Nur Hazahsha Shamsudin

Comparison of LQR and PID controller tuning using PSO for Coupled Tank System

Coupled Tank System is one of the widely used applications in industries. Like other process control, it require suitable controller to obtain the good system performances. Hence, this paper presents the study of Coupled Tank System using LQR and PID controller. Both controller parameters are tuned using Single-Objective Particle Swarm Optimization (PSO). The performance of the system is compared based on the transient response in term of of Rise Time (Tr), Settling Time (Ts), Steady State Error (ess) and Overshoot (OS). Simulation is conducted within MATLAB environment to verify the performances of the system. The result shows that both controller can be tuned using PSO, while LQR controller give slightly better results compared to PID controller.

Performance Evaluation of Real Power Quality Disturbances Analysis using S-transform

Power quality is main issue because of the impact to electricity suppliers, equipments, manufacturers and user.To solve the power quality problem, an analysis of power quality disturbances is required to identify and rectify any failures on power system. Most of researchers apply fourier transform in power quality analysis, however the ability of fourier transform is limited to spectral information extraction that can be applied on stationary disturbances. Thus, time-frequency analysis is introduced for analyzing the power quality distubances because of the limitation of fourier transform. This paper presents the analysis of real power quality disturbances using S-transform. This time-frequency distribution (TFD) is presented to analyze power quality disturbances in time-frequency representation (TFR). From the TFR, parameters of the disturbances such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total nonharmonic distortion (TnHD) and total waveform distortion (TWD) of the disturbances are estimated. The experimental of three phase voltage inverter and starting motor are conducted in laboratory to record the real power quality disturbances. The disturbances are recorded via data logger system which is mplemented using LabVIEW while the analysis is done using Matlab in offline condition. The results show that S-transform gives good performance in identifying, detecting and analyzing the real power quality disturbances, effectively.

A DNR and DG Sizing Simultaneously by Using EPSO

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an efficient hybridization of both Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) methods which is called the Evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration.

Power Quality Signals Classification System Using Time-Frequency Distribution

Power quality signals are an important issue to electricity consumers. The signals will affect manufacturing process, malfunction of equipment and economic losses. Thus, an automated monitoring system is required to identify and classify the signals for diagnosis purposes. This paper presents the development of power quality signals classification system using time-frequency analysis technique which is spectrogram. From the time-frequency representation (TFR), parameters of the signal are estimated to identify the characteristics of the signals. The signal parameters are instantaneous of RMS voltage, RMS fundamental voltage, total waveform distortion, total harmonic distortion and total non harmonic distortion. In this paper, major power quality signals are focused based on IEEE Std. 1159-2009 such as swell, sag, interruption, harmonic, interharmonic, and transient. An automated signal classification system using spectrogram is developed to identify, classify as well as provide the information of the signal.

Performance Verification of Power Quality Signals Classification System

Power quality has become a greater concern nowadays. The increasing number of power electronics equipment contributes to the poor quality of electrical power supply. The power quality signals will affect manufacturing process, malfunction of equipment and economic losses. This paper presents the verification analysis of power quality signals classification system. The developed system is based on linear time-frequency distribution (TFD) which is spectrogram that represents the signals jointly in time-frequency representation (TFR). The TFD is very appropriate to analyze power quality signals that have magnitude and frequency variations. Parameters of the signal such as root mean square (RMS) and fundamental RMS, total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonic distortion (TnHD) of voltage signal are estimated from the TFR to identify the characteristics of the signal. Then, the signal characteristics are used as input for signal classifier to classify power quality signals. In addition, standard power line measurements are also calculated from voltage and current such as RMS and fundamental RMS voltage and current, real power, apparent power, reactive power, frequency and power factor. The power quality signals focused are swell, sag, interruption, harmonic, interharmonic, and transient based on IEEE Std. 1159-2009. The power quality analysis has been tested using a set of data and the results show that, the spectrogram gives high accuracy measurement of signal characteristics. However, the system offers lower accuracy compare to simulation due to the limitation of the system.

Optimal Placement and Sizing of Renewable Distributed Generation via Gravitational Search Algorithm

The installation of distributed generation (DG) gives advantages to the environment such as, it contribute in the reduction of non-peak operating cost, diversification of energy resources, lower losses thus improving overall organization. These advantages might be rescinded if no proper location and sizing of DGs are considered before the DG’s installation. This paper offers an optimal location and sizing of multiple DGs using heuristic method called gravitational search algorithm (GSA). The suggested algorithm is tested on 13-bus radial distribution system. This method is being compared with particle swarm optimization (PSO) in terms of system power loss, voltage deviation and total voltage harmonic distortion (THDv). GSA shows the ability to locate and sized DG optimally with a better performance and more reliable than PSO.

sEMG signals analysis using time-frequency distribution for symmetric and asymmetric lifting

Following the industrial revolution, a third of the worlds economic output is derived from manufacturing industries. In the manufacturing industry sectors, manual lifting is commonly practiced even though mechanized material handling equipment are provided. Due to this repetitive lifting behavior, it will contributes to muscle fatigue and low back pain that can lead to work efficiency and low productivity. This will cause great losses to the company and to the country. The purpose of this study is to investigate changes in the surface electromyography (sEMG) signals during repetitive manual lifting for different twist angle values. The EMG signals are taken from right Biceps Branchii of five healthy subjects (age range 21 - 25 years) while performing symmetric and asymmetric lifting of twist angle 0° and 90° at lifting height of 140 cm with load mass of 10 kg. The analysis is done by applying time-frequency distribution (TFD) which is spectrogram technique to represent the EMG signal in time-frequency representation (TFR) before displaying the instantaneous voltage, Vrms (t) value. This study found that symmetric lifting requires less force to lift the load and that asymmetric lifting has faster tendency to experienced fatigue. This study concludes that the twist angle has a significant influence to the muscle performance of right Biceps Branchii.

Performance verification of real-time harmonic sources identification system

This paper presents the performance verification of accuracy between simulation and real-time harmonic source identification system. The simulation and real-time system are implementing spectrogram as analysis technique that offers high accuracy and represent signals in time-frequency representation (TFR). From the TFR, signal parameters such as instantaneous of root mean square (RMS), fundamental RMS, total harmonic distortion (THD), total non-harmonic distortion (TnHD) and total waveform distortion (TWD) are estimated. Then, the location of harmonic sources is determined by observing the significant characteristics between fundamental and harmonic impedances. The prediction accuracy of a forecasting method which is mean absolute percentage error (MAPE) is used in performance verification of accuracy. The small difference of 0.008% of average MAPE measured between the simulation and real-time system shows that the real-time system gives a high accuracy measurement. Furthermore, the real-time system also able to identify the location of harmonic sources with 100% correct identification of harmonic sources similar to simulation. Hence, it is proven that the real-time system is accurate and reliable enough to perform harmonic sources identification.

Losses minimization in network reconfiguration for fault restoration via a uniform crossover of genetic algorithm

Fault is a type of disturbance that affecting the continuity of the power supply to loads. Therefore, it is essential for a distribution power system to have a flexible, stable and more reliable load restoration system. The aim of the load restoration in this paper is to restore as many loads as possible through the network reconfiguration while minimizing the power losses after the occurrences of fault. Distribution network reconfiguration (DNR) is applied to determine the best combination of open switches that acts as the best route to optimize the reduction of power losses during load restoration process. An improved genetic algorithm (IGA) is proposed in this paper. The algorithm proposed is tested and validated on 69 IEEE bus using MATLAB software. A detail analysis is performed to demonstrate the effectiveness of IGA. The proposed method is applied and the effects of method on the power losses are examined. Results show that IGA method for load restoration via DNR is more effective compared with genetic algorithm (GA) solution.