Efficiency Of Power Transmission System Research Articles

Temperature Fluctuations in Power conductors: Analysis and Overcurrent Relay Adaptation Strategies for Mitigation

In electrical networks, the efficient flow of energy faces constraints primarily associated with the conductor’s temperature. By assessing transmission infrastructure, identifying critical geographical and climatic zones, one can compute the line’s temperature. Focusing on the importance of maintaining optimal conductor temperature for efficient power transmission, this paper evaluates the critical role of conductor temperature in overhead transmission lines and explores its dynamic variations influenced by various ambient conditions. Emphasizing the significance of maintaining optimal conductor temperature, the study delves into mitigation strategies and adaptive protection mechanism, with a focus on overcurrent relays, with specific emphasis on the efficacy of current-time graphs. By addressing these factors, the research aims to enhance the reliability and efficiency of power transmission systems, ensuring sustained performance under diverse environmental scenarios. Overall, the paper contributes valuable insights to optimize the flow of energy in overhead transmission lines, thereby enhance the robustness also, improving the resilience and functionality of electrical networks.

Examining electrical equivalent circuit models of insulators in transmission lines under pollution conditions: a comprehensive study and analysis

This research presents a comprehensive study investigating the behavior of insulators in transmission lines under various pollution conditions. Utilizing equivalent electrical circuits, the analysis aims to understand the impact of pollution on insulator performance, which is crucial for maintaining the reliability and efficiency of power transmission systems. By providing valuable insights into the relationship between pollution and insulator behavior, this study contributes to the development of improved strategies for mitigating the effects of pollution on transmission lines, ultimately enhancing the overall safety and stability of electrical power networks. The study focuses on a digital model that enables the visualization of potential and electric field distribution within a series of glass insulators utilized in Algerian electrical grids. Initially, a parallel RC network equivalent circuit was devised and its parameters were determined through finite element analysis using Comsol Multiphysics. This equivalent circuit was subsequently integrated into ATP/EMTP software to simulate leakage currents, yielding satisfactory outcomes. The model was then incorporated into the isolator's equivalent circuit. Results from various simulations indicate that the occurrence of discharge along the dry strip impacts leakage current. Furthermore, the re-initiation of discharge on the contaminated insulator's surface is influenced by the pollutant deposit's conductivity and distribution. Therefore, understanding the pollution level is vital for accurately evaluating and sizing the insulator chain at the installation site.

Implementation of Transmission Line Fault Detection System using Long Range Wireless Sensor Networks

This paper proposes a fault detection system designed for transmission lines using Long-Range Wireless Sensor Network (LoRAWSN). The system is designed to detect and locate faults across transmission lines in real-time, which can significantly improve the reliability and efficiency of power transmission systems. A WSN will be built across transmission lines over an area. The faults identified by these sensor nodes is then transmitted to a central control unit, which analyses and displays the data. The LoRaWAN technology enables the WSN to cover long distances while consuming minimal power, making it ideal for monitoring transmission lines. The proposed fault detection system is evaluated through real world experiments, which demonstrate the feasibility and effectiveness of the proposed system. Overall, this paper presents a novel and practical approach for fault detection on transmission lines, which has the potential to improve the reliability and efficiency of power transmission systems.

A new approach for fault diagnosis in TCSC compensated transmission lines using FWHT and machine learning techniques

In this research a new approach is introduced for detecting and classifying faults in a Thyristor-Controlled Series Capacitor (TCSC) compensated power system. Our proposed scheme utilizes both the Fast Walsh Hadamard Transform (FWHT) and machine learning algorithms. The FWHT is employed to extract fault features from current data obtained from the TCSC compensated transmission line, while machine learning algorithms such as K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) are used to classify the extracted features for the purpose of fault detection and identification. To evaluate the performance of our proposed scheme, simulation studies were conducted on a test system under various fault conditions. The simulation results demonstrate that our approach is highly effective in accurately and quickly detecting and classifying faults, even when noise and TCSC compensation are present. This scheme has the potential to enhance the reliability and efficiency of power transmission systems.

Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks

Gearbox fault diagnosis is expected to significantly improve the reliability, safety and efficiency of power transmission systems. However, planetary gearbox fault diagnosis remains a challenge due to complex responses caused by multiple planetary gears. Model-based gearbox fault diagnosis techniques extract hand-crafted features from sensor data based on underlying physics and statistical analysis, which are not effective in extracting spatial and temporal features automatically. While deep learning methods such as convolutional neural network (CNN) enable automatic feature extraction from multiple sensor sources, they are not capable of extracting spatial and temporal features simultaneously without losing critical feature information. To address this issue, we introduce a novel deep neural network based on bidirectional-convolutional long short-term memory (BiConvLSTM) networks to determine the type, location, and direction of planetary gearbox faults by extracting spatial and temporal features from both vibration and rotational speed measurements automatically and simultaneously. In particular, a CNN determines spatial correlations between two measurements within one time step automatically by combining signals collected from three accelerometers and one tachometer. Long short-term memory (LSTM) networks identify temporal dependencies between two adjacent time steps. By replacing input-to-state and state-to-state operations in the LSTM cell with convolutional operations, the BiConvLSTM can learn spatial correlations and temporal dependencies without losing critical features. Experimental results have shown that the BiConvLSTM network can detect the type, location, and direction of gearbox faults with higher accuracy than conventional deep learning approaches such as CNN, LSTM, and CNN-LSTM.

Design and evaluation of two-stage planetary gearbox for special-purpose industrial machinery

The gearbox, as a traditional machine that changes the transmission ratio and transfers power safely, stably, quietly and efficiently, is an irreplaceable component in the field of machinery design. The planetary gear train is the most widely used of the traditional gearbox designs. Although scholars have carried out a variety of research on gearboxes, gear failures, noise and other issues are still common. This article presents a study involving the initial macro geometry design, gear flank modification, static and dynamic analysis, and experimental verification of a two-stage planetary gearbox for special-purpose industrial machinery. Compared to traditional static transmission error simulation, this study presents a novel analysis of the gearbox overall dynamic transmission error. In this research, the modal flexibility is also analyzed to determine the possibility of resonance in the gearbox. As a result, the strength of this gear system is guaranteed by macro design. PPTE (noise evaluation index) of each gear pair is greatly improved after flank modification. The efficiency of power transmission system is also improved with the improvement of vibration. The result of final bench test of the prototype is also quite satisfactory. This also verified the correctness of the theoretical simulation method presented in this research.

Contingency Analysis of Fault and Minimization of Power System Outage using Fuzzy Controller

In power systems, voltage stability perform the major role in design and its operation. Major system failures are occur due to voltage variability and breakdown. To meet and compensate the rising power demand of regular usage in modern trends, transmission networks are enormously loaded which create the voltage instability. Contingency analysis is a recognized energy managing tool. It calculate the violation in the transmission line. In this paper a computational controller fuzzy system is suggested to handle the transmission line outage and overload in other branch kind of problems in Power system. The efficiency of power transmission system with fuzzy controller is inveterate by computation of various parameters of transmission bus under different loading situations. For the contingency analysis the transmission power flow several methods have been developed. Fast Decoupled load flow program is the effective method which provides a fast and effective solution to the contingency analysis in the transmission system and also it is incorporate with matrix alteration formula which gives additional advantage for the system.

Coupled aeropropulsive design optimisation of a boundary-layer ingestion propulsor

AbstractAirframe–propulsion integration concepts that use boundary-layer ingestion (BLI) have the potential to reduce aircraft fuel burn. One concept that has been recently explored is NASA’s STARC-ABL aircraft configuration, which offers the potential for fuel burn reduction by using a turboelectric propulsion system with an aft-mounted electrically driven BLI propulsor. So far, attempts to quantify this potential fuel burn reduction have not considered the full coupling between the aerodynamic and propulsive performance. To address the need for a more careful quantification of the aeropropulsive benefit of the STARC-ABL concept, we run a series of design optimisations based on a fully coupled aeropropulsive model. A 1D thermodynamic cycle analysis is coupled to a Reynolds-averaged Navier–Stokes simulation to model the aft propulsor at a cruise condition and the effects variation in propulsor design on overall performance. A series of design optimisation studies are performed to minimise the required cruise power, assuming different relative sizes of the BLI propulsor. The design variables consist of the fan pressure ratio, static pressure at the fan face, and 311 variables that control the shape of both the nacelle and the fuselage. The power required by the BLI propulsor is compared with a podded configuration. The results show that the BLI configuration offers 6–9% reduction in required power at cruise, depending on assumptions made about the efficiency of power transmission system between the under-wing engines and the aft propulsor. Additionally, the results indicate that the power transmission efficiency directly affects the relative size of the under-wing engines and the aft propulsor. This design optimisation, based on computational fluid dynamics, is shown to be essential to evaluate current BLI concepts and provides a powerful tool for the design of future concepts.