Comparison between the conventional methods and PSO based MPPT algorithm for photovoltaic systems

Since the output characteristics of photovoltaic (PV) system depends on the ambient temperature, solar radiation and load impedance, its maximum power point (MPP) is not constant. Under each condition PV module has a point at which it can produce its MPP. Therefore, a maximum power point tracking (MPPT) method is needed to uphold the PV panel operating at its MPP. This paper presents comparative study between the conventional MPPT methods used in (PV) system: Perturb and Observe (P&O), Incremental Conductance (IncCond), and Particle Swarm Optimization (PSO) algorithm for (MPPT) of (PV) system. To evaluate the study, the proposed PSO MPPT is implemented on a DC-DC cuk converter and has been compared with P&O and INcond methods in terms of their tracking speed, accuracy and performance by using the Matlab tool Simulink. The simulation result shows that the proposed algorithm is simple, and is superior to the P&O and IncCond methods

An improved maximum power point tracking for PV system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Working very far from maximum power point diminishes the created power from photovoltaic (PV) system. It is therefore vital, in order to ensure ideal operating conditions, to constantly track the Maximum Power Point (MPP) of the PV panel array. However, this is not easy to identify, due to considerable changes in external influences and the nonlinear relationship of the electrical attributes of PV panels. Therefore, Maximum Power Point Tracking (MPPT) methods can be used to uphold the PV panel operating at its MPP. To date, a number of MPPT methods have been developed, ranging from the simple to the more complex, depending on the weather conditions and the control strategies employed. This current study offers a novel approach to augment the MPPT method for the PV system, based on the Lagrange Interpolation (LI) formula and the Particle Swarm Optimisation (PSO) method. The LI method is used initially to determine the optimum value of the duty cycle in the case of the MPP, according to the operating point. Starting from that point, the PSO method can then be used to search for the true Global Peak (GP). The proposed MPPT controller essentially initialises the particles surrounding the MPP, thereby providing the initial swarm with information concerning the most effective position. This has the ability to improve PSO efficiency and lead to a more rapid convergence, with zero steady-state oscillations. Additionally, there is no need to restrict particle velocity, as the initial values are closer to MPP. Thus, the proposed technique aims to increase efficiency without adding additional complexity, thereby substantially enhancing potential tracking speeds, while also reducing the steady-state oscillation (i.e. to practically zero) once the MPP is located. This offers a number of significant improvements over the conventional PSO method, in which new operating points are at too great a distance from MPP, and thus require additional iterations. The algorithm put forward in this work is verified with an OPAL-RT real time simulator and Matlab Simulink tool. A number of simulations are undertaken and compared to: (1) the Perturb and Observe (P&O) method; (2) the Incremental Conductance (IncCond) method; and (3) the PSO based algorithm. The simulation results indicate that the proposed algorithm can effectively enhance stability and fast tracking capability under fast-changing non-uniform insolation conditions

Comparison between the Conventional Methods and PSO Based MPPT Algorithm for Photovoltaic Systems

Since the output characteristics of Photovoltaic (PV) system depends on the ambient temperature, solar radiation and load impedance, its maximum Power Point (MPP) is not constant. Under each condition PV module has a point at which it can produce its MPP. Therefore, a Maximum Power Point Tracking (MPPT) method is needed to uphold the PV panel operating at its MPP. This paper presents comparative study between the conventional MPPT methods used in (PV) system: Perturb and Observe (P&O), Incremental Conductance (IncCond), and Particle Swarm Optimization (PSO) algorithm for (MPPT) of (PV) system. To evaluate the study, the proposed PSO MPPT is implemented on a DC-DC converter and has been compared with P&O and INcond methods in terms of their tracking speed, accuracy and performance by using the Matlab tool Simulink. The simulation result shows that the proposed algorithm is simple, and is superior to the P&O and IncCond methods

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

Comparison study of five maximum power point tracking techniques for photovoltaic energy systems

Since the output characteristics of photovoltaic (PV) system depends on the ambient temperature, solar radiation and load impedance, its maximum power point (MPP) is not constant. Under each condition PV module has a point at which it can produce its MPP. Therefore, maximum power point tracking (MPPT) methods can be used to uphold the PV panel operating at its MPP. In this survey, five MPPT algorithms are presented and compared under different atmosphere conditions: Perturb and Observe (P&O) Methods, Incremental Conductance (IncCond) Methods, Constant Voltage (CV), Short Circuit Current (SCC) and Open Circuit Voltage (OCV). These algorithms are widely used in PV systems as a result of their easy implementation as well as their low cost. These techniques were analysed and their performance was evaluated by using the Matlab tool Simulink under various types of solar radiation and temperature. The IncCond method was the most efficient, at rapidly changing condition

A Novel MPPT Algorithm Based on Particle Swarm Optimisation for Photovoltaic Systems

This paper describes a new maximum-power-point-tracking method for a photovoltaic system based on the Lagrange Interpolation Formula and proposes the particle swarm optimization method. The proposed control scheme eliminates the problems of conventional methods by using only a simple numerical calculation to initialize the particles around the global maximum power point. Hence, the suggested control scheme will utilize less iterations to reach the maximum power point. Simulation study is carried out using MATLAB/SIMULINK and compared with the Perturb and Observe method, the Incremental Conductance method, and the conventional Particle Swarm Optimization algorithm. The proposed algorithm is verified with the OPAL-RT real-time simulator. The simulation results confirm that the proposed algorithm can effectively enhance the stability and the fast tracking capability under abnormal insolation conditions

A Secure Blockchain-Enabled Remote Healthcare Monitoring System for Home Isolation

This article presents a secure framework for remote healthcare monitoring in the context of home isolation, thereby addressing the concerns related to untrustworthy client connections to a hospital information system (HIS) within a secure network. Our proposed solution leverages a public blockchain network as a secure distributed database to buffer and transmit patient vital signs. The framework integrates an algorithm for the secure gathering and transmission of vital signs to the Ethereum network. Additionally, we introduce a publish/subscribe paradigm, thus enhancing security using the TLS channel to connect to the blockchain network. An analysis of the maintenance cost of the distributed database underscores the cost-effectiveness of our approach. In conclusion, our framework provides a highly secure and economical solution for remote healthcare monitoring in home isolation scenarios