Dynamic Simulation and Sensitivity Analysis of Steam Generation Solar Power Plant
Unified Power Quality Conditioner (UPQC) Research Study on Steady - State Power Flow
Photovoltaic Module Failure Detection using Machine Vision and Lazy Learning Technique
Design and Implementation of Wallace Tree Multiplier and Its Applications in FIR Filter
Review on Obstacle Detection in Solar Panel Cleaning Applications
Loss Distribution Methodology with a Sense Of Emission Dispatch
Low Power Optimization Technique Based Linear Feedback Shift Register
Leakage Power Reduction Using Multi Modal Driven Hierarchical Power Mode Switches
Validation of IOV chain using OVM Technique
Performance of Continuous and Discontinuous Space Vector Pwm Technique for Open End Winding Induction Motor Drive
Electronic Circuit Design for Electromagnetic Compliance through Problem-Based Learning
Trioinformatics: The Innovative and Novel Logic Notation That Defines, Explains, and Expresses the Rational Application of The Law of Trichotomy for Digital Instrumentation and Circuit Design
Design Of a Novel Gated 5T SRAM Cell with Low Power Dissipation in Active and Sleep Mode
A Two Stage Power Optimized Implantable Neural Amplifier Based on Cascoded Structures
An Efficient Hybrid PFSCL based Implementation of Asynchronous Pipeline
This paper presents exergy, exergoeconomic, and exergy cost sensitivity analyses of a cogeneration solar power plant cycle, along with a detailed parametric study based on Exergy Cost Theory. Mathematical models addressing mass, energy, exergy, and economic parameters were developed and presented. The thermodynamic properties and associated analyses were conducted using Thermax, Excel, and MATLAB Simulink software tools. The findings provide valuable benchmarks for evaluating the economic performance of the plant. Sensitivity and parametric analyses reveal that the exergoeconomic factor, total annual plant cost, and unit costs of work and steam increase with a higher interest rate, while they decrease as the annual operating hours rise. The solar direct steam generation analysis demonstrates that the exergoeconomic factor values are 0.64 in January and 0.34 in July, with corresponding total costs of $3010.4/hr and $5480/hr, respectively. These results offer significant insights into the parametric influences and their effects, providing practical guidance for site engineers and operators. This study highlights the interplay between energy, exergy, and cost, assisting professionals in optimizing plant performance while managing resource and economic trade-offs effectively.
This paper presents a conceptual study of the Unified Power Quality Conditioner (UPQC) in mitigating voltage swell and sag occurrences on power distribution networks. A distinctive feature of UPQC is its capability to inject voltage within a range of 0° to 360° relative to the utility or Point of Common Coupling (PCC) voltage phase angle. Leveraging this capability, UPQC can either inject or absorb active power, enabling operation in modes of zero active power consumption, active power absorption, or active power delivery. Specifically, during voltage swell and sag conditions, the series Active Power Filter (APF) component of UPQC operates in active power delivery and absorption modes, respectively. Simultaneously, the shunt APF component aids the series APF by stabilizing the DC link voltage at a constant level. The shunt APF also compensates for the load's reactive power demands and reduces harmonics generated by it. MATLAB/Simulink simulation results validate the analysis for different types of loads, such as linear, non-linear, sensitive, and EV charging stations.
Solar module efficiency and dependability can be improved by detecting faults and monitoring their condition. This study examines the limitations and challenges of diagnosing solar module malfunctions. The various issues associated with solar module failure are thoroughly discussed. A monitoring tool that combines thermography and intelligent computing is developed to detect issues in photovoltaic cells while filtering out trivial anomalies based on a review of relevant studies. Given the rapid growth of solar energy, the Photovoltaic (PV) component's fault detection plays a crucial role in enhancing the reliability of the entire solar power system and identifying fault types whenever a system issue arises. As a result, this paper presents a hybrid strategy that employs the Chaotic Synchronization Detection Method (CSDM) and a Convolutional Neural Network (CNN) for PV module fault detection. With the explosive growth of the global Photovoltaic (PV) market, problem identification and resolution in PV systems have become equally important. Early issue detection can improve the efficiency, performance, and lifespan of a solar system. If PV flaws are not identified and addressed promptly, the plant's electricity production will be severely affected. Both on-site and online monitoring are possible for defect detection. Additionally, certain faults, such as ground faults, arc faults, line-to-line errors, and points of ignition, may pose fire hazards. Recently, researchers have proposed various methods for diagnosing and detecting PV issues. This paper provides an in-depth discussion of significant photovoltaic defects. It also highlights the distinctions, benefits, and drawbacks of the various fault detection approaches proposed in the available literature. A brief discussion of different solar cell modeling techniques is also included.
Limited Drive Reaction channels are the most significant component in signal handling and correspondence. The architecture of a FIR filter includes a delay unit, multiplier, and adder. Along these lines, execution of FIR channel is chiefly founded on multiplier. This paper presents FIR filter execution of the Wallace multiplier. Delay, power, and area performance can all be improved with this method. VHDL is used to write the code, which is simulated using ModelSim 6.3c and synthesized using Xilinx ISE 9.2i. The design is then implemented in the Spartan-3 FPGA.
Solar energy has a number of concerns, such as dust buildup, environmental changes, and improper tilt of the panel, all of which drag the efficiency of the system. Sensors like temperature, voltage, and current connected with NodeMCU allow constant and accurate tracking of solar systems with invaluable data on energy efficiency for different purposes like agriculture or commercial usage. These systems are improved by ultrasonic sensors that help in the detection of obstacles and distance, although the accuracy of results may be affected by the prevailing conditions. Additionally, microcontrollers like Arduino facilitate autonomous cleaning and dual-axis tracking mechanisms to address issues such as dust layering and panel misalignment, which can drastically reduce power efficiency by up to 50%. Studies also show that these cleaning systems combined with tracking technologies increase that efficiency by 35% by increasing light exposure and decreasing maintenance required. Smart platforms based on Blynk mobile applications offer constant system operation control and thus simplify the process of system management and maintenance. More refined algorithms, such as the smart filters and the least squares methods, also sharpen monitoring accuracy with the ability to predict solar outputs and minimize calculation errors of energy. It seems that the future integration of AI and cloud computing may extend these systems' functions to include predictive maintenance, the use of data collected in a wider range of ways, and increase the systems' potential expandability. IoT-based solar monitoring systems are considered cost-efficient in enhancing solar energy performance, with future developments expected to address issues of network availability, reliability, and costs of maintenance and adaptation to geographical and physical scenarios.
