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This paper reviews alarm processing methods in electrical power systems, focusing on evolving strategies beyond traditional fault analysis to accommodate modern grid complexities. Historically, alarm processing has predominantly aimed at fault analysis, increasingly merging with technological advances in communication and computing. However, it still needs to fully meet the challenges posed by the dynamic characteristics of modern power systems. This review points out certain inadequacies in current practices, notably their limited adaptation to new grid conditions. The authors propose a novel generation of alarm processing methodologies designed for future grids, emphasizing managing rare events and enhancing operator decision-making through advanced anomaly detection and explainable artificial intelligence. This synthesis presents a prospective direction for future research and applications in alarm processing, advocating for methodologies better suited to supporting system operators amidst technological advancements.

For the process alarm management of chemical process safety analysis, its reliability mainly depends on the design of the safety limit and the optimization of the technical process, but there are few studies on the overall reliability level of process alarm based on the alarm disposal rate under the influence of human factor reliability. By focusing on independent layer analysis technique in the alarm and response, we combined the human reliability analysis (HRA) technology and Bayes theory, to form Human Reliability Analysis in chemical process safety(Chem-HRA), an optimized quantitative evaluation algorithm for chemical alarm failure frequency. The algorithm was applied to a risk event on tank farm’s cut over, ensuring the reliability and accuracy of petrochemical process analysis and disposal of the process alarm.

Summary Power grid facilities can be vulnerable with respect to malicious physical attacks. An advanced system to monitor physical security of substations and other facilities is essential to maintain system integrity. This paper describes a substation physical security monitoring (SPSM) system for remote monitoring of power substations. The proposed methodology consists of video surveillance for outdoors, motion detectors for indoors, and the classification of intrusion events. An important feature of the proposed method is the ability to determine the response to a threat based on the result of physical security monitoring. In addition, industry experience from two transmission system operators (TSOs) is incorporated in the development of the methodology. To validate the proposed SPSM system, an experiment was performed at a critical substation involving two physical intrusion scenarios. Copyright © 2015 John Wiley & Sons, Ltd.

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator's actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system.

Real time applications to control industrial, medical, scientific, consumer, environmental and other processes is rapidly growing. Today such systems can be found in nuclear power stations, computer-controlled chemical plants, flight control, etc. This growth, however, has also brought to the forefront some of problems with the existing technologies. In domains like real-time alarm processing in a power system control centre existing technologies like expert systems cannot efficiently cope with. These problems have pushed for research into new techniques which could be used for solving these problems. The problems range from among other aspects, the enormous size of the power system and the fast response time constraints in emergency situations. In this paper we describe the application of the Intelligent Multi-Agent Hybrid Distributed Architecture for real-time alarm processing in a power system control centre. We show how the IMAHDA architecture is able to model the complexity and size of the power system as well as meet the desired response time constraints. Implementation of a large scale real time system like alarm processing involves realization of various objectives. These include methodology related objectives, domain related objectives, and management related objectives. This paper also describes the realization of these objectives.

Abstract Within the exploitation of the automated production systems (APS) framework, supervision and monitoring take the dependability into account. In order to reach these objectives, a method is being developed at the Laboratoire d'Automatique et d'Informatique Industrielle de Lille in a supervision and monitoring framework. The main functions are detection, diagnosis, modes management and faults recovery. This paper focuses on two kinds of modelling: functional modelling and behavioural modelling. On the one hand, these models are used to implement the diagnosis and the modes management functions. On the other hand, the complementarity of these models enables them to be used within the alarm processing system framework. Indeed, the design of intelligent alarm processing systems (IAPS) is a critical problem. One common problem is the number of alarms that operators have to manage. This paper focuses on the problem of the management of avalanches of alarms after the occurrence of failures in a complex process. To deal with this problem, different types of alarm are defined from the functional and behavioural modelling of the process to be surveyed. Filtering rules, based on the concepts of validation and inhibition, are also proposed.

This work proposes a software tool for fault diagnosis in power systems. The solution was developed to monitoring real-time SCADA data of the transmission and generation network of a Brazilian electric utility in order to assist operators of control centres to analyse the occurrence and to decide the best procedures to re-establish the service in order to enhance the service reliability and reduce the power restoration time. The fault diagnosis system is formulated as an optimization problem and solved through two stages: event classification at equipment level. The developed approach also identifies the malfunctioned protective devices as well the missing and false alarms. Possible fault scenarios were considered in part of a real Brazilian power system to validate the methodology.

As technology continues to evolve, our society is becoming enriched with more intelligent devices that help us perform our daily activities more efficiently and effectively. One of the most significant technological advancements of our time is the Internet of Things (IoT), which interconnects various smart devices (such as smart mobiles, intelligent refrigerators, smartwatches, smart fire alarms, smart door locks, and many more) allowing them to communicate with each other and exchange data seamlessly. We now use IoT technology to carry out our daily activities, for example, transportation. In particular, the field of smart transportation has intrigued researchers due to its potential to revolutionize the way we move people and goods. IoT provides drivers in a smart city with many benefits, including traffic management, improved logistics, efficient parking systems, and enhanced safety measures. Smart transportation is the integration of all these benefits into applications for transportation systems. However, as a way of further improving the benefits provided by smart transportation, other technologies have been explored, such as machine learning, big data, and distributed ledgers. Some examples of their application are the optimization of routes, parking, street lighting, accident prevention, detection of abnormal traffic conditions, and maintenance of roads. In this paper, we aim to provide a detailed understanding of the developments in the applications mentioned earlier and examine current researches that base their applications on these sectors. We aim to conduct a self-contained review of the different technologies used in smart transportation today and their respective challenges. Our methodology encompassed identifying and screening articles on smart transportation technologies and its applications. To identify articles addressing our topic of review, we searched for articles in the four significant databases: IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Consequently, we examined the communication mechanisms, architectures, and frameworks that enable these smart transportation applications and systems. We also explored the communication protocols enabling smart transportation, including Wi-Fi, Bluetooth, and cellular networks, and how they contribute to seamless data exchange. We delved into the different architectures and frameworks used in smart transportation, including cloud computing, edge computing, and fog computing. Lastly, we outlined current challenges in the smart transportation field and suggested potential future research directions. We will examine data privacy and security issues, network scalability, and interoperability between different IoT devices.

Alarm systems are commonly deployed in complex industries to monitor the operation status of the production process in real time. Actual alarm systems generally have alarm overloading problems. One of the major factors leading to excessive alarms is the presence of many correlated or redundant alarms. Analyzing alarm correlations will not only be beneficial to the detection of and reduction in redundant alarm configurations, but also help to track the propagation of abnormalities among alarm variables. As a special problem in correlated alarm detection, the research on first-out alarm detection is very scarce. A first-out alarm is known as the first alarm that occurs in a series of alarms. Detection of first-out alarms aims at identifying the first alarm occurrence from a large number of alarms, thus ignoring the subsequent correlated alarms to effectively reduce the number of alarms and prevent alarm overloading. Accordingly, this paper proposes a new first-out alarm detection method based on association rule mining and correlation analysis. The contributions lie in the following aspects: (1) An association rule mining approach is presented to extract alarm association rules from historical sequences based on the FP-Growth algorithm and J-Measure; (2) a first-out alarm determination strategy is proposed to determine the first-out alarms and subsequent alarms through correlation analysis in the form of a hypothesis test on conditional probability; and (3) first-out rule screening criteria are proposed to judge whether the rules are redundant or not and then consolidated results of first-out rules are obtained. The effectiveness of the proposed method is tested based on the alarm data generated by a public simulation platform.

Intensive-care clinicians are presented with large quantities of measurements from multiple monitoring systems. The limited ability of humans to process complex information hinders early recognition of patient deterioration, and high numbers of monitoring alarms lead to alarm fatigue. We used machine learning to develop an early-warning system that integrates measurements from multiple organ systems using a high-resolution database with 240 patient-years of data. It predicts 90% of circulatory-failure events in the test set, with 82% identified more than 2 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an area under the precision-recall curve of 0.63. On average, the system raises 0.05 alarms per patient and hour. The model was externally validated in an independent patient cohort. Our model provides early identification of patients at risk for circulatory failure with a much lower false-alarm rate than conventional threshold-based systems. A machine-learning algorithm based on an array of demographic, physiological and clinical information is able to predict, hours in advance, circulatory failure of patients in the intensive-care unit.