Reliability Of Power Distribution System Research Articles

Determining target levels of power distribution system reliability indices using machine learning

Electrical distribution system (EDS) reliability has always been a critical subject due to the considerable share of distribution systems in power interruptions to end-users. Meeting regulatory service quality standards requires distribution companies to achieve defined reliability levels for performance improvement. Conventionally, specialists determine target reliability based on experience, experimental knowledge, and rules of thumb. However, these methods are error-prone and may result in suboptimal investments, economic inefficiency, and inadequate resource allocation in EDS operation and planning. Accordingly, this paper presents a machine learning-based method to determine the target level for EDS's reliability indices based on its historical performance. The approach involves comprehensive analysis of historical EDS reliability data, followed by a data envelopment analysis (DEA) to assess performance across subdivisions like substations or feeders, identifying the most effective units. Subsequently, machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) network—are trained on optimal subdivisions from the DEA stage. These models accurately estimate target reliability for all subdivisions, contributing to the overall determination of the EDS target reliability level. A three-scenario case study validates the proposed method, suggesting its potential applicability as a valuable tool for regulatory agencies in setting EDS reliability targets. While admitting the potential challenges, notably the method's reliance on the quality and completeness of the historical data collected by distribution companies, the paper emphasizes the importance of collaborative efforts to ensure data accuracy and sufficiency. The proposed method leads to a fair mechanism to determine the target level for a real EDS's reliability indices in comparison with a business-as-usual scenario. Moreover, it provides a reliability performance ranking for the EDS's subdivisions.

Graphical Environment to Study Reliability of Electric Power Distribution Systems.

In this paper we present an application for reliability studies of electric power distribution systems. These studies are easily carried out using a graphical interface that allows interaction with the user. The program is capable of evaluating a reliability index and allows the modification of features related to the distribution network or related to the way in which service restoration is done when there is a fault in the system. Thus, it is possible to study several reliability improvement measures as shown in an example in this paper.

Design and modeling of an all-solid-state high power bidirectional DC switch

In order to improve the reliability of the aircraft ring network power distribution system for the future development needs of advanced multi-power and all-power aircraft, a design scheme of an all-solid-state high-power bidirectional DC switch is proposed to upgrade the aircraft power distribution system originally composed of traditional mechanical switches and some low-power solid-state power controllers to an all-solid-state aircraft power distribution system. The solid-state high-power bi-directional DC switch uses SiC MOSFETs as switching elements is modeled and simulated by Saber simulation software and uses reverse time overcurrent protection and short circuit protection design to realize high voltage control distribution, circuit protection, and fault isolation functions. The simulation verifies that the designed all-solid-state high-power bidirectional DC switch has short-circuit protection and overcurrent protection intelligent protection functions. Applying the design to the aircraft distribution loop network greatly enhances the reliability of the aircraft distribution system drives the development of aircraft multi-electricity-all-electricity technology, and also promotes the application of digital solid-state series modular switches in civil transportation and high-voltage power supply fields.

Electric power distribution maintenance model for industrial customers: Total productive maintenance (TPM), reliability-centered maintenance (RCM), and four-discipline execution (4DX) approach

The reliability of the electric power distribution system is crucial for industrial consumers, particularly in Karawang Regency, one of the industrial centers in Indonesia. The management of electricity supply for industries in Karawang Regency is entrusted to the Indonesian State Electricity Enterprises, also known as PLN. However, there are still disturbances in the distribution system that affect industrial customers. The primary causes of these disturbances are 39% jointing damage, 36% damage caused by third parties, and 15% damage to cubicles. The occurrence of these disturbances is closely tied to maintenance activities on the electric power distribution system. To assess the distribution system's reliability, various indices are calculated, including the System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI), energy not supplied (ENS), and feeder disturbance indices. Currently, PT PLN (Persero) UP3 Karawang employs a maintenance model that combines corrective and preventive methods with the four-discipline execution (4DX) approach. This study aims to develop an improved maintenance model that enhances the overall performance of the electric power distribution system for industrial customers. In this study, a maintenance model is proposed by integrating the total productive maintenance (TPM) method, reliability-centered maintenance (RCM) method, and four-discipline execution (4DX) approach. The developed maintenance model is tested on 40 feeders serving industrial customers. A comparative analysis is performed before and after implementing the maintenance model, considering the SAIFI, SAIDI, ENS, feeder disturbance, and overall equipment effectiveness (OEE) indexes. The analysis of the 40 feeders reveals improvements in performance following the implementation of the maintenance model using the TPM, RCM, and 4DX methods. Prior to implementation, the SAIFI was 0.24 times/cust.yr, SAIDI was 1.877 min/cust.yr, ENS was 60,470 kWh, feeder disturbance occurred 8 times, and OEE was 0.99935. After implementation, the SAIFI improved to 0.016 times/cust.yr, SAIDI reduced to 1.342 min/cust.yr, ENS decreased to 41,241 kWh, feeder disturbance reduced to 4 times, and OEE increased to 0.99962. Consequently, the performance improvements are as follows: SAIFI by 33%, SAIDI by 29%, ENS by 32%, feeder interference by 50%, and OEE by 0.027%.

Use of Symmetrical Components in the Study of Electrical Disturbances in Distribution Systems with Charging Allocation for Electric Vehicles and Battery Banks

This paper presents a study of electric power distribution systems, which include battery banks and electric vehicle charging.Emphasis is placed to detect and distinguish voltage disturbances, conductor breaks, and other events that commonly occur in distribution systems. The research uses free license software ATPDraw™ to accurately model distribution systems and replicate voltage characteristics. The study investigates the ideal location of electric vehicle chargers, battery banks and electrical voltage meters in distribution systems. For this purpose, the General Algebraic Modeling System (GAMS) will be used, which provides high-level mathematical optimization capabilities to improve the observability of the system. Research will focus on possible mitigation to the problems these factors can cause in the power distribution system and explore strategies to minimize their negative effects. The discoveries will help improve the system performance, stability, and reliability of power distribution systems and support the integration of electric vehicles and energy storage.

Safeguarding the future: A comprehensive analysis of security measures for smart grids

With the rapid advancement of technology and the growing reliance on renewable energy sources, smart grids have emerged as a transformative solution for enhancing the efficiency and reliability of power distribution systems. As the smart grid ecosystem evolves, security concerns become paramount due to the interconnectedness of various components, including advanced metering infrastructure, communication networks, and distributed energy resources. This paper presents a comprehensive analysis of smart grid security, focusing on the challenges, vulnerabilities, and potential threats that must be addressed to ensure the resilience of these intelligent infrastructures. It explores the diverse attack vectors that malicious actors may exploit to compromise smart grid operations and highlights the potential consequences of such security breaches, including power disruptions, data breaches, and financial losses. To counteract these risks, this paper delves into various state-of-the-art security measures and strategies that can be implemented to safeguard smart grids effectively. Moreover, the importance of collaboration between various stakeholders is described, including utility companies, government agencies, researchers, and technology vendors, to foster a holistic approach to smart grid security. By emphasizing the significance of information sharing and collaboration, this paper advocates for a robust and adaptive security framework to respond promptly to emerging threats and vulnerabilities. Furthermore, the paper explores emerging technologies such as artificial intelligence, blockchain, and intrusion detection systems, showcasing their potential contributions in fortifying smart grid defenses. It was noted that the integration of these innovative technologies can enhance anomaly detection, facilitate secure data exchange, and reinforce the resilience of the smart grid against sophisticated cyber-attacks. Towards the end of this paper, insights into future research directions and policy implications to foster a more secure and sustainable smart grid environment is provided. By acknowledging the evolving threat landscape and the dynamic nature of the smart grid domain, this work encourages continuous research and development to stay ahead of adversaries and establish a secure foundation for the future of energy distribution.

A weather-based power distribution system reliability assessment

The continuity of power supply is affected by many factors; the weather conditions to which power system components are exposed are considered the most effect. A new methodology to assess the reliability of distribution system considering the impact of different weather conditions is presented in this paper. The proposed method considers possible future changes in the weather patterns that might occur differently from what it was in the past. Modified sets of reliability indices are presented for mathematically predicting reliability performance based on historical reliability data under specific past weather conditions combined with forecasted future weather conditions. The proposed method is applied to the distribution system components of RBTS; the results demonstrate the significance of considering the forecast of weather conditions in reliability assessment.

Power distribution system reliability evaluation using weight-optimised ANN approach

Power distribution system reliability evaluation using weight-optimised ANN approach

Artificial intelligence based optimal coordination of directional overcurrent relay in distribution systems considering vehicle to grid technology

Optimal coordination of overcurrent relays has become a major challenge in power distribution systems due to the increasing participation of distributed generation (DG) and electric vehicle (EV) charging stations, especially vehicle-to-grid (V2G) technology. This paper proposes a novel approach for optimal coordination of directional overcurrent relays (DOCRs) to achieve the minimum operating time and obtain optimal tuning in terms of time dial setting (TDS) and pick-up current (IP) considering V2G integration. The optimization was conducted using three different Artificial Intelligence (AI) techniques: grey wolf optimization (GWO), cuckoo search (CS), and Hunger Games Search (HGS), and was performed on two systems: IEEE 33-bus system and a local grid located in El-Shorouk City–district#8 (ShC-D8), Cairo, Egypt. The proposed methodology is constructed and validated on ETAP and MATLAB software packages. The simulation results demonstrate that the proposed optimization techniques provide high dynamic performance and accurate coordination of DOCRs. The results show that HGS achieves the minimum operating time of all overcurrent relays as compared with GWO and CS. The effect of V2G integration on the coordination methodology is also considered. The proposed approach provides a significant contribution to the field of power system protection and can help improve the reliability and efficiency of power distribution systems with V2G integration.

Research on Establishment and Application of Digital Twin for a Phase-Shift Full-Bridge Current Doubling Rectifier Converter

In order to improve the safety and reliability of airborne power supply and distribution system, a digital twin of a phase-shift full-bridge current doubling rectifier converter based on gallium nitride metal-oxide-semiconductor field-effect transistor (GaN MOSFET) is built. The digital twin can be regarded as virtual symmetry of the real converter. The particle swarm optimization (PSO) algorithm is applied to compare the voltage and current waveforms of the real converter with relative waveforms of its digital model established in simulation software, and the interior parameters of the model are constantly updated until the waveforms of the digital twin coincide with that of the converter. Applications of this digital twin are discussed through experimental verification, and the results show that the digital twin can deduce the parameter value of key components of the converter in the case of soft faults. In the case of hard faults, a back propagating artificial neural net (BP ANN) is trained by data collected from the digital twin, and the BP ANN is able to identify different running states of the converter. The outcomes of this paper present a non-intrusive and high real-time method of condition monitoring and fault diagnosis, which is beneficial to improve the reliability of airborne HVDC power supply system.

Optimised temperature fluctuation control strategy for lifetime improvement in aircraft electrical power distribution system

As a power electronic converter with bidirectional energy transmission, the Dual Active Bridge (DAB) has a broad application prospect in the electrical power distribution system of hybrid-electric flight. For the DAB application in the electrical power distribution system, high efficiency and high reliability are envisaged due to the mission-critical nature of the application. A new pulse width modulation optimization control strategy is proposed, which is based on the feedback temperature fluctuations of the GaN (Gallium Nitride) power transistors, to improve the lifetime of the whole circuit. The complete topology and simulations of the DAB converter are carried out in PLECS. Detailed simulation results manifest the precise lifetime of the GaN power transistors and verify the accuracy of the new optimization control strategy. Meanwhile, Insulated Gate Bipolar Translator is set as a control group to compare the lifetime of conventional silicon-based power devices with the GaN power transistor. Hardware In Loop platform test is built to further validate the proposed method.

Modeling of Adverse Weather Events in an Integrated Reliability and Power Quality Assessment of Distribution System Using Stochastic Diffusion Process

This paper presents the modeling of adverse weather events and their impact on the integrated reliability and power quality assessment of power distribution systems. With respect to previous works, stochastic modeling of adverse weather events is integrated into the Monte Carlo Simulation approach and its impacts on reliability and voltage sag indices are highlighted. In this paper, the aleatory and epistemic uncertainty are modeled into sampling of Time to Failure (TTF) using the Stochastic Diffusion Process. The Stochastic Diffusion Process, including the Jump diffusion is used to model the impact of adverse weather events on permanent and temporary failure rates. The proposed method is applied to the modified IEEE 34 node test feeder, and three case studies have been performed to investigate the impact of various uncertainties and adverse weather events. Numerical results for the IEEE 34 node test feeder are presented to quantify adverse weather impacts on both reliability and voltage sag indices.

Reliability Assessment and Improvement of Electrical Distribution Systems by Using Multinomial Monte Carlo Simulations and a Component Risk Priority Index

In this paper, a method of assessing and improving the reliability of power distribution systems based on Monte Carlo simulation and a novel risk priority index is proposed. The initialization of the assessment process is carried out by using Multinomial Monte Carlo simulation with a nonsequential technique to assess system reliability in the form of SAIFI and SAIDI indices. Then, the novel per-time-based component reliability indices representing the insights obtained from root-cause analysis for each component in the system are evaluated to make suitable decisions on improvement measures. The proposed indices are derived as a component risk priority index based on the principle of the failure mode and an effect analysis to prioritize and select the implementation points by the Pareto principle. By applying the proposed method, a reliability improvement should be achieved at the correct point with minimal operations. In addition, the proposed method can be used to study the effect of uncertainty regarding some device operations on the system reliability. To verify the performance of the proposed method and demonstrate its application, three case studies were performed on the IEEE RBTS Bus-2 test system. From the first case study, the results of the proposed assessment process were validated by comparison with a standard benchmark. The second case study showed the performance of applying the entire process to improve system reliability, and the results showed that system reliability can be improved significantly. The third case study was performed to determine the effect of uncertainty in protective device operations. The results of the third case showed that there was a significant decrease in overall reliability in terms of a higher level of power outages, while the performance of the protective components was slightly reduced.

Remaining Useful Life Prediction for AC Contactor Based on MMPE and LSTM With Dual Attention Mechanism

In order to realize the remaining useful life (RUL) prediction of AC contactor and improve the operation reliability of the low-voltage power distribution system, a RUL prediction method based on modified multi-scale permutation entropy (MMPE) and long short-term memory (LSTM) with dual attention (DA) mechanism is proposed. First of all, MMPE is used to analyze the performance degradation of AC contactor, mine the change law of feature parameters, and effectively detect the performance inflection point in the degradation process. Secondly, the LSTM with DA mechanism realizes the quantitative RUL prediction. In order to improve the RUL prediction performance, the feature attention mechanism and the temporal attention mechanism respectively assign weights to input features and time steps. Finally, a case analysis is carried out. The results show that the proposed method can effectively realize the quantitative RUL prediction, and the prediction error is smaller compared with the existing methods.

Improving reliability of the power distribution system in Goma (DRC) using solar distributed generation

The existing power distribution system of the city of Goma in the Democratic Republic of the Congo has many problems, including the scarcity of electric energy, power unreliability, the low access rate of electrification, poor flexibility in the network topology, and lack of demand response, which lead to shedding of load, unbalancing and overloading the system. The reliability of the Goma power distribution system is very poor by international standards. The major cause of this is the lack of adequate energy supply to meet demand. To mitigate the power reliability problem, a solar-based distributed generation (DG) is modelled and evaluated in this study. Each feeder has been considered separately with an appropriately designed DG. The work has evaluated initial investment cost and life cycle cost of the investment to assess the feasibility of the proposed solution. Based on the unserved energy and electricity tariff, an economic analysis was conducted. The reliability indices are computed and the modelled solution is designed for each feeder in PV syst software and simulated using ETAP, whose simulation results show that the reliability can be improved by up to 76%.

Statistical Modeling for the Effects of Vegetative Growth on Power Distribution System Reliability

The purpose of this paper is to examine the effects of vegetative growth on the reliability of electric power distribution system under normal (storm exclusion) operating conditions, and to determine an effective vegetation maintenance schedule. Generalized statistical linear regression models, including Poisson, Negative Binomial, Zero-Inflated, and their mixed model variants are developed and are applied into a 5-years outage data along with vegetation maintenance history from a power company in Midwestern United States. From the methodological point of view, advanced statistical models such as zero-inflated models and mixed models are utilized the first time on outage data and provided good fit to the occurrence of outages. In practice, numerical results from this study suggest that an optimal cycle length of every 6 years could be greatly helpful for power companies in devising a cost-effective schedule, improving system reliability, and maintaining customer satisfaction.

Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble

Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the reliability of power distribution systems. Earlier research with statistical models, neural networks, and committee machines to estimate weather-related and animal-related outages has reported some success. In this paper, a deep neural network ensemble model for outage estimation is proposed. The entire input space is partitioned with a distinct neural network in the ensemble performing outage estimate in each partition. A novel algorithm is proposed to train the neural networks in the ensemble, while simultaneously partitioning the input space in a suitable manner. The proposed approach has been compared with the earlier approaches for outage estimation for four U.S. cities. The results suggest that the proposed method significantly improves the estimates of outages caused by wind and lightning in power distribution systems. A comparative analysis with a previously published model for animal-related outages further establishes the overall effectiveness of the deep neural network ensemble.

ANALISA PENGUJIAN KUALITAS GAS SF6 PADA PEMUTUS TENAGA (PMT) 66 KV BAY REAKTOR 4R2 DI GITET BANDUNG SELATAN

Abstract Electrical energy is a necessity that is needed by everyone. To ensure the reliability of electrical energy, the electrical equipment used must be regularly maintained. One of the maintenance carried out is testing the SF6 gas on the power breaker (PMT). This test is carried out to determine the quality of SF6 gas which functions as an insulation medium for PMT. Because when there is a fault current or a normal current at the time the PMT is cut off, an arc will arise which can damage the PMT equipment itself. Therefore, the quality of SF6 gas in PMT must be maintained under normal conditions. In the SF6 gas test, the SF6 Multi Analyzer test tool is used which functions to ensure that the quality of the SF6 gas at PMT is still in good condition. In routine maintenance activities, SF6 gas testing is carried out at 66 kV PMT which is in the 4R2 Reactor Bay. From the test results, the gas purity value of SF6 is 99.9%, the product decomposition or SO2 content of SF6 gas is 26 ppmv, the dew point value is -21.2 oC, and the pressure value is 0.735 MPa. Based on the test results, there is an anomaly in PMT, namely the results of the SO2 content test in SF6 gas are not normal. The SO2 value exceeds the predetermined standard limit, which exceeds the maximum limit of 12 ppmv. Therefore, in accordance with the analysis of the FMEA method, that is, if there is a bad quality test result for SF6 gas, then SF6 gas replacement and SF6 gas purity testing must be carried out so that there is no widespread disturbance and to ensure the reliability of the electric power distribution system. After replacing SF6 gas, the test results show the value of SO2 = 0 ppmv.

Analysis of smart grid technology application for power distribution system reliability enhancement: A case study on Bahir Dar power distribution

Reliability improvement of power distribution systems is a pre-request for economic and social development of any country. Currently, there is a rapid growth of industrial, commercial, residential and other service supplying institutions. All of these institutions need reliable power supply. Due to this, utilities must work hard and ensure that the customers are getting a reliable power supply. However, higher power interruption frequency and longer interruption duration are the most serious problem facing Ethiopian power distribution systems and this problem affects the daily activity of customers. In this paper, smart grid technology applications using optimal placement of switching/automating devices, network reconfiguration and rapid power restoration is performed to improve the reliability problem of Bahir Dar power distribution system. Optimal placement of switching/automating devices and network reconfiguration is determined by the Genetic Algorithm optimization technique using mat lab software. The rapid power restoration is carried out by Prim‘s Algorithm used for constructing the Minimum Spanning Tree in mat lab software. The study is tested at Bata the 34 bus feeder of Bahir Dar distribution system. The existing network is assessed and the reliability indices such as SAIFI, SAIDI, ASAI, CAIDI and EENS are evaluated using ETAP 12.6.0 software.

Reliability evaluation of power distribution grids considering the dynamic charging mode of electric buses

Advances in wireless power transfer technology provide the possibility to construct electrified roads and charge electric vehicles driving on the road. Dynamic charging mode enables the contactless interaction of electric vehicles with the power grid and has a promising prospect, but it may also bring about potential challenges to the power grid such as reliability deterioration. Electric buses serve as the forerunner to use this new charging mode due to their fixed driving patterns. Thus, it is needed to investigate its potential impact on power distribution system reliability. In this paper, first, the electric bus dynamic charging model is constructed, and then the impacts of this model on power distribution system reliability are studied. Simulation results indicate that compared with the non-dynamic charging mode, the electric bus dynamic charging mode does not cause additional deterioration to the reliability performance and has a slightly better effect.