Empowering data-driven load forecasting by leveraging long short-term memory recurrent neural networks.

The integration of renewable energy sources has resulted in an increasing intricacy in the functioning and organization of power systems. Accurate load forecasting, particularly taking into account dynamic factors like as climatic and socioeconomic impacts, is essential for effective management. Conventional statistical analysis and machine learning methods struggle with accurately capturing the intricate temporal relationships present in load data.

A comprehensive analysis of advanced solar panel productivity and efficiency through numerical models and emotional neural networks.

This study presents an in-depth analysis and evaluation of the performance of a standard 200 W solar cell, focusing on the energy and exergy aspects. A significant research gap exists in the comprehensive integration of numerical models with advanced machine-learning approaches, specifically emotional artificial neural networks (EANN), to simulate and optimize the electrical characteristics and efficiency of solar panels. To address this gap, a numerical model alongside a novel EANN was employed to simulate the system's electrical characteristics, including open-circuit voltage, short-circuit current, system resistances, maximum power point characteristics, and characteristic curves.

Enhanced direct torque control based on intelligent approach for doubly-fed induction machine fed by three-level inverter.

This paper presents an adaptive neuro-fuzzy inference system (ANFIS) based on 24 sectors direct torque command (DTC) for a doubly-fed induction machine (DFIM) by using a 3-level neutral point clamped inverter. The DTC approach is used in this paper with 24 sectors based on the ANFIS regulator to minimize the torque fluctuations, flux fluctuations, and stator stream THD (Total Harmonic Distortion) of the DFIM drive. The composed technique is accomplished by replacing the hysteresis controllers of the flux and torque with the ANFIS controller.

Techno-economic and environmental analysis of hybrid energy system for industrial sector of Pakistan.

The industrial sector of Pakistan is currently facing severe load-shedding, which ultimately affects its unit production. The greater dependency on conventional energy resources (Thermal, Nuclear, etc.) results in higher production costs and environmental pollution.

Synthesis and analysis of a novel thermal interface material for DC-DC boost converter.

The rapid evolution of power electronics has triggered an intensified focus on thermal management within electronics circuits, stemming from the critical necessity to mitigate thermal-related failure rates. Thermal management in power electronics circuits relies heavily on efficient heat transfer to prevent overheating of components and ensure their reliable operation, optimal performance, and safety. To facilitate the effective heat transfer, a thermal interface material (TIM) is utilized between switching components such as MOSFETs and heat sinks to improve surface contact, which increases heat transfer.

A developed DQ control method for shunt active power filter to improve power quality in transformers.

Power transformers are the most important component in power system. Exposing these transformers to the harmonic distortions causes additional heat losses, insulation stress, decrease in lifetime of insulation, and reduced power factor with decrease in efficiency of the system. The lifespan of distribution transformers is influenced by the fragility of power quality in power networks.

Review of speed estimation algorithms for three- phase induction motor.

In the field of evolving industrial automation, there is a growing need for refined sensorless speed estimation techniques for induction drives to cater the demands of various applications. In this paper, the sensorless speed estimation algorithms for induction motor drives are investigated and reviewed detailly for real-time industrial usages. The main objective of this paper is to classify sensorless techniques by highlighting the characteristics, merits and drawbacks of each sensorless speed estimation techniques of induction motor drives.

MPPT control of a solar pumping system based five-phase impedance source inverter fed induction motor.

This paper presents a control method for a system composed of a photovoltaic (PV) array, five-phase impedance source inverter, five-phase induction motor and centrifugal pump. This method is based on controlling the motor speed to control the pump power as the insolation level or temperature change to attain the maximum power extraction from the PV-array. The motor speed is controlled by using artificial neural network (ANN) which is trained to provide the desired inverter frequency and modulation index at any insolation level and temperature to attain the maximum PV operating power.

Design and analysis of photovoltaic/wind operations at MPPT for hydrogen production using a PEM electrolyzer: Towards innovations in green technology.

In recent times, renewable energy systems (RESs) such as Photovoltaic (PV) and wind turbine (WT) are being employed to produce hydrogen. This paper aims to compare the efficiency and performance of PV and WT as sources of RESs to power polymer electrolyte membrane electrolyzer (PEMEL) under different conditions. The study assessed the input/output power of PV and WT, the efficiency of the MPPT controller, the calculation of the green hydrogen production rate, and the efficiency of each system separately.

High gain chopper supplied from PV system to fed synchronous reluctance motor drive for pumping water application.

Power generation for renewable energy sources increased rapidly during last few years. Similarly, the high gain dc-dc boost choppers are taking place of conventional power converters used for photovoltaic (PV) appliances. Researchers are developing different methods in order to provide high voltage gain, low ripple, reduced switch stress, low converter costs, and minimized variations of PV operating points.