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In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.

Doubly-fed induction generator (DFIG)-based wind farms (WFs) are interfaced with power electronic converters. Such interfaces are attributed to the low inertia generated in the WFs under high penetration and that becomes prevalent in a fault scenario. Therefore, transient stability enhancement along with frequency stability in DFIG-based WFs is a major concern in the present scenario. In this paper, a cooperative approach consisting of virtual inertia control (VIC) and a modified grid-side converter (GSC) approach for low voltage ride-through (LVRT) is proposed to achieve fault ride-through (FRT) capabilities as per the grid code requirements (GCRs) while providing frequency support to the grid through a synthetic inertia. The proposed approach provides LVRT and reactive power compensation in the system. The participation of the VIC in a rotor-side converter (RSC) provides frequency support to the DFIG-based WFs. The combined approach supports active power compensation and provides sufficient kinetic energy support to the system in a contingency scenario. Simulation studies are carried out in MATLAB/Simulink environment for symmetrical and unsymmetrical faults. The superiority of the proposed scheme is demonstrated through analysis of the performance of the scheme and that of a series resonance bridge-type fault current limiter (SR-BFCL).

In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques. [ABSTRACT FROM AUTHOR]

The integration of information and communication technology, and cyber components, electricity infrastructures have made power systems more open and accessible from outside networks. Thus, the system becomes more vulnerable to data manipulations. This paper aims at analyzing the impact of data manipulation, on power system state estimation under various system operating conditions and explores various options to recommend a robust solution approach under such uncertainties. In-state estimation, the measurements can be manipulated either by creating a contingency to the system or by interfering with injections or flows. Contribution of this work lies in analysing simulated impact, statistically through the computation of errors namely, mean error, standard error, standard deviation, and variance indices and also by a parametric test Further, the performance and robustness of the conventional and evolutionary methods, employed for state estimation, are analysed under different measurement uncertainties. Results of these estimators are compared with base values of state variables obtained through load flow solution and then the percentage error in the estimated values for each case study is calculated. The efficiency and robustness of the Weighted Least Square method, Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing techniques are evaluated on IEEE 6, 14, and 30 bus systems. Finally, a comparative analysis of the statistical results is conducted, proving that the GA-based state estimation is more efficient and robust than other conventional and evolutionary techniques. [ABSTRACT FROM AUTHOR]

In this paper, Hilbert-Huang Transform (HHT) and Discrete Wavelet Transform (DWT) based methods are employed on line to monitor the deviation in electrical quantities of the power system integrated with distributed generation. HHT employs an Empirical Mode Decomposition (EMD) method i.e. decomposition of the signal into Intrinsic Mode Functions (IMFs) in such a way that they are sorted from the highest frequency to the lowest frequency whereas, Wavelet Transform (WT) decomposes the signal into components that are indicative of signal details and trend. This paper presents a comparative analysis of HHT and DWT with the motive to provide the guidelines for deciding which one of these techniques is more suitable for Power Quality (PQ) event analysis in the case of Distributed Generation (DG) integrated system, in real-time domain. From the comparative analysis, it is observed that DWT is more precise in comparison with HHT in determining PQ events in real-time domain with high resolution. This work further explores the potential of WT for the real time application in measuring, monitoring, and analyzing the electrical signals during the occurrence of voltage sag, swell and harmonics. Real time implementation of WT necessitates signal sections to be serially processed. Towards the same, signal samples are collected in a buffer and then subjected to WT to extract the inherent details. The processing of the signal gives attributes that help in identifying the type of disturbance. Performance of the proposed method is tested through simulations on MATLAB/Simulink platform along with the experimental validation of the technique. Results reveal that the application of WT, in real time, can accurately detect the instant of disturbances with minimum delay to analyze the PQ issues.