Uniform Irradiance Conditions Research Articles

ANALYSIS OF A HYBRID CURRENT MODULATED DUAL ACTIVE BRIDGE PHOTOVOLTAIC DC/DC CONVERTER FOR UNIFORM IRRADIATION CONDITIONS

The energy of Photovoltaic (PV) modules can be connected to DC grid with high step-up ratio power electronics converters. Generally, two-stage isolated boost-based converters are used for this purpose. However, these types of converters consist of a high number of semiconductors which must withstand high voltage levels, and they utilize input inductance which has high value and large magnetic core size. To solve these problems, single-stage conversion-based Hybrid Current Modulated (HCM) isolated Dual Active Bridge (DAB) converter is proposed in the literature. However, this converter is designed for considering partial shading (PS), and PS rarely occurs in PV farms. In this study, the design methodology of DAB PV DC/DC converter is given for uniform irradiation (UI) conditions that is more suitable for PV farms. In addition, UI and PS designs of HCM DAB PV DC/DC converter are compared to each other. Analyses show that UI-based design has 3.88% smaller RMS2 current, 3.56% smaller peak current and 2.35% smaller core size than the PS-based design. Hence, UI-based design can be more efficient, cheaper, and smaller in size.

Performance analysis of drone sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions

In recent years, growing consumer demand for sustainable energy is driving down costs and paving the way for more clean energy in the future. Since these energies are sporadic, and the load pattern does not correlate to their generation pattern, a battery storage system is required. Operating dispersed alternative energy sources, connected to the grid in this situation makes energy control an unavoidable task. The difficulty is brought on by things like intermittent sources, daytime costs, solar panel and battery size restrictions, and limitations in battery charging and discharging rates. Moreover, integrating energy storage devices into the power system network is one of the recommendations made in this work to increase the performance and dependability of these systems. Compared to the existing works reported in the literature, this study offers enhanced control techniques for hybrid Photovoltaic and Battery Energy Storage System under fluctuating atmospheric scenario. Here, an innovative approach called Drone Squadron Optimisation, which tracks the Global Maximum Power Point, is used get maximum power. Further, suggested approach is tested with various partial shading instances and the uniform irradiance condition to validate it. This research also investigates photovoltaic models and the status of the battery storage device for improved energy management in the system. Finally, the proposed algorithm assists in quickly identifying the potential benefits of a grid-connected, battery-powered rooftop solar system.

Implementation of modified P&O and an adaptive fuzzy logic controller based MPPT tracking system under partial shading and variable environmental conditions

Most of the conventional Maximum Power Point Tracking (MPPT) Algorithms provide the finest efficiency during uniform irradiation conditions, but under variable irradiation or partial shading condition (PSC), the performance deteriorates and the solar PV system is unable to provide the maximum electricity out of the PV Modules. This degradation in performance occurs due to the presence of numerous local maximum power points (LMPP) and a single global maximum power point (GMPP) in the power versus voltage (P-V) characteristics and the perfect tracking of these LMPP is not possible with the prevailing MPPT algorithms. In order to eradicate this shortcoming, we have proposed the implementation of an adaptive Fuzzy Logic Controller (FLC) based on Perturb and Observe (P&O) technique by employing a boost converter with variable resistive load under uniform irradiance condition (UIC), dynamic atmospheric conditions (DAC) and PSC in this article. We also demonstrated the usage of a simple and improved P&O algorithm by employing a buck boost converter for faster tracking time. These two suggested approaches of FLC and P&O incorporate a variable load scenario and a stable load scenario respectively to address the problem of output voltage oscillation in a PV-based system. The FLC is designed to adjust the dynamic change in step size based on the rate of change of the yield power of the PV panel to reduce oscillations in output of the boost converter and the PV system. According to the results of this analysis, the recommended FLC-based P&O (FLC-P&O) algorithm outperforms the proposed P&O algorithm under DAC and PSC with respect to tracking speed, steady-state error, and dynamic response. Apparently, it can be concluded that the proposed FLC-P&O technique can be applicable to real-time systems for reliable and efficient operation of the boost converter and add to the stability and simplicity of the contemporary PV-based systems.

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

This study proposes a novel honey bee dancing (HBD) maximum power point tracking (MPPT) algorithm inspired by the foraging behavior of honey bees. When a bee finds nectar, it returns to the honeycomb and dances to inform others about the location of the nectar. Other bees then fly towards the location and gather the nectar. The proposed HBD algorithm uses five bees searching for the nectar who communicate with each other about the location and the quantity of nectar by dancing. Finally, the five bees found the location of the most nectar which is represented by the maximum power point. The proposed HBD algorithm applies to uniform irradiance condition (UIC) and partial shaded condition (PSC). It is then compared with the PV panel output power and load relationship (OPLR) algorithm and perturb and observe (P&O) algorithm. Experimental verification has been performed under both the UIC and PSC. At the UIC's irradiance level of 200 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 120 W, 118 W, and 94.5 W, respectively, with efficiencies of 99 %, 97 %, and 78 %. Additionally, under the PSC with an irradiance level of 600 W/m2, the PV module's output power for the proposed HBD algorithm, OPLR algorithm, and P&O algorithm are 218 W, 189.2 W, and 74.8 W, respectively, with efficiencies of 99 %, 86 %, and 34 %, and convergence times of 4.7 ms, 6.5 ms, and 6 ms, respectively. It is evident from the results that the solar MPPT performance of the proposed HBD algorithm is better than the OPLR algorithm and P&O algorithm. This method ingeniously combines the foraging behavior of bees with solar power generation to produce the maximum natural power. This approach does not require the development of photovoltaic (PV) panel specification data, complex calculations, and additional temperature meters and heliographs. It is highly efficient and has significant economic benefits.

A Coronavirus Optimization (CVO) algorithm to harvest maximum power from PV systems under partial and complex partial shading conditions

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

MPPT Techniques Exploration under Uniform and Non-Uniform Solar Irradiance Condition–A Survey

Solar PV has become increasingly popular in the past decade, both for standalone and grid-connected systems. Solar PV systems exhibit non-linear output characteristics due to weather fluctuations, which can substantially impact the overall PV system’s output. Consequently, selecting the optimal MPPT technique for a given PV system is a complex task, as each method has different strengths and weaknesses. Therefore, conducting a comprehensive examination of these techniques is crucial. A survey of academic publications on ‘Google Scholar’ spanning the past seven years (2015-2022) revealed a total of seventy-one review articles addressing various MPPT techniques. Among the studies, seven have directed their attention toward non-uniform solar irradiance, while four have dedicated their focus to uniform conditions. Surprisingly, none have ventured into the realm of hybrid optimization MPPT techniques. Moreover, several of these reviews have covered a restricted range of MPPT methods, and none have delivered an all-encompassing assessment that encompasses both online and offline strategies for uniform conditions, alongside an evaluation of hybrid MPPT techniques in the context of non-uniform solar irradiance conditions. Unfortunately, there have been minimal efforts in this direction. Consequently, the need of the hour is a comprehensive review paper that encompasses nearly all known MPPT techniques within a single publication. This paper places its primary emphasis on categorizing MPPT algorithms into offline, hybrid, and online optimization techniques, addressing both non-uniform and uniform irradiance scenarios. It serves as a comprehensive resource by consolidating diverse MPPT techniques, presenting their mathematical formulations, operational principles, and visual representations through block diagrams and flowcharts. The objective of this research endeavor is to provide a valuable reference for strategists, researchers, and energy engineers, offering them a roadmap to steer future research endeavors and practical implementations in the realm of optimization of MPPT.

An improved 0.8Voc model‐based global maximum power point tracking algorithm for photovoltaic system

AbstractIn this paper, a low‐cost and high‐speed partial shading condition (PSC) detection scheme and global maximum power point tracking (GMPPT) technique are presented. The proposed technique detects the PSC using a low‐cost voltage sensor without setting any threshold parameter. The proposed GMPPT algorithm utilizes the 0.8 model along with the load‐line observation to skip the unwanted interval of – curve. Moreover, in case of dynamic shading, a smart scanning mechanism is used along with the load‐line observation to reduce the voltage iterations which leads to an improvement in tracking speed. The proposed technique is implemented in a Matlab/Simulink environment and compared with the conventional 0.8 method and accurate PSC detection method. The results indicate the following advantages of the proposed algorithm: (1) iteration free and accurate detection of PSC and uniform irradiance condition, (2) enhancement in average tracking time by 67.44% and 69.48%, and (3) improvement in average transient efficiency by 17.36% and 21.25% compared with 0.8 method and accurate PSC detection method. Besides, the experimental results depict that the proposed method enhances the average tracking time by 68.6% and 72.69% and improves the average transient efficiency by 17.62% and 15.06% compared with 0.8 method and accurate PSC detection method, respectively.

MPPT Novel Controller Based on Passivity for the PV Solar Panel-Boost Power Converter Combination

This article deals with the MPPT controller based on passivity, which calculates through a dynamic average model between the PV solar panel and the boost power converter under uniform irradiance conditions. The MPPT algorithm is a linear controller based on the exact tracking error dynamics passive output feedback (ETEDPOF) controller design methodology. The algorithm includes suitable adaptive feedforward pre-compensation, which explicitly depends on the precisely estimated resistance of the boost converter. The MPP reaches by regulating the voltage and current of the boost converter to hold the PV maximum power in the system's input capacitor. The controller desired references such as the inductor current, the output voltage, and the average input control are in terms of the PV maximum power nominal values, which remain always fixed. A reduced-order extended state observer (RESO) develops to estimate the boost converter's output resistance, adapting to the controller's desired references. Simulation and experimental results demonstrate the effectiveness, stability, and robustness of the MPPT algorithm based on passivity as compared to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Incremental Conductance</i> (IC) algorithm, under sudden output resistor changes, high irradiance, and high temperature in the solar panels.

An enhanced scanning technique for flexible power point tracking under partial shading condition

Flexible power point tracking (FPPT) terminology has recently emergedto support additional functionality for grid-connected systems. Despite having a well-developed operation under uniform irradiance conditions, the FPPT algorithm's operation under partial shading conditions(PSC) reveals substantial challenges. The presented article introduces a global FPPT (GFPPT) algorithm that can operate under the PSC with a fast-trackingspeed. The proposed GFPPT algorithm employs the conventional search-skip-judge global maximum power point trackingmethod and the essential strategic scheme. The method conducts on the left side of the power-voltage curve to avoid the less robust nature ofthe right-sideoperation. Simulation and experimental tests are employed to validate the adequacy of the proposed approach. The simulation and experimental study adopted an indirect control approach using a proportional-integral controller and a buck-boost dc-dc converter. The uniform irradiance condition and PSC patterns with various peaks are chosen, and the presented GFPPT method is applied underdistinct criteria. The proposed method's effectiveness has been adequately validated based on the simulation and experimental outcomes.

Performance Analysis of Different Optimization Algorithms for MPPT Control Techniques under Complex Partial Shading Conditions in PV Systems

Classic algorithms show high performance in tracking the maximum power point (MPP) of photovoltaic (PV) panels under uniform irradiance and temperature conditions. However, when partial or complex partial shading conditions occur, they fail in capturing the global maximum power point (GMPP) and are trapped in one of the local maximum power points (LMPPs) leading to a loss in power. On the other hand, intelligent algorithms inspired by nature show successful performance in GMPP tracking. In this study, an MPPT system was set up in MATLAB/Simulink software consisting of six groups of serially connected PV panels, a DC-DC boost converter, and load. Using this system, the cuckoo search (CS) algorithm, the modified incremental conductivity (MIC) algorithm, the particle swarm optimization (PSO) algorithm, and the grey wolf optimization (GWO) algorithm were compared in terms of productivity, convergence speed, efficiency, and oscillation under complex shading conditions. The results showed that the GWO algorithm showed superior performance compared to the other algorithms under complex shading conditions. It was observed that GWO did not oscillate during GMPP tracking with an average convergence speed of 0.22 s and a tracking efficiency of 99%. All these evaluations show that GWO is a very fast, highly accurate, efficient, and stable MPPT method under complex partial shading conditions.

Influence of fatigue precracking and specimen size on Master Curve fracture toughness measurements of EUROFER97 and F82H steels

EUROFER97 and F82H steels are two leading reduced-activation ferritic-martensitic (RAFM) steels for fusion first wall and blanket applications. Exposure to the harsh environment of fusion reactors can result in severe degradation of fracture toughness. Thus, the post-irradiation evaluation of fracture toughness is critical for understanding the material behavior. Due to the space constraints of irradiation facilities and challenges in controlling a uniform irradiation condition for large size specimens, the development of small specimen test techniques (SSTT) is indispensable to evaluate the performance of irradiated materials. In this study, we evaluated specimen size effects on the Master Curve fracture toughness of EUROFER97 and F82H steels. A wide variety of specimens, including 0.5 T compact tension (C(T)) specimens, 0.16 T mini-compact tension (miniC(T)) specimens, and 1.65 mm miniature bend bar specimens, were tested. The testing methodology was based on the Master Curve method in the ASTM E1921 standard. No specimen size effect was observed in 0.5 T C(T) and 0.16 T miniC(T) specimens on the Master Curve reference temperature T0, while 1.65 mm miniature bend bar specimens yielded a higher T0Q. A strong effect of fatigue precracking on T0 for 0.5 T C(T) and 0.16 T miniC(T) specimens was observed, such that testing on specimens with skewed fatigue precrack fronts resulted in lower T0 than for specimens with ASTM standard qualified straight fatigue precrack fronts. The results highlight the importance of experimental quality control in developing SSTT for Master Curve fracture toughness testing. Lastly, we also evaluated and provided recommendations on the minimum number of specimens needed for each specimen type for yielding reliable T0Q values.

An improved genetic algorithm based fractional open circuit voltage MPPT for solar PV systems

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test the robustness of the proposed technique under uniform and non-uniform irradiance conditions. The performance is compared to the Perturb & Observe, Incremental Conductance, and other hybrid MPPT techniques. Furthermore, the efficacy of the proposed technique is also assessed against a commercial PV system’s power output over one day. The results demonstrate that the proposed GA-FOCV technique improves the efficiency of the conventional FOCV method by almost 3%, exhibiting an average tracking efficiency of 99.96% and tracking speed of around 0.07 s with minimal steady-state oscillations. Additionally, the proposed technique can also efficiently track the global MPP under partial shading conditions and offers faster tracking speed, higher efficiency, and fewer oscillations than other hybrid MPPT techniques.

Design and Implementation of a New Fast and Efficient MPPT Controller under Different Solar Irradiance Conditions

The power-voltage (P-V) characteristic curve of solar photovoltaic (PV) systems operating in partial shading conditions (PSC) is nonlinear and has multiple local maximum peak power (LMPP) points, rendering many of the maximum power point tracking (MPPT) algorithms ineffective at locating global maximum peak power (GMPP) points. This work proposes a novel slime mould algorithm- (SMA-) based MPPT controller to utilise maximum peak power (MPP) from solar PV systems during uniform irradiance conditions (UC) and nonuniform irradiance conditions (NUC). On the basis of the MPP they tracked, tracking time, and power efficiency, MPPT controller performance is assessed through MATLAB simulations and implemented experimentally with dSPACE MicroLabBox under various irradiance conditions. The effective performance of the proposed controller is validated and demonstrated in comparison to existing popular MPPT controllers.

Power maximization in standalone photovoltaic system: an adaptive PSO approach

A control strategy for power maximization which is an important mechanism to extract maximum power under changing environmental conditions using Adaptive Particle Swarm Optimization (APSO) is proposed in this paper. An Adaptive Inertia Weighting Factor (AIWF) is utilized in the velocity update equation of traditional PSO for the improvement in speed of convergence and precision in tracking Maximum Power Point (MPP) in standalone Photovoltaic system. Adaptation of weights based on the success rate of particles toward maximum power extraction is the most promising feature of AIWF. The inertia weight is kept constant in traditional PSO for the complete duration of optimization process. The MPPT in PV system poses a dynamic optimization problem and the proposed APSO approach paves way not only to track MPP under uniform irradiation conditions, but also to track MPP under non-uniform irradiation conditions. Simulations are done in MATLAB/Simulink environment to verify the effectiveness of proposed technique in comparison with the existing PSO technique. With change in irradiation and temperature, the APSO technique is found to provide better results in terms of tracking speed and efficiency. Hardware utilizing dSPACE DS1104 controller board is developed in the laboratory to verify the effectiveness of APSO method in real time.

The Shading Effect on Photovoltaic Generator (GPV) Based on Reverse Osmosis (RO) Desalination System in Algeria

This paper investigates the performances analysis of PV-RO desalination system under uniform and non-uniform irradiance conditions. The main objective is the application of Extremum Seeking Control (ESC) to the GPV side in order to overcome the generated power and water quantity losses to improve the performance of the whole system. The technical design of the developed system is based on an existing RO desalination unit beside that real climatic data taken from a local mast are involved in this study. The modeling of the whole system and the control techniques adopted in this study are fully formulated. The whole system is modeled and controlled using MATLAB/Simulink. Different scenarios (healthy and shaded) based on real climatic data have been used to carry out the results. The ESC used shows its effectiveness to extract maximum power under shaded conditions for all the proposed scenarios. Compared with conventional controllers, this technique can offer extra power surrounding 685W in several cases. Furthermore, the shading conditions can affect widely freshwater production whose losses are estimated at around 82.91% for critical scenarios. The findings results are very significant for industrialists working in this field.

Metaheuristic Algorithm Based Maximum Power Point Tracking Technique Combined With One Cycle Control For Solar Photovoltaic Water Pumping Systems

Solar photovoltaic technology has become eminent in the world because of its clean and abundant nature and can be effectively used for water pumping applications. A maximum power point tracking method is indispensable to achieve the best benefit from photovoltaic systems. Conventional maximum power tracking methods become successful only under uniform irradiance conditions and fail to track the maximum power under partial shading conditions (PSC). Hence, nature-inspired metaheuristic algorithms were proposed to track the optimum power under varying environmental conditions. This study proposes a method for improving the performance of the nature-inspired maximum power point tracking algorithms by using a nonlinear control technique called one cycle control. Based on the duty cycle obtained from the tracking algorithm the one cycle control technique generates pulses for the DC–DC converter, which is connected to a brushless DC motor pump system through a voltage source inverter. The performance of the proposed system under various PSCs is validated using the Cuckoo search, particle swarm optimization, and grey wolf algorithms. Simulation results for various shading patterns prove the supremacy of the system with respect to convergence speed, tracking efficiency, robustness, steady-state oscillations at maximum power point, and initial exploration oscillations in comparison with systems without one cycle control. In addition, the introduction of a KY converter as a DC–DC converter reduces the output voltage ripple in the system. Thus, the proposed system with one cycle control overcomes the disadvantages of the existing methods and can be effectively utilized for water pumping applications.

A Novel SLOPDM Solar Maximum Power Point Tracking Control Strategy for the Solar Photovoltaic Power System

This study proposes a novel maximum power point tracking (MPPT) control strategy for the solar photovoltaic power system (SPPS). The proposed system adopts two solar photovoltaic modules of 430 W, which are connected to a boost converter and an MPPT controller, since the traditional MPPT algorithm (such as perturbation and observation [P&amp;O] algorithm) can hardly reach maximum power point (MPP) under low irradiance level and partial shading conditions (PSC), which leads to the low efficiency of the SPPS. The speed of light optical path difference measurement (SLOPDM) MPPT control strategy has been developed in this study to overcome this problem. The estimation of the optical path angle difference is used as the basis for the proposed control strategy. This is done by determining the relationship between the optical path angle difference, solar photovoltaic power impedance Rspv and load Ro, and then calculating the duty cycle corresponding to the MPP, which then drives the boost converter to capture the MPP. The experimental results verify the proposed system, which shows the efficiency comparison between the SLOPDM MPPT algorithm, solar angle and horizon (SAH) algorithm, and P&amp;O algorithm under PSC and uniform irradiance conditions (UIC) at irradiance levels of 700 W/m2 and 65 W/m2. It is evident from the comparison that the efficiency of the SLOPDM MPPT algorithm is 99% under both conditions, which is higher than the SAH and P&amp;O algorithms. The SLOPDM MPPT algorithm can precisely, rapidly, and stably be operated at MPP. The contribution of this study is that the proposed MPPT control strategy can help achieve the high−performance of SPPS without changing the hardware circuit design and requiring any additional solar power meter. This reduces the cost and the complexity of the system significantly.

Design and implementation of a fault detection method for a PV‐fed DC‐microgrid with power control mechanism

This paper presents practical implementation of a fault detection, localisation, and categorisation (FDLC) method in PV-fed DC-microgrid (DCMG). The DCMG is implemented by utilising a group of two DC nanogrids (DCNG) that have power control mechanism (PCM). The FDLC uses a voltage calculating circuit comprising a single voltage sensor and diode network. Moreover, the architecture is based on six statements extracted from the investigation of line to line (L–L) and line to ground (L–G) faults at a DCNG of the cluster. The PCM in the proposed system utilises a power triggering circuit for effective power flow among the different units of the DCNG considering the load demands and the resource availability. Experimentation is carried out by creating L–L/L–G faults at different points in the DCMG. Detection, localisation, and classification of faults is performed by utilising the sensor's voltage and power of the individual DCNG. FDLC offers less computational burden, perform fast detection of fault, and is capable of distinguishing between the L–L/L–G faults and uniform irradiance and partial shading conditions in the PV-array. The proposed FDLC technique and its six statements are verified through experimental results.

Improved tunicate swarm search-based MPPT for photovoltaic on a "grid-connected" inverter system.

Photovoltaic (PV) systems are broadly utilized, especially for remote electrification. It is easier for installation and is free of greenhouse gases, so the impacts on the environment are reduced. The temperature and irradiance change during the day, and the circumstances are more dynamic on cloudy days. Hence, attaining maximum power under some environmental conditions is complex and also requires individual techniques for tracking the maximum available power. These approaches are named maximum power point tracking (MPPT) methods. These are designed through power electronic converters, which supply the PV power to the load. In recent years, meta-heuristic-based algorithms have emerged because of their local optima avoidance ability and flexibility. However, the maximum power point (MPP) will not be tracked efficiently due to the constraint factors between some circuit parameters. This limitation has to be solved in this study. Here, MPPT constraint conditions of the PV system with an inverter are found by analyzing its integrated mathematical model. The MPP of a PV system is tracked by diverse techniques. Though these approaches differ in complexity, effectiveness, and convergence speed, cost and sensors are required. With the effect of these challenges, the main intention of this paper is to design and develop a new modified MPPT algorithm for enhancing the efficiency of the power grid connected with a DC-AC single-phase full-bridge inverter and a proportional integral derivative (PID) controller. Here, the parameters of the PID controller are tuned or optimized by the newly improved meta-heuristic algorithm termed modified tunicate swarm algorithm with new condition (MTSA-NC) thus maximizing the energy extraction of the PV system. The search process for newly improved MTSA-NC works well to adapt the MPPT to cope with a "grid-connected" inverter by attaining a faster convergence rate. Here, MTSA-NC is adopted by introducing the fitness-based solutions for updating the swarm behavior of tunicates to maximize the energy efficiency of "grid-connected" inverter systems for PV arrays. In MTSA-NC, the optimal fitness value is known as a food source, which is used for determining the solution updating. Finally, the experimental tests validate the success of the designed scheme in a PV system during uniform irradiance and partial shading conditions.