a. Using network reduction

Combing elements 1 and 3 in series as in Eq. (12) gives:

$$r\_{1,3} = \frac{\lambda\_1 r\_1 + \lambda\_3 r\_3}{\lambda\_s} = \frac{0.5 \times 10 + 0.01 \times 100}{0.51} = 11.76 \quad \text{hr}$$

The indices of components 2 and 4 combined will be identical:

$$r\_{2,4} = \frac{\lambda\_2 r\_2 + \lambda\_4 r\_4}{\lambda\_s} = \frac{0.5 \times 10 + 0.01 \times 100}{0.51} = 11.76 \text{ hr}$$

The indices for the load point are

$$
\lambda\_s = 6.984 \times 10^{-4} \text{ } f/\text{y} \quad r\_2 = 5.88 \text{ hr}
$$

$$
U = 4.1066 \times 10^{-3} \quad \text{hr/yr}
$$

b. Using failure modes analysis



Although the second method seems longer, it is worth noting that it gives a greater deal of information. It indicates that the failure rate and unavailability are mainly due to the overlapping failures of the two lines; however, the average

The most widely used reliability indices are averages that weight each customer equally. Customer-based indices are popular with electric companies [14] since a small residential customer has just as much importance as a large industrial customer. Regardless of the limitations they have, these are generally considered acceptable techniques showing adequate measures of reliability. Indeed, they are often used as reliability benchmarks and improvement targets. The formulae for

SAIFI is a measure of how many sustained interruptions an average customer

For a fixed number of customers, the only way to improve SAIFI is to reduce the

SAIDI is a measure of how many interruption hours an average customer will experience over the course of a year. For a fixed number of customers, SAIDI can be improved by reducing the number of interruptions or by reducing the duration of these interruptions. Since both of these reflect reliability improvements, a reduction in SAIDI indicates an improvement in reliability. This measure can be defined as

CAIDI is a measure of how long an average interruption lasts and is used as a

measure of utility response time to the system contingencies. CAIDI can be improved by reducing the length of interruptions but can also be reduced by increasing the number of short interruptions. Consequently, a reduction in CAIDI does not necessarily reflect an improvement in system reliability. This measure can

Total number of customers served ð Þ inter*=*cust (24)

Total number of customers served ð Þ <sup>h</sup>*=*cust (25)

Total number of customers interruptions ð Þ <sup>h</sup>*=*cust (26)

will experience over the course of a year. This measure can be defined as

outage duration is mainly due to the overlapping outages of the two

**8.1 System average interruption frequency index (SAIFI)**

SAIFI <sup>¼</sup> Total number of customers interruptions

number of sustained interruptions experienced by customers.

**8.2 System average interruption duration index (SAIDI)**

*<sup>S</sup>*AIDI <sup>¼</sup> Total customers interruptions durations

CAIDI <sup>¼</sup> Total customers interruptions durations

**8.3 Customer average interruption duration index (CAIDI)**

reduction technique.

**8. Customer-based reliability indices**

*Reliability Evaluation of Power Systems DOI: http://dx.doi.org/10.5772/intechopen.85571*

customer-based indices include:

be defined as

**163**

transformers. This information, which is vital in assessing critical areas and indicating the areas requiring more investment, is not given by the network

**Figure 14.** *The tie-lines configuration with data load points.*

*Reliability Evaluation of Power Systems DOI: http://dx.doi.org/10.5772/intechopen.85571*

The following case study showcases the existing tie-line interconnecting the eastern region (ER) with the central region (CR) (400 km apart) in the Kingdom of Saudi Arabia (KSA). The ER is actually the incubator of the oil industry and all its refineries and infrastructures. Riyadh is located in the CR, which is the domicile of the Saudi Electric Company (SEC). The latter is envisioning tremendous expansion with vast increasing industrial future loads. Therefore, a huge bulk of electric power is transferred from the ER to the CR via the interconnecting tie-line. Therefore, to evaluate its reliability using the concepts and methodology stated above, the tie-line

(see **Figure 14**) is considered bearing the following data:

*Reliability and Maintenance - An Overview of Cases*

Combing elements 1 and 3 in series as in Eq. (12) gives:

The indices of components 2 and 4 combined will be identical:

**Overlapping outages** *λ* **(** *f/yr***)** *r* **(h)** *U* **h (h/yr)** 1 and 2 5.7080 <sup>10</sup><sup>14</sup> <sup>5</sup> 2.854 <sup>10</sup><sup>14</sup> 1 and 4 0.6279 <sup>10</sup><sup>14</sup> 9.091 5.708 <sup>10</sup><sup>14</sup> 2 and 3 0.6279 <sup>10</sup><sup>14</sup> 9.091 5.708 <sup>10</sup><sup>14</sup> 3 and 4 0.0228 <sup>10</sup><sup>14</sup> <sup>50</sup> 1.142 <sup>10</sup><sup>14</sup>

6.987 <sup>10</sup><sup>14</sup> <sup>=</sup> *<sup>λ</sup><sup>s</sup>* 5.88 = *rs* 4.110 <sup>10</sup><sup>14</sup>

*λ<sup>s</sup> rs*

a. Using network reduction

The indices for the load point are

b. Using failure modes analysis

*The tie-lines configuration with data load points.*

**Figure 14.**

**162**

Although the second method seems longer, it is worth noting that it gives a greater deal of information. It indicates that the failure rate and unavailability are mainly due to the overlapping failures of the two lines; however, the average outage duration is mainly due to the overlapping outages of the two transformers. This information, which is vital in assessing critical areas and indicating the areas requiring more investment, is not given by the network reduction technique.
