-This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

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Utility companies always struggle with the High Impedance Fault (HIF) in the electrical distribution systems. In this article, the current signal is seen in situations involving 10,400 different samples, with and without HIF, like linear, non-linear load, and capacitance switching. A better method that processes signals very fast and with low sample rates, requiring less memory and computational labor, is demonstrated by Mathematical Morphology (MM). For HIF identification, Deep Convolution Neural Networks (DCNNs) are being developed. This paper presents a novel method for signal processing with low sample rates, high signal processing speed, and low computational and memory requirements. The suggested six-layer DCNN is compared with other models, such as the four-layer and eight-layer DCNN models and the results are discussed.

The integration of Renewable Energy Sources (RES) brings along abnormalities that affect the grid, loads, and may degrade the performance of the system. These issues can be alleviated with the integration of RES with the use of a distribution Static Synchronous Compensator (STATCOM). Renewable generation with STATCOM provides quality of power during disturbances created by the AC loads and intermittent power from the RES. The STATCOM distribution of DC link plays a major role in the supply quality during abnormalities. In this work, an attempt has been made to provide supply quality in the distribution system with the integration of a renewable energy farm using Artificial Neural Network (ANN)-based DC link STATCOM control of distribution. The wind farm is analyzed for a Double Fed Induction Generator (DFIG) based wind turbine system and it is integrated into the distribution system. The system was simulated in MATLAB 2018A.

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