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This study focuses on detecting Power Quality Disturbance Events (PQDE) in microgrids integrated with a Solar Energy Conversion System (SECS). The research proposes a novel signal reduction technique called Clark Transformed Modal, which reduces three-phase voltage to a single unit signal, optimizing memory utilization and reducing computational load during feature extraction. A total of 16 features are extracted from the proposed modal signal by performing multi-resolution analysis through Maximum Overlap Discrete Wavelet Transform (MODWT). Various disturbances, including sag, swell, transients, notches, and flicker, are intentionally simulated in a PV-grid tied MATLAB/Simulink model to obtain a dataset of 10800 samples. Further, the dataset is randomly divided into training-testing subsets to verify the classification ability of a novel ensemble classifier called subspace weighted k-nearest Neighbor (SWKNN). In addition to that the optimum mother wavelet (dmay) is identified to even further boost the classifier performance. The results demonstrate the superior classification capabilities of the proposed MODWT-SWKNN classifier in terms of various performance metrics like precision, recall and F1-score. It also outperformed when compared with several competitive PQ classification models based on PV-integrated systems both under ideal and noisy conditions. Additionally, the disturbance detection system is validated in an OPAL-RT real-time environment to demonstrate its efficiency in terms of detection time. The accuracy of detection is found to be 99.74% in ideal case and fall back to no more than 3% regulation i.e., 97.28% even in dense noise of 20dB with as low as 8 WKNN subspaces. Further, average detection time with 500 trails is found to be 0.0285 seconds. The efficacy of the proposed PQ detection algorithm is also tested in a large PV integrated IEEE 13-bus system.

The modern era power system is constantly undergoing constructive changes and implementations both in source and load side. Certainly, the distributed generators, unconventional/nonlinear loads, charging stations etc are mostly integrated through power electronics interfaces. As a result, frequent power quality disturbances appear in the system that is to be mitigated at the earliest. Since detection is the prerequisite for mitigation, therefore the article presents a novel intelligent power quality detection scheme to detect and classify the PQ Events. At first, the energy feature of the 5 band limited modes are calculated from variational mode decomposed voltage signals. Then the mode energy features are utilized to train a novel Hybrid Arithmetic Whale Optimized light gradient boosting machine classifier. A total of 15 different PQ events have been investigated and exceptional classification results have obtained with optimum computational complexity, both under noiseless and noisy conditions. Moreover, the accuracy of the proposed PQ classification schemes found to be towering against other related pre-published works. Finally, the ability of the proposed detection scheme is validated in real time though OPAL-RT 4510 and grid simulator hardware in loop setup.

Current electricity sectors will be unable to keep up with commercial and residential customers’ increasing demand for data-enabled power systems. Therefore, next-generation power systems must be developed. It is possible for the smart grid, an advanced power system of the future, to make decisions, estimate loads, and execute other data-related jobs. Customers can adjust their needs in smart grid systems by monitoring bill information. Due to their reliance on data networks, smart grids are vulnerable to cyberattacks that could compromise billing data and cause power outages and other problems. A false data injection attack (FDIA) is a significant attack that targets the corruption of state estimation vectors. The primary goal of this paper is to show the impact of an FDIA attack on a power dataset and to use machine learning algorithms to detect the attack; to achieve this, the Python software is used. In the experiment, we used the power dataset from the IoT server of a 10 KV solar PV system (to mimic a smart grid system) in a controlled laboratory environment to test the effect of FDIA and detect this anomaly using a machine learning approach. Different machine learning models were used to detect the attack and find the most suitable approach to achieve this goal. This paper compares machine learning algorithms (such as random forest, isolation forest, logistic regression, decision tree, autoencoder, and feed-forward neural network) in terms of their effectiveness in detecting false data injection attacks (FDIAs). The highest F1 score of 0.99 was achieved by the decision tree algorithm, which was closely followed by the logistic regression method, which had an F1 score of 0.98. These algorithms also demonstrated high precision, recall, and model accuracy, demonstrating their efficacy in detecting FDIAs. The research presented in this paper indicates that combining logistic regression and decision tree in an ensemble leads to significant performance enhancements. The resulting model achieves an impressive accuracy of 0.99, a precision of 1, and an F1 score of 1.

Conventional DC-DC boost converters have played a vital role in electric vehicle (EVs) powertrains by enabling the necessary voltage to increase to meet the needs of electric motors. However, recent developments in high-gain converters have introduced new possibilities with enhanced voltage amplification capabilities and efficiency. This study discusses and evaluates the state-of-the-art high-gain DC-DC converters for EV applications based on the Quadratic Boost Converter (QBC). Research into innovative topologies has increased in response to the increasing demand for efficient and high-performance power electronic converters in the rapidly expanding EV industry. Due to its ability to provide more significant voltage gains than conventional boost converters, the QBC has become a viable option for meeting the unique requirements of EV power systems. This survey focuses on the efficiency, power density, and overall performance parameters of QBC-based high-gain converters. The literature review provides a foundation for comprehending power electronics converters’ trends, challenges, and opportunities. The acquired knowledge can enhance the design and optimization of high-gain converters based on the QBC, thereby fostering more sustainable and efficient power systems for the expanding electric mobility industry. In the future, the report suggests that investigating new high-gain converter design methodologies will reduce component stress and enhance the intact system efficiency.

Renewable energy utilization is the only suitable solution to diminish the increasing level of greenhouse gas emissions, fuel costs, and energy crisis in the next generation. Out of many renewable sources, solar energy sources that are clean, green, and emissions-free have gained wide utilization despite their intermittency nature. Several solar photovoltaic (PV) panels are connected in parallel to achieve the energy demand. In such a system, each panel operates differently due to uneven temperature and irradiation, resulting in a uniform and partial shading conditions. Thus, a unique and efficient mechanism is required to extract maximum power from uniformly and partially shaded PV systems. Many researchers across the world have developed various maximum power point tracking (MPPT) techniques to increase the efficiency and lifetime of PV systems. This study provides a unique, in-depth, and organized review of MPPT methods under four categories: classical, intelligent, optimization, and hybrid techniques. All possible selection benchmarks are considered to do a comprehensive review, which is not deliberated in the existing review literature. Based on the selection benchmarks, the advantages and disadvantages of each MPPT technique under different categories are summarized in tabulated form. To address the research gaps for further investigation in this field, a concise discussion is included at the end. This review article may find an accessible reference for engineers to understand the most useful MPPT method and to undertake extensive research in PV systems.

Abstract A relative assessment on conventional and adaptive current controllers used in reduced sensor‐maximum power point tracking (MPPT) based photovoltaic (PV)‐grid tied inverter systems for the improvement of system power quality is suggested. The steady‐state and transients errors produced in the conventional PI and proportional resonant controllers, which are used to generate the references, can be fixed by using an intelligent ADALINE‐LMS adaptive controller; moreover, it helps in reducing the %THD (total harmonic distortion) level measured at different power zones. Also, to track the maximum PV power, which is further integrated to DC‐bus, a reduced sensor‐based technology is added into the circuit that sidesteps the problem of tracking local MPP instead of global MPP and the drawbacks of using current sensors. The use of a reduced sensor‐based MPPT controller confirms extraction of maximum PV power and it guarantees a constant DC‐link voltage under all the possible test conditions. The overall control architectures and system performances, which are tested under different system dynamics, are validated through MATLAB/Simulink as well as experimental findings obtained using the dSPACE RTI 1202 interfacing kit. These experimental results confirm that the adaptive control technique used in reduced sensor‐MPPT based PV‐grid tied inverter systems performs unbeatably with balanced load and grid voltages, less harmonics, quick response time etc. under the operation of linear, non‐linear and transient loads, whereas, conventional controllers are best only for the linear loads.

Multilevel inverters (MLIs) with switched-capacitor (SC) combinations are widely recognized to improve the power quality and efficiency of the renewable energy and high-frequency power distribution systems. Self-balancing of SCs and voltage boosting ability are the salient features of the recently developed SC MLIs. This work presents a new SC MLI structure using a reduced number of switches and two dc sources. For increasing the voltage levels, the basic units of the proposed MLI can be extended without increasing the number of dc sources. By suitable charging-discharging patterns, the SCs are self-balanced and voltage boosting is achieved. Comparative analysis with prior-art MLIs demonstrates the merit of topological advancement. Further, extensive simulation and experimental results confirm the feasibility of the proposed MLI under different loading and dynamic operating conditions.

Electric vehicles (EVs) are universally recognized as an incredibly effective method of lowering gas emissions and dependence on oil for transportation. Electricity, rather than more traditional fuels like gasoline or diesel, is used as the main source of energy to recharge the batteries in EVs. Future oil demand should decline as a result of the predicted rise in the number of EVs on the road. The charging infrastructure is considered as a key element of EV technology where the recent research is mostly focused. A strong charging infrastructure that serves both urban and rural areas, especially those with an unstable or nonexistent electrical supply, is essential in promoting the global adoption of EVs. Followed by different EV structures such as fuel-cell- and battery-integrated EVs, the charging infrastructures are thoroughly reviewed in three modes, specifically—off-grid (standalone), grid-connected, and hybrid modes (capable of both standalone and grid-connected operations). It will be interesting for the readers to understand in detail several energy-source-based charging systems and the usage of charging stations for different power levels. Towards the improvement of the lifetime and efficiency of EVs, charging methods and charging stations in integration with microgrid architectures are thoroughly investigated. EVs are a multi-energy system, which requires effective power management and control to optimize energy utilization. This review article also includes an evaluation of several power management and control strategies followed by the impact assessment of EVs on the utility grid. The findings and the future research directions provided in this review article will be extremely beneficial for EV operators and research engineers.

Due to global warming and climate change, it is essential to produce power using renewable sources, such as solar, wind, fuel cells, etc. The traditional grid shifts towards the smart grid by infusing digital communication techniques and information technology. As the current power system is shifting towards a smart grid, the utility and prosumers participate in the energy trading process. Due to the distributed nature of the smart grid, providing a fair price among them is becoming a difficult task. The article introduces a model for energy trading in a smart grid by allowing participants to negotiate in multiple stages using a game-theory-based multi-stage Nash Bargaining Solution (NBS). The model’s application of game theory enables the participants to decide on a mutually acceptable price, thereby encouraging the utility, private parties and prosumers (those who are able to generate and consume energy) to participate in the trading process. Since all parties participate in the trading procedure, greenhouse gas emissions are reduced. The proposed model also balances the benefits of consumers and producers in the final agreed fixed price. To demonstrate the efficacy of the proposed work, we compare the analytical results with feed-in-tariff (FiT) techniques in terms of consumers’ energy bills and producers’ revenue. For experimental analysis, 20 participants are considered, where the percentage reduction in the bill of each consumer and the percentage increment of revenue of each producer are compared to FiT. On average, the overall bill of the consumer is reduced by 32.8%, and the producers’ revenue is increased by 64.83% compared to FiT. It has been shown further that the proposed model shows better performance as compared to FiT with an increase in the number of participants. The analysis of carbon emission reduction in the proposed model has been analyzed, where, for 10 participants, the carbon emission reduction is approximately 28.48 kg/kWh, and for 100 participants is 342.397 kg/kWh.

This paper presents a unique two-fold adaptive linear neural network (ADALINE) for fast and accurate measurement of fundamental, harmonics, sub-harmonics, inter-harmonics and decaying DC components of a distorted current signal with additive noise. The preceding parallel approach is termed as Master-Slave ADALINE (MS ADALINE). The Slave-ADALINE adopts least mean square (LMS) algorithm with a fixed and large step-size for weight vector adjustment. During the training interval or transients, this filter performs a significant role. On the other hand, the Master-ADALINE uses a variable step-size LMS algorithm for achieving a small steady-state error. At the end of each iteration, the local averages of the squared errors of both the ADALINE's are calculated and weights of the Master-ADALINE are updated accordingly. The amplitudes and phases of desired frequency components can be worked out from Master-ADALINE's weights. The proposed architecture improves the convergence speed by establishing an independent control action between the steady-state error and the speed of convergence. The simulation results of this method under various operating situations are analyzed and compared with single fold ADALINE structure that obeys dynamic step-size LMS (DSSLMS) adaptation rule. Eventually, a scaled laboratory prototype has been developed for the validation of the proposed technique in real-time utilization. This innovative research finding makes the power system smart and precise.

Multilevel inverters (MLIs) have drawn tremendous attention in the power sector. Application of MLI has grown extensively to improve the power quality and efficiency of the photovoltaic (PV) system. For an MLI interfacing PV system, the size, cost and voltage stress are the key constraints of the MLI that need to be minimized. This paper presents a novel reduced part count MLI interfacing single-stage grid-tied PV system along with a closed-loop control strategy. The proposed MLI consists of n repeating units and a level boosting circuit (LBC) that assists in generating 4n + 7 voltage levels instead of 2n + 3 levels. Three different algorithms are proposed for a proper selection of dc-link voltages to enhance the levels further. A comparative analysis is carried out to confirm the superiority of the developed MLI. The workability of the proposed MLI is investigated with a 1.3 kW PV system. The closed-loop control strategy ensures the maximum power tracking, dc-link voltage balancing, satisfactory operation of the MLI and injection of clean sinusoidal grid current under any dynamic changes. Comprehensive simulation analysis is carried out considering a 15-level MLI structure. Experimental tests further confirm the practicality of the topological advancement for a PV system under different dynamic conditions.

Recently, multilevel inverters (MLIs) have gained lots of interest in industry and academia, as they are changing into a viable technology for numerous applications, such as renewable power conversion system and drives. For these high power and high/medium voltage applications, MLIs are widely used as one of the advanced power converter topologies. To produce high-quality output without the need for a large number of switches, development of reduced switch MLI (RS MLI) topologies has been a major focus of current research. Therefore, this review paper focuses on a number of recently developed MLIs used in various applications. To assist with advanced current research in this field and in the selection of suitable inverter for various applications, significant understanding on these topologies is clearly summarized based on the three categories, i.e., symmetrical, asymmetrical, and modified topologies. This review paper also includes a comparison based on important performance parameters, detailed technical challenges, current focus, and future development trends. By a suitable combination of switches, the MLI produces a staircase output with low harmonic distortion. For a better understanding of the working principle, a single-phase RS MLI topology is experimentally illustrated for different level generation using both fundamental and high switching frequency techniques which will help the readers to gain the utmost knowledge for advance research.