Improved BPSO for optimal PMU placement

Optimal phasor measurement unit (PMU) placement involves the process of minimizing the number of PMU needed while ensuring entire power system network completely observable. This paper presents the improved binary particle swarm (IBPSO) method that converges faster and also manage to maximize the measurement redundancy compared to the existing BPSO method. This method is applied to IEEE-30 bus system for the case of considering zero-injection bus and its effectiveness is verified by the simulation results done by using MATLAB software

Improving frequency and ROCOF accuracy during faults, for P class phasor measurement units

Abstract—Many aspects of Phasor Measurement Unit (PMU) performance are tested using the existing (and evolving) IEEE C37.118 standard. However, at present the reaction of PMUs to power network faults is not assessed under C37.118. Nevertheless, the behaviour of PMUs under such conditions may be important when the entire closed loop of power system measurement, control and response is considered. This paper presents ways in which P class PMU algorithms may be augmented with software which reduces peak frequency excursions during unbalanced faults by factors of typically between 2.5 and 6 with no additional effect on response time, delay or latency. Peak ROCOF excursions are also reduced. In addition, extra filtering which still allows P class response requirements to be met can further reduce excursions, in particular ROCOF. Further improvement of triggering by using midpoint taps of the P class filter, and adaptive filtering, allows peak excursions to be reduced by total factors of between 8 and 40 (or up to 180 for ROCOF), compared to the C37.118 reference device. Steady-state frequency and ROCOF errors during sustained faults or unbalanced operation, particularly under unbalanced conditions, can be reduced by factors of hundreds or thousands compared to the C37.118 reference device

Learning from power system data stream: phasor-detective approach

Assuming access to synchronized stream of Phasor Measurement Unit (PMU) data over a significant portion of a power system interconnect, say controlled by an Independent System Operator (ISO), what can you extract about past, current and future state of the system? We have focused on answering this practical questions pragmatically - empowered with nothing but standard tools of data analysis, such as PCA, filtering and cross-correlation analysis. Quite surprisingly we have found that even during the quiet "no significant events" period this standard set of statistical tools allows the "phasor-detective" to extract from the data important hidden anomalies, such as problematic control loops at loads and wind farms, and mildly malfunctioning assets, such as transformers and generators. We also discuss and sketch future challenges a mature phasor-detective can possibly tackle by adding machine learning and physics modeling sophistication to the basic approach