Trade-offs in PMU and IED Deployment for Active Distribution State Estimation Using Multi-objective Evolutionary Algorithm

Prasad, Sachidananda; Kumar, D. M. Vinod

doi:10.1109/tim.2018.2792890

Cited by 59 publications

(42 citation statements)

References 34 publications

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“…Remark 3. Problem (10) is equivalent to minimizing the number of sensors or the total cost of the sensors, while enforcing a given accuracy, i.e., satisfying a performance threshold λ of the metric: min {x|f (x)≤λ, xi∈{0,1} ∀i} h(x), as in [15]- [18]. To achieve that, the problem (10) can be solved for different numbers of sensors or budgets until the performance value is below the threshold:…”

Section: B Measurementsmentioning

confidence: 99%

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

Picallo

Anta

Schutter

2019

IEEE Trans. Power Syst.

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The lack of measurements in distribution grids poses a severe challenge for their monitoring: since there may not be enough sensors to achieve numerical observability, load forecasts (pseudo-measurements) are typically used, and thus an accurate state estimation is not guaranteed. However, an estimation is required to control distribution grids given the increasing amount of distributed generation. Therefore, we consider the problem of optimal sensor placement to improve the state estimation accuracy in large-scale, 3-phase coupled, unbalanced distribution grids. This is a combinatorial optimization problem whose optimal solution is unpractical to obtain for large networks. We explore the properties of different metrics in the context of optimal experimental design, like convexity and modularity, to propose and compare several tight lower and upper bounds on the performance of the optimal solution. Moreover, we show how to use these bounds to choose near-optimal solutions. We test the method on two IEEE benchmark test feeders, the 123-bus and the 8500-node feeders, to show the effectiveness of the approach.

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Section: B Measurementsmentioning

confidence: 99%

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

Picallo

Anta

Schutter

2019

IEEE Trans. Power Syst.

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“…1). From (11) and (12), it can be found that the covariance is zero when branch b is not downstream branch j (or vice versa), whereas it is equal to the variance of branch b (or j) if this is downstream branch j (or b). It is worth noting that in the latter case it is Ψ = Γ b (or Ψ = Γ j ).…”

Section: Impact Of Pseudo-measurementsmentioning

confidence: 99%

“…The use of heterogeneous devices with different costs and accuracy characteristics, including last generation Phasor Measurement Units (PMUs), is also investigated in other papers (e.g. [9]- [11]). In [12], the uncertainties in the network parameters are also taken into account in addition to the meters uncertainties to ensure the achievement of the desired accuracy targets.…”

Section: Introduction Distribution System State Estimation (Dsse) mentioning

confidence: 99%

Impact of Current and Power Measurements on Distribution System State Estimation Uncertainty

Pau

Pegoraro

Monti

et al. 2019

IEEE Trans. Instrum. Meas.

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The effective management of future smart grids is strictly related to the accurate knowledge of the network operating conditions via Distribution System State Estimation (DSSE). To achieve this target, the measurement infrastructure of the distribution systems needs a substantial upgrade. However, obvious economic limitations prevent a massive deployment of measurement instruments on the field in short times. As a consequence, ad hoc meter placement techniques have to be applied to define location and type of a minimum set of measurements required to obtain the desired accuracy performance. This paper presents a mathematical analysis showing the impact of current and power measurements on the accuracy of DSSE results. The goal is to provide the analytical tools to identify the best placement for additional current or power measurements when applying an incremental deployment of meters in the distribution system. The analysis has a general validity but has also a clear impact on practical situations when the monitoring systems is upgraded starting from an existing infrastructure. Tests on a 95bus sample grid are presented in order to validate the found mathematical results and to highlight the associated benefits in a meter placement perspective.

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“…In literature, some alternative solutions have been developed with the aim of reducing the total measurement infrastructure costs. For example, in [16], in order to find a good tradeoff between measurement equipment costs and SE accuracy, a hybrid solution is proposed based on the use of different kinds of meters. In [25], the use of LV smart meter measurements is also considered, in order to save the costs of additional MV measurement system installation.…”

Section: Meter Placement Techniques and Measurement Systems For Distrmentioning

confidence: 99%

“…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]. Missing data are integrated with pseudo-measurements, exploiting historical information or an a priori estimate of the relative power magnitude of each load [17][18][19][20].…”

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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Trade-offs in PMU and IED Deployment for Active Distribution State Estimation Using Multi-objective Evolutionary Algorithm

Cited by 59 publications

References 34 publications

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

Comparison of Bounds for Optimal PMU Placement for State Estimation in Distribution Grids

Impact of Current and Power Measurements on Distribution System State Estimation Uncertainty

Incremental Heuristic Approach for Meter Placement in Radial Distribution Systems

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