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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.

Cyberattacks can be strategically counterfeited to replicate grid faults, thereby manipulating the protection system and leading to accidental disconnection of grid-tied converters. To prevent such setbacks, we propose a physics-informed spline learning approach-based anomaly diagnosis mechanism to distinguish between both events using minimal data for the first time in the realm of power electronics. This methodology not only provides compelling accuracy with limited data, but also reduces the training and computational resources significantly. We validate its effectiveness and accuracy under experimental conditions to conclude how data availability problem can be handled.

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.

As the penetration rate of the electric vehicles (EVs) increases, their uncontrolled charging could cause undervoltage and network congestion in the electric network. To mitigate these impacts, the controlled charging of the EVs has been investigated by earlier publications. However, controlled charging cannot be easily implemented as it involves multiple customers having individual interests. To overcome these drawbacks, the power prediction of charging and discharging of EVs plays a major role. A new realistic power prediction algorithm that accounts for the requirements of different patterns and consumers is developed in this study. The main objective of the study is to develop a charging and discharging coordination algorithm that effectively addresses the problem of power demand during peak time. Stochastic methods were used to develop the charging-discharging models and estimate the EV usage. The proposed algorithm aims to manage high power demands at peak times using vehicle-to-grid technologies. Intensive computer simulations are performed to test and estimate the power demand by adapting the proposed algorithm. The developed algorithm shows a significant improvement in the comprehensive index with a value of 0.649 which is very high compared with conventional charging strategies. The results depicted an efficient scheduling and power distribution without affecting the performance of the EV or the flexibility of EV owner's trip schedule.

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.

This paper signifies the development of an efficient and cost-effective control scheme for operation of grid integrated photovoltaic (PV) system under low voltage ride through (LVRT) condition. During LVRT If the PV system was to disconnect from the grid, the disconnection further aggravates the situation by causing larger disturbance than what was caused by the fault. To stabilize the system and prevent the flow of over current during LVRT it is necessary to control the reactive power injection in the system. The proposed scheme balances the active power though proportional gain regulation of plug in controller. Compared to the conventional LVRT method, this method is more cost effective. A normal maximum power point tracking (MPPT) scheme is implemented during normal operation. But in case of fault condition, an extra proportional controller is added to assist the droop characteristics of photovoltaic power voltage curve and attain system stability. Simulation has been performed on a grid integrated PV system of 4kW and LVRT conditions are tested. It is observed that the response of controller is fast and seamless during transition mode.

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.

In photovoltaic inverters, transformerless inverter has been emerging trend in recent years. Control of transformerless inverter keeping all other parameters in consideration is a major concern. This paper presents proportional–integral–derivative (PID) and fuzzy-based control strategies for H5 inverter topologies describing their principal functions are uses. Voltage balancing of H5 inverters under various load conditions was observed with the use of PID, fuzzy and fuzzy-PID controllers. Several specific requirements are taken into consideration and subsequently analysed, the simulation model and the results defined effectiveness of the system for various controllers in terms of stability, settling time and harmonic distortions. At the end, the experimental results are introduced, and they confirm the accuracy of propounded control strategies.

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.

This paper focuses on the development of approaches to design hybrid power systems for the needs of energy management and power quality. It develops an algorithm for determining an optimal structure of power supply system taking into account technical, environmental and social aspects. This research aims at achieving a regulatory for power management in a PV Wind hybrid power system based on simulated intellect practices. To safeguard efficient optimization of sources, Fuzzy Inference System approach is engaged to accomplish the Maximum Power Point for photovoltaic panels and wind turbine. In addition, the power quality aspects are studied and with the use of UPFC which improves the voltage and current regulations. The Total Harmonic Distortions of the Voltages and currents in the simulated system under different load conditions were analysed with IEEE Standards 519-2014. The whole system is analysed through simulation in MATLAB / Simulink environment.

Solar powered Electric vehicle (EV) charging stations are being widely adapted in the present-day scenarios. But the problems with varying irradiance for photo-voltaic (PV) systems, peak load, and disturbances due to grid penetration of PV and EV needs to be addressed. In this paper a PV powered electric vehicle charging station with an intelligent energy management strategy (EMS) is developed. The energy management approach adapts meteorological data and load demand statistics to optimize the use of solar and grid energy for charging station. The EMS controls the power flow between the PV, grid, battery and EV charging station resulting in reduced power demand peak by a factor of 2.