Periodic Reporting for period 1 - ARMOUR (smARt Monitoring Of distribUtion netwoRks for robust power quality)
Reporting period: 2020-10-15 to 2022-10-14
The project ARMOUR aimed to develop methodologies for root-cause analysis of power quality (PQ) issues by combining time series correlations and statistical data analysis and information derived from a knowledge highlighting the interlinkages between different PQ issues and external contributing factors. The purpose was to assist the distribution system operators (DSOs) in mitigation/ reduction of PQ emissions through automated recommendations based on the root-cause analysis.
Solutions for power quality (PQ) issue source-tracing and alleviation lead to lesser outages and ditribution losses towards customer end. This implies customer satisfaction and economic distribution network. Energy conserved is energy produced therefore the project ultimately contributes to both green and digital intiatives of the European society by utilizing digital tools to build tools for a more sustainable ecosystem.
The specific goal of the project was to provide maximum possible insights on PQ situation with minimal monitoring, without complete knowledge of network topology while respecting data privacy. The main objectives are: leveraging machine learning for condition monitoring and tracing power quality events, and to develop a smart grid technology which assists the distribution system operators in prevention and diagnosis of power quality events.
Solutions for power quality (PQ) issue source-tracing and alleviation lead to lesser outages and ditribution losses towards customer end. This implies customer satisfaction and economic distribution network. Energy conserved is energy produced therefore the project ultimately contributes to both green and digital intiatives of the European society by utilizing digital tools to build tools for a more sustainable ecosystem.
The specific goal of the project was to provide maximum possible insights on PQ situation with minimal monitoring, without complete knowledge of network topology while respecting data privacy. The main objectives are: leveraging machine learning for condition monitoring and tracing power quality events, and to develop a smart grid technology which assists the distribution system operators in prevention and diagnosis of power quality events.
The project was split into distinct activity streams. Monitor and acquire real-time PQ data from critical nodes identified by customer DSOs. This mostly included transformer and the subsequent node(s) in an area of PQ concern in the low-voltage distribution system. An analysis based on EN50160 also provided the weekly compliance reports for different PQ variations and events at the monitored nodes. The measurements and computations can be obtained simply and cost-effectively with GridEye measurement devices.
Next, the data available from GridEye can be used for a detailed analysis of the PQ violations and a subsequent root-cause analysis. A critical concern raised by DSOs, is their inability to trace the source of PQ issues in the distribution network, which in-turn leads to both energy and economic losses over time. Depsys simplifies and facilitates the understanding of PQ situation for DSOs, by providing weekly compliance reports based on EN50160. The location, number and duration of PQ issues is easily computed and reported for DSOs to help them in strengthening the weak sections of the grid.
Lastly, a real-world insight into the practical concerns and needs of grid operators and planners surrounding PQ root-cause analysis. To achieve this, a detailed analysis of PQ issues in the grid was done for various European DSOs and continuous feedback was taken to enrich the knowledge base and recommendations for typical causes behind PQ issues. Depsys was able to help several customers in reducing the PQ emissions in their grid and making it more reliable.
Manuscripts under consideration for publication are as follows.
Monitoring based localization of unbalances and root cause analysis in low voltage distribution systems. Under revision in IEEE Systems Journal
The role of hosting capacity study in power system advancements: A review. Provisionally accepted as a chapter in Soft Computing Applications for Advancements in Power Systems, River Publishers.
Next, the data available from GridEye can be used for a detailed analysis of the PQ violations and a subsequent root-cause analysis. A critical concern raised by DSOs, is their inability to trace the source of PQ issues in the distribution network, which in-turn leads to both energy and economic losses over time. Depsys simplifies and facilitates the understanding of PQ situation for DSOs, by providing weekly compliance reports based on EN50160. The location, number and duration of PQ issues is easily computed and reported for DSOs to help them in strengthening the weak sections of the grid.
Lastly, a real-world insight into the practical concerns and needs of grid operators and planners surrounding PQ root-cause analysis. To achieve this, a detailed analysis of PQ issues in the grid was done for various European DSOs and continuous feedback was taken to enrich the knowledge base and recommendations for typical causes behind PQ issues. Depsys was able to help several customers in reducing the PQ emissions in their grid and making it more reliable.
Manuscripts under consideration for publication are as follows.
Monitoring based localization of unbalances and root cause analysis in low voltage distribution systems. Under revision in IEEE Systems Journal
The role of hosting capacity study in power system advancements: A review. Provisionally accepted as a chapter in Soft Computing Applications for Advancements in Power Systems, River Publishers.
PQ monitoring at key locations:
The GridEye measurements were utilized to accurately assess PQ and generate emission/ compliance reports across the monitored network – all without a costly and complicated equipment and disruptive installation.
PQ analysis and reporting:
10-minute data stream was used for analysis of supply voltage variations, unbalance, harmonics, and total harmonic distortion. Flicker analysis is possible using 2-hour data and power frequency variations can be analyzed using 10-second data. The availability of weekly data at different time granularities facilitated the understanding of PQ emissions from top to bottom level, i.e. analyzing PQ indices at grid, node, week, and day level. The analysis also helps in feeder balance study, detecting anomaly and highlighting significant correlations between different PQ variations and PQ events during a time frame of interest. A summary of available margins at different nodes help in alerting the DSOs of critical situation, and the correlations based on a knowledge base of root-cause, consequences, and mitigation strategies help the DSOs with availability of list of possible actions which can be taken to strengthen the grid against PQ issues.
Recommendations and DSO feedback:
GridEye based analysis and the methods developed in ARMOUR were used to provide insights and solutions to DSOs to improve the PQ and system reliability. The customer DSOs were helped in detecting the source and reducing problems related to supply voltage
unbalance and harmonics. The feedback from the DSOs was used to strengthen the knowledge base of root-cause and creating an automated recommendations list. Solutions for power quality (PQ) issue source-tracing and alleviation lead to lesser outages and ditribution losses towards customer end. This implies customer satisfaction and economic distribution network. Energy conserved is energy produced therefore the project ultimately contributes to both green and digital intiatives of the European society by utilizing digital tools to build tools for a more sustainable ecosystem.
The GridEye measurements were utilized to accurately assess PQ and generate emission/ compliance reports across the monitored network – all without a costly and complicated equipment and disruptive installation.
PQ analysis and reporting:
10-minute data stream was used for analysis of supply voltage variations, unbalance, harmonics, and total harmonic distortion. Flicker analysis is possible using 2-hour data and power frequency variations can be analyzed using 10-second data. The availability of weekly data at different time granularities facilitated the understanding of PQ emissions from top to bottom level, i.e. analyzing PQ indices at grid, node, week, and day level. The analysis also helps in feeder balance study, detecting anomaly and highlighting significant correlations between different PQ variations and PQ events during a time frame of interest. A summary of available margins at different nodes help in alerting the DSOs of critical situation, and the correlations based on a knowledge base of root-cause, consequences, and mitigation strategies help the DSOs with availability of list of possible actions which can be taken to strengthen the grid against PQ issues.
Recommendations and DSO feedback:
GridEye based analysis and the methods developed in ARMOUR were used to provide insights and solutions to DSOs to improve the PQ and system reliability. The customer DSOs were helped in detecting the source and reducing problems related to supply voltage
unbalance and harmonics. The feedback from the DSOs was used to strengthen the knowledge base of root-cause and creating an automated recommendations list. Solutions for power quality (PQ) issue source-tracing and alleviation lead to lesser outages and ditribution losses towards customer end. This implies customer satisfaction and economic distribution network. Energy conserved is energy produced therefore the project ultimately contributes to both green and digital intiatives of the European society by utilizing digital tools to build tools for a more sustainable ecosystem.