| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 400389 | International Journal of Electrical Power & Energy Systems | 2016 | 7 Pages |
•A comprehensive model for forecasting distribution system reliability is proposed.•An ANN forecasting model for EDS failures is proposed using key influencing factors.•A reliability forecasting model for planned outage is built considering investments.•Priorities of improvement measures can be obtained using Gray Relational Analysis.
Many utilities in developing countries are investing in installation and renewing of Electrical Distribution System (EDS) components, such as overhead lines, cables and switching devices, to improve the EDS reliability and meet the rapid increase of load demand. In the beginning stage of investment, it is very difficult to evaluate the EDS reliability by using traditional methods due to EDS topology not being fully determined. This paper presents a comprehensive model for forecasting EDS reliability, which is built separately into two parts, i.e. the models for EDS failures and planned outages. Firstly, a three-layer Artificial Neural Network (ANN) model is proposed to forecast the EDS reliability considering EDS failures. Each neuron in the ANN input layer represents a key influencing factor of EDS failures, which are recognized by Gray Relational Analysis (GRA) method. The proposed ANN is trained using historical reliability data of an EDS. In addition, a planned outage reliability model is also built according to the magnitude of investment and type of planned outage. The priorities of improvement measures can also be obtained using the GRA to improve the EDS reliability. Case studies of practical EDSs illustrate the efficiency and applicability of the proposed techniques.
