Electric Pump Research Articles

Analysis of Power and Voltage Losses in Electric Submersible Pump Motor Installations

In area mining are quite extreme, where the level of safety from high temperatures and toxic gases which are explosive as well as well flow pressure is also high and can damage production equipment. Therefore, it is very necessary to install reliable electrical power, starting from analyzing the selection of cable types that used, voltage drop, power losses and transformer selection. The problems mentioned previously, this research carried out an analysis of improvements to voltage drops and power losses that occurred in the ESP motor installation system. The method used is to replace the type of cable used in three scenarios with three different types of cable. The installation system data obtained from this company was then processed using software which was then validated with theoretical calculation results. The result of voltage drops and power losses in ESP cables according to standards using scenario 3, replacing AWG 2/0 type with a voltage rating of 5 kV, diameter of 11.709 mm and has a KHA of up to 210 A. The voltage drop value after repairs are carried out based on theoretical calculation results has an average percentage index value of 4.5% and power losses of 4.6%. This shows that in the condition after repairs, both the results of simulation calculations and theoretical values have met the IEEE std 141-1993 standard which states that the value of voltage drop and power losses in industrial plan distribution systems must be below 5%.

Techno-economic analysis of onboard CO[formula omitted] capture for ultra-large container ships

Decarbonising the naval shipping sector is of paramount importance to decrease global CO2 emissions. This work presents a techno-economic analysis of onboard solvent-based carbon capture for an ultra-large container ship, powered by two dual-fuel engines fed with either heavy fuel oil or liquefied natural gas. Different case studies are proposed depending on the fuel used by the ship and on the chosen design to supply the necessary heat and electricity to the carbon capture unit. Aside a conventional strategy based on heat provided via fuel combustion in a boiler, a novel configuration based on electric heat pump is investigated. The economic analysis is based on a real inter-continental journey. The results highlight that onboard carbon capture determines an increase in fuel consumption with respect to the unabated ship, that varies depending on the exhaust gas CO2 content (hence, on the ship fuel) and on the selected process design. The integration of the electric heat pump leads to a significant decrease in this additional fuel consumption and better results in terms of CO2 avoidance and costs. The carbon abatement cost is found in the range of 64–149 €/t of CO2 avoided.

Utilization of machine learning for the estimation of production rates in wells operated by electrical submersible pumps

AbstractIn this study, a neural network model is developed for the prediction of oil flow rates in wells lifted by electrical submersible pumps (ESPs). Three attributes of the model in this work make this study unique. First, the knowledge on the computational cost of models has been presented, a rarity in most neural network models on this subject; second, the models have been explicitly presented, a feature uncommon in published ANN predictive modelling studies; and third, it includes a sensitivity analysis of input variables. The dataset utilized for the model development comprises 275 data points collected from ESP-lifted wells in the Middle East. Statistical evaluation of the model’s performance using the metrics such as mean square error, root mean square error and coefficient of determination demonstrates high predictive accuracy with respective values of 0.0000201861, 0.00449 and 0.999. In order to ascertain the parametric importance of the inputs, Garson’s algorithm was utilized. In this regard, choke size and upstream pressure had the highest influence (19% and 16%, respectively), while casing head pressure had the least effect (4.8%) on oil flow rate. In terms of memory requirements and processing speed for software applications, the model had a memory footprint of 888 bytes and required 191 multiply and accumulate operations to give an output. By utilizing the proposed models, the time-consuming separator tests measurements of flow rate would no longer be necessary and real-time results could be provided in the field. This work would be useful to production engineers who seek a quick and accurate means of estimating oil flow rate from ESP wells in real time.

The emission reduction potential and cost-effectiveness of low-GWP refrigerants in electric vehicle air conditionings

Electric vehicles play a crucial role in the carbon neutrality transformation of urban transportation, provided that they are powered by electricity generated from renewable (rather than fossil-based) generation sources. However, the substantial indirect emissions from electric vehicle air conditioning energy consumption and the significant direct emissions from HFCs (hydrofluorocarbons) refrigerants pose considerable challenges. In order to identify low-GWP (global warming potential) refrigerants -such as R1234yf, R744, and R290 - that can effectively minimize carbon emissions while maintaining certain cost-effectiveness, this paper establishes the relevant life-cycle analysis and life-cycle cost analysis models for electric vehicle air conditioning. The results obtained show that, in 2022, the total carbon emissions of China’s automotive air conditioning fleet were approximately 162 million tons CO2-eq. The nationwide average carbon emissions ranking of various refrigerant heat pumps is R290 < R1234yf < R744 < R134a. The Net Present Value (NPV) of the life cycle cost for R134a electric vehicle heat pump is estimated to be around 11,500 yuan. Among the three low-GWP refrigerants, the life cycle cost of R290 is significantly lower than that of R134a under nationwide average conditions, while R744 exhibits the best life cycle performance in certain cold cities/regions.

Physics-informed neural nets for control of dynamical systems

Physics-informed neural networks (PINNs) incorporate established physical principles into the training of deep neural networks, ensuring that they adhere to the underlying physics of the process while reducing the need for labeled data, since the desired output is not a prerequisite for physics-informed training. For modeling systems described by Ordinary Differential Equations (ODEs), traditional PINNs typically take continuous time as an input variable and produce the solution to the corresponding ODE. However, in their original form, PINNs neither accommodate control inputs nor do they effectively simulate variable long-range intervals without experiencing a significant decline in prediction accuracy. In this context, this work introduces a novel framework known as “Physics-Informed Neural Nets for Control” (PINC). PINC presents an innovative PINN-based architecture tailored to control problems, capable of simulating longer-range time horizons that are not predetermined during training. This increased flexibility sets it apart from traditional PINNs. The variable simulation time is achieved by adding inputs to the PINC network that convey the initial condition and the control signal for a particular time interval. Simulating variable long-range intervals involves running the PINC net across a sequence of shorter intervals. In this autoregressive process, the network predictions are linked in a self-feedback mode, with the initial state (input) of the next interval set to the last predicted state (network output) of the previous interval. We showcase the effectiveness of our proposal in identifying and controlling three nonlinear dynamic systems: the Van der Pol oscillator, the four-tank system, and an electric submersible pump. Crucially, these experiments demonstrate that learning the dynamics of these systems can be achieved without relying on any sample collected from the actual process, and it offers faster inference speed compared to numerical simulations.

Sub‐Nominal Resolution Fourier Transform Spectrometry with Chip‐Based Combs

AbstractChip‐based optical frequency combs address the demand for compact, bright, coherent light sources of equidistant phase‐locked lines. Traditionally, the Fourier Transform Spectroscopy (FTS) technique is considered a suboptimal choice for resolving comb lines in chip‐based sensing applications due to the requirement of long optical delays, and spectral distortion from the instrumental line shape. Here, a sub‐nominal resolution FTS technique is developed that extracts the comb's offset frequency in any spectral region directly from the measured interferogram without resorting to nonlinear ‐to‐ interferometry. This in turn enables 10's of MHz resolution spectrometry with millimeter optical retardations currently limited by the emission linewidth and phase noise of the used lasers. Low‐pressure Doppler‐broadened absorption lines probed by widely‐tunable chip‐scale mid‐infrared optical frequen with electrical pumping are fully resolved over a span of tens of nanometers. This versatile technique paves the way for compact, electrostatically‐actuated, or even all‐on‐chip high‐fidelity FTS, and can be readily applied to boost the resolution of existing commercial instruments with compact interferometers several hundred times.

Investigation of Electrical Configuration Topologies for Enhancing the Performance of Solar Photovoltaic Water Pumping System

The numerous solar energies provide humans with clean, environmentally friendly, and abundant energy resources. Electricity shortages and high diesel prices have an impact on rural water irrigation systems and drinking water supplies. Solar energy for water pumping could be an alternative to standard electric and diesel pumping systems. The rising importance of solar photovoltaic water pumping systems (SPVWPS) as a feasible power source in rural, inaccessible, and unconnected areas drives the study. These locations, often in poor nations like India, lack conventional power. Solar energy is converted into electrical energy via photovoltaic (PV) technology, which is then used to power a DC or AC submersible or surface water pump. PV module configurations are crucial in improving a PV system’s performance. Many researchers have studied the solar water pumping design on series parallel combination and acquired the best system efficiency, but the other configurations, Total Cross Tied (TCT) and Bridge Link (BL) have never been employed in a solar water pumping system. This research aims to investigate the experimental comparison of different PV configurations for better system efficiency on different operating parameters. These PV array layouts were considered in a uniform situation. The result shows that power increases with increase in radiation and the flow rate provided by (6Sx2P) configuration is 18,956 l/day of water and calculated the best average pump efficiency.

INCREASED RELIABILITY OF ELECTRIC CENTRIFUGAL PUMPING UNITS

Electric submersible pumping units are used for production in high-yield oil wells. Since existing pumping units come in different types depending on the complexity of the design and well conditions, a clear and responsible decision must be made in terms of reliability. In the article, in connection with the widespread use of electric submersible pumping units at oil fields, the methods of solving the issue of ensuring the performance of the equipment, namely, achieving reliability and control of technical condition in the conditions of its operation are considered. In relation to regional oilfields the well efficiency indicators were determined and a comparative analysis is presented. The analysis allows to study the problems of failures of wells equipped with submersible pumps with low service life. The aim of the study is to improve the reliability of the electric centrifugal pump during operation.

Evaluating the intake plugging effects on the electrical submersible pump (ESP) operating conditions using nodal analysis

The intake plugging of an electrical submersible pump (ESP) has presented a formidable challenge to conventional ESP wells. Attention to cumulative solid deposition is essential since it intensifies the intake plugging severity and impedes ESP performance. We present a new approach to evaluate the ESP performance degradation during increased intake plugging severity. In particular, we employ the intake plugging factor, rate-derating factor, and affinity law to calculate the new ESP speed at different plugging conditions. We used Schlumberger PIPESIM software to perform nodal analysis of the newly calculated ESP speed. The result was validated using the actual field data and compared to the field cases that reported the intake plugging issue. The nodal analysis showed a steady maximum ESP head with zero rate derating at the shut-in point. The intake plugging factor caused a significant reduction in the ESP operating rate and increased pump intake pressure and annulus liquid level. Based on the existing intake plugging field data, we established the quantitative standard for the normal and abnormal intake plugging factor range. The observed results agreed with the field downhole data recorded during the intake plugging problem. We identified that regulating the ESP speed to the reduced operating rate could minimize unexpected pump stoppage. It is also possible to carefully monitor the intake plugging problem by combining the annulus liquid level, the signature of pump intake pressure, and a deadhead test.

Comparison between variable displacement and variable speed compressors for electric vehicle heat pumps

Recent developments in electric vehicles have heightened the need for high-efficiency heat pump technology to improve cruising range and cabinet thermal comfort. As the key component for a heat pump system, compressor has a significant impact on heat pump system performance. In this work, a numerical model of a heat pump system consisting of a variable displacement compressor (VDC) model, a variable speed compressor (VSC) model, and a heat exchanger model is presented to compare the two major types of compressor. The simulation is executed across a diverse array of conditions to assess the performance disparity between two variants of compressors, particularly in challenging environments. The proposed model is validated based on the experimental data from a heat pump test rig which compressor section can be replaced with a prototype VDC or VSC based on validation demands. Overall, the modeling results demonstrate a favorable alignment with the measurements. The simulated power input of VDC under heat and cooling mode has a mean absolute percentage error (MPAE) of 4.4% and 6.0%, respectively, while the MAPEs for VSC under heating and cooling modes are 3.2%, and 4.5%. The modelling results indicate that the VDC system shows superior performance in specific mass flow rate, compressor power input, and system efficiency, especially at part load conditions.

Performance of a low-cost hydraulic ram pump as a function of variations in feed height and inlet pipe diameter

Access to water in rural areas is extremely important in the different activities that occur daily on agricultural properties, whether for human consumption, animal watering, irrigation and other purposes. This access is often difficult and costly, especially for small rural producers who do not have the financial capacity to purchase an electric pump, or do not have electricity on their property. In this sense, alternative water pumping, without the need to use electrical energy or energy from the burning of fossil fuels, can be a good alternative and deserves to be studied. Based on these premises, the objective of this research was to study the performance of the repressed flow, obtained by a low-cost hydraulic ram, installed in different hydraulic scenarios. The methodology consisted of installing equipment built in the Irrigation, Hydraulic and Hydrology Laboratory of the Agricultural Sciences Center of the State University of the Tocantina Region of Maranhão (LIHH/CCA/UEMASUL), for this, different hydraulic scenarios were created depending on the interaction of different feeding heights and different pipe diameters, which constituted the experimental treatments. The results found allowed us to conclude that the highest average pressure flow was obtained in the hydraulic scenario that presented the greatest geometric difference between the water source and the hydraulic ram with the largest diameter of the inlet pipe, with an average of 9,539.21 L.d-1 of repressed water. It was also possible to prove that this equipment constitutes a good alternative for pumping water on small rural properties.

A hybrid approach for PV based grid tied intelligent controlled water pump system

SummaryThis article proposes a hybrid POA‐RBFNN approach for PV (photovoltaic) based grid tied intelligent controlled water pump system. The proposed method is the hybrid wrapper of Pelican Optimization Algorithm (POA) and Radial Basis Function Neural Network (RBFNN) and later it is termed as POA‐RBFNN method. On the basis of bidirectional power grids, this research presents bidirectional power flow control for PV systems. The water pump is driven through a three‐phase synchronous reluctance motor (RSM). A novel Cuk integrated Landsman converter can operate at low duty cycles to provide larger DC voltages with negligible switching losses is used. In addition, to enhance the pumping system reliability, solar panels and electric pumps can be used to their maximum potential. Despite fluctuating weather conditions, the system supplies nominal displacement all over the day and surplus energy to the grid. Furthermore, if one of the sources fails, the pump performance is unaffected. Switching losses are minimized since the Voltage Source Inverter (VSI) operates at a fundamental frequency. In all operational modes, this control delivers unique grid‐side power quality and unity power factor functioning. Here, the performance of the proposed hybrid system is implemented in the MATLAB and is compared with several existing methods.

A fault detection strategy for an ePump during EOL tests based on a knowledge-based vibroacoustic tool and supervised machine learning classifiers

This paper presents a methodology for identifying faulty components in an electric pump during the end-of-line test based on accelerations and pressure pulsation data used to train an ensemble learning algorithm based on supervised machine learning classifiers. Despite various quality control measures in pump manufacturing, some out-of-tolerance components can pass through and end up on the assembly line, potentially leading to premature failure or abnormal noise during real-field operation. Because of the high impact, it is very important to put in place actions to mitigate the risk of delivering non-conform units, even if properly working in terms of pressure-flow rate performances. In this paper, an innovative knowledge-based vibroacoustic tool together with a machine learning built-in Python® library have been used to post-process acceleration and pressure pulsations data to generate features, which are then used to train, and test several supervised machine learning algorithms. The ensemble learning algorithm combines the best classifiers to identify healthy electric pump units with high accuracy, achieving above 95% accuracy in an experimental test campaign carried out on eighty electric pumps. Results are compared using principal component analysis for dimensionality reduction, and a sensor sensitivity study is conducted.

Systematic Evaluation of Possible Maximum Loads Caused by Electric Vehicle Charging and Heat Pumps and Their Effects on Common Structures of German Low-Voltage Grids

In this paper, we present a comprehensive assessment of the effects of residential loads, electric vehicles (EVs), and electric heat pumps (HPs) on low-voltage (LV) grids in urban, suburban, and rural areas of Germany. Firstly, real data are used to determine the typical structures for each LV grid region. Secondly, nine scenarios are defined with different levels of EV and HP penetration. Thirdly, the Low Voltage Load Flow Calculation in the DIgSILENT PowerFactory is performed for all scenarios while taking the simultaneity factor (SF) for each load type into consideration to calculate the minimum voltage and maximum loadings of transformer and lines in each grid; this allows for the grid’s potential bottlenecks to be identified. The network simulations are carried out with the consideration of charging powers of 11 kW and 22 kW in order to evaluate how an increasing EV load in the future may affect the grid’s parameters. To the best of our knowledge, no study in the literature has simultaneously addressed all of the aforementioned topics. The results of this study provide a useful framework that distribution system operators (DSOs) may apply to anticipate the forthcoming challenges and figure out when grid reinforcement will be required.

A new method for restoring the liquid level at the intake of an electric drive pumping system

An analysis of the system for stabilizing the hydrodynamic level in the wellbore is presented. It is shown that hydraulic losses increase with increasing difference between the coordinates of the valve installation location and the liquid level, as well as with increasing valve length. A method is developed for determining the parameters of the dynamic level stabilization system in the wellbore. An algorithm is obtained and criteria for selecting standard sizes of system elements are determined. Keywords: electric centrifugal pump, bypass device, annular cavity. urazakk@mail.ru

Sensorless control of submersible permanent magnet synchronous motor under long-line cable model

In the process of oil production, the submerged oil motor works 1-3 km downhole and is connected to the inverter by a long-wire cable, which generates overvoltage at the motor end due to the problem of matching the impedance of the motor with the impedance of the power cable. The downhole environment is not suitable for the installation of precision devices, such as position sensors. To solve these problems, a sensorless control method of submerged oil motors based on a high-frequency voltage injection method is proposed. A motor terminal voltage and current observer under a long-wire cable model is designed to estimate the actual values of motor terminal voltage and current by collecting the voltage and current at the inverter terminal. A three-level inverter is used to provide energy to the submersible oil electric pump through the long-wire cable, which can effectively suppress the motor terminal overvoltage compared to the two-level inverter. The control algorithm is verified by modeling the whole system using simulation software.

Experimental study on the internal temperature rise of a high-speed canned motor pump for liquid rocket engines

High-speed electric pumps are now gradually applied in liquid rocket engines. Compared with the pumps with dynamic seals, the canned motor pumps are more reliable, and thus suitable for delivering risky propellants. However, the partial flow diverted to the air-gap for cooling and lubrication faces the risk of overheating caused by its friction against the rotor. This paper performed an experiment on this issue with a high-speed canned motor pump for a notional rocket engine. Impellers with and without balance holes, as well as different configurations of jet holes, were adopted and their effects were analysed. Results showed that the internal temperature rise decreased by up to 78% when the balance holes were removed, and decreased by up to 40% when the jet hole configuration was modified. Both factors influenced the internal temperature rise by varying the flow rate of the cooling recirculation. The study suggested that the high-speed canned motor pump should be designed with an appropriate flow rate of the cooling recirculation, so as to control the temperature rise of the propellant and ensure safe operation of the rocket engine.

Distribution-Driven Optimization for Heat Pump and EV Hosting Capacity in LV Networks

The decarburization of heat and transport using electric vehicles (EVs) and heat pumps (HPs) needs an important investment in low-voltage (LV) networks. This article proposes an optimization approach to estimate the availability of headroom for domestic EV charging and optimization on LV networks under various penetrations of heat pumps. The proposed optimization approach is called dynamic differential annealed optimization (DDAO). The objective of the proposed approach is to maximize the hosting capability of low-voltage networks for EVs and HPs by optimizing the flexibility of EV charging. The DDAO is used to optimize different aspects of EV charging and LV networks. The DDAO approach is implemented in three steps: (a) HP headroom assessment, (b) EV optimization, and (c) validation. The performance of the proposed method is validated on a MATLAB platform and compared with the existing Binary Spring Search Algorithm (BSSA), Manta Ray Foraging Optimization Algorithm (MRFO), and Chaos Game Optimization methods. In comparison to the total prices of 3.5£ in BSSA, 4.5£ in MRFO, and 5.5£ in CGO, the proposed DDAO method achieves a lower total price of 3.2£. This suggests that the DDAO method is more cost-effective than the existing techniques.

Changes in the Concentration of fat, Protein and Carbohydrates During the Excretion of Milk by Vacuum Breast Pump and Breast Pump with Compression

Background: The purpose of the work is to investigate the contents of the main nutrients of milk (fat, protein, lactose), as well as the energy value of milk in the process of expression milk using an vacuum electric breast pump and vacuum electric breast pump with compression pulses simultaneously. Method: Surveys were conducted on 9 women 5-8 weeks of lactation. The analysis was carried out in 10 ml samples of milk, which were continuously expression with breast pump «Laktopuls»: only with vacuum stimuli, and simultaneously with vacuum and compression stimuli. The sampling continuity was ensured by a specially developed procedure. Milk analysis in the samples was performed 1-1.5 hours after gland expression on a Miris AB Uppsala, Sweden, human milk analyzer. Finding: The largest changes in milk samples were observed in fat concentration. The fat content in milk increased in each subsequent sample and in the latter samples could exceed its content in the initial samples by 3-4 times. The total amount of fat was about 25% more when milk ejected by breast pump with vacuum and compression stimuli than with vacuum stimulus only. When determining the protein concentration in milk samples, an increase in its concentration was also found during milk ejection, but on 10-20%. In contrast to the dynamics of fat and protein concentrations, the carbohydrate concentration did not change in all women during the milk ejection. Conclusion: A method has been developed that allows analyzing milk nutrients during the continuous removal of milk from the gland. Surveys have shown that ejection of milk with breast pump with vacuum and compression stimuli, increases the amount of fat and protein in breast milk.

An open source experimental framework and public dataset for vibration-based fault diagnosis of electrical submersible pumps used on offshore oil exploration

An Electrical Submersible Pump (ESP) is an important equipment used in the industry for lifting liquids in various types of wells. An ESP is widely used in the oil industry for offshore exploration. Detecting a faulty ESP before installation is a predictive maintenance measure in order to extend its operational time. Machine learning fault diagnosis is an effective way for performing this task. Machine learning fault diagnosis algorithms are highly dependent of the availability of an appropriate problem dataset. This paper describes in detail the problem of ESP fault diagnosis and the ESPset dataset, a real-world and public dataset for vibration-based fault diagnosis of electrical submersible pumps used on offshore oil exploration. In addition, the paper also proposes an experimental framework for adequately comparing research works based on the ESPset dataset and defines benchmark classifiers and respective results as referential to the fault diagnosis research community. The framework considers the fact that some subset of samples are not drawn independently, and therefore, proposes a cross-validation sampling strategy that mitigates the similarity bias among samples. Indeed, this work shows that a conventional k-fold cross-validation may lead to a clear overestimation of the average performance. This fact is supported by results which show that the best classification model drops from a mean F-measure of 0.887 to 0.733 when removing the similarity bias from the data.