
and National High -Tech Program of P. R. China (No. 863-511-9609-003)
FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations
Neural network-based methods for image processing are becoming widely used in
practical applications. Modern neural networks are computationally expensive
and require specialized hardware, such as graphics processing units. Since such
hardware is not always available in real life applications, there is a
compelling need for the design of neural networks for mobile devices. Mobile
neural networks typically have reduced number of parameters and require a
relatively small number of arithmetic operations. However, they usually still
are executed at the software level and use floating-point calculations. The use
of mobile networks without further optimization may not provide sufficient
performance when high processing speed is required, for example, in real-time
video processing (30 frames per second). In this study, we suggest
optimizations to speed up computations in order to efficiently use already
trained neural networks on a mobile device. Specifically, we propose an
approach for speeding up neural networks by moving computation from software to
hardware and by using fixed-point calculations instead of floating-point. We
propose a number of methods for neural network architecture design to improve
the performance with fixed-point calculations. We also show an example of how
existing datasets can be modified and adapted for the recognition task in hand.
Finally, we present the design and the implementation of a floating-point gate
array-based device to solve the practical problem of real-time handwritten
digit classification from mobile camera video feed
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