Power distribution system equipment failure identification using machine learning algorithms

2017 ◽  
Author(s):  
Milad Doostan ◽  
Badrul H. Chowdhury
Keyword(s):  
Machine Learning ◽  
Power Distribution ◽  
Distribution System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Equipment Failure ◽  
Power Distribution System ◽  
Failure Identification

Fault Identification in a Subsea ESP Power Distribution System Using Electrical Waveform Monitoring

2021 ◽  
Author(s):  
Larry Obst ◽  
Andrew Merlino ◽  
Alex Parlos ◽  
Dario Rubio
Keyword(s):  
Monitoring System ◽  
Power Distribution ◽  
Distribution System ◽  
High Frequency ◽  
Machine Learning Algorithms ◽  
In Situ Monitoring ◽  
Waveform Analysis ◽  
Normal Operation ◽  
Waveform Data ◽  
Power Distribution System

Abstract This paper describes the technology and processes used to identify in a timely matter the source of an Instantaneous Over Current (IOC) trip during an ESP re-start at Shell Perdido SPAR. Monitoring health condition of subsea ESPs is challenging. ESPs operate in harsh and remote environments which makes it difficult to implement and maintain any in-situ monitoring system. Shell operates five subsea ESPs and implemented a topside conditioning monitoring system using electrical waveform analysis. The Perdido SPAR had a scheduled maintenance shutdown in April 2019. While ramping the facility down on April 19, 2019 the variable frequency drive (VFD) for ESP-E tripped on a cell overvoltage fault. The cell was changed, but the VFD continued to trip on instantaneous overcurrent. During ramp up beginning April 29, 2019 most equipment came back online smoothly, but the VFD of the particular ESP labeled ESP-E continued to experience the problem that was causing overcurrent trips, preventing restart. Initial investigations could not pinpoint the source of the issue. On May 1, 2019 Shell sought to investigate this issue using high-frequency electrical waveform data recorded topside as an attempt to better pinpoint the source of this trip. Analysis of electrical waveform before, during and after the IOC trip found an intermittent shorting/arcing at the VFD and ruled out any issues with the 7,000-foot-long umbilical cable or ESP motor. Upon further inspection, a VFD technician was able to visually identify the source of the problem. Relying in part on electrical waveform findings, VFD technician found failed outer jackets in the MV shielded cables at the output filter section creating a ground path from the VFD output bus via the cable shield. The cables were replaced, and the problem was alleviated allowing the system to return to normal operation. Shell credits quick and accurate analysis of electrical waveform with accelerating troubleshooting activities on the VFD, saving approximately 1-2 days of troubleshooting time and associated downtime savings, that translate to approximately 50,000 BOE deferment reduction. Analysis of high-frequency electrical waveform using physics-based and machine learning algorithms enables one to extract long-term changes in ESP health, while filtering out the shorter-term changes caused by operating condition variations. This novel approach to analysis provides operators with a reliable source of information for troubleshooting and diagnosing failure events to reduce work-over costs and limit production losses.

scholarly journals An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3242 ◽  
Author(s):  
Muhammad Salman Saeed ◽  
Mohd Wazir Mustafa ◽  
Usman Ullah Sheikh ◽  
Touqeer Ahmed Jumani ◽  
Ilyas Khan ◽  
...  
Keyword(s):  
Decision Tree ◽  
Power Distribution ◽  
Distribution System ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Chi Square ◽  
Power Distribution System ◽  
Energy Theft ◽  
C5.0 Decision Tree

Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers’ consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson’s chi-square feature selection algorithm is adopted to select the most relevant features among the extracted ones. Finally, the Boosted C5.0 Decision Tree (DT) algorithm is used to classify the honest and the fraudster consumers based on the outcomes of the selected features. To validate the superiority of the proposed NTL detection approach, its performance is matched with that of few state-of-the-art machine learning algorithms (one of most exciting recent technologies in Artificial Intelligence), like Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Extreme Gradient Bossting (XGBoost). The proposed NTL detection method provides an accuracy of 94.6%, Sensitivity of 78.1%, Specificity of 98.2%, F1 score 84.9% and Precision of 93.2% which are significantly higher than that of the same for the above-mentioned algorithms.

scholarly journals Topology identification in distribution system via machine learning algorithms

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252436
Author(s):  
Peyman Razmi ◽  
Mahdi Ghaemi Asl ◽  
Giorgio Canarella ◽  
Afsaneh Sadat Emami
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Distribution Systems ◽  
Learning Algorithms ◽  
Distribution Networks ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Voltage Profile ◽  
Topology Identification ◽  
Switching Devices

This paper contributes to the literature on topology identification (TI) in distribution networks and, in particular, on change detection in switching devices’ status. The lack of measurements in distribution networks compared to transmission networks is a notable challenge. In this paper, we propose an approach to topology identification (TI) of distribution systems based on supervised machine learning (SML) algorithms. This methodology is capable of analyzing the feeder’s voltage profile without requiring the utilization of sensors or any other extraneous measurement device. We show that machine learning algorithms can track the voltage profile’s behavior in each feeder, detect the status of switching devices, identify the distribution system’s typologies, reveal the kind of loads connected or disconnected in the system, and estimate their values. Results are demonstrated under the implementation of the ANSI case study.

scholarly journals Microgrid Infrastructure Compendium Analysis with a Model Creation Tool and Guideline Based on Machine Learning Techniques

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4509
Author(s):  
Miguel Carpintero-Rentería ◽  
David Santos-Martín ◽  
Mónica Chinchilla ◽  
David Rebollal
Keyword(s):  
Machine Learning ◽  
Electric Power ◽  
Distributed Generation ◽  
Power Distribution ◽  
Distribution System ◽  
Machine Learning Techniques ◽  
Power Distribution System ◽  
Electric Power Distribution ◽  
Learning Techniques ◽  
Machine Learning Tool

A microgrid (MG) is an electric power distribution system that may provide a suitable ecosystem for distributed generation. Detailed information about the infrastructure layer in MG projects is available, so this study aimed to propose a compendium and a model creation guideline for MGs. The aggregated information based on 1618 MGs was summarized into different tables and analyzed based on various parameters. Two MG infrastructure model creation tools were developed. First, a simple guideline was created based on the information in the tables, and then a machine learning tool based on decision trees was proposed that generates more accurate MG models using two main inputs: latitude and the segment in which they operate.

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

2022 ◽  
Author(s):  
Danalakshmi D ◽  
Łukasz Wróblewski ◽  
Sheela A ◽  
A. Hariharasudan ◽  
Mariusz Urbański
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Smart Grid ◽  
Power Management ◽  
Power Distribution ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Results ◽  
Time Duration ◽  
Renewable Power

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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