7 results found
Batzelis EI, Anagnostou G, Chakraborty C, et al., 2020, Computation of the lambert W function in photovoltaic modeling, Electrimacs 2019, Publisher: Springer International Publishing, Pages: 583-595, ISSN: 1876-1100
Recently, the Lambert W function has emerged as a valuable mathematical tool in photovoltaic (PV) modeling and other scientific fields. This increasing interest is because it can be used to reformulate the implicit equations of the single-diode PV model into explicit form. However, the computation of the Lambert W function itself is still not clear in the literature; some studies use the iterative built-in functions in MATLAB or other computational platforms, while others adopt their own approximation formulae. This paper takes a deeper look at the ways the Lambert W function is evaluated in PV models and carries out a comparative study to assess the most commonly used methods in terms of accuracy, computational cost, and application range. These alternatives are implemented in a modern computer and a typical microcontroller to evaluate their performance in both simulations and embedded applications. The analysis concludes that some series expansions are good options for PV modeling applications, requiring less execution time than the built-in MATLAB lambertw function and exhibiting negligible approximation error.
Anagnostou G, Puthenpurayl Linash K, Pal B, 2019, Dynamic state estimation for wind turbine models with unknown wind velocity, IEEE Transactions on Power Systems, Vol: 34, Pages: 3879-3890, ISSN: 0885-8950
This paper proposes a novel Kalman filtering based dynamic state estimation method, which addresses cases of models with a nonlinear unknown input, and it is suitable for wind turbine model state estimation. Given the complexity characterising modern power networks, dynamic state estimation techniques applied on renewable energy based generators, such as wind turbines, enhance operators’ awareness of the components comprising modern power networks. In this context, the method developed here is implemented on a doubly-fed induction generator based wind turbine, under unknown wind velocity conditions, as opposed to similar studies so far, where all model inputs are considered to be known, and this does not always reflect the reality. The proposed technique is derivative-free and it relies on the formulation of the nonlinear output measurement equations as power series. The effectiveness of the suggested algorithm is tested on a modified version of the IEEE benchmark 68-bus, 16-machine system.
Batzelis E, Anagnostou G, Cole I, et al., 2019, A state-space dynamic model for photovoltaic systems with full ancillary services support, IEEE Transactions on Sustainable Energy, Vol: 10, Pages: 1399-1409, ISSN: 1949-3029
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 (DCM) 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.
Anagnostou G, Boem F, Kuenzel S, et al., 2018, Observer-based anomaly detection of synchronous generators for power systems monitoring, IEEE Transactions on Power Systems, Vol: 33, Pages: 4228-4237, ISSN: 0885-8950
This paper proposes a rigorous anomaly detectionscheme, developed to spot power system operational changeswhich are inconsistent with the models used by operators. Thisnovel technique relies on a state observer, with guaranteedestimation error convergence, suitable to be implemented in realtime, and it has been developed to fully address this importantissue in power systems. The proposed method is fitted to thehighly nonlinear characteristics of the network, with the statesof the nonlinear generator model being estimated by meansof a linear time-varying estimation scheme. Given the relianceof the existing dynamic security assessment tools in industryon nominal power system models, the suggested methodologyaddresses cases when there is deviation from assumed systemdynamics, enhancing operators’ awareness of system operation.It is based on a decision scheme relying on analytical computationof thresholds, not involving empirical criteria which are likely tointroduce inaccurate outcomes. Since false-alarms are guaranteedto be absent, the proposed technique turns out to be very usefulfor system monitoring and control. The effectiveness of theanomaly detection algorithm is shown through detailed realisticcase studies in two power system models.
Batzelis E, Anagnostou G, Pal B, 2018, A state-space representation of irradiance-driven dynamics in two-stage photovoltaic systems, IEEE Journal of Photovoltaics, Vol: 8, Pages: 1119-1124, ISSN: 2156-3381
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.
Anagnostou G, Pal BC, 2017, Derivative-free Kalman filtering based approaches to dynamic state estimation for power systems with unknown inputs, IEEE Transactions on Power Systems, Vol: 33, Pages: 116-130, ISSN: 1558-0679
This paper proposes a decentralized derivative-freedynamic state estimation method in the context of a power systemwith unknown inputs, to address cases when system linearisationis cumbersome or impossible. The suggested algorithm tacklessituations when several inputs, such as the excitation voltage,are characterized by uncertainty in terms of their status. Thetechnique engages one generation unit only and its associatedmeasurements, and it remains totally independent of other systemwide measurements and parameters, facilitating in this way theapplicability of this process on a decentralized basis. The robust-ness of the method is validated against different contingencies.The impact of parameter errors, process and measurement noiseon the unknown input estimation performance is discussed. Thisunderstanding is further supported through detailed studies in arealistic power system model.
Anagnostou G, Pal BC, 2015, Impact of Overexcitation Limiters on the Power System Stability Margin Under Stressed Conditions, IEEE Transactions on Power Systems, Vol: 31, Pages: 2327-2337, ISSN: 1558-0679
This paper investigates the impact of the overexcitation limiters (OELs) on the stability margin of a power system which is operating under stressed conditions. Several OEL modeling approaches are presented and the effect of their action has been examined in model power systems. It is realized that, more often than not, OEL operating status goes undetected by existing dynamic security assessment tools commonly used in the industry. It is found that the identification and accurate representation of OELs lead to significantly different transient stability margins. Unscented Kalman filtering is used to detect the OEL activation events. In the context of stressed system operation, such quantitative assessment is very useful for system control. This understanding is further reinforced through detailed studies in two model power systems.
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