Data-driven system reliability and failure behavior modelling using FMECA

System reliability modelling needs a large amount of data to estimate the parameters. In addition, reliability estimation is associated with uncertainty. This paper aims to propose a new method to evaluate the failure behavior and reliability of a large system using failure modes, effects and criticality analysis (FMECA). Therefore, qualitative data based on the judgment of experts is used when data is not sufficient. The subjective data of failure modes and causes has been aggregated through the system to develop an overall failure index (OFI). This index not only represents the system reliability behavior but also prioritizes corrective actions based on improvements in system failure. In addition, two optimization models are presented to select optimal actions subject to budget constraint. The associated costs of each corrective action are considered in risk evaluation. Finally, a case study of a manufacturing line is introduced to verify the applicability of the proposed method in industrial environments. The proposed method is compared with conventional FMECA approach. It is shown that the proposed method has a better performance in risk assessment. A sensitivity analysis is provided on the budget amount and the results are discussed.Hadi A. Khorshidi, Indra Gunawan, and M. Yousef Ibrahi

Optimal Switch Placement by Alliance Algorithm for Improving Microgrids Reliability

A method for optimal switches placement in distribution systems with distributed generation is presented in this paper. According to both technical and economical issues, the method allows minimizing the unsupplied loads in case of permanent faults, while limiting the number of installed switches. The problem is formulated as a mixed integer non linear programming problem (MINLP) and the solution is obtained by a new metaheuristic algorithm, i.e., the Alliance Algorithm. The method is based on self-microgrids forming and allows improving the continuity of the service as confirmed by simulation results on both an IEEE standard and a real test network

Modelagem e simulação integrada ao processamento e diagnóstico do desempenho de parâmetros elétricos no contexto de Redes Inteligentes.

Os sistemas elétricos de potência têm experimentado mudanças significativas ao longo dos últimos anos. A crescente inserção de energia por fontes renováveis tende a mudar a matriz energética, introduzindo novos conceitos, como redes ativas e microrredes. O uso de fontes renováveis é muito comum nessas redes, que podem operar conectadas à rede convencional ou ilhada. Quando na condição ilhada, trata-se de um sistema mais fraco, i.e., com baixa capacidade de curto-circuito, sujeita a sofrer grandes impactos diante de contingências. Isso faz com que o monitoramento das condições operativas da rede seja de grande importância. Este trabalho foca no estudo do monitoramento da qualidade de microrredes. A principal contribuição deste trabalho reside no desenvolvimento de duas ferramentas para monitoramento da rede. Uma é em uma janela de tempo executada no Simulink, que usa o filtro Discrete-Time Wavelet Transform (DTWT), para decompor a forma de onda em diferentes níveis de frequência. O monitoramento desses diferentes níveis, pode ser usado para detectar contingências na rede. Também foi proposto o monitoramento da distorção harmônica total (THD) das três fases de uma barra de interesse. Isso permite a entrega automatizada de um diagnóstico que avalia se a rede está operando em condições aceitáveis ou não. A outra ferramenta desenvolvida em uma interface do MATLAB, tem a proposta de decompor as formas de onda das tensões trifásicas através da ferramenta de processamento de sinais Wavelet Packet Tree (WPT), resultando em uma melhor visualização da fundamental e das harmônicas de maiores magnitudes em um dado intervalo de tempo. A ferramenta ainda permite visualizar o espectro de frequência no período selecionado e a amplitude de cada frequência ao longo do período de operação em um gráfico tempo - frequência. Os resultados mostraram eficiência em ambas ferramentas desenvolvidas

Investigating the impact of asset condition on distribution network reconfiguration and its capacity value

Ph. D. ThesisGenerally, decisions regarding Distribution Network (DN) operations are based only on operational parameters, such as voltages, currents and power flows. Asset condition is a key parameter that is usually not considered by Network Management Systems (NMSs) in their optimization process. The work in this thesis seeks to quantify the extent to which asset condition information can positively influence network operation and planning; specifically through Distribution Network Reconfiguration (DNR). Asset condition can be translated into Health Indices (HIs) and failure rates, allowing an NMS – or an optimization algorithm – to make better informed decisions. This is realized via appropriate asset condition assessment and failure rate models. The effect on optimal DNR is evaluated – focusing on substation condition and reliability; the idea of load transfer from one feeder or substation to a more reliable one is key in the proposed methodology. Condition-based risk is considered in the DNR problem, and the impact of transformer ageing on network reconfiguration is examined as well. The effect of asset condition assessment and ageing – which depends on the type of network branches (overhead lines or underground cables) – on the optimal distribution switch automation is also investigated. Finally, a probabilistic method is developed to quantify the contribution of DNR to network security considering asset condition and ageing. The results show that savings can be in the order of tens of thousands of U.S. dollars for a single DN; this corresponds approximately to 10% of the annual cost of active power losses. This can mean hundreds of thousands – or even millions – of U.S. dollars of savings for a single DN operator. Regarding the optimal placement of automated switches, neglecting the effect of asset ageing can result in an underestimation of expected outage cost by as much as $223,000 over a 20-year period. Finally, ignoring the contribution of DNR to security of supply can double the estimation of network risk; in addition to that, disregarding asset condition and ageing results in a reinforcement deferral overestimation of two years

Microgrid Formation-based Service Restoration Using Deep Reinforcement Learning and Optimal Switch Placement in Distribution Networks

A power distribution network that demonstrates resilience has the ability to minimize the duration and severity of power outages, ensure uninterrupted service delivery, and enhance overall reliability. Resilience in this context refers to the network's capacity to withstand and quickly recover from disruptive events, such as equipment failures, natural disasters, or cyber attacks. By effectively mitigating the effects of such incidents, a resilient power distribution network can contribute to enhanced operational performance, customer satisfaction, and economic productivity. The implementation of microgrids as a response to power outages constitutes a viable approach for enhancing the resilience of the system. In this work, a novel method for service restoration based on dynamic microgrid formation and deep reinforcement learning is proposed. To this end, microgrid formation-based service restoration is formulated as a Markov decision process. Then, by utilizing the node cell and route model concept, every distributed generation unit equipped with the black-start capability traverses the power system, thereby restoring power to the lines and nodes it visits. The deep Q-network is employed as a means to achieve optimal policy control, which guides agents in the selection of node cells that result in maximum load pick-up while adhering to operational constraints. In the next step, a solution has been proposed for the switch placement problem in distribution networks, which results in a substantial improvement in service restoration. Accordingly, an effective algorithm, utilizing binary particle swarm optimization, is employed to optimize the placement of switches in distribution networks. The input data necessary for the proposed algorithm comprises information related to the power system topology and load point data. The fitness of the solution is assessed by minimizing the unsupplied loads and the number of switches placed in distribution networks. The proposed methods are validated using a large-scale unbalanced distribution system consisting of 404 nodes, which is operated by Saskatoon Light and Power, a local utility in Saskatoon, Canada. Additionally, a balanced IEEE 33-node test system is also utilized for validation purposes

Multilevel Monte Carlo approach for estimating reliability of electric distribution systems

Most of the power outages experienced by the customers are due to the failures in the electric distribution systems. However, the ultimate goal of a distribution system is to meet customer electricity demand by maintaining a satisfactory level of reliability with less interruption frequency and duration as well as less outage costs. Quantitative evaluation of reliability is, therefore, a significant aspect of the decision-making process in planning and designing for future expansion of network or reinforcement. Simulation approach of reliability evaluation is generally based on sequential Monte Carlo (MC) method which can consider the random nature of system components. Use of MC method for obtaining accurate estimates of the reliability can be computationally costly particularly when dealing with rare events (i.e. when high accuracy is required). This thesis proposes a simple and effective methodology for accelerating MC simulation in distribution systems reliability evaluation. The proposed method is based on a novel Multilevel Monte Carlo (MLMC) simulation approach. MLMC approach is a variance reduction technique for MC simulation which can reduce the computational burden of the MC method dramatically while both sampling and discretisation errors are considered for converging to a controllable accuracy level. The idea of MLMC is to consider a hierarchy of computational meshes (levels) instead of using single time discretisation level in MC method. Most of the computational effort in MLMC method is transferred from the finest level to the coarsest one, leading to substantial computational saving. As the simulations are conducted using multiple approximations, therefore the less accurate estimate on the preceding coarse level can be sequentially corrected by averages of the differences of the estimations of two consecutive levels in the hierarchy. In this dissertation, we will find the answers to the following questions: can MLMC method be used for reliability evaluation? If so, how MLMC estimators for reliability evaluation are constructed? Finally, how much computational savings can we expect through MLMC method over MC method? MLMC approach is implemented through solving the stochastic differential equations of random variables related to the reliability indices. The differential equations are solved using different discretisation schemes. In this work, the performance of two different discretisation schemes, Euler-Maruyama and Milstein are investigated for this purpose. We use the benchmark Roy Billinton Test System as the test system. Based on the proposed MLMC method, a number of reliability studies of distribution systems have been carried out in this thesis including customer interruption frequency and duration based reliability assessment, cost/benefits estimation, reliability evaluation incorporating different time-varying factors such as weather-dependent failure rate and restoration time of components, time-varying load and cost models of supply points. The numerical results that demonstrate the computational performances of the proposed method are presented. The performances of the MLMC and MC methods are compared. The results prove that MLMC method is computationally efficient compared to those derived from standard MC method and it can retain an acceptable level of accuracy. The novel computational tool including examples presented in this thesis will help system planners and utility managers to provide useful information of reliability of distribution networks. With the help of such tool they can take necessary steps to speed up the decision-making process of reliability improvement.Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201