The assessment and improvement of angle stability condition of the power system using particle swarm optimization (PSO) technique / Nor Azwan Mohamed Kamari

This thesis presents the assessment and improvement of stability domains for the angle stability condition of the power system using particle swarm optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ks and damping torque coefficients Kd to solve angle stability problems was developed and used to identify the angle stability condition on single and multi machine system. In order to accelerate the determination of angle stability, particle swarm optimization (PSO) is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as evolutionary programming (EP) and artificial immune system (AIS). Subsequently, a newly control technique named as proportional-integral-derivative (PID) incorporated with flexible AC transmission (FACTS) device is proposed in this study to improve the damping capability of the system. The minimum damping ratio ξmin was applied as an indicator to precisely determine the angle stability condition based on PSO technique. The proposed optimization technique was compared with respect to EP and AIS

Kesan penjana fotovolta tersambung-grid terhadap kestabilan voltan dinamik dalam sistem kuasa

Penyepaduan penjana fotovolta (FV) tersambung-grid ke dalam sistem kuasa memberi kesan teknikal kerana reka bentuk rangkaian asalnya tidak mengambil kira penyepaduan penjanaan teragih. Pertumbuhan pesat pemasangan dan penembusan tinggi penjana fotovolta tersambung-grid boleh menimbulkan beberapa isu teknikal seperti kualiti kuasa dan kesan ke atas kestabilan voltan sistem kuasa. Kajian ini memberi tumpuan kepada analisis kestabilan voltan dinamik, serta menentukan parameter dinamik penjana FV yang memberi kesan terhadap kestabilan voltan dinamik. Model sistem yang dibangunkan untuk analisis kestabilan voltan dinamik menimbang model dinamik penjana FV dan beban dinamik. Simulasi domain-masa kestabilan voltan dinamik mengambil kira faktor seperti tenggelam-timbul sinaran suria, beban dinamik seperti motor aruhan dan putusan talian. Simulasi dilakukan pada tahap maksimum penembusan FV; 2% bagi sistem penghantaran IEEE 118 bas dan 60% bagi sistem agihan jejari IEEE 69 bas. Keputusan mendapati bahawa tenggelam-timbul sinaran suria yang berpunca dari kejadian lindungan awan boleh memberi kesan terhadap kestabilan voltan dalam sistem kuasa. Kejatuhan voltan secara mendadak menyebabkan ketakstabilan voltan dinamik yang mana bas mengalami kejatuhan voltan melebihi 6% sisihan voltan yang dibenarkan. Gangguan dinamik seperti permulaan motor aruhan turut menjejaskan kestabilan voltan. Selain itu, didapati berlakunya kepulauan yang disebabkan oleh putusan talian di talian tersambung pada bas penjana FV. Justeru, kehadiran sistem penjana FV tersambung grid dan motor aruhan tidak dapat membantu mengekalkan keseimbangan voltan bas yang akhirnya boleh membawa kepada keruntuhan voltan

Pengawasan beban tak mengganggu menggunakan mesin penyokong vektor

Kertas kerja ini membentangkan pembangunan pengawasan beban tak mengganggu (PBTM) untuk mengenal pasti beban dengan menggunakan pengelasan mesin penyokong vektor berbilang (MPVB). Suatu kaedah pengawasan beban diselia dilaksanakan untuk mengenal pasti tiga jenis beban yang kebiasaannya terdapat di bangunan komersial iaitu lampu pendaflour, penghawa dingin dan komputer peribadi. Parameter kuasa asas yang terdapat pada meter pintar dan penyarian sifat kuasa lain yang lebih terperinci dipertimbangkan dalam kertas kerja ini. Sifat kuasa yang berkesan ditentukan dengan melakukan pemilihan sifat mengikut kombinasi yang berpotensi. Selain itu, teknik baru penyarian sifat, iaitu, jelmaan masa-masa (MM) diperkenalkan dalam kajian ini. Suatu kaedah pemilihan sifat kuasa yang sistematik dilaksanakan dengan mempertimbangkan kombinasi terbaik untuk tujuan perbandingan. Berikutan penggunaan meter pintar komersial di sektor pengguna adalah majoriti dengan kadar pensampelan yang rendah, perlaksanaan eksperimen dan kajian yang dilakukan adalah di bawah pengukuran penggunaan yang sebenar dengan pensampelan yang rendah. Kadar pensampelan rendah yang sesuai untuk PBTM dikaji mengikut spesifikasi meter pintar komersial dengan tiga keadaan pensampelan iaitu 1 minit, 10 minit dan 30 minit. Satu set data pengesahsahihan dengan aktiviti beban secara rawak digunakan untuk menguji kemantapan kaedah PBTM yang dibangunkan. Justeru, teknik pengelasan beban menggunakan MPVB dibandingkan dengan teknik lain seperti bayes lurus dan K-kejiranan terdekat (KKT) untuk menilai prestasi MPVB yang dicadangkan untuk PBTM. Menerusi keputusan yang diperolehi, kaedah yang dicadangkan iaitu MPVB menunjukkan keputusan pengelasan yang terbaik dengan 99.94% ketepatan dalam mengenal pasti beban. Justeru, berdasarkan kadar pensampelan yang dikaji pensampelan 1 minit menunjukkan penggunaan pengawasan beban yang terbaik berbanding pensampelan lain yang dikaji untuk tujuan PBTM

Oscillatory Stability Prediction Using PSO Based Synchronizing and Damping Torque Coefficients

This paper presents the assessment of stability domains for the angle stability condition of the power system using Particle Swarm Optimization (PSO) technique. An efficient optimization method using PSO for synchronizing torque coefficients Ks and damping torque coefficients Kd to identify the angle stability condition on multi-machine system. In order to accelerate the determination of angle stability, PSO is proposed to be implemented in this study. The application of the proposed algorithm has been justified as the most accurate with lower computation time as compared to other optimization techniques such as Evolutionary Programming (EP) and Artificial Immune System (AIS). Validation with respect to eigenvalues determination, Least Square (LS) method and minimum damping ratio ξmin confirmed that the proposed technique is feasible to solve the angle stability problems

A review of automated micro-expression analysis

Micro-expression is a type of facial expression that is manifested for a very short duration. It is difficult to recognize the expression manually because it involves very subtle facial movements. Such expressions often occur unconsciously, and therefore are defined as a basis to help identify the real human emotions. Hence, an automated approach to micro-expression recognition has become a popular research topic of interest recently. Historically, the early researches on automated micro-expression have utilized traditional machine learning methods, while the more recent development has focused on the deep learning approach. Compared to traditional machine learning, which relies on manual feature processing and requires the use of formulated rules, deep learning networks produce more accurate micro-expression recognition performances through an end-to-end methodology, whereby the features of interest were extracted optimally through the training process, utilizing a large set of data. This paper reviews the developments and trends in micro-expression recognition from the earlier studies (hand-crafted approach) to the present studies (deep learning approach). Some of the important topics that will be covered include the detection of micro-expression from short videos, apex frame spotting, micro-expression recognition as well as performance discussion on the reviewed methods. Furthermore, major limitations that hamper the development of automated micro-expression recognition systems are also analyzed, followed by recommendations of possible future research directions

Design of optimal multi-objective-based facts component with proportional-integral-derivative controller using swarm optimization approach

This study proposes a multi-objective-based swarm intelligence method to improve angle stability. An optimization operation with single objective function only improves the performance of one perspective and ignores the other. The combination of two objective functions which derived from real and imaginary components of eigenvalue are able to provide better performance beyond the optimization capabilities of single objective function. Tested using MATLAB, the simulation is performed using a single machine attached to the infinite bus (SMIB) system equipped with static var compensator (SVC) that attached with PID controller (SVC-PID). The objective of this experiment is to explore the excellent parameters in SVC-PID to produce a more stable system. In addition to the comparison of objective functions, this study also compares particle swarm optimization (PSO) capabilities with evolutionary programming (EP) and artificial immune system (AIS) techniques

EP Based Optimization for Estimating Synchronizing and Damping Torque Coefficients

Abstract: This paper presents Evolutionary Programming (EP) based optimization technique for estimating synchronizing torque coefficients, K s and damping torque coefficients, K d of a synchronous machine. These coefficients are used to identify the angle stability of a system. Initially, a Simulink model was utilized to generate the time domain response of rotor angle under various loading conditions. EP was then implemented to optimize the values of K s and K d within the same loading conditions. Result obtained from the experiment are very promising and revealed that it outperformed the Least Square (LS) method and Artificial Immune System (AIS) during the comparative studies. Validation with respect to eigenvalues determination confirmed that the proposed technique is feasible to solve the angle stability problems

Techno-economic and carbon emission assessment of a large-scale floating solar pv system for sustainable energy generation in support of malaysia’s renewable energy roadmap

Energy generation from renewable sources is a global trend due to the carbon emissions generated by fossil fuels, which cause serious harm to the ecosystem. As per the long-term goals of the ASEAN countries, the Malaysian government established a target of 31% renewable energy generation by 2025 to facilitate ongoing carbon emission reductions. To reach the goal, a large-scale solar auction is one of the most impactful initiatives among the four potential strategies taken by the government. To assist the Malaysian government’s large-scale solar policy as detailed in the national renewable energy roadmap, this article investigated the techno-economic and feasibility aspects of a 10 MW floating solar PV system at UMP Lake. The PVsyst 7.3 software was used to develop and compute energy production and loss estimation. The plant is anticipated to produce 17,960 MWh of energy annually at a levelized cost of energy of USD 0.052/kWh. The facility requires USD 8.94 million in capital costs that would be recovered within a payback period of 9.5 years from the date of operation. The plant is expected to reduce carbon emissions by 11,135.2 tons annually. The proposed facility would ensure optimal usage of UMP Lake and contribute to the Malaysian government’s efforts toward sustainable growth

Optimal Compact Network for Micro-Expression Analysis System

Micro-expression analysis is the study of subtle and fleeting facial expressions that convey genuine human emotions. Since such expressions cannot be controlled, many believe that it is an excellent way to reveal a human’s inner thoughts. Analyzing micro-expressions manually is a very time-consuming and complicated task, hence many researchers have incorporated deep learning techniques to produce a more efficient analysis system. However, the insufficient amount of micro-expression data has limited the network’s ability to be fully optimized, as overfitting is likely to occur if a deeper network is utilized. In this paper, a complete deep learning-based micro-expression analysis system is introduced that covers the two main components of a general automated system: spotting and recognition, with also an additional element of synthetic data augmentation. For the spotting part, an optimized continuous labeling scheme is introduced to spot the apex frame in a video. Once the apex frames have been recognized, they are passed to the generative adversarial network to produce an additional set of augmented apex frames. Meanwhile, for the recognition part, a novel convolutional neural network, coined as Optimal Compact Network (OC-Net), is introduced for the purpose of emotion recognition. The proposed system achieved the best F1-score of 0.69 in categorizing the emotions with the highest accuracy of 79.14%. In addition, the generated synthetic data used in the training phase also contributed to performance improvement of at least 0.61% for all tested networks. Therefore, the proposed optimized and compact deep learning system is suitable for mobile-based micro-expression analysis to detect the genuine human emotions

Symmetrically Stacked Long Short-Term Memory Networks for Fall Event Recognition Using Compact Convolutional Neural Networks-Based Tracker

In recent years, the advancement of pattern recognition algorithms, specifically the deep learning-related techniques, have propelled a tremendous amount of researches in fall event recognition systems. It is important to detect a fall incident as early as possible, whereby a slight delay in providing immediate assistance can cause severe unrecoverable injuries. One of the main challenges in fall event recognition is the imbalanced training data between fall and no-fall events, where a real-life fall incident is a sporadic event that occurs infrequently. Most of the recent techniques produce a lot of false alarms, as it is hard to train them to cover a wide range of fall situations. Hence, this paper aims to detect the exact fall frame in a video sequence, as such it will not be dependent on the whole clip of the video sequence. Our proposed approach consists of a two-stage module where the first stage employs a compact convolutional neural network tracker to generate the object trajectory information. Features of interest will be sampled from the generated trajectory paths, which will be fed as the input to the second stage. The next stage network then models the temporal dependencies of the trajectory information using symmetrical Long Short-Term Memory (LSTM) architecture. This two-stage module is a novel approach as most of the techniques rely on the detection module rather than the tracking module. The simulation experiments were tested using Fall Detection Dataset (FDD). The proposed approach obtains an expected average overlap of 0.167, which is the best performance compared to Multi-Domain Network (MDNET) and Tree-structured Convolutional Neural Network (TCNN) trackers. Furthermore, the proposed 3-layers of stacked LSTM architecture also performs the best compared to the vanilla recurrent neural network and single-layer LSTM. This approach can be further improved if the tracker model is firstly pre-tuned in offline mode with respect to a specific type of object of interest, rather than a general object