View of REVIEW ON RELIABILITY ANALYSIS IN SMART GRIDS

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Vol. 05, Issue 06,June 2020 Available Online: www.ajeee.co.in/index.php/AJEEE

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REVIEW ON RELIABILITY ANALYSIS IN SMART GRIDS Tej Pratap Singh Bhadauriya

Guide, Prof. Dr. A K Jhala, RKDF Bhopal Co-Guide, Mr. Pramod Kumar Rathore, RKDF Bhopal

Abstract - Smart Grids, the next-generation power grids, are equipped with modern technologies to address the increasing demand on the quality of the power supply in the traditional power grids. Power grid is a critical infrastructure with relia-bility being one of its key quality attributes. In our research, we aim at developing an approach for reliability analysis that would take into account multi-layered nature of smart grid by incorporating the physical components, software components and service usage scenarios. Such approach would be able to evaluate the reliability of the system with respect to the offered services and their stakeholders. The goal of the approach is to create a general smart grid reference model parametrized with parameters relevant to the reliability analysis.

Afterwards, it will be possible to instantiate the reference model for a specific smart grid system. Next, it will be transformed into a formal model in order to obtain the estimation of system reliability. Finally, an open-source tool will be implemented to support the modeling and computation process.

Keywords: Smart Grid; Reliability analysis; Smart Grid Ref-erence Architecture Model 1 INTRODUCTION

The electrical grids, which are large-scale physical in-terconnected networks, have become the backbone of the energy supply. The current (also referred to as legacy or traditional) power grid infrastructure must face ever-growing electricity demand, pressure for environmental protection and pressure for power distribution efficiency.

Furthermore, the legacy grids cannot easily cope with the increasing availability of technologies for distributed renewable power sources, which can turn a simple customer to a power contributor. In order to address these issues, the power grid has begun its slow transformation into a next-generation power grid called "Smart grid", which is equipped with modern information and communication technologies.

Since the power grids are considered a critical infrastruc-ture, their reliability is essential [1]. There is a number of works proposing methods for reliability prediction in smart and legacy grids. However, none of these methods directly takes into account the software systems and components, nor they specify the analysis for various use cases of the smart grid. Additionally, their perception of failure is restricted to the (un)availability of the smart grid components or the overall system failure leading to power outage.

Nevertheless, one of the main advantages of the smart grid is its integration of the power grid network with

advanced communication and software components and the whole functionality of the smart grid is decomposed into multiple layers [2]. We argue that we should expand the perception of the reliability in the smart grid systems with consideration of also other layers than the physical one and also consider different types of failures that do not necessarily result in power outages. For example, let us assume a Billing use case. The smart grid allows to use the dynamic billing options. There might be a failure during the reading of the smart meter resulting in obtaining incorrect consumption data thus wrongly calculating the price for power con-sumption. Although such failure does not cause a power outage, it might have negative impact on the associated stakeholders and possibly on other use-cases as well (e.g. wrong information from smart meters might influence the load management algorithms).

Therefore, in our work we would like to develop an approach that would take into account multiple layers of smart grid. Such approach would be able to determine the reliability in terms of probability of failure of the system with respect to the offered services and it could show an impact of the service failure on the associated stakeholders.

2 RELATED WORKS

Currently, there is a number of methods to evaluate the reliability of power

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2 systems (see [3] for an overview). They target the traditional power grid infrastructures and do not consider the new components and communication possi-bilities available in smart grids.

Recently several authors proposed methods focusing particularly in smart grids.

Mahmood et al. [4] have introduced a method for reli-ability analysis of the protective systems in the smart grid infrastructure, such as circuit breakers, switches, transcuders and availability of redundant communication components.

Niyato et al. [5] presents a reliability analysis of the smart grid wireless communications system to support demand-side management. The system has a hierarchical structure consisting of home-area network (HAN), neighborhood-area network (NAN), and wide-area network (WAN)

Wafler and Heegaard [6] propose approach for analysis of reliability of a smart grid system. The reliability is represented by the probability of availability. They use a combination of Markov model and Reliability block dia- grams to model the system. The former is used to capture the dynamic behaviour of the system, the latter captures the structure of the system.

Zeng et al. [7] use Stochastic Petri Nets (SPN) to evaluate availability and reliability of a control system networks in the smart grid.

Albasrawi et al. [8] proposes reliability and resiliency analysis approach. The model uses Markov Imbeddable Structure (MIS) model that is suitable for evaluation of systems with the inter-dependent components.

Faza, A. et al. [9] introduces an approach of smart grid reliability evaluation based on the fault injection into the physical and control layer of the smart grid.

Although the authors above focus on the smart grids, they include only the physical components of the grid into account and address types of failures leading to power outages.

The software reliability engineering is a domain on its own and there exist lots of methods for evaluation of reliability of software components (see [10] for detailed survey). On the other hand, they rarely integrate hardware and service

layers into an analysis. One of the most advanced architecture-based approaches that consider also hardware and service layers is the Palladio Component Model with reliability analysis extension [11].

3 RESEARCH AIMS

The aim of our research is to develop an approach for reliability analysis and failure prediction in a smart grid. We intend to adopt the multi-layered nature of the smart grid architecture by integrating the physical (hardware components and resources), IT (software components) and business (services and stake-holders) layers. The approach will consist of three parts. First will be the reference model for smart grid reliability analysis. It will serve as a descrip-tion of elements which constitute the smart grid architecture. Furthermore, it will be annotated with parameters that are relevant for the reliability analysis.

Second part will be the reliability analysis method. The reference model can be instantiated for a specific smart grid system. We will refer to an instance of the reference model as model. Afterwards, our analysis method transforms the model into its formal representation and then performs the computations resulting in various reliability outputs. Third part will be an open-source software tool that will allow us to model the smart grid system and to execute the reliability analysis method.

A. Approach Requirements

In order to address the shortcomings of existing software and power grid reliability analysis methods, our approach should meet the following requirements:

• Multi-layered architecture and analysis - within our approach, the reference model should map the actions in the smart grid usage scenarios with actions of the software services implementing the scenario which are in turn mapped to he underlying hardware infrastruc-ture.

• State-based formal analysis method - at low-level the method will be using the state-based approach, such as Discrete Time Markov Chain (DTMC), which is currently used by the most mature software reliability tools.

• Scenario-based - There are multiple ways of repre-senting states in a state-based model. We chose the

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3 scenario-based approach, because it allows us to conve-niently capture the the steps from the smart grid usage scenarios and software services. It will also enable analysis of the system reliability with respect to the individual scenarios.

• Hierarchical decomposition of components - to limit the number of states by allowing the hierarchical de-composition of the model so that we can decide on the level of detail for reliability analysis. The hierarchical decomposition can be physical or logical. In the former, we can choose to perform the analysis while consider-ing the individual smart meters or we can do it on the level of substations and aggregating the smart meters in a single

component. The logical

decomposition means aggregating the components based on other criteria, for example geographic location.

• Automated identification of the failure types and points of failure - we would like to employ a technique for discovery of failure types and their location based on the analysis of the event logs from the smart grid infrastructure.

• Integration of severities for failure types - the per-ception of failure impact and importance might vary with the different stakeholders and usage scenarios.

• Enhanced reference model with additional artifacts and parameters - in addition to common reliability parameters, such as failure probability or Mean-Time-Between- Failures (MTBFs) of low-level components, operation profile (transition probabilities) etc., we want to include also artifacts, such as messages, that can be produced by the software components and then trans-ported through the communication links. This way, we can for example investigate the impact of the messaging in a smart grid on its reliability.

• Representation of uncertain inputs and outputs - Using point values for uncertain input parameters or for representation of the subsequently uncertain model outputs can lead to misleading interpretation of the result. Therefore, in our model we

want to support also additional means of input and output quantification, such as intervals or probabilistic representation.

B. Research questions

In our thesis, we would like to answer the following research questions:

1) What are the most useful formal methods for reliability analysis on smart grid scale?

2) What are the critical parameters and components in the reliability analysis of smart grid?

3) What is the effect of uncertainty propagation from the input data to the method outputs based on the input data representation?

4) What information should be available in the smart grid event logs so that they can be used for automated identification of failure types and points of failure?

4 RESEARCH METHODOLOGY A. Approach implementation

Some parts of our work will be implemented as an exten-sion to the existing software quality analysis tool Palladio[11]. It meets several of our requirements from Section III.A. First, it is multi-layered, state-based, scenario- based and supports modeling of different types of failures. Second, it implements optimization algorithms to reduce the size of the state-based model. Finally, it is an open source and can be extended with plugins. On the other hand, it cannot be used for our purposes as it is and needs to be extended in multiple areas. The main extensions will include:

• More general modelling of hardware resources - at this moment the analysis in Palladio assumes that the hardware resource is either completely available or unavailable. However, in our case we would like to model cases when the resource can be available only at the reduced capacity or the resource can be operating in degraded state caused by the recovery from the failure).

• Hierarchical decomposition - Palladio does not sup-port the decomposition of components (e.g.

hardware infrastructure) with the ability to select the level of detail for the analysis.

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• Reference model annotations and parameters - cur-rently, Palladio maps the actions within a service to the required hardware resources. In addition to that, we will need to implement the relationship between the service actions and associated artifacts.

• Uncertain parameters - we will need to implement a mechanisms for capturing the uncertain input parame-ters and the propagation of the uncertainty to the output parameters.

Some of the extensions, such as hierarchical decomposition of the state model and the uncertainty representation will be based on existing solutions [12]

and algorithms [13]. Still, the integration of the existing approaches will be non- trivial. The rest of the extensions - detailed hardware modelling, the smart grid-specific failure types and location analysis, the parametrized reference model for multi-layer mapping will be based on our original work.

B. Threats to validity

There are four main aspects of our approach that will need to be further validated:

• Feasibility of modeling abstraction - by capturing the real world system to an abstract model, we in-evitably introduce simplifications, e.g. in the modeling of the data flow or incomplete behaviour specification.

Validation of these simplifications can be achieved by comparison of the model outputs from the case-study with the measured values from the real or simulated system.

• Feasibility of estimation of model annotations - the computations in our model rely on various annotations assigned to the modeled components or artifacts, such as MTBF of the hardware resources. In the case study, we will show that all the necessary inputs can be derived from the data sources available in smart grid systems.

• Validity of the state-based (Markov) analysis - we further simplify the model due to assumptions required by the formal analysis method. For DTMCs, the assumptions are, for example abstractions from time-related

aspects. The validity of the analysis can be then evaluated by comparison of the results with another method, e.g. the simulation of the system under study.

• Significance and robustness of prediction results - the results obtained from the application of our approach are useful only when they allow answering the relevant questions regarding the design of smart grid system under study. To this end, the case study will focus on those usage scenarios that are considered by the domain expert to be the most important. The robustness of the approach will be evaluated using the sensitivity analysis.

C.Time plan of work

In Fall 2017, we will be continuing our work on the anomaly detection in the smart grid and we would like to publish a failure and fault taxonomy in smart grid as well as parametrized reference model.

In Spring 2018, we plan to extend the hardware modeling capabilities of the Palladio tool together with the hierarchical decomposition of the state-based model.

During the Fall 2018, we would like to implement the uncertainty representation in the Palladio model and the method for transformation of the reference model to the formal representation. In Spring 2019, we will evaluate and publish our approach on the realistic case study. I plan to defend the thesis at the beginning of the Fall 2019.

5 ACHIEVED RESULTS

Our Smart grid related research is based on the coopera-tion with the industrial partners which enables us to validate our results in the real smart grid environment.

One of the sub-tasks of our research is identification of possible failure and fault types together with their location that are not covered by existing practices in smart grid fault localization.

We want to do that by monitoring the smart grid event data streams and analysing the anomalies in the Smart Grid.

In [14], we presented a preliminary approach for detection of smart grid anomalies.

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5 In [15], we proposed a new algorithm and method for remote control of individual smart meters and connected water heating appliances by utilizing the Bayesian Network (BN) model to deal with the uncertainties in the historical consumption data that were collected from smart meters.

In [16], we investigated various aspects related to Ad-vanced Metering Infrastructure (AMI) of Smart grid with focus on standards, architectures and technological recom-mendations related to Smart grid security and privacy. This knowledge will help us with development of parametrized reference model for SG reliability analysis.

In [17], we surveyed methods for estimation of input parameters for software reliability prediction models.

Since our approach considers also the software components and their reliability parameters, the survey might help us to identify the suitable methods for parameter estimation and potential data sources.

6 CONCLUSION

Our research will contribute to the current state of the art with the following deliverables:

• Smart grid reliability analysis approach.

• Parametrized reference model capturing the mapping between the layers of the smart grid infrastructure.

• Analysis method that transforms the reference method into a formal representation and derives the reliability outputs.

• A tool implementing the reference model and analysis method.

• Method for extracting failure types and points of failure from the event logs collected from smart meters and other smart grid components.

• Taxonomy of failure and fault types in smart grid.

ACKNOWLEDGEMENTS

This research is done at the Faculty of Informatics, Masaryk University under the supervision of Barbora Buh-nova and Tomáš Pitner.

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