Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model
Introduction
Solar energy is a clean and renewable energy source that has been increasingly popular due to its ease of maintenance and cost-saving in the long term usage. It is widely known that the initial cost of photovoltaic (PV) system is normally high, an accurate investigation and analysis for the electrical characteristic of the applied PV component is of critical importance in the design stage. From previous experiences on working with maximum power point tracking (MPPT) algorithm in [1], [2], we found that an accurate model is crucial to obtain desirable results.
Over the past years, a variety of PV cell models has been developed to predict the electrical characteristics of PV cells. They are normally analytical equations predicting the values of the operating current I and voltage V of the PV cell with a series of model parameters, which are constant quantities that characterize the PV cell model. For instance, the single-diode model [3] is widely applicable in simulation tools such as PSIM [4] and PVsyst [5] due to its simplicity. PV cell model cannot be explicitly applied due to the lack of proper model parameters characterizing the PV cells. The parameter estimation is a discipline that provides tools for the efficient use of data assisting the mathematical modeling and estimation of parameter values existing in the model. In general, the methods of parameter estimation can be classified into two groups [6]:
- i.
Analytical method [7], [8]: it is a traditional approach that predicts parameters by mathematical expressions;
- ii.
Numerical extraction [9], [10]: it fits the operating points on the I–V characteristic curves via a numerical method.
More recently, the computational intelligence methods, such as cuckoo search (CS) [11], particle swarm optimization (PSO) [12], genetic algorithm (GA) [13], simulated annealing (SA) [14], bacterial foraging algorithm (BFA) [15], and differential evolution (DE) [16] are applied to determine the values of the model parameters. Albeit accurate and feasible output, these methods applied multiple agents or particles in a random search process, which consumed excessive time in a sequential computing platform. Thus, the approaches were impractical for online applications.
Today's computing environments are becoming more multifaceted, exploiting the capabilities of a range of multi-core microprocessors, central processing units (CPUs), digital signal processors (DSPs), reconfigurable hardware: field programmable gate arrays (FPGAs), and graphic processing units (GPUs) [17]. To distribute the workload of PSO appropriately to the available computing device, this paper proposes a parallel swarm algorithm (PSA) based parameter estimation method for PV cell models. Error and statistical analysis are carried out to illustrate the accuracy and efficiency of the proposed method. Henceforth, we organize the remaining part of this paper as follows. Section 2 briefly introduces the single-diode PV cell model. The development environment for parallel algorithms in general is introduced in Section 3. This section provides useful tips in adopting parallel tools in an optimization work. Further, in Section 4, the PSA – based parameter estimation is described. This is followed by Section 5, which discusses the simulation results, and finally is the conclusion, presented as Section 6.
Section snippets
PV cell model
When a PV cell that is connected to an external circuit, is exposed to incident light, the reverse current is generated across the p–n junction, this current is the photocurrent Ipv, shown in Fig. 1. By eliminating the effect of photocurrent, a PV cell behaves like a normal diode. Its I–V characteristics can be simply modeled as a linear independent current source in parallel to the diode. The single-diode model improves the simple model by recognizing the series resistance Rs and parallel
Development environment for parallel algorithms
This section aims to provide general guidelines for anyone who would like to convert their sequential works to parallel platform. The parallel platform in this context does not mean linking a cluster of computers in parallel to perform a task. In fact, the context of parallelism here covers the utilization of multi-core and graphic processing units (GPUs) computation capabilities. The motivation to apply the existing work in [22], [23] to parallel platform is based on this simple ideology: the
Parallel swarm algorithm for PV cell model's parameter estimation
In this section, the concept of the particle swarm optimization (PSO) algorithm is introduced, which is then followed by the parallel implementation of the PSO algorithm.
Results and discussions
Table 3 records the value of parameters of the single-diode model extracted by the PSA. By careful analysis of the values of the root-mean-square error frms, it is observed that frms decreases when the swarm size increases. This simply indicates that a good estimation of these parameters requires a large number of particles and iterations. This is in fact a computationally expensive task. Also, from this table, one can observe that some parameter values are highly deviated from their true
Conclusions
In this paper, the parallel swarm algorithm (PSA) has been applied to estimate the parameters of a single-diode model. The fitness evaluation functions are written in OpenCL kernel, and they are executed on the multicore central processing unit (CPU) and graphical processing units (GPUs). The feasibility and efficiency of the proposed method have been validated by parameter estimation results on a commercial PV cell. Simulation results prove the significant speedup and acceleration when
Acknowledgements
The work is supported by National Basic Research Program of China under grant no. 2012CB316301 and National Natural Science Foundation of China (NSFC) under grant no. 61473236
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