Distribution System Reliability Research Articles

Pemeliharaan Kubikel 20 KV di PT.Haleyora Powerindo

Maintenance of 20 kV cubicles at PT. Haleyora Powerindo is a crucial step in ensuring the reliability and safety of the electrical distribution system. This process includes preparation stages such as inspecting work equipment, coordinating power outages, and utilizing safety equipment (PPE) to minimize the risk of accidents. Subsequent steps involve voltage disconnection, verifying zero voltage using detection tools, cleaning components with specialized fluids, and drying to ensure that moisture does not cause disruptions. Once all stages are completed, the voltage is carefully reactivated, and a maintenance report is prepared to document the process. This study employs direct observation, interviews, and document analysis to evaluate the maintenance implementation and provide recommendations for quality improvement. The results are expected to serve as a guide for other companies in enhancing the efficiency and reliability of 20 kV cubicle maintenance to ensure a stable electricity supply.

Impact of wind speed and soil frost on electricity distribution system reliability

Impact of wind speed and soil frost on electricity distribution system reliability

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.

Distribution systems reliability improvement using optimal phase balancing and economic analysis

Distribution systems reliability improvement using optimal phase balancing and economic analysis

Allocation of Microgrid-Forming resources for distribution systems reliability improvement

This paper proposes a novel method to allocate distributed generation and energy storage as Microgrid-Forming Resources (MFR) to improve the reliability of medium voltage (MV) grids. A graph-based approach is used, which simplifies the circuit analysis and reduces computational time when compared to exhaustive search methods. First, a medium voltage substation-level Nodal Fault-Effect Matrix (NFEM) is derived from the feeder’s topology, which is used for predictive reliability calculations, and calibrated with historical data. Second, a reduced set of scenarios of MFR allocation are generated using a graph community approach and evaluated in terms of reliability improvement and power and energy requirements of the distributed resources. Two case studies are presented based on real grids with different reliability characteristics and the results indicate that the proposed approach is an insightful tool for distribution system planners to evaluate the integration of microgrids to improve system reliability.

Reliability and line loading enhancement of distribution systems using optimal integration of renewable energy and compressed air energy storages simultaneously under uncertainty

Recently, with the significant growth of load demand, further interest in renewable energy sources (RESs) incorporated into electric distribution systems (DSs) has become important. However, the output powers' stochastic fluctuations of the photovoltaic (PV) and wind turbine (WT) generation sources, as well as the load demand, result in obvious operational challenges related to the reliability and line loading of these systems. Therefore, this present study is intended to enhance the load-oriented reliability indices (LORIs), the customer-oriented reliability indices (CORIs), and the line loading index (LLI) of the DSs by assigning the optimal integration of RESs and compressed air energy storages (CAESs) through the determination of both the optimal placement and capacities of RESs and CAESs simultaneously, as well as the optimal charging and discharging powers of the CAESs and their initial state of charge based on the red-tailed hawk (RTH) optimization technique. The Monte Carlo Simulation (MCS) and the Scenario-based Reduction (SBR) uncertainty methods are implemented to address the uncertainties of RESs and loading. The influences of the linear failure rate of DS's feeders and the installation of protective devices are also demonstrated. The efficiency of the intended methodology is demonstrated on the IEEE 69-bus and IEEE 33-bus DSs associated with voltage-dependent, time-varying mixed loads. The outcomes obtained from the suggested RTH are compared to those of other reported optimization techniques to validate its effectiveness. The results reveal that the optimal integration of RESs along with CAESs in the IEEE 69-bus test DS can improve the energy not supplied (ENS), system average interruption duration index (SAIDI), system average interruption frequency index (SAIFI), average service unavailability index (ASUI), and LLI by 1.09 %, 1.86 %, 1.73 %, 1.89 %, and 64.53 %, respectively.

Efficient allocation of energy storage and renewable energy system for performance and reliability improvement of distribution system

This paper aims to investigate the impact of optimal planning of renewable energy systems and independent battery energy storage systems (BESS) on the performance and reliability of distribution systems (DS). A unique energy management scheme (EMS) controls the charging and discharging schedules of the BESS. The transit search optimization (TSO) algorithm determines the optimal allocation of wind turbine (WT) and solar photovoltaic (SPV) generator units, along with BESS units. The optimization focuses on improving the DS reliability in terms of energy supply, increasing the feeder security margin, and reducing apparent power loss. Multiple case studies are designed to study the efficacy of the problem outcomes and are validated using Bus 4 of the Roy Bollington Test System (RBTS). To obtain results that are close to actual operating conditions, the modelling process takes into account the time-varying load profile and the dynamic power outputs from the SPV and WT units. The findings show that the suggested method works well by enhancing the feeder security margin by a maximum of 11.25%, reducing expected energy not supplied (EENS) by 4.3%, and improving the power loss profile by 11.5%. In addition, the feeder upgrade deferral (FUD) years also show a maximum improvement of 20.8%.

Fault Localization in Multi-Terminal DC Distribution Networks Based on PSO Algorithm

Flexible DC power grids are widely recognized as an important component of building smart grids. Compared with traditional AC power grids, flexible DC power grids have strong technical advantages in islanding power supplies, distributed power supplies, regional power supplies, and AC system interconnection. In multi-terminal flexible DC power grids containing renewable energy sources such as solar and wind power, due to the instability and intermittency of renewable energy, it is usually necessary to add energy storage units to pre-regulate the power of the multi-terminal flexible DC power grid in islanded operation. Aiming at the important problem of large current impact and serious consequences when the flexible DC distribution network fails, a combined location method combining an improved impedance method (series current-limiting reactors at both ends of the line to obtain a more accurate current differential value) and a particle swarm optimization algorithm is proposed. Initially, by establishing the enhanced impedance model, the differential variables under the conditions of inter-electrode short-circuit and single-pole grounding fault can be obtained. Then tailor-made fitness functions are designed for these two models to optimize parameter identification. Subsequently, the iterative parameters of the particle swarm optimization algorithm are fine-tuned, giving it dynamic sociality and self-learning ability in the iterative process, which significantly improves the convergence speed and successfully avoids local optimization. Finally, various fault types in a six-terminal DC distribution network are simulated and analyzed by MATLAB, and the results show that this method has good accuracy and robustness. This research provides strong theoretical and methodological support for improving the safety and reliability of DC distribution systems.

Smooth control strategy for emergency switching of multi-port flexible interconnected distribution system modes

IntroductionWith the rise of distributed energy resources, the interconnection of distribution networks and Flexible Multi-State Switch (FMSS) has become a key technology in the construction of new distribution networks. FMSS plays a significant role in enhancing the reliability and flexibility of the system.MethodsThis paper investigates the impact of mode-switching in FMSSs on voltage shocks, current shocks, and power fluctuations in the event of a feeder fault in a multi-port flexible interconnected distribution system. Firstly, an improved state-tracking control method is proposed to effectively mitigate these impacts. Secondly, for feeder faults connected to the fixed DC bus voltage (Udc-Q) control port, a reselection method for the Udc-Q control port at the main station is introduced, aiming to select the optimal port to maintain the stability of the DC bus voltage.ResultsSimulation experiments have validated the effectiveness of the proposed control method in reducing voltage and current shocks as well as power fluctuations. Additionally, the proposed control strategy has demonstrated an enhancement in the safe and stable operation of the system under emergency fault conditions.DiscussionThe control strategy presented in this paper is capable of addressing the challenges posed by feeder faults and ensuring the stability of the DC bus voltage and reliable power supply to feeder loads, thereby enhancing the overall performance and reliability of the flexible interconnected distribution system. The research findings have been verified on the MATLAB/Simulink simulation platform.

Electric eel foraging optimization algorithm for distribution network reconfiguration with distributed generation for power system performance enhancement considerations different load models

Optimizing the placement of distributed generators (DGs) alongside network reconfiguration is crucial for enhancing the efficiency and reliability of modern distribution systems amidst growing energy demands. This paper introduces a novel Electric Eel Foraging Optimization (EEFO) algorithm for the optimal reconfiguration and placement of DGs in radial distribution networks (RDNs), considering different load models. EEFO utilizes an adaptive energy factor to balance exploration and exploitation across four search strategies, such as exploration, resting, hunting, and migrating. Additionally, its free-parameter feature eliminates the need for fixed settings, simplifying the optimization process. The effectiveness of EEFO is demonstrated through its application to the 33-node and practical 84-node RDNs under different DG power factor scenarios with and without network reconfiguration. The primary objective is to minimize active and reactive power losses while reducing voltage deviation. The results indicate that the scenario with DG allocation at optimal power factor and network reconfiguration achieves significant reductions in voltage deviation, active power loss, and reactive power loss, with improvements of up to 92.20 %, 94.03 %, and 92.33 % in the 33-node network and 55.95 %, 60.20 %, and 59.36 % in the 84-node network, respectively. Furthermore, the comparative analysis with the zebra optimizer, whale optimizer, and moth flame optimizer highlights EEFO's superior efficiency and effectiveness in optimal DG allocation and distribution network reconfiguration.

Optimization of isolation valve placement to improve reliability and resilience of water distribution systems

Optimization of isolation valve layout is critical to improve post-failure response restoration of water distribution systems (WDSs) when pipes are damaged. Many studies have been done to optimize isolation valves to improve reliability of WDSs, but less attention has been given to improving the seismic resilience. This paper proposes a multi-objective optimization model for isolation valves placement considering reliability and seismic resilience simultaneously. The newly developed model employs topology-based reliability and resilience indicators to improve efficiency of optimization algorithm by identification of accidental segments and unintended isolation segments using graph theory without hydraulic simulation. The optimization model was applied to two networks with different loop structures. Subsequently, searching trajectory guided by constraints is investigated and performance of isolation valve placements under different valve densities are assessed including topological structure of isolation valves, criticality of segments and isolation valves, and seismic resilience. The results showed that optimal layout of isolation valves obtained through optimization can improve the reliability and resilience of the system. Our novel approach can benefit water utilities that frequently encounter optimal placement of isolation valves to improve the reliability and resilience of WDSs.

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Control of D-Statcom in Appropriation Framework Involving ANFIS for Further Developing Power Quality

This paper introduces an intelligent controller designed for the optimal operation of a D-STATCOM (Distribution Static Compensator) utilizing the ANFIS. In primary focus is improve the power quality within a 3 phase, four wire system. And distribution of fortifying and overall consistency. Then the ANFIS controller in-corporates fuzzy logic’s, devices are applied in “adaptive network framework”. To use the ANFIS method are established and using the MATLAB Simulink software. D-STATCOM, featuring a VSI and a DC side capacitance, excels in reactive power compensation and harmonic mitigation, even when subjected to dynamic load conditions and advice of the DC link capacitor V, the proposed ANFIS controller generates a compensation current reference. This reference adequately addresses the reactive power requirements imposed by nonlinear loads, ensuring the maintenance of DC voltage regulation. Through comprehensive simulation results, it is evident that the ANFIS controller surpasses in providing superior harmonic compensation and robust DC link voltage regulation under varying operating conditions. This research contributes to the advancement of intelligent control strategies for enhancing power quality and reliability in distribution systems.

Fault Tolerant Control of Fuzzy Stochastic Distribution Systems With Packet Dropout and Time Delay

The fault diagnosis (FD) and fault tolerant control (FTC) problem of the fuzzy stochastic distribution system (SDS) over packet dropout and time delay is studied. Firstly, the static model of linear fuzzy logic system that approximates the output PDF and the dynamic model with the multiplicative fault in a fuzzy system are established. The random packet dropout and time delay caused by the network are described in a unified framework, in which the lost data is compensated with the latest data received by the buffer. On this basis, an adaptive observer is devised to estimate the unknown multiplicative fault. The design of sliding mode predictive fault tolerant controller is discussed to ensure that the system still has good tracking performance after the fault occurs. Numerical simulations on a molecular weight distribution control system are supplied to prove the effectiveness of the presented scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to improve the reliability of the fuzzy stochastic distribution system with packet dropout and time delay. This kind of stochastic distribution systems is described by the relationship between the input and the output PDF rather than the normal relationship between the input and the output. When the multiplicative fault occurs in the system, the design of fault diagnosis strategy and fault-tolerant controller becomes an important challenge, especially when the system is subject to packet dropout and time delay. To address the above problems, a new fault diagnosis and fault-tolerant control scheme is proposed, which can accurately estimate the time and size of the fault and obtain the satisfactory fault-tolerant control effect.

Comparative analysis of the reliability assessment of commercial and residential feeders in the power distribution utility of Nigeria

Reliability studies serve as valuable tools for assessing and optimizing system performance. Utilities with higher reliability indices are more likely to achieve break-even points due to significantly reduced downtime. This paper explores a comparative assessment of two 11 kV feeders supplying electricity to residential and commercial customers, addressing concerns about distribution system reliability in Nigeria and its impact on the country's GDP. The study involves a comprehensive reliability analysis, utilizing a flowchart to outline procedural steps and employing the ETAP Software program for data analysis collected over a one-month period from a power utility company. The data encompass operational parameters such as day-hourly consumption, outage records, and network equipment data. Results indicate higher reliability indices in the commercial feeder compared to the residential feeder, with the Customer Average Interruption Duration Index (CAIDI) being lower in the commercial feeder. The research underscores the significance of reliability assessment in improving operational efficiency, facilitating maintenance planning, and enhancing customer satisfaction.

Applications of tunable-Q factor wavelet transform and AdaBoost classier for identification of high impedance faults: Towards the reliability of electrical distribution systems

This study presents a novel approach that employs a mixture of the tunable-Q wavelet transform (TQWT) and enhanced AdaBoost to address the issue of high impedance fault (HIF) recognition in power distribution networks. Traditional overcurrent protection relays frequently have lower fault current levels than normal current, making it exceedingly difficult to detect this HIF problem with the necessity to use a quick and effective approach to find HIF problems. Since the TQWT performs better with signals that exhibit oscillatory behavior, it has been utilized to extract special features for the training of the improved AdaBoost model. The procedure is accelerated by calculating the Kourtosis (K) value for each level and selecting the ideal level of decomposition to minimize computing work. Faulted zones are categorized using an enhanced AdaBoost approach. Under normal, noisy, and unbalanced conditions, the recommended approach is applied to an imbalanced 123-bus test system and an IEEE 33-bus test system. The efficiency of the recommended method is also being assessed for imbalanced distribution networks incorporating dispersed generation into real-time platforms. This procedure is quick compared to previous methods since it uses an upgraded AdaBoost classifier and optimal decomposition level.

Robust optimization model for relief supplies distribution considering fairness

AbstractThe emergency management agency (EMA) needs to distribute limited relief supplies efficiently. However, it is difficult to develop a reliable system for relief supplies distribution owing to the uncertainties in emergencies. This paper investigates a robust multiperiod relief supplies distribution problem that considers the uncertainties of transportation time, the amount of donation amount, and the secondary disasters. First, a satisfaction model is constructed by considering the relief supplies and the transportation time. The negative effect of the delay in transportation time is considered in the satisfaction model. Second, based on the satisfaction model, a comprehensive fairness strategy is constructed that considers both vertical fairness and horizontal fairness. Finally, a relief supplies distribution model is built for the EMA that considers the actions of the non‐governmental organization (NGO) and donors. Both the utility and the fairness are considered in the objective of the proposed model. In numerical experiments, the Wenchuan earthquake is conducted to illustrate the applicability of the model and provide implications for decision‐makers. The results show the benefits of considering both vertical fairness and horizontal fairness. Then the suitable threshold is given by the analysis of the time delay. Finally, managerial insights and recommendations for the EMA and the NGO derived from the numerical experiments are presented. The findings in this paper help improve the reliability of the relief supplies distribution system.

Smart Water Management Grid

The Smart Water Management Grid (SWMG) lever- age the power of the Internet of Things (IoT) to revolutionize water management, ensuring efficiency, sustainability, and reliability in water distribution systems. By integrating sensors such as level monitoring, pressure monitoring, leakage control, and pH sensors, these grids enable real-time monitoring and intelligent decision-making to optimize water usage and minimize wastage. One crucial aspect of smart water grids is level monitoring, which tracks water levels in reservoirs, tanks, and pipelines. By continuously monitoring water levels, authorities can ensure optimal storage and distribution, preventing shortages or over- flows. Pressure monitoring sensors further enhance efficiency by maintaining optimal pressure throughout the distribution network, minimizing energy consumption and reducing the risk of pipe bursts. Leakage control is another significant feature facilitated by IoT sensors in smart water grids. Advanced leak detection sensors can pinpoint even minor leaks in the system, enabling swift repairs and preventing significant water losses. This proactive approach not only conserves water but also reduces operational costs and mitigates potential damage to infrastructure and the environment. Moreover, integrating pH sensors into the water grid allows for real-time monitoring of water quality. Maintaining proper pH levels is critical for ensuring safe and potable water supply. By detecting variations in pH levels, authorities can promptly identify and address potential contamination issues, safeguarding public health and preventing waterborne diseases.

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.

Frequency and power shaving controller for grid-connected vanadium redox flow batteries for improved energy storage systems

In this research, the performance of vanadium redox flow batteries (VRFBs) in grid-connected energy storage systems centering on frequency and power sharing using voltage source inverters was evaluated. VRFBs are increasingly promising due to their scalability and long lifespan. We explore the impact of voltage source inverters on the frequency of the power supply when there is a change in load and power sharing between the grid and VRFB through MATLAB simulations. The test system demonstrates a storage system at an 11-kV substation and considers a load profile commencing from residential and commercial consumers. The outcome of this study reveals that VRFBs provide the required power to maintain load demand. Due to fast response time, VRFBs also regulate the frequency efficiently during the change in load demand and maintaining current total harmonic distortions (THDs). This research confirms the design and operation of VRFB-based energy storage systems, contributing to resilient grid infrastructure and reliable power distribution systems.