Today's complex civilization requires a steady and constant supply of electrical energy. It's crucial because electrical power creation necessitates effective management. This is because the amount of energy generated must equal the amount of energy used, also accurate and timely forecasting is required. In order to characterize future load in sufficient quantitative clarity, the estimated load demand is determined using previous knowledge. The load time series has a significant level of uncertainty, making it difficult to create an accurate short-term load estimate. Therefore, this work examines numerous load forecasting problems, as well as related recent constraints, utilizing various methodologies.
Our food security is built on the foundation of soil. Farmers would be unable to feed us with fiber, food, and fuel if the soils were not healthy. Accurately predicting the type of soil helps in planning the usage of the soil and thus increasing productivity. This research employs state-of- the-art Visual Transformers and also compares performance with different models such as SVM, Alexnet, Resnet, and CNN. Furthermore, this study also focuses on differentiating different Visual Transformers architectures. For the classification of soil type, the dataset consists of 4 different types of soil samples such as alluvial, red, black, and clay. The Visual Transformer model outperforms other models in terms of both test and train accuracies by attaining 98.13% on training and 93.62% while testing. The performance of the Visual Transformer exceeds the performance of other models by at least 2%. Hence, the novel Visual Transformers can be used for Computer Vision tasks including Soil Classification.
Nonlinear dynamics are critical in nuclear power reactors such as Advanced Heavy Water Reactor (AHWR). The core of AHWR must be governed and controlled by a suitable and reliable controller to deliver the demand electricity properly. In this paper, a fractional-order nonlinear controller (FO-NPD) comprises of fractional-order (FO), nonlinear (N), and proportional plus derivative (PD) terms are proposed and incorporated into the normalized point kinetics (NPKs) model of AHWR core for trajectory tracking performance, disturbance rejection, and noise suppression analysis. The FO- NPD controller gains are modified at run-time using the cuckoo search algorithm (CSA) based on a performance metric index termed as the sum of the integral of square error (ISE). The tuned gains get modified in real-time which makes the recommended controller more resilient and robust. To validate the obtained simulation results, a comparative performance study is carried out between the proportional plus derivative (PD), the fractional-order proportional plus derivative (FO- PD), and the nonlinear proportional plus derivative (NPD) with the proposed FO-NPD controller. And it is observed that the proposed controller demonstrates the superiority over other three controllers.
The present study employs a fair bidding blueprint acceptable in the Electricity market in real-time. For market players, reliable electricity prices prediction is crucial. Price projections are used by market participants to determine their trading strategies, portfolio allocation and risk mitigation. For proper incorporation, the appropriate bidding patterns have been analyzed so as to employ maximum revenue generation by the power producing companies. Contribution in terms of mathematical framework and modelling under certain constraints like seasonality, weather, previous loads and electrical prices, has been presented here, so that overall profits in the intraday market get amplified. A proper balance in the bidding aspects of Day-Ahead and Intraday Markets with incorporation of Deep-Learning in the electricity price data has been achieved, that too specifically using the concept of Artificial Neural Network modelling via MATLAB.
With the increased penetration of distributed generators (DGs) in the distribution system, the protection schemes based on standard inverse time characteristics are unsuitable as DGs alter the power flow from unidirectional to bidirectional and change the fault current level depending upon the operating mode of the microgrid. All of these result in the need for directional elements and communication links for proper coordination, which increase the cost and complication of the protection scheme. In this paper, the study of optimal relay coordination in a DG-based system is performed with user-defined characteristics, which is a logarithmic function of fault voltage and has a grading of relays independent of operating modes of the microgrid. Further, the performance of the voltage-based relay in terms of optimal settings and total operating time is compared to the conventional overcurrent relay. The optimal solution to the relay coordination problem is determined by a genetic algorithm (GA) and simulated annealing (SA), and the results of both the methods were compared for the user-defined and the standard relay characteristics.
