Study on the performance indicators for smart grids: a comprehensive review

This paper presents a detailed review on performance indicators for smart grid (SG) such as voltage stability enhancement, reliability evaluation, vulnerability assessment, Supervisory Control and Data Acquisition (SCADA) and communication systems. Smart grids reliability assessment can be performed by analytically or by simulation. Analytical method utilizes the load point assessment techniques, whereas the simulation technique uses the Monte Carlo simulation (MCS) technique. The reliability index evaluations will consider the presence or absence of energy storage elements using the simulation technologies such as MCS, and the analytical methods such as systems average interruption frequency index (SAIFI), and other load point indices. This paper also presents the difference between SCADA and substation automation, and the fact that substation automation, though it uses the basic concepts of SCADA, is far more advanced in nature

A hybrid model to evaluate human error probability (HEP) in a pharmaceutical plant

The aim of the present research is to propose a hybrid model to evaluate Human Error Probability (HEP) called Logit Human Reliability (LHR). The new approach is based on logit normal distribution, Nuclear Action Reliability Assessment (NARA), and Performance Shaping Factors (PSFs) relationship. The present paper analyzed some shortcomings related to literature approaches, especially the limitations of the working time. We estimated PSFs after 8 hours (work standard) during emergency conditions. Therefore, the correlation between the advantages of these three methodologies allows proposing a HEP analysis during accident scenario and emergencies. The proposed approach considers internal and external factors that affect the operator's ability. LHR has been applied in a pharmaceutical accident scenario, considering 24 hours of working time (more than 8 working hours)

Quality Checks Logit Human Reliability (LHR): A New Model to Evaluate Human Error Probability (HEP)

In the years, several approaches for human reliability analysis (HRA) have been developed. The aim of the present research is to propose a hybrid model to evaluate Human Error Probability (HEP). The new approach is based on logit-normal distribution, Nuclear Action Reliability Assessment (NARA), and Performance Shaping Factors (PSFs) relationship. In the research, shortcomings related to literature approaches are analyzed, especially the limitations of the working time. For this reason, PSFs after 8 hours (work standard) during emergency conditions were estimated. Therefore, the correlation between the advantages of these three methodologies allows proposing a HEP analysis during accident scenarios and emergencies; a fundamental issue to ensure the safety and reliability in industrial plants is emergency Mmnagement (EM). Applying EM methodology, two main aspects are analyzed: system reliability and human reliability. System reliability is strongly related to the reliability of its weakest component. During incidental situations, the weakest parts of the whole system are workers (human reliability) and accidental scenarios influence the operator’s ability to make decisions. This article proposes a new approach called Logit Human Reliability (LHR) that considers internal and external factors to estimate human reliability during emergencies. LHR has been applied in a pharmaceutical accident scenario, considering 24 hours of working time (more than 8 working hours). The results highlighted that the LHR method gives output data more in conformity with data banks than the conventional methods during the stress phase in an accident scenario

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Alert Diagnostic System: SDA

Currently, there is a trend in reduction of the number of industrial plant operators. The challenges are mainly during emergency situations: how to support operator time management without increasing operational risks? SDA focuses on this area and aims to increase operator situational awareness (ability to perceive, understand and predict the future behavior of a process) through new technological paradigms, such as Expert System and Ecological Human Machine Interface (HMI) in order to provide operational support, maintenance and optimization of refining, exploration and system of production of oil and gas plants. In SDA, the most critical alerts are shown by priority, along with decision trees, trend charts and variable comparison charts. SDA aims to assist control room operators in solving a critical problem in the oil industry, that is the loss of safety function, associated with alarms, during alarm flood. The SDA results of the SDA are presented through its implementation in Sulfur Recovery Units—URE, in the state of Rio de Janeiro, in Brazil

Web Supervision System of a Freight Elevator

Nowadays, automation and industrial control is an area in which there are innovations ev- ery day in terms of process digitalization, equipment interconnection and human-machine interaction, which results in a constant learning and adaptation to new technologies and methodologies developed. With this comes the responsibility to keep systems robust and prepared for eventual failures, while moving towards an increasing dependence on remote communication between different controllers and different processes. This fact leads to the need to create supervision and monitoring tools capable of detecting and transmitting existing failures, while ensuring that the system continues to operate with the same stability and performance. Therefore, in this work it is proposed the development of a supervisory tool based on industrial automation that has a fault detection component and a human-machine interface in order to incorporate all the essential features of an industrial supervisor. Using industrial programming languages for Programmable Logic Controllers, it was possible to develop an algorithm that is based on inference mechanisms to identify potential faults in the system, which are then transmitted to the user in an interface that can be accessed either locally or remotely via the Web.Nos dias de hoje, a automação e controlo industrial é uma área onde existe todos os dias inovações ao nível da digitalização de processos, da interconexão de equipamentos e na interação Homem-máquina, o que resulta numa constante aprendizagem e adaptação às novas tecnologias e metodologias desenvolvidas. Com isto, vem a responsabilidade de manter os sistemas robustos e preparados para eventuais falhas, ao mesmo tempo que se avança no sentido da cada vez maior dependência da comunicação remota entre diferentes controladores e diferentes processos. Este facto leva a que tenham de ser criadas ferramentas de supervisão e monitorização capazes de detetar e transmitir as falhas existentes, enquanto se garante que o sistema continua em funcionamento garantindo a mesma estabilidade e performance. Assim, neste trabalho é proposto o desenvolvimento de uma ferramenta de supervisão baseada em automação industrial que possua uma componente de deteção de falhas e uma interface Homem-máquina de forma a incorporar todas as funcionalidades essenciais de um supervisor industrial. Recorrendo a linguagens de programação industrial para controladores lógicos programáveis, foi possível desenvolver um algoritmo que se baseia em mecanismos de inferência para identificar potenciais avarias no sistema que são posteriormente transmitidas ao utilizador numa interface que pode ser acedida quer localmente, quer remotamente via Web

Anomaly detection for resilient control systems using fuzzy-neural data fusion engine

Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting

Human factors consideration in the automation design of a safety-critical installation

M.Ing. (Engineering Management)Abstract: Human factors consideration should form an integral part of any system’s design. The aim is to ensure the designed system is compatible with human skills and limitations. Benefits of this consideration include reduction in the required level of training once the system is deployed. Unfortunately, even though the requirement of humans in systems design is well known, systems are continuously designed with little or no input from the eventual operators. This study aims to investigate the human factors aspect in the automation design of a safety-critical installation. Automation in its noble form is intended to improve factors such as safety, efficiency, and costs. However, this is not always the case. Part of the problem is that human operators are not always adequately considered during the design. It is the aim of this study to elicit the important human factors that must be considered in the automation design. This is done using a case study method. The case study was undertaken at the major radioisotopes production institution in the Republic of South Africa. The use of this study method is adopted as it provides enough in-depth knowledge that can be used in other safety-critical facilities