A PSO-based Global MPPT technique for Distributed PV Power Generation

International audienc

Maximum Power Point Tracking for Cascaded PV-Converter Modules Using Two-Stage Particle Swarm Optimization

The paper presents a novel two-stage particle swarm optimization (PSO) for the maximum power point tracking (MPPT) control of a PV system consisting of cascaded PV-converter modules, under partial shading conditions (PSCs). In this scheme, the grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated with the basic PSO algorithm, ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced to improve its convergence speed. A PWM algorithm enabling permuted switching of the PV sources is applied. The method enables this PV system to achieve the maximum power generation for any number of PV and converter modules. Simulation studies of the proposed MPPT scheme are performed on a system having two chained PV buck-converter modules and a dc-ac H-bridge connected at its terminals for supplying an AC load. The results show that this type of PV system allows each module to achieve the maximum power generation according its illumination level without affecting the others, and the proposed new control method gives significantly higher power output compared with the conventional P&O and PSO methods

A Novel Intelligent Neural Network Techniques of UPQC with Integrated Solar PV System for Power Quality Enhancement

A Novel, Intelligent control of a Unified Power Quality Conditioner (UPQC), coupled with a Photovoltaic (PV) system, is proposed in this work. It enhances the decarbonizes clean energy generation and maintains Power Quality (PQ) to the grid. In PV integrated UPQC, Crow Search Algorithm (CSA) assisted Perturb and Observation (P&O) Maximum Power Point Tracking (MPPT) technique. A d-q theory-based control is employed with the assistance of a Proportional Integral (PI) controller for controlling the working of UPQC and maintaining the power quality. The dynamic working of the PV-based UPQC is evaluated based on simulation outcomes attained from MATLAB

A Novel Intelligent Neural Network Techniques of UPQC with Integrated Solar PV System for Power Quality Enhancement

A Novel, Intelligent control of a Unified Power Quality Conditioner (UPQC), coupled with a Photovoltaic (PV) system, is proposed in this work. It enhances the decarbonizes clean energy generation and maintains Power Quality (PQ) to the grid. In PV integrated UPQC, Crow Search Algorithm (CSA) assisted Perturb and Observation (P&O) Maximum Power Point Tracking (MPPT) technique. A d-q theory-based control is employed with the assistance of a Proportional Integral (PI) controller for controlling the working of UPQC and maintaining the power quality. The dynamic working of the PV-based UPQC is evaluated based on simulation outcomes attained from MATLAB

Marine Predator Algorithm (MPA)-Based MPPT Technique for Solar PV Systems under Partial Shading Conditions

To satisfy global electrical energy requirements, photovoltaic (PV) energy is a promising source that can be obtained from the available alternative sources, but partial shading conditions (PSCs), which trap the local maxima power point instead of the global maxima peak power point (GMPP), are a major problem that needs to be addressed in PV systems to achieve the uninterruptable continuous power supply desired by consumers. To avoid these difficulties, a marine predator algorithm (MPA), which is a bio-inspired meta-heuristic algorithm, is applied in this work. The work is validated and executed using MATLAB/Simulink software along with hardware experimentation. The superiority of the proposed MPA method is validated using four different PSCs on the PV system, and their characteristics are compared to those of existing algorithms. The four different PSC outcomes in terms of GMPP are case 1 at 0.07 s 995.0 Watts; case 2 at 0.06 s 674.5 Watts; case 3 at 0.04 s 654.1 Watts; and case 4 at 0.04 s 364.2 Watts. The software- and hardware-validated results of the proposed MPA method show its supremacy in terms of convergence time, efficiency, accuracy, and extracted power.publishedVersio

Maximum power point tracking implementation by Dspace controller integrated through Z-Source inverter using particle swarm optimization technique for photovoltaic applications

Maximum Power Point Tracking (MPPT) technique is used to extract maximum power from the photovoltaic system. This paper involves working on an enhanced Particle Swarm Optimization (PSO) based MPPT method for the photovoltaic (PV) system integrated through Z-Source inverter. The main benefit of the proposed method is the diminishing of the steady-state oscillation when the maximum power point (MPP) is located. Additionally, during an extreme environmental condition, such as partial shading and large fluctuations of irradiance and temperature, the proposed method has the capability to track the MPP. This algorithm is implemented in dspace 1104 controller. MATLAB simulations are carried out under varying irradiance and temperature conditions to evaluate its effectiveness. Its performance is compared with a conventional method like Perturb and observe (P&O) method

A novel MPPT algorithm based on particle swarm optimization for photovoltaic systems

In this paper a new maximum-power-point-tracking (MPPT) method for the photovoltaic (PV) system using an improved particle swarm optimization (PSO) algorithm is proposed. The proposed control scheme eliminates the problems in the conventional methods as it uses only a simple numerical calculation to initialize the particles around the global maximum power point (GMPP). Hence, the PSO will utilize less iteration to reach the MPP. The proposed algorithm is verified by using MATLAB SIMULINK tool, several simulations being carried out, and compared to Perturb and Observe (P&O) Method, Incremental Conductance (IncCond) Method, and conventional Practical Swarm Optimization based MPPT Algorithm. The simulation results indicate that the proposed algorithm can effectively enhance stability and fast tracking capability even under rapidly changing atmospheric conditions

A Review and Classification of Most Used MPPT Algorithms for Photovoltaic Systems

Energy has become an indispensable need to sustain our lives. Approximately 80% of the energy consumed in the world is produced from fossil sources. For the reasons such as the depletion of fossil resources and their damages to the environment, the interest in renewable resources is increasing and the importance of solar systems, which draws attention with unlimited energy resource, is increasing day by day. The biggest disadvantages of solar systems are seen as low production efficiency and high setup cost. A PV cell can convert only 5-20% of the solar energy coming on it to electricity. Based on this, it is very important to provide the power obtained from PV with maximum efficiency and minimum cost. Accordingly, many different maximum power point tracking (MPPT) algorithms have been proposed over the years. Although the purpose of all proposed algorithms is the same, they have many advantages and disadvantages compared to each other. In this study, the most used MPPT algorithms have been examined and compared by considering many parameters such as tracking speed, stability, and cost etc. and a new classification of these algorithms is proposed