This paper proposes a control strategy for grid-following inverter control and grid-forming inverter control developed for a Solar Photovoltaic (PV)–battery-integrated microgrid network. A grid-following (GFL) inverter with real and reactive power control in a solar PV-fed system is developed; it uses a Phase Lock Loop (PLL) to track the phase angle of the voltages at the PCC and adopts a vector control strategy to adjust the active and reactive currents that are injected into the power grid. The drawback of a GFL inverter is that it lacks the capability to operate independently when the utility grid is down due to outages or disturbances. The proposed grid-forming (GFM) inverter control with a virtual synchronous machine provides inertia to the grid, generates a stable grid-like voltage and frequency and enables the integration of the grid. The proposed system incorporates a battery energy storage system (BESS) which has inherent energy storage capability and is independent of geographical areas. The GFM control includes voltage and frequency control, enhanced islanding and black start capability and the maintenance of the stability of the grid-integrated system. The proposed model is validated under varying irradiance conditions, load switching, grid outages and temporary faults with fault ride-through (FRT) capability, and fast frequency response and stability are achieved. The proposed model is validated under varying irradiance conditions, load switching, grid outages and line faults incorporating fault ride-through capability in GFM-based control. The proposed controller was simulated in a 100 MW solar PV system and 60 MW BESS using the MATLAB/Simulink 2023 tool, and the experimental setup was validated in a 1 kW grid-connected system. The percentage improvement of the system frequency and voltage with FRT-capable GFM control is 69.3% and 70%, respectively, and the percentage improvement is only 3% for system frequency and 52% for grid voltage in the case of an FRT-capable GFL controller. The simulation and experimental results prove that GFM-based inverter control achieves fast frequency response, and grid stability is also ensured.

Grid Connected Photo Voltaic (GCPV) system should be susceptible to grid faults and load curtailment without disconnection and supports in grid stability. During grid faults, there is an increase in dc link voltage, dip in grid voltage which leads to over-current on the grid side. Similarly, when demand is suddenly removed, the voltage at the PCC rises above its nominal value. This leads to possible damage in the PV inverters and hence need to disconnection of GCPV system leading to islanding scenarios. Hence, the GCPV system need to be equipped with Fault Ride Through (FRT) capability to address the issues related to low voltage and high voltage conditions in the grid side. In this work, multimode inverter control strategy is proposed with FRT capability according to grid code compliance. An improved current control technique is proposed as FRT based protection strategy during grid faults and sudden removal of load. According to severity of faults, a fault level detection is developed which triggers the control strategies to generate the reference real and reactive power with respect to the grid code compliance. The performance analysis of the proposed FRT control strategy is implemented in 100 kW two-stage GCPV system in MATLAB/Simulink 2018b environment and tested under low voltage and high voltage ride through conditions. The proposed control strategy is implemented in hardware setup of 1 kW GCPV system. The experimental results prove the effectiveness of the proposed inverter FRT based control strategy in enhancing the system parameters during system disturbances under different modes of operation.

Purpose: The artificial intelligence (AI) based total productive maintenance (TPM) condition based maintenance (CBM) approach through Industry 4.0 transformation can well predict the breakdown in advance to eliminate breakdown.Design/methodology/approach: Meeting the customer requirement as per the delivery schedule with the existing resources are always a big challenge in industries. Any catastrophic breakdown in the equipment leads to increase in production loss, damage to machines, repair cost, time and affects delivery. If these breakdowns are predicted in advance, the breakdown can be addressed before its occurrence and the demand supply chain can be met. TPM is one of the essential operational excellence tool used in industries to utilize the existing resources of a plant in a optimal way. The conventional time based maintenance (TBM) and CBM approach of TPM in Industry 3.0 is time consuming and not accurate enough to achieve zero down time.Findings: The proposed AI and IIoT based TPM is achieved in a digitalized data oriented platform to monitor and control the health status of the machine which may reduce the catastrophic breakdown by 95% and also improves the quality rate and machine performance rate. Based on the identified key signature parameters related to major breakdown are measured using the sensors, digitalised by programmable logic controller (PLC) and monitored by supervisory control and data acquisition (SCADA) and predicted in server or cloud.Originality/value: Long short term memory based deep learning network was developed as a regression forecasting model to predict the remaining useful life RUL of the part or assembly and based on the predictions, corrective action has been implemented before the occurrence of breakdown. The reliability and consistency of the proposed approach are validated and horizontally deployed in similar machines to achieve zero downtime.