A Performance Comparison of PSO based MPPT Algorithms for Various Partial Shading Conditions

Collaborative Beamforming in Wireless Sensor Networks Using a Novel Particle Swarm Optimization Algorithm Variant

ABSTRACTThe power-voltage (P-V) characteristic curves of a PV array are nonlinear and have multiple peaks under partially shaded conditions (PSCs). This paper proposes a novel maximum power point tracking (MPPT) method for a PV system with reduced steady-state oscillation based on a two-stage particle swarm optimization (PSO) algorithm. The grouping method of the shuffled frog leaping algorithm (SFLA) is incorporated in the basic PSO algorithm (PSO-SFLA), ensuring fast and accurate searching of the global extremum. An adaptive speed factor is also introduced into the improved PSO to further enhance its convergence speed. Test results show that the proposed method converges in less than half the time taken by the conventional PSO method, and the power is improved by 33% under the worst PSCs, which confirms the superiority of the proposed method over the standard PSO algorithm in terms of tracking speed and steady-state oscillations under different PSCs.

The power-voltage (P-V) characteristic curve of photovoltaic (PV) system have nonlinear and multiple peaks characteristics under partial shading condition. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system based on an improved particle swarm optimization (PSO) algorithm and variable step perturb and observe (P&O) method. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced into the basic PSO algorithm, ensuring the differences among particles and the searching of global extremum. And then, the variable step P&O method is used to track the global maximum power point (GMPP) accurately with the change of environment. Finally, the superiority of the proposed method over the traditional PSO algorithm in terms of tracking speed and steady-state oscillations is highlighted by simulation and experimental results under partial shading condition.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

A modified particle swarm optimization algorithm for a batch-processing machine scheduling problem with arbitrary release times and non-identical job sizes

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

A hybrid intelligent GMPPT algorithm for partial shading PV system

Recently Particle Swarm Optimization (PSO) algorithm gained popularity and employed in many engineering applications because of its simplicity and efficiency. The performance of the PSO algorithm can further be improved by using hybrid techniques. There are various hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristics algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and basic PSO (B-PSO). The performance of the proposed hybrid algorithm, examined through four typically nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms basic PSO.

Distributed generation is a vital component of the national economic sustainable development strategy and environmental protection, and also the inevitable way to optimize energy structure and promote energy diversification. The power generated by renewable energy is unstable, which easily causes voltage and frequency fluctuations and power quality problems. An adaptive online adjustment particle swarm optimization (AOA-PSO) algorithm for system optimization is proposed to solve the technical issues of large-scale wind and light abandonment. Firstly, a linear adjustment factor is introduced into the particle swarm optimization (PSO) algorithm to adaptively adjust the search range of the maximum power point voltage when the environment changes. In addition, the maximum power point tracking method of the photovoltaic generator set with direct duty cycle control is put forward based on the basic PSO algorithm. Secondly, the concept of recognition is introduced. The particles with strong recognition ability directly enter the next iteration, ensuring the search accuracy and speed of the PSO algorithm in the later stage. Finally, the effectiveness of the AOA-PSO algorithm is verified by simulation and compared with the traditional control algorithm. The results demonstrate that the method is effective. The system successfully tracks the maximum power point within 0.89 s, 1.2 s faster than the traditional perturbation and observation method (TPOM), and 0.8 s faster than the incremental admittance method (IAM). The average maximum power point is 274.73 W, which is 98.87 W higher than the TPOM and 109.98 W more elevated than the IAM. Besides, the power oscillation range near the maximum power point is small, and the power loss is slight. The method reported here provides some guidance for the practical development of the system.

In Particle Swarm Optimization (PSO) algorithm, the acting of gBest particle during evolutionary process is important for attaining convergence. A new improved PSO algorithm called SLS-PSO was proposed in this paper. Based on the structure of basic PSO algorithm, the proposed algorithm searched local optimal solutions to gBest particle by adopting Stochastic Local Search (SLS) algorithm to improve the convergence performance during evolutionary process of PSO algorithm. Four well-designed test problems were used to evaluate the proposed algorithm. Compared with the basic PSO algorithm, the proposed algorithm shows its effectiveness and efficiency.

The partial shading of a photovoltaic array repeatedly occurs in the natural environment, which can cause a failure of a conventional maximum power point tracking (MPPT) algorithm. In this paper, the convergence conditions of the standard particle swarm optimization (PSO) algorithm are deduced by the functional analysis, and then the influence of the random variables and inertia factor of the algorithm on the trajectory in the particle swarm optimization is analyzed. Based on the analysis results, an improved particle swarm optimization (IPSO) algorithm, which adopts both global and local modes to locate the maximum power point, is proposed. Compared to the standard PSO algorithm, in the improved PSO algorithm, many random and interfered variables are removed, and the structure is optimized significantly. The proposed algorithm is first simulated in MATLAB to ensure its capability. The feasibility of the approach is validated through physical implementation and experimentation. Results demonstrate that the proposed algorithm has the capability to track the global maximum power point within 3.3 s with an accuracy of 99%. Compared with five recently developed Global MPPT algorithms, the proposed IPSO algorithm achieved better performance in the maximum power tracking in the partial shading conditions.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

To solve the strong randomicity and slow convergence of the Particle Swarm Optimization(PSO) algorithms, two new particle’s position renewal formulas were analyzed on the basis of extrapolation in mathematics. A new modified PSO algorithms (called Leading PSO algorithms) was put forward. The Direct Torque Control(DTC) System was built in the environment of Matlab(Simulink). The weight and threshold values of BP Neural Network were trained using the modified PSO algorithms. Some disadvantages such as slow convergence speed and easily plunging into the local solution were avoided effectively. The simulation result shows that the system works well, and the rotor speed identifier has great static and dynamic performance.

A comparative study between deterministic and two meta-heuristic algorithms for solar PV MPPT control under partial shading conditions


 
 
 
 Fossil energy sources experience a decrease each year when the demand increases significantly. In the case of environmental issues, renewable energy sources (RES) can be energy alternatives. The photovoltaic module is RES with unique characteristics, especially partial shading conditions. This condition leads to the PV characteristic curve experiencing multiple peaks. The paper conducted the simulation of the PV solar panel module using MATLAB Simulink. The Maximum Power Point Tracking (MPPT) PV is also described based on a particle swarm optimization (PSO) algorithm. The proposed algorithm can address multiple peak curve problems due to partial shading conditions. For comparison, the conventional algorithm, perturb & observe, is presented. The PV module is divided into three group cells with irradiance differences for each group to illustrate the partial shading condition. The result shows that the PSO algorithm guarantees optimal and fast response for the operating PowerPoint. It needs about 0.04 seconds to maintain at the optimal power point, 129 Watt, compared with the perturb and observe algorithm performance that only kept at the lower operating power point, 67 Watt at 0.06 second. Thus, the PSO algorithm can tackle the partial shading condition with a fast response to maintain the maximum PowerPoint. Therefore, the PSO algorithm is the proper solution for tracking the optimum operating power point under partial shading conditions.