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Need of Power quality information in Smart Grid

Today's grid is pacing towards smartness with use of smart appliances, smart communication technologies and lot of integration of OT/IT systems which inurbanely introduces more number of  electronics devices within utilities operations umbrella. While doing this we not only have to benchmark the grid reliability but must also focus on the sustainability and efficiency of grid.  One of the ways is to address more seriously the quality of power delivery and information on quality of power. It's not that utilities are not doing this presently but they have to do it more precisely as now utilities have the technologies that can provide more frequently and lot of information on quality of power. Now utilities do not have to do the aftermath analysis to find out the root cause of a problem and mitigate the future occurrences of it. The smart meters and other smart devices in the network has the capability to provide information on quality of power which can be used to detect and alert signs of an equipment failure beforehand to the utilities where a prognostic approach helps in time to prevent the occurrence of any serious situations. The key performance indices of utilities such as SAIFI, MAIFI, SARFI etc. not only improves but can be projected on the go to all the executive level across the departments of an utilities on a mobility devices which in a way help utilities to be in sync with updated information and operations point of view anytime and anywhere.

With increase participation of distributed energy resources in grid and with nature of intermittency the impact on the grid voltage variations are high and grid is more prone to power quality issues such as voltage sags, transients, overvoltage, increase in unbalance, flickers etc. When distributed generations are connected to grid, the steady sate voltage limits are challenged in case of lightly loaded long feeders and intermittency nature represents the source of flickers and sags in the electrical distribution network. When a fault occurs repeatedly for very short duration on distribution feeder it is not significant enough for tripping of protective devices and the feeder is exposed to high currents for very short durations with repeated voltage drops several times in a day. The connected electrical equipments on this feeder are exposed to extreme thermal and electrical conditions which degrade equipments performance and life. The smart meters with capabilities of capturing the total harmonic distortion and other power quality parameters can send this information to a centric location such as distribution control center where power quality and voltage monitoring applications can play a significant role to highlight and alarm such conditions of the network for immediate attention of operator.

 In smart grid world the need of solutions with more emphasize on power quality monitoring & predictive analysis applications will play crucial role in pre-diagnosing the problem and avoid the circumstances leading to abnormalities. The advance distribution management system with applications like Fault Location isolation and restoration (FLISR), integrated volt-var control (ivvc), demand response operations management, power quality &voltage monitoring, distribution system energy analytics and condition based monitoring will provide enormous capabilities to improve utilities reliability, sustainability and efficiency.

Need of power quality monitoring and analysis will no more be an offline study but will be one of the critical applications on the dashboard of a utility operators. Such applications will ensure the quality power deliverance respecting all the regulations and recommendations. Power quality aspect in smart grid will ensure the reliable, sustainable and efficient power delivery model.


Not sure what is SARFI?

Hi Vijay

SARFI is System Average RMS (Variation) Frequency Index.
It describe the expected system performance for momentary voltage variations, such as voltage sags. The index represents the average number of voltage sags with a specified characteristic experienced by a customer. For SARFI(x), the index would include all of the voltage dips where the minimum voltage was less than x. For example, SARFI(70) represents the expected number of voltage sags in situations where the minimum voltage is less than 70%.

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