Electrical Research Articles

An analysis of circuit breaker capacity according to IEC 60909 and IEC 60059 standards at the PT Borneo Alumina Indonesia electrical system

At PT Borneo Alumina Indonesia (PT BAI), the D01 Power Distribution System uses circuit breakers (CB) with a current rating capacity of 1250A for both incoming and outgoing 10kV busbars. Replacing outgoing CBs with the same capacity during maintenance leads to unnecessary waste. This study was conducted to examine the appropriate CB capacity as a substitute for the old CBs in the outgoing 10 kV busbars for loads of 10 kV induction motors and transformers so that recommendations can be made to improve efficiency during maintenance on the D01 Power Distribution System. The Electronic Transient Analyzer Program (ETAP) 19.0.1 software was used to perform short circuit simulations based on IEC 60909 and load flow simulations, which were then adjusted to IEC 60059 and CB products from Siemens. The results show that we can replace the current Siemens CB, which has a rated breaking current specification of 31.5kA, with 25kA during the maintenance period. We can also replace the rated peak value of 80kA with 63kA, and the rated current of 1250A with 630A. Replacement of CB specifications does not change/modify the switchgear panel because the replacement CB matches the brand, dimensions, and size of the currently installed CB. These findings would also help increase efficiency during maintenance on the D01 Power Distribution System. The discussion includes limitations and recommendations for future research.

Research on application of electric submersible pumps to increase oil recovery field Y, lot 05-1a, the Nam Con Son basin

The application method of Electric Submersible Pump increases the oil recovery factor Y at 05-1A block, the Nam Con Son basin, which is affected by many factors such as energy regime, fluid regime, fractures, etc. All these factors are based on the simulation model from Prosper simulation software. The process starts from the appropriate PVT data, exploitation data, selecting necessary parameters, designing wells, and predicting exploitation to find the best case for the Electric Submersible Pump exploitation plan. The results showed that if oil is only exploited by natural energy, it will not be effective, so it is possible to equip a Electric submersible pump system with 2 exploitation wells for the most efficient exploitation. The Electric Submersible Pump is applied and effective in the case of wells with high WTC and low GOR.

Optimizing Word Embeddings for Patient Portal Message Datasets with a Small Number of Samples.

Patient portal messages often relate to specific clinical phenomena (e.g., patients undergoing treatment for breast cancer) and, as a result, have received increasing attention in biomedical research. These messages require natural language processing and, while word embedding models, such as word2vec, have the potential to extract meaningful signals from text, they are not readily applicable to patient portal messages. This is because embedding models typically require millions of training samples to sufficiently represent semantics, while the volume of patient portal messages associated with a particular clinical phenomenon is often relatively small. We introduce a novel adaptation of the word2vec model, PK-word2vec, for small-scale messages. PK-word2vec incorporates the most similar terms for medical words (including problems, treatments, and tests) and non-medical words from two pre-trained embedding models as prior knowledge to improve the training process. We applied PK-word2vec on patient portal messages in the Vanderbilt University Medical Center electric health record system sent by patients diagnosed with breast cancer from December 2004 to November 2017. We evaluated the model through a set of 1000 tasks, each of which compared the relevance of a given word to a group of the five most similar words generated by PK-word2vec and a group of the five most similar words generated by the standard word2vec model. We recruited 200 Amazon Mechanical Turk (AMT) workers and 7 medical students to perform the tasks. The dataset was composed of 1,389 patient records and included 137,554 messages with 10,683 unique words. Prior knowledge was available for 7,981 non-medical and 1,116 medical words. In over 90% of the tasks, both reviewers indicated PK-word2vec generated more similar words than standard word2vec (p=0.01).The difference in the evaluation by AMT workers versus medical students was negligible for all comparisons of tasks' choices between the two groups of reviewers (p = 0.774 under a paired t-test). PK-word2vec can effectively learn word representations from a small message corpus, marking a significant advancement in processing patient portal messages.

Coordinated Reconfiguration with Energy Storage System for Load Restoration in Integrated Electric and Heating Systems

Coordinated load restoration of integrated electric and heating systems (IEHSs) has become indispensable following natural disasters due to the increasingly relevant integration between power distribution systems (PDS) and district heating systems (DHS). In this paper, a coordinated reconfiguration with an energy storage system is introduced to optimize load restoration in the aftermath of natural catastrophes. By modifying the DHS network topology, it is possible to maintain an uninterrupted energy supply in unfaulty zones by shifting heat loads among sources and adjusting the operation of coupled devices. Additionally, energy storage systems with rapid response times are implemented to enhance load restoration efficiency, especially when working in conjunction with multiple energy sources. Comprehensive case analyses have been systematically conducted to demonstrate the impact of coordinated reconfiguration with energy storage systems on improving load restoration.

Hybrid optimal-FOPID based UPQC for reducing harmonics and compensate load power in renewable energy sources grid connected system.

Integration of renewable energy sources (RES) to the grid in today's electrical system is being encouraged to meet the increase in demand of electrical power and also overcome the environmental related problems by reducing the usage of fossil fuels. Power Quality (PQ) is a critical problem that could have an effect on utilities and consumers. PQ issues in the modern electric power system were turned on by a linkage of RES, smart grid technologies and widespread usage of power electronics equipment. Unified Power Quality Conditioner (UPQC) is widely employed for solving issues with the distribution grid caused by anomalous voltage, current, or frequency. To enhance UPQC performance, Fractional Order Proportional Integral Derivative (FOPID) is developed; nevertheless, a number of tuning parameters restricts its performance. The best solution for the FOPID controller problem is found by using a Coati Optimization Algorithm (COA) and Osprey Optimization Algorithm (OOA) are combined to make a hybrid optimization CO-OA algorithm approach to mitigate these problems. This paper proposes an improved FOPID controller to reduce PQ problems while taking load power into account. In the suggested model, a RES is connected to the grid system to supply the necessary load demand during the PQ problems period. Through the use of an enhanced FOPID controller, both current and voltage PQ concerns are separately modified. The pulse signal of UPQC was done using the optimal controller, which analyzes the error value of reference value and actual value to generate pulses. The integrated design mitigates PQ issues in a system at non-linear load and linear load conditions. The proposed model provides THD of 12.15% and 0.82% at the sag period, 10.18% and 0.48% at the swell period, and 10.07% and 1.01% at the interruption period of non-linear load condition. A comparison between the FOPID controller and the traditional PI controller was additionally taken. The results showed that the recommended improved FOPID controller for UPQC has been successful in reducing the PQ challenges in the grid-connected RESs system.

High-Frequency and High-Current Transmission Techniques for Multiple Earth Electrical Characteristic Measurement Systems Based on Adaptive Impedance Matching through Phase Comparison.

With the increase in groundwater exploration, underground mineral resource exploration, and non-destructive investigation of cultural relics, high-resolution earth electrical characteristic measurement has emerged as a mainstream technique owing to its advantageous non-destructive detection capability. To enhance the transmission power of the high-frequency transmitter in high-resolution multiple earth electrical characteristic measurement systems (MECS), this study proposes a high-frequency, high-current transmission technique based on adaptive impedance matching and implemented through the integration of resonant capacitors, a controllable reactor, high-frequency transformers, and corresponding control circuits. A high-current precisely controllable reactor with a 94% inductance variation range was designed and combined with resonant capacitors to reduce circuit impedance. Additionally, high-frequency transformers were employed to further increase the transmission voltage. A prototype was developed and tested, demonstrating an increase in transmission current at frequencies between 10 and 120 kHz with a peak active power of 200 W. Under the same transmission voltage, compared to the transmission circuit without impedance matching, the transmission current increased to a maximum of 16.7 times (average of 10.8 times), whereas compared to the transmission circuit using only traditional impedance matching, the transmission current increased by a maximum of 10.0 times (average of 4.2 times), effectively improving the exploration resolution.

POWERING THE EV WITH DYNAMOS

The dynamo, traditionally used in bicycles to convert mechanical energy into electrical energy, can be adapted for use in EVs to supplement battery power. As the vehicle moves, the dynamo harnesses kinetic energy, converting it into electricity to recharge the EV's battery. This regenerative braking mechanism not only extends the vehicle's range but also reduces reliance on charging stations, enhancing the EV's practicality and autonomy. The integration of a dynamo system introduces a sustainable element by utilizing kinetic energy that would otherwise be dissipated as heat during braking. By capturing and converting this energy, EVs equipped with dynamos can operate more efficiently, reducing overall energy consumption and carbon emissions. This abstract explores the technical feasibility, potential benefits, and challenges associated with implementing a dynamo system in EVs. Considerations such as optimal placement, efficiency optimization, and compatibility with existing battery and electrical systems are discussed. Additionally, the economic viability and environmental impact of widespread adoption of dynamo-equipped EVs are evaluated. The integrating dynamos into EVs presents a promising avenue to address range anxiety and charging infrastructure limitations, while also promoting sustainability and energy efficiency in the transportation sector. Further research and development are warranted to optimize the design and implementation of this innovative solution for powering electric vehicles.

Integration of Building Services in Modular Construction: A PRISMA Approach

Modular construction is gaining worldwide attention. Building services are the systems that make buildings comfortable and efficient. Mechanical, electrical, and plumbing (MEP) systems provide heating, cooling, lighting, energy distribution, and water services. Modular construction integrates building services into prefabricated modules at the manufacturing site, which are then transported to the construction site. A systematic review (SR) of building services integration within modular construction is thus necessary. Prior to the SR, a quantitative analysis of the retrieved 115 publications from the Scopus database was explored. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide, 13 relevant publications from the Scopus database and via the Research Rabbit application were profiled. MEP systems integration approaches and research needs were examined. Recent literature emphasizes technology integration and sustainability, while early studies laid the framework for modular approaches. The findings highlight the potential of digital technologies like building information modeling (BIM) and recommend a holistic framework for the entire building’s lifetime, from design to operation. Future research directions include performance studies, modular building service adaptation, and industry-wide standards building. Researchers and practitioners seeking to improve modular construction methods and integrate complex building services will gain insights from this study.

Simulation Of Electrical Load With An Over Load System Shedding

A break in one of the circuits in a double circuit transmission network system can cause the system to overload. Where excess load on the transmission network system can cause quite dangerous impacts. The Over Load Shedding system is a protection system for the electric power transmission network which aims to prevent widespread blackouts by removing certain predetermined loads. By using the Arduino Mega 2560 as a control center, security against load abnormalities can be carried out and can be monitored automatically using Scada, apart from that, PMT taping for load balancing can also be done using Scada. The Arduino Mega 2560 will read the current on each phase wire using the ACS712 current sensor to detect the load of one of the circuits to be aware of whether it is overloaded or not. This simulator tool has carried out several measurements and tested how to operate it. This tool has a fairly high level of work accuracy, reaching 85% success in operation. So it is hoped that this tool can provide knowledge to the wider community about over load shedding systems in maintaining the electric power transmission system properly and easily to understand.

Enhancing the performance of zero energy buildings with boosted coyote optimization and elman neural networks

This research paper aims to optimize electrical system modeling to minimize CO2 emissions, improve reliability, and reduce costs. It proposes integrating Elman Neural Network (ENN) and metaheuristic algorithm to evaluate environmental impact and optimize system performance. The goal is to create a mathematical model considering renewable energy sources, grid energy, CO2 emissions, and maintenance costs, and use metaheuristic algorithms to determine the optimal combination of wind turbines and photovoltaic panels. To train the ENN model and optimize the system, a three-stage approach is proposed. Quasi-random numbers are generated using Sobol techniques to create input data, which is then processed by TRNSYS for simulation. The proposed approach incorporates an ENN with Boosted version of the Coyote optimization algorithm to accurately model and predict the behavior of Hybrid Renewable Energy Systems for Zero Energy Buildings. The results demonstrate the effectiveness of the proposed approach. Our method significantly reduces energy consumption, costs, and system reliability, achieving a 25% reduction in energy consumption and 12% reduction in system cost, and is effective in optimizing hybrid solar/wind and hydrogen storage systems. The optimal system configuration significantly reduces CO2 emissions, achieves a high level of system reliability, and minimizes costs. Comparative analysis with previous studies showcases the superiority of the proposed method in achieving comprehensive optimization. In conclusion, this study combines AI techniques and metaheuristic algorithms to optimize electrical system design, considering environmental, technical, and economic factors. The proposed approach overcomes limitations in previous studies and offers an effective solution to address the optimization problem.

Trend Research on Maritime Autonomous Surface Ships (MASSs) Based on Shipboard Electronics: Focusing on Text Mining and Network Analysis

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry.

Transactive energy management system for smart grids using Multi-Agent Modeling and Blockchain

Technological approaches for effective energy regulation are required due to incorporating contemporary electrical systems with sustainable power resources. Our article suggests a Transactive Energy Managing System (T.E.M.S.) using Blockchain-based technologies and Multiple-Agent Modelling (M.A.M.) to improve the long-term viability and dependability of energy-efficient grids. The framework utilizes a decentralized methodology enabled by self-governing agents. These units include services, sellers, and customers in the electricity system. Such entities engage in vibrant interactions, trading energy according to current economic circumstances, choices, and facts. Blockchain-based technology promotes a more robust and decentralized power industry by eliminating the demand for centralized mediators and improving information security. The suggested T.E.M.S. aims to tackle issues such as demand-response administration, incorporation of sustainable energy resources, and grid reliability. The efficiency of the mechanism in maximizing power use, lowering load spikes, and fostering an improved power environment is proved via modelling and evaluation. The research advances intelligent grid technology by providing an extensive, decentralized approach that aligns with the changing power industry. In this era of connected layouts, integrating Blockchain-based technologies with Multiple-Agent Modelling offers a solid basis for creating flexible and adaptable power control technologies.

Detailed modelling and performance analysis of power flow topology in a hybrid electric vehicle having series-parallel architecture

Despite massive investment and carbon-neutral transition goals set around the world, gasoline-based internal combustion engine vehicles still form an absolute majority in the transportation sector. With the advent of technology, access to electricity, uncertainties in fuel prices and health awareness, people worldwide are moving towards a better, reliable, cost-effective and environmentally friendly mode of transportation, a hybrid electric vehicle. Such a vehicle, with its powerful electric motor and compact gasoline-based engine, offers better efficiency in terms of operating cost and reliability. Considering the advent of hybrid electric vehicles taking pace, studies related to its architecture types, power flow dynamics, control and modelling of its various components will form an essential part of the automobile industry and research. This paper proposes a power flow topology for a hybrid electric vehicle with series-parallel architecture. The developed vehicle model with such an architecture type consists of three main sub-systems: the electrical system, the control system and the mechanical system. The presented power flow topology being modelled and analysed in detail in the Simulink tool is being implemented via a mode logic controller, which forms part of the central control system. The developed hybrid electric vehicle model demonstrates various modes of operation, from starting to accelerating to de-accelerating and then finally coming to a complete rest. Each mode yields and explains the following: the vehicle reference speed; the engine and generator turn functions on/off; the dc bus and battery voltage; the motor, battery, and generator current; the motor, generator, and engine speed; engine torque; engine power; throttle demand; and the vehicle’s actual speed. The results thus obtained show that the waveforms associated with such topology, during its various modes of operation, are pretty stable and acceptable, thereby depicting and validating the operation of the developed hybrid electric vehicle model with proposed power flow topology in a precise and transparent manner.

Voltage Optimization on Low Voltage Distribution Transformer Zones Using Batteries in Uganda

In the context of Uganda's rapidly growing energy demands and the need for sustainable solutions, this study explores the implementation of voltage optimization techniques in Low Voltage (LV) distribution transformer zones. The research focuses on the innovative integration of batteries to optimize voltage levels, thereby enhancing the efficiency and reliability of the electrical distribution system. By analyzing real-time data from various LV transformer zones in Uganda, this study investigates the impact of voltage fluctuations on the overall power distribution network. The research methodology involves the design and deployment of battery energy storage systems (BESS) strategically placed within LV distribution transformer zones. These BESS units are utilized to store excess energy during periods of low demand and release it during peak hours, ensuring consistent voltage levels and minimizing losses in the distribution network. The study evaluates the effectiveness of this approach through extensive simulations and on-site experiments, considering factors such as battery capacity, charging/discharging rates, and load variations. A comprehensive cost-benefit analysis is conducted to evaluate the potential financial savings and environmental impact associated with this sustainable energy solution. The findings of this research indicate significant improvements in voltage regulation, reduced system losses, and enhanced reliability in LV distribution transformer zones. Additionally, the study demonstrates the feasibility of integrating batteries into the existing infrastructure, thereby contributing to the optimization of the energy distribution system in Uganda. The outcomes of this research provide valuable insights for policymakers, utility companies, and researchers, emphasizing the importance of embracing innovative technologies to address the energy challenges faced by developing nations like Uganda.

Full-Scale Modeling and FBGs Experimental Measurements for Thermal Analysis of Converter Transformer.

As the imbalance between power demand and load capacity in electrical systems becomes increasingly severe, investigating the temperature variations in transformers under different load stresses is crucial for ensuring their safe operation. The thermal analysis of converter transformers poses challenges due to the complexity of model construction. This paper develops a full-scale model of a converter transformer using a multi-core high-performance computer and explores its thermal state at 80%, 100%, and 120% loading ratios using the COUPLED iteration method. Additionally, to validate the simulation model, 24 FBGs are installed in the experimental transformer to record the temperature data. The results indicate a general upward trend in winding the temperature from bottom to top. However, an internal temperature rise followed by a decrease is observed within certain sections. Moreover, as the loading ratio increases, both the peak temperature and temperature differential of the transformer windings rise, reaching a peak temperature of 107.9 °C at a 120% loading ratio. The maximum discrepancy between the simulation and experimental results does not exceed 3.5%, providing effective guidance for the transformer design and operational maintenance.

Traveling-wave-based Fault Location using Time Delays between Modal Components in Electrical Distribution Systems

Background: The distribution feeders usually include many laterals, too many sensors need to be installed to locate the fault positions in electrical distribution systems by using the traditional double-ended traveling-wave-based fault-location methods Objective: Fault location based on the time delays between the moments of the first wavefronts of zero-mode voltages and that of aerial-mode voltages arriving at one end of the distribution feeder. Methods: Large decomposition level in wavelet transformation is adopted in this paper to obtain the zero-mode velocity. Results: Both theoretical research and simulation experiments have proved that the proposed fault location method can greatly improve the accuracy of the fault location in the electrical distribution systems without many sensors. Conclusion: The proposed fault location method can provide one feasible way to locate the faults in the electrical distribution systems.

Computational study of the effect of the number of splitter plates considering blow-out coil voltage in arc chamber of moulded case circuit breaker

To attain current limitation in low voltage circuit breaker (LVCB) apparatus, the approach involves elevating the arc voltage. This improvement is realised primarily in the arc chamber's splitter plates because of the higher drop voltages at the anode and cathode sheath areas. One of the most crucial ways to better describe arc behaviour in LVCB is to account for voltage in the vicinity of the electrodes. To reduce the current, LVCB works by raising the arc voltage. Voltage is raised in several ways, including increase in losses, elongating the arc, and multiplying the splitter plates. The aim of this research is to present readers with an additional technique for analysing electric arcs in moulded case circuit breaker (MCCB) which is one of the types of low voltage circuit breakers which is used to prevent overload and short circuit in electrical systems. 2-D model of MCCB with splitter plate has been developed and simulated using ANSYS software. Simulation results with the effect of increase in the number of Splitter plates considering blow-out coil voltage in arc chamber has been discussed. Modelling and simulation technologies provide the mechanism through which this understanding may be attained.

Enhancing control disorder and implementing V2X-Based suppression methods for electric vehicle CO2 thermal management systems

In recent years, the development of electric vehicles (EVs) thermal management systems has underscored the crucial role in ensuring driving safety and optimizing driving range has become increasingly prominent. However, the inherent dynamic complexity of EV operation coupled with automatic control systems, can sometimes lead to unstable behavior, resulting in performance degradation and safety risks for compressors and batteries. To effectively address this issue, an evaluation was conducted on the dynamic control characteristics of an EV thermal management system utilizing CO2 as the refrigerant in this study. Through mathematical modeling and experimental analysis, the erratic nature of the dynamic thermal process was first identified. The underlying reasons were elucidated, focusing on system control characteristics and intrinsic mechanisms. It was found that control disorder could induce abnormal actions in thermal management system components like compressors and expansion valve, leading to significant performance decline and issues such as liquid carryover in compressor suction. Furthermore, specific control disorder regions of CO2 heat pumps for EVs were delineated, providing a framework for assessing the likelihood of system control disorder. Notably, control disorder was more likely to occur under conditions of low indoor air flow rate, high ambient temperature, and low supply air temperature. Given the widespread nature of this issue and the lack of suitable solutions, two control disorder suppression schemes were developed using V2X technology and validated through simulation. Results showed that adoption of V2X communication technology prevented an average of 70.1 % COP degradation, ensuring stability and safety of compressors and batteries under various operating conditions. The research provides useful information for exploring the dynamic characteristics of CO2 thermal management systems, offering a novel approach to enhance the system stability and efficiency.

Brief Analysis of the Location and Determination of Maximum Capacity of Distributed Generation in Electrical Systems Considering Demand Scenarios in Ecuador

This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.

Design of Radial Distribution System to Study Load-Flow and Short Circuit Analysis Using ETAP Software

This work's analysis provides information on fault analysis and load flow studies, which help to understand how radial distribution networks operate both in normal and emergency circumstances. The research explores optimization methods, describing capacitor placement, transformer size, and feeder reconfiguration in order to lower losses and increase system efficacy. Moreover, the work looks at reliability-focused maintenance techniques, stressing the need of predictive maintenance and condition monitoring in raising the dependability of radial distribution networks. Modern electrical power networks depend heavily on their radial distribution system, which effectively and dependable distributes electricity to customers. Many radial distribution system-related topics are covered in detail on this website, including analytical methods, optimization processes, and dependability enhancement strategies. Power flow, voltage control, and losses in radial networks are investigated in this paper under a range of operational situations. The last and crucial stage of a power system, the electrical distribution system makes sure that customers have a steady supply of electricity. Regretfully, because it is often neglected and exposed to environmental risks, our distribution network has a number of problems. Our distribution feeder is rigorously analyzed for load flow and short-circuits in order to overcome these challenges and find and fix problems. The main topics of this study are the load flow and short-circuit analysis of a radial distribution feeder, which is mostly constructed with (ETAP). The final and crucial part of a power system, the electrical distribution system ensures that consumers receive an uninterrupted and steady supply of electricity. Our distribution feeder undergoes a rigorous and cautious process to address these issues front on because of the uncontrollable nature and susceptibility of our network to environmental hazards. This paper delves deeply into the issues of short-circuiting and load flow in radial distribution feeders. Both symmetrical and asymmetrical faults are thoroughly investigated by mathematical computations and ETAP simulations; the results of the software simulations are contrasted with those of the mathematical analysis. These discoveries will definitely influence the development and management of our system and point us in the direction of workable and practical solutions.