An Advanced Particle Swarm Optimization Algorithm for MPPTs in PV Systems

Abstract: The condition of the ambient air must be continuously checked to detect hazardous gas leakage and ensure the safety of employees in hazardous gas production and storage areas. Detection and elimination of hazardous gas leaks can be done with various complex measurement, safety, and disposal systems. These measurement systems consist of different types of sensor and require control and monitoring system. This leads to high investment and operating costs. In this study, semi-continuous measurement system automation with a single set of sensors working by taking air samples from different points over specific periods has been designed to reduce investment, operation, and maintenance repair costs. In the design, the measurement points are divided into zones and codes in the automation system. Thus, it is aimed to keep a constant record of what the air sample is coming from and what its content is. As a result, it has been determined that it is possible to establish a measurement and automation system similar to existing hazardous gas measurement systems with less investment and operating costs. The proposed automation system has been applied in the industrial storage area. In addition, a cost analysis has been performed and compared with conventional systems.

Abstract: Solar energy turns into a promising supply of electricity, so structures of Photo-voltaic (PV) regularly use a maximum power point tracking (MPPT) way to deliver the highest probable power to the load continuously. This paper presents the methodology of PI controller tuning of PV employing Particle Swarm Optimization (PSO). The aim is to obtain the maximum power and maintain its value using the PI controller. It is employed to trace this highest power point value.

Abstract: This paper proposes an improved maximum power point tracking (MPPT) method for the photovoltaic (PV) system using a modified particle swarm optimization (PSO) algorithm. The main advantage of the method is the reduction of the steady- state oscillation (to practically zero) once the maximum power point (MPP) is located. Furthermore, the proposed method has the ability to track the MPP for the extreme environmental condition, e.g., large fluctuations of insolation and partial shading condition. The algorithm is simple and can be computed very rapidly; thus, its implementation using a low-cost microcontroller is possible. To evaluate the effectiveness of the proposed method, MATLAB simulations are carried out under very challenging conditions, namely step changes in irradiance, step changes in load, and partial shading of the PV array. Its performance is compared with the conventional Hill Climbing (HC) method. Finally, an experimental rig that comprises of a buck-boost converter fed by a custom-designed solar array simulator is set up to emulate the simulation. The soft- ware development is carried out in the Dspace 1104 environment using a TMS320F240 digital signal processor. The superiority of the proposed method over the HC in terms of tracking speed and steady-state oscillations is highlighted by simulation and experimental results.

Abstract: Multiple photovoltaic (PV) modules feeding a common load is the most common form of power distribution used in solar PV systems. In such systems, providing individual maximum power point tracking (MPPT) schemes for each of the PV modules increases the cost. Furthermore, its v-i characteristic exhibits multiple local maximum power points (MPPs) during partial shading, making it difficult to find the global MPP using conventional single-stage (CSS) tracking. To overcome this difficulty, the authors propose a novel MPPT algorithm by introducing a particle swarm optimization (PSO) technique. The proposed algorithm uses only one pair of sensors to control multiple PV arrays, thereby resulting in lower cost, higher overall efficiency, and simplicity with respect to its implementation. The validity of the proposed algorithm is demonstrated through experimental studies. In addition, a detailed performance comparison with conventional fixed voltage, hill climbing, and Fibonacci search MPPT schemes are presented. Algorithm robustness was verified for several complicated partial shading conditions, and in all cases this method took about 2 s to find the global MPP.

Abstract: An artificial bee colony based MPPT under partially shaded conditions is proposed.Photovoltaic systems are considered.A co-simulation methodology combining Simulink and Pspice has been adopted.Excellent efficiency and tracking performance compared to the PSO-based MPPT.The effectiveness of the proposed method has been confirmed experimentally. Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink? and Cadence/Pspice?, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.

Abstract: This paper present the modeling and simulation of hill climbing (HC) maximum power point tracking (MPPT) using novel Single Ended Primary Inductance Converter (SEPIC). This article firstly introduces a practical model of photovoltaic by which the photovoltaic array's model is obtained. The HC algorithm is used to track the maximum power from the solar panel. The MPP of solar panel varies with irritation and temperature. A dc to dc improved SEPIC converter is used in connection with pv array's for achieving operation in maximum power point and keeping output voltage constant. In this improved converter, two inductors are used for feeding the load by two independent switch. The whole system is simulated using MATLAB/Simulink and simulation results presented.

Abstract: In solar photovoltaic (SPV) system it's always a challenge to extract the maximum output power. In this paper, a simple design has been developed for maximum power point tracking (MPPT) algorithms by Incremental Conductance (IC) and Perturb and Observe (P&O) methods in MATLAB/SIMULINK environment. Modeling in Matlab/Simulink environment facilitates design and simulation of the MPPT algorithms and provides a modular visual simulation and user-friendly environment which can be easily reconfigurable for the electrical response of solar photovoltaic (SPV) module or system. This paper also offers a useful comparison between incremental conductance (IC) and perturb and observe (P&O) algorithms for photovoltaic module by which it's make convenient to choice the right algorithms for solar photovoltaic systems.