Robust Measurement Placement for Distribution System State Estimation

Yao, Yiyun; Li, Xuan; Li, Zuyi

doi:10.1109/tste.2017.2775862

Cited by 42 publications

(33 citation statements)

References 34 publications

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“…Optimizing the location of metering instruments in distribution systems is a significant subject for research, given the size of the system and potentially limited financial resources [69]. Different objectives have been proposed in the literature to address this problem, including improving system observability, minimizing installation/maintenance costs, bad data detection capability, and improving the DSSE accuracy [ [80]. Different algorithms have been tried for solving the placement problem, including Genetic-Algorithm (GA), Mixed Integer Linear Programming (MILP), Mixed Integer Semi-Definite Programming (MISDP), and Multi-Objective Evolutionary (MOE) methods.…”

Section: Distribution Network Metering System Design and Analysismentioning

confidence: 99%

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Dehghanpour

Wang

et al. 2019

IEEE Trans. Smart Grid

357

173

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This paper presents a review of the literature on State Estimation (SE) in power systems. While covering some works related to SE in transmission systems, the main focus of this paper is Distribution System State Estimation (DSSE). The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudomeasurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security. Both conventional and modern data-driven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE.

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Section: Distribution Network Metering System Design and Analysismentioning

confidence: 99%

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Dehghanpour

Wang

et al. 2019

IEEE Trans. Smart Grid

357

173

View full text Add to dashboard Cite

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“…This solution, though, is still uncomfortable, as it involves converting parameter to a variable dependent on the square of the tap ratio (if k = m), or on the tap ratio (if k≠m). This increases the non-linearity degree of the ORPF model, making some rigid power factor limit -e q (14) elastic power factor limit -e q (15) distance is minimized in eq (16) Figure 7. Modeled capability curved for a RES generator.…”

Section: Modeling the Oltc Transformermentioning

confidence: 99%

“…If this is the case, the results show that Local Control requires a careful tuning of its defining parameters, as it is prone to produce violations under peculiar operating conditions of the DN. The authors studied various optimal tuning solutions for Local Control laws in [15,56]; these approaches are promising, but in the absence of proper network monitoring, they depend heavily on the statistical estimation of generation/demand profiles, which may not ensure a strong correlation with the actual operating conditions of the grid. On the other hand, if the DN's control and supervision architecture allows the implementation of Coordinated Control, then Local Control could either be abandoned or, in correspondence with the regulatory framework, could work in tandem with Local Control; the base operation could be provided by Local Control (with a less stiff set-up), which, in spite of the disadvantages, tries to keep the voltage profile close to nominal, while the Coordinated Control would override the Local Control in cases of detected violations.…”

Section: Local Vs Coordinated Controlmentioning

confidence: 99%

Voltage Control Methodologies in Active Distribution Networks

et al. 2020

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Renewable Energy Sources are becoming widely spread, as they are sustainable and low-carbon emission. They are mostly penetrating the MV Distribution Networks as Distributed Generators, which has determined the evolution of the networks’ control and supervision systems, from almost a complete lack to becoming fully centralized. This paper proposes innovative voltage control architectures for the distribution networks, tailored for different development levels of the control and supervision systems encountered in real life: a Coordinated Control for networks with basic development, and an optimization-based Centralized Control for networks with fully articulated systems. The Centralized Control fits the requirements of the network: the challenging harmonization of the generator’s capability curves with the regulatory framework, and modelling of the discrete control of the On-Load Tap Changer transformer. A realistic network is used for tests and comparisons with the Local Strategy currently specified by regulations. The proposed Coordinated Control gives much better results with respect to the Local Strategy, in terms of loss minimization and voltage violations mitigation, and can be used for networks with poorly developed supervision and control systems, while Centralized Control proves the best solution, but can be applied only in fully supervised and controlled networks.

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“…Thus, several works can be found in the literature approaching the problem of determining the best MP configuration for a given power network. The problem has been addressed in literature, proposing different specific targets including system observability, installation/maintenance cost minimization, poor data detection capability, and SE accuracy [8][9][10][11]. To solve the issue, different optimization algorithms have been proposed, such as genetic algorithms (GA), particle swarm optimization (PSO) or heuristic techniques.…”

Section: Meter Placement Techniques and Measurement Systems For Distrmentioning

confidence: 99%

“…Traditionally, SE techniques make use of few actual measurements of medium-voltage (MV) branch voltages or current/power flows for collecting the input data of SE algorithms. The determination of the best possible combination of meters for distribution system monitoring is referred to as the optimal meter placement (MP) [8][9][10][11][12][13][14]. For the measurement of the SE input data, different kinds of measurement equipment can be used, i.e., phasor measurement units (PMUs), smart meters (SMs), power quality analyzers (PQAs) and so on, with different accuracy features and costs [15,16].…”

Section: Introductionmentioning

confidence: 99%

Incremental Heuristic Approach for Meter Placement in Radial Distribution Systems

et al. 2019

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The evolution of modern power distribution systems into smart grids requires the development of dedicated state estimation (SE) algorithms for real-time identification of the overall system state variables. This paper proposes a strategy to evaluate the minimum number and best position of power injection meters in radial distribution systems for SE purposes. Measurement points are identified with the aim of reducing uncertainty in branch power flow estimations. An incremental heuristic meter placement (IHMP) approach is proposed to select the locations and total number of power measurements. The meter placement procedure was implemented for a backward/forward load flow algorithm proposed by the authors, which allows the evaluation of medium-voltage power flows starting from low-voltage load measurements. This allows the reduction of the overall costs of measurement equipment and setup. The IHMP method was tested in the real 25-bus medium-voltage (MV) radial distribution network of the Island of Ustica (Mediterranean Sea). The proposed method is useful both for finding the best measurement configuration in a new distribution network and also for implementing an incremental enhancement of an existing measurement configuration, reaching a good tradeoff between instrumentation costs and measurement uncertainty.

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Robust Measurement Placement for Distribution System State Estimation

Cited by 42 publications

References 34 publications

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Voltage Control Methodologies in Active Distribution Networks

Incremental Heuristic Approach for Meter Placement in Radial Distribution Systems

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