Ting-Chia Ou

Hybrid Control of a Wind Induction Generator Based on Grey–Elman Neural Network

This brief presents the design of an optimal wind energy control system for maximum power point tracking. With the help of a grey predictor for the preprocessor, a high-performance online training Elman neural network (ENN) is designed to derive the turbine speed needed to extract maximum power from wind. Moreover, the connective weights of the improved ENN are trained online by the backpropagation learning algorithm. Compared to earlier methods, better results are obtained when the ENN controller is used together with the grey system modeling approach. Performance of the proposed approach is verified by the experimental results.

Optimal location and capacity of STATCOM for voltage stability enhancement using ACO plus GA

Voltage security is a crucial issue in power systems especially under heavily loaded condition. In the new scheme of restructuring, voltage stability problem becomes even more serious. To solve the problem, we integrate reactive power compensation concept by Static Synchronous Compensator (STATCOM) with Equivalent - current Injection (ECI). We derive a new STATCOM with ECI model. This paper shows the application of Ant Colony Optimization (ACO) plus Genetic Algorithms (GA) for optimal capacity and location of a new STATCOM with ECI model in a power system. Finally simulation shows the optimal location and capacity of new STATCOM with ECI model to enhance power system voltage stability by using GACO. The proposed method demonstrates the improvement of voltage stability margin.

Application of Enhanced Honey-Bee Mating Optimization Algorithm to Fault Section Estimation in Power Systems

An application of plant growth simulation algorithm-enhanced honey-bee mating optimization is proposed to solve the fault section estimation problem in a power system. This approach is proposed because the local search capability of the honey-bee mating optimization is highly dependent on the parameter decisions. An inappropriate selection of parameters may affect the computation efficiency. This paper hence has added the plant growth simulation algorithm to enhance the honey-bee mating optimization so that the work of external parameter settings can be economized while the overall computation can be improved. In order to validate the effectiveness of this method, we have conducted the approach on practical power systems. Test results confirm the feasibility of this proposed method for the application considered.

A novel power flow analysis for microgrid distribution system

A microgrid distribution system is proposed in the paper, consisting of solar power, wind power, microturbine power and a battery energy storage system (BESS). By using the proposed algorithm, a more efficient network configuration can be obtained. The problem is optimized in a stochastic searching manner similar to that of the evolutionary programming. A direct building algorithm for microgrid distribution power flow analysis is proposed in this paper. Simulation results show that the proposed algorithm has advantages than the earlier developed algorithms. The optimization strategy is general and can be used to solve other hybrid power system optimization problems as well.

A study for price-based unit commitment with carbon trading by DI&C simulation

In this paper, the Hybrid Genetic Algorithm-Ant Colony Optimization (GACO) approach is presented to solve the unit commitment problem, and comparison with the results obtained using literature methods by nuclear-grade Digital Instrumentation and Control (DI&C) simulation. Then this paper applied the ability of the Genetic Algorithm (GA) operated after Ant Colony Optimization (ACO) can promote the ACO efficiency. The objective of GA is to improve the searching quality of ants by optimizing themselves to generate a better result, because the ants produced randomly by pheromone process are not necessary better. This method can not only enhance the neighborhood search, but can also search the optimum solution quickly to advance convergence. The other objective of this paper is to investigate an influence of emission constraints on generation scheduling. The motivation for this objective comes from the efforts to reduce negative trends in a climate change. In this market structure, the nuclear power plants (NPPs) and independent power producers (IPPs) have to deal with several complex issues arising from uncertainties in spot market prices, and technical constraints which need to be considered while scheduling generation and trading for the next day. In addition to finding dispatch and unit commitment decisions while maximizing its profit, their scheduling models should include trading decisions like spot-market buy and sell. The model proposed in this paper build on the combined carbon finance and spot market formulation, and help generators in deciding on when these commitments could be beneficial.

MRAS-based sensorless wind energy control for wind generation system using RFNN

This paper presents an analysis of a high-performance model reference adaptive system (MRAS) observer for the sensorless control of a induction generator (IG). The sensorless control is based on a model reference adaptive system observer for estimating the rotational speed. The proposed output maximization control is achieved without mechanical sensors such as wind speed or position sensor, and the new control system will deliver maximum electric power with light weight, high efficiency, and high reliability. The concept has been developed and analyzed using a turbine directly driven IG. The estimation of the rotor speed is on the basis of the MRAS control theory. A sensorless vector-control strategy for an IG operating in a grid-connected variable speed wind energy conversion system is presented.

Sliding mode speed control of Brushless DC Motor using Pulse-Width-Modulated current regulator

This paper proposes a sliding mode controller (SMC) design for a Brushless DC Motor (BLDCM) to achieve high-performance speed control. Based on Pulse-Width-Modulation (PWM) technique, a PWM-based controller in the inner current loop is first developed to perform a fast current control. A sliding mode speed controller is then designed in its outer control loop to enhance the speed control. Finally, the effectiveness of the proposed control scheme under the load disturbances and parameter uncertainties is verified by simulation results and comparison with proportional-integral control (PIC) approach.

Intelligent fuzzy logic controller for a solar charging system

This paper presents an intelligent solar charging system with fuzzy logic control method. With the scarce energy source and the worsening environmental pollution, how to create and use a clean and never exhausted energy is becoming very important day by day. This solar charging system is composed of a solar cell, a charger, batteries, a buck converter and a digital signal processor. In the meantime, it also combines the fuzzy logic method with the tactics of charging to improve the efficiency of charging, suppress the abnormal battery temperature rise, lengthen the batterys life, and reduce the waste used. Finally, experimental and simulation results are shown to demonstrate the effectiveness and validity of the system.

Polynomial Fitting Approach for Wireless Signal Field under Power Lines in Taiwan Railway

The purpose of this paper is to research wireless systems for the message communicated to trolleys and level-crossings under power lines. The purpose used with wireless communication, linked with level-crossings, and uses the radio propagation to dispatch message from the wireless facilities to the trolleys along Taiwan railway when the urgent situation is taken place or the emergency button is pushed by passersby to permit the train driver to practice the critical situation previously. The results for static and dynamic measurement of this wireless system, concerned with the level-crossing and the trolley, is able to offer an improved assignment regulation. In order to estimate the wireless signal field, we perform some practical measurements under power lines, then use the polynomial approximation to verify the wireless signal field, therefore we create a optimal propagating model for Taiwan railway from Tainan to Fangliao separated by Western Line, Pingtung line, and Kaohsiung city line respectively

A MULTI-STAGE FAULT-TOLERANT MULTIPLIER WITH TRIPLE MODULE REDUNDANCY (TMR) TECHNIQUE

This study proposes a multistage fault-tolerant (MSFT) scheme for fixed-width array multipliers. The proposed MSFT multipliers divide the array multiplier into multiple stages, and implement a single processing element (PE) by regarding multiple computation cycles to achieve a low area design. To tolerate the fault that occurs in the integrated circuit, three redundancy replicas of PE (TMR-PE) architecture are proposed. Thus, the MSFT multiplier employs the TMR-PEs to achieve a low-cost fault-tolerant design. The TMR-PEs are designed by using compressors with multiple operands, such as 4-2 compressors or other compressors with more operands, to reduce computation cycles and speed up the execution time. Because of implementation with a 0:18-um CMOS process, the long word-length MSFT multiplier saves a significant amount of the circuit area. The proposed 64 × 64 MSFT multiplier has only 13% of the circuit area and 3% of the delay overhead of the original multiplier. Based on the measurements of the area-delay product (AT) metric, the value of the 64 x 64 MSFT multiplier is only 0:21 fold of the value of the original multiplier. Consequently, the proposed MSFT multipliers achieve a low-cost fault-tolerant design.