Placement Of Fault Indicators Research Articles

An unsupervised learning based MCDM approach for optimal placement of fault indicators in distribution networks

This paper proposes a novel integrated model based on multi-criteria decision-making (MCDM) method to assess and rank the feeder sections to optimally locate fault indicators in a large-scale distribution network. First, decision makers’ weights are computed based on the subjective weighting information. Then, dataset including data calculated for every alternative under active power flow, failure rate, repair time, number of customers, and customer type criteria are grouped using mixed-type clustering algorithm. This leads to the number of clusters extracted from the dataset, then obtained number of clusters are utilized to set the required linguistic terms. Finally, the ranking order of alternative is assessed by using the fuzzy similarity measure-based technique for order of preference by similarity to ideal solution (TOPSIS). The newly proposed model combines the merits of integrated enhanced particle swarm optimization (EPSO) fuzzy analytic hierarchy process (AHP) model in dealing with fuzziness and vagueness of linguistic assessments, the merits of clustering algorithm focused on representing performance values of each alternative with respect to each decision criterion and the merits of similarity measure-based TOPSIS in solving complex decision-making problems. The proposed model is applied to the RBTS4 and then sensitivity analysis is performed in the optimal solution.

Reliability Enhancement on Distribution System Using Modified Multi-Objective Particle Swarm Optimization Technique

This research paper introduces an innovative approach for optimizing the placement of fault indicators (FIs) within electric distribution systems. The primary focus is on addressing how the presence of existing protection and control devices affects the time it takes to restore service to customers in the event of a fault. Unlike conventional FI placement methods, this extended approach considers the uncertainties associated with automatic switching and introduces a novel technical objective function known as the Customers' Average Restoration Time Index (CARTI). To tackle the complex task of multi-objective optimization, a solution approach has been devised, with the goal of simultaneously minimizing both economic and technical objectives. This methodology harnesses a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and incorporates a Modified Particle Swarm Optimization technique to select the most suitable solution from the set of Pareto optimal solutions generated. The proposed method's effectiveness is demonstrated through its application in two scenarios: firstly, in the context of bus number four within the Roy Billinton test system (RBTS4), and secondly, in a practical real-life distribution network. The study assesses technical objectives, specifically the System Average Interruption Duration Index (SAIDI) and CARTI, providing insights into the method's practical utility and its capacity to enhance FI placement for improved distribution system performance.

Fault indicator placement optimization using the cross-entropy method and traffic simulation data

This paper presents an approach to optimize the placement of fault indicator devices in distribution systems using the cross-entropy method and results from traffic simulations. The problem formulation takes into account the impact of the devices on restoration times and costs due to fines related to service interruption reliability indices. Candidate solutions to the problem are evaluated using sequential Monte Carlo simulations, where travel times of maintenance crews are sampled according to data acquired from mobility traffic simulations. Results show the applicability of the approach in different simulation scenarios and the benefits of installing the devices in distribution networks.

Optimal Placement of Fault Indicators to Identify Fault Zones in Distribution Systems

The optimal placement of fault indicatorss (FIs) is one of the most viable alternatives to improve the reliability of customer service of the distribution utilities. This letter approaches the concept of fault zone as being the portion of the system characterized by the actuation of FI devices. The proposed method consists of FI placement in locations of the system that provide smaller fault zones. Moreover, these devices must be close to customers with high power demand to decrease the service interruption costs. The proposed method is tested on the IEEE 34-node test feeder, in which the results demonstrated that it is efficient to reach the stated objectives.

Optimal Placement of IoT-Based Fault Indicator to Shorten Outage Time in Integrated Cyber-Physical Medium-Voltage Distribution Network

Traditional fault indicators based on 3G and 4G cannot send out fault-generated information if the distribution lines are located in the system across remote mountainous or forest areas. Hence, power distribution systems in rural areas only rely on patrol to find faults currently, which wastes time and lacks efficiency. With the development of the Internet of things (IoT) technology, some studies have suggested combining the long-range (LoRa) and the narrowband Internet of Things (NB-IoT) technologies to increase the data transmission distance and reduce the self-built communication system operating cost. In this paper, we propose an optimal configuration scheme for novel intelligent IoT-based fault indicators. The proposed fault indicator combines LoRa and NB-IoT communication technologies with a long communication distance to achieve minimum power consumption and high-efficiency maintenance. Under this given cyber network and physical power distribution network, the whole fault location process depends on the fault indicator placement and the deployment of the communication network. The overall framework and the working principle of the fault indicators based on LoRa and NB-IoT are first illustrated to establish the optimization placement model of the proposed novel IoT-based fault indicator. Secondly, an optimization placement method has been proposed to obtain the optimal number of the acquisition and collection units of the fault indicators, as well as their locations. In the proposed method, the attenuation of the communication network and the power-supply reliability have been specially considered in the fault location process under the investment restrictions of the fault indicators. The effectiveness of the proposed method has been validated by the analysis results in an IEEE Roy Billinton Test System (IEEE-RBTS) typical system.

Optimal Placement of Fault Indicator and Sectionalizing Switch in Distribution Networks

Distribution network automation is considered by power supply companies as an effective investment strategy to improve reliability and service quality. Switching devices and protective devices play an important role in the distribution automation system (DAS). This paper presents a novel method to optimize placement of fault indicators and sectionalizing switches in distribution networks with branch lines. The objective function of the proposed method includes the total cost of fault indicators and sectionalizing switches as well as interruption cost. Among different automation equipment, this paper considers fault indicators and remote controlled switches. Besides, manual switches are taken into account since their number and location have a significant impact on the optimal placement problem. Mixed-integer linear programming is used to model the problem, and the proposed model can be solved by large-scale commercial solvers. The solution to the problem is composed of the optimal number and location of fault indicators and sectionalizing switches. The validity of the proposed method is demonstrated by relevant case studies and sensitivity analysis. Moreover, the proposed method is applied to a real distribution network to verify its practicability.

An MIP-based model for the deployment of fault indicators and sectionalizing switches in distribution networks

Fault indicators (FIs) and sectionalizing switches (SSs) are of great importance in distribution automation for reliability enhancement and efficiency improvement. However, their types and locations have great effects on the resulting service quality. This paper intends to develop a model for the optimal simultaneous allocation of FIs and SSs in a complex distribution network based on reliability and cost-benefit analyses. Three position matrices are obtained by preprocessing the topology of distribution networks to enable the establishment of the proposed model. Furthermore, a quantification method for the customer interruption duration under different contingencies is constructed to achieve a more precise and practical reliability evaluation. The resultant optimization problem is formulated as a mixed-integer linear programming that can obtain the global optimal solution in a finite number of iterations. An IEEE 33-bus network and a real urban distribution system are used to verify the effectiveness and efficiency of this model. The obtained outcomes show that simultaneously considering the placement of FIs and SSs can expedite the fault location and isolation processes, reduce the overall costs of the system and improve the service reliability.

A new mixed-integer linear formulation for optimal placement of fault indicators in distribution systems

A new mixed-integer linear formulation for optimal placement of fault indicators in distribution systems

Application of BICA-NM hybrid algorithm for optimal locating of fault indicators in distribution networks

Fast and accurate detection of fault location in distribution networks, is one of the important issues in operation of the system. In this paper, an approach based on Binary Imperialistic Competitive algorithm and Nelder-Mead simplex search has been proposed for placement of fault indicators in distribution networks. By using this method, the problem of locating fault indicators can be solved faster and more accurately by minimizing of cost function. In this paper, in addition to describing the BICA-NM hybrid algorithm, the fault indicator placement has been implemented using this method on a test system, and the simulation results have been compared with Genetic algorithm GA, Binary Shuffled Frog Leap Algorithm BSFLA and Improved BSFLA IBSFLA and the accuracy and fastness of the proposed method has been demonstrated.

Alocação eficiente de indicadores de faltas em um sistema de distribuição real usando computação evolutiva

Este trabalho propõe uma abordagem computacional evolutiva para a resolução do problema de alocação de dispositivos indicadores de faltas (IFs) em alimentadores primários de distribuição de energia elétrica. De forma mais específica, o problema de se obter o melhor local de instalação é solucionado por meio da técnica de Algoritmos Genéticos (AGs) que busca obter uma configuração eficiente de instalação de IFs no tronco principal de um alimentador de distribuição. Assim, faz-se a modelagem do mesmo na forma de um problema de otimização orientado à melhoria dos indicadores de qualidade do serviço e ao encontro de uma solução economicamente atraente. Os resultados com dados reais comprovam a eficiência da metodologia proposta.

Optimal Placement of Fault Indicators Using the Immune Algorithm

This paper examines the application of the immune algorithm for the problem of optimal placement of fault indicators to minimize the total cost of customer service outage and investment cost of fault indicators. The reliability index of each service zone is derived to solve the expected energy not served due to fault contingency, and the customer interruption cost is then determined according to the customer type and power consumption within the service zone. To demonstrate the effectiveness of the proposed IA methodology and solve the optimal placement of fault indicators, a practical distribution feeder of Taiwan Power Company is selected for computer simulation to explore the cost benefit of fault indicator placement.