Tri-Objective Optimal PMU Placement Including Accurate State Estimation: The Case of Distribution Systems

Abstract

This paper presents a tri-objective Optimal Phasor Measurement Units (PMUs) Placement (OPP) strategy that is focused on the minimization of i) the total number of PMU channels, ii) the maximum state estimation uncertainty based only on high-rate PMU data and iii) the sensitivity of state estimation to line parameter tolerances. The proposed formulation keeps into account system observability with and without contingencies due to single-line and PMU faults. Also, it includes the effect of possible zero-injection buses and a-priori constraints on both the number of PMU channels and the type of PMU measurements performed at each bus. Due to the nonlinear combinatorial nature of the proposed OPP problem, this is solved through a custom implementation of the nondominated sorting genetic algorithm II (NSGA-II). The analysis of the proposed OPP strategy is focused on distribution systems. The placement results obtained using four test systems of a different size show that increasing the number of buses instrumented with PMUs and/or the number of PMU channels beyond given thresholds just leads to larger costs with negligible further reductions in both state estimation uncertainty and sensitivity to line parameters tolerances. Moreover, if PMUs with just two three-phase channels are used, we can avoid instrumenting between 30% and 40% of buses with just a minor impact on state estimation performance even in the case of contingencies. This percentage can be slightly increased if multi-channel PMUs are used. However, this choice generally is not profitable.

Developing an Optimal Framework for PMU Placement Based on Active Distribution System State Estimation Considering Cost-Worth Analysis

“…DSSE has been widely studied considering the accuracy of the state estimation process (SEP), measurement placement, network observability, and investment cost issues [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16]. In these works, traditional measurements such as substation measurement, line flow measurements, pseudomeasurements based on historical network data, and modern measurements such as phasor measurement units (PMUs) as well as smart metering measurements have been used for an effective state estimation.…”

Simplified and Low-Cost Characterization of Medium-Voltage Low-Power Voltage Transformers in the Power Quality Frequency Range

“…The result is the availability of distributed measurement systems (DMSs) capable of assessing the network conditions. For example, PMUs are used, thanks to their synchrophasor measurements, to have an accurate state estimation or voltage control of the network [ 2 , 3 , 4 , 5 ]. DMSs are used to run algorithms for the fault location or to assess the health status of the electrical assets (see [ 6 , 7 ] and [ 8 , 9 , 10 ], respectively).…”

Allocation of Optimal PMUs for Power System Observability Using PROMETHEE Approach

“…Meenakshi Devi and Geethanjali [34] obtained optimal allocation of PMUs by combining the genetic algorithm approach with the minimum spanning tree method. In [35], the authors applied non-dominated sorting genetic algorithm II (NSGA II) for determining optimal locations of PMUs. A realistic method that implements a modified whale optimization algorithm [36] has been suggested with the dual objective of minimizing the required number of PMUs and enhancing the power system reliability.…”