Electric Power Distribution Networks Research Articles

External Multi-Gap Lightning Arrester Modeling Using the Integration Method

Electric power distribution networks are exposed to both internal and external disturbances. Lightning strikes are among the latter and are responsible for a significant percentage of damage in distribution transformers, especially in rural areas. Electric utilities must pay special attention to prevent damage and service interruption due to these unforeseeable events. In this context, Surge Protection Devices (SPDs) combined with a series of external air gaps are designed to safeguard electric equipment and systems from transient over-voltages. There are several well-known models of SPDs in the specialized literature; nonetheless, few studies have been carried out with external gaps and multi-gaps. The main contribution of this paper is a methodology to model the disruptive effect in an external air gap by determining the parameters of Kind’s and Chowdhuri’s models using the integration method. The adjustment of the model parameters is carried out by a genetic algorithm (GA). The proposed model was tested and validated using experimental measurements, and its capability to predict the time-to-breakdown under different impulse voltages was verified.

Reconfiguration of low-voltage distributed power sources within electric power's distribution network based on improved particle swarm-fish swarm fusibility algorithm

With the development of distributed power sources in the distribution network, the algorithm of distribution network reconfiguration is gaining attention from experts and scholars. Its goal is to reduce the power loss during power transmission, so as to reduce the power grid loss during power transmission. And weaken the electric heating effect in the process of electric energy transmission, thus maintaining the safety of the surrounding residents. Due to the wire impedance effect, a lot of electric energy of the circuit is lost to electric heating, which is easy to cause local overheating and lead to fire. This will not only cause power loss, but also endanger the safety of surrounding residents. To address the issue, experiments on distribution grid reconstruction are performed using the enhanced particle swarm-fish swarm algorithm with the Elecgrid self-constructed dataset. Initially, low-voltage distributed power sources in parallel are connected to the circuit, thereby decreasing internal resistance and electrical heat. Then, by controlling the circuit in the system, the double separation relay adjusts the inductance and capacitance of the conductor, thus reducing the reactance length. Additionally, particle swarm particles are mutated to enable them to jump out of the local optimum, and elite fish approach is used to expand the search area. Finally, the proposed fusion algorithm is applied to the self-built data set of Elecgrid and compared with the other three algorithms. The fusion algorithm serves as the standard test system for this comparison. The active power loss of the hybrid algorithm is 63 kW at an operating voltage of 0.74 V. The loss work of the other three algorithms is 74 kW, 97 kW and 109 kW respectively. The mixed algorithm has the lowest loss among the four algorithms. The experiments are repeated for six times, and the linear fitting degrees of the four algorithms are 0.9804, 0.9527, 0.9612 and 0.9503, respectively. The experimental results show that the application of this algorithm can effectively reduce the active loss in the process of distribution network reconfiguration, thus reducing energy consumption; At the same time, it can reduce the electric heating in the process of electric energy transmission, and then prevent the occurrence of fire. There are three main contributions of this study. Firstly, the resistance in the transmission path is reduced by using this algorithm, so that the power transmission efficiency can be analyzed more accurately. Secondly, the new algorithm enriches the power safety maintenance method; Finally, the fire caused by local overheating of the line is reduced by fusion algorithm.

Impact of EV charging on electrical distribution network and mitigating solutions – A review

AbstractRapidly increasing uptake of Electric Vehicles (EVs) is expected to have a significant impact on electrical power distribution networks. Considerable work has been carried out to understand this impact and quantify the distribution networks hosting capacity, with and without network management solutions. However, the current body of knowledge does not have a comprehensive review of the research done to‐date on this topic which is vital to understand the scope of the existing studies, the data used in analysing the impact, and, most importantly, the findings. A comprehensive yet focused review of impact of EV charging on distribution networks is presented by delving into the main factors restricting EV hosting capacity and the strategies used to maximise EV hosting capacity by managing the aforementioned impacts. The authors comprehensively summarise the approaches used to quantify the impact, network and data types, and the proposed solutions to increase network hosting capacity. Moreover, the shortcomings in the existing work are identified and recommendations for future research are provided to help stakeholders understand the current state‐of‐the‐art, make informed decisions, and to be considered by future researchers.

Study of the Dynamic Behavior of a DSTATCOM Installed in a Distribution Network in Case of Single and Multiphase Short Circuit

This paper investigated the behavior of a DSTATCOM in the face of contingencies that may arise in an electrical power distribution network. It shows that DSTATCOM is an effective attenuator to limit the impacts of perturbations on the distribution network. The model equations are simulated in a MATLAB environment and have shown that the exactitude of the network parameters is a key determinant of the resilience of a disrupted distribution network in the presence of an optimally positioned DSTATCOM at a node. The capacitive current of the DSTATCOM increases inversely with a bad power factor and a critical voltage drop. The DSTATCOM in a network is therefore more solicited when the operating parameters of this network degrade as a result of single-phase or multiphase short circuits. Distribution system operators must therefore design protection plans that truly and sustainably limit the impacts of short circuits on distribution systems.

Efficient cloud-based digital-physical testing method for feeder automation system in electrical power distribution network

Efficient cloud-based digital-physical testing method for feeder automation system in electrical power distribution network

Optimal Planning of Fast EV Charging Stations in a Coupled Transportation and Electrical Power Distribution Network

Optimal Planning of Fast EV Charging Stations in a Coupled Transportation and Electrical Power Distribution Network

Analysis of Radial Distribution Systems by using Particle Swarm Optimization under Uncertain Conditions

Abstract: Losses in the network are one of the most important parts of a power distribution network, and work should be done to lower their value. The research used the Particle Swarm Optimisation (PSO) metaheuristic algorithm to investigate the impact of concurrently optimising phase balance and conductor size on the planning issues and objective functions of an imbalanced distribution system. These objective functions include power loss, voltage unbalance, total neutral current, and complicated power unbalance. Firstly, the optimisation process is applied to each goal function. Then, they are put together with weights to form a multi-objective optimisation problem. In this study, it was tried to find out how to minimise losses in electrical power distribution networks that aren't fair. Power flow and optimal DG placement are two PSO techniques that may be used to reduce losses. These changes may be applied to existing distribution systems using an effective load-flow method for a three-phase imbalanced radial distribution network. Knowing the node voltage, angle, branch current, actual power loss, wattles power loss, branch losses, etc. helps determine the network's true state. Simple formulae may be used to describe the relationship between the voltage at one end of the distribution system, the voltage at the other end, and the voltage drops throughout the whole system. An approach is developed to identify the relevant variables. The voltage's angle at the target is calculated with its magnitude. It's a process that requires time and effort. From the substation to each terminal node, the constant voltage of 1p.u. is considered. Voltage magnitude and phase angle are varied between repetitions, and voltage reductions are computed using the new parameters. The suggested approach has been applied to 19- and 25-node networks with unequal distribution. To demonstrate its efficacy, the recommended approach's speed requirements were compared to those of another recently developed technology. Good outcomes are achieved, and DG proves to be a viable option for reducing costs and improving performance.

Hybrid DGA Method for Power Transformer Faults Diagnosis Based on Evolutionary k-Means Clustering and Dissolved Gas Subsets Analysis

Considered as the heart of electrical power transmission and distribution networks, power transformers are essential part of the electricity transmission grid. Among the condition monitoring and fault diagnosis tools for these machines, dissolved gas analysis (DGA) has proven its effectiveness in their early detection and classification of faults. Up to date, many methods have been proposed in the literature for the interpretation of DGA data, classified into traditional and intelligent methods. This paper proposes a two-steps hybrid method, which uses the strengths of both methods. The approach uses the evolutionary k-means clustering algorithm based on the genetic algorithm for subset formation and subset analysis by human expertise. In the diagnostic procedure, to determine the condition of a sample, the subset to which it belongs is first identified and then the corresponding diagnostic sub-model is applied. The proposed method has been implemented with 595 DGA data, tested on 254 DGA data and validated on the International Electrotechnical Commission (IEC) TC10 database. Their performances were evaluated and compared with existing traditional, intelligent and hybrid methods. From the results obtained with the IEC TC10 database, the newly proposed approach depicts the best overall diagnosis accuracies. Indeed, the best performance is achieved with the proposed method compared to other models in the literature, with diagnostic accuracy of 98.29% compared to 88.89% of the Gouda triangle method, to 88.03% of the Hyosun Corporation gas ratio method or to 86.32% of the three ratios technique.

Kajian Manajerial Efektifitas Pemeliharaan Jaringan dan Transformator Distribusi 20 kV Menggunakan Uji ANOVA

PT PLN (Persero) is an electricity company in Indonesia which supports the development and industrial sectors. Process Business PLN starts from power plant, Transmission and distribution until Retail to Customers. For Supply the needed for customer in electricity power, PLN keep the reliability electricity network for fulfil it. The reliability of electric power distribution network depends on several factors, including the quality of materials, maintenance, operating patterns, protective equipment used and also network configuration. One of the ways to maintain electricity service is to carry out distribution transformer maintenance. After the maintenance is carried out, it is necessary to evaluate the distribution transformer maintenance activities that have been carried out, whether the maintenance activities have an impact or not on management, this is measured by the quality of the voltage and the rupiah per kWh that can be distributed and is denominated in rupiah. The purpose of this study is to find out whether the network and transformer maintenance has had an impact or not. One way that can be done statistically is through the ANOVA test. From the result, Minitab results obtained a value of 0.321 > α = 0.05 and the results of the autocorrelation do not show plots that are outside the lines or margins. This means that the maintenance activities of the 20 kV distribution transformers in data collection unit affect the company’s income. This means that maintenance activities affect the company's income. Therefore, the maintenance factor affects the company's income. Thus, it can be the main focus for improving distribution reliability by doing transformer 20 kV maintenance.

Application of Artificial Neural Network Model for Improved Power System Protection in Port Harcourt 33 kV Network


 
 
 Electric power distribution networks are exposed to the environment due to their length, for this reason, they are mostly affected by faults. These faults disrupt the continuous flow of power supply. There is also an associated loss of power that is generated that also determines the state of the economy. In order to reduce system downtime, it is necessary to integrate a system that detects and classifies faults quickly in order to hasten its clearance. This will bring about improvement in the efficiency and integrity of the power network. The artificial neural network as proposed in this study is meant to detect, classify and locate fault on the Rukpokwu 33-kV
 feeder of Port Harcourt Electricity Distribution Company (PHEDC). Fault detector, classifier and locator, with feed-forward back propagation were employed in the research. Matlab Simulink software was used to model and simulate the distribution network. The model was trained using s values of voltages and currents. With the simulation results, the efficiency of the proposed network was demonstrated for fault detection, classification and location. Mean square error (MSE) and confusion matrix were used to evaluate the performance of the proposed model. Results showed that the acceptable MSE of 0.00000027736 and an accuracy of 100% were achieved, which is satisfactory.
 
 

Power distribution network reliability calculation method based on collective multi-label classifier and energy entropy

Reliable power supply from the grid is vital to life and work, production activities, and national security. At present, the simulation calculation method of distribution network reliability needs a lot of time. In view of this shortcoming, this paper proposes a multi-label classifier model using the importance sampling method, which can shorten the calculation time by reasonably extracting samples. At the same time, the multi-label classifier can evaluate the failure probability of the electric power distribution network at the bus level. Finally, we use the IEEE RTS model to test the proposed method, and the effectiveness of the proposed method is verified.

Optimal truck-mounted mobile generators allocation to improve distribution network resilience using the consolidation index

Electrical energy supply is a vital part of a modern network, so enhancing its resilience to natural hazards is of paramount importance. The present study aims to determine the number and capacity of truck-mounted mobile generators that can be utilized in electric power distribution networks to provide a certain level of resilience, taking into account the costs and revenues associated with their use. To this aim, a "consolidation index" is used, which is based on the "curve analysis index" and considers "cost (either of the type of investment or of the type of repairs imposed by the event)" and "lack of load supply (with critical and non-critical load separation)" simultaneously. Presenting a "decision curve" allows the planner or policymaker to perform the best by applying the amount of capital or budget at his/her disposal to improve a certain percentage of the resilience in the evaluated network. Finally, the effectiveness and efficiency results of the proposed method are shown in the modified IEEE 33-bus network.

Time-synchronized carbon flow metering scheme for electric power transmission, transformation, and distribution networks

Faced with the pressure of energy conservation and emission reduction, the power industry is urgently requires low-carbon transformation. The carbon flow calculation theory redistributes the actual carbon generated by the power plant to the branch and loads customers with the power flow. This paper first introduces the theory of carbon flow calculation and the carbon metrics corresponding to the electricity metrics. Second, a time-synchronous technology is introduced for the carbon flow calculation of transmission, transformation, and distribution networks, and a time-synchronous-based carbon metering system is conceived. The impact of time deviation on carbon metering is elucidated through simulation experiments of IEEE14 standard nodes, and finally, relevant suggestions are made for future research ideas and technical routes.

Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles

With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

Energy saving management technology for electrical automation and power distribution network dispatching

With the rapid development of the economy, the demand for electric power is increasing, and the operation quality of the power system directly affects the quality of people’s production and life. The electric energy provided by the electric power system is the foundation of social operation. Through continuous optimization of the functions of the electric power system, the efficiency of social operation can be improved, and economic benefits can be continuously created, thereby promoting social progress and people’s quality of life. In the power system, the responsibility of the power distribution network (PDN) is to transmit electricity to all parts of the country, and its transmission efficiency would directly affect the operational efficiency of the power system. PDN scheduling plays an important role in improving power supply reliability, optimizing resource allocation, reducing energy waste, and reducing environmental pollution. It is of great significance for promoting social and economic development and environmental protection. However, in the PDN scheduling, due to the inflexibility of the power system scheduling, it leads to the loss and waste of electric energy. Therefore, it is necessary to upgrade the operation of the PDN automatically and use automation technology to improve the operational efficiency and energy utilization rate of the power system. This article optimized the energy-saving management of PDN dispatching through electrical automation technology. The algorithm proposed in this paper was a distribution scheduling algorithm based on electrical automation technology. Through this algorithm, real-time monitoring, analysis, and scheduling of PDNs can be achieved, thereby improving the efficiency and reliability of distribution systems and reducing energy consumption. The experimental results showed that before using the distribution scheduling algorithm based on electrical automation technology, the high loss distribution to transformation ratios of power distribution stations in the first to fourth quarters were 21.93%, 22.95%, 23.61%, and 22.47%, respectively. After using the distribution scheduling algorithm, the high loss distribution to transformation ratios for the four quarters were 15.75%, 13.81%, 14.77%, and 13.12%, respectively. This showed that the algorithm can reduce the high loss distribution to transformation ratio of power distribution stations and reduce their distribution losses, which saved electric energy. The research results of this article indicated that electrical automation technology can play an excellent role in the field of PDN scheduling, which optimized the energy-saving management technology of PDN scheduling, indicating an advanced development direction for intelligent management of PDN scheduling.

Group Method of Data Handling Using Christiano-Fitzgerald Random Walk Filter for Insulator Fault Prediction.

Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano-Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10-12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.

Ant Colony Optimization based algorithm to determine the optimum route for overhead power transmission lines

Electrical power distribution networks present one of the most essential infrastructure projects. Despite of the vitality of this sector and the huge budgets of their projects, but it is still depends on traditional manual routing techniques which are slow, expensive and inefficient comparing with the current AI revolutionary techniques. Hence, this research presented a novel AI model for optimizing “overhead power transmitting lines” (OHPTL) routing using Ant Colony Optimization (ACO) technique. The proposed technique depends on the core routine (which can generate random and valid routes and estimate their total cost) and searching algorithm (which uses ACO technique to find the optimum route among the generated routes). As a prototype, the proposed technique was codes in “Visual Basic for Applications” VBA, while MS-EXCEL was used to handle the graphical inputs and outputs. The functionality of the technique was validated using three virtual case studies and its optimization capability was tested using a real-life case study. The results showed that the total cost of the optimized route is about 75% of the existing route.

On-Line Partial Discharge Localization of 10-kV Covered Conductor Lines

This paper proposes an innovative partial discharge (PD) location technique for overhead electrical power distribution networks. It is aimed at improving the condition-based maintenance of the network. PD localization is carried out via an improved double-sided traveling-wave method. The method is driven by a hybrid detection technique, which integrates a pulse-based synchronization mechanism and a global positioning system (GPS). The proposed solution offers a number of benefits. It has the nice inherent feature of being immune to varying physical parameters of the transmission line, and it has been proven be offer improved accuracy with respect of the conventional GPS-based location methods. Also, an in-house designed portable and non-invasive test setup is presented and thoroughly discussed, thus demonstrating the feasibility of the proposed method. Moreover, an enhanced algorithm is embedded into the PD location system to improve robustness to high-level noise. Finally, the proposed tool relies on a well-established automatic procedure which requires neither parameter tuning nor any expert intervention. The features and strengths of the method are validated on a real case consisting of a 2125-m long 10-kV overhead covered conductor line.

Effective Deterministic Methodology for Enhanced Distribution Network Performance and Plug-in Electric Vehicles

The rapid depletion of fossil fuel motivates researchers and policymakers to switch from the internal combustion engine (ICE) to plug-in electric vehicles (PEVs). However, the electric power distribution networks are congested, which lowers the accommodation of PEVs and produces higher power losses. Therefore, the study proposes an effective deterministic methodology to maximize the accommodation of PEVs and percentage power loss reduction (%PLR) in radial distribution networks (RDNs). In the first stage, the PEVs are allocated to the best bus, which is chosen based on the loading capacity to power loss index (LCPLI), and the accommodation profile of PEVs is developed based on varying states of charge (SoC) and battery capacities (BCs). In the second stage, the power losses are minimized in PEV integrated networks with the allocation of DG units using a recently developed parallel-operated arithmetic optimization algorithm salp swarm algorithm (AOASSA). In the third stage, the charging and discharging ratios of PEVs are optimized analytically to minimize power losses after planning PEVs and DGs. The outcomes reveal that bus-2 is the most optimal bus for accommodation of PEVs, as it has the highest level of LCPLI, which is 9.81 in the 33-bus system and 28.24 in the 69-bus system. The optimal bus can safely accommodate the largest number of electric vehicles, with a capacity of 31,988 units in the 33-bus system and 92,519 units in the 69-bus system. Additionally, the parallel-operated AOASSA mechanism leads to a reduction in power losses of at least 0.09% and 0.25% compared with other algorithms that have been previously applied to the 33-bus and 69-bus systems, respectively. Moreover, with an optimal charging and discharging ratio of PEVs in the IEEE-33-bus radial distribution network (RDN), the %PLR further improved by 3.08%, 4.19%, and 2.29% in the presence of the optimal allocation of one, two and three DG units, respectively. In the IEEE-69-bus RDN, the %PLR further improved by 0.09%, 0.09%, and 0.08% with optimal charge and discharge ratios in the presence of one, two, and three DG units, respectively. The proposed study intends to help the local power distribution companies to maximize accommodation of PEV units and minimize power losses in RDNs.

A collaborative, immersive, virtual reality environment for training electricians

This paper presents the development of a multi-user, collaborative virtual reality environment for training electricians to  work on the electric power distribution network. Typically, traditional trainings in this field have lots of constraints and  don’t allow learners to experience a wide set of different equipment that is installed in the network nor bad weather  conditions and the different risk analysis that should be performed. With the proposed virtual reality environment,  learners will be able to practice several realistic conditions without risking themselves.