Error reduction of phasor measurement unit data considering practical constraints

Chatterjee, Paroma; Pal, Anamitra; Thorp, James S.; Lopez, Jaime De La Ree; Centeno, Virgilio

doi:10.1049/iet-gtd.2017.1359

Cited by 19 publications

(7 citation statements)

References 25 publications

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“…Based on the above analysis, the conjugate symmetric data transmission path is optimized. In the actual data transmission process, the energy consumption between the same distance and different links will be different due to the influence of objective factors, so energy-based the path optimization method adds transmission consumption weights to each link, so that the network can better integrate with the objective environment in which it is located, thereby better improving the reliability of data transmission [11][12][13][14]. The transmission consumption weight matrix is as follows:…”

Section: Formulation Of Conjugate Symmetric Data Transmission Protocolmentioning

confidence: 99%

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Wang¹

2023

IJACSA

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In conjugate symmetric data transmission, due to insufficient judgment of congestion during transmission, the amount of data is large and the transmission rate is low. In order to improve the data transmission rate, a conjugate symmetric data transmission control method based on machine learning is designed. Firstly, the data to be transmitted is tracked and determined, and then conjugate symmetric data fusion is completed according to the calculation result of the best tracking signal. According to the fusion results, the framework of the conjugate symmetric data coding system is established, and the data coding is completed. The average congestion mark value is calculated by the machine learning method, and the congestion judgment of data transmission is completed. On the basis of congestion determination, the efficient transmission control of conjugate symmetric data is realized by specifying the conjugate symmetric data transmission protocol. Experimental results show that compared with traditional control methods, this control method has the advantages of a high delivery rate, low message transmission overhead and low data transmission delay. Compared to the traditional two-way path model, the scheduling method proposed in this study increases the transmission delivery rate by 5%, while reducing the transmission cost and delay by 0.7 cost index and 1.1 min delay, respectively. In comparison with the performance of accurate error tracking equalization, the transmission delivery rate of the research method increased by 21%, and in transmission cost index and delay analysis, it also decreased by 3.1 and 2.9 minutes. Based on the above performance comparison analysis, it can be concluded that the machine learning method has more superior transmission control performance.

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Section: Formulation Of Conjugate Symmetric Data Transmission Protocolmentioning

confidence: 99%

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Wang¹

2023

IJACSA

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“…The quantization behavior of the three-phase PMU output data is described in [10] [23]. All three-phase voltage and current data received by PMUs are quantized by certain scales due to the analog-to-digital conversion [24].…”

Section: ) Pmu Quantization Errormentioning

confidence: 99%

Transmission Lines Positive Sequence Parameters Estimation and Instrument Transformers Calibration Based on PMU Measurement Error Model

et al. 2019

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Phasor Measurement Unit measurement data have been widely used in nowadays power system applications both in steady state and dynamic analysis. The performance of these applications running in utilities' energy management system depends heavily on an accurate positive sequence power system model. However, it is impractical to find this accurate model with transmission line parameters calculated directly with the PMU measurements due to ratio errors brought by instrument transformers and communication errors brought by PMUs. Therefore, a methodology is proposed in this paper to estimate the actual transmission lines parameters throughout the whole system and, at the same time, calibrate the corresponding instrument transformers. A PMU positive sequence measurement error model is proposed targeting at the aforementioned errors, which is applicable to both transposed and un-transposed transmission lines. A single line parameters estimation method is designed based on Least Squares Estimation and this error model. This method requires only one set of reference measurements and the accuracy can be propagated throughout the whole network along with the topology acquired by the introduced Edge-based Breadth-first Search algorithm. The IEEE 118-bus system and the Texas 2000-bus system are used to demonstrate the effectiveness and efficiency of the proposed method. The potential for deployment in reality is also discussed. P REV PS ratio error on voltage measurement , P ut REV PS ratio error on voltage measurement of untransposed TL ph  Complex value indicating single-phase deviation from PS Z Impedance matrix of one single TL i KV Correction factor of i REV i KI Correction factor of i REI iM V  PS quantization error on iM V , iM q V Actual quantized PS voltage measurement on bus i output by PMU , iM q I Actual quantized PS current measurement on bus i output by PMU real K Real part of correction factors imag K Imaginary part of correction factors   true real

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“…Proof: Let 𝜃 𝑥 𝑡 and 𝜃 𝑥 𝑚 denote the true voltage angle and the measured voltage angle, respectively, i.e. 𝜃 𝑥 𝑚 = 𝜃 𝑥 𝑡 + 𝑒 𝑥 holds true for every time instant, where 𝑒 𝑥 denotes the fixed but unknown instrumentation error [18]. Now, let a contingency occur at 𝑡 = 𝑡 𝑐 that causes the voltage angles to change in the manner shown in Fig.…”

Section: Lemma 1: Cumulative Sum Of Change In Voltage Phase Angle Com...mentioning

confidence: 99%

A PMU Based Islanding Detection Scheme Immune to Additive Instrumentation Channel Errors

Barkakati

Biswas

Pal

2019

2019 North American Power Symposium (NAPS)

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Traditional synchrophasor measurement-based islanding detection techniques have primarily relied on voltage angle measurements and/or their derivatives for successfully detecting islands. However, relatively high instrumentation channel errors associated with phasor measurement unit (PMU) data, can significantly degrade islanding detection accuracies. In this paper, a new islanding detection scheme employing cumulative sum of change in voltage phase angle difference (CUSPAD) is proposed, which is immune to additive instrumentation channel errors in the PMU measurements. The robustness of the proposed islanding detection algorithm is established through application to an 18-bus test system and the IEEE 118-bus system having different wind energy penetration levels. Comparative analysis of the accuracies of the proposed approach (CUSPAD) and the conventional angle difference (AD) approach prove the former's superior performance when additive instrumentation channel errors are present.

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Error reduction of phasor measurement unit data considering practical constraints

Cited by 19 publications

References 25 publications

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Conjugate Symmetric Data Transmission Control Method based on Machine Learning

Transmission Lines Positive Sequence Parameters Estimation and Instrument Transformers Calibration Based on PMU Measurement Error Model

A PMU Based Islanding Detection Scheme Immune to Additive Instrumentation Channel Errors

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