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This research study explains the design and performance of a Static VAR Compensator (SVC) coupled Hybrid Distribution Static Compensator (DSTATCOM) for low voltage power distribution system (PDS). A comparative analysisis done between Voltage Source Inverter (VSI)-based DSTATCOM and SVC Hybrid DSTATCOM using Jordan Least Mean Square (JLMS) algorithm to alleviate the shunt-related power quality (PQ) glitches. The specific design is obtained as compared to the existing neural network toolbox algorithm to improve the estimation speed and accuracy. The main objective is to extract the tuned weighted values of fundamental active and reactive power components of distorted load currents for the switching signal generation using an adaptive JLMS algorithm.Several functions of DSTATCOM and hybrid DSTATCOM using this JLMS controller are showcased for harmonics curtailment, power factor (p.f.) improvement, load balancing, voltage regulation, etc. Simulation results on an SVC Hybrid DSTATCOM have shown an acceptable level of performance under unbalanced loading situations. Finally, some comparative results are provided to validate the feasibility and effectiveness of the suggested topology.

This paper presents the shunt compensation performance of quasi-Z-source inverter (QZSI) and back to back connected QZSIs (BB-QZSI) to address the power quality (PQ) issues in the three-phase three-wire power utility network (PUN). Generally, these PQ issues are poor voltage regulation, low power factor (PF), source current distortion, unbalanced voltage, etc. The proposed BBQZSI-based distribution static compensator (DSTATCOM) consists of two QZSIs with a common dc-link capacitor. Because the QZSI could achieve buck/boost conversion as well as DC to AC inversion in a single-stage and the back to back configuration decreases the system down time cost (if a fault occurs in one QZSI the other can continue the shunt compensation). Particularly, icos ϕ control algorithmcontrol algorithm is implemented to generate proper switching pulses for the switches of DSTATCOM. The effectiveness of the BB-QZSI is verified through simulation studies over QZSI using MATLAB/Simulink software satisfying the recommended grid code.

With the rapid advancement of the technology, deep learning supported voltage source converter (VSC)-based distributed static compensator (DSTATCOM) for power quality (PQ) improvement has attracted significant interest due to its high accuracy. In this paper, six subnets are structured for the proposed deep learning approach (DL-Approach) algorithm by using its own mathematical equations. Three subnets for active and the other three for reactive weight components are used to extract the fundamental component of the load current. These updated weights are utilised for the generation of the reference source currents for VSC. Hysteresis current controllers (HCCs) are employed in each phase in which generated switching signal patterns need to be carried out from both predicted reference source current and actual source current. As a result, the proposed technique achieves better dynamic performance, less computation burden and better estimation speed. Consequently, the results were obtained for different loading conditions using MATLAB/Simulink software. Finally, the feasibility was effective as per the benchmark of IEEE guidelines in response to harmonics curtailment, power factor (p.f) improvement, load balancing and voltage regulation.