By penetrating the SCADA system, an intruder may remotely operate a power system using supervisory control privileges. Hence, cybersecurity has been recognized as a major threat due to the potential intrusion to the online system. This paper proposes a methodology to evaluate the cybersecurity vulnerability using attack trees. The attack tree formulation based on power system control networks is used to evaluate the system, scenario, and leaf vulnerabilities. The measure of vulnerabilities in the power system control framework is determined based on existing cybersecurity conditions before the vulnerability indices are evaluated. After the indices are evaluated, an upper bound is imposed on each scenario vulnerability in order to determine the pivotal attack leaves that require countermeasure improvements. The proposed framework can be extended to security investment analysis.

Vulnerability Assessment of Cybersecurity for SCADA Systems Using Attack Trees

The basic concepts of the multimodeling approach to the representation of physical systems are presented. Emphasis is placed on the exploitation of many, diverse models of a system for the execution of complex problem solving tasks, such as interpretation, diagnosis, design, simulation, etc. The considered models are based on different ontologies, representational assumptions, epistemological types, and aggregation levels. After a brief survey of the techniques adopted for representing structural and behavioral knowledge, attention is focused on function and teleology. A novel approach is proposed for defining, representing, and using these two types of knowledge which play a fundamental role both from the representation and reasoning perspectives. The fundamental claim is that while teleological knowledge concerns the specific purposes for which the system has been designed, functional knowledge is devoted to bridge the gap between such abstract purposes and the actual structure and behavior of the system, through the concepts of phenomena, processes, and functional roles. A clear definition is provided of all the various epistemological and ontological links existing between the different models. >

Functional and teleological knowledge in the multimodeling approach for reasoning about physical systems: a case study in diagnosis

An Expert System 10 for providing diagnostics to a data communications network 5. Alarms from a Network Manager 24 are received and queued by an Event Manager 117 and then filtered by an Alarm Filter 118 to remove redundant alarms. Alarms which are ready for processing are then posted to a queue referred to as a Bulletin Board 120. A Controller 112 determines which one of the posted goals has the highest priority by considering a priority number associated with the goal plus a time of arrival of the goal. An Inference Engine 122 uses information from an Expert Information Structure 111 to solve the highest priority goal by a process called instantiation. The process of solving the goal may be interrupted by a pause or suspension in order to perform tests under the direction of a Network Test Manager 124 or retrieve other information during which time other goals may be processed. Expert information is entered using a user friendly User Interface 104 which reduces need for the participation of a Knowledge Engineer. Configuration information about the network is maintained in a Network Structure Knowledge Base 109 by a Network Configuration Module 108. The Expert System 10 may operate in any of three modes: manual, wherein tests must be approved by or directed by an operator; automatic, where the tests are run automatically without operator intervention; and semiautomatic, where operator approval is required for certain tests such as interruptive tests and other tests such as non-interruptive tests may proceed without operator intervention.

Expert system for communications network

A survey on intelligent system application to fault diagnosis in electric power system transmission lines

When a fault occurs on a section or a component in a given power system, if one or more protective relays (PRs) and/or circuit breakers (CBs) associated do not work properly, or in other words, a malfunction or malfunctions happen with these PRs and/or CBs, the outage area could be extended. As a result, the complexity of the fault diagnosis could be greatly increased. The existing analytic models for power system fault diagnosis do not systematically address the possible malfunctions of PRs and/or CBs, and hence may lead to incorrect diagnosis results if such malfunctions do occur. Given this background, based on the existing analytic models, an effort is made to develop a new analytic model to well take into account of the possible malfunctions of PRs and/or CBs, and further to improve the accuracy of fault diagnosis results. The developed model does not only estimate the faulted section(s), but also identify the malfunctioned PRs and/or CBs as well as the missing and/or false alarms. A software system is developed for practical applications, and realistic fault scenarios from an actual power system are served for demonstrating the correctness of the presented model and the efficiency of the developed software system.

An Analytic Model for Fault Diagnosis in Power Systems Considering Malfunctions of Protective Relays and Circuit Breakers

In this study, a new method that handles multiple hypotheses is presented for fault diagnosis using sequence of event recorders (SERs). To quantify the certainty of hypotheses, a method to calculate their credibility is provided. The proposed techniques are integrated in a generalized alarm analysis module (GAAM) and have been tested with numerous scenarios from the Italian power system.

Multiple hypotheses and their credibility in on-line fault diagnosis

The paper presents a novel expert system architecture which supports explicit representation and effective use of both declarative and procedural knowledge These two types of expert knowledge are represented by means of production rules and event-graphs respectively, and they are processed by a unified inference engine Communication between the rule level and the event-graph level is based on a full visibility of each level on the internal state of the other, and it is structured in such a way as to allow each level to expert control on the other

This structure offers several advantages over more traditional architectures Knowledge representation is more natural and transparent; knowledge acquisition turns out to be easier as pieces of knowledge can be immediately represented without the need of complex transformation and restructuring; inference is more effective due to reduced non-determinism resulting from explicit representation of fragments of procedural knowledge in event-graphs; finally, explanations are more natural and understandable

The proposed architecture has been adopted for the design of PROP, an expert system for on-line monitoring of the cycle water pollution in a thermal power plant PROP is running on a SUN-2 workstation and has been tested on a sample of real cases

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Representing procedural knowledge in expert systems: an application to process control

The difficulties encountered in applying knowledge-based system technology to complex industrial environments have made the need for representing and using deep knowledge about physical systems increasingly clear to system designers. A rather large number of approaches to modeling and reasoning with deep knowledge have been experimented, but the impact of these new techniques, often referred to as model based reasoning, on real applications is still poor. This paper presents a novel model-based diagnostic method, whose distinctive features make it practical for diagnostic problem solving in automated systems for monitoring continuous processes. The method we introduce makes use of models at different levels of abstraction, qualitative and quantitative. In particular, we discuss an algorithm based on a quantitative, real-valued algebraic model, and a qualitative causal model that can be easily derived from the former in an automated way. The causal model is used for candidate generation, and the real-valued model for validation/rejection of candidates.

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A diagnostic algorithm based on models at different level of abstraction

Flat tariff has always been the default condition for residential customers in Italy and only starting from July, 1st 2010, the Italian Authority for Electricity and Gas (AEEG) approved the entry into force of a mandatory time-of-use (ToU) tariff at two-part rate periods. In order to assess the impact of the tariff in the short and medium term among Italian customers, R.S.E. has started a research project on residential loads and demand, in collaboration and under the patronage of AEEG. The results of such analysis show that, even if there has been a limited shift of consumptions from peak hours to off-peak hours, the change in the behavior of the users is not negligible. Particular attention has been paid to the possible causes that have contributed to limit the amount of energy shifted from peak hours to off-peak hours and to analyse some possible solutions in order to make the tariff more effective.

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Massimo Gallanti

Papers

Multiple hypotheses and their credibility in on-line fault diagnosis

Representing procedural knowledge in expert systems: an application to process control

A diagnostic algorithm based on models at different level of abstraction

Impact of the enforcement of a time-of-use tariff to residential customers in Italy

Exploiting physical and design knowledge in the diagnosis of complex industrial systems