In low and medium voltage distribution networks, the LCL integrated conventional converter based distributed static compensator (D-Statcom) has shown to be a practical solution for shunt compensation. Despite numerous efforts in this area, the traditional control approach still has a number of issues. This article describes the development of LCL integrated D-Statcom for shunt compensation utilizing a deep learning technique. The voltage source converter (VSC) and LCL filter are included in the new framework. The operation and control of the distribution network are directly impacted by the proposed system's performance. MATLAB Simulink software and an experimental research based on d-SPACE are used to demonstrate the synchronization, which solves current related power quality (PQ) issues such as poor power factor (p.f.), current harmonics, unbalanced voltage at point of common coupling (PCC) and poor voltage regulation. In order to provide precise reference currents for control, the deep learning technique is used to monitor the essential active and reactive components of load currents. Furthermore, it precisely ascertains the remaining constituents, attaining a swifter transient reaction and superior system stability. Comparisons are then made between VSC and LCL integrated VSC using deep learning technique by considering the implementation procedure. With a lower DC-link voltage and a smaller converter power rating, the LCL integrated VSC system improves PQ of distribution network more than the VSC.

Green energy and clean power are the recent trends of modern power distribution net-works (PDN). In recent years, great attention has been focused on T type inverters due to their advantages over conventional voltage source inverters (VSI), such as fault-tolerant,overload capability, less total harmonic distortion (THD), better output waveform and high efficiency. An inductor coupled T type (IC-T type) inverter-based distribution static com-pensator (DSTATCOM) is built for active power filtering of 3-phase 3-wire PDN connected nonlinear load in this paper. The proposed topology is composed of three inductors connected between the VSI and common source. The proposed PDN is obstructed by the DSTATCOMusing icos control algorithm for the inverter DC link voltage reduction, filter inductor rating minimization, decreasing the switching stress, increasing the life span of an inverter, reliable operation, stress balancing, loss reduction and increase in efficiency. Apart from these, other improvements such as power factor (PF) correction, better voltage regulation, harmonics re-duction and load balancing are obtained. The efficacy of the IC-T type inverter in different loading scenarios is justified using MATLAB/Simulink software captivating in reflection ofthe IEEE-514-2017 and IEC- 61000-1-3 benchmark

The voltage source active power filter (VS-APF) is being significantly improved the dynamic performance in the power distribution networks (PDN). In this paper, the superconducting magnetic energy storage (SMES) is deployed with VS-APF to increase the range of the shunt compensation with reduced DC link voltage. The proposed SMES is characterized by the physical parameter, inductive coil, diodes and insulated gate bipolar transistors (IGBTs). The deep Q- learning (DQL) algorithm is suggested to operate SMES based VS-APF for the elimination of harmonics under different loading scenarios. Apart from this, the other benefits like improvement in power factor (PF), load balancing, potential regulation are attained. The simulation studies obtained from the proposed method demonstrates the correctness of the design and analysis compared to the VS-APF. To show the power quality (PQ) effectiveness, balanced and unbalanced loading are considered for the shunt compensation as per the guidelines imposed by IEEE-519-2017 and IEC-61000-1 grid code by using dSPACE-1104-based experimental study.

This study presents the design and functional performance of Cross Least Mean Square (X-LMS) neural network based distributed static compensator for shunt compensation. First, considering the adaptive least mean square (ALMS) technique, harmonics correction, power factor (p.f) upgrading, load balancing, and voltage regulation at the supply bus are attained. Second, based on the X-LMS principle, active and reactive components of load current by updating their respective weights are mathematically analyzed. The main objective is to extract the tuned weighted values of fundamental active and reactive power components of distorted load currents using the suggested adaptive principle. The X-LMS exhibits better convergence speed provides a significant computational saving and avoids the additional filtering needed with the ALMS structure. Finally, the X-LMS model is built using MATLAB/Simulink for simulation study and validated experimentally by d-SPACE laboratory setup results. The parameter design of X-LMS can be used to improve the harmonic reduction with other compensation performances to showcase better power quality of the distribution utility.

This article proposes an adaptive Kernel-Hebbian least mean square (KHLMS) controller for a dual voltage source inverter (VSI). The recommended topology consists of a distributed energy resource (DER) supported VSI called main VSI (MVSI) and split capacitor supported VSI termed as auxiliary VSI (AVSI). Both the MVSI and AVSI are used to serve the shunt compensation when DER is not integrated with MVSI. The DER scenario is considered to suppress the active power flow shortage in the utility grid. Here, optimal active power flow control (OAPFC) is managed by MVSI and shunt compensation is achieved by AVSI during DER operated mode. Hence, a dual VSI based distribution static compensator (DSTATCOM) facilitates the configuration merits such as reduction in system downtime cost, filter rating switching stress etc. Supremacy of both the neural network (NN) based controller and topology is presented by comparing VSI (called AVSI) in the context of harmonic reduction in source side, voltage balancing, power factor (PF) enhancement, better voltage regulation and OAPFC. The experimental results are obtained through field programmable gate array (FPGA) based hardware units which exhibit radical improvement in the power quality (PQ) conferring as per the international standard grid code (IEEE-519-2017).

Two Back to Back connected 2-level voltage source inverters (BBC-VSI) under three phase three wire weak utility grid is examined. Generally, the challenges addressed in the modern utility grid are end users’ nonlinear loads and dependency on conventional energy sources. The end users’ nonlinear loads generate power quality (PQ) issues and dependency on conventional energy sources raises environment pollution and economic crises. The BBC-VSI based distribution static compensator (DSTATCOM) topology consists of two voltage source inverters (VSIs) supplied by a distributed energy resources (DERs) supported common DC-link capacitor. A sparse least mean squares (SLMS) technique is selected for generating the pulses IGBTs. The SLMS technique offers high estimation speed, less than one cycle weight convergence, fast transient response and small error in steady state over conventional technique. A holistic comparison is performed between the BBC-VSI and VSI using the field programmable gate arrays (FPGA) SPARTAN-6 control board regarding optimal power flow control, which shows the BBC-VSI is competitive. Also, it is authenticated under different conditions like source current shaping before and after compensation, source power failure, DER power fluctuation, nonlinear load variation, etc., which are naturally encountered in a modern utility grid.

This article proposes the shunt compensation capability improvement using a deep belief learning network approach (DBLN) with anti-wind-up regulator-supported distributed static compensator (DSTATCOM). Six subnets make up this proposed DBLN controller. Three subnets for each active and reactive mass part are employed to isolate the basic component of the output current. Numerous issues such as past and normalising weight and learning rates are engaged in the DBLN-based weight-updating formula to have a superior dynamic presence, reduce the computational load and achievesfaster estimation, etc. This proposed DBLN is suggested for both proportional-integral (PI) and anti-wind-up regulator to showcase the better DC link voltage which further leads to providing better PQ improvement. This method offers excellent dynamics and resilience to outside disturbances. The suggested study is examined by simulation and experimental development using MATLAB/Simulink by a real-time interface based on a dSPACE 1104 for healthier potential regulation, potential balancing, input current harmonic distortion and PF correction under different load scenarios. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

The Distributed Static Compensator (DSTATCOM) is being recognized as a shunt compensator in the power distribution networks (PDN). In this research study, the superconducting magnetic energy storage (SMES) is deployed with DSTATCOM to augment the assortment compensation capability with reduced DC link voltage. The proposed SMES is characterized by a DC-DC converter with different circuit elements like one inductor, two diodes and two insulated gate bipolar transistors. The Deep Bayesian Active Learning algorithm is suggested to operate SMES supported DSTATCOM for the elimination of harmonics under different loading scenarios. Apart from this, the other benefits like improvement in power factor, load balancing, potential regulation are attained. The simulation studies obtained from the proposed method demonstrates the correctness of the design and analysis compared to the DSTATCOM. To show the power quality effectiveness, balanced and unbalanced loading are considered for the shunt compensation as per the guidelines imposed by IEEE-519-2017 and IEC- 61000-1 grid code. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Concerning the power quality issues in the power distribution network due to load uncertainties and improper impedance matching of the inductances, deep reinforced learning (DRL)-based inductively coupled DSTATCOM (IC-DSTATCOM) is proposed. First, by analyzing the impedance matching principle, the expression of source, load and filter current is derived with the help of inductive filtering transformer. And second, an individual DRL subnet structure is accumulated for each phase using mathematical equations to perform the better dynamic response. A 10-kVA, 230-V, 50-Hz prototype direct coupled distributed static compensator (DC-DSTATCOM) and IC-DSTATCOM experimental setup is buit to verify the experimental performance under uncertainties of loading. The IC-DSTATCOM is augmented better dynamic performance in terms of harmonics curtailment, improvement in power factor, load balancing, potential regulation, etc. The benchmark IEEE-519-2017, IEC-61727 and IEC-61000-1 grid code are used to evaluate the effectiveness of the simulation and experimental study. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

Microgrids have emerged as a feasible solution for consumers, comprising Distributed Energy Resources (DERs) and local loads within a smaller geographical area. They are capable of operating either autonomously or in coordination with the main power grid. As compared to Alternating Current (AC) microgrid, Direct Current (DC) microgrid helps with grid modernisation, which enhances the integration of Distributed and Renewable energy sources, which promotes energy efficiency and reduces losses. The integration of energy storage systems (ESS) into microgrids has garnered significant attention due to the capability of ESS to store energy during periods of low demand and then provide it during periods of high demand. This research includes planning, operation, control, and protection of the DC microgrid. At the beginning of the chapter, a quick explanation of DC microgrids and their advantages over AC microgrids is provided, along with a thorough evaluation of the various concerns, control techniques, challenges, solutions, applications, and overall management prospects associated with this integration. Additionally, this study provides an analysis of future trends and real-time applications, which significantly contributes to the development of a cost-effective and durable energy storage system architecture with an extended lifespan for renewable microgrids. Therefore, providing a summary of the anticipated findings of this scholarly paper contributes to the advancement of a techno-economic and efficient integration of ESS with a prolonged lifespan for the use of green microgrids.

The directly coupled distributed static compensator (DC-DSTATCOM) is often utilized to achieve better power quality (PQ) in the power distribution network (PDN). However, this compensator faced challenges like poor adaptability performance and more maintenance costs due to the integration of several types of energy resources. To overcome the above-said limitations, the inductively coupled distributed static compensator (IC-DSTATCOM) using Deep Belief Learning Network (DBLN) technique is proposed. The power transfer capability of the IC-DSTATCOM is examined by considering the impedance matching principle of the distributed static compensator (DSTATCOM), source and load. Besides this, the dependent parameters are combined with the convergence factor and learning rate to achieve the approximate tuned weight by using the suggested learning mechanism. The generalized mathematical equations are illustrated using MATLAB/Simulink to generate the switching pulses. The simulation studies of both DC-DSTATCOM & IC-DSTATCOM are performed to evaluate the transient behaviour and robustness under different states of loading. The proposed system is augmented with a superior performance in terms of harmonics curtailment, improvement in power factor (p.f), load balancing, potential regulation etc. The international standard regulatory guidelines IEEE-519–2017 and IEC- 61,000–1 are imposed to evaluate the effectiveness of the simulation and d-SPACE-based experimental study.

A Thyristor controlled reactor supported inductively coupled hybrid distributed static compensator (ICH-DSTATCOM) for three-phase three-wire (3P3W) distribution system (DS) is planned. The hybrid distributed static compensator (H-DSTATCOM) is a hot topic among scientists studying power quality (PQ) problems. The majority of published papers, however, avoid discussing H-DSTATCOM design. This paper proposes a design of a distributed static compensator (DSTATCOM) utilised to compensate for an uncontrolled rectifier load jointed to a 3P3W network. The filtering mechanism is performed by inductively coupled voltage source converter using the deep learning approach. The Thyristor controlled reactor (TCR) is incorporated in series with compensator inductance. The detailed design of TCR for the elimination of error caused by the system unbalance is discussed by using the mathematical equations. The MATLAB/ Simulation outcomes show that enhanced potential control & balancing, harmonic mitigation, and good power factor (pf) with the proposed topology as per the guidelines imposed by IEEE-519-2017.