Search

The modern-day urban energy sector possesses the integrated operation of various microgrids located in a vicinity, named cluster microgrids, which helps to reduce the utility grid burden. However, these cluster microgrids require a precise electric load projection to manage the operations, as the integrated operation of multiple microgrids leads to dynamic load demand. Thus, load forecasting is a complicated operation that requires more than statistical methods. There are different machine learning methods available in the literature that are applied to single microgrid cases. In this line, the cluster microgrids concept is a new application, which is very limitedly discussed in the literature. Thus, to identify the best load forecasting method in cluster microgrids, this article implements a variety of machine learning algorithms, including linear regression (quadratic), support vector machines, long short-term memory, and artificial neural networks (ANN) to forecast the load demand in the short term. The effectiveness of these methods is analyzed by computing various factors such as root mean square error, R-square, mean square error, mean absolute error, mean absolute percentage error, and time of computation. From this, it is observed that the ANN provides effective forecasting results. In addition, three distinct optimization techniques are used to find the optimum ANN training algorithm: Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient. The effectiveness of these optimization algorithms is verified in terms of training, test, validation, and error analysis. The proposed system simulation is carried out using the MATLAB/Simulink-2021a® software. From the results, it is found that the Levenberg–Marquardt optimization algorithm-based ANN model gives the best electrical load forecasting results.

Power system networks are one of the most widely used methods in the real world for transferring large amounts of electrical energy from one location to another. At present, High Voltage Direct Current Transmission is preferred for long distances over hundreds of miles due to minimal power loss and transmission cost of transmission. Due to an increase in power demand, integration of renewable sources to minimise the voltage fluctuations and compensate for power loss is necessary. This is a mandatory requirement to produce sophisticated protection methods for mainly smart systems under various balanced and unbalanced fault conditions. The system protection scheme must respond as quickly as possible to protect the connected devices in a smart environment. The network must be monitored and protected under various weather conditions as well as electrical parametric problems. The proposed research work is carried on the basis of physical monitoring with the aid of the Internet-of-Things and electrical parameters calibrated with the help of wavelet analysis. A wavelet is a mathematical tool to investigate the behaviour of transient signals at different frequencies, which provides important information related to the detailed analysis of faults in power networks. The major goals of this research are to analyse faults using detailed coefficients of current signals through the bior-1.5 mother wavelet for fault identification and artificial neural network analysis for fault localization. This proposed approach furnishes an IoT supervised Photovoltaic High Voltage Direct Current (HVDC) combined wide area power network security scheme using wavelet detailed coefficients under various types of faults with Fault-Inception-Angles. [ABSTRACT FROM AUTHOR]

Power system network is one of the most widely used in engineering system in the field of electrical field for moving bulk amount of power from one location to the other several directions of the country over thousands of kilometres. Integration of power projects typically involves adding new energy sources like wind, solar generation units with shunt and series compensating devices to an existing power system network. It is essential to design new protection scheme due to changes in the topology and dynamic behaviour of the system. Now fast fault detection algorithmic approaches are necessary to integrate different types of generating sources and loads under smart environment. The protection scheme must provide physical monitoring as well as para-metrical with the help new technologies. Internet-ofthings(IoT) is one of the source to monitor electrical systems under various environmental conditions of the system. Wavelet (WT) basically investigates the fault transient signals of different frequency and divides the waveform into different approximate and detailed coefficient values, which provides the important knowledge about the classification and location of fault. The detection of faulty-line and the location of fault by implementation wavelet detailed coefficients of Bior1.5 mother wavelet and for location of transmission line fault using Artificial neural network technique. This proposed method provide Condition monitoring Analysis of IoT based Wind-SVC Integrated Two Area Power System Network Protection scheme using artificial neural networks and wavelet approach under various types of faults. [ABSTRACT FROM AUTHOR]

This paper incorporates the SSSC FACTS device in optimal power flow solutions to enhance the performance of the power systems. The particle swarm optimization is used for solving the optimal power flow problem for steady-state studies. The effectiveness of the proposed approach was tested on IEEE 14-bus and IEEE 30-bus systems with SSSC FACTS device. Results show that the proposed PSO algorithm gives better solution to enhance the system performance with SSSC device compared to without SSSC device. [ABSTRACT FROM AUTHOR]