Direct Torque Control of Induction Motor Load Generator

DTC （direct torque control） of induction motor drives has many outstanding performance and implementation properties. This paper presents an innovative direct torque controlled load generator based on space vector modulation suitable for the emerging applications such as electrical test benches that require extremely fast large-signal torque and dynamic responses. To realize system performance, two methods based on the classical DTC （with six-sector switch table） and the proposed twelve-sector DTC have been analyzed and used for modelling the dynamometer. The performance of the proposed dynamometer is investigated by simulating different parts of the system and results are presented for several industrial load profiles. Finally, limitations and advantages of each controller are presented and analyzed.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Modeling of Cross-Spring Pivots Subjected to Generalized Planar Loads

Cross-spring pivots, formed by crossing two identical flexural beams at their midpoint, have been broadly used in precision engineering and aerospace fields. Many researches have been conducted on modeling and analysis of cross-spring pivots. However the influence of application position and magnitude of the external loads on the load-rotation and parasitic motion characteristics has not yet been discussed. In order to reveal the effect of the external loads, this paper develops the accurate load-rotation and center shift models of cross-spring pivots, with generalized planar loads applied including bending moment, horizontal and vertical forces. Firstly, by using the energy method, the load-displacement models of the pivot are derived with the assumption of small rotational angles. Based on the models, the influence of generalized planar loads on the load-rotation relationship is discussed, which shows that both application position and magnitude of the vertical and horizontal forces influence the load-rotation behaviors. Then the accurate center shift expressions of the pivot with generalized planar loads are developed, which shows that the rotational angle is the dominant term for both components of the center shift while the vertical and horizontal forces are small. Finally, the accuracy of the proposed model is validated by finite element analysis（FEA）. Comparing the model data with the results obtained from FEA, the relative error of the load-rotation is less than 6% even if the rotational angle reaches 20°; the relative errors of the two components of center shift are less than 5% and 10% respectively when the rotational angle reaches 10°. The proposed model and analytical conclusions can be used to analyze and preliminarily design the compliant mechanisms containing cross-spring pivots.

Large deflections of non-prismatic nonlinearly elastic cantilever beams subjected to non-uniform continuous load and a concentrated load at the free end

This work studies large deflections of slen- der, non-prismatic cantilever beams subjected to a combined loading which consists of a non-uniformly distributed con- tinuous load and a concentrated load at the free end of the beam. The material of the cantilever is assumed to be non- linearly elastic. Different nonlinear relations between stress and strain in tensile and compressive domain are considered. The accuracy of numerical solutions is evaluated by com- paring them with results from previous studies and with a laboratory experiment.

MODEL B-I FOR MACRO RECTANGULAR ELEMENT AND PROBLEMS OF INFLUENCE SURFACE FOR PLATE STRUCTURES

Model B-I for marco rectangular element is presented for the first time in this paper. To establish the influence surf ace for resultant R of bending plates, a number of generalized distributive loads q are defined. It is shown by numerical examples that Model B-I and the formula for the generalized distributive loads advanced in this paper are featured by high accuracy, low memory space and flexibility in practical application, and that they are especially effective for plate structures subject to moving loads, such as the two-dimensional continuous plates of highway bridges and the flat stabs in piled jetty engineering.

Optimal power flow calculation in AC/DC hybrid power system based on adaptive simplified human learning optimization algorithm

This paper employs an efficacious analytical tool,adaptive simplified human learning optimization(ASHLO)algorithm,to solve optimal power flow(OPF)problem in AC/DC hybrid power system,considering valve-point loading effects of generators,carbon tax,and prohibited operating zones of generators,respectively.ASHLO algorithm,involves random learning operator,individual learning operator,social learning operator and adaptive strategies.To compare and analyze the computation performance of the ASHLO method,the proposed ASHLO method and other heuristic intelligent optimization methods are employed to solve OPF problem on the modified IEEE 30-bus and 118-bus AC/DC hybrid test system.Numerical results indicate that the ASHLO method has good convergent property and robustness.Meanwhile,the impacts of wind speeds and locations of HVDC transmission line integrated into the AC network on the OPF results are systematically analyzed.

Experimental investigations of a prototype reversible pump turbine in generating mode with water head variations

Influences of water head variations on the performances of a prototype reversible pump turbine are experimentally studied in generating mode within a wide range of load conditions(from 25% to 96% of the rated power). The pressure fluctuations of the reversible pump turbine at three different water heads(with non-dimensional values being 0.48, 0.71 and 0.90) are measured and compared based on the pressure data recorded in the whole flow passage of the turbine. Furthermore, effects of monitoring points and load variations on the impeller-induced unstable behavior(e.g. blade passing frequency and its harmonics) are quantitatively discussed. Our findings reveal that water head variations play a significant role on the pressure fluctuations and their propagation mechanisms inside the reversible pump turbine.

Design optimization of a grid-tied microgrid for a residential community in southern Bangladesh

Growing energy demand,diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security.These factors have a greater impact on developing countries,as many of them rely largely on traditional energy resources.Cleaner energy generation is the viable alternative for mitigating these problems,as well as achieving energy independ-ence and tackling climate change.The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources.In particular,the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region,Urir Char,located in southern Bangladesh,was addressed.Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy.In addition to the analysis,a predictive load-shifting-based demand management is also introduced.Several cases were considered for the studies and,after considering several criteria,a grid-tied system comprising a photovoltaic array,wind turbine and energy storage system was found to be the best fit for powering the loads.The suggested system reduces the life-cycle cost by 18.3%,the levelized cost of energy by 61.9%and emissions by 77.2%when compared with the grid-only option.Along with the microgrid design,cooking emissions and energy categorization were also discussed.

High-resolution peak demand estimation using generalized additive models and deep neural networks

This paper covers predicting high-resolution electricity peak demand features given lower-resolution data.This is a relevant setup as it answers whether limited higher-resolution monitoring helps to estimate future high-resolution peak loads when the high-resolution data is no longer available.That question is particularly interesting for network operators considering replacing high-resolution monitoring by predictive models due to economic considerations.We propose models to predict half-hourly minima and maxima of high-resolution(every minute)electricity load data while model inputs are of a lower resolution(30 min).We combine predictions of generalized additive models(GAM)and deep artificial neural networks(DNN),which are popular in load forecasting.We extensively analyze the prediction models,including the input parameters’importance,focusing on load,weather,and seasonal effects.The proposed method won a data competition organized by Western Power Distribution,a British distribution network operator.In addition,we provide a rigorous evaluation study that goes beyond the competition frame to analyze the models’robustness.The results show that the proposed methods are superior to the competition benchmark concerning the out-of-sample root mean squared error(RMSE).This holds regarding the competition month and the supplementary evaluation study,which covers an additional eleven months.Overall,our proposed model combination reduces the out-of-sample RMSE by 57.4%compared to the benchmark.