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The increasing distributed generation (DG) systems and their integration with the utilities has complexed the operation of the power system network by subjecting them various abnormalities. Numerous studies are aimed to explore the ability of an inverter in a DG system to operate in standalone (SA) mode during the abnormal operation of the utility or when it is scheduled for maintenance. In this paper, a grid condition monitoring based transition approach is proposed by developing a hybrid control strategy to control the photovoltaic (PV) inverter operating in the grid-connected (GC) mode. In the case of Intentional or unintentional islanding conditions, the PV is disconnected, and voltage loop-based control is implemented for controlling the PV inverter in the SA mode of operation. The switching between the control strategies of GC and SA is performed by the central controller ensuring a smooth transition and continuous power delivery to the load. For validating the claims, a 4 kWp PV system is designed on MATLAB Simulink. The system is operated in both SA and GC mode of operation and the result depicts smooth transition without any transient peaks or harmonics.

The installation of grid-connected microgrids ( $\mu {\rm{Gs}}$ ) is considered a suitable solution to enhance the modernization of distributed generation systems into smart grids. This realization has raised the need for the development of an energy management system $({{\rm{EMS}}})$ for achieving efficient monitoring, control, and management of the energy flows in the system. In this article, the fuzzy inference system (FIS) based EMS synthesis is proposed to efficiently control the distribution of energy flows of $\mu {\rm{G}}$ in real time. The FIS is further optimized using a genetic algorithm approach to achieve faster evaluation and robust EMS development. In the development process of the EMS, the graph theory-based representation of the $\mu {\rm{G}}$ s is proposed for efficient representation of the energy demand and energy generation with the energy systems connected in the $\mu {\rm{G}}$ . To assess the performance of the proposed EMS, a photovoltaic generation of 19.95 kWp and an aggregated load of 8 kWp are characterized along with a battery energy storage system to form a $\mu {\rm{G}}$ . The results identified the benefits of the proposed approach regarding profit generated and battery usage during the $\mu {\rm{G}}$ operation.

This paper proposes an intelligent seamless transition controller for smooth transition between grid-connected (GC) and standalone modes of distributed generation (DG) units in the grid. The development of this seamless controller contributes to two main processes in the transition modes: the synchronization process and an islanding process. For the synchronization process, the stationary reference frame phase-locked loop (SRF-PLL) associated with the voltage source inverter (VSI) is modified using the frequency, voltage deviation, and phase angle information. Furthermore, the islanding process is classified as intentional and unintentional islanding scenarios for achieving efficient transition control. Here, the intentional islanding process is achieved with the information that is available in the system due to the planned disconnection. For the unintentional islanding process, a fuzzy inference system (FIS) is used to modify the conventional droop control using the information of change in active power, voltage, and frequency. To identify the action of the proposed approach during the transition process, numerical simulations are conducted with the hardware-in-loop (HIL) simulator by developing a 10kWp three-phase grid-connected DG system. The results identified the efficient control of the VSI for both islanding and grid connection processes. In the islanding conditions, the proposed controller provides advantage with less detection and disconnection time, and during synchronization, it instantly minimizes the phase-angle deviation to achieve efficient control.

This paper develops an islanding classification mechanism to overcome the problems of non-detection zones in conventional islanding detection mechanisms. This process is achieved by adapting the support vector-based data description technique with Gaussian radial basis function kernels for islanding and non-islanding events in single phase grid-connected photovoltaic (PV) systems. To overcome the non-detection zone, excess and deficit power imbalance conditions are considered for different loading conditions. These imbalances are characterized by the voltage dip scenario and were subjected to feature extraction for training with the machine learning technique. This is experimentally realized by training the machine learning classifier with different events on a 5 &nbsp, kW grid-connected system. Using the concept of detection and false alarm rates, the performance of the trained classifier is tested for multiple faults and power imbalance conditions. The results showed the effective operation of the classifier with a detection rate of 99.2% and a false alarm rate of 0.2%.

This paper focus on developing a new islanding detection method with the help of machine learning and signal processing technique. The islanding detection method make sure that there is a proper remote monitoring of the grid integrated photovoltaic (PV) system. In case of grid fault or maintained of the grid, a proper informed signal is provided to various distributed generation (DG) networks so that they can disconnect with the grid and operate in isolated mode. A simulation of 1kW grid connected PV system is performed. The signal such as voltage, current and frequency are recorded at point of common coupling (PCC). The feature of recorded signals are extracted using wavelet transformation. The extracted features are used to form a islanding scenarios matrix. The matrix is further utilized to train a classifier using machine learning algorithm. From the result it can be observed that the trained classifier depicted 97.9% training accuracy with a training time of 16.9 sec which is better when compared with the literature. Further the trained classifier is subjected to test with an unknown islanding condition to observe the robustness of the classifier.

This paper develops a coordinated reactive power control system for grid integrated solar photovoltaic (PV) inverters. The proposed algorithm combines the reactive power control capabilities of PV inverter with flexible AC transmission devices for stabilizing the PV system during grid faults. The proposed control method works as per a predefined hierarchical structure by prioritizing the reactive power control with PV inverters. The complete methodology is realized by testing them with a two-stage single-phase grid-connected PV system simulated in MATLAB/Simulink software. The simulation results verify the accuracy of the classification algorithm and depict the effectiveness of the proposed controller in both the under and overvoltage situations.

Electric vehicles (EVs) are envisaged to be the future transportation medium, and demonstrate energy efficiency levels much higher than conventional gasoline or diesel-based vehicles. However, the sustainability of EVs is only justified if the electricity used to charge these EVs is availed from a sustainable source of energy and not from any fossil fuel or carbon generating source. In this paper, the challenges of the EV charging stations are discussed while highlighting the growing use of distributed generators in the modern electrical grid system. The benefits of the adoption of photovoltaic (PV) sources along with battery storage devices are studied. A multiport converter is proposed for integrating the PV, charging docks, and energy storage device (ESD) with the grid system. In order to control the bidirectional flow between the generating sources and the loads, an intelligent energy management system is proposed by adapting particle swarm optimization for efficient switching between the sources. The proposed system is simulated using MATLAB/Simulink environment, and the results depicted fast switching between the sources and less switching time without obstructing the fast charging to the EVs.

A fault classification and identification system for prognosis of single-phase inverters was presented in this paper. This concept involves the resolution and analysis of different technical constraints associated with the operation and integration of the inverters. The work presented is centered on fault identification for single-phase inverters by developing a suitable technique utilizing signal processing and machine learning techniques. This task is approached by observing the behavior of inverters under various faults and the factors affecting them. Thereafter, the output voltage under both normal and faulted operation was subjected to signal processing techniques to observe the features. The obtained features are trained for classification and identification of faults using support vector machines. Simulation was carried out for operation of inverter under different events of a fault. By combining output voltages and their features with wavelet transforms and feature detection, the developed algorithms are capable of capturing the key features of inverters. The results depicted how to perform numerical fault analysis calculations using MATLAB classification learner by adapting the trained data for identifying the fault.

Photovoltaic (PV) energy has become one of the main sources of renewable energy and is currently the fastest-growing energy technology. As PV energy continues to grow in importance, the investigation of the faults and degradation of PV systems is crucial for better stability and performance of electrical systems. In this work, a fault classification algorithm is proposed to achieve accurate and early failure detection in PV systems. The analysis is carried out considering the feature extraction capabilities of the wavelet transform and classification attributes of radial basis function networks (RBFNs). In order to improve the performance of the proposed classifier, the dynamic fusion of kernels is performed. The performance of the proposed technique is tested on a 1 kW single-phase stand-alone PV system, which depicted a 100% training efficiency under 13 s and 97% testing efficiency under 0.2 s, which is better than the techniques in the literature. The obtained results indicate that the developed method can effectively detect faults with low misclassification.