Data compression techniques for Phasor Measurement Unit (PMU) applications in smart transmission grid

AbstractThe advances in Wide Area Measurement Systems (WAMS) and deployment of a huge number of phasor measurement units (PMUs) in the grid are generating big data volume. This data can be used for a variety of applications related to grid monitoring, management, operation, protection, and control. With the increase in this data size, the respective storage capacity needs to be enhanced. Also, communication infrastructure readiness remains bottleneck to transfer this big data. One of the probable solutions could be transmitting compressed data. This paper presents techniques for data compression in the smart transmission system using singular values decomposition (SVD) and the eigenvalues decomposition (EVD). The SVD and EVD based principal component analysis (PCA) techniques are applied to the real-time PMU data collected from extra-high voltage (EHV) substations of transmission utility in the western regional grid of India. Adequacy of data is checked by Kaiser-Meyer-Olkin (KMO) test in order to have the satisfactory performance of these techniques towards achieving the objective of efficient data compression. Results are found satisfactory gives compression more than 80% using real time data.

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DC Microgrid Utilizing Artificial Intelligence and Phasor Measurement Unit Assisted Inverter

Energies ◽

10.3390/en14196086 ◽

2021 ◽

Vol 14 (19) ◽

pp. 6086

Author(s):

Raziq Yaqub ◽

Mohamed Ali ◽

Hassan Ali

Keyword(s):

Artificial Intelligence ◽

Real Time ◽

Distribution System ◽

Reference Signal ◽

Phasor Measurement Unit ◽

Measurement Unit ◽

Time Data ◽

Dc Microgrid ◽

Utility Company ◽

Phasor Measurement

Community microgrids are set to change the landscape of future energy markets. The technology is being deployed in many cities around the globe. However, a wide-scale deployment faces three major issues: initial synchronization of microgrids with the utility grids, slip management during its operation, and mitigation of distortions produced by the inverter. This paper proposes a Phasor Measurement Unit (PMU) Assisted Inverter (PAI) that addresses these three issues in a single solution. The proposed PAI continually receives real-time data from a Phasor Measurement Unit installed in the distribution system of a utility company and keeps constructing a real-time reference signal for the inverter. To validate the concept, a unique intelligent DC microgrid architecture that employs the proposed Phasor Measurement Unit (PMU) Assisted Inverter (PAI) is also presented, alongside the cloud-based Artificial Intelligence (AI), which harnesses energy from community shared resources, such as batteries and the community’s rooftop solar resources. The results show that the proposed system produces quality output and is 98.5% efficient.

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Phasor Measurement Unit Assisted Inverter—A Novel Approach for DC Microgrids Performance Enhancement

Electricity ◽

10.3390/electricity2030020 ◽

2021 ◽

Vol 2 (3) ◽

pp. 330-341

Author(s):

Raziq Yaqub

Keyword(s):

Real Time ◽

Distribution System ◽

Performance Enhancement ◽

Reference Signal ◽

Vital Role ◽

Phasor Measurement Unit ◽

Measurement Unit ◽

Time Data ◽

Dc Microgrids ◽

Phasor Measurement

DC microgrids are set to change the landscape of future energy markets. However, a wide-scale deployment faces three major issues: initial synchronization of microgrid with the utility grid, slip management during its operation, and mitigation of distortions produced by the inverter. This paper proposes a Phasor Measurement Unit (PMU) Assisted Inverter (PAI) that addresses these three issues in a single solution. The proposed PAI continually receives real-time data from a Phasor Measurement Unit installed in the distribution system of a utility company and keeps constructing a real-time reference signal for the inverter. A well-constructed, real-time reference signal plays a vital role in addressing the above issues. The results show that the proposed PAI is 97.95% efficient.

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Communication infrastructure for situational awareness enhancement in WAMS with optimal PMU placement

Protection and Control of Modern Power Systems ◽

10.1186/s41601-021-00189-9 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Bhargav Appasani ◽

Amitkumar Vidyakant Jha ◽

Sunil Kumar Mishra ◽

Abu Nasar Ghazali

Keyword(s):

Real Time ◽

Situational Awareness ◽

Area Measurement ◽

Measurement Unit ◽

Monitoring And Control ◽

Communication Infrastructure ◽

Wide Area Measurement System ◽

Digital Elevation ◽

Elevation Model ◽

And Control

AbstractReal time monitoring and control of a modern power system has achieved significant development since the incorporation of the phasor measurement unit (PMU). Due to the time-synchronized capabilities, PMU has increased the situational awareness (SA) in a wide area measurement system (WAMS). Operator SA depends on the data pertaining to the real-time health of the grid. This is measured by PMUs and is accessible for data analytics at the data monitoring station referred to as the phasor data concentrator (PDC). Availability of the communication system and communication delay are two of the decisive factors governing the operator SA. This paper presents a pragmatic metric to assess the operator SA and ensure optimal locations for the placement of PMUs, PDC, and the underlying communication infrastructure to increase the efficacy of operator SA. The uses of digital elevation model (DEM) data of the surface topography to determine the optimal locations for the placement of the PMU, and the microwave technology for communicating synchrophasor data is another important contribution carried out in this paper. The practical power grid system of Bihar in India is considered as a case study, and extensive simulation results and analysis are presented for validating the proposed methodology.

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Big data actionable intelligence architecture

Journal Of Big Data ◽

10.1186/s40537-020-00378-7 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Tian J. Ma ◽

Rudy J. Garcia ◽

Forest Danford ◽

Laura Patrizi ◽

Jennifer Galasso ◽

...

Keyword(s):

Big Data ◽

Real Time ◽

Digital Media ◽

Decision Makers ◽

Time Data ◽

Human In The Loop ◽

Course Of Action ◽

Insufficient Time ◽

Data Source ◽

Different Sources

AbstractThe amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

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The real-time big data processing method based on LSTM or GRU for the smart job shop production process

Journal of Algorithms & Computational Technology ◽

10.1177/1748302620962390 ◽

2020 ◽

Vol 14 ◽

pp. 174830262096239 ◽

Cited By ~ 1

Author(s):

Chuang Wang ◽

Wenbo Du ◽

Zhixiang Zhu ◽

Zhifeng Yue

Keyword(s):

Big Data ◽

Data Processing ◽

Production Process ◽

Real Time ◽

Job Shop ◽

Production Data ◽

Time Data ◽

Big Data Processing ◽

The Real ◽

Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

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