Single-Submodule Open-Circuit Fault Diagnosis for a Modular Multi-level Converter Using Artificial Intelligent-based Techniques

Today, the amount of biological data types obtained are increasing every day. Among these data types are micro arrays that play an important role in cancer diagnosis. The data analysis that are carried out through traditional approaches have proven unsuccessful in delivering efficient results on data types where data complexity is high and where sampling is low. For this reason, using a hybrid algorithm by merging the effective features of two distinct algorithms will yield effective results. In this study, a classification process was performed firstly by dimension reduction on micro array data that were obtained from the tissues from patients with a tumor in their central nervous system and then by using an artificial neural network algorithm that was optimized through Fire Fly Algorithm (FF), a hybrid approach. The data obtained were compared to K Nearest Neighbors (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) classification algorithms, which are frequently used in the literature. Also, the results were compared to the findings that were obtained from artificial neural networks, which are reinforced by Genetic Algorithm (GA), another hybrid approach. Then the results were shared. The performance results obtained show that hybrid approaches present a highly precise and more efficient classification process but they show a slower performance than basic classification algorithms.

Submodule (SM) fault diagnosis is necessary for the reliability of modular multilevel converters (MMC) and the complex operating environment of MMC may cause missing data and noise in signal detections, where some of the existing open circuit fault diagnosis methods can be disabled. An artificial intelligence (AI) diagnosis method for open circuit faults in MMC based on extreme gradient boosting system (XG-Boost) is proposed in this paper which can diagnose the open circuit fault quickly and accurately in the mentioned conditions. Compared with neural network diagnosis method, the proposed method has higher accuracy with the same non-ideal data. Faster speed and better processing ability of the proposed method are also validated by simulation of a three-phase MMC in MATLAB/Simulink.

There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

Dual three-phase (asymmetrical six-phase) induction motors have received significant attention in high power medium voltage drive applications. Typically, two three-phase voltage source converters are needed to feed such a drive system. In medium voltage applications, multilevel inverters are preferred as they provide a stepped output voltage waveform that reduces dv/dt stresses and provides a better output current waveform, which reflects positively on the machine developed torque and overall drive performance. Among different multilevel inverters, the Modular Multilevel Converter (MMC) has recently shown promise in medium and high voltage applications. For a dual three-phase machine, two MMCs with 6 arms each, are needed. This paper proposes a nine-arm MMC (9A-MMC) to drive such a motor. The proposed approach reduces the number of involved arms by 25%, i.e. reduces the number of required dc capacitors, semiconductor devices and their gate drive circuits, which reduces system complexity and cost. A complete analysis of the proposed system is presented and a simulation model is used to verify the proposed concept.

According to Vision Indonesia, data on people with eye diseases in Indonesia in 2018-2019 were 3 million people or about 1.5% of the total population. So far, public information or knowledge about the recognition of eye disease disorders is still lacking. The problem in this study is how to educate the public about the introduction of eye diseases based on information on symptoms of the disease and how to apply the web-based Artificial Neural Network (ANN) algorithm for the introduction of eye diseases. The ANN algorithm in the eye disease recognition education system can conclude knowledge even though it does not have certainty and takes it into account sequentially so that the process is faster. In terms of educational content about eye disease recognition, this is a novelty to use. This research aims to create an educational system for introducing eye diseases based on information on symptoms of the disease and applying a web-based Artificial Neural Network (ANN) algorithm for the recognition of eye diseases. The method used is the Artificial Neural Network algorithm method. The work of ANN in the education system for the introduction of eye diseases is to make parameters of eye disease symptoms or indicators that will produce the type of eye disease. The research material used is data on types of eye diseases and symptoms of each type of eye disease. The research results are to create an education system that can help the public recognise eye diseases based on the symptoms of these eye diseases that can be run on a web platform. The Artificial Neural Network (ANN) algorithm can manage input analysis data from disease indicators and show the initial results of eye diseases that can be detected. suffered by someone based on Training Results Weights and Bias v11= 1.6769, v01= 0.4356, w11= -1.5233, w01= 0.3242. Based on white box testing, the test results are free from logical errors. The results of this study indicate that the use of the ANN algorithm for eye disease recognition shows accurate results based on eye disease symptom data.

Detection and location of open-circuit fault for modular multilevel converter

Machine learning algorithms are used to analyze medical data sets effectively in present. Today machine learning gives us several necessary tools for intelligent data analysis and research. Especially in very recent years, the digital world has provided relatively inexpensive and available means to collect and store the data. The main aim is to implement supervised machine learning concept by using datasets regarding blood cells collected from blood cells detecting and counting sensors, of a human as the input which is trained by artificial neural network algorithm and apply decision tree classification learning algorithm to perform classification which results in recognizing and also possibly predict the disease based on the nature of blood cells and classify accordingly. Artificial Neural network algorithm seems to avoid pruning problem and has higher efficiency and accuracy in training the datasets. Also the using of Decision tree is because they are easy to interpret, understand and also possess non-linear characteristics between values. This holds well the performance of the tree constructed which gives better outputs. Beside all the application developed using machine learning in day today life, the use of such learning algorithm in such medical application will enhance and benefit the medical field.

Artificial intelligence through deep neural networks is now widely used in a variety of applications that have profoundly altered human livelihoods in a variety of ways. People's daily lives have become much more convenient. Image recognition, smart recommendations, self-driving vehicles, voice translation, and a slew of other neural network innovations have had a lot of success in their respective fields. The authors present the ANN applied in weather forecasting. The prediction technique relies solely upon learning previous input values from intervals in order to forecast future values. And also, Convolutional Neural Networks (CNNs) are a form of deep learning technique that can help classify, recognize, and predict trends in climate change and environmental data. However, due to the inherent difficulties of such results, which are often independently identified, non-stationary, and unstable CNN algorithms should be built and tested with each dataset and system separately. On the other hand, to eradicate error and provides us with data that is virtually identical to the real value we need Artificial Neural Networks (ANN) algorithms or benefit from it. The presented CNN model's forecasting efficiency was compared to some state-of-the-art ANN algorithms. The analysis shows that weather prediction applications become more efficient when using ANN algorithms because it is really easy to put into practice.

Walking gait event detection based on electromyography signals using artificial neural network

Modular Multilevel Converter (MMC) is an emerging converter topology for medium and high voltage applications. Nearest level Modulation (NLM) is the conventional control topology used to control the MMC that produces the N+1 AC output waveform. In previous research work, the Modified NLM has been already proposed, producing a 2N+1 and 4N+1 output waveform while utilizing a half-bridge (HB) submodule (SM) topology. However, half-bridge-based MMC has a similar behavior as two-level Voltage Source Converter (VSC) and cannot block DC fault current in case of DC-side short circuit fault. So, in recent years, full-bridge-based MMC topology is preferably used by manufacturers as it has DC fault blocking capabilities. This paper presents the Modified NLM for Full bridge (FB) SM topology to take the critical benefits of FB SM topology and improve power quality. The proposed method is simpler to implement and produces a 4N+1 AC output waveform. The THD of the output voltage and current reduces to half compared to the conventional NLM method. The proposed method is verified using LabVIEW Multisim co-simulation and as well as real-time simulation.

The modular multilevel converter (MMC) is a highly efficient multilevel converter topology suitable for high and medium voltage applications. However, one of the negative traits of MMC is the existence of circulating current. The detrimental effects of circulating currents include distortion in arm voltages, converter power loss and high switching stresses on semiconductor devices. This paper aims to focus on different circulating current mitigation techniques and their comparative analysis. This paper also proposes a circulating current suppressing controller (CCSC) which depends upon the repetitive control (RC) to minimize the inner circulating current in an MMC. The proposed model is simulated in MATLAB/Simulink and the results are quite in proximity with theoretical analysis.

An open-set fault diagnosis framework for MMCs based on optimized temporal convolutional network

Modular multilevel converter (MMC) is a promising converter in medium and high voltage applications due to its modularity and easiness to meet any voltage level by cascading the sub-modules. However, large amount of modules not only increase the failure probability of the devices, but also increase the difficulty of the fault diagnosis, which have brought a challenging for popularization and application of the MMC. Therefore, an extended state observer based fault diagnosis strategy for MMC is proposed in this paper. The extended state observer is designed based on the circulating current equation. The output of the extended state observer is compared with the theoretical value calculated by the modified switching models to locate the fault. The simulation is modeled and carried out in Matlab/Simulink. The effectiveness of the proposed strategy is verified by the simulation results.

The computational cost of training artificial neural network (ANN) algorithms limits the use of large systems capable of processing complex problems. Implementing ANNs on a parallel or distributed platform to improve performance is therefore desirable. This work illustrates a method to predict and evaluate the performance of distributed ANN algorithms by analyzing the performance of the comparatively simple mathematical operations, which are used to construct the ANN. The ANN algorithms are divided into simple components: matrix and vector multiplication, matrix processed through a function, competition in a matrix. These basic operational parts are examined individually and it is demonstrated that the computation processes of distributed neural networks can be derived from the composition of these basic operations. Three popular network architectures are examined: multi-layer perceptrons with back-propagation learning, self-organizing map, and radial basis functions network.

This study analyzed consumer intention to repurchase a smartphone, using an analysis of previous research and suggesting this as a research model. To this end, factors influencing “intention to repurchase” the smartphone were derived as social influence, consumer satisfaction, emotional loyalty, and habit. In addition, statistical analysis was conducted to investigate consumer repurchase intentions and the causal relationships. It is also possible to investigate whether consumer habits are linked to repurchases by analyzing their psychological inclinations; therefore, in this study, 390 people who repurchased a smartphone over the past two years were surveyed, and data were collected. This study analyzed the causal relationships among the factors using SPSS 24.0. Moreover, the causal relationship analysis was enhanced using the artificial neural network (ANN) algorithm. With multiple regression analysis and the ANN algorithm, consumer satisfaction (0.71), emotional loyalty (0.108), and social influence (0.062) were determined to affect the intention to repurchase. This means that the ANN algorithm can be used over multiple regression analysis and improve the results of the analysis. In addition, this study also provided practitioners with a way to improve their understanding of consumer behavior intention to repurchase smartphones.