Jian-Liung Chen

Using Self-Synchronization Error Dynamics Formulation Based Controller for Maximum Photovoltaic Power Tracking in Micro-Grid Systems

This paper proposes a self-synchronization error dynamics formulation based controller for maximum photovoltaic power tracking (MPPT) of a photovoltaic (PV) array. The output power conversion of a PV array depends on atmospheric conditions, such as the solar radiation and ambient temperature, and its conversion efficiency is low. Therefore, a MPPT controller is necessary for a PV conversion system, in order to improve the output power. A PV cell is a p-n semiconductor junction. Photon motion, temperature, or electricity conduction cause anomalous diffusion phenomena in inhomogeneous media. In order to describe nonlinear-characteristics, fractional-order calculus can be used to express the dynamic behaviors using fractional-order incremental conductance and to adjust the terminal voltage to the maximum power point. Inspired by the synchronization of Sprott system, a voltage detector is formulated to trace the desired voltage and to control the duty cycle of a boost converter. For a small photovoltaic system, the numerical experiments demonstrate that the proposed method can reduce the tracking time and can improve the conversion efficiency.

Nontechnical Loss and Outage Detection Using Fractional-Order Self-Synchronization Error-Based Fuzzy Petri Nets in Micro-Distribution Systems

Load management is a challenging issue in micro-distribution systems dealing with power utilities. To efficiently detect fraudulent and abnormal consumption, this paper proposes the use of fractional-order self-synchronization error-based Fuzzy Petri nets (FPNs) to detect nontechnical losses and outage events. Under the advanced metering infrastructure technique, the Sprott system is a feature extractor, which tracks the differences between profiled usages and irregular usages, such as illegal and fault events. Thus, fraudulent consumption, outages, and service restoration activities can be pointed out, randomly initiated, and terminated in a real-time application. Multiple FPNs-based making-decision systems are used to locate abnormalities. Computer simulations are conducted using an IEEE 30-bus power system and medium-scale micro-distribution systems to show the effectiveness of the proposed method.

Non-Cooperative Game Model Applied to an Advanced Metering Infrastructure for Non-Technical Loss Screening in Micro-Distribution Systems

This paper proposes a non-cooperative game (NCG) model for non-technical loss (NTL) screening in micro-distribution systems. This model can compound an infra-structure with smart electric meters and fractional-order self-synchronization error formulation to distinguish illegal activities between profiled usages and non-technical usages. Then a NCG-based decision-making model is used to locate the abnormalities. Experimental results are presented to demonstrate the effectiveness of the proposed method.

Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition

This paper proposes combining the biometric fractal pattern and particle swarm optimization (PSO)-based classifier for fingerprint recognition. Fingerprints have arch, loop, whorl, and accidental morphologies, and embed singular points, resulting in the establishment of fingerprint individuality. An automatic fingerprint identification system consists of two stages: digital image processing (DIP) and pattern recognition. DIP is used to convert to binary images, refine out noise, and locate the reference point. For binary images, Katzs algorithm is employed to estimate the fractal dimension (FD) from a two-dimensional (2D) image. Biometric features are extracted as fractal patterns using different FDs. Probabilistic neural network (PNN) as a classifier performs to compare the fractal patterns among the small-scale database. A PSO algorithm is used to tune the optimal parameters and heighten the accuracy. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Dissolved gases forecast to enhance oil-immersed transformer fault diagnosis with grey prediction–clustering analysis

: A method is proposed for dissolved gases forecast and fault diagnosis in oil-immersed transformers using grey prediction–clustering analysis. Incipient faults can produce hydrocarbon molecules and carbon oxides due to the thermal decomposition of mineral oil, cellulose and other solid insulation. Dissolved gas analysis is employed to detect and monitor abnormal conditions in oil-immersed power transformers. However, the procedure takes a long time to decompose overall key gases and monitor conditions. The grey prediction GM(1, 2) model uses the variant information of hydrogen to forecast the further trends of both combustible and non-combustible gases. Grey clustering analysis is applied to diagnose internal faults including thermal faults, electrical faults and faults involving cellulose degradation. Numerical tests with field gas records were conducted to show the effectiveness of the proposed model, and are easy to implement with the help of portable devices.

Optical sensor measurement and biometric-based fractal pattern classifier for fingerprint recognition

Research highlights? This paper proposes biometric-based fractal pattern classifier for fingerprint recognition. ? Biometric characteristics are extracted as fractal patterns using Weierstrass cosine function with different fractal dimensions. ? Grey relational analysis is used to determine the relational grade between a given reference pattern and a given set of comparative patterns. ? For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition. This paper proposes biometric-based fractal pattern classifier for fingerprint recognition using grey relational analysis (GRA). Fingerprint patterns have arch, loop, whorl, and accidental morphologys, and embed singular points, which result in establishing fingerprint individuality. An automatic fingerprint identification system consists of three stages: image acquisition and processing, feature extraction, and pattern recognition. Fingerprint images are captured from subjects using an optical fingerprint reader (OFR). Digital image preprocessing (DIP) is used to refine out noise, enhance the image, convert to binary image, and locate the reference point. For binary images, Katzs algorithm is employed to estimate the fractal dimension (FD) from two-dimension (2D) image. Biometric characteristics are extracted as fractal patterns using Weierstrass cosine function (WCF) with different FDs. GRA performs to compare the fractal patterns among the small-scale database. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Photovoltaic Energy Conversion System Fault Detection Using Fractional-Order Color Relation Classifier in Microdistribution Systems

In this paper, we propose a photovoltaic (PV) energy conversion system (PVECS) fault detection scheme using a fractional-order color relation classifier in microdistribution systems. Based on electrical examination method, output power degradation is used to monitor physical conditions with changes in a PV array’s circuitry, including grounded faults, mismatch faults, bridged faults between two PV panels, and open-circuit faults. The PV array power depends on solar radiation and temperature, and maximum power point tracking (MPPT) control is used to maintain stable power supply to a microdistribution system in the event of a fault in the PVECS. The MPPT algorithm is employed to estimate the desired maximum power, which is then compared with the meter-read power. Fractional-order dynamic errors are determined to quantify output power degradation between the desired maximum power and the meter-read power. Then, a color relation analysis is used to separate normal conditions from fault events. For a PVECS with two panels in parallel, the simulation results demonstrate that the proposed method is suitable for real-time applications and is flexible for fault identification. Its detection rates exceeded 88.23% for six events.

Combining fractional-order edge detection and chaos synchronisation classifier for fingerprint identification

This study proposes the combination of fractional-order edge detection (FOED) and a chaos synchronisation classifier for fingerprint identification. Fingerprints have various morphologies and exhibit singular points, which result in fingerprint individuality. Thumbprint images are captured from subjects using an optical fingerprint reader. The identification procedure consists of three stages: image enhancement, feature extraction and pattern identification. The adjustment of grey-scale values is used to enhance the contrast of the image. In order to overcome the limitations of the integral-order method, FOED is used to improve the clarity of the ridge and valley structures in fingerprint images. Using a reference point, it provides a stable sampling window for fingerprint extraction. Multiple CS-based detectors are used to track the differences as dynamic errors between heterogeneous fingerprints, on a one-to-one basis. The maximum-likelihood method performs a comparison of these different dynamic errors to identify individuals. Using 30 laboratory subjects, the proposed hybrid methods have a faster processing time and provide more accurate fingerprint identification.

Using Sprott Chaos Synchronization-Based Voltage Relays for Protection of Microdistribution Systems Against Faults

This paper proposes the use of Sprott chaos synchronization (SCS)-based voltage relays for fault protection in microdistribution systems (MDSs). The Sprott chaos system is used as a voltage detector to track the dynamic errors between the normal signal and disturbance signals. The proposed voltage relay is used to detect disturbances, such as serious voltage sag and swell, and uses a critical trigger time to isolate the fault section. It is proposed that the restoration strategy then restores the unfaulted but blackout zones, and confirms security operations, including load-power balance, power generation limits, voltage limits, and power-flow limits. Computer simulations are conducted by using an IEEE 30-bus power system and MDSs, to show the effectiveness of the proposed method.

Fractal features for cardiac arrhythmias recognition using neural network based classifier

This paper proposes a method for cardiac arrhythmias recognition using fractal transformation (FT) and neural network based classifier. Iterated function system (IFS) uses the nonlinear interpolation in the map and uses similarity maps to construct various fractal features including supraventricular ectopic beat, bundle branch ectopic beat, and ventricular ectopic beat. Probabilistic neural network (PNN) is proposed to recognize normal heartbeat and multiple cardiac arrhythmias. The neural network based classifier with fractal features is tested by using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. The results will appear the efficiency of the proposed method, and also show high accuracy for recognizing electrocardiogram (ECG) signals.