Electric Power Distribution Research Articles

Reduction of losses in electric power distribution system–dynamic reconfiguration case study

Reduction of losses in electric power distribution system–dynamic reconfiguration case study

Parametrized fragility, failure analysis and cost-effectively strengthening of electrical power distribution system under ice and concurrent wind hazards

In the past decades, the increasing frequency and severity of winter storms due to climate change have revealed the vulnerability of power systems against such events. This study aims to investigate the impact of ice and concurrent wind hazards on power systems by proposing a set of parametrized fragility functions for utility poles and conductors, and then applying them to system failure analysis and cost-effectively strengthening. The fragility model is generated by logistic regression to estimate the failure probability of poles and conductors as a function of hazard and structural characteristics. Applying proposed functions, system failure analysis is performed using IEEE123 testbed in four cities with various hazard characteristics. The results show that wind speed significantly affects pole rupture, while extreme ice and wind combination influences conductor breakage. Meanwhile, even minor structural failure leads to severe outages. Moreover, the risk-based Expected Outage Reduction (EOR) is calculated with developed fragility models as pole replacement ranking index. By comparing different replacement strategies, the 70-year system performance can be cost-effectively optimized by risk-based pole ranking and replacement pace and timing. Overall, the models and applications presented in this paper serve as a critical part of power system resilience analysis against winter-related hazards.

Short-term Load Forecasting using Genetic Algorithm based Artificial Neural Network

The electrical load Forecasting is considered one of the main and important points for the planning for the future in the electric power networks in both of transmission and distribution systems. In this paper, the genetic optimization algorithm (GA) with the artificial intelligent network (ANN) methods are proposed to forecast the electrical load of the building of Electrical Power Distribution office in the Iraq /Anbar Governorate. Moreover, the optimization method of GA is presented to train the ANN weights using Matlab software with simple programming code. Then, the proposed method is applied to the actual load and the results are compared with that obtained in traditional method such as ANN and regression trees (RG trees) methods. Hence, from the simulation results and the comparison, we can confirm that the proposed method is better than the traditional methods, and it achieved a good agreement with actual load and it provide a good total RMSE of 6% when compared with other methods

An overview of Applied Artificial Intelligence in Power Grids

Power grids are networks of lines and equipment used for electrical power generation, transmission, distribution, and consumption. Power grids are undergoing a process of digital transformation based on emerging information and communication technologies, with the aim of optimising the operation of power systems, increasing efficiency, reliability, sustainability, security, and quality of service, while reducing CO2 emissions. In this process of digital transformation, Artificial Intelligence (AI) has become a strategic element within power grids. This paper provides an overview of AI applications in power grids and how they can improve the efficiency, reliability, and availability of their processes. It analyses the main challenges and prospects for the future application of AI in power grids. AI applications help process large volumes of data and generate insights, with a focus on two primary functions: supporting operations and aiding strategic decision-making. These factors highlight the significant potential for AI application in power grids and solidify AI as a strategic key to the development of smart grids.

Decoupling carbon dioxide emissions from economic growth across multiregional supply chains within China

Decoupling carbon dioxide emissions from economic growth is crucial for China’s dual carbon targets and sustainable development. Amidst the backdrop of a new development paradigm emphasizing domestic circulation, the decoupling analysis of embodied carbon emissions and embodied value added among Chinese regions is vital for green multiregional supply chain management to reduce carbon emissions while keeping economic growth. This study first comprehensively explores the decoupling status of carbon emissions and economic growth at the provincial level. Moreover, we focus on the embodied carbon emissions and value added across multiregional supply chains within China during 2012-2017 from both demand and supply perspectives. Results illustrate that the decoupling status of 22 provinces (e.g., Beijing, Shandong, and Xinjiang) displays diversity across different perspectives. Furthermore, the different multi-perspective decoupling statuses of critical sectors (e.g., the production and distribution of electric power and heat power sector) underscore the necessity for multi-side measures to reconcile carbon emission reduction with economic growth. Notably, this study identifies the decoupling status of most interregional pairs (i.e., two regions with carbon emissions and value added flows) are undesirable. It highlights the importance of interregional cooperation for decoupling embodied carbon emissions from value added growth along multiregional supply chains. Our findings can provide valuable insights for policymakers in advancing carbon emissions decoupling from economic expansion to achieve national sustainable development.

Analysis of the Electric Power Distribution System Service Reliability Index at PLN Lancang Garam

The level of electrical energy demand in an area reflects the welfare of that community. The greater the use of electrical energy, the higher the community's standard of living, followed by the public's critical attitude towards everything that happens and influences lives, including electricity services. Generally, a network's reliability level can be seen from the size of the SAIDI and SAIFI values on the PLN (state electricity company) side. The calculation and reliability analysis results of the Lhokseumawe ULP above show that the total SAIFI index value is 2.68 times/year. The SAIFI Rayon Lhokseumawe reliability index is categorized as reliable because its value does not exceed the standard set by PLN in SPLN 68-2 of 1986, namely 3.2 times/year. The total value of the SAIDI index is 45.30 hours/year. The Lhokseumawe Rayon SAIDI reliability index is categorized as unreliable because its value exceeds the standard set by PLN in SPLN 68-2 of 1986, namely 21 hours/year. There are certainly differences in infrastructure, electrical equipment, and number of customers in 1986 compared to current years; this has a different influence on electricity reliability values such as SAIDI and SAIFI.

Dynamic stability analysis of the aircraft electrical power system in the more electric aircraft concept

This paper presents the modelling and analysis process of the electrical power management system on board an electrified aircraft conforming to the More/All Electric Aircraft concept. The main objective of the study is to assess the stability of the Electric Power Distribution System under different operational conditions, including the aspect of constant power loads. The paper describes the structure of the system, in which the key role is played by a synchronous generator and a three-phase rectifier converting alternating current AC into direct current DC. To solve the problem, modeling methods in the Matlab/Simulink environment were used, which enabled the analysis of the dynamic properties of the system and the assessment of stability under various types of loads. The obtained test results show that the presence of constant power loads can lead to instability in the EPDS system, manifested by voltage and current oscillations exceeding the standards specified in the MIL-STD-704E standard. The paper also provides tools for predicting system stability without the need to conduct full stability tests, which is important for the design of modern aviation power systems. The final section of the work presents the research results obtained and the resulting significant insights and practical conclusions.

Quantitative assessment of PM2.5-related human health impacts at the provincial level in China and analysis of its heterogeneity affected by economic structural transformation

Rapid economic development has led to massive fossil energy consumption and emissions of air pollutants such as PM2.5, which have severely impacted human health and the environment. By uncovering the primary regions and pivotal sectors of PM2.5-related human health impacts (PM2.5-HHI) and evaluating the influence of economic structural factors on them, we can facilitate a more targeted strategy for managing PM2.5 pollution sources. This study employs a structural decomposition analysis method based on input–output analysis to evaluate the impact of China’s provincial economic structural transformation and changes in final demand on PM2.5-HHI in the years 2012, 2015, and 2017. Results indicated that PM2.5-HHI is primarily concentrated in economically developed provinces (e.g., Shandong and Guangdong), which is compared to Shanghai, Heilongjiang, Liaoning, and Hebei experienced negative growth in PM2.5-HHI during 2007–2017. The production-based PM2.5-HHI is primarily driven by energy-intensive sectors such as the production and distribution of electric power and heat power. By contrast, the building sector is key to driving consumption-based PM2.5-HHI. An increasing number of regions are reducing PM2.5-HHI by implementing production structure changes. Moreover, the driving effect of production structure changes on PM2.5-HHI growth is strengthening in Beijing and Tianjin. Changes in the final demand structure mainly led to the growth of PM2.5-HHI in areas with higher economic development levels, such as Beijing and Shandong, but this driving effect is weakening. The final demand–driven PM2.5-HHI shows an evolutionary trend of an increasing share driven by fixed capital formation and exports and a decreasing share driven by household consumption. Changes in emission intensity play a key role in decreasing PM2.5-HHI in each region. Alternatively, changes in the structure of emission sources have a relatively minor impact on PM2.5-HHI. To mitigate PM2.5-HHI, regional economic and resource endowment advantages should be used to promote regional coordinated development and strengthen green production-process innovation in energy-intensive industries. Meanwhcile, it is necessary to optimize urban construction planning and improve the energy efficiency of buildings.

Power Flow Analysis in Unbalanced Three-Phase Distribution Systems using Backward/Forward Sweep and Current Injection Methods

The electrical power distribution system is a part of the power system that distributes electricity from the transmission network to customers. In the distribution system, imbalances often occur due to the varying load profiles in each phase. This can cause voltage imbalances in the distribution system. This study aims to compare two power flow analysis methods, Backward/Forward Sweep and Current Injection. The study analyses the voltage and power loss conditions on each phase at each bus and line in the three-phase distribution system under unbalanced conditions. Simulations were conducted on two IEEE test buses, IEEE 19-Bus and IEEE 33-Bus with radial configurations. The power flow calculation results using the Backward and Forward Sweep method showed that in the IEEE 19-Bus system, the highest voltage drop percentage occurred on phase b at bus 19, at 3.14%, the highest voltage imbalance percentage occurred at bus 19, at 0.1409%, and the total active and reactive power losses were 7.352 kW and 3.164 kVAR. In the IEEE 33-Bus system, the highest voltage drop percentage occurred on phase c at bus 18, at 5.85%, the highest imbalance percentage occurred at bus 15, at 0.2077%, and the total active and reactive power losses were 19.107 kW and 8.22 kVAR. The percentage difference between the two methods used is less than one percent, indicating that both methods are sufficiently accurate in analyzing power flow in an unbalanced distribution system.

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

Analysis of Relay Coordination in Electrical Distribution Networks with Optimally Positioned Distributed Generation Units

As power demand increases and transmission and distribution capacity is limited, Distributed Generation (DG) units are gradually being incorporated into electrical power distribution networks. However, this integration may result in a significant impact on the coordination of protective relays, potentially generating issues such as blindness of protection and false tripping of overcurrent relays. To overcome these issues, an adaptive protection method is presented that adjusts relay sensitivity in response to changes in network design. The suggested technique dynamically modifies relay settings based on the line current, which fluctuates depending on the system design. Digital relays are needed for this method in order to save operational data in real time. Based on periodic observations, the Plug Multiplier Setting (PMS) and Time Multiplier Setting (TMS) parameters are optimized using a Fuzzy Inference System (FIS). Fuzzy rules are used by the adaptive algorithm for every relay. These rules are produced using the fuzzy editor using the inputs provided. An initial investigation was carried out to evaluate the effect of DG on the coordination of relay protection., as well as the optimal placement of DG to reduce losses and improve voltage profiles, using two base distribution networks. The algorithm was further tested on various configurations of IEEE distribution networks, and its effectiveness in achieving optimal relay coordination with adaptive settings was validated through the relay coordination module in ETAP.

Power Distribution Transformer Maintenance Skills Required byElectrical Engineering Technology Students ofPolytechnics inNorth-East Nigeria

Electric power distribution industry in North-East, Nigeria is faced with inadequate number of power transformer maintenance personnel which has led to numerous abandonment of broken down power transformers. Transformer faults have been blamed for many power outages of long duration in many towns and communities in North-East, Nigeria.It is against this backdrop that thisstudy determined the power distribution transformer maintenance skills required by Electrical Engineering Technology students of polytechnics in North-East Nigeria. Three research questions and three hypotheses guided the study which employed a descriptive survey research design. The population of the study was 144 comprising of 135 Electrical Engineering Technology Lecturers, 3 Power Transformer Maintenance Technicians and 6 Electric Power Distribution Company-Based Supervisors. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Power Distribution Transformer Maintenance Skills Required Questionnaire (PDTMSRQ) developed by the researchers. The instrument was validated by three experts and a reliability of 0.74 was obtained using Cronbach Alpha reliability method. Mean statistic was used to answer the research questions while ANOVA was used to test the two null hypotheses of the study at 0.05 level of significance. The findings of the study revealed that the Electrical Engineering Technology students of polytechnics in North-East Nigeria required preventive, predictive and corrective maintenance skills. The study recommended the following: Lecturers should encourage the students’ skill acquisition process by exposing them to various maintenance activities in order for them to imbibe the different methods of power transformer maintenance; Students should be trained to recognize sounds and sights of faulty equipment in order to predict the parts or components that needed replacement or repairs.

Comparison Between Maintenance of Distribution Substations Using the Mobile Substation Unit (UGB) Method and Maintenance of Blackout Distribution Substations regarding Energy Not Served (ENS) which is not distributed to PLN UP3 Serpong

Electricity has become an important part of human life along with technological developments in this modern era. Economic progress, quality of life and the business world are very dependent on the electricity supply supplied by PT. PLN (Persero) UP3 Serpong. Electric power distribution is the final stage in the electric power delivery process. This is the process of distributing electric power from the electricity transmission system to electricity consumers. One of the main components in the electric power distribution system is the distribution substation. A distribution substation is an electrical installation device which contains disconnecting, connecting, safety and distribution transformer equipment to distribute electrical power according to consumer needs. Maintenance at distribution substations is carried out to support services to electricity consumers so that the substation equipment used is in reliable condition. and guaranteed continuity. Regular maintenance on distribution substations is important so that distribution substations do not experience damage and disruption while operating.

OPTIMAL DISTRIBUTION OF ADDITIONAL ELECTRICAL POWER

The problem of optimal distribution of additional electric power for private houses and households with solar panels or stations in adverse weather conditions over a long period of time is considered. This problem is very relevant for regions where the supply of electric power to houses and households is carried out mainly by green technology. Based on this, it is shown that when weather conditions worsen, which can last for a long time, a problem arises of providing these objects with energy allocated from backup power lines, the capacity of which is insufficient for full energy supply. To solve this problem, especially in the case of a shortage of allocated power, when it is not enough to satisfy all consumers to the maximum, the criteria for distributing the allocated limited power among houses are analyzed, examples are given for calculating the residual energy of a deep-discharge battery for each household in the event of prolonged bad weather conditions. Two options for solving the problem of optimal distribution of the allocated power are considered, taking into account the full simulation model of houses and households with electricity supply from solar panels: - distribution by residual energy taking into account the energy required to charge the batteries of private houses; - distribution by residual energy without charging the batteries. The solution to the problem of distributing limited energy between houses with solar panels is shown using the example of 5 households. For this purpose, the initial conditions and restrictions for each house are modeled. The problem of optimal distribution of allocated energy between these objects is formulated. The model of the problem itself is built and solved using the SOLVER subroutine of the Excel program. The main tables are given, in which it is necessary to enter restrictions, optimization criteria and the objective function, screenshots of the solution of this problem in two versions with the introduction of all restrictions in separate Excel windows. It is shown that solution of the issue of optimal distribution of additional energy allocated to eliminate the shortage of energy in conditions of limitation allows to the following opportunities: - provision of additional energy depending on the time of complete depletion of energy in the house prevents overloading of the electric network, incorrect openings in power lines and other unpleasant situations; - the inclusion of the module of optimal distribution of additional energy into the model during simulation, allows to save time for the seeking of additional energy and thus to increase the overall efficiency of the system. Keywords: solar panels, bad weather, additional energy, optimal distribution.

Cost - benefit analysis of DISCOs by Optimal allocation of DGs and DSTATCOM using GTO algorithm

Electric power Distribution Companies (DISCOs) provide low-cost, reliable, and consistent voltage power from distribution substations. DISCOs have radial transmission lines with all buses as load buses and no generating buses. The voltage at each bus decreases, resulting in larger network losses and voltage variations, which lowers DISCO and customer costs and benefits. This study strategically uses Distributed Generation (DGs) and DSTATCOM to optimize cost-benefit analysis and voltage stability for Distribution Companies (DISCOs). We planned DG unit and DSTATCOM deployment using a new and comprehensive Group Teaching Optimization (GTO) method in this study. The algorithm considers Distribution Company and DG Owner profitability simultaneously. Group Teaching Optimization (GTO) is used on a 33-node test system. MATLAB simulations show the GTO's usefulness in Distribution System Operators.

Efficiency Of Generator Set On Changes In Electrical Load On Fishery Vessels

Electrical energy is an essential requirement for fishing vessels, and it is primarily generated by diesel-driven electric generators. This energy is utilized for various purposes onboard such as lighting, navigation, and supporting fishing activities including loading and unloading. The efficient operation and longevity of the electric generator onboard depend on ensuring that the load imposed on it does not surpass its capacity. Researchers conducted several phases in this study, including identifying the electrical power distribution system, determining the generator capacity and the electrical load it receives, and calculating the electrical load under various operational conditions. Additionally, the study examined the operational efficiency of the generator under four different conditions. The findings reveal that the fishing vessel employs a radial electrical distribution system and is equipped with two electric generators, each having a capacity of 225 kVA. The generators demonstrate efficient performance across all operational scenarios, with load factors ranging from 79.70% to 83.08%.

Design and Testing of a Multi-Cylinder Piezopump for Hydraulic Actuation

Hydraulic actuation systems are widely used in industries such as aerospace, the marine industry, off-highway vehicles, and manufacturing. There has been a shift from the hydraulic distribution of power from a centralized supply to electrical power distribution, to reduce the maintenance requirements and weight and improve the efficiency. However, hydraulic actuators have many advantages, such as power density, durability, and controllability, so the ability to convert electrical to hydraulic power locally to drive an actuator is important. Traditional hydraulic pumps are inefficient and unsuitable for low-power applications, making piezopumps a promising alternative for the conversion of electrical to hydraulic power in the sub-100 W range. Currently, the use of piezopumps is limited by their maximum power (typically a few watts or less) and low flows. This paper details the design, simulation, and testing of a multi-cylinder piezopump designed to push the envelope of the power output. The simulation results demonstrate that pumps with two or three cylinders show increasing benefits in terms of hydraulic and electrical performance due to the reduced flow and current ripple compared to a single-cylinder pump. The experimental results from a two-cylinder pump confirm this, and the effect of the phase relationship between the drive signals is investigated in detail. The experimental pump has fast-acting disc-style reed non-return valves, allowing piezostack drive frequencies of up to 1.4 kHz to be used. Custom power electronics tailored to the pump are developed. These features are critical in demonstrating the potential for multi-cylinder piezopumps to play an important role as a future actuation solution.

Sliding mode control based dynamic voltage restorer for voltage sag compensation

The reliability and efficiency of electrical power systems are critical for modern industries, which increasingly rely on electric machines and devices. However, power electronic converters add non-linear demands to the electric power distribution systems (EPDS), that can cause power quality (PQ) problems, which could harm electric devices. As a result, EPDS are pointed for the most effective and economical techniques to enhance PQ. Therefore, Dynamic Voltage Restorer (DVR) is employed to maintain voltage stability in modern EPDS. This study focuses on the application of Sliding Mode Control (SMC) in DVRs to protect against voltage dips, enhancing power system sustainability. It details the construction, operation, and control methods of DVRs, comparing the conventional PI-controller with SMC. The arctic puffin optimizer is utilized to get the optimum gains of the PI regulator, and at the same time, it is used to specify the optimal sliding surface required for SMC. A distribution system contains DVR is suggested and implemented utilizing Simulink/MATLAB. The simulation results of the DVR employing SMC and the conventional PI-controller demonstrate a marked improvement in voltage regulation and power quality. Specifically, the SMC-based DVR exhibited a reduction in voltage sag duration and magnitude by approximately 30 % compared to the PI-controller. Additionally, the total harmonic distortion (THD) in the load voltage was reduced by 25 %, indicating a significant enhancement in power quality. These improvements of the enhanced performance of the SMC-based DVR supports the seamless integration of renewable energy sources, which often introduce variability into the power grid.

Comparative experimental study of structural behavior and economic aspects of circular pre-stressed and H-shaped reinforced concrete electrical power distribution poles

Comparative experimental study of structural behavior and economic aspects of circular pre-stressed and H-shaped reinforced concrete electrical power distribution poles

Predictive Modeling and Forecasting of Solar Power Generation Using Machine Learning Techniques

The purpose of this research study is to investigate the predictive modelling and forecasting of solar power generation by utilising a variety of machine learning approaches. The paper addresses the significant obstacle that is presented by the variable production of solar energy, which is a barrier to the effective incorporation of solar power into the network of electrical power distribution systems. This research investigates the application of machine learning models such as Bayesian Ridge Regression, Gradient Boosting, and Linear Regression. The research makes use of historical meteorological data and solar power output to investigate the application of these models. The effectiveness of these models in estimating the amount of solar energy that will be produced under a variety of different weather situations is being investigated. Based on the findings, it is evident that the application of machine learning techniques has the potential to considerably improve the accuracy of solar power projections. This, in turn, will facilitate better grid integration and promote wider adoption of solar energy products. Through the demonstration of the potential of advanced machine learning approaches in enhancing the reliability and efficiency of solar power generation, this study makes a contribution to the expanding body of literature on the forecasting of renewable energy sources.