Albert W. L. Yao

An improved Grey-based approach for electricity demand forecasting

The aim of this project is to develop an online electricity demand predictor. In this paper, we present an improved Grey-based prediction algorithm to forecast a very-short-term electric power demand for the demand-control of electricity. We adopted Grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. However, our preliminary study shows that the general Grey model, GM(1,1) is inadequate to handle a volatile electrical system. The general GM(1,1) prediction generates the dilemmas of dissipation and overshoots. In this study, the prediction is improved significantly by applying the transformed Grey model and the concept of average system slope. The adaptive value of α in the Grey differential equation is obtained quickly with the average system slope technique. The present intelligent Grey-based electric demand-control system is able to provide an instrument to save operation costs for high energy consuming enterprises. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant.

Developing a PC-based automated monitoring and control platform for electric power systems

Abstract Power companies generally have many different rate schedules and penalty policies for poor-power-factor loads. In order to cut down on electric bills and avoid penalties, companies concern themselves with the efficient use of electricity. In this paper, we present a cost-effective automated monitoring and control electric system (AMCES), based on open modular controllers (OMCs) and a PC-based visual human–machine interface and data acquisition system (HMI/DAS). The AMCES is an intelligent supervisory control and data acquisition (SCADA) platform, which provides economical and user-friendly solutions to electric power facility management. The key aspect of this AMCES platform is its easily accessible flow of information and wiring through network. The OMC provides feasible solutions to the demand control of electricity, using PC-based HMI/DAS, whereby OMCs are optimized for executing data collection and rapid sequential control strategies. Information from this platform can be distributed through an enterprises Intranet or Internet. Nowadays with the convergence of underlying microprocessor technology and software programming techniques, the hybrid AMCES provides a cost-effective solution to real time control in small- to medium-sized process plants on electric power management. The major purpose of this paper is to demonstrate that OMCs are responsive to rapid and repetitious control tasks. By using PCs that present the flow of information automation and accept operator instructions, it therefore provides the user a tool to monitor and control the use of electric power economically and efficiently.

A Petri nets and genetic algorithm based optimal scheduling for job shop manufacturing systems

An optimal production scheduling solution to meet the order is a must for enterprise to gain profit. This paper presents a novel Petri nets and Genetic Algorithm (PNGA) optimal scheduling method for job shop manufacturing systems. Using the job shop production of a mold factory as a case study, we examined the capability of the proposed PNGA method and compared its results with the ordinary Genetic Algorithm (GA) and Hybrid Taguchi-Genetic Algorithm (HTGA) methods. The MATLAB software was adopted to model the Petri nets in this study. Taguchis method was used to optimize these experiment parameters. The optimal parameter settings were then programmed into the PNGA program. In conjunction with the Petri nets model, the process time was then estimated. The simulation results show that the average process time of PNGA is about 287 (unit time). It is less than 289.55 of the GA and 288.8 of the HTGA. The standard deviation of process time of PNGA is about 5.20. It is less than 6.0 of the GA and 5.88 of the HTGA. That is, the proposed PNGA is able to provide a better production scheduling solution.

A PETRI NET BASED OFFLINE SIMULATION AND ONLINE DIAGNOSTIC PLATFORM FOR MANUFACTURING SYSTEMS

ABSTRACT To simulate and debug or diagnose manufacturing systems is always challenging. This paper presents a hybrid PLC-controlled and PC Visual Basic (VB)-based Human-Machine Interface (HMI) platform based on Petri net (PN) theory for Manufacturing Systems. An augmented PN model was adopted to design and simulate the operational sequence of the Flexible Manufacturing System (FMS) at the National Kaohsiung First University of Science and Technology (NKFUST). With the simulated PN model, the control sequences were quickly translated into PLC programs. A PC-based HMI monitoring system based on the PN model was developed to provide an intuitive offline simulation and online diagnostic tools for the FMS. This hybrid system shows many of advantages by using the economical discrete control unit with PLC, and PC-based HMI using PN model as a base. The discrete event control sequence of an FMS can be easily modeled and evaluated before its build-up. More importantly, it can be diagnosed online when the system is malfunctioning. That is, the presented system provides an adequate means for offline system design, simulation, performance evaluation, and quick online system diagnostic.

Development of A Petri Net-Based Reengineering Simulation System: A Case Study

Researchers always intend to pursue the convenient and integrated tools for restoring the complexity of industrial automated systems. In this paper, a PC-based with PC visual basic-based (VB-Based) human-machine interface (HMI) is developed by using Petri nets (PN) technique for the simulation of an industrial automated system. The result brings many advantages to the reality of the automated control world. It not only reduces the restore time of the automated systems, prevents the cost of equipment malfunction, but also builds the panel of the humanity for the system. The developed sequence table has proved as an important role of control process for a discrete-event control system (DECS). This paper also discusses a systematic method to convert the sequence table to Petri nets so the barriers of the conversion technique can be easily reduced.

Development of a Cloud Based Remote Mobile Monitoring and Control System for Manufacturing

The purpose of this study is to develop a remote mobile monitoring and control system for manufacturing factory by using the cloud computing technology and information and communication (ICT) technology. In conjunction with the cloud and ICT technologies and mobile devices, modern manufacturing systems can be integrated to promote competitive strength to transmit the information and control commands toward the remote monitoring systems instantly via mobile devices. In such a way, this remote system could enhance the efficiency of information flow and the competitive strength and add unknown value to industries. This study adopts the cloud and ICT technologies as the main communication media to develop a remote monitoring and control system with smart mobile devices. The results of this study show the successful integration of the intelligent mobile devices and cloud networks for manufacturing system.

Development of an Intelligent Grey-RSS Navigation System for Mobile Robots

This study aims at developing an intelligent navigation system for mobile robots to eliminate the cumulative errors caused by using a conventional optical encoder. The optical encoder for mobile robots is supplanted by an intelligent Grey-RSS navigation system (IGRNS) with a RFID system embedded with a RSS (Received Signal Strength) location estimator and a Grey predictor for robot rotation angle prediction. The RFID system identifies the target with active RFID tag. The RSS location estimator then calculates the possible route of robot. The Grey controller optimizes the traveling route quickly. The simulation results show that the proposed IGRNS can reduce robot’s unstable rotation when locating the target. With IGRNS, the mobile robot takes fewer steps to reach target quickly. The performance of IGRNS is better than the one with the RSS navigation system alone.© 2012 ASME

A Virtual Factory Planning and Estimation System for Engineering and Education

Virtual manufacturing (VM) is an emerging concept in which real manufacturing operations are simulated using computer models. In this study, we developed a virtual flexible manufacturing system (V-FMS) using virtual reality (VR) technology at National Kaohsiung First University of Science and Technology (NKFUST), Taiwan. Technologies employed include EON Studio virtual reality software, Visual Basic (VB) programming, scheduling and production management methods, and numerical control (NC) interpretation. This integrated system provides operating simulation, factory planning, scheduling, and performance estimation functions. It can also serve as a safe, relevant, and cost-effective tool for education and training.Copyright

Development of A Long-Stroke Precision Positioning System with Force Feedback Effect

This study develops a positioning system with force feedback effect for a long-stroke platform. Conventionally, a positioning platform usually adopts keyboard and mouse as input devices for operators. However, the operators of this system are unable to experience the movement of platform. In this study, a user interface was designed by using the Borland C++ Builder, to access the force feedback signal of a joystick. With the force feedback effect, it allows operators to control the poisoning platform more precisely. We utilized a HP-35670A frequency analyzer and the curve fitting method to identify the transfer function of positioning system, and to design an efficient controller. The result of the study effectively improves the positioning accuracy with the designed controller.