Abstract
A smart grid concept has been defined in recent years, which emphasizes the importance on smart protection and measurement devices, reliable data communication and high security, optimal energy management system, and fault detection, location, isolation and service restoration (FLISR) of distribution networks (DNs). The main objectives of the FLISR approach are to achieve fast fault processing time, reduce the minimum number of interrupted customers, and improve the power supply reliability of the distribution. The conventional FLISR approach is to use signals of fault indicators (FIs) with distribution network states. The discrete installation of FIs to switches or reclosers may slow the processing time of fault detection and location, so it is necessary to develop a more efficient FLISR approach for smart distribution networks using functions of feeder terminal units (FTUs). In this paper, pick-up and tripping signals of overcurrent (OC) relays in combination with distribution grid states (e.g., switching status of devices, loss of voltage…) sent from feeder terminal units (FTUs) are used to detect and locate different fault types. Fault isolation and service restoration of black-out areas are then performed by solving an objective function with two main constraints, including (i) restoring the possible maximum number of out-of-service loads; and (ii) limiting the minimum number of switching operation. Thirteen performance factors (PF) are used for the post-fault service restoration process, consisting of: (i) Power Flow Violations (PFV), (ii) Bus Voltage Violations (BVV), (iii) Total Operation Cost (TOC), (iv) Lost Power (LP), (v) Outage Customer (OC), (vi) Number of Switching Steps (NSS), (vii) Power Losses (LOSS); (viii) Customer Minutes Interruption (CMI), (ix) Load Minutes Interruption (LMI), (x) MAIFI, (xi) SAIFI, (xii) SAIDI, and (xiii) Protection Validation (PRV). E-Terra platform of a distribution management system (DMS) is used to implement the proposed FLISR approach. Simulation and experiment results from a real 22 kV distribution network are also analysed to validate this FLISR approach. As a result, the novel FLISR approach has the ability to identify effectively the over-reaching of OC relays, indicate a mis-coordination risk of adjacent protection devices on the same feeder, and get the total processing time of fault detection, location and isolation as well as ranking all possible service restoration plans in distribution network at less than two minutes.
Highlights
For the Centralized FLISR (C-FLISR) architecture, all real-time data are recorded at measurement and switching devices, and remote terminal units (RTUs)/gateways (GWs) which will be transmitted to Front End Servers (FEP) through IEC 60870-5-101/104 protocol, as referred to
Simulation results of the proposed FLISR approach for a 22 kV distribution system are shown in Tables 21 and 22
A main reason is that the fault cases F2.3 and F3.3 cause the mal-operation of Outage Customer (OC) relays and reclosers which are adjacently placed on the
Summary
A major challenge of distribution networks is to provide customers with high power quality and reliability. Compared to a conventional distribution network, a smart distribution network should achieve self-healing control with respect to any faults, fault detection and location, fault isolation, and fast restoration of power supply of outage areas where there is no fault. How to quickly detect different types of faults, precisely locate the faulted sections and accurately isolate these faulted sections is the basis and prerequisite of self-healing control functions in smart distribution networks. Fault isolation and service restoration of black-out areas in a distribution network should meet main constraints such as: (i) to restore the possible maximum number of out-of-service loads; and (ii) to limit the minimum number of switching operation. Various performance factors for the post-fault service restoration process should be considered, mainly consisting of power flow violations, bus voltage violations, total operation cost, lost power, outage customer, the number of switching operation, power losses, customer minutes interruption, load minutes interruption, momentary average interruption frequency index (MAIFI), system average interruption frequency index (SAIFI), system average interruption duration (SAIDI), and protection validation
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