Phasor Measurement Units In Power Systems Research Articles

Co-optimal PMU and communication system placement using hybrid wireless sensors

Abstract In smart distribution networks, the problem of measuring and estimating the state of the system is vital. Communication infrastructure, as an inseparable part of the wide area measurement system (WAMS), has to be optimally designed and placed to collect information from phasor measurement units (PMUs) and deliver them to control centers. In this paper, a novel hybrid wireless sensor network (WSN) is proposed for connecting of PMUs throughout the system to enable a convenient and low cost communication media. Having found the optimal placement of PMUs in power system to reach a full observability, the problem of observability in communication system will be handled to provide proper transmission of measured data to the control centers. In the proposed co-optimal PMU-sensor placement problem, the main objective is to minimize the total cost of PMU placement in power system and related communication system considering full observability and reliability constraints. For increasing the reliability of communication system, a hybrid WSN including plug-in powered sensor nodes (PPSNs) and energy harvesting sensor nodes (EHSNs) is utilized. A binary-coded genetic algorithm (BCGA) is used for solving the proposed mixed-objective optimization problem subject to different technical operating constraints. The proposed method in examined on a sample 11-bus test system and IEEE 37-bus test feeder systems. The results show the applicability and effectiveness of the proposed algorithm compared to the conventional methods in this subject area.

A new measurement algorithm for PMU in power system based on all-phase Fourier transform

One new vector estimation approach for phasor measurement units (PMUs) within power system is put forward, which is built upon the basis of apFFT, namely all-phase fast Fourier transform. On account of the remarkable accomplishment of apFFT when subduing property of “phase invariant” and spectral leakage, the estimation approach is capable of achieving quick evaluation of PMU phase angles, and subsequently, the spectrum correction of time-shift phase discrepancy is used so as to evaluate the amplitude and frequency parameters. The calculation flow of the algorithm is given, and the hardware design of the new PMU measuring system is completed by using ARM9 microprocessor. Simulation result indicates that the method in this paper is roughly the same to an estimator without bias for noise-free conditions, and this calculation precision is also better than the existing approach.

Recent Developments in Optimal Placement of Phasor Measurement Units Considering in Incomplete Observability

Independent System Operator (ISO) monitors the SCADA system to continuously check the health of power system. SCADA system refreshing rate is several seconds which may cause delay or even miss important information and the ISO may not be able to take appropriate action when most needed. An emerging technology is the Phasor Measurement Unit (PMU) which takes the help of the Global Positioning System (GPS) and timestamps the whole power system network with much faster refreshing rates. In this paper difference of Phasor Measurement Unit (PMU) and SCADA is represented. Rules and conditions to optimally place the PMUs in power system are discussed. A review on methods used for placement of the PMU in the power system based on incomplete observability is given. Also the spanning tree search based optimal placement method is also discussed.

Optimal Configuration of PMU in Power System

In order to use the least number of phasor measurement unit (PMU) to guarantee the power system completely observable, an optimal PMU configuration method in power system was put forward. The pre-configuration of PMU was done considering the actual situation of power grid. The genetic algorithm (GA) was used for PMU configuration. Modify the formulae of crossover probability and mutation probability in traditional genetic algorithm to overcome the evolutionary stagnation when the maximum fitness value and the average fitness value in group were equal. The improved adaptive genetic algorithm (IAGA) was obtained. In order to eliminate the premature convergence of GA resulted from the chance and randomness of the crossover operation and mutation operation, the preventing premature operation was introduced. This method combined the IAGA and the preventing premature operation. It has good global astringency, and it can ensure the network complete observability with the minimum number of PMU.