Increase In Power Loss Research Articles

Voltage enhancement and loss minimization in a radial network through optimal capacitor sizing and placement based on Crow Search Algorithm

In power systems, the distribution network is crucial as it serves as the final stage in delivering energy from generation plants to end users. However, reactive power demand from consumers can create challenges such as low power factor, voltage drops, and increased power losses depending on the distribution system used. This study aims to enhance the performance of radial distribution networks (RDNs) by optimizing capacitor placement and sizing based on the Crow Search Algorithm (CSA). Additionally, the study addresses the Optimal Power Flow (OPF) within the network by employing the Backward/Forward Sweep (BFS) method. The study accesses CSA’s effectiveness compared to other metaheuristic optimization techniques through simulation. The simulation results showed that the Voltage Stability Index improved from 0.61 for base case to 0.68 pu with CSA, compared to 0.66 for Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Teacher Learner Based Optimization (TLBO and 0.67 for Invasive Weed Optimization (IWO). CSA also achieved a 30.41 % reduction in active power losses, outperforming PSO, ABC, GA, and TLBO having 26.61 %, and IWO at 24.92 %. Additionally, application of CSA led to a 29.33 % reduction in reactive power losses, compared to 21.91 % for PSO, ABC, GA, and TLBO, 18.2 % for IWO. In the simulated IEEE 33 bus Radial Distribution Network (RDN) topology, the lowest bus voltage at bus 18 increased from 0.8820 in the base case to 0.9080 with CSA, indicating a 32.9 % improvement in voltage deviation. Furthermore, the optimization resulted in a significant reduction of 3.841 pu in capacitor cost, demonstrating CSA’s efficiency in both technical and economic aspects. CSA not only showed favorable results in minimizing power losses and improving voltage profiles but also provided substantial cost savings in capacitor implementation, making it a robust and cost-effective solution for enhancing RDN performance.

A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks

The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.

Experimental Characterization of a Bladeless Air Compressor

Abstract The Tesla compressor is an innovative technology that offers a unique approach to fluid compression. Unlike traditional compressors that use rotating blades, bladeless compressors utilize closely spaced disks to create compression. The purpose of this article is to design a prototype Tesla air compressor with optimal design parameters and investigate the performance and loss characteristics based on numerical analysis and experimental demonstration. The prototype model has been numerically investigated at different rotational speeds, and the results have been compared with those obtained in experiments. Computational fluid dynamics (CFD) simulations indicate that the rotor-only efficiency is greater than 90% at very low mass flowrates, while the coupling of the rotor and volute leads to a total-to-static efficiency of approximately 58% (without losses) at 14 g/s. At a nominal mass flow of 4 g/s, the highest total-to-static pressure ratio would be around 1.27. Experimental results indicate leakage losses greatly reduce net mass flow, while pressure ratio values are in good agreement with CFD predictions. During this experiment, a maximum isentropic efficiency of 32.4% is measured. Indeed, the prototype included ventilation and leakage losses, which were not modeled in the CFD analysis. It is remarkable that the compressor does not show any unstable behavior down to zero mass flow (closed valve test), where the CFD and the experiment show consistent pressure ratios. An estimation of the losses from end-wall friction and leakage flow is carried out using numerical simulations at different exit radial clearances. Increasing radial clearance results in an increase in leakage and end-wall power loss, the latter being driven mainly by the axial clearance with the casing, which remained unchanged. To minimize leakage, a Teflon ring has been used as a first measure. Numerical calculations have indicated that the leakage rate is approximately 6 g/s at design speed. A brush seal-type solution can improve the sealing system to reduce leakage.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Implementasi Metode Artificial Bee Colony Untuk Penempatan Distributed Generator Pada Penyulang Rayon Tanjung

Distributed Generation (DG) is a power plant that is spread over the power distribution network. In principle, electrical power output is generated by power plants located far from the load center and electrical power is transmitted to the load center through transmission and distribution networks. The longer the cable from the power plant or substation, the lower the voltage at the end load. Sub-optimal DG placement and size can result in increased power losses and affect the system voltage profile. In addition, suboptimal generation capacity can lead to voltage instability, reduced system security, and potential impacts on system frequency and emissions. Therefore, this study will use the Artificial Bee Colony (ABC) method to determine the optimal DG capacity and location. In the base case condition, the power losses in the system are 118 KW and 99 Kvar. Power losses after the installation of 1 DG active power loss becomes 37.4 KW and reactive power loss becomes 31.65 Kvar. Placement of 2 DGs reduces active power loss to 17 KW and reactive power loss to 15 Kvar. While the placement of 3 DG reduces active power loss to 33 KW and reactive power loss to 28 KVar.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Optimal EV Charging and PV Siting in Prosumers towards Loss Reduction and Voltage Profile Improvement in Distribution Networks

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction.

Evaluating the Harmonic Effects on the Thermal Performance of a Power Transformer

Harmonics in the power grid contribute to increased power losses in both the core and windings of power transformers. These losses lead to abnormal rises in temperature causing overheating and reduce the efficiency of the transformer. If the losses and temperature exceed the values set during the design stage for linear load conditions, it can damage the transformer’s insulating materials and shorten its lifespan. To assess the thermal impact of power system harmonics on transformers under steady-state and transient conditions, the rated losses and harmonic losses of the transformer are calculated. These losses are then inputted into a developed thermal 3D finite element method (FEM) performance model to determine the temperature distribution of transformer components. The numerical results from the thermal model will be compared with data from a Hyundai test report and real measurements from Egypt’s Kureimat power plant, specifically a 750 MW combined cycle power plant. The thermal modeling is focused on a step-up (16.5/240 kV), 240 ± 4 × 2.5%, 180/240/300 MVA power transformer operating in ONAN, ONAF1, and ONAF2 modes. This paper shows that the developed model aligns closely with actual measurements and the HYUNDAI test report. The loss calculations reveal that the discrepancy in total losses, with and without accounting for harmonics, becomes more pronounced as the load increases. Using this model, the presence of grid harmonics results in a higher temperature distribution across transformer components, leading to an increase in the hot spot temperature.

Probabilistic Assessment of the Impact of Electric Vehicle Fast Charging Stations Integration into MV Distribution Networks Considering Annual and Seasonal Time-Series Data

Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies.

Optimal location and sizing of renewable distributed generations and electric vehicle charging stations

Many countries are aiming to replace gasoline-based vehicles with electric vehicles (EVs). The increasing adoption of EVs has led to a surge in the number of charging stations, significantly impacting the electrical grid with issues such as power quality degradation, increased losses, and voltage fluctuations. In response to these challenges, there is a growing interest in integrating distributed generation from unconventional and renewable sources into the grid to power EV Charging Stations (EVCSs). However, this integration poses new complexities, including increased power losses and voltage instability. Consequently, the optimal allocation and sizing of Renewable Distributed Generations (RDGs) and EVCSs have become critical planning considerations. This paper addresses these issues by formulating a multi-objective optimization problem aimed at minimizing power losses and improving the voltage profile of distribution systems. The study introduces several constraints, including the placement of EVCSs and RDGs at separate buses, and employs particle swarm optimization and cuckoo search algorithm to simultaneously determine the optimal locations and sizes of the RDGs and the optimal locations of the EVCSs. The effectiveness of the proposed techniques is demonstrated through simulations on IEEE 33 radial and meshed distribution systems, illustrating their capability to identify optimal configurations for integrating RDGs and EVCSs.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

Multiscale multiphysics modeling of ammonia-fueled solid oxide fuel cell: Effects of temperature and pre-cracking on reliability and performance of stack and system

Ammonia is a promising carbon-free fuel for solid oxide fuel cells (SOFCs). However, direct feeding of ammonia into the SOFC may lead to serious degradation due to the nitriding. Conversely, the pre-cracking of ammonia introduces complexity to the system design and causes increased power losses in the system. Therefore, it is crucial to explore all these aspects simultaneously. In this context, this study introduces a novel multiscale modeling approach by integrating a 3D multiphysics simulation of the ammonia-fueled SOFC stack with system-level modeling to investigate the reliability and performance of the stack and system. Two different cell technologies developed for low temperature (LT) and high temperature (HT) operation are investigated at LT (600 – 700°C) and HT (700 – 800°C) ranges. The results indicate that fuel inlet temperature should be 55°C and 18°C higher than the minimum temperature in 0% pre-cracking case for HT and LT cases, respectively. The increase in the required air flow rate for cooling in the 100% pre-cracking case compared to the 0% pre-cracking case is around 100% and 216% for the HT and LT cases, respectively. However, stack power production and power losses in the system components are comparable for LT and HT cases which leads to similar system performance. A larger share of the active area is affected by nitriding in LT cases than HT ones. However, a smaller cracking ratio at LT (∼ 82%) compared to HT conditions (∼ 92%) is needed for elimination of nitriding. While the LT and HT cases are comparable in terms of system power production, the lower stack outlet temperatures in LT cases require novel and more expensive catalysts for ammonia pre-cracking and HT cases need more expensive steels.

An optimal operation strategy of wind farm for frequency regulation reserve considering wake effects

When wind farms (WFs) participate in power system frequency regulation, deloaded control can increase the stored rotational kinetic energy in the wind turbines (WTs), thereby enhancing their frequency support capability. However, due to the wake effects and deloaded control of turbines, this method can also lead to increased power losses. An optimal operation strategy has been proposed to balance power generation and frequency regulation reserve in WFs, taking wake effects into account. Initially, a WF deloaded control model that considers wake effects, and an adaptive combined frequency control model based on kinetic energy reserve, are established for participation in power system frequency regulation. Subsequently, the Covariance Matrix Adaptation Evolution Strategy (CMAES) is utilized to determine the optimal operation allocation of maximum WF output power within the FLORIS wake model, meeting frequency regulation reserve requirements. A comparative analysis of the optimal allocation results under various wind conditions is conducted to test the frequency support capabilities. The results indicate that the proposed strategy can achieve varying levels of frequency regulation reserve, reduce wake losses to maximize output power under deloaded control, and improve frequency support capability.

Enhancing power conversion efficiency in five-level multilevel inverters using reduced switch topology

This paper presents extensive research on improving the power conversion efficiency of five-level multilevel inverters (MLIs) by utilizing a reduced switch topology. MLIs have received an abundance of focus because of their ability to generate high-quality output waveforms and have better harmonic outcomes than traditional two-level inverters. The high number of switches in MLIs, on the other hand, can result in increased power losses and lower overall efficiency. In this paper, a novel reduced switch topology for five-level MLIs, which is having five switches is proposed with the aim of minimizing power losses while preserving superior performance due to lesser number of switches. To achieve efficient power conversion, the proposed topology employs advanced pulse width modulation control strategies and optimized switching patterns. The simulation results show that the minimized switch topology improves the power conversion efficiency of the five-level MLI, resulting in lower losses and better overall system performance. The total harmonic distortion (THD) value of the output current has been reduced to 7.12% and the efficiency has been achieved to 96.92%. The findings of this investigation help to advance MLI technology, allowing for more efficient and reliable power conversion in a variety of applications such as renewable energy systems, electric vehicles, and industrial drives.

Multi-time-scale voltage control of the distribution network with energy storage equipped soft open points

The integration of distributed generation (DG) units into distribution networks (DNs) has brought about several operational challenges, including voltage issues and increased power loss. Energy storage equipped soft open points (E-SOPs) can accurately and flexibly control active and reactive power flows to address these problems. Additionally, the photovoltaic (PV) inverter and the network reconfiguration (NR) play a significant role in voltage control by adjusting the reactive power and the topology of the DN, respectively. However, due to differences in response times, there is a lack of systematic coordination between NR and the inverters of the E-SOP and PV. This paper proposes a multi-time-scale voltage control model that includes day-ahead NR scheduling, inter-day droop control optimization of the PV and E-SOP, and real-time local droop control. Considering the uncertainties of renewable DG outputs and loads, a robust optimization method is used in the day-ahead stage to obtain a reliable network structure. Then, with more accurate intra-day predictions, a stochastic optimization method is used to obtain the optimal state-of-charge interval, aiming to provide a flexible regulation range for battery energy storage to cope with the power fluctuations during the real-time stage. In addition, to address the intra-day voltage control model with bilinear constraints of the droop control function, a particle swarm optimization method is used. The results are verified on a 33-bus DN system through comparative analyses, showing effective performance.

Monitoring Energy Flows for Efficient Electricity Control in Low-Voltage Smart Grids

Modern low-voltage distribution lines, especially those linked with renewable energy sources, face technical hurdles like unaccounted and illegal electricity use, increased power losses, voltage control issues, and overheating. Tackling these challenges effectively requires continuously monitoring power flows and identifying problematic network spots. This study introduces a method involving ongoing energy flow monitoring from distribution transformers and other sources to end-users through auxiliary facilities. The algorithm seamlessly integrates with consumers’ existing smart power meters and supporting infrastructure, eliminating the need for extra equipment or data. Deployed in several distribution networks totaling about 40 GWh/year over two years, this diagnostic system showed promising results. It notably cut total power consumption by around 6% by detecting and mitigating illegal energy waste and addressing technical issues. Additionally, it reduced technical personnel involvement in operational tasks by approximately twentyfold, significantly enhancing network profitability overall.

Collaborative strategy for electric vehicle charging scheduling and route planning

AbstractDue to varying energy demands and supply levels in different regions, the distribution of power load exhibits an imbalanced state. It contributes to increased power loss and poses a threat to the security constraints of the electrical grid. Simultaneously, the global energy transition has led to a continuous increase in the proportion of renewable energy integrated into the grid. Electric vehicles (EVs), serving as representative of renewable energy, further magnify this load imbalance with their charging requirements, which poses a significant challenge to the stable operation of the grid. Therefore, to ensure the smooth operation of the grid under the context of renewable energy integration, the authors investigate the coordinated strategies of EV charging scheduling and route planning. The authors first model the coupling of the transportation network with the smart grid as a cyber‐physical system. Subsequently, the authors simulate and analyse the daily charging load curve of the network, capturing the travel characteristics of EVs. Based on this, the authors research the EV charging scheduling in both individual and collective travel scenarios during peak and off‐peak hours. For the off‐peak travel period of EVs, a charging schedule strategy based on travel plans is proposed, which reduces the time cost of EV owners' travel. Furthermore, for the collective travel of a large number of EVs within the system, a multi‐EV charging scheduling strategy based on charging station load balancing is presented. This strategy effectively balances the load levels of various charging stations while reducing the overall system travel time. Ultimately, through experimental results, the authors demonstrate that by deploying appropriate charging scheduling strategies, EVs cease to be a burden on the grid and can be transformed into tools for balancing the loads across different regions.

Design and Control Performance Optimization of Variable Structure Hydrostatic Drive Systems for Wheel Loaders

The traditional loader drive system is based on the hydraulic torque converter as the key component, and its gear is shifted through the coordination of the clutch and gearbox, which greatly increases power loss and operator fatigue. To address the above problem, a variable-structure hydrostatic drive system is proposed. This system adopts a closed-loop design and adjusts the displacement of the pump and dual motors to follow the throttle opening of the vehicle, achieving automatic gear shifting and smooth speed regulation. It can also automatically change the system structure according to the vehicle’s speed, meeting the vehicle’s demand for rapid switching output of high torque and high speed. At the same time, in the displacement matching control process, an adaptive sliding mode control scheme based on radial basis function neural network compensation is proposed. This scheme designs an adaptive hyperparameter update strategy according to the characteristics of the system to effectively compensate for changes in uncertain factors. Experimental results show that, compared to traditional drive systems, this system has the characteristics of simple operation, smooth speed regulation, and high fuel economy.

Analysis the Impact of Distributed Generation Interconnection on the 20 kV Distribution Network: A Study Case of the Waste-to-Energy in Bantar Gebang and the Tambun Area Distribution Network

The need for electricity in Indonesia is increasing along with the growth of world industry in Indonesia. The use of power plants with fossil energy sources is decreasing with the aim of improving the environmental system. Electricity needs can be fulfilled by Distributed Generation (DG), namely generation that is connected to a distribution network in order to fulfill power needs and improve electricity quality. In Bekasi City, the Directorate General of the Bantar Gebang Waste-to-Energy Plant (WtE) is being built with a drained power of around 786 kW. In this research, an analysis will be carried out on the Bantar Gebang WtE, which is connected to the Tambun area distribution network. The simulation results show that the voltage drop at the transformer load is 0.3% to 3% in general. With PVUR values on sample transformers of 0.171%, 0.578%, and 0.187% and real and reactive power losses on Underground Lines, Overhead Lines, and transformers of 144 kW and 173 kvar leading before interconnection. After interconnection, there was an increase in real power loss to 147 kW and an improvement in PVUR values to 0.156%, 0.563%, and 0.183%, and reactive power to 164 kvar. The use of power at the Tambun feeder Dodge Substation has also decreased due to the interconnection of the Bantar Gebang WtE, which can generate around 786 kW of power to supply power to the distribution network connected to the Bantar Gebang WtE DG.

Power Loss and Damage Behavior of Gears Operating Under Loss of Lubrication

Abstract Geared turbofans need to withstand loss of lubrication due to inevitable off-design conditions. Additionally, the loss of lubrication specifications that have to be fulfilled are getting stricter. For a loss of lubrication event, today's geared turbofans are equipped with secondary oil supply systems. Their omission would reduce space and weight and, consequently, carbon emissions. However, this requires geared transmissions to withstand loss of lubrication events. In order to enable this, knowledge of the power loss and damage behavior of gears under loss of lubrication is required first. In this study, power loss and bulk temperature measurements of test gears under loss of lubrication on an FZG gear efficiency test rig are presented. Hertzian pressures in the pitch point up to 1723 N/mm2 and circumferential speeds up to 20 m/s were considered. The experimental results show the characteristics of increasing power loss and bulk temperatures under loss of lubrication depending on load and speed. At moderate operating conditions, no damage occurs within 20 s under loss of lubrication, whereas a load increase results in slight scuffing, and a speed increase results in severe scuffing. Oil centrifugation has a strong effect on the loss of lubrication performance. Additional experiments under reduced quantity lubrication demonstrate the impact of the remaining oil on the survivability of gears facing the loss of lubrication.