Kuei-Hsiang Chao

An intelligent maximum power point tracking method based on extension theory for PV systems

The purpose of this paper is to propose a novel maximum power point tracking (MPPT) technique to fully utilize photovoltaic (PV) array output power that depends on solar insolation and ambient temperature. The proposed intelligent MPPT algorithm based on extension theory can automatically adjust the step size to track the PV array maximum power point (MPP). Compared with the conventional fixed step size perturbation and observation (P&O) and incremental conductance (INC) methods, the presented approach is able to effectively improve the dynamic response and steady-state performance of the PV systems simultaneously. Theoretical analysis and the design principle of the proposed method are described in detail. Some simulations are performed to demonstrate the effectiveness of the proposed extension MPPT method.

A novel clustering algorithm based on the extension theory and genetic algorithm

This paper presents a novel clustering method this is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustering problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in clustering processes. In order to improve this defect, the paper uses the EGA to find the best parameter of classical domain. Through the simulations, we prove that this new method can eliminate try and error adjustment of modeling parameters and increase the accuracy of clustering problems. Experimental results from three different examples, including two benchmark data sets and one practical application, verify the effectiveness and applicability of the proposed work.

Modeling and fault simulation of photovoltaic generation systems using circuit-based model

In this paper, a circuit-based simulation model of a photovoltaic (PV) panel using PSIM software package is first developed. Then, 3 kW PV arrays established using the proposed PSIM model with series and parallel connections are not only employed to represent its I-V and P-V characteristics at variable surface temperatures and isolations under normal operation, but also to carry out the fault simulation. The proposed model can be applied to any brand of PV modules by setting the parameters properly. A 3 kW PV system constructed with the SIEMENS SP75 solar modules is selected as an example to verify the validity of the proposed simulation model.

A VSS speed controller with model reference response for induction motor drive

This paper is mainly concerned with the development of a variable-structure system (VSS) controller with model reference speed response for an induction motor drive. An indirect-field-oriented (IFO) induction motor drive is first implemented, and its dynamic model at a nominal operating condition is estimated from measured data. Then, a two-degrees-of-freedom linear model-following controller (2DOFLMFC) is designed to meet the prescribed tracking and load regulation speed responses at the nominal case. As the variations of system parameters and operating condition occur, the prescribed control specifications may not be satisfied further. To improve this, a VSS controller is developed to generate a compensation control signal to reduce the control performance degradation. The proposed VSS controller is easy to implement, since only the output variable is sensed. The existence condition of sliding-mode control is derived, and the chattering suppression during the static period is also considered. Good model-following tracking and load regulation speed responses are obtained by the designed VSS controller. Effectiveness of the proposed controller and the performance of the resulting drive system are confirmed by some simulation and measured results.

A Maximum Power Point Tracking Method Based on Extension Neural Network for PV Systems

In this paper, a maximum power point tracking (MPPT) technique based on extension neural network (ENN) was proposed to make full utilization of photovoltaic (PV) array output power which depends on solar insolation and ambient temperature. The proposed ENN MPPT algorithm can automatically adjust the step size to track the PV array maximum power point (MPP). Compared with the conventional fixed step size perturbation and observation (P&O) and incremental conductance (INC) methods, the presented method is able to effectively improve the dynamic response and steady state performance of the PV systems simultaneously. A theoretical analysis and the designed principle of the proposed method are described in detail. And some simulation results are made to demonstrate the effectiveness of the proposed MPPT method.

A discrete adaptive field-oriented induction motor drive

A discrete model reference adaptive controller (MRAC) is designed and implemented. This MRAC makes the performance of the field-oriented induction motor drives insensitive to parameter changes. Only the information of the reference model and the plant output are required. Hence, the proposed controller is easy to implement practically. For designing the proposed adaptive controller, the dynamic model of the drive system is estimated from the sampled input-output data using the stochastic modeling technique. The theoretical basis of the adaptive control is derived and simulation is made. The hardware of the drive system and the microprocessor-based adaptive controller are discussed. Some experimental results are given to demonstrate the effectiveness of the proposed controller. >

State-of-health estimator based-on extension theory with a learning mechanism for lead-acid batteries

The main objective of this paper is to design and implement an improved intelligent state-of-health (SOH) estimator for estimating the useful life of lead-acid batteries. Laboratory studies were carried out to measure and record the distributed range of characteristic values in each SOH cycle for the battery subject to cycles of charging and discharging experiments. The measured coup de fouet voltage, internal resistance, and transient current are used as characteristics to develop an intelligent SOH evaluation algorithm. This method is based on the extension matter-element model that has been modified in this research by adding a learning mechanism for evaluation SOH of batteries. The proposed algorithm is relatively simple so that it can be easily implemented with a programmable system-on-chip (PSOC) microcontroller achieve rapid evaluation of battery SOH with precision by using a concise hardware circuit.

Maximum Power Point Tracking Method Based on Modified Particle Swarm Optimization for Photovoltaic Systems

This study investigated the output characteristics of photovoltaic module arrays with partial module shading. Accordingly, we presented a maximum power point tracking (MPPT) method that can effectively track the global optimum of multipeak curves. This method was based on particle swarm optimization (PSO). The concept of linear decreases in weighting was added to improve the tracking performance of the maximum power point tracker. Simulation results were used to verify that this method could successfully track maximum power points in the output characteristic curves of photovoltaic modules with multipeak values. The results also established that the performance of the modified PSO-based MPPT method was superior to that of conventional PSO methods.

A novel neural network with simple learning algorithm for islanding phenomenon detection of photovoltaic systems

This study aimed to propose an intelligent islanding phenomenon detection method for a photovoltaic power generation system. First, a PSIM software package was employed to establish a simulation environment of a grid-connected photovoltaic (PV) power generation system. A 516W PV array system formed by Kyocera KC40T photovoltaic modules was used to complete the simulation of the islanding phenomenon detection method. The proposed islanding phenomenon detection technology was based on an extension neural network (ENN), which combined the extension distance of extension theory, as well as the learning, recalling, generalization and parallel computing characteristics of a neural network (NN). The proposed extension neural network was used to distinguish whether the trouble signals at the grid power end were power quality interference or actual islanding operations, in order that the islanding phenomenon detection system could cut off the load correctly and promptly when a real islanding operation occurs. Finally, the feasibility of the proposed intelligent islanding detection technology was verified through simulation results.

Design and Implementation of a Simulator for Photovoltaic Modules

Proposed in this paper is the development of a photovoltaic module simulator, one capable of running an output characteristic simulation under normal operation according to various electrical parameters specified and exhibiting multiple advantages of being low cost, small sized, and easy to implement. In comparison with commercial simulation tools, Pspice and Solar Pro, the simulator developed demonstrates a comparable I-V as well as a P-V output characteristic curve. In addition, a series-parallel configuration of individual modules constitutes a photovoltaic module array, which turns into a photovoltaic power generation system with an integrated power conditioner.