Distributed Kalman Filter for Large-Scale Power Systems With State Inequality Constraints

Cheng, Zhijian; Ren, Hongru; Zhang, Bin

doi:10.1109/tie.2020.2994874

Cited by 23 publications

(7 citation statements)

References 45 publications

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“…Cheng et al 26 designed a modified distributed Kalman filter (KF) based on the mixed phasor measurement unit (PMU) and RTU measurement model that independently estimated local states via local measurements. Munsi et al 27 proposed the use of an algorithm based on the unscented Kalman filter (UKF) to control the voltage and current of a microgrid against unknown noise. Che and Shahidehpour 28 investigated the power flow between a DC microgrid and a main grid from the perspectives of economy, optimization and stability and designed a three-level control strategy for a DC microgrid.…”

Section: Multisource DC Microgrid Group Control Research Status and C...mentioning

confidence: 99%

Development of a distributed group control strategy for pumping well groups connected by multisource DC microgrids

Yue,

Sun,

et al. 2024

Sci Rep

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Due to the alternating loads on pumping units and the integration of new energy sources, multisource DC microgrid pumping unit well groups experience increased fluctuations in voltage and power as well as superimposed peak and valley values. This work presents a distributed control strategy for pumping unit well groups on a multisource DC microgrid based on the weighted moving average algorithm. A centralized control program is implanted in the RTU of the single-well controller of each pumping unit, and communication with each well is realized via SCADA and multicast communication, resulting in a distributed well group system. The real-time power values of the pumping well group are calculated by grouping the power values, and each group is weighted using the total power fluctuation threshold of the well group as the control target. Then, a weighted moving average algorithm is used to predict the next power value and form a table of predicted real-time power spectra. According to the power values in the community power spectrum table, the inverter frequency is proportionally adjusted downwards to reach the power peak before deceleration; after the power peak is crossed, the frequency is increased in the same way to reach the power valley before acceleration. Finally, the peak and valley power values of the bus system level off and further learn to reach the set impulse; ultimately, a stable impulse is formed. In laboratory testing and field application in the Shengli Oilfield XIN-11 block, the group control software module effectively suppressed the active power peak and valley values and voltage fluctuations of the bus system, the active power fluctuation rate range decreased by more than 70%, and the DC bus voltage fluctuation range decreased by more than 80%; moreover, the active power decreased by approximately 6% without additional hardware costs.

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Section: Multisource DC Microgrid Group Control Research Status and C...mentioning

confidence: 99%

Development of a distributed group control strategy for pumping well groups connected by multisource DC microgrids

Yue,

Sun,

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In ( 6), the measurement functions h k (•) typically represent the standard real and reactive power observation of node power injection or line power flow. With the advancement of PMU technology, direct measuring states of voltage magnitude and phase angle are also possible [27], [28]. However, the extensive collection of real-time observations may not only raise meter installation costs but also lead to filtering information redundancy.…”

Section: B Fase Modelmentioning

confidence: 99%

“…The efficiency and robustness of nonlinear and linear power flow model-based FASE estimators are included in comparisons under different simulation scenarios. Different state forecasting and filtering configurations for the six estimators are shown in Table I, where |V | and θ represents the measurement of voltage magnitude and phase angle; nonlinear PJ and PF represents the nonlinear measurement equations of power injection and power flow in filtering stage (i.e., extended Kalman filter, EKF [28]). For EKF-FASE-II and DC-FASE-II, the full system state derivation on the nonlinear PJ and DC PJ equations are implemented as the state transition model in forecasting stage.…”

Section: A Performance Comparisonsmentioning

confidence: 99%

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Approximate Power Flow Solutions-Based Forecasting-Aided State Estimation for Power Distribution Networks

WANG,

Xu,

et al. 2024

Preprint

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Emerging forecasting-aided state estimation (FASE) frequently encounters complicated parameter analysis and observation calculation tasks, especially when confronted with intricate and uncertain scenarios. To this end, a concise FASE estimator is developed by combining the precise depiction of dynamic state change and linear power flow approximation. Designing the dynamic system state as a voltage perturbation vector around the nominal value, the forecasted state is firstly derived from the linear approximation of power injection equation solutions. The state forecasting model relies solely on nodal impedance information as the state transition matrix, eliminating the onerous parameter tuning effort. After that, the optimal filtered state is efficiently obtained utilizing line power flow measurements, with branch admittance information to construct the approximate observation matrix. Numerical simulation comparisons on a symmetric balanced 56-node distribution system verify the performance of the proposed estimator in terms of accuracy and robustness.

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“…The well group production used DC bus power supply components to form a small DC microgrid, which incorporated several new energy sources such as wind power, photovoltaic power generation and network electricity to form a distributed multi-source DC microgrid. The DC microgrid connects the control cabinets of multiple pumping units in parallel through the DC bus, and the integrated inverter and Remote Terminal Unit (RTU) in the control cabinet can realize the mutual feed-sharing and recycling of the power generated by multiple pumping units [12][13][14][15]. The multi-source DC microgrid has commonness and mutual influence among each other [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Research on Multi-source DC microgrid Distributed Group Control Strategy for Pumping Well Cluste

Yue,

Sun,

et al. 2023

Preprint

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Due to the pumping units' dual role as alternating loads and new energy sources, multi-source DC microgrid pumping unit well clusters experience increased fluctuation in voltage and power as well as peak and valley values that are superimposed. This work presents a distributed cooperation strategy based on the grouping weighted moving average algorithm for pumping unit well groups in multi-source DC microgrid. Centralized control program is implanted in the RTU of the single well controller of each pumping unit, and the communication of each single well is realized by SCADA system to construct the distributed well group system. The real-time power values of the pumping well group are calculated by grouping the power values and granting weighted weights to each group, using the total power fluctuation threshold of the well group as the control target, and further using a weighted moving average algorithm to predict the next power value and form a table of predicted real-time power spectra. According to the power value in the community power spectrum table, the inverter frequency is proportionally adjusted downward to achieve the power peak before deceleration to reach; after crossing the power peak, the frequency is raised in the same way to accelerate the compensation to achieve the power valley before acceleration to reach, that is, acceleration compensation, and finally achieve the power peak and valley values of the bus system to level off, and further learn to reach the set impulse, and finally form a stable impulse. Through laboratory testing and field application in Shengli XIN-11 block application proved: the group control software module not only effectively suppresses the active power peak and valley values and voltage fluctuations of the bus system, the active power fluctuation rate range decreases by more than 70%, the DC bus voltage fluctuation range decreases by more than 80%, but also reduces active power by about 6% without increasing the cost of new hardware.

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Distributed Kalman Filter for Large-Scale Power Systems With State Inequality Constraints

Cited by 23 publications

References 45 publications

Development of a distributed group control strategy for pumping well groups connected by multisource DC microgrids

Development of a distributed group control strategy for pumping well groups connected by multisource DC microgrids

Approximate Power Flow Solutions-Based Forecasting-Aided State Estimation for Power Distribution Networks

Research on Multi-source DC microgrid Distributed Group Control Strategy for Pumping Well Cluste

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