The modernisation of the world has significantly reduced the prime sources of energy such as coal, diesel and gas. Thus, alternative energy sources based on renewable energy have been a major focus nowadays to meet the world's energy demand and at the same time to reduce global warming. Among these energy sources, solar energy is a major source of alternative energy that is used to generate electricity through photovoltaic (PV) system. However, the performance of the power generated is highly sensitive on climate and seasonal factors. The unpredictable behaviour of the climate affects the power output and causes an unfavourable impact on the stability, reliability and operation of the grid. Thus an accurate forecasting of PV output is a crucial requirement to ensure the stability and reliability of the grid. This study provides a systematic and critical review on the methods used to forecast PV power output with main focus on the metaheuristic and machine learning methods. Advantages and disadvantages of each method are summarised, based on historical data along with forecasting horizons and input parameters. Finally, a comprehensive comparison between machine learning and metaheuristic methods is compiled to assist researchers in choosing the best forecasting technique for future research.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-rpg.2018.5649

Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques

Recently, multilevel inverters (MLIs) have gained lots of interest in industry and academia, as they are changing into a viable technology for numerous applications, such as renewable power conversion system and drives. For these high power and high/medium voltage applications, MLIs are widely used as one of the advanced power converter topologies. To produce high-quality output without the need for a large number of switches, development of reduced switch MLI (RS MLI) topologies has been a major focus of current research. Therefore, this review paper focuses on a number of recently developed MLIs used in various applications. To assist with advanced current research in this field and in the selection of suitable inverter for various applications, significant understanding on these topologies is clearly summarized based on the three categories, i.e., symmetrical, asymmetrical, and modified topologies. This review paper also includes a comparison based on important performance parameters, detailed technical challenges, current focus, and future development trends. By a suitable combination of switches, the MLI produces a staircase output with low harmonic distortion. For a better understanding of the working principle, a single-phase RS MLI topology is experimentally illustrated for different level generation using both fundamental and high switching frequency techniques which will help the readers to gain the utmost knowledge for advance research.

/pdf/recently-developed-reduced-switch-multilevel-inverter-for-4lxrfejht6.pdf

Recently Developed Reduced Switch Multilevel Inverter for Renewable Energy Integration and Drives Application: Topologies, Comprehensive Analysis and Comparative Evaluation

http://dspace.uniten.edu.my/jspui/bitstream/123456789/13396/1/Gridconnected-renewable-energy-sources-Review-of-the-recent-integration-requirements-and-control-methodsJournal-of-Cleaner-Production.pdf

Grid-connected renewable energy sources: Review of the recent integration requirements and control methods

In this paper, hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed. Hybridization combining more than two modalities is a new trend in brain imaging and prosthesis control. Electroencephalography (EEG), due to its easy use and fast temporal resolution, is most widely utilized in combination with other brain/non-brain signal acquisition modalities, for instance, functional near-infrared spectroscopy (fNIRS), electromyography (EMG), electrooculography (EOG), eye tracker, etc. Three main purposes of hybridization are to increase the number of control commands, improve classification accuracy, and reduce the signal detection time. Currently, such combinations of EEG+fNIRS and EEG+EOG are most commonly employed. Four principal components (i.e., hardware, paradigm, classifiers, features) relevant to accuracy improvement are discussed. In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active BCI accuracy. Active and reactive tasks sometimes are combined: motor imagination with steady-state evoked visual potentials (SSVEP), and motor imagination with P300, etc. In the case of reactive tasks, SSVEP is most widely combined with P300 to increase the number of commands. Passive BCIs, however, are rare. After discussing the hardware and strategies involved in the development of hBCI, the second part examines the approaches used to increase the number of control commands and to enhance classification accuracy. The future prospects and the extension of hBCI in real-time applications for daily life scenarios are provided.

/pdf/hybrid-brain-computer-interface-techniques-for-improved-x6kgxccccc.pdf

Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Multilevel inverters (MLIs) are a great development for industrial and renewable energy applications due to their dominance over conventional two-level inverter with respect to size, rating of switches, filter requirement, and efficiency. A new single-phase cascaded MLI topology is suggested in this paper. The proposed MLI topology is designed with the aim of reducing the number of switches and the number of dc voltage sources with modularity while having a higher number of levels at the output. For the determination of the magnitude of dc voltage sources and a number of levels in the cascade connection, three different algorithms are proposed. The optimization of the proposed topology is aimed at achieving a higher number of levels while minimizing other parameters. A detailed comparison is made with other comparable MLI topologies to prove the superiority of the proposed structure. A selective harmonic elimination pulse width modulation technique is used to produce the pulses for the switches to achieve high-quality voltage at the output. Finally, the experimental results are provided for the basic unit with 11 levels and for cascading of two such units to achieve 71 levels at the output.

/pdf/optimal-design-of-a-new-cascaded-multilevel-inverter-92toxxo1kk.pdf

Optimal Design of a New Cascaded Multilevel Inverter Topology With Reduced Switch Count

This paper presents a new drive control system for electric car with GPS system to measure the speed of the car-body. This drive control system uses a non-linear controller designed by following the Lyapunov stability theorem. The controller is designed in order to control the wheel slip when the car starts to accelerate on the slippery road condition. The effectiveness of this control system is verified by experiments.

Non-linear Drive Control System for Electric Car

This paper discusses the factors contributing to the poor academic performance of undergraduate students at
the Department of Electrical Engineering, in the Faculty of Engineering, University of Malaya. Amongst the
factors found are social issues, poor self discipline, language problems, financial constraint and adjustment
issues into university life. The Department has taken steps in its effort to overcome these problems. Part of these initiatives was the formation of a committee to oversee the situation of the poor performing students, and efforts have been stepped up to monitor and guide these students.

Recent Findings Pertaining To Factors Contributing To The Poor Academic Performance Of Undergraduate Students In The Department Of Electrical Engineering, University Of Malaya, Kuala Lumpur, Malaysia

The perturb and observe (P&O) algorithm is the most popular of the numerous maximum power point tracking (MPPT) algorithms due to its simplicity and ease of implementation. However, the method has limitations regarding convergence speed, steady-state oscillation, and drift problem under sudden irradiance variations. This paper presents an improved P&O-MPPT algorithm to enhance the tracking convergence speed using adaptive duty cycle perturbation step fuzzy logic (FL-∆D) size. Moreover, a new strategy is added to the algorithm to stop the steady-state oscillation once MPP is tracked. The proposed algorithm was verified using MATLAB/Simulink under constant and varied irradiance levels. The results showed that the proposed algorithm with an adaptive FL-∆D size could efficiently track an accurate MPP with zero-state oscillation at different irradiance levels. Moreover, the algorithm achieves fast tracking under a rapid change in irradiance. The proposed algorithm was compared with the traditional P&O algorithm and the modified P&O algorithms that have recently been published. The comparison results validated the effectiveness of the proposed algorithm.

A Modified P&O-MPPT Technique Using Fuzzy Logic Controller for PV Systems

The original Simulated Kalman Filter (SKF) is an optimizer that employs synchronous update mechanism. The agents in SKF update their solutions after all fitness calculations, prediction process, and measurement process are completed. An alternative to synchronous update is asynchronous update. In asynchronous update, only one agent does fitness calculation, prediction, measurement, and estimation processes at one time. Recent study found that the original SKF is subjected to premature convergence. Thus, synchronous and asynchronous mechanisms are combined in SKF to address the premature convergence problem in SKF. At first, the SKF starts with synchronous update. If no improved solution is found, the SKF changes its update mechanism. The decision to switch from synchronous to asynchronous or vice versa is made based on the information of the population. In this paper, population’s diversity is used as switching indicator. Using the CEC2014 benchmark test suite, experimental results indicate that the proposed diversity-based adaptive switching synchronous-asynchronous SKF outperforms the original SKF significantly.

A Diversity-Based Adaptive Synchronous-Asynchronous Switching Simulated Kalman Filter Optimizer

Each UAV require a robust collision detection in its operating system to ensure it can aware the potential of collision in near future. Collision detection algorithm is one of the important parts that need to be concerned in the CAS. This paper proposes a design of collision avoidance algorithm for unmanned aerial vehicle based on circle overlapping test. Assuming two moving UAV are flying in the same altitude and in straight path within a two dimensional plane as conflict problem. Both UAV are cooperative aircrafts which communication of position, heading angle, waypoint, and velocity are allowed. The proposed algorithm will determine the collision potential between the surrounding intruders according to the conflict information.

Marizan Mubin

Papers

Non-linear Drive Control System for Electric Car

Recent Findings Pertaining To Factors Contributing To The Poor Academic Performance Of Undergraduate Students In The Department Of Electrical Engineering, University Of Malaya, Kuala Lumpur, Malaysia

A Modified P&O-MPPT Technique Using Fuzzy Logic Controller for PV Systems

A Diversity-Based Adaptive Synchronous-Asynchronous Switching Simulated Kalman Filter Optimizer

UAV Collision Detection Algorithm Design Based on Circle Overlapping Test