Deep Learning Based Non-Intrusive Load Monitoring for a Three-Phase System

Non-Intrusive Load Monitoring (NILM) is a method to determine the power consumption of individual appliances from the overall power consumption measured by a single measurement device, which is usually the main meter. Increase in the adoption of smart meters has facilitated large scale implementation of NILM, which can provide information about individual loads to the utilities and consumers. This will lead to significant energy savings as well as better demand-side management. Researchers have proposed several methods and have successfully implemented NILM for residential sectors that have a single-phase supply. However, NILM has not been successfully implemented for industrial and commercial buildings that have a three-phase supply, due to several challenges. These buildings consume significant amount of power and implementing NILM to these buildings has the potential to yield substantial benefits. In this paper, we propose a novel deep learning-based approach to address some of the key challenges in implementing NILM for buildings that have a three-phase supply. Our approach introduces an ensemble learning technique that does not require training of multiple neural network models, which reduces the computational requirements and makes it economically feasible. The model was tested on a three-phase system that consists of both three- phase loads and single-phase loads. The results show significant improvement in load disaggregation compared to the existing methods and indicate its applicability

Development of intelligent collision avoidance systems

This paper introduces a new paradigm for avoiding vehicle collisions in Intelligent Transportation Systems. The key feature of the concept is that we introduce interactive control of different vehicular sub-systems ultimately leading to interdependent maneuvering of vehicles in order to avoid collision encounters. A general scenario of motion of vehicular systems passing a common point at the same time is studied here. Using a fuzzy logic-based controller, collision avoidance maneuvers of the individual vehicular sub-systems are realized. An inter-vehicle communication (IVC) system that facilitates interactions between the vehicles is studied as a top tier above the individual vehicular controllers. The concept that is adopted here is that in order to control a general system with participatory sub-systems of vehicles, all the influencing members have to interactively work together in order to effectively negotiate to achieve an optimal result in avoiding collision encounters. While the interactive operations are being carried out, each vehicular sub-system may act as a ‘Master’ one time and a ‘Slave’ at another time. This switching between the states of‘Master’ and ‘Slave’ should be done efficiently to better prevent a collision encounter. Even though above paradigm is proved only for two interactive sub-systems, it is emphasized that the concept can be extended to any number of vehicular sub-systems to realize effective collision avoidance maneuvers.Senate Research Gran

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

[[abstract]]Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are (1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control;(2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller-observer design; and (3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties as well as severe and adverse driving conditions.[[incitationindex]]SCI[[booktype]]紙

Neuroadaptive Combined Lateral and Longitudinal Control of Highway Vehicles Using RBF Networks

A neural network (NN) adaptive model-based combined lateral and longitudinal vehicle control algorithm for highway applications is presented in this paper. The controller is synthesized using a proportional plus derivative control coupled with an online adaptive neural module that acts as a dynamic compensator to counteract inherent model discrepancies, strong nonlinearities, and coupling effects. The closed-loop stability issues of this combined control scheme are analyzed using a Lyapunov-based method. The neurocontrol approach can guarantee the uniform ultimate bounds of the tracking errors and bounds of NN weights. A complex nonlinear three-degreeof- freedom dynamic model of a passenger wagon is developed to simulate the vehicle motion and for controller design. The controller is tested and verified via computer simulations in the presence of parametric uncertainties and severe driving condition

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are 1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control; 2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller–observer design; and 3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties, as well as severe and adverse driving condition

A novel active inrush suppression method for SC converter

A new type of Inrush suppression circuit in SC buck converter is proposed. The method is realized by the hybrid of passive and active circuits. It adapts PIC micro controller to generate multi pulse to drive multi switches. Computer simulation shows that this method suppresses inrush current drastically

A compact and high performance SC DC/DC buck converter

A novel paralleling interleaved discharging (PID) approach is presented to reduce the output ripple and continuous input current waveform in step-down switched capacitor (SC). Theoretical analysis and the computer simulation show that PID method can reduce output ripple by a factor of three and improve output power level by 8. 7%. The PID method can provide a large range of constant desired values of the output voltage for a given input voltage by paralleling

Discrete-time neuroadaptive control using dynamic state feedback with application to vehicle motion control for intelligent vehicle highway systems

Discrete time neuro-compensated dynamic state feedback control system for lateral and longitudinal control of intelligent vehicle highway systems (IVHS) is developed. A discrete time counterpart of the continuous time non-linear IHVS model is obtained in state-space form and the controller is analysed in three stages, with and without compensation mechanisms resulting in an implementation from low to high complexity. Gain parameters of the dynamic state feedback control are optimised with respect to a minimisation of a linear quadratic cost function. The weight convergence of the neuro-compensation algorithm is established in discrete time Lyapunov sense via a graphical method. The performance enhancement of each design stage of the controller is presented and compared with the aid of computer simulations

Discrete-time neuroadaptive control using dynamic state feedback with application to vehicle motion control for intelligent vehicle highway systems

Discrete time neuro-compensated dynamic state feedback control system for lateral and longitudinal control of intelligent vehicle highway systems (IVHS) is developed. A discrete time counterpart of the continuous time non-linear IHVS model is obtained in state-space form and the controller is analysed in three stages, with and without compensation mechanisms resulting in an implementation from low to high complexity. Gain parameters of the dynamic state feedback control are optimised with respect to a minimisation of a linear quadratic cost function. The weight convergence of the neuro-compensation algorithm is established in discrete time Lyapunov sense via a graphical method. The performance enhancement of each design stage of the controller is presented and compared with the aid of computer simulations