Abstract:
The variable demand creates a burden on the power system that causes challenges for
consumers as well as generating stations. To meet the fluctuating demand of consumers
and to make the system more efficient, penetration of distributed generators (DGs) has
increased from the past recent years. But, apart from providing an advantage, it creates the major challenge of unintentional islanding into the system. To prevail over this
complication, a dual islanding detection index (𝐷𝐼2) based modified passive islanding detection technique has been proffered in the thesis using the Unscented Kalman Filter
(UKF) signal processing scheme. Primarily, a three-phase voltage signature is extricated
at the PCC bus (𝑉𝑃𝐶𝐶) and fed into the 12-bit ADC Filter. The filter has built in feature of
noise removal using the Bessel Filter in addition to a low pass filter holding a breakpoint
frequency (𝑓𝑐𝑢𝑡𝑡) of 1.6kHz to discard the turbulence present in the signal. The ADC Filter
& implicit Bessel low pass filter work simultaneously to filter out the signal & to convert
𝑉𝑃𝐶𝐶 to a digital signal at a switching frequency (𝑓𝑠𝑤𝑖) 3.6kHz. The RMS value of the
digitalized ADC signals of all three phases (𝑉𝑟𝑚𝑠𝐴𝐷𝐶_𝑎, 𝑉𝑟𝑚𝑠𝐴𝐷𝐶_𝑏,𝑉𝑟𝑚𝑠𝐴𝐷𝐶_𝑐
) has been given to UKF having sampling time (𝑡𝑠𝑎𝑚) 1/3600sec, to extract the features using sigma points of the signals separately. Ultimately, the UKF output is fed to SSSV and CTHD indices to discern between the planned and un-planned conditions/situations.
Accomplishment of recommended UKF strategy is manifested using the IEEE-13 Node
Test system simulated on MATLAB/Simulink version 2023a. Various test results reveal
that the suggested scheme is highly efficient and precise, having an accuracy of 98%, and
can perceive the islanding events within 0.05ms. Moreover, the efficacy of the presented
technique has been highlighted by the comparative analysis of the various operating signal processing and other techniques in the literature presented.
