Explore the vast range of titles available.

In this article, an efficient long-term novel scheduling technique is proposed for allocating capacitors in a combined system involving distributed generation (DG) along with radial distribution systems (RDS). We introduce a unique multi-objective function that focuses on the reduction of power loss with the maximization of voltage stability index (VSI) subjected to constraints of equality and inequality systems. Loss sensitivity factor and VSI together are involved in pre-identifying the locations of capacitors and DG. Determination of the optimal size of capacitor and DG is performed by utilizing the Bat algorithm (BA) for all the loads in RDS. The conventional approach considers the medium load of (1.0) condition generally, but the proposed method changes the feeder loads linearly, ranging from light load (0.5) to peak load (1.6) with the value of step size as 1%. BA determines the optimal size of the capacitor and DG for each step load. The curve fitting technique is used for deducing the generalized equation of capacitor size and DG for all conditions of the load with the various loading condition sized by distributed network operators (DNOs). Further, various load models such as industrial, residential, and commercial loads have been considered to show the efficiency of the present approach. Validation of results is performed in different scenarios on a 69-bus test system and on a standard IEEE 33-bus system. The results exhibit improved accuracy with less power loss value, superior bus voltage, and stability of system voltage with a higher rate of convergence.

The common design of thermal power plants is fundamentally oriented towards achieving a high-process performance, with market demands necessitating enhanced operational stability as a result of ongoing global support for renewable energy sources. Indeed, dynamic simulation represents one useful and cost-effective choice for optimizing the flexibility of parabolic trough power plants (PTPP) in a range of transient operating conditions, such as weather changes, resulting again in variations of the output load as well as varying start-up times. The purpose of this review is to provide an overview of steady-state and dynamic modelling for PTPP design, development, and optimization. This gives us a greater opportunity for a broad understanding of the PTPPs subjected to a variety of irradiance solar constraints. The most important features of the steady-state and their uses are reviewed, and the most important programs used in steady-state modelling are also highlighted. In addition, the start-up process of the plant, thermal storage system capacities and response dynamics (charging and discharging modes), and yearly electricity yield can be analyzed using dynamic modelling. Depending on the dynamic simulation, specific uses can be realized, including control loop optimization, load estimation for critical in-service equipment, and emergency safety assessment of power plants in the event of an outage. Based on this review, a detailed overview of the dynamic simulation of PTPP, and its development and application in various simulation programs, is presented. Here, a survey of computational dynamic modelling software commonly applied for commercial and academic applications is performed, accompanied by various sample models of simulation programs such as APROS, DYNAMICS, DYMOLA, and ASPEN PLUS. The simulation programs generally depend on the conservation equations of mass, momentum, species, and energy. However, for the equation of equilibrium, specific mathematical expressions rely on the basic flow model. The essential flow models involved, together with the basic assumptions, are presented, and are supplemented through a general survey covering popular simulation programs. Various previous research on the dynamic simulation of the PTPP are reviewed and analyzed in this paper. Here, several studies in the literature regarding the dynamic simulation of the PTPP are addressed and analyzed. Specific consideration is given to the studies including model verification, in order to explore the effect of modelling assumptions regarding the simulation outputs.

Artificial Neural Network (ANN) has been recognized as a powerful method for short-term load forecasting (STLF) of power systems. However, traditional ANNs are mostly trained by gradient-based learning algorithms which usually suffer from excessive training and tuning burden as well as unsatisfactory generalization performance. Based on the ensemble learning strategy, this paper develops an ensemble model of a promising novel learning technology called extreme learning machine (ELM) for high-quality STLF of Australian National Electricity Market (NEM). The model consists of a series of single ELMs. During the training, the ensemble model generalizes the randomness of single ELMs by selecting not only random input parameters but also random hidden nodes within a pre-defined range. The forecast result is taken as the median value the single ELM outputs. Owing to the very fast training/tuning speed of ELM, the model can be efficiently updated to on-line track the variation trend of the electricity load and maintain the accuracy. The developed model is tested with the NEM historical load data and its performance is compared with some state-of-the-art learning algorithms. The results show that the training efficiency and the forecasting accuracy of the developed model are superior over the competitive algorithms.

The bi-directional energy flow between prosumers (wind energy) and smart grid (SG) provides pertinent benefits, such as (i) load-sharing, (ii) peak-load shaving, (iii) load reduction with energy market programs, (iv) ancillary services-based energy transactions, and (v) mutual beneficial frameworks based on rewards and penalties. However, the load variations of SG, intermittent wind speed in energy district (ED) of prosumers, and stochastic energy price are the major constraints that must be considered in wind energy prosumers (WEPs) interaction with utility. Further, the interfacing and interactions of WEPs with SG incur an enormous volume of data to be processed, stored, accessed, and managed. Therefore, the authors proposed a stochastic bi-directional energy management model (BEMM) to manage the aforementioned constraints. Moreover, the BEMM is empowered with cloud-based service level agreement (C-SLA) that provides massive storage capabilities to the enormous data incurred due to WEPs interactions with SG. Two sub-models of BEMM are incorporated, namely stochastic wind estimation model and stochastic energy pricing model. The wind estimation model deals the stochasticity of wind speed for energy generation, while energy price model manages and controls the uncertainty of pricing tariffs based on real-time pricing and day-a-head pricing mechanisms for efficient energy trade between SG and WEPs under the principle of C-SLA.

A PV system’s operation highly depends on weather conditions. In case of varying irradiances or load changes, there is a power mismatch between various modules of the PV array. This power mismatch causes instability in the output of the PV system and deteriorates the overall system efficiency. To overcome instability and lower efficiency problems, and to extract maximum power from the PV system, various maximum power point tracking (MPPT) techniques are employed. The success of these techniques depends on the identification of the actual operating conditions of the system. This article proposes a hybrid maximum power point tracking (MPPT) technique that is capable of efficiently differentiating between uniform irradiance, non-uniform irradiance, and load variations on the PV system. Based on the identified operating conditions, the proposed method uses modified perturb and observe (Modified P&O) to cope with uniform irradiance variations and chimp optimization algorithms (ChOA) for non-uniform conditions to track the oscillation free maximum power-point. The proposed method is implemented and verified using a 4 × 3 PV array model in MATLAB Simulink software. Different cases of uniformly changing irradiance and non-uniformly changing irradiance are applied to test the performance of the proposed hybrid technique. The load varying conditions are performed by applying a variable load resistor. The authenticity of the proposed hybrid technique is critically evaluated against the well-known and most widely used optimization techniques of modified perturb and observe (Modified P&O), particle swarm optimization (PSO), flower pollination algorithm (FPA), and grey wolf optimization (GWO). The results demonstrate the superiority of the proposed technique in oscillation-free tracking of global maximum power point (GMPP) in a minimum tracking time of 0.4 s and 0.15 s, and steady-state MPPT efficiency of 96.92% and 99.54% under uniform and non-uniform irradiance conditions, respectively.

This study aims to address three problems in current studies in decoding the ankle movement intention for robot-assisted bilateral rehabilitation using surface electromyogram (sEMG) signals: (1) only up to four ankle movements could be identified while six ankle movements should be classified to provide better training; (2) feeding the raw sEMG signals directly into the neural network leads to high computational cost; and (3) load variation has large influence on classification accuracy. To achieve this, a convolutional neural network (CNN)—long short-term memory (LSTM) model, a time-domain feature selection method of the sEMG, and a two-step method are proposed. For the first time, the Boruta algorithm is used to select time-domain features of sEMG. The selected features, rather than raw sEMG signals are fed into the CNN-LSTM model. Hence, the number of model’s parameters is reduced from 331,938 to 155,042, by half. Experiments are conducted to validate the proposed method. The results show that our method could classify six ankle movements with relatively good accuracy (95.73%). The accuracy of CNN-LSTM, CNN, and LSTM models with sEMG features as input are all higher than that of corresponding models with raw sEMG as input. The overall accuracy is improved from 73.23% to 93.50% using our two-step method for identifying the ankle movements with different loads. Our proposed CNN-LSTM model have the highest accuracy for ankle movements classification compared with CNN, LSTM, and Support Vector Machine (SVM).

AbstractFor the fatigue failure and tribological property of a rolling element bearing, the contact load variation plays a significant role while the most loaded position of the bearing outer raceway takes the greatest risk of failure. This paper focuses on the variation in contact load on the most loaded position of the outer raceway of a gearbox bearing in high-speed train. Under operation conditions of different input speeds and torques, the dynamic contact load distribution in a gearbox bearing of high-speed train was measured by instrumenting the bearing with strain gauges. The most loaded position was identified accordingly and the features and reasons of the variation in contact load on this position were suggested. Three factors were found to have varying degrees of impact on the contact load variation under different gear meshing conditions: modal vibration of the cage or shaft, radial geometrical differences among the rollers and vibration of the gearbox housing.Graphic abstract

The high-level penetration of renewable energy sources (RESs) is the main reason for shifting the conventional centralized power system control paradigm into distributed power system control. This massive integration of RESs faces two main problems: complex controller structure and reduced inertia. Since the system frequency stability is directly linked to the system’s total inertia, the renewable integrated system frequency control is badly affected. Thus, a fractional order controller (FOC)-based superconducting magnetic energy storage (SMES) is proposed in this work. The detailed modeling of SMES, FOC, wind, and solar systems, along with the power network, is introduced to facilitate analysis. The FOC-based SMES virtually augments the inertia to stabilize the system frequency in generation and load mismatches. Since the tuning of FOC and SMES controller parameters is challenging due to nonlinearities, the whale optimization algorithm (WOA) is used to optimize the parameters. The optimized FOC-based SMES is tested under fluctuating wind and solar powers. The extensive simulations are carried out using MATLAB Simulink environment considering different scenarios, such as light and high load profile variations, multiple load profile variations, and reduced system inertia. It is observed that the proposed FOC-based SMES improves several performance indices, such as settling time, overshoot, undershoot compared to the conventional technique.

The uncertainty associated with the determination of load parameters, which is a key step in the design of wastewater treatment plants (WWTPs), was investigated on the basis of data sets from 58 WWTPs. A further analysed aspect was the organic load variations associated with variable sewage temperatures. Data from 26 WWTPs with a high inflow sampling frequency was used to simulate scenarios to investigate the effect of lower sampling frequencies through a Monte Carlo approach. The calculation of 85-percentile values for chemical oxygen demand (COD) loadings based on only 26 samples per year is associated with a variability of up to ±18%. Approximately 90 samples per year will be necessary to reduce this uncertainty for estimation of COD loadings below 10%. Hence, a low sampling frequency can potentially lead to under- or overestimation of design parameters. Through an analogous approach, it was possible to identify uncertainties of ±11% in COD loading when weekly average data was used with four samples per week. Finally, a tendency to lower COD input loads with increasing temperatures was identified, with a reduction of about 1% of the average loading per degree Celsius.