Optimal Placement of PMUs and Related Sensor-based Communication Infrastructures for Full Observability of Distribution Networks

Bashian, Amir; Assili, Mohsen; Anvari‐Moghaddam, Amjad

doi:10.1109/pesgm41954.2020.9281586

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…Demand side fluctuations, supply side volatility, network harmonics and inter-harmonics can affect the proper operation and control of smart grids [2]- [3]. To detect and counteract possible critical operating conditions, the state of the system should be estimated also at the distribution level [4]- [6]. The Phasor Measurement Units (PMUs) are particularly useful to this purpose, since they are able to estimate the synchronized phasors of voltage and current AC waveforms at a high rate under both static and dynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filtering with Harmonics Whitening for P Class Phasor Measurement Units

Bashian

Macii

Fontanelli

et al. 2021

2021 IEEE 11th International Workshop on Applied Measurements for Power Systems (AMPS)

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The need for increasingly accurate and fast Phasor Measurement Units (PMUs), especially for active distribution systems monitoring, requires to achieve challenging trade-offs between measurement uncertainty and responsiveness. This is particularly important for protection-oriented (i.e., P Class) PMUs. In order to improve estimation accuracy with no need to prolong the data record size and the related delays, this paper presents a Taylor Kalman Filter (TKF) enhanced with a preliminary stage able to whiten possible narrowband disturbances over short observation intervals. The use of dynamic estimators such as the TKF is motivated by the need to track possible sudden amplitude or phase changes of voltage or current AC waveforms, which are likely to occur in smart grids. However, while a basic TKF is very sensitive to disturbances different from white noise, the proposed whitening-technique is able to greatly improve the estimation accuracy of synchrophasor amplitude, phase, frequency and Rate of Change of Frequency (ROCOF) under the influence of harmonics and amplitude or phase step changes even over one-cycle observation intervals, with just a minor performance degradation in the other P Class testing conditions reported in the IEEE/IEC Standard 60255-118-1:2018.

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Section: Introductionmentioning

confidence: 99%

Kalman Filtering with Harmonics Whitening for P Class Phasor Measurement Units

Bashian

Macii

Fontanelli

et al. 2021

2021 IEEE 11th International Workshop on Applied Measurements for Power Systems (AMPS)

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“…Different values of ϒ ̂, ϒ ͂ , and Π ˉ would also lead to different solutions. According to [14], [32], [45], the conventional measurements utilized in this study include the injected power and power flow. Consider a π-model for the grid branch connecting buses i and j.…”

Section: Opp Problem Considering Pmu Cls Zibs Single Pmu Loss Conting...mentioning

confidence: 99%

Optimal Placement of Phasor Measurement Unit in Smart Grids Considering Multiple Constraints

Chen

He²,

Sun³

et al. 2023

Journal of Modern Power Systems and Clean Energy

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The distribution of measurement noise is usually assumed to be Gaussian in the optimal phasor measurement unit (PMU) placement (OPP) problem. However, this is not always accurate in practice. This paper proposes a new OPP method for smart grids in which the effects of conventional measurements, limited channels of PMUs, zero-injection buses (ZIBs), single PMU loss contingency, state estimation error (SEE), and the maximum SEE variance (MSEEV) are considered. The SEE and MSEEV are both obtained using a robust t-distribution maximum likelihood estimator (MLE) because t-distribution is more flexible for modeling both Gaussian and non-Gaussian noises. The A-and G-optimal experimental criteria are utilized to form the SEE and MSEEV constraints. This allows the optimization problem to be converted into a linear objective function subject to linear matrix inequality observability constraints. The performance of the proposed OPP method is verified by the simulations of the IEEE 14-bus, 30-bus, and 118-bus systems as well as the 211-bus practical distribution system in China.

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“…The Jacobian matrices H and H corresponding to the real and reactive power injection (or power flows) measurements can be derived as follows. According to [50,54], the conventional measurements utilized in this paper include the power injection and power flow. Consider the π-model for the grid branch connecting buses i and j.…”

Section: Dpmu Placement Considering Multiple Constraintsmentioning

confidence: 99%

“…The proposed optimization problem ( 26) can be obtained directly by using YALMIP with the CUTSDP solver [55] in MATLAB. Note that many OPP methods assume that the topology of the power system is known in advance [7,15,29,32,54], from where the measurement matrix H can be calculated beforehand. This paper follows the same assumption.…”

Section: Dpmu Placement Considering Multiple Constraintsmentioning

confidence: 99%

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Optimal placement of distribution-level synchrophasor sensors for distribution system

Chen¹,

Liu²,

Chen³

et al. 2022

Meas. Sci. Technol.

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This paper proposes a new method for the placement of the important synchrophasor sensors, the Distribution-level Phasor Measurement Units (DPMUs). The new objective function and constraints are developed to achieve the minimization of distribution system state estimation error, in the presence of the limited number of DPMUs, the zero injection buses, the limited DPMU channels and the Supervisory Control And Data Acquisition (SCADA) measurements. The A-optimal experimental design criterion is utilized to formulate the proposed objective function and constraints. The state estimation error is obtained using the maximum likelihood estimator under non-Gaussian measurement noise since the traditional assumption of Gaussian noise may violate the reality. The performance of the proposed method is demonstrated by simulations on the IEEE 34-bus and 123-bus distribution systems.

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Optimal Placement of PMUs and Related Sensor-based Communication Infrastructures for Full Observability of Distribution Networks

Cited by 6 publications

References 14 publications

Kalman Filtering with Harmonics Whitening for P Class Phasor Measurement Units

Kalman Filtering with Harmonics Whitening for P Class Phasor Measurement Units

Optimal Placement of Phasor Measurement Unit in Smart Grids Considering Multiple Constraints

Optimal placement of distribution-level synchrophasor sensors for distribution system

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