Jieming Ma

Parameter Estimation of Photovoltaic Models via Cuckoo Search

Since conventional methods are incapable of estimating the parameters of Photovoltaic (PV) models with high accuracy, bioinspired algorithms have attracted significant attention in the last decade. Cuckoo Search (CS) is invented based on the inspiration of brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior. In this paper, a CS-based parameter estimation method is proposed to extract the parameters of single-diode models for commercial PV generators. Simulation results and experimental data show that the CS algorithm is capable of obtaining all the parameters with extremely high accuracy, depicted by a low Root-Mean-Squared-Error (RMSE) value. The proposed method outperforms other algorithms applied in this study.

Approximate Single-Diode Photovoltaic Model for Efficient I-V Characteristics Estimation

Precise photovoltaic (PV) behavior models are normally described by nonlinear analytical equations. To solve such equations, it is necessary to use iterative procedures. Aiming to make the computation easier, this paper proposes an approximate single-diode PV model that enables high-speed predictions for the electrical characteristics of commercial PV modules. Based on the experimental data, statistical analysis is conducted to validate the approximate model. Simulation results show that the calculated current-voltage (I-V) characteristics fit the measured data with high accuracy. Furthermore, compared with the existing modeling methods, the proposed model reduces the simulation time by approximately 30% in this work.

DEM: Direct Estimation Method for Photovoltaic Maximum Power Point Tracking

Abstract Since the electrical characteristics of a Photovoltaic (PV) Module vary with the changing atmospheric conditions, re- searchers have shown an increasing interest in Maximum Power Point Tracking (MPPT) approaches. This paper presents a direct Maximum Power Point (MPP) estimation method derived from the mathematical expressions of the Current-Voltage (I-V) characteristics of a PV module. Simulation results demonstrate that the proposed approach is sufficiently accurate for practical applications.

A hybrid MPPT method for Photovoltaic systems via estimation and revision method

Maximum Power Point Tracking (MPPT) methods can be classified into direct and indirect approaches. They are used to improve the efficiency of power conversion in Photovoltaic (PV) systems. However, a review of present literature implies that the indirect methods never produce accurate results. Meanwhile, the conventional direct Perturb and Observe (P&O) method has two problems: oscillations at steady state and slow dynamic response under changing environment conditions. Estimation and Revision (ER) method is proposed in this paper to overcome these limitations by the alternative use of MPP estimation and MPP revision process. The efficiency of the ER method is verified in an MPPT system implemented with a specific DC-DC converter and an adopted PV module.

Multicores and GPU utilization in parallel swarm algorithm for parameter estimation of photovoltaic cell model

HighlightsBetter estimation of parameters, on two models.In this work here, we successfully identified the relevant parameters of two photovoltaic models. To prove the efficacy of the proposed method, we included a comparison study.Utilization of multicores and GPU facilities.We have implemented the parallel swarm algorithm utilizing the multicores and GPU computing capabilities of a computer. Bio-inspired metaheuristic algorithms have been widely applied in estimating the extrinsic parameters of a photovoltaic (PV) model. These methods are capable of handling the nonlinearity of objective functions whose derivatives are often not defined as well. However, these algorithms normally utilize multiple agents in the search process, and thus the solution process is extremely time-consuming. In this regard, it takes much time to search the possible solutions in the whole search domain by sequential computing devices. To overcome the limitation of sequential computing devices, parallel swarm algorithm (PSA) is proposed in this work with the aim of extracting and estimating the parameters of the PV cell model by utilizing the power of multicore central processing unit (CPU) and graphical processing unit (GPU). We implement this PSA in the OpenCL platform with the execution on Nvidia multi-core GPUs. Simulation results demonstrate that the proposed method significantly increases the computational speed in comparison to the sequential algorithm, which means that given a time requirement, the accuracy of a solution from the PSA can be improved compared to that from the sequential one by using a larger swarm size.

Low-cost global MPPT scheme for Photovoltaic systems under partially shaded conditions

Maximum Power Point Tracking (MPPT) is a technique applied to improve the efficiency of power conversion in Photovoltaic (PV) systems. Under partially shadowed conditions, the Power-Voltage (P-V) characteristic exhibits multiple peaks and the existing MPPT methods such as the Perturb and Observe (P&O) are incapable of searching for the Global Maximum Power Point (GMPP). This paper proposes a low-cost on-line MPPT scheme to overcome this drawback. By using hybrid numerical searching process, the operating point approaches Local Maximum Power Points (LMPPs) gradually and the GMPP is caught by comparing all the LMPPs. Simulation results prove the effectiveness and correctness of the proposed method.

Design, analysis, tools and applications for programmable high-speed and power-aware 4G processors

Data rate traffic and communication capacity demand have been increased continuously. Therefore, a highly advanced 4G wireless system is required to meet a high demand for modern mobile terminals. For getting a further improvement for 4G communication systems, new paradigms of design, analysis tools and applications for 4G communication processors are necessary. In this paper, some of these new paradigms are discussed. Furthermore, a single-step discrete cosine transform truncation (DCTT) method is proposed for the modeling-simulation in signal integrity verification for high-speed communication processors.

Accelerating Parameter Estimation for Photovoltaic Models via Parallel Particle Swarm Optimization

Bio-inspired metaheuristic algorithms have been widely proposed to estimate parameters of photovoltaic (PV) models in recent years due to its ability to handle nonlinear functions regardless of the derivatives information. However, these algorithms normally utilize multiple agents/particles in the search process, and it takes much time to search the possible solutions in the whole search domain by sequential computing devices. This paper proposes parallel particle swarm optimization (PPSO) method to extract and estimate the parameters of a PV model. The algorithm is implemented in OpenCL and is executed on Nvidia multi-core GPUs. From the simulation results, it is observed that the proposed method is capable of accelerating the computational speed with the same accuracy in comparison to sequential particle swarm optimization (PSO).

High-speed area-efficient and power-aware multiplier design using approximate compressors along with bottom-up tree topology

Estimating arithmetic is a design paradigm for DSP hardware. By allowing structurally incomplete arithmetic circuits to occasionally perform imprecise calculations, higher performance can be achieved in many different electronic systems. By means of approximate compressor design and bottom-up tree topology, this paper presents a novel approach of implementing high-speed, area-efficient and power-aware multipliers. Experimental results are given to show the applicability and effectiveness of our proposed approach.

OL-SVR based soft-sensor for real-time estimation of solar irradiance

An accurate measurement of the solar irradiance is of importance for evaluating and developing of solar renewable energy systems. However, devices for solar irradiance sensing (e.g. pyranometers and pyrheliometers) are usually expensive and difficult to calibrate. In this paper, a low-cost soft-sensor, implemented with a solar cell, is proposed for real-time estimation of solar irradiance. It applies On-Line Support Vector Regression (OL-SVR) soft model to represent the knowledge of the measuring system that is exploited to improve the quality of measurements. The Approximation Parameter Dependence (APD) condition is used to select new samples to reconstruct the model, maintaining the prediction accuracy even when the electrical characteristics of the solar cell vary with irradiance, temperature and age. The proposed approach is validated through simulations and experimental prototyping using real outdoor measurements.