An Adaptive Undervoltage Load Shedding Against Voltage Collapse Based Power Transfer Stability Index

Muhammad Nizam,Azah Mohamed,Aini Hussain 대한전기학회 2007 Journal of Electrical Engineering & Technology Vol.2 No.4

This paper highlights the comparison of a proposed methods named adaptive undervoltage load shedding based PTSI techniques for undervoltage load shedding and two previous methods named Fixed Shed Fixed Delay (FSFD) and Variable Shed Variable Delay (VSVD) for avoiding voltage collapse. There are three main area considerations in load shedding schemes as the amount of load to be shed, the timing of load shedding event, and the location where load shed is to be shed. The proposed method, named as adaptive UVLS based PTSI seem to be most appropriate among the uncoordinated schemes. From the simulation result can be shown the Adaptive UVLS based PTSI give faster response, accurate and very sensitive control for the UVLS control technique. This technique is effectively when calculating the amount to be shed. Therefore, it is possible to bring the voltage to the threshold value in one step. Thus, the adaptive load shedding can effectively reduce the computational time for control strategy.

An Adaptive Undervoltage Load Shedding Against Voltage Collapse Based Power Transfer Stability Index

Nizam, Muhammad,Mohamed, Azah,Hussain, Aini The Korean Institute of Electrical Engineers 2007 Journal of Electrical Engineering & Technology Vol.2 No.4

This paper highlights the comparison of a proposed methods named adaptive undervoltage load shedding based PTSI techniques for undervoltage load shedding and two previous methods named Fixed Shed Fixed Delay (FSFD) and Variable Shed Variable Delay (VSVD) for avoiding voltage collapse. There are three main area considerations in load shedding schemes as the amount of load to be shed, the timing of load shedding event, and the location where load shed is to be shed. The proposed method, named as adaptive UVLS based PTSI seem to be most appropriate among the uncoordinated schemes. From the simulation result can be shown the Adaptive UVLS based PTSI give faster response, accurate and very sensitive control for the UVLS control technique. This technique is effectively when calculating the amount to be shed. Therefore, it is possible to bring the voltage to the threshold value in one step. Thus, the adaptive load shedding can effectively reduce the computational time for control strategy.

Vulnerability Assessment of a Large Sized Power System Using Neural Network Considering Various Feature Extraction Methods

Ahmed M. A Haidar,Azah Mohamed,Aini Hussian 대한전기학회 2008 Journal of Electrical Engineering & Technology Vol.3 No.2

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network (ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

Vulnerability Assessment of a Large Sized Power System Using Neural Network Considering Various Feature Extraction Methods

Haidar, Ahmed M. A,Mohamed, Azah,Hussian, Aini The Korean Institute of Electrical Engineers 2008 Journal of Electrical Engineering & Technology Vol.3 No.2

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network(ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

Nickel-cobalt oxide/activated carbon composite electrodes for electrochemical capacitors

Sook-Keng Chang,Zulkarnain Zainal,Kar-Ban Tan,Nor Azah Yusof,Wan Mohamad Daud Wan Yusoff,S.R.S. Prabaharan 한국물리학회 2012 Current Applied Physics Vol.12 No.6

Nanostructured synthesis of nickelecobalt oxide/activated carbon composite by adapting a coprecipitation protocol was revealed by transmission electron microscopy. X-ray diffraction analysis confirmed that nickelecobalt oxide spinel phase was maintained in the pure and composite phases. Cyclic voltammetry, galvanostatic chargeedischarge tests and ac impedance spectroscopy were employed to elucidate the electrochemical properties of the composite electrodes in 1.0 M KCl. The specific capacitance which was the sum of double-layer capacitance of the activated carbon and pseudocapacitance of the metal oxide increased with the composition of nickelecobalt oxide before showing a decrement for heavily-loaded electrodes. Utilisation of nickelecobalt oxide component in the composite with 50 wt. % loading displayed a capacitance value of ~59 F g-1. The prepared composite electrodes exhibited good electrochemical stability. Nanostructured synthesis of nickelecobalt oxide/activated carbon composite by adapting a coprecipitation protocol was revealed by transmission electron microscopy. X-ray diffraction analysis confirmed that nickelecobalt oxide spinel phase was maintained in the pure and composite phases. Cyclic voltammetry, galvanostatic chargeedischarge tests and ac impedance spectroscopy were employed to elucidate the electrochemical properties of the composite electrodes in 1.0 M KCl. The specific capacitance which was the sum of double-layer capacitance of the activated carbon and pseudocapacitance of the metal oxide increased with the composition of nickelecobalt oxide before showing a decrement for heavily-loaded electrodes. Utilisation of nickelecobalt oxide component in the composite with 50 wt. % loading displayed a capacitance value of ~59 F g-1. The prepared composite electrodes exhibited good electrochemical stability.

An Ensemble Classification of Mental Health in Malaysia related to the Covid-19 Pandemic using Social Media Sentiment Analysis

Nur ‘Aisyah Binti Zakaria Adli,Muneer Ahmad,Norjihan Abdul Ghani,Sri Devi Ravana,Azah Anir Norman 한국인터넷정보학회 2024 KSII Transactions on Internet and Information Syst Vol.18 No.2

COVID-19 was declared a pandemic by theWorld Health Organization (WHO) on 30 January 2020. The lifestyle of people all over the world has changed since. In most cases, the pandemic has appeared to create severe mental disorders, anxieties, and depression among people. Mostly, the researchers have been conducting surveys to identify the impacts of the pandemic on the mental health of people. Despite the better quality, tailored, and more specific data that can be generated by surveys, social media offers great insights into revealing the impact of the pandemic on mental health. Since people feel connected on social media, thus, this study aims to get the people’s sentiments about the pandemic related to mental issues. Word Cloud was used to visualize and identify the most frequent keywords related to COVID-19 and mental health disorders. This study employs Majority Voting Ensemble (MVE) classification and individual classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) to classify the sentiment through tweets. The tweets were classified into either positive, neutral, or negative using the Valence Aware Dictionary or sEntiment Reasoner (VADER). Confusion matrix and classification reports bestow the precision, recall, and F1-score in identifying the best algorithm for classifying the sentiments.