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

Modular multilevel converters (MMCs) are considered very promising converters due to their modularity structure and high reliability from fault-tolerant. A fault within a submodule (SM) is one of the main issues in half-bridge MMCs with substantial switching devices. In this paper, an adaptive carrier based phase disposition pulse width modulation (PDPWM) technique for MMCs, which uses only one carrier having flexibility with fault-tolerant capability, is presented. The energy-based control is also used in this study to regulate the balancing of SMs during and after a fault. In order to investigate the performance of the proposed method, a laboratory single-phase MMC prototype has been built by using four SMs to generate nine-level. The single-phase MMC prototype is tested by assuming one of the SMs in the failure condition. The result revealed that the proposed method had been successfully applied to the MMC prototype to control the upper and lower arm during the failure. In addition, the reference command will correct the fault according to the adaptive carrier and the computational burden is lesser since it only uses one single carrier.

/pdf/adaptive-carrier-based-pdpwm-control-for-modular-multilevel-3fpdwjbllg.pdf

Adaptive Carrier-Based PDPWM Control for Modular Multilevel Converter With Fault-Tolerant Capability

Performances of the adaptive conventional maximum power point tracking algorithms for solar photovoltaic system

Peak detection is a significant step in analyzing the electroencephalography (EEG) signal because peaks may represent meaningful brain activities. Several approaches can be used for peak point detection such as time domain, frequency domain, time-frequency domain, and nonlinear approaches. The main intention of this study is to find the significant peak features in time domain approach and this can be done using feature selection methods such as gravitational search algorithm (GSA) and particle swarm optimization (PSO). This study focuses on using GSA method, a new computational intelligence algorithm. Moreover, a rule-based classifier is employed to distinguish a peak point based on the selected features. Using GSA, the parameter estimation of the classifier and the peak feature selection can be done simultaneously. Based on the experimental results, the significant peak features of the peak detection algorithm were obtained where the average test accuracy is 77.74%.

Feature Selection and Classifier Parameter Estimation for EEG Signal Peak Detection Using Gravitational Search Algorithm

Now-a-days many protective problems have been encountered due to the existence of distributed generators. The major problem is unintentional islanding phenomenon. This can cause hazardous conditions for people who are working and equipment connected to it. So it is required to detect the phenomena of islanding within 2 sec according to IEEE 1547 standards. In this paper, a new hybrid islanding detection method to identify islanding states has been proposed based on combined changes of the rate of change of active power (ROCOAP) and rate of change of reactive power (ROCORP) at the point of common coupling (PCC). Due to hybrid nature, the performance of this method is better compared with previous methods. To validate the performance of the proposed detection techniques simulations are carried out on PSCAD/EMTDC. From the simulation results, it has been observed that the proposed technique can detect islanding faster which is less than 2 sec.

Hybrid Islanding Detection Technique for Malaysian Power Distribution System

Harmonic contents in the output voltage of the multilevel inverter (MLI) affects the performance of different appliances and decreases the reliability of the system. Selective harmonic elimination pulse width modulation (SHEPWM) technique based Bio-inspired intelligent algorithms (BIAs) are found superior to eliminate the undesired lower-order harmonics and lower switching losses. To control the fundamental at the desired level and eliminate the undesired harmonics, mainly two approaches are utilized i.e. fixed input DC sources and optimal input DC sources. In this paper, a detailed comparative analysis is performed among the aforementioned techniques for harmonic elimination. The 5-level inverter is considered and PSO algorithm is applied for the calculation of optimized switching angles for both cases. The Simulation results show that in optimal input DC sources, the fundamental voltage is accurately regulated for each modulation index with an increased number of elimination of lower-order harmonics over a wide range of modulation indexes and achieved lower THD in comparison to fixed input DC sources.

Marizan Mubin

Papers

Adaptive Carrier-Based PDPWM Control for Modular Multilevel Converter With Fault-Tolerant Capability

Performances of the adaptive conventional maximum power point tracking algorithms for solar photovoltaic system

Feature Selection and Classifier Parameter Estimation for EEG Signal Peak Detection Using Gravitational Search Algorithm

Hybrid Islanding Detection Technique for Malaysian Power Distribution System

Comparative Analysis of Optimal and Fixed Input DC Sources with Selective Harmonic Elimination Pulse Width Modulation