The generation of active power in renewable energy is dependent on several factors. These variables are related to the areas of weather, physical structure, control, and load behavior. Estimating the future value of the active power to be generated is difficult due to their unpredictable character. However, because of the higher precision required of the estimation, this problem becomes more complex if we examine a short-term temporal prediction. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms to perform the Short-term estimate. The environment, the operation, and the generated (normal or faulty) signal are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been realized to conduct an experiment. In addition, the LSTM and the GRU are compared to see how well they perform in this system. The proposed method's end findings outperform the current state-of-the-art.

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid there is a ready testbed to estimate the future failures.

The purpose of this paper is to describe the design of a Hybrid Series Active Power Filter (HSeAPF) system to improve the quality of power on three-phase power distribution grids. The system controls are comprise of Pulse Width Modulation (PWM) based on the Synchronous Reference Frame (SRF) theory, and supported by Phase Locked Loop (PLL) for generating the switching pulses to control a Voltage Source Converter (VSC). The DC link voltage is controlled by Non-Linear Sliding Mode Control (SMC) for faster response and to ensure that it is maintained at a constant value. When this voltage is compared with Proportional Integral (PI), then the improvements made can be shown. The function of HSeAPF control is to eliminate voltage fluctuations, voltage swell/sag, and prevent voltage/current harmonics are produced by both non-linear loads and small inverters connected to the distribution network. A digital Phase Locked Loop that generates frequencies and an oscillating phase-locked output signal controls the voltage. The results from the simulation indicate that the HSeAPF can effectively suppress the dynamic and harmonic reactive power compensation system. Also, the distribution network has a low Total Harmonic Distortion (< 5%), demonstrating that the designed system is efficient, which is an essential requirement when it comes to the IEEE-519 and IEC 61,000–3-6 standards.