Chih-Ming Hong

Neural-Network-Based MPPT Control of a Stand-Alone Hybrid Power Generation System

A stand-alone hybrid power system is proposed in this paper. The system consists of solar power, wind power, diesel engine, and an intelligent power controller. MATLAB/Simulink was used to build the dynamic model and simulate the system. To achieve a fast and stable response for the real power control, the intelligent controller consists of a radial basis function network (RBFN) and an improved Elman neural network (ENN) for maximum power point tracking (MPPT). The pitch angle of wind turbine is controlled by the ENN, and the solar system uses RBFN, where the output signal is used to control the dc/dc boost converters to achieve the MPPT.

A New Elman Neural Network-Based Control Algorithm for Adjustable-Pitch Variable-Speed Wind-Energy Conversion Systems

This paper presents an improved Elman neural network (IENN)-based algorithm for optimal wind-energy control with maximum power point tracking. An online training IENN controller using back-propagation (BP) learning algorithm with modified particle swarm optimization (MPSO) is designed to allow the pitch adjustment for power regulation. The node connecting weights of the IENN are trained online by BP methodology. MPSO is adopted to adjust the learning rates in the BP process to improve the learning capability. Performance of the proposed ENN with MPSO is verified by many experimental results.

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.

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.

Hybrid fuzzy control of wind turbine generator by pitch control using RNN

This article presents the design of a hybrid fuzzy sliding mode loss-minimisation control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training recurrent neural network (RNN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RNN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low- and high-wind speeds, and the maximum energy can be captured from the wind. The sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilised to estimate the upper bound of uncertainties.

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.

Fuzzy sliding mode-based control for PMSG maximum wind energy capture with compensated pitch angle

This paper presents the design of a fuzzy sliding mode loss-minimization control for the speed of a permanent magnet synchronous generator (PMSG) and proportional-integral (PI) for the turbine pitch angle control. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. A sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilized to estimate the upper bound of uncertainties. Furthermore, the fuzzy inference mechanism with center adaptation is investigated to estimate the optimal bound of uncertainties.

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.

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.

Application of Fuzzy Neural Network Sliding Mode Controller for Wind Driven Induction Generator System

An induction generator (IG) speed drive with the application of a sliding mode controller and a proposed fuzzy neural network (FNN) controller is introduced in this paper. Grid connected wind energy conversion system (WECS) present interesting control demands, due to the intrinsic nonlinear characteristic of wind mills and electric generators. The FNN torque compensation is feedforward to increase the robustness of the wind driven induction generator system. A multivariable controller is designed to drive the turbine speed to extract maximum power from the wind and adjust to the power regulation. Moreover, a sliding mode speed controller is designed based on an integral-proportional (IP) sliding surface. When sliding mode occurs on the sliding surface, the control system acts as a robust state feedback system.