Nonlinear control of WECS based on PMSG for optimal power extraction

This paper proposes a robust control strategy for optimizing the maximum power captured in Wind Energy Conversion Systems (WECS) based on permanent magnet synchronous generators (PMSG), which is integrated into the grid. In order to achieve the maximum power point (MPPT) the machine side converter regulates the rotational speed of the PMSG to track the optimal speed. To evaluate the performance and effectiveness of the proposed controller, a comparative study between the IBC control and the vector control based on PI controller was carried out through computer simulation. This analysis consists of two case studies including stochastic variation in wind speed and step change in wind speed

DFIG use with combined strategy in case of failure of wind farm

In the wind power area, Doubly Fed Induction Generator (DFIG) has many advantages due to its ability to provide power to voltage and constant frequency during rotor speed changes, which provides better wind capture as compared to fixed speed wind turbines (WTs). The high sensitivity of the DFIG towards electrical faults brings up many challenges in terms of compliance with requirements imposed by the operators of electrical networks. Indeed, in case of a fault in the network, wind power stations are switched off automatically to avoid damage in wind turbines, but now the network connection requirements impose stricter regulations on wind farms in particular in terms of Low Voltage Ride through (LVRT), and network support capabilities. In order to comply with these codes, it is crucial for wind turbines to redesign advanced control, for which wind turbines must, when detecting an abnormal voltage, stay connected to provide reactive power ensuring a safe and reliable operation of the network during and after the fault. The objective of this work is to offer solutions that enable wind turbines remain connected generators, after such a significant voltage drop. We managed to make an improvement of classical control, whose effectiveness has been verified for low voltage dips. For voltage descents, we proposed protection devices as the Stator Damping Resistance (SDR) and the CROWBAR. Finally, we developed a strategy of combining the solutions, and depending on the depth of the sag, the choice of the optimal solution is performed

Variable Recursive Least Square Algorithm for Lithium-ion Battery Equivalent Circuit Model Parameters Identification

For SOC (state of charge) assessment techniques based on electrical circuit models, the parameters of the model are strongly biased by: battery aging, temperature, causing some errors in the estimation of the SOC. One approach to solve this problem is to update the model parameters constantly. We suggest a new algorithm VRLS (variable recursive least squares) to update the parameters of a 2-resistor-capacitor (RC) network and to estimate the output battery voltage. VRLS is compared to the recursive least squares (RLS) and the adaptive forgetting factor recursive least squares (AFFRLS) algorithms. For algorithm assessment, we utilized real experimental data conducted on the Samsung 18650-20R lithium-ion cell. The tests indicate that compared to RLS and AFFRLS methods, VRLS recorded a low distribution in the high error range, in addition to small predictive performance indicators (RMSE, MAE, and MAPE) in all tests, which implies that VRLS has a good parameter identification ability

Viscosity of Ar-Cu nanofluids by molecular dynamics simulations Effects of nanoparticle content, temperature and potential interaction

International audienceIn this study, Molecular Dynamics Simulations is used to calculate the viscosity of Ar-Cu nanofluid within the Green-Kubo framework considering the influence of nanoparticle volume fraction and the nanofluid temperature. First, the simulation method is developed and favourably compared to previous works. Then, simulation results show that the viscosity of Ar-Cu nanofluid is significantly larger compared to that of the Argon base fluid. We also demonstrated that the viscosity of the nanofluid systematically increases with the increase in the particle volume fraction and decreases with increasing temperature. Our results were compared to existing analytical model and previous works involving one common element either Argon or Copper evidencing the role of adjacent liquid layer to a nanoparticle at the solid–liquid interface. Finally, the influence of the solid-solid inter-atomic potential type on the viscosity of nanofluid (Ar-Cu) was finally investigated to evidence the density effect of the ordered liquid layer at liquid-nanoparticle interface. © 2018 Elsevier B.V

Enhancing fault ride-through capacity of DFIG-Based WPs by adaptive backstepping command using parametric estimation in non-linear forward power controller design

The principal issue associated with wind parks (WPs) based on doubly-fed induction generators (DFIGs) is their vulnerability to network faults. This paper presents a novel nonlinear forward power controller design with an adaptive backstepping command using parametric estimation (NFPC_ABC-PE) to enhance fault ride-through (FRT) capacities in WP utilizing DFIGs. The suggested NFPC_ABC-PE manupiles both rotor and network-side power converters (i.e., RSPCs and NSPCs). Specifically, RSPCs are manipulated to maintain the targeted voltage at dc-bus terminals, while NSPCs are manipulated to supply the reactive energy (power) necessary if the network is disturbed. As a result, the NFPC_ABC-PE proposed precisely supplies reactive energy to ensure the smooth execution of FRT ability. The method developed comprehends the dynamics of RSPC, NSPC-side filters, and dc-bus terminal voltage in the form of electrical active and reactive output power. The parameters of the RSPC and NSPC-side filters, including those associated with the dc-bus capacitor, are regarded as entirely unknown. To estimate and regulate these parameters, adaptation algorithms are utilized. The NFPC_ABC-PE employs parameter adaptation algorithms and switching control inputs designed to safeguard the overall stability of WP. The stability analysis of the DFIG-based WPs with the proposed NFPC_ABC-PE involves applying stability in the sense of the Lyapunov function (LF). To validate its efficacy, simulations are carried out on a single 10 MW power generation unit. The results of the simulation highlight a clear enhancement in the stability and FRT capability of WP, contrasting with the nonlinear forward power controller employing the sliding mode command (NFPC-SMC)

Enhancing battery capacity estimation accuracy using the bald eagle search algorithm

Accurate assessment of metrics such as state of health (SOH) is of paramount importance in effective battery management systems (BMS), given the propensity of batteries to experience capacity degradation with aging. This research endeavors to significantly enhance the precision of battery capacity estimation by effectively mitigating the inherent uncertainties associated with state of charge (SOC) estimation and measurement. To address this challenge, we introduce an innovative approach leveraging the bald eagle search algorithm (BES), a method inspired by the systematic hunting behavior of bald eagles. BES strategically navigates the search space, identifying and selecting promising solutions through fitness evaluations. Our principal aim, utilizing the inherent capabilities of BES, is to pinpoint the optimal candidate that minimizes a designated cost function, while ensuring real-time cell capacity updates facilitated by the incorporation of a memory forgetting factor. The distinctiveness of this study is twofold: firstly, the strategic integration of the BES algorithm within the context of battery capacity optimization, and secondly, the inclusion of a memory forgetting factor to enhance real-time capacity estimations. The efficacy of our approach is rigorously substantiated through validation using NASA’s Prognostic Data, along with three battery scenarios for plug-in hybrid and electric vehicles. BES consistently outperformed four aggressive algorithms, demonstrating heightened accuracy with a peak error rate of only 1.06% in the most demanding scenario. Furthermore, the predictive performance measures remained consistently below 0.41%, underscoring the robustness of our proposed methodology