Pan Duan

An Improved Variable Step PO and Global Scanning MPPT Method for PV Systems under Partial Shading Condition

In the case of partially shading PV array would appear multi-peak characteristics of the P-U curve, and it is difficult to track the maximum power point with the conventional method. Therefore, this paper proposes an improved variable step PO (perturb and observe) and Global scanning method (VSPO&GS) to achieve the maximum power point tracking. This algorithm is guaranteed not to fall into local optimum, and to make the power oscillation less. This scheme is divided into two steps: 1) The improved variable step size PO is used to track the local optimum, 2) Then the global scanning is used to obtain the global optimum. Finally, the proposed algorithm is compared with a conventional fixed step size PO algorithm and validated for sudden changes in the irradiance. The simulation results show the proposed algorithm can accurately track the maximum power point (MPP) to avoid the power loss and improve the efficiency of PV array under partial shaded conditions.

An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory

Purpose – The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed. Design/methodology/approach – In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory fac...

A Two-Stage Particle Swarm Optimization Algorithm for MPPT of Partially Shaded PV Arrays

ABSTRACT The 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.

An intelligent algorithm for maximum power point tracking in photovoltaic system under partial shading conditions

In a photovoltaic (PV) system, maximum power point tracking (MPPT) under partial shading (PS) conditions is a challenging task due to the presence of multiple peaks in the power voltage characteristics. This paper puts forward a novel artificial fish-swarm algorithm (FSA), which is optimized by particle swarm optimization with extended memory (PSOEM-FSA). In this algorithm, both the velocity inertia factor and the memory and learning capacity of PSOEM are introduced into the FSA. To validate the effectiveness of the novel algorithm, the PV system along with the proposed MPPT algorithm was simulated using Matlab/Simulink Simscape tool box. The simulation results show that the proposed approach is effective in MPPT under PS conditions and has a more stable performance when compared with the traditional methods in convergence speed and searching precision.

Comprehensive improvement of artificial fish swarm algorithm for global MPPT in PV system under partial shading conditions

Due to the non-linear characteristics I–V of the photovoltaic (PV) curve, the tracking of the maximum power point (MPP) under partial shading (PS) conditions can sometimes be a challenging task. This paper presents a modified artificial fish swarm algorithm (AFSA) for MPP tracking (MPPT) in PV modules under PS. In this algorithm, the AFSA optimized by particle swarm optimization (PSO) algorithm with extended memory (PSOEM-FSA) is improved by hybridizing it with adaptive visual and step, and the resulting algorithm is a comprehensive improvement on the AFSA (abbreviated as CIAFSA). Combining the searching capabilities of the PSOEM-FSA and the self-learning ability of adaptive visual and step for AFSA, CIAFSA is developed. To validate the effectiveness of this novel MPPT technique, the PV system along with the proposed MPPT algorithm is simulated using the Matlab/Simulink Simscape toolbox. Results show that the proposed approach is more effective in MPPT in PV systems under PS conditions when compared with other methods in searching precision.

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.

Power forecasting approach of PV plant based on similar time periods and Elman neural network

A forecasting model based on similar time period model and Elman neural network forecasting model is presented. Similar time period model divides the forecasting day and historical days into 4 parts, according to the real-time weather forecast, the best historical data were selected to match each time period. K-fold cross volition was used to do the structure selection and parameter optimization for the Elman neural network forecasting model to get the minimum error model. Tests were conducted and results indicate that the model in the paper has a better performance in forecasting the changing law of output power in the single weather day, in addition, it also offers a better performance for the complicated weather day.

Saliency detection based on BP-neural Network

Saliency detection has a significant influence on improving image analysis and processing techniques. We propose a novel saliency detection method based on BP-neural Network in this paper. First, we need to segment images into superpixels by the SLIC approach. Secondly, from these superpixels, we extract 10-dimensional feature to describe a superpixel. Finally, we use BP-neural Network to learn the relationship between 10-dimensional feature value and saliency. We show the results that our proposed method outperform the several state-of-the-art methods on MSRA1000 public database.

Application of improved radial basis function neural network method in global MPPT for PV array

To solve the problem that the structure and parameters of neural network are hard to be tuned, a modified radial basis function neural network (RBFNN) method based on improved particle swarm optimization algorithm (IMPSO-RBFNN, for short) is proposed. In the proposed method, the IMPSO algorithm is utilized to optimize RBFNN, and the nearest neighbor cluster algorithm (NNCA) is introduced into RBFNN. Finally, the experimental results show that the proposed method is effective for MPPT under partially shaded (PS) conditions and has a more stable performance in searching precision when compared to the other methods.

Speed Sensor-Less Vector Control Used in DFIG Wind Power Generation Based on EKF Algorithm

Wind power generation system is a typical nonlinear system. As wind speed is changing constantly, induction motor with conventional speed sensor-less control method has great estimated error. Wind power generation system with the control method cannot be good at tracking maximum power point. The paper put forward a novel control method that estimates the angular velocity and the rotor flux of the wind turbine based on EKF, As a result, induction motor with double-loop sensor-less Vector Control is realized. Simulation results show that the control scheme is effective.