Topology and Parameter Identification of Distribution Network Using Smart Meter and µPMU Measurements

Srinivas, Vedantham Lakshmi; Wu, Jianzhong

doi:10.1109/tim.2022.3175043

Cited by 41 publications

(12 citation statements)

References 32 publications

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“…Topology connection errors may exist during the initialization of the static power grid model, and under the condition of a determined static power grid model, incremental topological changes (telesignalling changes) can also lead to this issue. Traditional methods for identifying power grid topology errors typically involve obtaining and analyzing real-time information such as PMU node voltages and phase angles or leveraging load node statuses provided by smart meters to aid in topology identification [23][24][25]. Another approach is to compare the results of power system state estimation with the topological model, analyzing the connection relationships of power grid nodes and branches to identify topological errors or inaccurate model parameters [26,27].…”

Section: Topological Error Identificationmentioning

confidence: 99%

Rapid Topological Analysis and State Estimation Based on Spatiotemporal “Power Grid One Graph”

Dai,

Liang,

Tang

et al. 2024

Preprint

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The integrated application of rapid and accurate topological analysis with state estimation is crucial for ensuring the reliability, stability, and efficiency of power systems. In response to this need, this paper proposed an innovative approach to constructing a spatiotemporal “Power Grid One Graph” model using a graph database and achieving fast topological analysis and state estimation. Firstly, a spatiotemporal power grid model was constructed by combining grid topology with temporally dynamic updates of the power grid telemetric and telesignalling. Subsequently, based on the graph model and employing entity mapping, the spatiotemporal node-breaker graph model was generated. Building upon this model, the topological error identification was executed, and a fast topological analysis optimization algorithm considering the functionality of components was utilized to create the corresponding dynamic bus-branch graph model, facilitating graph-based state estimation. Finally, the proposed method on a real power grid system was validated, and the application and performance enhancement of the spatiotemporal power grid model considering topological changes were explored. The introduced method provides theoretical and practical support for the digital transformation of power systems and the developing of digital twin power grids.

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Section: Topological Error Identificationmentioning

confidence: 99%

Rapid Topological Analysis and State Estimation Based on Spatiotemporal “Power Grid One Graph”

Dai,

Liang,

Tang

et al. 2024

Preprint

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“…Superscript R indicates the reference value of each quantity. Deviations ξ h and η ij are the relative errors of voltage and current magnitude measurements, while 1 When designed specifically for distribution systems such instruments are often referred to as distribution PMUs even though up to now no specific standards are available.…”

Section: A Background On Synchrophasor Estimation In the Presence Of ...mentioning

confidence: 99%

“…In the evolving scenario of distribution systems (DSs), new monitoring and management tools are emerging. Each of them should rely on an accurate model of the network itself, in terms of topology [1], loads, generators, etc. Many applications proposed for DS management assume indeed known line parameters.…”

Section: Introductionmentioning

confidence: 99%

Refined Modeling and Compensation of Current Transformers Behavior for Line Parameters Estimation Based on Synchronized Measurements

Laurano

Pegoraro

Sitzia

et al. 2023

IEEE Open J. Instrum. Meas.

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Nowadays, in modern management and control applications, line parameters need to be known more accurately than in the past to achieve a reliable operation of the distribution grids. Phasor Measurement Units (PMUs) may improve line parameters estimation processes, but the accuracy of the result is affected by all the elements of the PMU-based measurement chain, in particular by the instrument transformers. Current transformers (CTs) are nonlinear and therefore their behavior is not easily described: their models cannot be straightforwardly included in estimation problem. In this regard, the paper refines modeling and compensation of CT systematic errors in line parameters estimation processes, based on different methods to describe the transformer behavior under various operating conditions. As main result, the systematic errors of CTs are remarkably identified and mitigated. Moreover, estimation of shunt susceptance values is significantly improved.

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“…Topology identification plays a pivotal role in the realm of energy management systems and distribution management systems by enabling the mathematical modeling of a power network’s physical structure for subsequent analysis and computation. It furnishes crucial network structure data indispensable for various functions within the power system, including power flow calculation, state estimation, dynamic security analysis, fault analysis, and reactive power optimization, among others ( Xie et al, 2022 ; Srinivas & Wu, 2022 ). The advent of the Power Internet of Things (PIoT) has ushered in a transformative era within the power system, where diverse electrical devices and personnel are interconnected through information sensing devices.…”

Section: Introductionmentioning

confidence: 99%

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

Xu,

Lv,

Wang

et al. 2024

PeerJ Computer Science

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At present, the reconfiguration, maintenance, and review of power lines play a pivotal role in maintaining the stability of electrical grid operations and ensuring the accuracy of electrical energy measurements. These essential tasks not only guarantee the uninterrupted functioning of the power system, thereby improving the reliability of the electricity supply but also facilitate precise electricity consumption measurement. In view of these considerations, this article endeavors to address the challenges posed by power line restructuring, maintenance, and inspections on the stability of power grid operations and the accuracy of energy metering. To accomplish this goal, this article introduces an enhanced methodology based on the hidden Markov model (HMM) for identifying the topology of distribution substations. This approach involves a thorough analysis of the characteristic topology structures found in low-voltage distribution network (LVDN) substations. A topology identification model is also developed for LVDN substations by leveraging time series data of electricity consumption measurements and adhering to the principles of energy conservation. The HMM is employed to streamline the dimensionality of the electricity consumption data matrix, thereby transforming the topology identification challenge of LVDN substations into a solvable convex optimization problem. Experimental results substantiate the effectiveness of the proposed model, with convergence to minimal error achieved after a mere 50 iterations for long time series data. Notably, the method attains an impressive discriminative accuracy of 0.9 while incurring only a modest increase in computational time, requiring a mere 35.1 milliseconds. By comparison, the full-day data analysis method exhibits the shortest computational time at 16.1 milliseconds but falls short of achieving the desired accuracy level of 0.9. Meanwhile, the sliding time window analysis method achieves the highest accuracy of 0.95 but at the cost of a 50-fold increase in computational time compared to the proposed method. Furthermore, the algorithm reported here excels in terms of energy efficiency (0.89) and load balancing (0.85). In summary, the proposed methodology outperforms alternative approaches across a spectrum of performance metrics. This article delivers valuable insights to the industry by fortifying the stability of power grid operations and elevating the precision of energy metering. The proposed approach serves as an effective solution to the challenges entailed by power line restructuring, maintenance, and inspections.

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Topology and Parameter Identification of Distribution Network Using Smart Meter and µPMU Measurements

Cited by 41 publications

References 32 publications

Rapid Topological Analysis and State Estimation Based on Spatiotemporal “Power Grid One Graph”

Rapid Topological Analysis and State Estimation Based on Spatiotemporal “Power Grid One Graph”

Refined Modeling and Compensation of Current Transformers Behavior for Line Parameters Estimation Based on Synchronized Measurements

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

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