A Particle Swarm Optimization-Based Maximum Power Point Tracking Algorithm for PV Systems Operating Under Partially Shaded Conditions

An improved particle swarm optimization based maximum power point tracking algorithm for PV system operating under partial shading conditions

It is highly expected that partially shaded condition (PSC) occurs due to the moving clouds in a large photovoltaic (PV) generation system (PGS). Several peaks can be seen in the P-V curve of a PGS under such PSC which decreases the efficiency of conventional maximum power point tracking (MPPT) methods. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is proposed based on particle swarm optimization (PSO) for MPPT of PV modules. After tuning the parameters of the fuzzy system, including membership function parameters and consequent part parameters, to obtain maximum power point (MPP), a DC/DC boost converter connects the PV array to a resistive load. ANFIS reference model is used to control duty cycle of the DC/DC boost converter, so that maximum power is transferred to the resistive load. Comparing the proposed method with PSO alone method and firefly algorithm (FA) alone shows its efficacy and high speed tracking of MPP under PSC. Due to the fact that these optimization algorithms have online applications, the convergence time of the algorithms is very important. The simulation results show that the convergence time for the proposed ANFIS-based method is lower than 0.15 second, while it is nearly three second for PSO and FA methods.

The quasi-Z-source inverter (qZSI) in a photovoltaic (PV) generation system (PGS) has been very popular among researchers due to its ability to boost the PV voltage with its single-stage topology. In comparison to the conventional two-stage inverter, the qZSI comes with lower cost and higher efficiency. So far, research was focused on different qZSI topologies and their control methods. Not much attention has been given to one of the most important parts of a PGS, i.e. the maximum power point tracker (MPPT). This paper proposes a hybrid MPPT method for a PGS under partial shading condition (PSC) by combining an intelligent particle swarm optimization (PSO) algorithm with a simple but effective perturb and observe (P&O) technique. With these two MPPT algorithms, a much more accurate detection of the maximum power point (MPP) of a PGS under PSC can be attained, which is otherwise unachievable if they work independently. Simulation results based on MATLAB-Simulink are presented to verify the proposed method.

A review on MPPT techniques of PV system under partial shading condition

Photovoltaic generation systems (PVGSs) face significant efficiency challenges under partial shading conditions and rapidly changing irradiance due to the limitations of conventional maximum power point tracking (MPPT) methods. To address these challenges, this paper proposes a Transfer Learning-based Fractional-Order Recurrent Neural Network (TL-FRNN) for robust global maximum power point (GMPP) tracking across diverse operating conditions. The incorporation of fractional-order dynamics introduces long-term memory and non-local behavior, enabling smoother state evolution and improved discrimination between local and global maxima, particularly under weak and partially shaded conditions. The proposed approach leverages Caputo fractional derivatives with Grünwald–Letnikov approximation to capture the history-dependent behavior of PVGSs while implementing a parameter-partitioning strategy that separates shared features from task-specific parameters. The architecture employs a multi-head design with GMPP regression and partial shading classification capabilities, trained through a two-stage process of pretraining on general PV data followed by efficient fine-tuning on target systems with limited site-specific data. The TL-FRNN achieved 99.2% tracking efficiency with 98.7% GMPP detection accuracy, reducing convergence time by 53% compared to state-of-the-art alternatives while requiring 72% less retraining time through transfer learning. This approach represents a significant advancement in adaptive, intelligent MPPT control for real-world photovoltaic energy-harvesting systems.

Photovoltaic generation system (PGS) has become an attractive option among renewable energy sources because it is clean, maintenance-free and environmental friendly. For large PGS, the probability for partially shaded condition (PSC) to occur is high. Under PSC, when the entire array does not receive uniform insolation, the P-V curves of PGS get more complex, exhibiting multiple peaks. In this paper, a particle swarm optimization (PSO)-based MPPT algorithm for PGS operating under PSC is proposed. The problem formulation and design procedure are described and explained in detail. The proposed method boasts the advantages such as easy to implement, system-independent and high tracking efficiency. To validate the correctness of the proposed method, simulation and experimental results of a 2 kW PGS will also be provided to demonstrate the effectiveness of the proposed technique.

Hotspots in maximum power point tracking algorithms for photovoltaic systems – A comprehensive and comparative review

Neural-network-based maximum power point tracking methods for photovoltaic systems operating under fast changing environments

The partial shading condition (PSC) often occur in large photovoltaic (PV) generation system (PGS), it causes system losses and many problems in reliability of power system. The power voltage ( p-v) curve under the PSC have more peaks local and global peak and this makes the track of maximum power is very difficult and the conventional algorithms can't track the global maximum power point in this case. In this paper, the maximum power point tracking (MPPT) under PSC are evaluated using the particle swarm optimization (PSO). The proposed model tracks the global maximum power point (GMPP) very fast and in very short time and don't exceed 30 iterations under minimum solar irradiation cases.

To harvest maximum amount of solar energy and to attain higher efficiency, photovoltaic generation (PVG) systems are to be operated at their maximum power point (MPP) under both variable climatic and partial shaded condition (PSC). From literature most of conventional MPP tracking (MPPT) methods are able to guarantee MPP successfully under uniform shading condition but fails to get global MPP as they may trap at local MPP under PSC, which adversely deteriorates the efficiency of Photovoltaic Generation (PVG) system. In this paper a novel MPPT based on Whale Optimization Algorithm (WOA) is proposed to analyze analytic modeling of PV system considering both series and shunt resistances for MPP tracking under PSC. The proposed algorithm is tested on 6S, 3S2P and 2S3P Photovoltaic array configurations for different shading patterns and results are presented. To compare the performance, GWO and PSO MPPT algorithms are also simulated and results are also presented. From the results it is noticed that proposed MPPT method is superior to other MPPT methods with reference to accuracy and tracking speed.Article History: Received July 23rd 2016; Received in revised form September 15th 2016; Accepted October 1st 2016; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2016) A Novel Global MPP Tracking of Photovoltaic System based on Whale Optimization Algorithm. Int. Journal of Renewable Energy Development, 5(3), 225-232.http://dx.doi.org/10.14710/ijred.5.3.225-232

Higher solar energy potential from the Sun has increased the rate of development in solar power plants worldwide. The output power from the Photovoltaic (PV) panel is greatly influenced by the intensity of light falling on its surface. Because of non-linear characteristics of the PV array, a Maximum Power Point Tracking (MPPT) algorithm is employed to extract maximum power from the PV array. Uniform light on the modules in a PV array produces a single power peak on the P–V characteristic curve, in contrast to non-uniform shading on the PV array causes multiple peaks to occur on the PV curve. Conventional MPPT algorithms, Perturb and Observe (P&O), and Incremental Conductance (INC) perform well in tracking the maximum power during uniform conditions. But they lag in detecting the presence of partial shading on the PV modules. Generally, light sensors are placed at different locations along the PV array to identify the non-uniformity in the light incident on the array. This increases the cost and complicity of the system. Sensorless MPPT artificial intelligence algorithms detect the Global Maximum Power Point (GMPP) by regularly scanning the P–V characteristics curve to detect the multiple peaks. Fuzzy logic, neural networks, particle swarm optimization, and ripple correlation algorithms show improvement in tracking the GMPP. All the conventional algorithms are categorized under two methods, current-based and voltage-based control. None of the control algorithms have proved best among the two control strategies. In this chapter, a hybrid sensorless MPPT algorithm employing both voltage and current based control is proposed for identifying the presence of partial shading on the PV modules. The proposed method finds the GMPP with less number of iterations and the effectiveness of the algorithm is compared with conventional MPPT methods. Performance of the algorithm is evaluated by experimenting with severe changes in uniform and partial shading conditions and tested with a single-stage transfromerless grid-tied PV inverter.

The solar PV generation systems are well established in modern scenario where the problem of partial shading (PS) condition is a major threat on generation efficiency. In partially shaded PV system where multiple cells are arranged in cascaded mode, the output characteristics, e.g., power versus voltage or current versus voltage (P–V or I–V) displays multiple peaks. This results in enhancing the complexity of tracking global maximum using maximum power point tracking (MPPT) algorithms. The existing conventional MPPT algorithms like, hill climbing (HC) method, perturb and observe (P&O) method or modified P&O method, etc., used under such conditions, fail to extract maximum power due to their tendencies to lock at local maxima. Optimization-based MPPT procedures such as artificial bee colony (ABC) method, Cuckoo search (CS) algorithm and particle swarm optimization (PSO), etc., achieve global maxima detection under partial shading condition. These optimization-based algorithms require more computational load, resulting in difficulty while coming to real-time implementation. In this paper, an evolved sensor-based (SB) MPPT is projected under PS condition for detecting maximum power point (MPP) to overcome such disadvantages of optimization-based techniques. The proposed SB MPPT imposes not any computational burden on the working processor besides its simple hardware implementation. The proposed SB MPPT scheme shows comparable accuracy of tracking with excellent dynamic performance showing good tracking efficiency for considered 3S configuration while implementing in MATAB SIMUILINK atmosphere. The comparative results with existing techniques are achieved to justify the suitability of the proposed concept with relatively much easier implementation using existing available drive compatible hardware.KeywordsMPP trackingPartial shadingOptimization techniquesBoost converter

The power–voltage curve of a photovoltaic (PV) array shows multiple power peaks under partially shading conditions (PSCs). Hence, conventional maximum power point tracking (MPPT) algorithms can not guarantee the maximum power output of the PV array. In this study, a novel Lipschitz optimization (LIPO) MPPT algorithm, which is effective under PSCs, is proposed and analyzed. Its tracking speed is very fast and tracking efficiency is above 98%. The characteristics of a PV array under PSCs are first analyzed and then the working principle of the proposed LIPO MPPT algorithm is explained. In order to validate the performance of the proposed algorithm, two popular MPPT algorithms, i.e., the modified particle swarm optimization (M-PSO) algorithm and the modified firefly optimization (M-firefly) algorithm, are chosen to compare with it. All three algorithms are fulfilled and compared with each other through both simulations and experiments and the results show that the proposed MPPT algorithm has good performance.

A modified cat swarm optimization based maximum power point tracking method for photovoltaic system under partially shaded condition

Photovoltaic systems have become essentially needful among the renewable energy sources. Due to low conversion efficiency of the PV based generation system, one of the most crucial factors in the operation of the system is the extraction of the maximum power from the PV array which is indispensable especially under Partial shading condition. There are several conventional maximum power point tracking (MPPT) methods like P&O, INC etc. that work fine under uniform irradiance condition. But under partial shading conditions, the power-voltage curve exhibits multimodal nature with multiple peaks with local maximum power point (LMPPs) and global maximum power point (GMPP), where these methods fail to track the maximum power point. This paper proposes a novel and fast maximum power point tracking algorithm that scans the entire voltage range by shifting the operating point along the P-V curve by controlling the duty of the DC-DC converter interface. This method tracks the GMPP subsequently by changing the duty in steps thus varying the voltage over the entire operating range of the PV -converter interface. The proposed model exhibit improved tracking accuracy and enhanced tracking speed without additional circuit requirements with improved efficiency of 96.49% under PSC and 99.34% under UIC. The performance of the proposed algorithm has also been validated through MATLAB/Simulink simulation results.