Hierarchical Reasoning about Faults in Cyber-Physical Energy Systems using Temporal Causal Diagrams

Fault management systems that observe the state of the system, decide if there is an anomaly and then take automated actions to isolate faults. For example, in electrical networks relays and breaks isolate faults in order to arrest failure propagation and protect the healthy parts of the system. However, due to the limited situational awareness and hidden failures the protection devices themselves, through their operation (or mis-operation) may cause overloading and the disconnection of parts of an otherwise healthy system. Additionally, often there can be faults in the management system itself leading to situations where it is difficult to isolate failures. Our work presented in this paper is geared towards solution of this problem by describing the formalism of Temporal Causal Diagrams (TCD-s) that augment the failure models for the physical systems with discrete time models of protection elements, accounting for the complex interactions between the protection devices and the physical plants. We use the case study of the standard Western System Coordinating Council (WSCC) 9 bus system to describe four different fault scenarios and illustrate how our approach can help isolate these failures. Though, we use power networks as exemplars in this paper our approach can be applied to other distributed cyberphysical systems, for example water networks

Intelligent monitoring of the health and performance of distribution automation

With a move to 'smarter' distribution networks through an increase in distribution automation and active network management, the volume of monitoring data available to engineers also increases. It can be onerous to interpret such data to produce meaningful information about the health and performance of automation and control equipment. Moreover, indicators of incipient failure may have to be tracked over several hours or days. This paper discusses some of the data analysis challenges inherent in assessing the health and performance of distribution automation based on available monitoring data. A rule-based expert system approach is proposed to provide decision support for engineers regarding the condition of these components. Implementation of such a system using a complex event processing system shell, to remove the manual task of tracking alarms over a number of days, is discussed

Knowledge and model based reasoning for power system protection performance analysis

Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available.Technological advances within the field of power systems has led to engineers, at all levels, being confronted with an ever increasing amount of data to be analysed. This coincides with greater pressure on engineers to work more efficiently and cost effectively, due to the increasingly competitive nature of the electricity supply industry. As a result, there is now the requirement for intelligent systems to interpret the available data and provide information which is relevant, manageable and readily assimilated by engineers. This thesis concerns the application of intelligent systems to the data interpretation tasks of protection engineers. An on-line decision support system is discussed which integrates two expert system paradigms in order to perform power system protection performance analysis. Knowledge based system techniques are used to interpret the data from supervisory, control and data acquisition systems, whereas a model based diagnosis approach to the comprehensive validation of protection performance, using the more detailed data which is available from fault records or equivalent, is assessed. Such a decision support system removes the requirement for time consuming manual analysis of data. An assessment of power system protection performance is provided in an on-line fashion, quickly alerting the engineers to failures or problems within the protection system. This improves efficiency and maximises the benefit of having an abundance of data available

Connected system for monitoring electrical power transformers using thermal imaging

The stable supply of electricity is essential for the industrial activity and economic development as well as for human welfare. For this reason, electrical system devices are equipped with monitoring systems that facilitate their management and ensure an uninterrupted operation. This is the case of electrical power transformers, which usually have monitoring systems that allow early detection of anomalies in order to prevent potential malfunctions. These monitoring systems typically make use of sensors that are in physical contact with the transformer devices and can therefore be affected by transformer problems. In this work we demonstrate a monitoring system for electrical power transformers based on temperature measurements obtained by means of thermal cameras. Properly positioned, the cameras provide thermal data of the transformer, the incoming and outgoing lines and their surroundings. Subsequently, by appropriate image processing, it is possible to obtain temperature series to monitor the transformer operation. In addition, the system stores and processes thermal data in external equipment (placed in locations other than the transformers) and is equipped with a communications module that allows secure data transmission independent of the power grid. This aspect, along with the fact that there is no need to have physical contact with the transformer, make this approach safer and more reliable than standard approaches based on sensors. The proposed system has been evaluated in 14 stations belonging to the Spanish power grid, obtaining accurate and reliable temperature time seriesConsejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía)FEDER under B-TIC-586-UGR20P20-00525 projects and by the University of GranadaEndesa Distribución under the PASTORA (ref. EXP – 00111351/ITC-20181102)RESISTO (ref. 2021/C005/00144188) contract

Exploratory Models in a time of Big Data

This paper aims to trigger discourse about the emergence of a new type of social scientific model — Exploratory Models — which draw on Big Data, computer modeling and interdisciplinary research to tackle complex social scientific processes. First, we define Exploratory Models referring to Batty and Morgan and Morrison. We then present changes to the traditional modeling paradigm. We show how Exploratory Models circumvent challenges related to the idiosyncracy, self-reflexivity and acceleration of social phenomena, which limit predictive effectiveness of traditional models. We show that Exploratory Models are better equipped to tackle complex problems due to their capacity to process heterogeneous datasets. Having established that Exploratory Models are predominantly problem- and data-driven, we emphasize that scientific theory is indispensable to their progress. Finally, the development of an integrative platform is suggested as a way of maximizing the benefits of this approach. Discussion concludes by flagging areas for further research

Economic Forecasting: Some Lessons from Recent Research

This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes a general theoretical background against which recent results can be judged. We compare this framework to a previous formulation, which was silent on the very issues of most concern to the forecaster. We describe a number of aspects which it illuminates, and draw out the implications for model selection. Finally, we discuss the areas where research remains needed to clarify empirical findings which lack theoretical explanations.

Economic Forecasting: Some Lessons from Recent Research

We describe a general theoretical framework against which recent results in economic forecasting can be judged, including explanations for the findings of forecasting competitions, the prevalence of forecast failure, and the role of causal variables. We compare this framework to a previous formulation which was silent on the very issues of most concern to the forecaster, then describe ten aspects which our approach illuminates, and draw out their implications for model selection. Finally, we discuss ten areas where research is needed to clarify empirical findings that still lack theoretical explanations.

Space station automation of common module power management and distribution

The purpose is to automate a breadboard level Power Management and Distribution (PMAD) system which possesses many functional characteristics of a specified Space Station power system. The automation system was built upon 20 kHz ac source with redundancy of the power buses. There are two power distribution control units which furnish power to six load centers which in turn enable load circuits based upon a system generated schedule. The progress in building this specified autonomous system is described. Automation of Space Station Module PMAD was accomplished by segmenting the complete task in the following four independent tasks: (1) develop a detailed approach for PMAD automation; (2) define the software and hardware elements of automation; (3) develop the automation system for the PMAD breadboard; and (4) select an appropriate host processing environment

Economic forecasting: some lessons from recent research

This paper describes some recent advances and contributions to our understanding of economic forecasting. The framework we develop helps explain the findings of forecasting competitions and the prevalence of forecast failure. It constitutes a general theoretical background against which recent results can be judged. We compare this framework to a previous formulation, which was silent on the very issues of most concern to the forecaster. We describe a number of aspects which it illuminates, and draw out the implications for model selection. Finally, we discuss the areas where research remains needed to clarify empirical findings which lack theoretical explanations. JEL Classification: C32