Voltage Unbalance Compensation in Distribution Feeders Using Soft Open Points

You, Rui; Lu, Xiaonan

doi:10.35833/mpce.2021.000565

Cited by 37 publications

(13 citation statements)

References 28 publications

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“…Furthermore, the idea of the dual‐rate sampled AEKF method can also be applied to else fields, such as the super capacitors, 37 the fuel cell systems, 38 and the power electronic devices 39 …”

Section: Discussionmentioning

confidence: 99%

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

Wang

2022

Intl J of Energy Research

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Summary For a lithium battery state space model, to solve the unsuitable problems caused by the single‐rate (same rate) sampled state space model facing resistor‐capacity (R‐C) couples with different time scales, a dual‐rate sampled exponential weighted multiple innovation adaptive extended Kalman filter (AEKF) method is investigated. The details include: (a) By using dual‐rate sampled method, select the suitable sampled rate for their own time scale for different couples (R1‐C1 and R2‐C2) to establish dual‐rate sampled state space equations; (b) Considering noise variation, a recursive noise estimation based AEKF algorithm is adopted to realize adaptive correction of noise; (c) Considering the influences of present and past data, an AEKF algorithm based exponential weighted multiple innovation is studied. Experiment results show that the investigated method is clearly better than the classical single‐rate sampled method with high precision.

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Section: Discussionmentioning

confidence: 99%

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

Wang

2022

Intl J of Energy Research

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“…The MSC is controlled by appropriate duty cycles to extract the maximum power based on an MPPT algorithm when the wind speed is lower than the rated speed, or to limit the maximum extracted power at the rated value for speeds above the rated speed. Besides, the GSC is controlled to regulate the active and reactive power of the grid-connected WECS via a directcurrent (DC) link regulator or satisfy the requirements about quality of voltage of the distribution feeders [26]. Within this context, the proposed method focuses on controlling the MSC to achieve the MPPT control capability, while assuming that another controller is adopted for the GSC control.…”

Section: System Modelingmentioning

confidence: 99%

Reinforcement Learning-Based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine

Nguyen

2022

IEEE Access

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A reinforcement learning-based adaptive optimal fuzzy controller is proposed for maximum power point tracking (MPPT) control of a variable-speed permanent magnet synchronous generator-based wind energy generation system. The algorithm consists of a critic, an adaptive optimal fuzzy controller, and an adaptive optimal fuzzy estimator. The critic is built based on an adaptive neuro-fuzzy inference system (ANFIS) network instead of the neural network as normal to reduce the computation. The error between the system output and the estimator output is used as the input of the critic. In addition, the critic is used to calculate the update law for the parameters of the adaptive optimal fuzzy controller and adaptive optimal fuzzy estimator based on minimizing the input error function. Moreover, the proposed control scheme is output feedback instead of state feedback, which does not require a system model as well as system parameters, so the system is robust to uncertainties and external disturbances. Besides, the stabilization proof is accomplished by using the Lyapunov stability theorem for the closed-loop system and the convergence of the update law. Finally, the effectiveness of the proposed reinforcement learning-based adaptive optimal fuzzy control scheme is verified through simulation with various scenarios such as step wind speed, random wind speed, and system parameter variations. Also, the comparisons with other control schemes in the stateof-art (neural network reinforcement learning based adaptive optimal fuzzy controller, PI controller) are executed to demonstrate the advantages of the proposed control scheme. 17 18 numerous control strategies have been introduced such as 49 PI control [5], sliding mode control (SMC) [6], [7], [8], 50 [9], adaptive control [10], [11], and model predictive control 51 (MPC) [12]. In [5], a PI controller is employed for the 52 current control loop of the multi-motor wind turbine system. 53 However, this PI controller cannot ensure good performance 54 under condition variations due to the nonlinearity of WEGS 55 and the affection of the environment (e.g., wind speed, air 56 density). Next, SMC is considered as the nonlinear control 57 technique used to deal with the parametric uncertainties and 58 disturbances of WECS for the MPPT control. In [6] and [7], 59 the high-order SMCs are applied to improve the performance 60 of the WECS by reducing the chattering phenomenon with 61 the continuous control input. However, the fluctuation of the 62 output voltage and power is still high [7]. In [8], an enhanced 63 reaching law-based SMC method is introduced for the MPPT 64 control of offshore WECS, which consists of two loops: the 65 current control loop with a conventional PI controller and 66 the speed control loop with a finite time reaching SMC, 67 and a mechanical torque observer. This control strategy 68 significantly improves the ability of the WEGS to resist 69 uncertainties and disturbances. In [9], a fixed-time fractional-70 order SMC is designed for both rotor side converter (...

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“…Based on the highly modular converter structure, the configuration of ESOP is diversified [6], and the number of terminals and capacities of each converter can be flexibly designed. Because of the multi-stage expansion requirement of ESOP [7], the integrating location and configured capacities should be considered. Through appropriate device configuration, the flexible power flow regulation ability of ESOP can be fully utilized [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-stage expansion planning of energy storage integrated soft open points considering tie-line reconstruction

Chen²,

et al. 2022

Prot Control Mod Power Syst

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With the rapid development of flexible interconnection technology in active distribution networks (ADNs), many power electronic devices have been employed to improve system operational performance. As a novel fully-controlled power electronic device, energy storage integrated soft open point (ESOP) is gradually replacing traditional switches. This can significantly enhance the controllability of ADNs. To facilitate the utilization of ESOP, device locations and capacities should be configured optimally. Thus, this paper proposes a multi-stage expansion planning method of ESOP with the consideration of tie-line reconstruction. First, based on multi-terminal modular design characteristics, the ESOP planning model is established. A multi-stage planning framework of ESOP is then presented, in which the evolutionary relationship among different planning schemes is analyzed. Based on this framework, a multi-stage planning method of ESOP with consideration of tie-line reconstruction is subsequently proposed. Finally, case studies are conducted on a modified practical distribution network, and the cost–benefit analysis of device and multiple impact factors are given to prove the effectiveness of the proposed method.

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Voltage Unbalance Compensation in Distribution Feeders Using Soft Open Points

Cited by 37 publications

References 28 publications

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

A dual‐rate sampled multiple innovation adaptive extended Kalman filter algorithm for state of charge estimation

Reinforcement Learning-Based Adaptive Optimal Fuzzy MPPT Control for Variable Speed Wind Turbine

Multi-stage expansion planning of energy storage integrated soft open points considering tie-line reconstruction

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