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This paper presents an effective resonance suppression control for grid-interactive inverters with LCL filter in weak grid conditions. The resonance suppression mechanism is based on an adaptive model predictive control (AMPC). The large parasitic impedance and low SCR of weak grid challenges the operation of grid-interactive inverters. Specifically, the LCL filter resonance may get excited, resulting in collapse of the inverter operation. The proposed AMPC scheme autonomously alter the controller objectives for suppressing the resonance. The proposed control scheme alters the feedback currents based on the grid-condition. During the stiff grid conditions, grid current serves as feedback to the controller. While in the weak grid conditions, the inverter current is selected as feedback signal. The toggling action between these two feedback currents is determined by comparing moving RMS of grid current with desired current as constraint in the proposed AMPC cost function. The performance of proposed controller is verified experimentally. The results demonstrate that the proposed controller features robust performance under various grid conditions, model parameter mismatch, seamless transition between controller modes of operation and feedback alteration to attain resonance suppression, eliminates required tuning efforts in comparison to classical multi-nested loop control schemes, and provides high power quality.

Nowadays, to meet the vision of employing 100% renewable-based electricity generation, the conventional power system is evolving into power electronics-dominated grid (PEDG). This transition leads to an amplified complexity and significance for device and system-level control schemes to maintain resiliency, reliability, and operational stability. Recently, in various fields of engineering and science, the machine learning (ML)-based schemes have exhibited outstanding performance. Considering abundance of data in the PEDG, MLbased approaches illustrate promising potential to be adopted in this new energy paradigm. Similarly, the MLinspired approaches have been attracting many researchers in power electronics and power systems fields, who are trying to solve the challenges posed by the PEDG concept. This article presents cutting-edge ML applications in the PEDG and provides a futuristic research roadmap.

Microgrids in power electronics dominated grid (PEDG) should be able to operate under islanded and grid-connected modes and switch between them seamlessly. During the islanding events severe fluctuations in voltage and frequency of the microgrid may arise that can cause unstable operation and ultimately blackout. Moreover, under this transition the inverter may lose synchronism or point of synchronization because of disconnection from grid. This paper proposes: (i) a predictive control scheme that enables dual-mode (grid-forming and grid-following) operation of inverters and enables seamless transition between these two modes; and (ii) a swift single-point synchronization scheme for the network of grid-following and grid-forming inverters. The proposed control scheme mitigates voltage, current, and frequency fluctuations and enables seamless transition while needing limited time for synchronization between the grid-connected and the islanded modes of operation. Moreover, the predictive controller ensures smooth toggling between grid-following and grid-forming modes of operation and extracting maximum power from photovoltaic energy sources. The effectiveness of the proposed control scheme with its self-synchronization capability is validated by various case studies under grid-tied, islanded and transition from grid-connected to islanded mode of operations.

This paper builds upon an autonomous indexing-based model predictive control (MPC) framework for smart photovoltaic (PV) inverters in a connected community with dynamic topology and changing configurations. The proposed controller offers black start capability for connected communities with a high penetration of PV sources after a failure or power outage event. A higher margin of robustness and flexibility is achieved by enabling autonomous PV inverters operating modes while remaining fully synchronized. The autonomous indexing scheme assigns operational modes of distributed PV inverters, i.e. voltage or current control, in a sequential manner and enhances the resiliency of islanded connected communities against the contingency of grid-forming PV inverter interruption. The proposed controller is demonstrated to optimize the black start performance of an islanded and fully de-energized system. Simulated case studies explore the controller’s functionality and validate its black start restoration performance.

This paper presents an adaptive rank-based model predictive control (MPC) scheme for voltage source converters (VSCs) operating in a low voltage, AC community microgrid with dynamically changing topology. The proposed framework enables a fully synchronized microgrid with the ability to connect multiple VSCs to the utility grid simultaneously through multiple points of common coupling (MPCC). Furthermore, the VSCs autonomously adjust their operational mode from grid-forming to grid-following and vice versa, attaining a higher margin of stability, robustness, and flexibility. The MPC framework features an adaptive ranking system that assigns operational modes of the VSCs, i.e. voltage control (grid-forming) or current control (grid-following). The MATLAB/Simulink simulated case studies validate the controller’s functionality and flexibility while operating in changing microgrid configurations. A small-scale hardware testbed validates the practical implementation of the proposed controller.

This paper proposes a hybrid constant power generation - power ramp rate control (CPG-PRRC) scheme to regulate the ramp-rate of the power injected into the grid by a cluster of photovoltaic (PV) sources. The ramp-rate regulation of the injected PV active power is achieved dynamically in real-time under highly fluctuating PV ambient conditions and without considering PV forecasting data. The proposed hybrid CPG-PRRC control scheme integrates the concept of constant power generation with power ramp rate control to ensure mitigation of the potential voltage/ power fluctuations seen by the grid and protects it from source side instabilities. Moreover, the proposed scheme is capable of regulating the PV power ramp-rate within the set limit during power ramp-up as well as ramp-down scenarios and does so without the use of energy storage devices. The ramp-rate is regulated by maintaining the PV operation set– point on the left side of the maximum power point (MPP) and dynamically increasing or decreasing the power reference at a rate which is within the ramp limit. Furthermore, this paper addresses potential stability concerns for the PV architecture in hand with the CPG-PRRC. Several test scenarios are provided to verify the theoretical expectations.

This paper presents an effective resonance suppression control when grid interactive inverter with LCL filter experience weak grid conditions. The resonance suppression mechanism is based on finite-set model predictive control (FS- MPC) with adaptive cost function to alter the controller objectives seamlessly. The large parasitic impedance and low short circuit ratio (SCR) of weak grid challenges the operation of grid- connected inverters. Specifically, the LCL filter resonance may get excited, resulting in collapse of the inverter operation. To address this issue, an effective active damping approach is implemented in the proposed control. In the proposed active damping scheme for resonance suppression, the feedback currents are switched based on the operating conditions of inverter. During the stiff grid conditions, grid current serves as feedback to the controller. While in the weak grid conditions, the inverter current is selected as feedback signal. The toggling action between these two feedback currents is determined by comparing moving RMS of grid current with threshold current as constraint in the cost function of the proposed MPC scheme. The threshold current for toggling action is based on the reference active and reactive power setpoints plus an acceptable band of variation. The theoretical analysis is verified by several case studies.

This paper presents a self-stabilization mechanism based on finite-set model predictive control (FCS-MPC) framework for smart inverters operating in weak grid conditions. As weak grid's large parasitic impedance and low short-circuit-ratio (SCR) challenge the stable operation of grid-connected inverters. Specifically, the inverter may experience frequencies that might excite the LCL filter resonance phenomenon. The inverter stability collapses if this LCL resonance is triggered. To address this issue, a robust predictive controller is proposed that features a self-stabilization mechanism for smart inverters interacting with a weak grid. The proposed methodology utilizes the idea that in stiff grid conditions the grid current feedback (GCF) is stable and in weak grid conditions the inverter current feedback (ICF) is stable. Therefore, the proposed FCS-MPC toggles between GCF and ICF to achieve inherent LCL filter resonance damping. The toggling action between GCF and ICF is leveraged by comparing the moving RMS grid current with threshold current as a constrained in the proposed FCS-MPC cost function. The theoretical analyses are verified by several case studies for a single-phase grid-connected inverter. The analysis and results demonstrated that the proposed FCS-MPC operates well under weak, ultra-weak and stiff grid conditions.

Although, in Pakistan the electric motor industry is indigenous one, meeting all its demand and requirement through local production, but the percentage share of motors exported is quite minimal. The primary reason for this is that in international market there are specific standards and minimum efficiency criteria that should be met before exporting the motors. But the local Small-Medium Enterprises (SMEs) of Pakistan are unable to produce standard complaint motors because there is no such facility available in Pakistan that can give compliance certificates. This paper presents the design of a three-phase induction motor test bed which measures the efficiency of the motor according to the International standards. The standard experimental procedure for measuring the efficiency of 175 W induction motor according to IEEE 112 standard is discussed and results are presented. Moreover, simulation results of Electric drive are also discussed and presented.

Extracting the maximum energy from solar panels throughout the day deliver the maximum power at runtime that helps to attain early breakdown between solar panel cost and benefit for customer. Maximum power point tracking is limited by capabilities of power electronics converter topologies and its control systems. Wrong selection of topology can leads towards sub optimal operation and total lack of tractability. Several converter topologies are used to enhance the solar panel efficiency by extracting the maximum power from solar modules. These topologies work on the principle of impedance matching between load and solar module. In this paper non-isolated converter topologies and their comparison using different loads for maximum power point tracking has been discussed. Certain limitations of these topologies have been presented in this paper. Suitability of particular topology with respect to minimum number of passive components and different load ranges has also been discussed in this paper.