Photovoltaic System Dynamics Research Articles

NLBBODE optimizer for accurate and fast modeling of photovoltaic module/string generator and its application to solve real-world constrained optimization problems

In this paper, a new optimizer is presented to quickly and accurately identify parameters of the photovoltaic (PV) module/string models. This optimizer is named Nested Loop Biogeography-based Optimization - Differential Evolution referred to as (NLBBODE). It has been developed to identify the PV parameters with reasonable computational effort and minimum execution time, despite the nonlinearity of the PV system dynamics and the insufficiency of data. In addition, the NLBBODE optimizer is used to solve some engineering design problems known as highly constrained, nonlinear, and non-convex. The weaknesses of the original versions of BBO and DE approaches have been overpassed. Furthermore, the proposed optimizer is compared to the state-of-the-art metaheuristic methods using performance evaluation metrics. The computational resources needed to obtain the optimum solution using NLBBODE are significantly reduced due to the nested loop design. The results obtained prove that the NLBBODE optimizer is a suitable candidate to solve the problem of the PV modeling as well as to solve various real-world constrained optimization problems, with high accuracy and low processor runtime, which is a necessary condition for online applications. For instance, the well-known Photowatt-PWP-201 module model represented in the single diode model, NLBBODE registers a standard deviation value of 1.4682E-17 within a time of 13.9 s. For the STM6-40/36 module model represented in the single diode model, the NLBBODE optimizer records a standard deviation value of 6.191583E-18 within an execution time of 6 s. For the speed reducer design problem, the standard deviation obtained by the NLBBODE is 1.515824E-13 and needs a time of less than 1 s to obtain the optimum solution.

Dynamic performance evaluation and improvement of PV energy generation systems using Moth Flame Optimization with combined fractional order PID and sliding mode controller

The output power of Photovoltaic (PV) energy generation systems depends mainly on operating conditions that include climatic conditions and occurrence of faults. Current-Voltage characteristic curves show different operating regions that are characterized by different transient responses in varying operating conditions and in the special case of the presence of the partial shading condition. In this paper, a dynamic performance evaluation of the PV system is performed both in open loop and in the presence of a feedback controller. Analysis shows that the PV system is affected when operating in different regions and environmental conditions and is characterized by a complex dynamic behavior. To improve the PV system dynamics, an adapted control strategy is proposed where the PV voltage is regulated using a Fractional Order PID controller tuned in various regions and partial shading patterns using Particle Swarm Optimization, whereas the PV current is regulated using a sliding mode controller that does not require using Pulse Width Modulation (PWM). In the present study, it is also shown that MPPT algorithms are affected by the conventional tuning approach of the feedback controller. To remedy to this issue, the Moth Flame Optimization based MPPT technique is implemented associated with the proposed control strategy to improve the performance PV system in different operating conditions. The proposed dynamic performance improvement strategy shows excellent transient responses in various operating scenarios.

A State-Space Dynamic Model for Photovoltaic Systems With Full Ancillary Services Support

Large-scale photovoltaic (PV) integration to the network necessitates accurate modeling of PV system dynamics under solar irradiance changes and disturbances in the power system. Most of the available PV dynamic models in the literature are scope-specific, neglecting some control functions and employing simplifications. In this paper, a complete dynamic model for two-stage PV systems is presented, given in entirely state-space form and explicit equations that takes into account all power circuit dynamics and modern control functions. This is a holistic approach that considers a full range of ancillary services required by modern grid codes, supports both balanced and unbalanced grid operation, and accounts for the discontinuous conduction mode of the dc/dc converter of the system. The proposed dynamic model is evaluated and compared to other approaches based on the literature, against scenarios of irradiance variation, voltage sags, and frequency distortion. Simulation results in MATLAB/Simulink indicate high accuracy at low computational cost and complexity.

A State-Space Representation of Irradiance-Driven Dynamics in Two-Stage Photovoltaic Systems

In electric grids with large photovoltaic (PV) integration, the PV system dynamics triggered by irradiance variation is an important factor for the power system stability. Although there are models in the literature that describe these dynamics, they are usually formulated as block diagrams or flowcharts and employ implicit equations for the PV generator, thus requiring application-specific software and iterative solution algorithms. Alternatively, to provide a rigorous mathematical formulation, a state-space representation of the PV system dynamics driven by irradiance variation is presented in this paper. This is the first PV dynamic model in entirely state-space form that incorporates the maximum power point tracking function. To this end, the Lambert W function is used to express the PV generator's equations in an explicit form. Simulations are performed in MATLAB/Simulink to evaluate and compare the proposed dynamic model over the detailed switching modeling approach in terms of accuracy and computational performance.

Photovoltaic Power Forecasting Model Based on Nonlinear System Identification

Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared with conventional fossil fuel sources. As the merging of the PV systems with existing power sources increases, reliable and accurate PV system identification is essential to address the highly nonlinear change in the PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic in the real-life environment in Kuwait. The studied PV system is located on the top of the Ministry of Electricity and Water and the Ministry of Public Works buildings. The identification methodology is discussed. A Hammerstein–Wiener model is identified and selected due to its suitability to capture the PV system dynamics. Measured input–output data are collected from the PV system to be used for the identification process. The data are divided into estimation and validation sets. Results and discussions are provided to demonstrate the accuracy of the selected model structure.

Data-Driven Photovoltaic System Modeling Based on Nonlinear System Identification

Solar photovoltaic (PV) energy sources are rapidly gaining potential growth and popularity compared to conventional fossil fuel sources. As the merging of PV systems with existing power sources increases, reliable and accurate PV system identification is essential, to address the highly nonlinear change in PV system dynamic and operational characteristics. This paper deals with the identification of a PV system characteristic with a switch-mode power converter. Measured input-output data are collected from a real PV panel to be used for the identification. The data are divided into estimation and validation sets. The identification methodology is discussed. A Hammerstein-Wiener model is identified and selected due to its suitability to best capture the PV system dynamics, and results and discussion are provided to demonstrate the accuracy of the selected model structure.

Improvement of the Performance of PV System with CUK Converter by MPPT Technology

Maximum Power Point Tracking (MPPT) algorithms are used in PV system to maximize the output power. In this paper MPPT algorithm is implemented using Cuk converter in PV system. The dynamics of photovoltaic system is simulated at different solar irradiance and cell temperature and is validated by using Perturb and Observe (P&O) method in Maximum Power Point Tracking algorithm.

A Control Methodology and Characterization of Dynamics for a Photovoltaic (PV) System Interfaced With a Distribution Network

This paper proposes a control strategy for a single-stage, three-phase, photovoltaic (PV) system that is connected to a distribution network. The control is based on an inner current-control loop and an outer DC-link voltage regulator. The current-control mechanism decouples the PV system dynamics from those of the network and the loads. The DC-link voltage-control scheme enables control and maximization of the real power output. Proper feedforward actions are proposed for the current-control loop to make its dynamics independent of those of the rest of the system. Further, a feedforward compensation mechanism is proposed for the DC-link voltage-control loop, to make the PV system dynamics immune to the PV array nonlinear characteristic. This, in turn, permits the design and optimization of the PV system controllers for a wide range of operating conditions. A modal/sensitivity analysis is also conducted on a linearized model of the overall system, to characterize dynamic properties of the system, to evaluate robustness of the controllers, and to identify the nature of interactions between the PV system and the network/loads. The results of the modal analysis confirm that under the proposed control strategy, dynamics of the PV system are decoupled from those of the distribution network and, therefore, the PV system does not destabilize the distribution network. It is also shown that the PV system dynamics are not influenced by those of the network (i.e., the PV system maintains its stability and dynamic properties despite major variations in the line length, line X/R ratio, load type, and load distance from the PV system).