One major concern in using over current relays (OCRs) is their coordination in power system protection to decrease the pressure of electrical equipment and avoid mal‐trip in the primary and backup relays. The issue has recently become more challenging with the increasing use of distributed generation (DG). This study proposes the use of firefly algorithm (FA) to achieve an optimised coordination of OCRs in power system protection when DG is present in the system. Typically, the FA is done by selecting plug setting and time setting multiplier parameters. However, the algorithm associated with such method requires a long convergence time and has the risk of getting trapped in local optima. Therefore, an adaptive modified FA (AMFA) has been developed to overcome these issues and obtain the optimised coordination of OCRs. The AMFA is tested in five case studies of a power system with DG whose results are then compared with those obtained from the conventional and FA methods. The results demonstrate that the proposed AMFA is able to achieve the optimised coordination of OCRs in all test cases with significant improvement in time reduction reaching 40.446%.

Visual inspection process for weld defects still manually operated by human vision, so the result of the test still highly subjective. In this research, the visual inspection process will be done through image processing on the image sequence to make data accuracy more better. CNN as one of the image processing technique can determine the feature automatically which is suitable for this problem in order to classify the variation of each weld defect pattern. Classification using Convolution Neural Network (CNN) consist of two stages: extraction image using image convolution and image classification using neural network. Gaussian kernel used for blurring image, it helps the extraction of images without losing the main information from the original image, this filter also minimize the occurrence of interference or noise. Results of the classification used to get the category of weld defects with high accuracy as a variable of a weld inspection process whether the weld is pass the standard or not. The proposed system has obtained classification with validation accuracy of 95.83% for four different type of welding defect. The data input of this research is the result of images captured by a webcam.

Wind energy conversion systems (WECSs) can extract maximum power by controlling the wind turbine rotational speed. This study presents a novel sensorless maximum power extraction control for small‐scale WECS using a permanent magnet synchronous generator (PMSG), to improve the maximum power extraction. The proposed method uses the output voltage and current of a rectifier to determine the duty cycle of the boost converter, without requiring the wind speed information and turbine characteristics. The step size of the duty cycle is changed adaptively, based on the difference between the rectifier output power and the previous duty cycle to obtain fast convergence, until the maximum power point is attained. The performance of the proposed sensorless maximum power extraction control is evaluated both by simulation, using PowerSIM and laboratory experiments, for variable wind speed conditions. The proposed maximum power extraction controller has a simple structure, low cost, and a good response to wind speed variations. The proposed method can extract a higher maximum power and has a higher efficiency of 93.87%, than the conventional perturb and observe method.

High accuracy in pattern recognition based on electromyography (EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neural network (CNN) algorithm for classifying ten hand motion from two raw EMG signals. The main contribution of this study is the simplicity of pre-processing stage in classifier machine. For instance, the feature extraction process is not required. Furthermore, the performance of the classifier was improved by evaluating the best hyperparameter in deep learning architecture. To validate the performance of deep learning, the public dataset from ten subjects was evaluated. The performance of the proposed method was compared to other conventional machine learning, specifically LDA, SVM, and KNN. The CNN can discriminate the ten hand-motion based on raw EMG signal without handcrafts feature extraction. The results of the evaluation showed that CNN outperformed other classifiers. The average accuracy for all motion ranges between 0.77 and 0.93. The statistical t-test between using two-channel (CH1 and CH2) and single-channel (CH2) shows that there is no significant difference in accuracy with p-value>0.05. The proposed method was useful in the study of prosthetic hand, which required the simple architecture of machine learning and high performance in the classification.