Power Transmission Loss Research Articles

A weighted hybrid indoor positioning method based on path loss exponent estimation

With the rapid development of the Internet of Things (IoT), location-based services (LBS) have gained significant attention due to their widespread applications in daily life. This paper addresses the indoor target positioning problem in wireless sensor networks (WSNs). A weighted constrained linear least squares algorithm based on path loss exponent estimation (PLE-WCLLS) with received signal strength (RSS) and angle of arrival (AoA) hybrid measurements is proposed. To address the challenges of unknown transmission power and path loss exponent (PLE), the proposed method employs a linear least squares (LLS) estimation approach based on the ranging maximum likelihood (ML) estimation model to estimate both parameters. Subsequently, a confidence weight adjustment strategy is designed to reduce positioning errors. To handle the highly non-convex and nonlinear nature of the RSS/AoA hybrid optimization model, a linearization method based on Taylor series expansion is presented. Accurate target position estimation is achieved by solving a constrained quadratic programming problem. The effectiveness of the proposed algorithm is validated through numerical simulations and experimental evaluation in a real indoor environment. Compared to traditional positioning methods, the PLE-WCLLS algorithm improves positioning accuracy by 13.2%, and it performs exceptionally well even in scenarios with fewer sensor nodes. This gives it broad application prospects in areas such as IoT device management, personnel tracking in smart buildings, and asset localization in industrial automation.

Effect of Planetary Gear Set Designs on Thrust Bearing Power Loss in Automatic Transmission

Effect of Planetary Gear Set Designs on Thrust Bearing Power Loss in Automatic Transmission

Optimal Placement of HVDC-VSC in AC System Using Self-Adaptive Bonobo Optimizer to Solve Optimal Power Flows: A Case Study of the Algerian Electrical Network

Modern electrical power networks make extensive use of high voltage direct current transmission systems based on voltage source converters due to their advantages in terms of both cost and flexibility. Moreover, incorporating a direct current link adds more complexity to the optimal power flow computation. This paper presents a new meta-heuristic technique, named self-adaptive bonobo optimizer, which is an improved version of bonobo optimizer. It aims to solve the optimal power flow for alternating current power systems and hybrid systems AC/DC, to find the optimal location of the high voltage direct current line in the network, with a view to minimize the total generation costs and the total active power transmission losses. The self-adaptive bonobo optimizer was tested on the IEEE 30-bus system, and the large-scale Algerian 114-bus electric network. The obtained results were assessed and contrasted with those previously published in the literature in order to demonstrate the effectiveness and potential of the suggested strategy.

Energy efficiency and greenhouse gas emissions: A panel ARDL model of top five emitters in Africa

The Paris Agreement has mandated drastic decarbonization by developed and developing economies. Many of the top African emitters are now exploring drastic restructuring of the production processes in a way that promotes energy utilization efficiency. This is critical for ensuring an environmentally benign growth process. This study presents a unique addition to the body of existing knowledge by analysing the effect of some indicators of energy efficiency on three indicators of environmental quality among the top five emitters of GHG in Africa. The data were from the World Development Indicators (WDI), covering the 1990–2020 periods for South Africa, Egypt, Nigeria, Algeria, and Libya. The panel Autoregressive Distributed Lag (ARDL) model was used for data analysis. The results showed that all the included series were stationary at first difference. The optimum lag lengths were determined independently in STATA 17 software, and the most frequent lags across the countries were selected. The Hausman test revealed that the pooled mean group (PMG) estimation was preferred to other estimators. The results showed that in the long-run, particulate emission damages, CO2 intensity, and energy use intensity positively impacted GHG emissions, while GDP per capita and renewable energy utilization produced mixed results. In the short run, the error correction mechanism parameters were all significant (p < 0.01) in Libya for all emission indicators, while Algeria showed significance (p < 0.01) in total GHG and CO2 per capita models. The coefficient of particulate emission damages significantly reduced (p < 0.01) total GHG emissions in Libya and Nigeria and renewable energy significantly increased (p < 0.01) GHG emissions in South Africa but reduced them in Nigeria. Also, CO2 emission intensity significantly (p < 0.01) and positively impacted GHG emissions in South Africa, Egypt, Libya, and Nigeria, while electricity power transmission losses increased total emissions and CO2 emissions in Libya and Algeria. It was concluded that initiatives to reduce GHG emissions should focus on developing countries’ sensitivity to emission damages, and promotion of initiatives for using renewable energies. Also, promotion of energy efficiency across industries offers a prospect for ensuring environmental sustainability.

Multi-objective Approach for Dynamic Economic Emission Dispatch Problem Considering Power System Reliability and Transmission Loss Prediction Using Cascaded Forward Neural Network

This study addresses the significant problem of Dynamic Economic Emission Dispatch (DEED), a critical consideration in power systems from both economic and environmental protection viewpoints. Reliability stands as another vital facet, impacting maintenance and operation perspectives. The integration of Artificial Neural Network (ANN)-based transmission loss prediction into the DEED model is also essential to address specific limitations and enhance the overall performance of the dispatch process. Traditionally, the DEED model relies on a single B-loss coefficient to estimate transmission losses. While this approach simplifies calculations, it fails to account for the significant variations in demand that occur throughout the dispatch period and it leads to inaccuracies in loss prediction, especially in dynamic environments. Using a single coefficient, the model cannot adequately capture the complex, non-linear relationships between power generation, load, and transmission losses under different operating conditions. To overcome this limitation, this study introduces an ANN-based loss prediction method integrated into the DEED model and uses trained ANN to replace the process of finding B-loss coefficients during each dispatch period. This paper also introduces a strategy leveraging the multi-objective northern goshawk optimizer algorithm, characterized by a non-dominated sorting and crowding distance mechanism, to enhance DEED considerations incorporating reliability (DEEDR). This novel algorithm improves the solution space effectively, maintains high population diversity and enables an even distribution of individuals sharing the same rank in the objective space. The fundamental objective of this study is to balance fuel cost, emission, and system reliability in power system operations. Compared with a few existing multi-objective optimization algorithms, this study demonstrates superior performance in generating a series of non-dominated solutions. The experimental results highlight its competitive and potential as an efficient tool in the DEED and DEEDR problems, promising a synergistic coordination of economy, environmental protection, and system reliability benefits in power system management.

Two-stage data-driven optimal energy management and dynamic real-time operation in networked microgrid based on a deep reinforcement learning approach

Given the significant challenges posed by the vast and diverse data in energy management, this study introduces a two-stage approach: optimal energy management system (OEMS) and dynamic real-time operation (DRTOP). These stages employ a multi-agent policy-oriented deep reinforcement learning (DRL) approach, aiming to minimize operating and energy exchange costs through interactions in the networked microgrid (NMG) energy market. The primary objectives include minimizing the distribution system operator (DSO) cost and optimizing the exchanged power between DSO and NMG, and the power transmission losses and the secondary include minimizing MG’s operating cost, optimal use of renewable energy resources (RER) and energy storage systems (ESS), minimizing the exchanged power cost with the main grid and, risk analysis. The OEMS&DRTOP model is developed based on the Stackelberg game theory and the DRL structure. The DRL model is developed in two offline learning and online distributed operation phases to minimize the computational burden, time, and DRL operation process. This study’s results show the high efficiency of the presented approach to minimizing the operating cost, the exchanged power based on the price uncertainty, power transmission losses, and, RER and ESSs optimal participation. In addition, regarding computational load, the proposed concept demonstrates a 12.9% reduction compared to the dueling deep Q-network method and a 17% reduction compared to the deep Q-network method. Also regarding computational time, the proposed concept demonstrates a 17.13% reduction compared to the dueling deep Q-network method and a 25.6% reduction compared to the deep Q-network method.

Renewable energy mechanisms: An examination of the progress achieved in wireless power transmission techniques

Technology for “wireless power transfer” (WPT) has advanced greatly in recent years, especially when used for renewable power sources. Environmental issues and the growing need for renewable energy sources have prompted a great deal of study in this area. This report provided an overview of the evolution of WPT technologies for renewable energy systems, including their advantages, disadvantages, and future possibilities. An introduction to WTP’s foundational ideas is provided, including techniques such as resonant pairing, capacitive connection, and microwave-based approaches. It delves into how these systems have been modified and improved to facilitate the long-distance transmission of renewable energy sources like wind and solar power. The study emphasizes the benefits of WPT in renewable energy systems, such as improved convenience, lower maintenance costs, and the opportunity to lessen negative effects on the environment. Problems with WPT technology in renewable energy applications are also analyzed critically. Power loss in transmission, misalignment, and electromagnetic interference are all examples of technical difficulties, and these are on top of the financial and legal obstacles. WPT’s effects on humans and animals are also explored since they must be taken into account before broad use of the technology.

Electricity power production, transmission and distribution losses in Ghana: the role of regulatory quality

ABSTRACT Ghana faces annual productivity losses of $320–$924 million due to electricity crises, impacting GDP by 2%-6% and stifling economic growth with over 5% power losses. The study, therefore, utilized time series data to explore the relationship between electricity production, transmission, and distribution losses in Ghana from 1983–2018, specifically focusing on the role of regulatory quality in this relationship. Using statistical tests like Philip-Perron and Augmented Dickey-Fuller, the study confirmed cointegration among the variables. The Autoregressive Distributed Lag model was then employed, analyzing data from 1983 to 2018. The research compared a base model with variables affecting electricity losses to an extended model that incorporated Institutions and their interaction with electricity production. The study reveals that improving regulatory quality and institutional strength significantly reduces electricity losses in Ghana, highlighting the need for strategic policies to enhance efficiency and reliability in the electricity sector. Results also showed that the labor force had a positive impact on losses, while economic growth and electricity production had a negative effect. Foreign direct investment and gross fixed capital formation were not significant. The study’s short-term findings confirmed these relationships. The inclusion of Institutions maintained similar signs and significance in variables except for foreign direct investment. The study recommends enhancing institutional quality, attracting more FDI into the energy sector, and implementing targeted measures to manage the labor force’s impact on electricity demand and losses.

Hybrid Optimized LMMSE-Based Channel Estimation with Low Power Trellis Coded Modulation

Data transfer in a wireless channel environment is plagued by numerous security issues and power transmission loss. Channel state information is crucial to attaining the performance benefit of reconfigurable intelligent surfaces (RISs) in wireless systems. Because of the Doppler effect, mobility makes accurate channel estimate (CE) more difficult. The parameter estimation approach is employed as an alternative to direct estimation in the channel matrix to address the aforementioned problems. Also, to lower the Mean Square Error (MSE) between the estimated and original channel, training-based CE, such as Linear Minimum Mean Square Error (LMMSE), and Least Square Error (LSE), are widely used in some wireless standards. To optimize the channel, certain intelligent optimized strategies are presented. For the Optimal CE, a Trellis Coded Modulation (TCM) with hybrid optimization in LMMSE has been provided. Moth amalgamated Elephant Herding Optimization (MAEHO) algorithm is the proposed hybrid optimization. Finally, the performance of the accepted model is determined in comparison to other existing techniques using a variety of metrics.

ANALISIS GANGGUAN DAN IDENTIFIKASI KABEL FIBER OPTIC MENGGUNAKAN OTDR DI OTB CIREBON-BREBES R4

Fiber optic cables that experience interference and breaks are a common problem in modern communications infrastructure. Interference and breaks in optical cables can disrupt data transmission between locations and cause a decrease in signal quality. The use of an Optical Time Domain Reflectometer (OTDR) has become a common method for detecting faults in optical cables by displaying the attenuation of the cable under test. Although optical fiber has a large capacity and high speed for transmitting information, relatively small attenuation still has a significant impact on transmission quality. The importance of maintaining and handling this interference is because attenuation can cause power loss in fiber optic transmission, hamper data transmission, and affect the quality of internet services

Development of correction coefficients for cable cross-sections selection in polymeric cable channel

Relevance. The need to develop correction coefficients for selecting cable sections when laying in the polymeric cable channel in the Russian Federation. Currently, there are no recommendations fixed by the state standards for laying cable power lines in polymer pipes. Aim. To develop correction factors for laying cable power lines in the polymer cable channel. Objects. Cable transmission lines laid underground in polymer pipes. Methods. Numerical simulation of combined frequency-stationary method; determination of correction factors based on interpolation of model results; evaluation and analysis of comparison of cables in corrugated and smooth polymer pipes. Results. Calculations of the long-term current load for cables laid in the polymeric cable channel showed the effectiveness of this method of laying cable lines. The use of pipes with smooth walls makes it much more efficient to remove heat from cables into the environment (compared to a corrugated pipe), which allows you to increase the capacity of the same cable by about 25%, depending on the number of pipes laid nearby. Based on the results of the simulation of the thermal mode of corrugated and smooth polymer pipes, it can be concluded that corrugated pipe significantly complicates heat removal from the cable system to the environment (soil). This is due to the presence of air gaps in the ribbed structure of the corrugated pipe. The gaps filled with air act as a heat-insulating layer, unlike a smooth pipe in which this gap is absent. The developed coefficients will take into account the influence of the location of cables on their allowable current already at the design stage, which will reduce the cost of power transmission losses due to the wrong cable cross-section and high temperature.

A Partially Distributed Fixed-Time Economic Dispatch Algorithm With Kron's Modeled Power Transmission Losses

A Partially Distributed Fixed-Time Economic Dispatch Algorithm With Kron's Modeled Power Transmission Losses

Renewable energy integration and distributed generation in Kosovo: Challenges and solutions for enhanced energy quality

&lt;abstract&gt; &lt;p&gt;The growing demand for energy, driven by rapid economic development, necessitates higher electricity consumption. However, conventional energy systems relying on fossil fuels present environmental challenges, prompting a shift towards renewable energy sources. In Kosovo, coal-fired power plants dominate electricity production, highlighting the need for cleaner alternatives. Worldwide efforts are underway to increase the efficiency of photovoltaic systems using sustainable materials, essential for ecological and human health. Solar and wind energy are emerging as sustainable alternatives to traditional fossil fuels. However, global concerns about energy security and environmental sustainability are driving countries to prioritize renewable energy development.&lt;/p&gt; &lt;p&gt;In Kosovo, the integration of renewable energy sources, such as wind and solar energy, is progressing rapidly. However, challenges such as voltage stability and power losses need to be addressed. Distributed generation offers a solution by increasing energy reliability and reducing greenhouse gas emissions. Further research is needed to assess the technical, economic, and environmental implications of integrating renewable resources into Kosovo's energy system, focusing on power quality, system reliability, and voltage stability. The research focused on the eastern region of the country, operating at the 110 kV substation level. Challenges in energy quality arise due to the lack of 400 kV supply and the continuous increase in energy consumption, especially in the Gjilan area. This paper investigated integrating renewable energy, especially wind and solar sources, into the medium- and long-term plans at the Gjilan 5 substation to enhance energy quality in the area. Successful integration requires detailed analysis of energy flows, considering the impact of photovoltaics (PVs) on distribution system operation and stability. To simulate and analyze the effects of renewables on the transmission system, voltage profile, and power losses, a case study was conducted using ETAP software. The simulation results present a comparison between scenarios before and after integrating renewable systems to improve energy quality in the identified area.&lt;/p&gt; &lt;/abstract&gt;

CAVOA: A chaotic optimization algorithm for optimal power flow with facts devices and stochastic wind power generation

AbstractThe study proposes a modified version of African Vultures Optimization Algorithm (AVOA) to address the optimal power flow (OPF) issue. The developed optimizer is called Chaotic AVOA (CAVOA). Prior to applying CAVOA to the problem at hand, its accuracy is evaluated using 23 benchmark functions. The production powers of WTs that are interrupted are predicted using Weibull probability density functions (PDFs). Two additional costs, namely penalty cost and reserve cost, are incorporated into the goal function of the OPF. The simulation results of CAVOA are compared with those of the original AVOA to solve the OPF. The proposed OPF technique and its solution methodology are verified on the IEEE 30‐bus test system, taking into account the flexible alternating current transmission systems (FACTS) devices, which have several benefits such as reducing active power transmission loss, regulating power flow, and improving voltage stability/profile. The research's simulation results demonstrate that CAVOA is more effective in identifying the OPF's optimal solution by reducing the overall power cost and power losses. The results indicate that the CAVOA algorithm outperforms AVOA as a metaheuristic optimization algorithm due to its superior capability in addressing challenging OPF problems with a minimal convergence rate. For example, when comparing the two algorithms, CAVOA achieved a significant improvement. It successfully reduced the cost function by approximately 0.023% in Case I and 0.035% in Case II, while also decreasing power loss by 2.57% in Case II and 0.57% in Case I. Additionally, CAVOA exhibited a remarkable reduction of 5.36% in voltage deviation compared to AVOA in Case I. Furthermore, in Case III, CAVOA demonstrated a decrease of 0.64% in gross cost when compared to AVOA.

Optimal dispatch of combined heat and power generating units with prohibited operating zones using improved heap‐based optimizer

AbstractIn the present market scenario, energy cost has been increased and also restrictions have been faced in fossil fuels energy sources. This leads to evolvement of combined heat and power generating units having higher efficiency as compared to the existing systems. The optimal dispatch of CGUs involves a number of intricate constraints, making it a practical optimization problem. So, an improved heap‐based optimizer algorithm has been implemented in this work for solving optimal dispatch problem by accomplishing a proper balance between initial and final stages of global search and convergence, respectively. This problem provides optimal scheduling of heat and power generating units and seeks to minimize the overall fuel cost supply of combined units considering their operational limits. Here, three test systems, that is, 7‐units, 24‐units and 48 units system have been taken into consideration to verify the performance of proposed IHBO algorithm. The impact of prohibited zones on power dispatch has been analysed for both the system in an electricity market. In addition, the valve‐point loading effect, mutual dependency of produced heat and power of combined units and also transmission power losses in transmission lines have been studied. Comparisons among different methods have been made based on their respective solutions obtained and from that it manifests the robustness and superior performance of the proposed technique. Verification studies showed that impact of prohibited zones on generating units, increases the production cost with respect to without prohibited zones. However the proposed technique yields least production cost among other techniques.

Combined heat and power economic emission dispatch using dynamic switched crowding based multi-objective symbiotic organism search algorithm

Combined heat and power economic emission dispatch (CHPEED) problem is a highly complex, non-linear, non-convex multi-objective optimization problem due to two conflicting objectives and various operational constraints such as valve-point loading effect, power transmission loss, prohibited operating zone, and the feasible operating region of combined heat and power unit. In order to overcome these challenges, it is necessary to design an algorithm that exhibits a search behavior, which is suitable for the characteristics of objective and constraint space of the CHPEED problem. For these reasons, a dynamic switched crowding based multi-objective symbiotic organism search (DSC-MOSOS) algorithm was designed to meet the requirements and geometric space of the CHPEED problem. By applying the DSC method in the MOSOS algorithm, it was aimed to improve the exploration ability, to strengthen exploitation-exploration balance, and to prevent the catching into local solution traps. A comprehensive experimental study was carried out to prove the performance of the proposed algorithm on IEEE CEC 2020 multi-modal multi-objective problems (MMOPs) and CHPEED problem. In the experimental study conducted among eleven versions of MOSOS variations created with DSC-method and the base MOSOS algorithm on IEEE CEC 2020 MMOPs, according to Friedman scores based on the four performance metrics, the base MOSOS algorithm ranked the last. In other experimental study, the best DSC-MOSOS variant was applied to solve the CHPEED problem, where 5-, 7-, 10- and 14-unit test systems and eight case studies were considered. The important points of this study were that 10-unit and 14-unit test systems were presented to the literature, and the prohibited operating zone was considered in CHPEED problem for the first time. According to the results obtained from eight case studies obtained from the DSC-MOSOS and fourteen competitor algorithms, while the improvement in cost was between 0.2% and 16.55%, the reduction of the emission value was between 0.2 kg and 42.97 kg compared to the competitor algorithms. On the other hand, the stability of the DSC-MOSOS and the base MOSOS was evaluated using stability analysis. While the MOSOS algorithms was not able to perform a success in any case study, the DSC-MOSOS was achieved an average success rate with 91.16%. Thus, the performance of the DSC-MOSOS over the MOSOS was verified by the results of experimental studies and analysis.

Model predictive control for multimode power-split hybrid electric vehicles: Parametric internal model with integrated mode switch and variable meshing losses

Model predictive control (MPC) is one of the most promising energy management strategies for hybrid electric vehicles. However, owing to constructive complexity, the multimode power-split powertrain requires dedicated mathematical tools to model the mode switch and transmission power losses within the internal model of the controller. Thus, the transmission losses are usually neglected and the mode switch is optimised through offline simulations. This paper proposes an MPC internal model relying on a parametric approach available in the literature, which provides a unique formulation for modelling any power-split transmission and assesses the transmission meshing losses. The objectives, which cover a gap in the literature, are: 1) to integrate the discrete problem of the mode switch in a continuous formulation of the internal model; 2) to compare MPC internal models with different complexity, and evaluate how the consideration of meshing losses and efficiency of the electric machines affect the controller performance. The results on a case study vehicle, i.e., the Chevrolet Volt, suggest that a simplified internal model deteriorates the fuel consumption performance by less than 2 %, while the integrated mode switch is comparable to the offline strategy.

Power Flow Optimization with Energy Storage - Sal Island Case Study

The correlation between energy costs and the country's economic competitiveness is an unquestionable reality also responsible for the improvement of the population's life conditions. In the past Cape Verdean electric power system (EPS), expansion was based on fossil-fuel power plants, nowadays it shifted to renewable energy (RE) which is abundant in the Cape Verde archipelago. However, no reduction in the electricity tariffs occurred, due to renewable curtailment and other pendent questions related to power transmission losses in the EPS. This paper presents an approach, that supports an implementation of a distributed electric energy storage system (ESS) on the Sal Island of Cape Verde archipelago, as a solution to increase the RE integration and power Transmission congestion relief. Thus, a power flow optimization is only achievable by storing excess RE as near as possible to consumption buses that can reduce overall transmission losses. The most advantageous allocation of ESSs along the EPS buses is combinational which faces a maximization of transmission loss reduction and minimization of ESS investment capital. The proposed tool to manage the “trade-off” between cost and avoided losses, is based on a genetic algorithm (GA) that is broadly applied to multi-objective problems like this.

Energy Optimization Management Strategy for DC Nano-Grid Cluster With High Comprehensive Energy Efficiency

Nano-grid (NG) clusters (NGCs) have the potential to serve as as an essential configuration for future low-voltage distribution networks. To address the comprehensive energy efficiency (CEE) problem over a longer time horizon, this paper proposes an energy optimization management strategy. First of all, three typical models are developed, namely equivalent circuit model, cluster loss model and energy sharing model. In the modelling, influences of port characteristics and power transmission losses of interconnecting converters are considered to more accurately describe the cluster power flow. Then, an optimal energy scheduling mechanism with high CEE is proposed based on the three established models. The strategy decomposes the complex CEE problem into three interdependent sub-problems, corresponding to the three established models. First, ensuring the bus power quality and battery requirements are satisfied based on the proposed equivalent circuit model. Second, improving cluster self-sufficiency based on cluster energy sharing model. Third, achieving high energy transfer efficiency based on cluster loss model. Extensive simulation results demonstrate that the CEE under the proposed strategy achieves significant performance improvements, with increases of 8% and 32% in energy transfer efficiency and energy self-sufficiency respectively.

Distributed Generation in Electric Power Systems: An Overview and Important Issues

This paper discusses distributed generation (DG) in electric power systems. Various popular DG technologies that are currently used are also described, along with brief explanations of their working principles. It has been acknowledged that the integration of DG with renewable energy sources in power systems is increasing and will grow further. The main reason for this growth is the rising cost and environmental concerns of non-renewable energy sources (fossil fuels). Furthermore, DG offers some advantages, such as reducing power losses in transmission and distribution lines and improving power supply security. However, the increasing DG penetration brings technical implications for the power system to which the DG is connected. These critical issues are also highlighted in the present paper.