The primary objective of the conventional optimal phasor measurement unit (PMU) placement problem is the minimization of the number of PMU devices that, when placed in a power system, measures all bus voltages. However, due to advancements made in the field of relay technology, digital relays can now act as PMUs. This has significantly reduced device costs. Moreover, although the goal is to observe all the buses, the devices themselves can only be placed in substations, whose upgrade costs are much higher than those of the devices. Considering these factors, the approach proposed here optimizes the number of substations where traditional bus type PMUs, as well as the more modern dualuse line relay PMUs, can be placed. Called the general optimal substation coverage (GOSC) algorithm, it is also able to incorporate practical challenges such as redundancy in measurement of critical elements of the system, and estimate tap ratios of the transformers present. The results indicate that the proposed GOSC algorithm has significant techno-economic benefits.

Phasor measurement units (PMUs) play an important role in the wide-area monitoring and protection of modern power systems. Historically, their deployment was limited by the prohibitive cost of the device itself. Therefore, the objective of the conventional optimal PMU placement problem was to find minimum number of devices, which when carefully placed throughout the network, maximised observability subject to different constraints. Due to improvements in relay technology, digital relays can now serve as both relays and PMUs. Under such circumstances, the substation installations consume the largest portion of the deployment cost, and not the devices themselves. Thus, for minimising cost of synchrophasor deployment, number of substation installations must be minimised. This study uses binary particle swarm optimisation to minimise number of substations in which installations must be performed for making all voltage levels observable, while being subject to various practical constraints. Standard IEEE systems have been used to explain the technique. Finally, a large-scale network of Dominion Virginia Power is used as the test bed for implementation.

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