Abstract
Present-day power systems are mostly equipped with conventional meters and intended for the installation of highly accurate phasor measurement units (PMUs) to ensure better protection, monitoring and control of the network. PMU is a deliberate choice due to its unique capacity in providing accurate phasor readings of bus voltages and currents. However, due to the high expense and a requirement for communication facilities, the installation of a limited number of PMUs in a network is common practice. This paper presents an optimal approach to selecting the locations of PMUs to be installed with the objective of ensuring maximum accuracy of the state estimation (SE). The optimization technique ensures that the critical locations of the system will be covered by PMU meters which lower the negative impact of bad data on state-estimation performance. One of the well-known intelligent optimization techniques, the genetic algorithm (GA), is used to search for the optimal set of PMUs. The proposed technique is compared with a heuristic approach of PMU placement. The weighted least square (WLS), with a modified Jacobian to deal with the phasor quantities, is used to compute the estimation accuracy. IEEE 30-bus and 118-bus systems are used to demonstrate the suggested technique.
Highlights
The classical state estimation of a power system is based upon measurements collected from a supervisory control and data acquisition (SCADA) system
Covering the critical location in phasor measurement units (PMUs) optimization will ensure that the bad-data presence, even around the critical location, will not hamper the estimation performance of the system
That is why a PMU is placed in bus 12 to cover the critical zone
Summary
The classical state estimation of a power system is based upon measurements collected from a supervisory control and data acquisition (SCADA) system. The review papers present all the relevant work in the field of PMU optimization along with further information on objective functions, constraints, and optimization tools tried by researchers. Most of these papers, exclude conventional measurements from the formulation and only. Paper [21] proposes a heuristic PMU placement technique to enhance the state-estimation precision which is assessed by the performance indicator of average mean average percentage error (MAPEave ). This paper presents an optimization strategy of selecting any limited number of PMU locations in the power system.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
