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Photovoltaic (PV) installations have become integral for harnessing solar energy, yet ensuring uninterrupted power generation remains crucial. This study addresses the challenge of maintaining reliability in PV systems by proposing a method to detect and identify simultaneous faults, using kernel principal component analysis (KPCA) and statistical metrics. The proposed method employs KPCA, a machine learning technique adept at identifying patterns in complex data. By utilizing statistical metrics in a feature space generated by KPCA, potential faults in PV system performance data are flagged. Unlike prior research that focused on single faults, this work extends the application of KPCA to detect and identify multiple faults occurring simultaneously, such as partial shading combined with open or short circuit faults. Through extensive simulations, including 100 samples of different faults under varying irradiance conditions, the method demonstrates high accuracy rates: 93.33% for partial shading, 100% for open circuit, 100% for short circuit, and 81.81% for combinations of partial shading with either open or short circuit faults. Results from a Matlab‐Simulink model validate the effectiveness of KPCA in detecting both single and simultaneous faults in PV systems' DC side. [ABSTRACT FROM AUTHOR]

Summary: Review transcript presents copious solar tracking methods to investigate their output potential and also outline tracker type, construction, cost, and design parameter. The solar follower systems encompassed five classifications based on the tracking methods are active, passive, semi‐passive, manual, and chronological tracking are also reviewed with their application and feasibility. These all method‐based research paper are compared on the basis of complexity of circuit, cost, tracking based on (radiation or astronomical equation), degree of freedom, control strategy, accuracy, energy intact, power or energy gain, tracing error, and panel efficiency. Basically, one axis and two axis sun following system review revels compare study against stable system which addressed their suitability and capability of maximum energy generation and MPPT technologies. [ABSTRACT FROM AUTHOR]

Despite the advantages pertaining to efficiency, stability, and ease of integration with renewable energy sources, the wider acceptance of DC microgrids has been hindered by the complexity of the protection task. In addition to the absence of zero crossings, the weather‐dependent fluctuations in the operation of renewable sources and the high probability of distributed energy resources (DERs) outage further complicate the fault detection and classification task. This paper proposes a reliable DC microgrid protection approach with improved robustness against weather intermittency and possible source outages. The protection framework involves identifying the network topology from local information followed by executing a set of convolution neural networks (CNNs) to perform the protection tasks. The proposed CNN‐based scheme reduces the complexity and the associated computational cost of feature extraction for performing the intended protection tasks. The novelty of the scheme pertains to the inclusion of the stochastic model for representing weather variation and a mechanism for adapting the relaying mechanism during changes in the network structure. The same allows for avoiding relay malfunction during weather intermittency and source outage, thereby improving grid resilience and maintaining system integrity. The DC microgrid model has been simulated using MATLAB and Simulink. The proposed scheme has been extensively validated for various operating scenarios involving variations in wind speed, solar irradiance, fault parameters, and network configuration. Improvement toward reduction in memory requirement is still a challenge for the hardware implementation of CNN, which can be considered as a potential area for future research. [ABSTRACT FROM AUTHOR]

The main objective of this article is to apply the fractional calculus for establishing a novel design of photovoltaic (PV) system. In order to enhance the efficiency and robustness of the maximum power point tracking (MPPT) approach, a fractional‐order (FO) DC‐DC boost converter is proposed for a PV system. Due to the nonlinearity of the PV module, an artificial neural network (ANN) loop has been used to consistently generate an optimal reference voltage. Using FO control, an incommensurate FO backstepping controller (FO‐BSC) has been ultimately integrated for tracking the maximum power point in the presence of tremendously atmospheric conditions and load changes. In this context, the asymptotic stability is guaranteed via fractional Lyapunov function. According to Grünwald‐Letnikov fractional definition, the FO dynamic equations of the proposed converter have been derived using the principle of the average method, and the FO components of the converter are successfully approximated via Oustaloup rational approximation. MATLAB/Simulink has been used to confirm the validity and the accuracy of the constructed model, which is simulated by changing orders of the fractional components. In addition, the performance of the proposed boost converter has been verified under an open‐loop test and a closed‐loop test. The results have proved that the efficiency of the extracted maximum power has been improved with an average up to 99% in comparison with the conventional model based on a BSC. [ABSTRACT FROM AUTHOR]

This study proposes a novel strategy to utilize photovoltaic (PV) system energy coupled with grid connection and dual battery storage system. First, the problem is formulated in mathematical form with a linear model approach; later, a cost function is defined to evaluate the tariff in different scenarios. The cost function is designed to minimize the burden on consumers' shoulders in a constrained environment. The battery storage systems are used intelligently to reduce imported and exported energy, hence the respective cost. The secondary battery system has been added and discharges during high tariff hours provided the state of charge of the primary battery system is at the minimum level. Various types of tariff incentives are considered for comprehensive analysis. Various cases are considered for the analysis: grid supply, PV-system alone, grid connection coupled with PV-system, and grid connection connected with PV-system and battery storage banks. The statistical analysis is performed in each case to have a detailed observation on resultant distributions of imported energy, exported power, and stored energy based on unmet and excess power. The results conclude that the proposed methodology reduces the imported and exported power requirement with a net profit of £ 46. [ABSTRACT FROM AUTHOR]

Summary: It is known that the demand for renewable energy systems during recent years has increased significantly, especially for wind and photovoltaic systems. The continuous improvement of the efficiency and performance of these systems is important to their growth, particularly in terms of harvesting the maximum power. Many types of methods and techniques are available for tracking the maximum power point, the most important of which is the concept of artificial intelligence. Artificial intelligence (eg, neural networks and fuzzy neural networks) has great potential to handle complex systems or those that have non‐linear parameters and are influenced by environmental conditions. In this paper, artificial intelligence is used to predict the optimal reference values for wind turbines and photovoltaic panels used in maximum power point tracking (MPPT). Neural networks were used in the wind turbine system, while fuzzy neural networks were used in the photovoltaic systems. The networks were trained in real‐time using an incremental training mode based on the back‐propagation algorithm. The proposed system was designed practically based on the microcontroller and has been tested, and the results are extracted in real‐time. The results have proven their effectiveness and conformity with the required optimal values. [ABSTRACT FROM AUTHOR]

This article shows the motivation towards the development of a novel hybrid fuzzy‐based sliding mode control scheme for photovoltaic tied DSTATCOM topology for eliminations of perturbations in source currents and reactive power compensation. A LCLC type passive filter is employed at the output of PV fed DSTATCOM, which helps to connect the solar system to a grid. The presence of the series capacitor in the passive filters decreases dc link voltage, which helps in reducing the ratings of DSTATCOM in the proposed system. The switching signals for the PV fed DSTATCOM is generated by the utilization of novel hybrid fuzzy‐based sliding mode control scheme. To improve the efficiency of dc link voltage controller and to regulate the dc link voltage, fuzzy logic‐based approach has been utilized. The controller performance has been compared with conventional algorithms of reference signal generation to analyze the efficiency. An experimental prototype has been developed in the laboratory and results are analyzed to validate the efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Summary: The performance of the Photovoltaic (PV) system is highly affected by fluctuating climatic conditions. Due to these changing climatic conditions, the irradiance diminishes, and it results in the non‐linearity in voltage‐power (P‐V) curves of the PV system. Therefore, it is one of the reasons for the occurrence of the multiple peaks on P‐V curves instead of a single peak. Therefore, it is challenging to track the global maximum power point tracking (MPPT) under these circumstances with traditional MPPT techniques viz. perturb and observe (P&O) and fuzzy logic control (FLC). Concerning global MPP tracking, this paper proposes a novel MPPT method based on a Rao‐1 optimization algorithm. The proposed algorithm is used to overcome the drawbacks of the classical P&O, FLC, and PSO based MPPT techniques under partical shading conditions (PSCs) and rapidly changing atmospheric conditions viz. slow convergence speed and oscillation around the MPP. The proposed MPPT tracks the global MPP under any abnormal conditions like PSCs. To show the efficiency of the proposed MPPT a comparative assessment is performed with well‐established MPPT methods in the literature. The simulation results witnessed that the proposed MPPT is more superior in improving the PV system performance under normal as well as under PSCs. The proposed MPPT over the PSO technique decreases the tracking time by 72.7% for pattern 1, 29.8% for pattern 2, 66.7% for pattern 3, 61.9% for pattern 5, 27.8% for pattern 6, and improves the tracking efficiency in all shading patterns. [ABSTRACT FROM AUTHOR]

Summary: Hybrid renewable energy systems (HRES) are proving a promising solution for rural electrification in India, where grid power availability is limited. This paper aims to identify the optimal design and pre‐feasibility techno‐economic analysis of the hybrid grid‐independent renewable system for rural electrification. The proposed hybrid system integrates the various renewable energy system configurations to provide continuous power for the electric load demands of Kanakadri palle village, Andhra Pradesh, India. HOMER software is utilized to design the proposed hybrid system, perform optimization, techno‐economic analysis, and simulation. Here, six different hybrid system configurations were taken into account to perform a comparative economic analysis. The results obtained shows that one of the hybrid systems, PV/DG/converter/battery system (120 kW‐PV modules, 54 kW‐DG, 200 number of batteries with 260 Ah ratings, and 50 kW‐power converters), is the least economical optimal system based on least net present cost and Levelized cost of energy (COE), highest renewable fraction, and zero unmet electric loads percentage (NPC: $341 280, COE: $0.217, highest renewable fraction: 96.6% and unmet electric loads: 0%). For the site considered, this optimal hybrid system can provide a reliable power supply to the total energy requirements of 332.97 kWh/d and achieve 75.68% carbon emission reduction compared to a diesel generator. Moreover, the comparison of different system configurations was carried out based on economic analysis. This analysis shows that proposed hybrid system can be an appropriate model to provide reliable power at a low generation cost for the village considered in this study and can be recommended for future electrification projects. [ABSTRACT FROM AUTHOR]

Summary: The application of renewable energy sources in electrical energy generation is becoming widespread due to the decrease of installation costs and the increase of environmental concerns. Hybrid power generation systems are advantageous to meet the load demand, but optimal sizing is the main concern for having a cost‐effective system based on given load demand and techno‐economic indicators. This paper proposes a deterministic algorithm and utilizes genetic and artificial bee colony (ABC) optimization algorithms for optimal sizing of PV/battery and PV/WT/battery hybrid systems with minimum levelized cost of electricity (LCOE) constraint for two locations, Nigde and Bozcaada, in Turkey. The loss of power supply probability (LPSP) is used to build a reliable system and to make sure that the system produces required energy. Experimental results showed that optimal sizing of each location is different due to different wind and solar characteristics of locations. PV/battery model is more suitable for Nigde location with 1.22% LPSP and 0.1514 [$/kWh] LCOE, while PV/WT/battery model is more cost‐efficient for Bozcaada location with 1.952% LPSP and 0.0872 [$/kWh] LCOE. Time performances of the algorithms are also investigated. It has been seen that the ABC algorithm has better performance and less execution time. This study demonstrated that heuristic algorithms are more applicable than deterministic algorithms, due to fast discovery of optimal solutions for hybrid renewable energy systems. [ABSTRACT FROM AUTHOR]

Summary: One of the energy management system (EMS) goals in the smart home (SH) is to achieve cost reduction besides consumer risk minimization. For these purposes, SH will be equipped with combined heat and power (CHP) generation, wind turbine (WT), and photovoltaic (PV) resources. EMS in SH faces uncertainty due to variable generation of these resources and in‐operation of the switch connected to the network. In this article, proposed comprehensive algorithm for EMS of SH including WT, PV, battery energy storage system, CHP considering probability of mal‐operation of tie‐switch between SH and grid. In this regard suggested algorithm provides consumer risk reduction in SH EMS problem regarding to uncertainty of market price. It is so crucial that all of triple uncertainties of PV and WT resources and tie switch mal‐operation are considered as residential consumer risk constraints to achieve accurate results. Genetic algorithm (GA) is used as optimization method for solving of risk‐based SH EMS problem. Proposed EMS algorithm is implemented for test SH via simulation studies using MATLAB software. Results indicate presented risk‐based GA increases the thermal and energy storage by 20.25% and 14.28% and reduces the consumer risk when a blackout occurs by increasing the spinning reserve. [ABSTRACT FROM AUTHOR]

Summary: This paper presents the energy efficiency performances of a proposed Low Voltage Direct Current supply concept and other conventional on‐grid photovoltaic chains architectures. A photovoltaic power source was considered in the studied nano‐grids. The proposed chain architecture allows the photovoltaic generated energy to take a shorter path heading toward the load. The notion of relative saved energy was introduced to compare the studied PV systems energy performances. The obtained results revealed that the use of the low voltage direct current supply concept is very interesting and the use of direct current loads instead of alternative current loads, when a photovoltaic power is generated locally, is more efficient. The installed PV power source in the building should be well sized regarding to the consumed power in order to register a high system RSE. The efficiency of the new low voltage direct current architecture is 10% higher than the conventional one. [ABSTRACT FROM AUTHOR]

Summary: The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV‐generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV‐generated energy. We propose a novel integrated energy‐efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)‐based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV‐generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL‐based forecasting models for PV and EHP are accurate and reliable (ie, R2 above 0.95). Also, the results show that the proposed integrated energy‐efficient PV‐ESS‐EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV‐generated energy resulting in reduced dependency on fossil fuels for building energy needs. [ABSTRACT FROM AUTHOR]

Summary: Tariffs for ancillary services like reactive power are a significant tool for raising energy efficiency in the distribution systems. In this paper, a new method for the valuation of reactive power support provided by photovoltaic (PV) systems is proposed. As a valuation approach, it is assumed that the voltage regulation service can be individually provided by other sources like shunt reactors or demand response programs whose service prices have been formerly regulated. The proposed voltage control method is modeled by a network partitioning approach and the distributed algorithm called the alternating direction method of multipliers (ADMMs). Moreover, the uncertain nature of PV generation and load demand is modeled by a stochastic method. The proposed method is tested on a real rural distribution system and 123 bus IEEE distribution test system. The result evidence that the proposed method has a lower computation time compared to the centralized method in addition to having the same accuracy. For distribution companies, also, the proposed method of reactive power valuation will be a basis for pricing reactive power service provided by PVs. [ABSTRACT FROM AUTHOR]

Back ground and Aim: In this work, a new control scheme is developed to track the peak power from a solar energized grid‐tied three‐level diode‐clamped split source inverter (DCSSI). Material and method: Two isolated photovoltaic sources (PV) are considered as sources for the topology. The impedance network serves as a voltage booster and enhances the input PV voltage. Two individual perturb and observe maximum power tracking algorithms are used to pursue the peak power and to generate two individual dc‐link voltage references. A new dc‐link voltage reference is developed by the addition of the two individual voltage references. Results: The proposed control scheme is tested with different cases to investigate the optimum performance of the DCSSI. Primarily, the control scheme is examined with MATLAB/SIMULINK. To validate experimentally, a 5KVA DCSSI prototype is designed along with two individual impedance networks. An SP‐110 pyranometer is used to sense the variable irradiance. A Spartan‐6 XC6SLX25 field programmable logic array (FPGA) is used to generate the control algorithm for the DCSSI. Conclusion: Simulation and experimental results demonstrate the robustness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The direct and indirect control methods are commonly used for maximum power point tracker (MPPT) of photovoltaic (PV) systems. Due to the absence of the literature that assess their performance in a comprehensive way, this work attempts to analyze the effectiveness of both control methods for the perturb and observe (P&O) MPPT algorithm. The MATLAB/Simulink simulation is verified experimentally using a PV array simulator and the dSPACE DS1104 DSP board driving a buck‐boost converter. The results show that the indirect method exhibits lower steady‐state oscillation and is less sensitive to rapid irradiance change and load variations. It increases the steady‐state efficiency by 1.6%. Furthermore, under irradiance and load step changes, the transient efficiency increases by 29% and 30%, respectively. Based on these findings, it is envisaged that the indirect method is more suitable to be used as the controller in conjunction with the MPPT algorithm. [ABSTRACT FROM AUTHOR]

Summary: Background: In this paper, a new hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. It is a combination of modified particle swarm optimization (PSO) and perturb and observe (P&O) methods. The proposed GMPPT technique, adaptive butterfly PSO (ABF‐PSO) uses butterfly swarm intelligence for modifying the conventional PSO algorithm with parameter tuning to avoid premature convergence. Aims: Hybrid global maximum power point tracking (GMPPT) technique is proposed for faster and accurate tracking of global maximum power point (GMPP) without premature convergence. Further, a new reinitialization of particles for any irradiance change is proposed to get faster tracking. Materials & Methods: In the proposed hybrid GMPPT technique, first GP region is easily identified with adaptive sensitivity parameter of the ABF‐PSO algorithm and in the region identified, GMPP tracking is continued with P&O algorithm with variable length perturbations to avoid the unnecessary exploration of search space even after reaching global peak (GP) region. Results: The combined effect of adaptive parameters, global region identification with adaptive sensitivity, proposed reinitialization method, and steady‐state tracking with variable step P&O results in fast and accurate tracking of GMPP with low power oscillations during GP region identification stage and steady‐state. Discussion: Boost DC‐DC converter is used as an MPPT controller to test the performance of the proposed algorithm under different irradiance patterns by using MATLAB/Simulink model and hardware prototype developed. Conclusion: The combined effect of adaptive parameters, global region identification with adaptive sensitivity, proposed reinitialization method, and steady‐state tracking with variable step P&O results in fast and accurate tracking of GMPP with low power oscillations during GP region identification stage and steady‐state. [ABSTRACT FROM AUTHOR]

Summary: Photovoltaic (PV) energy represents one of the most important renewable energies, but its disadvantage resides in its maximum power point, which varies according to meteorological changes that make the efficiency low. Intelligent techniques, using the maximum power point tracking (MPPT) method, can achieve an efficient real‐time tracking of this point in order to ensure optimal functioning of the system. The output power of the PV system is removed from solar irradiation and cell temperature of the PV panel type SOLON 55W. Therefore, it is essential to harvest the generated power of the PV system and optimally exploit the collected solar energy. For this objective, this work treats on a new artificial neural network‐particle swarm optimization approach (ANN‐PSO). The ANN is used to predict the solar irradiation level and cell temperature followed by PSO to optimize the power generation and optimally track the solar power of the PV panel type SOLON 55W based on various operation conditions under changes in environmental conditions. The simulation results of the proposed approach give a minimum error with a relevant efficiency, that is, the power provided by ANN‐PSO approach is optimal and closer to the PV power. Consequently, this novel approach ANN‐PSO shows its major capability to extract the optimal power with excellent efficiency up of 97%. For this objective, this work treats a new hybrid ANN‐PSO approach. [ABSTRACT FROM AUTHOR]

Summary: Partial shading is a common occurrence in residential and commercial photovoltaic (PV) installations. It causes mismatch losses, particularly in string and central inverter‐based systems, leading to output power loss and in turn lower performance ratio under partial shading conditions. The choice of string configuration itself is critical in lowering mismatch losses and the levelized energy cost. While a range of commercial inverters with different string configurations and prices are available, many standard string topologies are not optimum for common settings under various shading scenarios. Therefore, in this article, we evaluate various string configurations attached to state‐of‐the‐art commercial inverters widely used in the industry from both mismatch loss and cost perspective. We quantify the impact of various shading scenarios on multiple inverter configurations to ascertain the relative performance of PV systems under symmetrical shading as well as random shading in field settings. For multiple PV system configurations (on a single rooftop), the mismatch loss varied up to 2.3% and 6% under symmetrical shading and field settings, respectively. The levelized cost of electricity also varies from 0.062$/kWh to 0.041$/kWh and is dependent on the type of inverter and string configurations. [ABSTRACT FROM AUTHOR]

Summary: The motor speed is variable in stand‐alone solar water pumping system (SWPS) due to intermittent nature of solar source. Hence, water flow is fluctuating under variation of sun radiation in stand‐alone SWPS, which leads to issues like underutilization of motor capacity and increase in irrigation time. Therefore, the reliability of stand‐alone SWPS is less in terms of water delivery. To increase water delivery over a day, grid‐connected SWPS is developed in this article utilizing the diode bridge as grid‐interface converter. The presented system consists of boost converter for maximum power tracking and voltage source inverter (VSI) for motor power control. In grid‐connected mode, the solar photovoltaic (SPV) power varies under the variation of ambient conditions, but the system assures maximum water delivery by drawing deficit power from the grid. This system also supports stand‐alone operation where the motor speed and corresponding water flow vary with change in ambient conditions. Because of unidirectional diode rectifier, the DC‐bus voltage rises to a high value when SPV power is more than motor power. This happens during starting of the motor when motor power rises slowly and does not match with a maximum SPV power. To tackle this issue, finite state machine (FSM)‐based power limit maximum power point tracking (MPPT) algorithm is implemented in this development. The algorithm ensures SPV power below the motor power under DC‐bus voltage rise condition. An experiment prototype is developed, and the control system is implemented on 32‐bit ARM Cortex M4 microcontroller. Experiments are performed to investigate the performance of the presented system under steady‐state and transient conditions. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel automated design of the photovoltaic maximum power point tracking (MPPT), based on the robust sliding mode control, using the automation programmable logic controller (PLC) software. The aim is to maintain the photovoltaic system operating at its maximum by continuously tracking the maximum power point, with considerable oscillation reduction to overcome the drawbacks usually witnessed with standard MPPT algorithms using automation PLC. The studied system is modeled and simulated using the Siemens automation software (TIA portal), and an empirical implementation is implemented using the Siemens S7‐1200 PLC as a controller. To validate the robustness of the proposed sliding mode control, the system outcomes under variant meteorological conditions using both the sliding mode controller and the well‐known incremental conductance algorithm are compared. The results reveal that the sliding mode control provides smooth and precise MPPT that far exceeds the incremental conductance performance. [ABSTRACT FROM AUTHOR]

Summary This paper presents an integrated operation model of renewable energy, distributed generators (DGs), energy storage, and demand response to overcome the challenges of renewable energy market participation. Additionally, an accurate prediction model for wind, photovoltaic (PV) power, and market price is introduced based on ensemble empirical model decomposition. In this model, radial basis function neural network (RBFNN) is used for the forecasting of subsignals from the analysis model. The training model of RBFNN is shaped based on the B‐water cycle algorithm (WCA) optimization method. Additionally, by considering the adjusted market, demand response, and uncertainties analysis, this method reduced the economic damages of generators and loads. A case study test has consisted of a wind turbine, photovoltaic energy, fuel cell energy, demand response, and energy storage. [ABSTRACT FROM AUTHOR]

Summary: In recent years, the attention towards the utilization of solar and wind power systems are becoming more popular because of its nonpolluted and nondepleting nature. However, these power sources rely on environmental conditions for power generation. So, extracting maximum power available from the wind and solar systems is essential. Many of the existing maximum power point tracking (MPPT) techniques' performances are not convenient in the hybrid operation of wind and solar power systems. So, to provide an optimum MPPT operation, this paper proposes a fuzzy logic controll er (FLC)–based elephant herding optimization (EHO) technique for MPPT in the hybrid wind‐solar system. The proposed fuzzy‐EHO (FEHO) method has the tendency to track the maximum power under the partial shading condition of the photovoltaic (PV) system and low wind speed situations. The results are compared with the conventional MPPT methods, and it shows that the proposed optimization method efficiently tracks the maximum power point of the wind‐solar power systems even with the variations in the climatic conditions. MATLAB tool is used in the design and operation of the proposed method. Furthermore, the performance of the proposed system is tested with an experimental setup and provides better efficiency. [ABSTRACT FROM AUTHOR]