Revised Fault Free Analysis Method for Reliability Evaluation of Smart Grids

SONG Guo-peng(宋國鵬), LEI Hong-tao(雷洪濤), GUO Bo(郭 波)

School of Information System and Management, National University of Defence Technology, Changsha 410073, China

Revised Fault Free Analysis Method for Reliability Evaluation of Smart Grids

SONG Guo-peng(宋國鵬)*, LEI Hong-tao(雷洪濤), GUO Bo(郭 波)

SchoolofInformationSystemandManagement,NationalUniversityofDefenceTechnology,Changsha410073,China

Smart grid was proposed as a practical form of future power distribution system. Evaluating the reliability of smart grids was of great importance and significance. A revised fault tree model was proposed to distinguish and separate grid-connected operation mode and islanded operation mode of smart grids, focusing on the perspective of the consumers. A hierarchical Monte Carlo simulation method for reliability evaluation was also proposed based on the proposed fault tree model. A case of reliability evaluation for the future renewable electric energy delivery and management (FREEDM) system was carried out and analyzed. The proposed methods can be applicable to other forms of smart grids.

smartgrid;faulttreeanalysis(FTA);MonteCarlosimulation;futurerenewableelectricenergydeliveryandmanagement(FREEDM)system

Introduction

Smart grid is also called smart electrical/power grid or intelligent grid. It has been proposed as an enhancement of the traditional power grid[1]. Traditional power grids generally distribute power from a few central generators to a large number of users or customers. On the contrary, the smart grid uses two-way flows of electricity and information to create an automated and distributed advanced energy delivery network[2].

For a smart grid, one of its major objectives is to enhance power distribution reliability[3-4]. Consequently, it is important and significant to evaluate the reliability of the system in order to make comparisons and improve the designs[5-6]. Procedures have been proposed to evaluate the reliability in distribution networks as well as smart grids[7-9]. In this paper, a modified layered fault tree model is proposed, aiming to distinguish and separate grid-connected operation mode and islanded operation mode of smart grids. A hierarchical Monte Carlo simulation method is applied based on the layered fault tree for reliability evaluation. Consumers’ requirement can be reflected in the selection of the top events and two types of importance for the basic events are used to find weak parts and improve designs. Finally, a case study of the future renewable electric energy delivery and management (FREEDM) system is carried out and discussed.

1 Revised Fault Tree Model

With microgrid and smart grid proposed, great changes take place in the topology structure of power system. Using traditional methods to do reliability analysis and evaluation for smart grids brings a series of difficulties. Based on specific architectures of smart grids, this paper uses a layered fault tree to do reliability modeling, and puts forward a hierarchical Monte Carlo simulation method for reliability analysis and evaluation.

1.1 Revised fault tree

Fault tree analysis (FTA) is a modeling method to reflect the relationship between the failures of the components and the system[10]. Using FTA, the concerned failure mode of the system is taken as the top event, and deductive method is used to find out the sets of events which may make the top event happen.

For smart grids, the construction of fault tree and simulation strategy can be different and difficult: (1) the fault is difficult to define; (2) the state of running is varied; (3) de-composition of the fault tree extends to a mass of questions. However, this classical method can still be utilized with improvement and modification. The concern should be concentrated on the two operation modes, which are islanded mode and grid-connected mode. Actually, grid-connected mode is the common state for smart grids. For most smart grids, only when utility supply fails, system turns to islanded mode. Therefore, this paper proposes a layered fault tree construction strategy and hierarchical simulation method. The consumers’ concern should be reflected when evaluating the reliability of the distribution system. This thought gives a starting point of the selection for top event of the fault tree. Different definitions of fault can be given by choosing different top events, taking consideration of power demand of consumers or loads in different priorities[11].

The fault tree should be layered, because for a smart grid, there are two operation modes, and the two different modes have different runtime requirements. Moreover, each mode has its own proper conditions and these two modes cannot be running at the same time. Grid-connected running is the normal running state, and islanded mode can be regarded as its sub-procedure. It provides a condition for layer division of the fault tree.

In Fig.1, an inhibit gate symbolized by a hexagon is used as the logical operation connected to the event of utility supply failure, which is a condition event in the form of ellipse. The triangleαis used to draw forth the next layer of fault tree considering local supply in islanded mode, which has a different runtime requirement and has to be considered separately.

From the perspective of time, the event of utility supply failure can be seen as the trigger of the secondary fault tree. Under the circumstance that utility supply is failed, the secondary fault tree is linked upward. Then only when the event of local supply failure occurs, can top event be led to. With the utility supply recovered, link between the two layers of fault tree is disconnected and the considered local framework will go on working with utility supply.

Fig.1 Logical operation connecting the two layers of fault tree

1.2 Simulation logic

Monte Carlo simulation is a kind of numerical simulation method based on the theory of probability and statistics. It is feasible through computer programming. Based on the layered fault tree model proposed for smart grids, this paper presents a hierarchical simulation strategy to assess fault tree model built for the load points, in order to evaluate the reliability of the overall system.

In a Monte Carlo run, the time to failure is generated for each component, then the components states are set to “failed”, one at a time, until the top event is produced[12]. Typically, the lives of electronic devices obey exponential distribution. Random sampling will generate the occurrence time of each basic event which obeys exponential distribution during the simulation as the following process:

F(t)=1-e-λt=ξ,

(1)

(2)

Fig.2 Main steps and processes of the simulation

A fixed time cycle is set for islanded mode of the local smart gird framework in this simulation procedure. The assessment for inadequacy judgment function of the secondary fault tree is integrated in the simulation process. In each single time of simulation, the repetition of utility supply failure is ignored due to its bare possibility. The simulation strategy can be seen as a hierarchical process, taking the simulation for the secondary fault tree as a conditioned sub-process of simulation. With this simulation strategy proposed, quantitative assessment of the layered fault tree for smart grid can be realized. Importance measures for the layered fault tree refer to Ref.[13] which are network risk achievement worth (NRAW) and network risk reduction worth (NRRW).

2 Case Study

2.1 A case of FREEDM system

The FREEDM system is a practical form of the envisioned concept of “Energy Internet”. It is characterized by its ability of integrating highly distributed and scalable alternative generating sources and storage with existing power systems. Therefore, it has been seen as an efficient electric power grid to facilitate a green and sustainable energy-based society and mitigate the growing energy crisis[14]. According to the architecture of FREEDM system, this paper provides a grid-connected case of FREEDM system shown in Fig.3.

Fig.3 A case of the FREEDM system

In the constructed local framework of FREEDM system, Load 1, Load 3, Load 4 and Load 7 are 5 kW. Load 6 and Load 8 are 2.5 kW. Load 2 is 3 kW, while Load 5 is 2 kW. The priorities of loads are coincided with the labels. The power supply services include three storage batteries with the same power of 5 kW and four diesel generators with the same power of 10 kW. Assume that the power factor can be up to 1.

2.2 Reliability evaluation

Figure 4 shows the fault tree taking outage of Load 1 with the highest priority for top event as an illustration. The components of intelligent energy management (IEM) and intelligent fault management (IFM), corresponding to intelligent control and protection systems of the FREEDM system, are not further analyzed and decomposed. Basic events of the fault tree are listed in Table 1.

Fig.4 Fault tree showing key events leading to outage of Load 1

No.BasiceventNo.Basicevent1Community-levelcircuitbreakerfailure20Startingfailureofdieselgenerator12Circuitbreaker1failure21Rectifier2failure3IFMcooperativeisolationfailure22Operationfailureofdieselgenerator24IEMcooperativecontrolfailure23Startingfailureofdieselgenerator2512kVACbusfailure24DC/DCconverter2failure6120VACbus1failure25Intermittentoutputofbattery27Localcommunicationfailure26Degradedoutputofbattery28Cross-systemcommunicationfailure27Shortofbattery29400VDCbus1failure28Rectifier3failure10400VDCbus2failure29Operationfailureofdieselgenerator311400VDCbus3failure30Startingfailureofdieselgenerator312Circuitbreaker2failure31DC/DCconverter3failure13Circuitbreaker3failure32Intermittentoutputofbattery314DC/DCconverter1failure33Degradedoutputofbattery315Intermittentoutputofbattery134Shortofbattery316Degradedoutputofbattery135Rectifier4failure17Shortofbattery136Operationfailureofdieselgenerator418Rectifier1failure37Startingfailureofdieselgenerator419Operationfailureofdieselgenerator1

The life of the electric equipment is generally exponentially distributed. Fault rates of the key components refer to Ref. [15]. The fault rate of starting failure of diesel generator is a fixed value of 0.05. For communication failure, IEM and IFM devices, an order of magnitude of 1.00E-8 failures/hour is given, considering their paramount importance in the system.

With layered fault tree model, we tend to analyze the reliability for different loads in the local framework of FREEDM system, using the simulation procedure for reliability assessment. The assessment is executed within a one-year cycle and the islanded cycle is set as one week (168 h). Results of reliability for each load point in the local framework are presented in Table 2. Reliability for different loads decreases with priority, which accords with the original intention of the system design.

For the loads with higher priorities, there is no significant difference with their reliability. As for Load 7 and Load 8, their unreliabilities are significantly greater. This difference is mainly caused by the different reliability levels of two different kinds of DGs, as batteries have higher reliability and can only supply 20 kW power.

Table 2 Assessment results for load points in one year

Importance measures for some selected components are given in Table 3. Components with the highest value of NRAW are the IEM devices, the 12 kV AC bus and communication components. These components should be maintained well, in order that the reliability of the system is not reduced significantly. In consequence, the maintenance priority for those components should be set high. The important components with the highest values of NRRW are the diesel generators. Reliability of these respective components is worth increasing, in order that the system reliability can be significantly increased.

Table 3 Importance measures for selected components

3 Conclusions

A layered fault tree model is presented to evaluate the reliability of smart grid, which has been seen as practical form of future power distribution system. The revised model can distinguish and separate grid-connected operation mode and islanded operation mode of smart grids, for two different modes have different working conditions and different runtime requirements. The state transition process of different power supply modes can also be described in the revised fault tree model. A case of reliability evaluation for the FREEDM system is carried out and the result is analyzed. The proposed method appears to be applicable to other forms of smart grids.

[1] Gharavi H, Ghafurian R. Smart Grid: the Electric Energy System of the Future [J].ProceedingsoftheIEEE, 2011, 99(6): 917-921.

[2] Fang X, Misra S, Xue G L,etal. Smart Grid-the New and Improved Power Grid: a Survey [J]. IEEE onCommunicationsSurveys&Tutorials, 2012, 14(4): 944-980.

[3] Al-Muhaini M, Heydt G T. Evaluating Future Power Distribution System Reliability Including Distributed Generation [J].IEEETransactionsonPowerDelivery, 2013, 28(4): 2264-2272.

[4] Rahman F A, Varuttamaseni A, Kintner-Meyer M,etal. Application of Fault Tree Analysis for Customer Reliability Assessment of a Distribution Power System [J].ReliabilityEngineering&SystemSafety, 2013, 111: 76-85.

[5] Atwa Y M, El-Saadany E F. Reliability Evaluation for Distribution System with Renewable Distributed Generation during Islanded Mode of Operation [J].IEEETransactionsonPowerSystems, 2009, 24(2): 572-581.

[6] Bae I, Kim J. Reliability Evaluation of Customers in a Microgrid [J].IEEETransactionsonPowerSystems, 2008, 23(3): 1416-1422.

[7] Brown R E, Gupta S, Christie R D,etal. Distribution System Reliability Assessment Using Hierarchical Markov Modeling [J].IEEETransactionsonPowerDelivery, 1996, 11(4): 1929-1934.

[8] Wang P, Billinton R. Time Sequential Distribution System Reliability Worth Analysis Considering Time Varying Load and Cost Models [J].IEEETransactionsonPowerDelivery,, 1999, 14(3): 1046-1051.

[10] Lee W S, Grosh D L, Tillman F A,etal. Fault Tree Analysis, Methods, and Applications-a Review [J].IEEETransactionsonReliability, 1985, R-34(3): 194-203.

[11] Chuang A, McGranaghan M. Functions of a Local Controller to Coordinate Distributed Resources in a Smart Grid [C]. 2008 IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, PA, 2008:1-6.

[12] Salem S L, Apostolakis G E, Okrent D. A Computer-Oriented Approach to Fault-Tree Construction [R]. EPRI NP-228. Los Angeles (USA): California University, 1976.

[14] Huang A Q, Crow M L, Heydt G T,etal. The Future Renewable Electric Energy Delivery and Management (FREEDM) System: the Energy Internet [J].ProceedingsoftheIEEE, 2011, 99(1): 133-148.

[15] Rahmat M K, Jovanovic S. Reliability Modelling of Uninterruptible Power Supply Systems Using Fault Tree Analysis Method [J].EuropeanTransactionsonElectricalPower, 2009, 19(6): 814-826.

1672-5220(2014)06-0870-05

Received date: 2014-08-08

*Correspondence should be addressed to SONG Guo-peng, E-mail: rocsgp@163.com

CLC number:TB114.3 Document code: A