Abstract:
Electrical power consumption and distribution along with ensuring its quality is important for
industries as the power sector mandates a clean and green process with the least possible carbon
footprints and avoid damage of expensive electrical equipments. The harmonics elimination has
emerged as a topic of prime importance for researchers and industry to realize the maintenance of
power quality in the light of the 7th Sustainable Development Goals (SDGs). These harmonics
reduce the equipment life and degrades the overall electric power quality due to excessive use of
non-linear load in industries. In this research, a Hybrid Shunt Active Harmonic Power Filter
(HSAHPF) has been implemented to reduce harmonic pollution. An ANN-based control algorithm
has been used to implement Hardware in the Loop (HIL) configuration and the network is trained
on the model of pq0 theory for harmonic elimination. The HIL configuration is applied to integrate
a physical processor with the designed filter. In this configuration, an external microprocessor
(Raspberry PI 3B+) has been employed as a primary data server for the ANN-based algorithm to
provide reference current signals for HSAHPF. The ANN model uses backpropagation and
gradient descent for the prediction of output based on received 7 inputs i.e., 3-phase source voltage,
3-phase applied load current and the compensated voltage across the DC-link capacitors of the
designed filter. Moreover, a real-time data visualization platform has been provided through an
Application Programming Interface (API) of a JAVA script called Node-RED. Additionally, the
Node-RED also performs data transmission between SIMULINK and external processors through
serial socket TCP/IP data communication for real-time data transceiving. It also develops a realtime Supervisory Control and Data Acquisition (SCADA) system to monitor and control the data
flow within the two HIL platforms. Furthermore, we have demonstrated real-time testing of
HSAHPF using the topology based on HIL topology that enables the control algorithms to run on
an embedded microprocessor for a physical system. The presented results validate the proposed
design of the filter and the implementation of real-time system visualization. The statistical values
showing a significant decrease in Total Harmonic Distortion (THD) from 35.76% to 3.56% for
conventional Pq0 thoery that perfectly lies within the set range of IEEE standard. On the basis of
this result an ANN model of tensor flow is trained upto the 99.9% efficiency. The trained model
is then applied in the SIMULINK environment which gives 3.75% THD on the source current. In
further, the ANN-based algorithm provides negligible THD with improved stability time while
bearing the computational overheads of the microprocessor. The implemented HIL approach along
with experimental verification confirms significant mitigation of harmonics by the acquisition of
quality power with power factor closer to unity.
