Maximum Power Point Tracking Using the Incremental Conductance Algorithm for PV Systems Operating in Rapidly Changing Environmental Conditions

Maximum Power Point Tracking (MPPT) is an important process in Photovoltaic (PV) systems because of the need to extract maximum power from PV panels used in these systems. Without the ability to track and have PV panels operate at its maximum power point (MPP) entails power losses;resulting in high cost since more panels will be required to provide specified energy needs. To achieve high efficiency and low cost, MPPT has therefore become an imperative in PV systems. In this study, an MPP tracker is modeled using the IC algorithm and its behavior under rapidly changing environmental conditions of temperature and irradiation levels is investigated. This algorithm, based on knowledge of the variation of the conductance of PV cells and the operating point with respect to the voltage and current of the panel calculates the slope of the power characteristics to determine the MPP as the peak of the curve. A simple circuit model of the DC-DC boost converter connected to a PV panel is used in the simulation;and the output of the boost converter is fed through a 3-phase inverter to an electricity grid. The model was simulated and tested using MATLAB/Simulink. Simulation results show the effectiveness of the IC algorithm for tracking the MPP in PV systems operating under rapidly changing temperatures and irradiations with a settling time of 2 seconds.

An improved MPPT control strategy based on incremental conductance algorithm

PV power production is highly dependent on environmental and weather conditions,such as solar irradiance and ambient temperature.Because of the single control condition and any change in the external environment,the first step response of the converter duty cycle of the traditional MPPT incremental conductance algorithm is not accurate,resulting in misjudgment.To improve the efficiency and economy of PV systems,an improved incremental conductance algorithm of MPPT control strategy is proposed.From the traditional incremental conductance algorithm,this algorithm is simple in structure and can discriminate the instantaneous increment of current,voltage and power when the external environment changes,and so can improve tracking efficiency.MATLAB simulations are carried out under rapidly changing solar radiation level,and the results of the improved and conventional incremental conductance algorithm are compared.The results show that the proposed algorithm can effectively identify the misjudgment and avoid its occurrence.It not only optimizes the system,but also improves the efficiency,response speed and tracking efficiency of the PV system,thus ensuring the stable operation of the power grid.

Research on the MPPT of Photovoltaic Power Generation Based on the CSA-INC Algorithm

The existing Maximum Power Point Tracking(MPPT)method has low tracking efficiency and poor stability.It is easy to fall into the Local Maximum Power Point(LMPP)in Partial Shading Condition(PSC),resulting in the degradation of output power quality and efficiency.It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms,and their performance in tracking the Global Maximum Power Point(GMPP)varies.Thus,a Cuckoo search algorithm(CSA)combined with the Incremental conductance Algorithm(INC)is proposed(CSA-INC)is put forward for the MPPT method of photovoltaic power generation.The method can improve the tracking speed by more than 52%compared with the traditional Cuckoo Search Algorithm(CSA),and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization(PSO)and the Gravitational Search Algorithm(GSA).CSA-INC has an average tracking efficiency of 99.99%and an average tracking time of 0.19 s when tracking the GMPP,which improves PV power generation’s efficiency and power quality.

Improved Control in Single Phase Inverter Grid-Tied PV System Using Modified PQ Theory

Grid-connected reactive-load compensation and harmonic control are becoming a central topic as photovoltaic(PV)grid-connected systems diversified.This research aims to produce a high-performance inverter with a fast dynamic response for accurate reference tracking and a low total har-monic distortion(THD)even under nonlinear load applications by improving its control scheme.The proposed system is expected to operate in both stand-alone mode and grid-connected mode.In stand-alone mode,the proposed controller supplies power to critical loads,alternatively during grid-connected mode provide excess energy to the utility.A modified variable step incremental conductance(VS-InCond)algorithm is designed to extract maximum power from PV.Whereas the proposed inverter controller is achieved by using a modified PQ theory with double-band hysteresis current controller(PQ-DBHCC)to produce a reference current based on a decomposition of a single-phase load current.The nonlinear rectifier loads often create significant distortion in the output voltage of single-phase inverters,due to excessive current harmonics in the grid.Therefore,the proposed method generates a close-loop reference current for the switching scheme,hence,minimizing the inverter voltage distortion caused by the excessive grid current harmonics.The simulation findings suggest the proposed control technique can effectively yield more than 97%of power conversion efficiency while suppressing the grid current THD by less than 2%and maintaining the unity power factor at the grid side.The efficacy of the proposed controller is simulated using MATLAB/Simulink.

Evaluation of On-Line MPPT Algorithms for PV-Based Battery Storage Systems

This paper presents a novel Simulink models with an evaluation study of more widely used On-Line Maximum Power Point tracking(MPPT)techniques for Photo-Voltaic based Battery Storage Systems(PV-BSS).To have a full comparative study in terms of the dynamic response,battery state of charge(SOC),and oscillations around the Maximum Power Point(MPP)of the PV-BSS to variations in climate conditions,these techniques are simulated in Matlab/Simulink.The introduced methodologies are classified into two types;the first type is conventional hill-climbing techniques which are based on instantaneous PV data measurements such as Perturb&Observe and Incremental Conductance techniques.The second type is a novel proposed methodology is based on using solar irradiance and cell temperature measurements with pre-build Adaptive Neuro-Fuzzy Inference System(ANFIS)model to predict DC–DC converter optimum duty cycle to track MPP.Then evaluation study is introduced for conventional and proposed On-Line MPPT techniques.This comparative study can be useful in specifying the appropriateness of the MPPT techniques for PV-BSS.Also the introduced model can be used as a valued reference model for future research related to Soft Computing(SC)MPPT techniques.A significant improvement of SOC is achieved by the proposed model and methodology with high accuracy and lower oscillations.

Stability and Control of Three-Phase Photovoltaic with LC Filter

The aim of this work is to study the stability and control of input of three-phase photovoltaic voltage source inverters connected to the LC （inductor and capacitor） filter. A PI （proportional-integral） classical controller approach is established in the control, including a MPPT （maximum power point tracking） based on incremental conductance algorithm, the MPPT improves the speed and the tracking accuracy, to rapidly track the MPPT from photovoltaic with various weather conditions, and effectively eliminate the oscillation phenomena near the MPPT. The control system can stabilize and improve the system performances, and the simulation results show the high stability, high efficiency and robustness of the proposed control with good performances.

Interactions of Cations with Electrodialyzed Clay Fraction of Soils as Inferred from Wien Effect in Soil Suspensions

The electrical conductivities (ECs) of suspensions containing 25 and 30 gkg^(-1) solids prepared from the electrodialyzed clay fraction (< 2μm in diameter) of latosol,yellow-brown soil, and black soil, dispersed in various nitrate solutions having concentrations of 1X 10^(-4)/z mol L^(-1), where z is the valence, and in distilled water, were measured at fieldstrengths ranging from 14 kV cm^(-1) to 210 kV cm^(-1). On the basis of analyses of the chargedensity and exchangeable ion composition on the surfaces of soil particles in the suspensions, andof the characters of the EC-field strength curves of the various suspensions, it was inferred thatthe increment of EC (ΔEC) and/or relative electrical conductivity (REC) can indicate the bondingstrength between cations and soil particles. The bonding strengths of various cations with the soilsdiminished in the order: K^+ > Zn^(2+) > Mg^(2+) = Ca^(2+) > Na^+ for latosol, Ca^(2+) > Zn^(2+) >Mg^(2+) = K^+ > Na^+ for yellow-brown soil, and Zn^(2+) ≥ Ca^(2+) ≥ Mg^(2+) > K^+ > Na^+ for blacksoil.

Pressure Tuning of Magnetism and Drastic Increment of Thermal Conductivity under Applied Magnetic Field in HgCr_2S_4

HgCr2S4 is a typical compound manifesting competing ferromagnetic （FM） and antiferromagnetic （AFM） exchanges as well as strong spin-lattice coupling. Here we study these effects by intentionally choosing a combination of magnetization under external hydrostatic pressure and thermal conductivity at various magnetic fields. Upon applying pressure up to 10 kbar at 1 kOe, while the magnitude of magnetization reduces progressively, the AFM ordering temperature TN enhances concomitantly at a rate of about 1.5 K/kbar. Strikingly, at lO kOe the field polarized FM state is found to be driven readily back to an AFM one even at only 5kbar. In addition, the thermal conductivity exhibits drastic increments at various fields in the temperature range with strong spin fluctuations, reaching about 30% at 50 kOe. Consequently, the results give new experimental evidence of spin-lattice coupling. Apart from the colossal magnetoeapacitance and colossal magnetoresistance reported previously, the findings here may enable new promising functionalities for potential applications.

Enhancing fault-clearing algorithm for renewable-energy-based distribution systems using artificial neural networks

Integrating small and large-scale photovoltaic(PV)solar systems into electrical distribution systems has become mandatory due to increased electricity bills and the concern for limiting greenhouse gases.However,the reliable and efficient operation of PV-based distribution systems can be confronted by the intermittence and high variability of solar sources and their consequential faults.In this regard,this article suggests a moderated fault-clearing strategy based on the incremental conductance–maximum power point tracking(IC–MPPT)technique and artificial neural networks(ANNs)to enhance fault detection,localization,and restoration pro-cesses in PV-based distribution systems.The proposed strategy leverages IC–MPPT to ensure optimal power generation from the PV solar system,even in the presence of faults.By tracking the maximum power point,the algorithm maintains the performance of the system and mitigates against the impact of faults on the output power.Furthermore,an ANN is employed to improve fault detec-tion and localization accuracy.The developed ANN-based moderated fault-clearing strategy is trained using historical data and fault scenarios,enabling it to recognize fault patterns and make informed decisions through extensive simulations and comparisons with traditional fault-clearing methods.To accomplish this study,benchmarks in PV-based distribution systems are constructed and em-ployed using the MATLAB®/Simulink®software package.Moreover,to validate the efficacy of the developed ANN-based moderated fault-clearing strategy,a real case study of a 1-MW PV-based distribution system in an industrial field located in Giza governorate,Egypt,is tested and investigated.The obtained results demonstrate the effectiveness of the IC–MPPT and ANN-based moderated fault-clearing strategy in achieving faster fault detection,precise fault localization,and efficient restoration in PV solar-based dis-tribution systems while preserving maximum power extraction under small and large system disturbances.Furthermore,IC–MPPT based on an ANN achieves an average power of 98.556 kW and 299.632 kWh energy availability,whereas the IC–MPPT based on a pro-portional–integral controller achieves 95.7996 kW and 283.4036 kWh,and the classic perturb-and-observe MPPT algorithm achieves 92.2657 kW and 276.8014 kWh.

Improved MPPT Control Strategy for PV Connected to Grid Using IncCond-PSO-MPC Approach

This paper proposes a new hybrid maximum power point tracking(MPPT)control strategy for grid-connected solar systems based on Incremental conductance—Particle Swarm Optimization and Model Predictive Controller(IncCond-PSOMPC).The purpose of the suggested method is to create as much power as feasible from a PV system during environmental changes,then transfer it to the power grid.To accomplish this,a hybrid combination of incremental conductance(IncCond)and particle swarm optimization(PSO)is proposed to locate maximum power,followed by model predictive control(MPC)to track maximum power and control the boost converter to achieve high performance regardless of parameter variations.A two-level inverter,likewise,controlled by Model Predictive Control,is employed to inject the PV power generated.In this application,the MPC is based on minimizing the difference between the reference and prediction powers,which is computed to select the switching state of the inverter.The proposed system is simulated and evaluated in a variety of dynamic conditions using Matlab/Simulink.Results reveal that the proposed control mechanism is effective at tracking the maximum power point(MPP)with fewer power oscillations.

A Combined Reinforcement Learning and Sliding Mode Control Scheme for Grid Integration of a PV System

The paper presents development of a reinforcement learning(RL)and sliding mode control(SMC)algorithm for a 3-phase PV system integrated to a grid.The PV system is integrated to grid through a voltage source inverter(VSI),in which PVVSI combination supplies active power and compensates reactive power of the local non-linear load connected to the point of common coupling(PCC).For extraction of maximum power from the PV panel,we develop a RL based maximum power point tracking(MPPT)algorithm.The instantaneous power theory(IPT)is adopted for generation reference inverter current(RIC).An SMC algorithm has been developed for injecting current to the local non-linear load at a reference value.The RL-SMC scheme is implemented in both simulation using MATLAB/SIMULINK software and on a prototype PV experimental.The performance of the proposed RL-SMC scheme is compared with that of fuzzy logic-sliding mode control(FL-SMC)and incremental conductance-sliding mode control(IC-SMC)algorithms.From the obtained results,it is observed that the proposed RL-SMC scheme provides better maximum power extraction and active power control than the FL-SMC and IC-SMC schemes.

Optimized Fuzzy Controller for MPPT of Grid-connected PV Systems in Rapidly Changing Atmospheric Conditions

Due to nonlinear behavior of power production of photovoltaic(PV)systems,it is necessary to apply the maximum power point tracking(MPPT)techniques to generate the maximum power.The conventional MPPT methods do not function properly in rapidly changing atmospheric conditions.In this study,a fuzzy logic controller(FLC)optimized by a combination of particle swarm optimization(PSO)and genetic algorithm(GA)is proposed to obtain the maximum power point(MPP).The proposed FLC uses the ratio of power variations to voltage variations and the derivative of power variations to voltage variations as inputs and uses the duty cycle as the output.The range of changes in fuzzy membership functions and fuzzy rules are proposed as an optimization problem optimized by the PSO-GA.The proposed design is validated for MPPT of a PV system using MATLAB/Simulink software.The results indicate a better performance of the proposed FLC compared to the common methods.