Fortescue Transformations for three-phase power flow analysis in distribution networks

Džafić, Izudin; Donlagic, Tarik; Henselmeyer, S.

doi:10.1109/pesgm.2012.6345581

“…2, the probability of bad data reception in measure signal 2 is 20%. Therefore, we can calculate Γ = � 0.5 0 0 0.8 � , Υ = � 0.25 0 0 0.16 � (48) Based on these matrices, we can obtain the state estimates using (41), (42), (43). We have performed a number of EKF and FTEKF state estimation experiments applied to grid synchronization.…”

Section: Section VI Computer Simulation Resultsmentioning

confidence: 99%

“…Denote ( ) = [ ( ), ( ), ( )] as the three-phase voltage vector. According to symmetrical component transformation (Fortescue's transformation), three phase voltages can be expressed in term of positive, negative, and zero sequence voltages 47,48 ( ) = 0 ( ) + ( ) + ( ) (3) where ( ) represents the instantaneous three phase voltages, and ( = 0, , ) denote the zero, positive, and negative sequence voltages…”

Section: Problem Formulationmentioning

confidence: 99%

Smart power grid synchronization with Fault Tolerant nonlinear estimation

Wang

¹

,

Yaz

²

2016

2016 American Control Conference (ACC)

View full text Add to dashboard Cite

Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke's transformation is used to transform the sequence networks into the stationary coordinate frame, which leads to system model formulation. A novel fault tolerant extended Kalman filter based real-time estimation framework is proposed for smart grid synchronization with noisy bad data measurements. Computer simulation studies have demonstrated that the proposed fault tolerant extended Kalman filter (FTEKF) provides more accurate voltage synchronization results than the extended Kalman filter (EKF). The proposed approach has been implemented with dSPACE DS1103 and National Instruments CompactRIO hardware platforms. Computer simulation and hardware instrumentation results have shown the potential applications of FTEKF in smart grid synchronization. SECTION I.

show abstract

“…To calculate VUFs, sequence voltages such as positive sequence voltage V + , negative sequence voltage V − , and zero-sequence voltage V 0 , are required, which are computed by the Fortescue transformation. Equation (1) shows the Fortescue transformation, where α = e j2π/3 = 1∠120 • , and V A , V B , V C are phase-to-neutral voltages [20].…”

Section: Voltage Unbalance Factors and Sequence Voltagementioning

confidence: 99%

“…2021, 11, 8979 4 of 14 voltage , are required, which are computed by the Fortescue transformation. Equation (1) shows the Fortescue transformation, where = / = 1∠120°, and , , are phase-to-neutral voltages [20].…”

Section: Volt-var Curvementioning

confidence: 99%

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

Jung

¹

,

Han

²

,

Lee

³

et al. 2021

View full text Add to dashboard Cite

Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.

show abstract

“…Denote ( ) = [ ( ), ( ), ( )] as the three-phase voltage vector. According to symmetrical component transformation (Fortescue's transformation), three phase voltages can be expressed in term of positive, negative, and zero sequence voltages 47,48 ( ) = 0 ( ) + ( ) + ( ) (3) where ( ) represents the instantaneous three phase voltages, and ( = 0, , ) denote the zero, positive, and negative sequence voltages where for = (0, , ) are zero, positive, and negative sequence voltage phasors, and = 1∠120 ∘ .…”

Section: Problem Formulationmentioning

confidence: 99%

Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation

Wang

¹

,

Yaz

²

2016

IEEE Trans. Power Syst.

View full text Add to dashboard Cite

Effective real-time state estimation is essential for smart grid synchronization, as electricity demand continues to grow, and renewable energy resources increase their penetration into the grid. In order to provide a more reliable state estimation technique to address the problem of bad data in the PMU-based power synchronization, this paper presents a novel nonlinear estimation framework to dynamically track frequency, voltage magnitudes and phase angles. Instead of directly analyzing in abc coordinate frame, symmetrical component transformation is employed to separate the positive, negative, and zero sequence networks. Then, Clarke's transformation is used to transform the sequence networks into the stationary coordinate frame, which leads to system model formulation. A novel fault tolerant extended Kalman filter based real-time estimation framework is proposed for smart grid synchronization with noisy bad data measurements. Computer simulation studies have demonstrated that the proposed fault tolerant extended Kalman filter (FTEKF) provides more accurate voltage synchronization results than the extended Kalman filter (EKF). The proposed approach has been implemented with dSPACE DS1103 and National Instruments CompactRIO hardware platforms. Computer simulation and hardware instrumentation results have shown the potential applications of FTEKF in smart grid synchronization. SECTION I.

show abstract

Fortescue Transformations for three-phase power flow analysis in distribution networks

Cited by 23 publications

References 17 publications

Smart power grid synchronization with Fault Tolerant nonlinear estimation

Smart power grid synchronization with Fault Tolerant nonlinear estimation

Adaptive Volt–Var Control in Smart PV Inverter for Mitigating Voltage Unbalance at PCC Using Multiagent Deep Reinforcement Learning

Smart Power Grid Synchronization With Fault Tolerant Nonlinear Estimation

Contact Info

Product

Resources

About