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 research proposes a new metaheuristic algorithm based on World Cup Optimization (WCO) to determine the optimal values of the Proportional Integral Deviation (PID) controller for the load frequency control problem. An islanded hybrid power system consists of a Wind Turbine Generator (WTG), Diesel Generation (DEG), and an Energy Storage System (ESS), such as a Battery Energy Storage System (BESS), Super Magnetic Energy Storage (SMES), and an Ultra Capacitor (UC) unit. A single-area power system was designed as a model network for the MATLAB-Simulink simulation. Furthermore, a hybrid system was employed with Proportional-Integral (PI) controllers and PI derivative controllers with proportionate gains to the DEG, and an ESS. A comparative performance assessment of the WCP was conducted to tune the PI and PID controllers. The simulation results indicate that a renewable integrated isolated power system with an Energy Storage System (ESS) tuned with a PID controller provides better responses than the PI controller for different loading conditions. Based on time settling, transient, and overshoot analysis, it is proven that a WCO-tuned PID controller is better than a PI controller.
The Current Mirror (CM) technique is widely used in mixed-mode and analog integrated circuits for tasks such as current amplification, biasing, and active loading. The overall effectiveness of these circuits relies heavily on their efficient designs. Current mirrors are primarily employed to accurately replicate currents in a circuit, offering high stability, simplicity, and scalability. They have become indispensable building blocks in analog and mixed-signal circuits, with their significance growing along with the demand for high-performance and low-power designs. Numerous techniques have been proposed to improve the performance metrics of current mirrors, including accuracy, input resistance, output resistance, and bandwidth. This study compares the advantages and disadvantages of these different current mirror techniques on a unified platform. It includes a comprehensive analysis of various contemporary mirror topologies, and classifies them based on their distinct characteristics. The performances of different current mirrors, including the basic CM, Wilson CM, cascode CM, and folded cascode CM circuits, were thoroughly examined in this analysis. The objective of this study is to select an appropriate current mirror for specific applications. The circuits considered in this study accurately mirror a current of 100 µA with a ±2% error using the Cadence Virtuoso software and UMC 65 nm technology. Process, Voltage, and Temperature (PVT) analysis, along with Monte Carlo simulations, were conducted under similar conditions using a supply voltage of 1.2V to ensure a fair comparison across the various current mirror approaches.
This study focuses on investigating the impact of temperature on solar panels and explores various cooling techniques to enhance their efficiency. The electric power output and efficiency of Photovoltaic (PV) cells decrease as the temperature increases. The temperature reduction depends on the type of solar panels and their temperature coefficient, which typically falls within the range of -0.3% to -0.5% per degree °C. The objective of this study was to determine the most effective cooling technique for solar systems. This study analyzed and compared various cooling methods and revealed that when water cooling is applied on both sides of a solar panel, it is the most efficient method to increase the efficiency by approximately 14.1%. Furthermore, the water-cooling system achieved a temperature reduction of up to 20 °C. The advantage of water cooling supports the self-cleaning of the solar panels, which further enhances their efficiency. The ability of water to remove dust and debris from the panel surface helps maintain optimal performance over time. The findings of this study provide valuable insights into the selection of the most suitable cooling technique for solar systems. Water cooling in solar panels offers substantial improvements in efficiency and temperature reduction.
This study focuses on improving power electronic components and devices, which have gained popularity due to their compact size and precise control over the output voltage and current. They are widely used in renewable energy systems, such as converters and inverters, and in industrial drives as control devices. However, the switching process in power electronic components introduces nonlinear behavior, leading to the generation of harmonics and power quality problems. To address these issues, various methods and techniques recommended by industry standards have been proposed to mitigate or eliminate harmonics and ensure the power quality. These standards provide limits and guidelines for assisting customers and manufacturers in maintaining acceptable power quality levels. Machine-learning algorithms offer a promising approach for improving power quality by leveraging data from a system to make predictions or classifications. In the context of power electronics, machine-learning algorithms can be trained to classify fault types, identify the exact location of faults, predict the remaining life of power electronic components, and detect voltage disturbances. Different machine learning techniques can be employed, depending on the specific application. For example, fault classification and fault location identification can be achieved through supervised learning algorithms, whereas predicting component life and detecting voltage disturbances can utilize techniques such as regression or anomaly detection. By leveraging the power of machine learning, enhanced reliability, performance, and efficiency can be achieved in various applications, thereby contributing to the overall advancement of power electronics technology.
The integration of Radio Frequency Identification (RFID) technology with the Internet of Things (IoT) has resulted in significant advancements in various domains, including supply chain management. This study explores the potential of integrating RFID-based Automated Public Distribution Systems (APDS) with IoT technologies to enhance the efficiency of sensor data analytics. By leveraging the capabilities of IoT sensors and RFID tags, this integration enables a comprehensive view of the distribution process, facilitates proactive decision making, and optimizes resource allocation. In this study, the key components and benefits of this integration are discussed, and practical use cases are presented, along with the challenges and opportunities of implementation. Furthermore, discussions were conducted on the implications for stakeholders, including government agencies, distributors, and end-consumers, emphasizing the positive impact on overall distribution efficiency and effectiveness.
