hh.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Mashad Nemati, HassanORCID iD iconorcid.org/0000-0002-5863-0748
Alternative names
Publications (10 of 13) Show all publications
Mashad Nemati, H. (2019). Data analytics for weak spot detection in power distribution grids. (Doctoral dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Data analytics for weak spot detection in power distribution grids
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This research aims to develop data-driven methods that extract information from the available data in distribution grids for detecting weak spots, including the components with degraded reliability and areas with power quality problems. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from available data. In particular, the data is exploited for three purposes: (a) failure pattern discovery, (b) reliability evaluation of power cables, and (c) analyzing and modeling propagation of power quality disturbances (PQDs) in low-voltage grids.

To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose a visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset.

The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data.

To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We discuss that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components.

In low-voltage distribution grids, analyzing PQDs is important as we are moving towards smart grids with the next generation of producers and consumers. Installing Power Quality and Monitoring Systems (PQMS) at all the nodes in the network, for monitoring the impacts of the new consumer/producer, is prohibitively expensive. Instead, we demonstrate that power companies can utilize the available smart meters, which are widely deployed in the low-voltage grids, for monitoring power quality events and identifying areas with power quality problems. In particular, several models for propagation of PQDs, within neighbor customers in different levels of the grid topology, are investigated. The results show that meters data can be used to detect and describe propagation in low-voltage grids.

The developed methods of (a) failure pattern discovery are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) reliability evaluation of power cables and (c) analyzing and modeling propagation of PQDs are applied on data from HEM Nät.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2019. p. 117
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-39067 (URN)978-91-88749-18-5 (ISBN)978-91-88749-19-2 (ISBN)
Public defence
2019-04-24, Haldasalen, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2019-03-19 Created: 2019-03-18 Last updated: 2019-04-03Bibliographically approved
Mashad Nemati, H., Pinheiro Sant'Anna, A., Nowaczyk, S., Jürgensen, J. H. & Hilber, P. (2019). Reliability Evaluation of Power Cables Considering the Restoration Characteristic. International Journal of Electrical Power & Energy Systems, 105, 622-631
Open this publication in new window or tab >>Reliability Evaluation of Power Cables Considering the Restoration Characteristic
Show others...
2019 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 105, p. 622-631Article in journal (Refereed) Published
Abstract [en]

In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions.

Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables.

The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
London: Elsevier, 2019
Keywords
Power cable, historical data, reliability, proportional hazard model, preventive maintenance.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-35470 (URN)10.1016/j.ijepes.2018.08.047 (DOI)000449447200055 ()2-s2.0-85053080255 (Scopus ID)
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2019-03-19Bibliographically approved
Mashad Nemati, H., Laso, A., Manana, M., Pinheiro Sant'Anna, A. & Nowaczyk, S. (2018). Stream Data Cleaning for Dynamic Line Rating Application. Energies, 11(8), Article ID 2007.
Open this publication in new window or tab >>Stream Data Cleaning for Dynamic Line Rating Application
Show others...
2018 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 11, no 8, article id 2007Article in journal (Refereed) Published
Abstract [en]

The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.

Place, publisher, year, edition, pages
Basel: MDPI, 2018
Keywords
smart grids, dynamic line rating, stream data cleaning, data mining
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-37676 (URN)000446604100086 ()2-s2.0-85052824538 (Scopus ID)
Note

Funding: This research was partially funded by Spanish Government under Spanish R+D initiative with reference ENE2013-42720-R and RETOS RTC-2015-3795-3.

Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2019-01-08Bibliographically approved
Mashad Nemati, H. (2017). Data-Driven Methods for Reliability Evaluation of Power Cables in Smart Distribution Grids. (Licentiate dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Data-Driven Methods for Reliability Evaluation of Power Cables in Smart Distribution Grids
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This research aims to develop data-driven methods that automatically exploit historical data in smart distribution grids for reliability evaluation, i.e., analyzing frequency of failures, and modeling components’ lifetime. The results enable power distribution companies to change from reactive maintenance to predictive maintenance by deriving benefits from historical data. In particular, the data is exploited for two purposes: (a) failure pattern discovery, and (b) reliability evaluation of power cables. To analyze failure characteristics it is important to discover which failures share common features, e.g., if there are any types of failures that happen mostly in certain parts of the grid or at certain times. This analysis provides information about correlation between different features and identifying the most vulnerable components. In this case, we applied statistical analysis and association rules to discover failure patterns. Furthermore, we propose an easy-to-understand visualization of the correlations between different factors representing failures by using an approximated Bayesian network. We show that the Bayesian Network constructed based on the interesting rules of two items is a good approximation of the real dataset. The main focus of reliability evaluation is on failure rate estimation and reliability ranking. In case of power cables, the limited amount of recorded events makes it difficult to perform failure rate modeling, i.e., estimating the function that describes changes in the rate of failure depending on age. Therefore, we propose a method for interpreting the results of goodness-of-fit measures with confidence intervals, estimated using synthetic data. To perform reliability ranking of power cables, in addition to the age of cables, we consider other factors. Then, we use the Cox proportional hazard model (PHM) to assess the impact of the factors and calculate the failure rate of each individual cable. In reliability evaluation, it is important to consider the fact that power cables are repairable components. We show that the conclusions about different factors in PHM and cables ranking will be misleading if one considers the cables as non-repairable components. The developed methods of (a) are applied on data from Halmstad Energi och Miljö (HEM Nät), Öresundskraft, Göteborg Energy, and Växjö Energy, four different distribution system operators in Sweden. The developed methods of (b) are applied on data from HEM Nät.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2017. p. 70
Series
Halmstad University Dissertations ; 34
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-35147 (URN)978-91-87045-70-7 (ISBN)978-91-87045-71-4 (ISBN)
Presentation
2017-09-29, Halda, Visionen/house J, Halmstad University, Kristian IV:s väg 3, Halmstad, 10:00 (English)
Opponent
Supervisors
Funder
Knowledge Foundation
Note

Funding: Knowledge Foundation & HEM Nät

Available from: 2017-11-24 Created: 2017-10-04 Last updated: 2018-01-13Bibliographically approved
Mashad Nemati, H., Sant´Anna, A. & Nowaczyk, S. (2016). Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids. In: 2016 IEEE International Energy Conference (ENERGYCON): . Paper presented at 2016 IEEE International Energy Conference (ENERGYCON), 4-8 April, Leuven, Belgium, 4-8 april, 2016.
Open this publication in new window or tab >>Bayesian Network Representation of Meaningful Patterns in Electricity Distribution Grids
2016 (English)In: 2016 IEEE International Energy Conference (ENERGYCON), 2016Conference paper, Published paper (Refereed)
Abstract [en]

The diversity of components in electricity distribution grids makes it impossible, or at least very expensive, to deploy monitoring and fault diagnostics to every individual element. Therefore, power distribution companies are looking for cheap and reliable approaches that can help them to estimate the condition of their assets and to predict the when and where the faults may occur. In this paper we propose a simplified representation of failure patterns within historical faults database, which facilitates visualization of association rules using Bayesian Networks. Our approach is based on exploring the failure history and detecting correlations between different features available in those records. We show that a small subset of the most interesting rules is enough to obtain a good and sufficiently accurate approximation of the original dataset. A Bayesian Network created from those rules can serve as an easy to understand visualization of the most relevant failure patterns. In addition, by varying the threshold values of support and confidence that we consider interesting, we are able to control the tradeoff between accuracy of the model and its complexity in an intuitive way. © 2016 IEEE

Keywords
Smart Grids, Condition Monitoring, Data Mining, Failure Statistics, Association Rules, Bayesian Networks
National Category
Computer Sciences Probability Theory and Statistics Bioinformatics (Computational Biology) Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-31710 (URN)10.1109/ENERGYCON.2016.7513929 (DOI)000390822900059 ()2-s2.0-84982836497 (Scopus ID)978-1-4673-8463-6 (ISBN)
Conference
2016 IEEE International Energy Conference (ENERGYCON), 4-8 April, Leuven, Belgium, 4-8 april, 2016
Available from: 2016-08-04 Created: 2016-08-04 Last updated: 2019-03-19Bibliographically approved
Mashad Nemati, H., Fan, Y. & Alonso-Fernandez, F. (2016). Hand Detection and Gesture Recognition Using Symmetric Patterns. Paper presented at 8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016), Da Nang, Vietnam, 14–16 March, 2016. Studies in Computational Intelligence, 642, 365-375
Open this publication in new window or tab >>Hand Detection and Gesture Recognition Using Symmetric Patterns
2016 (English)In: Studies in Computational Intelligence, ISSN 1860-949X, E-ISSN 1860-9503, Vol. 642, p. 365-375Article in journal (Refereed) Published
Abstract [en]

Hand detection and gesture recognition is one of the challenging issues in human-robot interaction. In this paper we proposed a novel method to detect human hands and recognize gestures from video stream by utilizing a family of symmetric patterns: log-spiral codes. In this case, several log-family spirals mounted on a hand glove were extracted and utilized for positioning the palm and fingers. The proposed method can be applied in real time and even on a low quality camera stream. The experiments are implemented in different conditions to evaluatethe illumination, scale, and rotation invariance of the proposed method. The results show that using the proposed technique we can have a precise and reliable detection and tracking of the hand and fingers with accuracy about 98 %.

Place, publisher, year, edition, pages
Heidelberg: Springer Berlin/Heidelberg, 2016
Keywords
Hand detection, Gesture recognition, Symmetric patterns, Log-spiral codes, Human-robot interaction
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-30576 (URN)10.1007/978-3-319-31277-4_32 (DOI)000390824900032 ()2-s2.0-84966539110 (Scopus ID)
Conference
8th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2016), Da Nang, Vietnam, 14–16 March, 2016
Note

ISBN: 978-3-319-31276-7 (Print) 978-3-319-31277-4 (Online). Source Type: Book series. Book Title: Recent Developments in Intelligent Information and Database Systems. Volume Editors: Dariusz Król, Lech Madeyski & Ngoc Thanh Nguyen.

Available from: 2016-03-23 Created: 2016-03-23 Last updated: 2018-02-14Bibliographically approved
Mashad Nemati, H., Gholami Shahbandi, S. & Åstrand, B. (2016). Human Tracking in Occlusion based on Reappearance Event Estimation. In: Oleg Gusikhin, Dimitri Peaucelle & Kurosh Madani (Ed.), ICINCO 2016: 13th International Conference on Informatics in Control, Automation and Robotics: Proceedings, Volume 2. Paper presented at 13th International Conference on Informatics in Control, Automation and Robotics, Lisbon, Portugal, 29-31 July, 2016 (pp. 505-512). SciTePress, 2
Open this publication in new window or tab >>Human Tracking in Occlusion based on Reappearance Event Estimation
2016 (English)In: ICINCO 2016: 13th International Conference on Informatics in Control, Automation and Robotics: Proceedings, Volume 2 / [ed] Oleg Gusikhin, Dimitri Peaucelle & Kurosh Madani, SciTePress, 2016, Vol. 2, p. 505-512Conference paper, Published paper (Refereed)
Abstract [en]

Relying on the commonsense knowledge that the trajectory of any physical entity in the spatio-temporal domain is continuous, we propose a heuristic data association technique. The technique is used in conjunction with an Extended Kalman Filter (EKF) for human tracking under occlusion. Our method is capable of tracking moving objects, maintain their state hypothesis even in the period of occlusion, and associate the target reappeared from occlusion with the existing hypothesis. The technique relies on the estimation of the reappearance event both in time and location, accompanied with an alert signal that would enable more intelligent behavior (e.g. in path planning). We implemented the proposed method, and evaluated its performance with real-world data. The result validates the expected capabilities, even in case of tracking multiple humans simultaneously.

Place, publisher, year, edition, pages
SciTePress, 2016
Keywords
Detection and Tracking Moving Objects, Extended Kalman Filter, Human Tracking, Occlusion, Intelligent Vehicles, Mobile Robots
National Category
Robotics Signal Processing Computer Vision and Robotics (Autonomous Systems) Medical Image Processing
Identifiers
urn:nbn:se:hh:diva-31709 (URN)10.5220/0006006805050512 (DOI)000392601900061 ()2-s2.0-85013059501 (Scopus ID)978-989-758-198-4 (ISBN)
Conference
13th International Conference on Informatics in Control, Automation and Robotics, Lisbon, Portugal, 29-31 July, 2016
Available from: 2016-08-04 Created: 2016-08-04 Last updated: 2018-01-10Bibliographically approved
Fan, Y., Aramrattana, M., Shahbandi, S. G., Nemati, H. M. & Åstrand, B. (2016). Infrastructure Mapping in Well-Structured Environments Using MAV. Paper presented at 17th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2016, Sheffield, United Kingdom, 26 June-1 July, 2016. Lecture Notes in Computer Science, 9716, 116-126
Open this publication in new window or tab >>Infrastructure Mapping in Well-Structured Environments Using MAV
Show others...
2016 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 9716, p. 116-126Article in journal (Refereed) Published
Abstract [en]

In this paper, we present a design of a surveying system for warehouse environment using low cost quadcopter. The system focus on mapping the infrastructure of surveyed environment. As a unique and essential parts of the warehouse, pillars from storing shelves are chosen as landmark objects for representing the environment. The map are generated based on fusing the outputs of two different methods, point cloud of corner features from Parallel Tracking and Mapping (PTAM) algorithm with estimated pillar position from a multi-stage image analysis method. Localization of the drone relies on PTAM algorithm. The system is implemented in Robot Operating System(ROS) and MATLAB, and has been successfully tested in real-world experiments. The result map after scaling has a metric error less than 20 cm. © Springer International Publishing Switzerland 2016.

Place, publisher, year, edition, pages
Cham, Switzerland: Springer, 2016
Keywords
Robotic mapping, parallel tracking and mapping, MAV
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-31645 (URN)10.1007/978-3-319-40379-3_12 (DOI)000386324700012 ()2-s2.0-84977496781 (Scopus ID)978-3-319-40378-6 (ISBN)978-3-319-40379-3 (ISBN)
Conference
17th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2016, Sheffield, United Kingdom, 26 June-1 July, 2016
Available from: 2016-07-14 Created: 2016-07-14 Last updated: 2018-03-22Bibliographically approved
Mashad Nemati, H., Sant'Anna, A. & Nowaczyk, S. (2015). Reliability Evaluation of Underground Power Cables with Probabilistic Models. In: DMIN'15: The 2015 International Conference on Data Mining. Paper presented at The 11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada, USA, July 27-30, 2015 (pp. 37-43).
Open this publication in new window or tab >>Reliability Evaluation of Underground Power Cables with Probabilistic Models
2015 (English)In: DMIN'15: The 2015 International Conference on Data Mining, 2015, p. 37-43Conference paper, Published paper (Refereed)
Abstract [en]

Underground power cables are one of the fundamental elements in power grids, but also one of the more difficult ones to monitor. Those cables are heavily affected by ionization, as well as thermal and mechanical stresses. At the same time, both pinpointing and repairing faults is very costly and time consuming. This has caused many power distribution companies to search for ways of predicting cable failures based on available historical data.

In this paper, we investigate five different models estimating the probability of failures for in-service underground cables. In particular, we focus on a methodology for evaluating how well different models fit the historical data. In many practical cases, the amount of data available is very limited, and it is difficult to know how much confidence should one have in the goodness-of-fit results.

We use two goodness-of-fit measures, a commonly used one based on mean square error and a new one based on calculating the probability of generating the data from a given model. The corresponding results for a real data set can then be interpreted by comparing against confidence intervals obtained from synthetic data generated according to different models.

Our results show that the goodness-of-fit of several commonly used failure rate models, such as linear, piecewise linear and exponential, are virtually identical. In addition, they do not explain the data as well as a new model we introduce: piecewise constant.

National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-29331 (URN)
Conference
The 11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada, USA, July 27-30, 2015
Funder
Knowledge Foundation
Available from: 2015-08-31 Created: 2015-08-31 Last updated: 2019-03-19Bibliographically approved
Mashad Nemati, H., Sant'Anna, A. & Nowaczyk, S. (2014). Overview of Smart Grid Challenges in Sweden. In: The SAIS Workshop 2014 Proceedings: . Paper presented at 28th annual workshop of the Swedish Artificial Intelligence Society (SAIS), Stockholm, Sweden, May 22-23, 2014 (pp. 155-164). Swedish Artificial Intelligence Society (SAIS)
Open this publication in new window or tab >>Overview of Smart Grid Challenges in Sweden
2014 (English)In: The SAIS Workshop 2014 Proceedings, Swedish Artificial Intelligence Society (SAIS) , 2014, p. 155-164Conference paper, Published paper (Refereed)
Abstract [en]

Smart grids are advanced power grids that use modern hardware and software technologies to provide clean, safe, secure, reliable, ecient and sustainable energy. However, there are many challenges in the eld of smart grids in terms of communication, reliability, interoperability, and big data that should be considered. In this paper we present a brief overview of some of the challenges and solutions in the smart grids, focusing especially on the Swedish point of view. We discuss thirty articles, from 2006 until 2013, with the main interest on datarelated challenges.

Place, publisher, year, edition, pages
Swedish Artificial Intelligence Society (SAIS), 2014
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-26445 (URN)
Conference
28th annual workshop of the Swedish Artificial Intelligence Society (SAIS), Stockholm, Sweden, May 22-23, 2014
Funder
Knowledge Foundation
Available from: 2014-09-13 Created: 2014-09-13 Last updated: 2018-03-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5863-0748

Search in DiVA

Show all publications