Modelling and simulation of KHLMS algorithm‐based DSTATCOM

Abstract

The assessment of the shunt compensation allied power quality glitches bounds the design and modelling of a controller for the d-FACTS (Distribution Flexible Alternating current Transmission System) device. So, Kernel Hebbian least mean square (KHLMS) is proposed for controlling the distributed static compensator (DSTATCOM) in this study. The KHLMS is the improved version of an adaptive LMS (ALMS), which is designed by using a suitable pattern of learning mechanism. Both the controller algorithms are formulated on the basis of mathematical equations using MATLAB/Simulink. In accordance with the system variation adaptability and experimental application, each phase controller is configured as an independent neural network structure. The objective of control algorithms is used for the extraction of fundamental active and reactive components from load currents for the generation of the reference source currents. In comparison with the ALMS algorithm, the KHLMS demonstrates more effective in improving the voltage regulation at DC link capacitor, voltage balancing, source current harmonic reduction, and power factor correction under the disturbance influences caused by even and uneven loading. Nomenclature w pa , w pb , w pc weighting values of active component of load current w qa , w qb , w qc weighting values of reactive component of load current w a mean active weight w r mean reactive weight α learning factor γ step size i la , i lb , i lc load currents i sa , i sb , i sc supply currents u pa , u pb , u pc in phase unit voltage template u qa , u qb , u qc quadrature unit voltage template w cp , w cq output of proportional integral (PI) controller at both DC and AC sides w sp, w sq total active and reactive components of the reference source current i aa , i ab , i ac total active weight i ra , i rb , i rc total reactive weight v sa , v sb , v sc supply voltages i sa *, i sb *, i sc * reference supply currents

Sparse LMS algorithm for two‐level DSTATCOM

“…Voltages and currents from various parts of the system are sensed using hall-effect voltage transducer LEM LV25-P, current transducer LA55-P, current probes (A622) and voltage probes (RP1050D). The dSPACE module DS1104 along with MAT-LAB2014a/Simulink and connector panel module CP1104 for analog feedback signal are used to implement the proposed control algorithm [27][28][29][30]. The supply voltage and load current are tracked through the dSPACE 1104 panel via DAC (digital to Analog converter) channel.…”

“…Here, sampling time of 20 µs is chosen to achieve efficient performance of the algorithm. The detailed execution process of d-SPACE is mention in the cited literature [25][26][27][28][29][30]. The current is determined with scale 25 A/div whereas the voltage scale is determined with 200 V/div.…”

ANN Based DC Link Control of STATCOM in Wind Integrated Distribution System for Power Quality Conditioning

“…The VSC is usually in PWM current control mode to inject the necessary current into the system. STATCOM also requires passive elements like a DC link capacitor, AC inductors, injection transformers and passive filters [31][32]. The required rating of the distribution STATCOM depends on the reactive power demand in the system.…”

Operation of Hebbian least mean square controlled distributed static compensator

“…These are leaky least mean square, adaptive neuro-fuzzy inference system LMS algorithm, artificial neural network-based discrete-fuzzy logic, Type-1 and Type-2 fuzzy logic [8][9][10][11][12]. Besides these, other algorithms are LMS based NN [13], Kernel Hebbian LMS [14], LMS-LMF [15] etc. These controller algorithms show promising results for the power quality (PQ) improvement [16][17][18][19].…”