Conventional Incremental Conductance Research Articles

An amended low-cost indirect MPPT strategy with a PID controller for boosting PV system efficiency

Operating a photovoltaic (PV) array at its optimum power point (MPP) despite fluctuating weather conditions is challenging. However, tracking this point enhances system efficiency and energy yields. Various maximum power point tracker (MPPT) techniques have been developed, differing in convergence speed, tracking efficiency, and implementation complexity. The Incremental Conductance (IC) strategy is popular due to its simplicity and reliability, but its fixed perturbation step size results in low tracking efficiency, MPP fluctuation, high power loss, and poor performance. This study proposes an improved IC (IMP-IC) method using indirect control based on a PID controller with an adjustable step size. The IMP-IC approach is simple, cost-effective, and implementable using a low-cost ATmega328 microcontroller on the Arduino Uno board.Simulations in the Proteus environment compared its performance to other MPPT techniques like conventional IC, zone voltage (ZV), fuzzy logic (FL), and novel adjustable step InC (NAS-INC). Results under various weather conditions, including rapid changes and EN50530 testing standards, indicate the IMP-IC approach outperforms existing methods. Specifically, it achieved an average tracking time of 0.12 seconds and tracking efficiencies of 99.40 % (static) and 99.88 % (dynamic). The IMP-IC method showed negligible power losses and fluctuations, a quick convergence speed and significantly lower implementation complexity. These attributes position the IMP-IC method as an effective solution for enhancing the reliability and performance of PV systems.

Efficient and cost-effective maximum power point tracking technique for solar photovoltaic systems with Li-ion battery charging

This paper presents an effective approach to achieve maximum power point tracking (MPPT) in photovoltaic (PV) systems for battery charging using a single-sensor incremental conductance (InC) method. The objective is to optimize the MPPT process while minimizing the number of sensors required. The suggested technique leverages the relationship between the PV module's output voltage and the duty cycle to automatically adjust and reach the MPP, resulting in optimal power generation. By eliminating the PV current sensor from the control circuit, the developed method reduces both the cost and size of the MPPT circuit. Compared to the conventional InC method, the developed approach demonstrates improved response speed and accuracy in steady-state operation, along with the ability to damp oscillations near the MPP. Extensive simulations using MATLAB/Simulink validate the performance of the developed technique across various environmental conditions. The results highlight the recommended method's realistic and effective MPP tracking capabilities, achieving higher efficiency (99.12 %) compared to the classical method (97.8 %) under high irradiance levels.

Maximizing off-grid solar photovoltaic system efficiency through cutting-edge performance optimization technique for incremental conductance algorithm

The maximum power point tracking (MPPT) algorithms are required to deliver the optimal energy from solar photovoltaic cells/array (PV) under numerous weather conditions. Therefore, MPPT circuits driven by defined rules called algorithms are designed. These algorithms range from simple to complex in design and implementation and are selected based on the scenarios of the surroundings. However, the incremental conductance (InC) MPPT algorithm is one of the market's most simple, easy to implement, and demanding algorithms. The drawback associated with the InC algorithm is its tracking speed. To overcome this weakness various researchers have made multiple improvements. Although the performance became better but was not satisfied. Further, the improvements introduce steady-state oscillations of the operating point around the MPP. So, the user needs to pick and choose based on demand. Keeping the target in focus, we have introduced a couple of modifications in the structure of INC. that remain fruitful. The proposed structure named the augmented InC algorithm has shown marvelous improvement in tracking speed and steady-state oscillations. The results have been compared with the conventional InC algorithm, where the proposed augmented InC algorithm has outperformed the conventional InC algorithm in tracking speed and steady-state oscillations. We have used the MATLAB script to code the conventional InC algorithm and proposed augmented InC algorithms based on their designed flowchart. Both algorithms have been applied to the standalone solar photovoltaic system composed of a solar photovoltaic array, DC/DC boost converter, illumination and temperature inputs, MPPT algorithm, and a DC load. The model is designed in Simulink/MATLAB.

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

A New Design and Embedded Implementation of a Low‐Cost Maximum Power Point Tracking Charge Controller for Stand‐Alone Photovoltaic Systems

This article presents a low‐cost maximum power point tracking (MPPT) charge controller designed to efficiently charge solar batteries using a simple and robust MPPT method, while providing protection against over‐currents and over‐voltages. The proposed MPPT approach utilizes a modified Incremental Conductance (INC) algorithm adapted to low‐cost embedded controllers, as it avoids all the mathematical division calculations typically used in the conventional INC algorithm. It also introduces a modified step size based only on photovoltaic (PV) power change to enhance tracking accuracy and convergence speed. Furthermore, to reduce the stress and switching losses of DC–DC converter, a feedback control with two proportional and integral (PI) compensators is also used to control both the PV voltage and to protect the battery from over‐currents using PV power limitation technique. Simulation results, carried out under MATLAB/Simulink, demonstrate the good performance of the proposed control scheme regarding any variations in atmospheric conditions, both for the MPPT control and for the battery charge control. Finally, a processor‐in‐the‐loop (PIL) test using a low‐cost Arduino MKR ZERO board is also performed to verify the hardware implementation of the proposed method. PIL results are in totally accordance with MATLAB/Simulink simulation results, which further proves the effectiveness of the proposed method.

Modelling and performance analysis of improved incremental conductance MPPT technique for water pumping system

Utilization of Solar Photo Voltaic Cell (SPVC) has significantly increased in residential, commercial, and industrial due to freely available. From the power-voltage (P–V) curve, fluctuation has been revealed over an hour because of varying irradiance, and temperature of sun rays. Maximum power point tracking (MPPT) has been essential to track power and maximize the output from SPVC constantly. In this study, a novel Improved Incremental Conductance (IIC) MPPT technique is proposed, and integrated with a boost converter to take out the maximum power from SPVC. The output power has been provided to a brushless DC motor (BLDC). Pulse Width Modulation (PWM) commutation system has been employed to control the BLDC motor through the 3-level bridge converter. The proposed study has been implemented in MATLAB/Simulink. The complete analysis has been examined at different irradiation conditions. Power consumption, loss, and efficiency have been estimated. Simulation results have been compared with conventional incremental conductance (IC). The proposed IIC MPPT has an improved 5% of tracking efficiency than conventional MPPT techniques. The results show that the effectiveness of the proposed IIC technique has attained a steady state in varying irradiation levels. In spite of the low irradiation level of 250 W/m2, the system remains effective.

Photovoltaic MPPT Control Strategy Based on Improved Conductance Increment Method

In this research, a new incremental conductance approach with variable step size is suggested in order to address the inadequacies of the conventional incremental conductance method in tracking accuracy and dynamic reaction speed. In this paper, an absolute value of dP / (dV – dI) is used as the change in step size, and the step size of the tracking process is dynamically adjusted using the exponential function as the adjustment coefficient to reduce the maximum power point’s tracking duration. Finally, on the Matlab platform, a simulation system was created. According to the simulation findings, the suggested approach has less steady-state oscillations and can track the maximum power point more quickly than the conventional conductance increment method, which increases the efficiency of solar power generation.

Evaluation of series-parallel-cross-tied PV array configuration performance with maximum power point tracking techniques under partial shading conditions

Abstract Tracking the maximum power point is a critical issue with solar systems. The power output of the solar panel varies due to variations in irradiance and temperature. Nonuniform irradiation due to partial shading conditions has a direct impact on the characteristics of photovoltaic (PV) systems. To build a diversity of maximum power point tracking algorithms in solar PV systems, this work focuses on perturb and observe, incremental conductance, and fuzzy logic control methodologies. The suggested fuzzy logic control method outperformed the conventional incremental conductance and perturb and observe algorithms with a collection of 49 rules. This paper presents a novel series-parallel-cross-tied PV array configuration with a developed fuzzy methodology. To comment on the performance of a proposed system under various partial shading conditions, a series-parallel PV array configuration has been considered. The simulation result demonstrates that the fuzzy method has a percentage improvement in the global maximum power point tracking efficiency of 24.85% when compared to the perturb and observe method and a 65.5% improvement when compared to the incremental conductance method under long wide partial shading conditions. In the case of the middle partial shading condition, the fuzzy method has a percentage improvement in the global maximum power point tracking efficiency of 12.4% compared to the perturb and observe method and a 60.7% improvement compared to the incremental conductance method.

Maximum power point tracking techniques using improved incremental conductance and particle swarm optimizer for solar power generation systems

Abstract The generation of power from solar energy by using Photovoltaic (PV) systems to convert the irradiation of the sun into electricity has been adopted over the past years. However, the PV system’s P–V and I–V characteristics become unstable when solar irradiation and temperature change. In this paper, the incremental conductance (INC) has been improved using signals to measure the current and voltage from the PV systems directly which quickly changes with the environmental conditions, and the conventional particle swarm optimization (PSO) is modified so that under multiple shaded peak PV array curves with fast-changing solar irradiance and temperature, more power is extracted at a faster rate without any tracking failure at high-speed tracking of both individual maximum power point (IMPP) and global maximum power point (GMPP) under varying solar irradiance and temperature at a longer distance to enhance the power generated. The individual and global coefficients are also improved to change with multiple shaded peak PV array curves with fast-changing solar irradiance and temperature. DC-DC converter converts DC power from one circuit to another and DC-AC inverter converts DC power to AC power. Simulation was carried out in MATLAB Simulink with different solar irradiance and temperature whereby the conventional INC and PSO were compared with the proposed INC and PSO. An experiment was carried out for a whole day from 8 am to 5 pm to test the validity of the proposed algorithm and compared it with the conventional INC and PSO by using the solar irradiance and temperature received. From both the simulation and experimental results, the proposed INC and PSO performed better by attaining high power and tracking speed with stable output results than the conventional INC and PSO.

A novel hybrid image processing‐based reconfiguration with RBF neural network MPPT approach for improving global maximum power and effective tracking of PV system

SummaryThe solar photovoltaic (PV) array output is reduced significantly by the frequently occurring inevitable partial shading conditions. In consequence, the array exhibits multiple peaks in its characteristics that cause the conventional maximum power point tracking (MPPT) algorithms to get stuck at the local maximum. So, to track the global maximum power (GMP) among the multiple peaks, a novel radial basis function (RBF)‐based neural network approach has been proposed for predicting the optimal GMP. Additionally, a novel and intelligent encryption‐based ruler transform (RT) reconfiguration approach is proposed to disperse the shading effect enhancing the GMP and mitigating the multiple peaks. The effectiveness of the proposed RBF‐MPPT and novel RT‐reconfiguration strategies has been tested and analyzed for a 5 × 7 PV array under distinct dynamic, uniform, and nonuniform shading conditions. The results of the proposed RBF have been compared with the conventional incremental conductance (INC) algorithm before and after reconfiguration of the PV array. Further, the ease of GMP tracking by a simple conventional INC due to the reduction of peaks after the array reconfiguration under shading conditions has been demonstrated and discussed in detail. After reconfiguration, the GMP is enhanced by 37.35%, 31.41%, 30.86%, 21.46%, 13.69%, and 8.88%, using the proposed RBF for the considered five shading conditions. The steady‐state oscillations are also considerably mitigated by employing the proposed reconfiguration and RBF strategies.

PERFORMANCE ANALYSIS ON PMSG BASED WIND GENERATION SYSTEM INTERFACED MULTILEVEL CONVERTER WITH ARTIFICIAL INTELLIGENCE TECHNIQUE

This paper focuses on the development of ANN based MPPT interfaced Permanent magnet synchronous generator (PMSG) for wind energy conversion system (WECS). There are many drawbacks to conventional energy sources, such as higher fossil fuel prices, damaging to the environment, a shortage of resources, and increased pollution. Due to the unpredictable and arbitrary nature of wind energy, it is crucial to introduce several control strategies in order to maximize its efficiency. In this work, Levenberg algorithms are developed for Artificial Intelligence Technique using MPPT algorithms for PMSG-based wind energy conversion systems. The proposed system consists of 3 kW PMSG integrated WECS, DC to DC boost converter, multilevel inverter and different loads. The DC to DC converter is connected to the multilevel inverter through a DC link capacitor. The inverter power is fed to the grid through a step-down transformer. MPPT controllers are used in the standalone wind energy conversion system. The goal of this research is to develop an MPPT controller using Levenberg-based Artificial Intelligence Technique for wind energy conversion systems. With the proposed Levenberg-based Artificial Intelligence Technique, MPPT uses voltage and current as input variables for DC to DC boost converter duty ratios. The Artificial Intelligence Technique of MPPT controller for standalone wind energy conversion systems with various different input/output conditions is modeled and simulated in MATLAB/ Simulink environment. The performance of the Artificial Intelligence Technique-based MPPT controller versus the conventional incremental conductance MPPT controller. Simulation results show that for a wide range of input wind speed, Artificial Intelligence Technique based MPPT controller shows improved performance than the conventional MPPT at various operating conditions. The response of the developed Levenberg based Artificial Intelligence Technique based MPPT algorithms for PMSG based wind energy conversion system in MATLAB/ Simulink environment to investigate the robustness of the developed control strategy and validate the reliability and stability under different input/output conditions for grid connected wind generation system.

Maximum Power Point Tracking Method Application to Increase the Efficiency of Photovoltaic Installations

Photovoltaic systems using boost converters based on the traditional incremental conduction method have delayed convergence dynamics to the maximum power point (MPP). This article presents a simulation and hardware implementation of a variable step size MPPT with automatic scaling at low cost and low power consumption. The production of photovoltaic energy is highly dependent on weather and environmental conditions such as temperature and solar radiation. Due to any change in the external environment and the only control condition, the response of the first step of the working cycle of the converter of the traditional incremental conduction MPPT algorithm is not accurate, resulting in an incorrect estimate. Furthermore, this study clarifies the usual technique and presents a modified incremental conductance approach that appropriately responds as solar radiation levels rise. In order to improve the economy and efficiency of photo-voltaic systems, we proposed an improved incremental conduction algorithm for the MPP control strategy. This tech-nique is simpler in structure than the classic incremental conduction algorithm, and it can assign immediate incre-ments of power, voltage, and current as the environment changes, as well as increase tracking performance. The MATLAB/Simulink program runs under conditions of rapidly changing solar radiation levels, and the results of the enhanced and conventional incremental conductance algorithm are compared. The results show that the pro-posed algorithm can effectively detect erroneous judgments and prevent their occurrence. This 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 system.

ENHANCE EFFICIENCY OF SOLAR ENERGY CONVERSION SYSTEM USING ANFIS WITH ISOLATED MODE

This paper shows performance investigation on enhance efficiency of standalone solar energy conversion system using ANFIS based MPPT controller. Now a days many researches are going in the field of renewable energy technologies to bridge the gap between supply and demand. Due to the Intermittent nature of renewable sources, operating standalone operation of them is highly unreliable causes interruption in power supply to the load. A solar photovoltaic system with MPPT based on Adaptive-Network-Based Fuzzy Inference System (ANFIS) is used for stable operation and to meet the supply with the load demand. The application of ANFIS based MPPT technique requires detailed study and analysis of the Solar Photovoltaic (SPV) system. Adaptive-Network-Based Fuzzy Inference System based MPPT technique is used along with Solar PV system as they can provide fast and accurate response to various environmental conditions. The Levenberg-Marquardt algorithm is used to train the neural network for MPPT in the photovoltaic system. This developed control strategy is able to control the devices and various power interface circuitry used therein. The main aim of this paper is to ensure a maximum power output coordinating appropriate control strategy with sources & compare the performance analysis of the ANFIS based MPPT with conventional incremental conductance based MPPT for the SPV system. The simulation studies have been carried out to find out the SPV system performance with different input conditions such as typical solar radiation and temperature. The Simulation test results show variable power generation and verifies that the performance of the integrated system with this control strategy is effective for the real-time installation. The simulation results shows that the performance of this developed control strategy for receiving maximum power output from the standalone SPV system is farfetched. Simulation test results show variable power generation and verify the performance of the integrated system with control strategy is overall effective for real-time installation. The developed system is essential in an isolated region where an existing grid is unable to supply secure power generation and system is beneficial with smartly feed ultimate power generation sources.

Efficient MPPT Controller for Solar PV System Using GWO-CS Optimized Fuzzy Logic Control and Conventional Incremental Conductance Technique

Efficient MPPT Controller for Solar PV System Using GWO-CS Optimized Fuzzy Logic Control and Conventional Incremental Conductance Technique

Optimization of a Solar Water Pumping System in Varying Weather Conditions by a New Hybrid Method Based on Fuzzy Logic and Incremental Conductance

The present work consists of developing a new hybrid FL-INC optimization algorithm for the solar water pumping system (SWPS) through a SEPIC converter whose objective is to improve these performances. This technique is based on the combination of the fuzzy logic of artificial intelligence and the incremental conductance (INC) technique. Indeed, the introduction of fuzzy logic to the INC algorithm allows the extraction of a maximum amount of power and an improvement in the efficiency of the SWPS. The performance of the system through the SEPIC converter is compared with those of the direct coupling to show the interest of the indirect coupling, which requires an adaptation stage driven by an optimal control algorithm. In addition, a comparative analysis between the proposed hybrid algorithm and the conventional optimization techniques, namely, P&O and INC Modified (M-INC), was carried out to confirm improvements related to the SWPS in terms of efficiency, tracking speed, power quality, tracking of the maximum power point under different weather changes, and pumped water flow.

Critical Performance Comparison Between Single-Stage and Two-Stage Incremental Conductance MPPT Algorithms for DC/DC Boost-Converter Applied in PV Systems

Operating with maximum power point tracking (MPPT) strategy is the key functions of the solar energy management system in solar PV deployment. Therefore, in this paper, the strengths and weaknesses of the MPPT based on an incremental conductance (IC) algorithm are analyzed. Furthermore, assessments in terms of performance of two MPPT-IC techniques are presented. More specifically, a two-stage optimized MPPT-IC algorithm in this study is developed and implemented for isolated solar PV system with the conventional single-stage MPPT-IC algorithm. Both MPPT-IC methods are analyzed and compared based on their tracking efficiencies, steady state error and transient speed responses. For the case of the two-mode MPPT-IC algorithm, significant modification is carried-out by combining the conventional IC and modified constant voltage methods. As a result, a better performance under both sunny and cloudy weather scenarios respectively is achieved with higher tracking efficiencies than the conventional IC algorithm. The two stage-algorithm not only rapidly tracks the maximum power point (MPP) under dynamic weather conditions, but also significantly reduces the fluctuations in power around the MPP when subjected to fast and slow speed’s change of irradiations.

A Complete review of FLC MPPT Technique and Comparison with INC Technique

Abstract: In recent years, all around the world, considerable technological growth has been observed to improve the availability of electrical energy in the most ecological way. Under partial shading conditions, maximum power point tracking techniques track the point at which full power can be taken out. Thus the net efficiency of a photovoltaic system is improved. This paper evaluates, methods such as incremental conductance (INC) and fuzzy logic controller (FLC) are evaluated. The simulation results obtained are developed under the software MATLAB / Simulink. Both techniques (INC) and (FLC) are used with a boost DC / DC converter and a load. Theseresults show that the fuzzy logic controller is superior to and faster than the conventional incremental conductance (INC) technique in dynamic response and steady-state in regular operation. Keywords: MPPT; PV; technique INC; technique FLC; Boost DC/DC.

Biased Maximum Power Extraction from a PV during Low Irradiation and a Highly Stiffed Grid Condition

Growing electrical demand is to be met dynamically through conventional and nonconventional power sources. PV power generation plays a vital role. Because of low irradiation and overcast weather condition, the installed PV sources are not fully utilized. Many research papers were presented to extract maximum power from PV using various MPPT techniques. This paper presents a new idea of implementing a biased transformer to extract maximum power during the above condition. The proposed method uses three winding transformers to give sufficient biased voltage and current. The biased current and voltage were obtained from the grid that is fed to the primary winding—two of the three winding transformers through the controller. A 10 kW panel is checked for low irradiation and overcast weather condition using the biased MPPT technique. The proposed method is compared with the conventional incremental conductance method with power, cost, and efficiency analysis. Simulation was carried out in MATLAB/Simulink software and experimentally evaluated through a suitable hardware setup.

A Novel Hybrid MPPT Technique to Maximize Power Harvesting from PV System under Partial and Complex Partial Shading

Photovoltaic (PV) system has been extensively used over the last few years because it is a noise-free, clean, and environmentally friendly source of energy. Maximum Power Point (MPP) from the PV energy systems is a challenging task under modules mismatching and partial shading. Up till now, various MPP tracking algorithms have been used for solar PV energy systems. Classical algorithms are simple, fast, and useful in quick tracing the MPP, but restricted to uniform weather conditions. Moreover, these algorithms do not search the Global Maxima (GM) and get stuck on Local Maxima (LM). However, bio-inspired algorithms help find the GM but their main drawback is that they take more time to track the GM. This paper addresses the issue by using the combination of conventional Incremental Conductance (InC) with variable step size and bio-inspired Dragonfly Optimization (DFO) algorithms leading to a hybrid (InC-DFO) technique under multiple weather conditions, for instance, Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS). To check the robustness of the proposed algorithm, a comparative analysis is done with six already implemented techniques. The results indicate that the proposed technique is simple, efficient with a quicker power tracking capability. Furthermore, it reduces undesired oscillation around the MPP especially, under PS and CPS conditions. The proposed algorithm has the highest efficiencies of 99.93%, 99.88%, 99.92%, and 99.98% for UI, PS1, PS2, and CPS accordingly among all techniques. It has also reduced the settling time of 0.75 s even in the case of the CPS condition. The performance of the suggested method is also verified using real-time data from the Beijing database.

ARTIFICIAL INTELLIGENCE TECHNIQUE BASED MPPT CONTROLLER FOR STANDALONE SOLAR ENERGY CONVERSION SYSTEM

This paper presents the development and performance analysis of Artificial Intelligence Technique based MPPT controller for a standalone solar energy conversion system. The proposed system consists of 2.5 kW PV array, DC to DC boost converter, inverter and different load. The DC to DC Converter connected to Inverter through DC link capacitor. The inverter power fed to the load through step down transformer. The standalone solar energy conversion system consists of MPPT controller. In this research Artificial Intelligence Technique based MPPT controller on Levenberg algorithm developed. For the proposed Artificial Intelligence Technique based MPPT takes two variables as PV voltage and PV current as an input and output the duty ratio for DC to DC boost converter. The Artificial Intelligence Technique based MPPT controller for standalone solar energy conversion system with various load is modeled and simulated in MATLAB/ Simulink environment. Performance of both the Artificial Intelligence Technique based MPPT controller compared with conventional incremental conductance MPPT. Simulation results show that for a wide range of input irradiance, Artificial Intelligence Technique based MPPT controller shows improved performance than the conventional incremental conductance MPPT with at various operating conditions.