PMU Placement for Maximum Observability of Power System under Different Contingencies

Review of Different Methods for Optimal Placement of Phasor Measurement Unit on the Power System Network

“…Integer Linear Programming methods, also recognised as binary integer programming, considering both the system's injection and power measurement as well as PMU error calculation by state estimation. Integer linear programming algorithm is based on the individual vectors derived from the adjacency matrix of the transverse tree [7]. Upon decomposition, the PMUs are positioned ideally in the networks utilising the integer linear programming principle to reduce the deployment rate.…”

Real‐time estimation of ATC using PMU data and ANN

“…The SFA begins with data matrix D having n rows and p columns, where they (rows, columns) represent observations and measurements, respectively. The algorithm then takes either an initial random p-by-q (required number of features) weight matrix W and minimises (36). The method shortens the initial feature set D to a smaller set D r using matrix W obtained by SFA (36)…”

“…To compute objective function (36), which depends on the n-by-p data matrix D and weight matrix W the procedure given in Table 5 has been employed. The schematic representation SFA method has been illustrated in Fig.…”

Wide area monitoring, protection, and control application in islanding detection for grid integrated distributed generation: A review

“…6 In a WAMPAC system, local information is carried to a remote location for state estimations using Phasor measurement units (PMU) which are sensors deployed over a wide area to capture voltage, current, and frequency, rate of change of frequency measurements. All of this measured data is synchronized with universal time coordinated (UTC) Time 25 Phasor Data Concentrators (PDC) processes PMU data streams and monitor power indices. 7 Because the introduction of more DGs into the power system increases its non-linearity, and intense islanding due to data deficiency, PMUs eliminates this by being very accurate and advanced in data synchronization.…”

“…7 Because the introduction of more DGs into the power system increases its non-linearity, and intense islanding due to data deficiency, PMUs eliminates this by being very accurate and advanced in data synchronization. Singh et al 25 proposed a method that used Integer Linear Programming (ILP) for the Placement of PMUs to insure the entire observability of the system while using a minimum PMU number possible to detect Islanding. Castello et al 26 the authors proposed an Active PDC with an adaptive with latency management of the data streams received by all PMUs on its periphery, this was tested in Castello et al 27,28 to establish its applicability in finding faults that may lead to Islanding using different PDC vendor technology.…”