Precise modelling of switching and conduction losses in cascaded h-bridge multilevel inverters

Nowadays, voltage source multilevel inverters are being used extensively in industry due to its many advantages, compared to conventional two level inverters, such as higher output voltage at low switching frequency, low voltage stress(dv/dt), lower total harmonic distortion (THD), less electro-magnetic interference (EMI), smaller output filter and higher fundamental output. However, the evaluation of multilevel inverter losses is much more complicated compared to two level inverters. This paper proposes an on-line model for precise calculation of conduction and switching losses for cascaded h-bridge multilevel inverter. The model is simple and efficient and gives clear process of loss calculation. A singlephase 7-level cascaded h-bridge with IGBT's as switching devices has been used as a case study of the proposed model. The inverter has been controlled using selective harmonic elimination in which the switching angles were determined using the Genetic Algorithm (GA). MATLAB-SIMULINK is used for the modelling and simulation

Power loss investigation in HVDC for cascaded H-bridge multilevel inverters (CHB-MLI)

In the last decade, the use of voltage-source multilevel inverters in industrial and utility power applications has been increased significantly mainly due to the many advantages of multilevel inverters, compared to conventional two level inverters. These advantages include: 1) higher output voltage at low switching frequency, 2) low voltage stress (dv/dt), 3) lower total harmonic distortion (THD), 4) less electro-magnetic interference (EMI), 5) smaller output filter, and 6) higher fundamental output. However, the computation of multilevel inverter power losses is much more complicated compared to conventional two level inverters. This paper presents a detailed investigation of CHB-MLI losses for HVDC. Different levels, and IGBT switching devices have been considered in the study. The inverter has been controlled using selective harmonic elimination in which the switching angles were determined using the Genetic Algorithm (GA). MATLAB-SIMULINK is used for the modelling and simulation. This investigation should result in a deeper knowledge and understanding of the performance of CHB-MLI using different IGBT switching devices

Optimum SHE for cascaded H-bridge multilevel inverters using: NR-GA-PSO, comparative study

Selective Harmonic Elimination (SHE) is very widely applied technique in the control of multilevel inverters that can be used to eliminate the low order dominant harmonics. This is considered a low frequency technique, in which the switching angles are predetermined based on solving a system of transcendental equations. Iterative techniques such as NR and Heuristic techniques such as GA and PSO have been used widely in literatures for the problem of SHE. This paper presents a detailed comparative study of these three techniques when applied for a 7-level CHB-MLI

A Turbo Detection and Sphere-Packing-Modulation-Aided Space-Time Coding Scheme

Arecently proposed space-time block-coding (STBC) signal-construction method that combines orthogonal design with sphere packing (SP), referred to here as STBC-SP, has shown useful performance improvements over Alamouti’s conventional orthogonal design. In this contribution, we demonstrate that the performance of STBC-SP systems can be further improved by concatenating SP-aided modulation with channel coding and performing demapping as well as channel decoding iteratively. We also investigate the convergence behavior of this concatenated scheme with the aid of extrinsic-information-transfer charts. The proposed turbo-detected STBC-SP scheme exhibits a “turbo-cliff” at Eb/N0 = 2.5 dB and provides Eb/N0 gains of approximately 20.2 and 2.0 dB at a bit error rate of 10?5 over an equivalent throughput uncoded STBC-SP scheme and a turbo-detected quadrature phase shift keying (QPSK) modulated STBC scheme, respectively, when communicating over a correlated Rayleigh fading channel. Index Terms—EXIT charts, iterative demapping, multidimensional mapping, space-time coding, sphere packing, turbo detection

Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model.

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks

Irrigation water allocation at farm level based on temporal cultivation-related data using meta-heuristic optimisation algorithms

© 2019 by the authors. The present water crisis necessitates a frugal water management strategy. Deficit irrigation can be regarded as an efficient strategy for agricultural water management. Optimal allocation of water to agricultural farms is a computationally complex problem because of many factors, including limitations and constraints related to irrigation, numerous allocation states, and non-linearity and complexity of the objective function. Meta-heuristic algorithms are typically used to solve complex problems. The main objective of this study is to represent water allocation at farm level using temporal cultivation data as an optimisation problem, solve this problem using various meta-heuristic algorithms, and compare the results. The objective of the optimisation is to maximise the total income of all considered lands. The criteria of objective function value, convergence trend, robustness, runtime, and complexity of use and modelling are used to compare the algorithms. Finally, the algorithms are ranked using the technique for order of preference by similarity to ideal solution (TOPSIS). The income resulting from the allocation of water by the imperialist competitive algorithm (ICA) was 1.006, 1.084, and 1.098 times that of particle swarm optimisation (PSO), bees algorithm (BA), and genetic algorithm (GA), respectively. The ICA and PSO were superior to the other algorithms in most evaluations. According to the results of TOPSIS, the algorithms, by order of priority, are ICA PSO, BA, and GA. In addition, the experience showed that using meta-heuristic algorithms, such as ICA, results in higher income (4.747 times) and improved management of water deficit than the commonly used area-based water allocation method

Do Termitaria Indicate the Presence of Groundwater? A Case Study of Hydrogeophysical Investigation on a Land Parcel with Termite Activity.

Termite nests have long been suggested to be good indicators of groundwater but only a few studies are available to demonstrate the relationship between the two. This study therefore aims at investigating the most favourable spots for locating groundwater structures on a small parcel of land with conspicuous termite activity. To achieve this, geophysical soundings using the renowned vertical electrical sounding (VES) technique was carried out on the gridded study area. A total of nine VESs with one at the foot of a termitarium were conducted. The VES results were interpreted and assessed via two different techniques: (1) physical evaluation as performed by drillers in the field and (2) integration of primary and secondary geoelectrical parameters in a geographic information system (GIS). The result of the physical evaluation indicated a clear case of subjectivity in the interpretation but was consistent with the choice of VES points 1 and 6 (termitarium location) as being the most prospective points to be considered for drilling. Similarly, the integration of the geoelectrical parameters led to the mapping of the most prospective groundwater portion of the study area with the termitarium chiefly in the center of the most suitable region. This shows that termitaria are valuable landscape features that can be employed as biomarkers in the search of groundwater

Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia.

In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques.

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area

Systematic sample subdividing strategy for training landslide susceptibility models

© 2019 Elsevier B.V. Current practice in choosing training samples for landslide susceptibility modelling (LSM) is to randomly subdivide inventory information into training and testing samples. Where inventory data differ in distribution, the selection of training samples by a random process may cause inefficient training of machine learning (ML)/statistical models. A systematic technique may, however, produce efficient training samples that well represent the entire inventory data. This is particularly true when inventory information is scarce. This research proposed a systemic strategy to deal with this problem based on the fundamental distribution of probabilities (i.e. Hellinger) and a novel graphical representation of information contained in inventory data (i.e. inventory information curve, IIC). This graphical representation illustrates the relative increase in available information with the growth of the training sample size. Experiments on a selected dataset over the Cameron Highlands, Malaysia were conducted to validate the proposed methods. The dataset contained 104 landslide inventories and 7 landslide-conditioning factors (i.e. altitude, slope, aspect, land use, distance from the stream, distance from the road and distance from lineament) derived from a LiDAR-based digital elevation model and thematic maps acquired from government authorities. In addition, three ML/statistical models, namely, k-nearest neighbour (KNN), support vector machine (SVM) and decision tree (DT), were utilised to assess the proposed sampling strategy for LSM. The impacts of model's hyperparameters, noise and outliers on the performance of the models and the shape of IICs were also investigated and discussed. To evaluate the proposed method further, it was compared with other standard methods such as random sampling (RS), stratified RS (SRS) and cross-validation (CV). The evaluations were based on the area under the receiving characteristic curves. The results show that IICs are useful in explaining the information content in the training subset and their differences from the original inventory datasets. The quantitative evaluation with KNN, SVM and DT shows that the proposed method outperforms the RS and SRS in all the models and the CV method in KNN and DT models. The proposed sampling strategy enables new applications in landslide modelling, such as measuring inventory data content and complexity and selecting effective training samples to improve the predictive capability of landslide susceptibility models