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

AC voltage regulation of a bidirectional high-frequency link converter using a deadbeat controller

This paper presents a digital controller for AC voltage regulation of a bidirectional high-frequency link (BHFL) inverter using Deadbeat control. The proposed controller consists of inner current loop, outer voltage loop and a feed-forward controller, which imposes a gain scheduling effect according to the reference signal to compensate the steady-state error of the system. The main property of the proposed controller is that the current- and the voltage-loop controllers have the same structure, and use the same sampling period. This simplifies the design and implementation processes. To improve the overall performance of the system, additional disturbance decoupling networks are employed. This takes into account the model discretization effect. Therefore, accurate disturbance decoupling can be achieved, and the system robustness towards load variations is increased. To avoid transformer saturation due to low frequency voltage envelopes, an equalized pulse width modulation (PWM) technique has been introduced. The proposed controller has been realized using the DS1104 digital signal processor (DSP) from dSPACE. Its performances have been tested on a one kVA prototype inverter. Experimental results showed that the proposed controller has very fast dynamic and good steady-state responses even under highly nonlinear loads

Adaptive, fast walking in a biped robot under neuronal control and learning

Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks

Discussion of the technology and research in fuel injectors common rail system

Common rail is one of the most important components in a diesel and gasoline direct injection system. It features a high-pressure (100 bar) fuel rail feeding solenoid valves, as opposed to a low-pressure fuel pump feeding unit injectors. Third-generation common rail diesels now feature piezoelectric injectors for increased precision, with fuel pressures up to 2,500 bar. The purpose of this review paper is to investigate the technology and research in fuel injectors common rail system. This review paper focuses on component of common rail injection system, pioneer of common rail injection, characteristics of common rail injection system, method to reduce smoke and NOx emission simultaneously and impact of common rail injection system. Based on our research, it can be concluded that common rail injection gives many benefit such as good for the engine performance, safe to use, and for to reduce the emission of the vehicle. Fuel injection common rail system is the modern technology that must be developed. Nowadays, our earth is polluting by vehicle output such as smoke. If the common rail system is developed, it can reduce the pollution and keep our atmosphere clean and safe

Method And System For Dynamic Stochastic Optimal Electric Power Flow Control

A dynamic stochastic optimal power flow (DSOPF) control system is described for performing multi-objective optimal control capability in complex electrical power systems. The DSOPF system and method replaces the traditional adaptive critic designs (ACDs) and secondary voltage control, and provides a coordinated AC power flow control solution to the smart grid operation in an environment with high short-term uncertainty and variability. The DSOPF system and method is used to provide nonlinear optimal control, where the control objective is explicitly formulated to incorporate power system economy, stability and security considerations. The system and method dynamically drives a power system to its optimal operating point by continuously adjusting the steady-state set points sent by a traditional optimal power flow algorithm.Clemson UniversityGeorgia Tech Research CorporationThe Curators Of The University Of Missour

Analytical Optimal Currents for Multiphase PMSMs Under Fault Conditions and Saturation

An original analytical expression is presented in this paper to obtain optimal currents minimizing the copper losses of a multi-phase Permanent Magnet Synchronous Motor (PMSM) under fault conditions. Based on the existing solutions [i]opt1 (without zero sequence of current constraint) and [i]opt2 (with zero sequence constraint), this new expression of currents [i]opt3 is obtained by means of a geometrical representation and can be applied to open-circuit, defect of current regulation, current saturation and machine phase short-circuit fault. Simulation results are presented to validate the proposed approach

Advanced control techniques for modern inertia based inverters

”In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques”--Abstract, page iv