Phasor measurement units in the Indian smart grid

Phasor Measurement Units (PMUs) have potential role in power system monitoring, protection and control. PMUs directly measure and provide synchronized measurements of voltage and current phasors at various system locations. This offer numerous possibilities for ascertaining information related to the state and health of a power system. The aim of an optimal PMU placement is to provide minimal PMU installations to ensure complete observability of a power system. Several methods have been proposed in the past and significant work has been carried out till date to ensure observability of transmission networks. Penetration of new technologies at distribution level raises concern about system operation. These concerns can be addressed by synchrophasor applications. Hence, PMUs play an equally important role at distribution level also. Since, distribution systems are large in size as compared to transmission systems, the algorithms reported may take longer computational time for optimization. Hence, an approach towards optimal PMU placement for distribution system is required. This paper presents a simple and effective algorithm of PMU placement for distribution networks that saves considerable amount of computational time. The proposed algorithm is tested on IEEE-13, 34 and 123 Node Test Feeders and results show that computational time is considerably reduced without significantly affecting the number of PMUs.

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“…If j=1, 2, 3…, n Y , then, Y is given as: 3(j -1)) ; for j=1, 2, …(n Y -1) bus prim (n prim -1) ; j=n Y (11) For discussed bus system:…”

Section: Stage-1mentioning

confidence: 99%

“…Since solution vector X 1 can be obtained by solving (8) to (11) only, problem of optimization is converted into a simple mathematical problem. Hence, it reduces computational time.…”

Section: Stage-1mentioning

confidence: 99%

Towards optimal placement of phasor measurement units for smart distribution systems

Jamil

Rihan

Anees

2014

2014 6th IEEE Power India International Conference (PIICON)

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“…WAMS comprise a network of synchronized PMUs [1], [4], which provide data at sampling rates typically equivalent to one cycle of the power systems fundamental frequency (50Hz on the GB system). This data, if efficiently managed and processed, can be used to enhance the reliability, stability and security of power systems.…”

Section: Introductionmentioning

confidence: 99%

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Khan

Ashton

et al. 2015

IEEE Trans. Smart Grid

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Abstract-Phasor Measurement Units (PMUs) are being rapidly deployed in power grids due to their high sampling rates and synchronised measurements. The devices high data reporting rates present major computational challenges, in the requirement to process potentially massive volumes of data, in addition to new issues surrounding data storage. Fast algorithms capable of processing massive volumes of data are now required in the field of power systems. This paper presents PDFA, a parallel detrended fluctuation analysis approach for fast event detection on massive volumes of PMU data, taking advantage of a cluster computing platform. The PDFA algorithm is evaluated using data from installed PMUs on the transmission system of Great Britain, from the aspects of speedup, scalability and accuracy. The speedup of the PDFA in computation is initially analysed through Amdahl's Law, a revision to the law is then proposed, suggesting enhancements to its capability to analyse the performance gain in computation when parallelizing data intensive applications in a cluster computing environment.

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“…The length of a gene head is set to 20, which covers all the possible combinations of the functions. The length of a gene tail can be computed using Length (Gene Tail ) = Length (Gene Head ) × ( − 1) + 1, (2) where is the largest number of input arguments of a function. In the following section, we present how the GEP method evolves when mining a correlation from the Hadoop configuration parameters in order to construct an objective function.…”

Section: Gep Designmentioning

confidence: 99%

“…WAMS provide a far more immediate and accurate view of the power grid than traditional Supervisory Control and Data Acquisition (SCADA) monitoring [1,2], collecting real-time synchronized measurements at a typical rate of 1 sample per cycle of the system frequency. This brings in new challenges in terms of data management that need to be addressed to fully realize the benefits of the technology.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Hadoop Performance for Big Data Analytics in Smart Grid

Khan

Huang

et al. 2017

Mathematical Problems in Engineering

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The rapid deployment of Phasor Measurement Units (PMUs) in power systems globally is leading to Big Data challenges. New high performance computing techniques are now required to process an ever increasing volume of data from PMUs. To that extent the Hadoop framework, an open source implementation of the MapReduce computing model, is gaining momentum for Big Data analytics in smart grid applications. However, Hadoop has over 190 configuration parameters, which can have a significant impact on the performance of the Hadoop framework. This paper presents an Enhanced Parallel Detrended Fluctuation Analysis (EPDFA) algorithm for scalable analytics on massive volumes of PMU data. The novel EPDFA algorithm builds on an enhanced Hadoop platform whose configuration parameters are optimized by Gene Expression Programming. Experimental results show that the EPDFA is 29 times faster than the sequential DFA in processing PMU data and 1.87 times faster than a parallel DFA, which utilizes the default Hadoop configuration settings.

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Phasor measurement units in the Indian smart grid

Cited by 19 publications

References 22 publications

Towards optimal placement of phasor measurement units for smart distribution systems

Towards optimal placement of phasor measurement units for smart distribution systems

Parallel Detrended Fluctuation Analysis for Fast Event Detection on Massive PMU Data

Optimizing Hadoop Performance for Big Data Analytics in Smart Grid

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