Development of autonomous monitoring and performance evaluation system of grid-tied photovoltaic station

This work aims to improve the existing monitoring systems MS for two grid-connected PV stations GCPVS of URERMS ADRAR, to eliminate its limitations. This improvement consists of developing an MS which is used for two PV stations with different configurations. This MS contains new LabVIEW-based monitoring software for visualizing real-time measured data and evaluating GCPVS performance. In addition, it illustrates the 2D and 3D real-time relationships of PV system parameters, which allow us to understand the dynamic behavior of PV system components. This developed monitoring software synchronizes also the various data acquisition units DAU of GCPVS, allowing simultaneous data access.To perform a reliable performance analysis and a comparative study of different GCPVS based on accurate measurements, the sensor's calibration is performed with its DAU. The MS autonomy is ensured by integrating developed PV-UPS. A graphical user interface is provided for the evaluation of PV-UPS performance.     

Cost effective second generation AC-modules: Development and testing aspects

In the framework of the European research project PV2GO, a new AC-module inverter was developed, taking into account all relevant aspects from a European market's point of view (standards, market, application, and research and development goals). The project goal was to achieve the overall system costs of 3 Euro per Wp for a modular plug-and-play photovoltaic system. For the photovoltaic-module, a standard 130-Wp Eurosolare module was chosen. The research and development (R&D) goal was to develop an advanced DC-control system consisting of a state-of-the-art programmable digital device and an Application Specific Integrated Circuit (ASIC) for the AC-control of the inverter. According to the topology concept, thermal and magnetic designs were optimized with regard to production technology and packaging for large-scale production. The new AC-modules were tested in a number of field-test sites in various parts of Europe and their reliability was assessed through Highly Accelerated Stress Tests. Efficiency and power quality have been tested in the laboratory. Further in the PV2GO project an optimization study of the manufacturing process of the new generation of AC-modules for high volume output was done. Another task was the pre-certification procedure to assure compliance with the European guidelines and standards.     

Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults, the conventional maximum power point tracking (MPPT) and inverter control methods of a PV power generation system cannot achieve optimal power output. They can also lead to misjudgments and poor dynamic performance. To address these issues, this paper proposes a new MPPT method of PV modules based on model predictive control (MPC) and a finite control set model predictive current control (FCS-MPCC) of an inverter. Using the identification model of PV arrays, the module-based MPC controller is designed, and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature. An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors, the optimal voltage vector is selected according to the optimal value function, and the corresponding optimal switching state is applied to power semiconductor devices of the inverter. The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified, and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink. The results show that MPC has better tracking performance under constraints, and the system has faster and more accurate dynamic response and flexibility than conventional PI control.     

并网系统逆变器锁相环的研究

在太阳能光伏发电系统中,并网逆变器是实现光伏电能馈送电网的重要环节,并网光伏逆变器的控制目标为：控制逆变电路的输出为稳定的、高品质的正弦波,且与电网电压同频、同相。鉴于此,本文提出了一种数字电流锁相的方法,并且进行了系统仿真。仿真结果表明,TMS320C2000系列DSP芯片适用于该数字锁相技术,其并网输出电流波形良好...     

准滑模逆变控制单相光伏并网系统的试验研究

逆变控制是光伏并网发电技术的一个难点问题。针对这一问题,提出了准滑模控制的解决方法。全面介绍了单相光伏并网发电系统的设计方案,在给出系统拓扑结构之后,建立了系统主电路的动态数学模型。简要分析了系统的最大功率点跟踪算法和准滑模控制逆变原理,完成了准滑模并网逆变控制试验。试验表明,基于准滑模控制的单相光伏并网发电系统具有动态响应快、稳态跟踪性能优良的特点,系统设计正确且可行。     

Design optimization of grid-connected PV-Hydrogen for energy prosumers considering sector-coupling paradigm: Case study of a university building in Algeria

Integrating sector coupling technologies into Hydrogen (H₂) based hybrid renewable energy systems (HRES) is becoming a promising way to create energy prosumers, despite the very little research work being done in this largely unexplored field. In this paper, a sector coupling strategy (building and transportation) is developed and applied to a grid-connected PV/battery/H₂ HRES, to maximise self-sufficiency for a University campus and to produce power and H₂ for driving electric tram in Ouargla, Algeria. A multi-objective size optimization problem is solved as a single objective problem using the ε-constraint method, in which the cost of energy (COE) is defined as the main objective function to be minimized, while both loss of power supply probability (LPSP) and non-renewable usage (NRU) are defined as constraints. Particle swarm optimization and HOMER software are then employed for simulation and optimization purposes. Prior to the two scenarios investigated, a sensitivity study is performed to determine the effects of H₂ demand by tram and NRU on the techno-economic feasibility of the proposed system, followed by a new reliability factor introduced in the optimization, namely loss of H₂ supply probability (LHSP). The results of the first scenario show that by setting NRU^max = 100%, the system without H₂ provides the best solution with COE of 0.016 $/kWh that reaches grid parity and has 13% NRU. However, by setting NRU^max = 1% in the second scenario, an optimized configuration consisting of grid/PV/Electrolyzer/Fuel cell/Storage tank is obtained, which has 0% NRU and COE of 0.1 $/kWh. In the second scenario, it is also observed that an increased number of trams (i.e. increased H₂ demands) causes a significant reduction in LHSP, COE, NRU and CO₂ emissions. It is thus concluded that the grid/PV combination is the optimal choice for the studied system when considering economic aspects. However, taking into account the growing requirements of future energy systems, grid-connected PV with H₂ will be the best solution.     

Control of a grid-connected direct-drive wind energy conversion system

This paper investigates the current control for a grid-connected direct-drive wind energy conversion system (DDWECS) with a permanent magnet synchronous generator (PMSG), which utilizes a back-to-back pulse width modulation (PWM) converter. For the machine-side, the controller adopts a current vector control method based on the rotating reference frame (RRF) and the maximum power extraction (MPE) is realised through the tip speed ratio (TSR) method. For the grid-side, a novel controller is proposed for the first time to be successfully used for the DDWECS, which combining a proportional complex integral (PCI) current inner loop based on stationary reference frame (SRF) for regulating the grid-side current with a dc voltage outer loop for stabilizing the dc bus voltage and compare with the proportional resonant (PR) controller. A system simulation model is established by using the Matlab/Simulink to simulate the performance of the DDWECS and a prototype system has been build and tested to verify the validity of the developed control methods for both machine-side and grid-side and the excellent performance of the DDWECS.     

Robust pole location with experimental validation for three-phase grid-connected converters

This paper provides design and experimental validation of robust current controllers for three-phase grid-connected converters. The main objectives here are: (i) to show that a simple polytopic model can be used for designing robust controllers for predominately inductive grids; (ii) to help in the choice of the control design parameter, based on a trade-off between an upper bound of the transient settling times and the control gain sizes. Linear matrix inequality based conditions are used to design the robust control gains with lower numerical complexity than similar conditions on literature. It is shown that small values the radius of pole location lead to better bounds for the transient responses, at the price of higher control gains. Good tracking of references for the grid currents is also illustrated in practice, allowing the closed-loop system to inject active and reactive power into the grid. Simulation and experimental results prove that the system connected to the grid can provide three-phase currents complying with requirements of an important international standard.     

微电网故障下三相电压型并网变换器瞬态行为分析

三相电压源型变换器(VSC)是交直流混合电力系统中重要的功率接口装置。交直流混合系统存在诸如电网侧故障或直流侧负载变化等扰动,对其中功率接口变换器造成难以预计的影响。着重研究微电网故障时并网变换器的瞬态响应过程及其机理。首先,给出了电网三相电压暂降时三相电压源型变换器的失稳的瞬态响应现象。然后,通过非线性分析揭示了该不稳定现象的物理本质,并通过系统建模计算给出了系统参数空间中的稳定域范围;最后,通过微电网与变换器仿真验证了分析计算的有效性。     

A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P&O) algorithm dispositive.