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N.K. Veera Kumar
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In Real Time
Situational Awareness for Hybrid AC–DC Grids
Journal article
(2026)
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Nidarshan Veerakumar, José de Jesús Chávez Muro, Arjen Jongepier, Maarten van Riet, Marjan Popov
As the energy sector transitions toward increased renewable integration and bidirectional grid operation, the complexity and frequency of disturbance events rise, necessitating advanced situational awareness. Robust expert systems are needed that leverage a multilayered wide area monitoring, protection, and control (WAMPAC) architecture, integrating device-level detection, data aggregation, and an adaptive event classification and validation framework facilitated by dynamic incremental learning (DIL). These expert systems address challenges such as concept drift, catastrophic forgetting, and the need for human-in-the-loop oversight, enabling rapid and accurate identification of both known and novel disturbance events that can be expected in the future
...
As the energy sector transitions toward increased renewable integration and bidirectional grid operation, the complexity and frequency of disturbance events rise, necessitating advanced situational awareness. Robust expert systems are needed that leverage a multilayered wide area monitoring, protection, and control (WAMPAC) architecture, integrating device-level detection, data aggregation, and an adaptive event classification and validation framework facilitated by dynamic incremental learning (DIL). These expert systems address challenges such as concept drift, catastrophic forgetting, and the need for human-in-the-loop oversight, enabling rapid and accurate identification of both known and novel disturbance events that can be expected in the future
The urgent need to decrease global carbon emissions to meet the Paris Climate Agreement calls for sustainable methods to manage growing electrical demands. This energy transition requires the decommissioning of large fuel-based power plants and simultaneously replacing them with power-electronics interfaced intermittent renewable energy sources and loads. These developments pose high stress on our ageing grid infrastructure, leading to an increased level of unanticipated electrical disturbances that, if left unchecked, might lead to total grid collapse. The thesis presents an expert system that fortifies the ever-evolving grid with advanced event identification and learning architecture engineered to protect against contemporary and evolving grid disturbances. The chosen design perspectives are intended to assure trust in academic algorithms and bridge the expanding gap between the academia-industry. In this context, the dissertation has three main contributions namely:
♦ A real-time PMU-based distribution state estimation.
♦ A near-real-time dynamic incremental learning-based event classifier.
♦ An adaptive human-in-the-loop event identification methodology.
First, to conduct extensive simulations on variety of model-driven and data-driven algorithms, a close to real-life simulation environment needs to be set-up. Using RTDS a cyber-physical replica of a 50 kV ring network operated by Stedin B.V. in the Zeeland area of the Netherlands is developed. Further, the grid is upgraded in 3 operational stages to meet steady-state, quasi-steady-state and dynamic-state conditions. This forms the benchmark grid for all further studies. Subsequently, as a first step towards real-time grid situational awareness, state-of-the-art EKF- and UKF-based state estimation algorithms are developed, tested and validated to achieve complete grid observability in terms of determined node voltage phasors for the grid. With enough confidence in terms of SE accuracy and computational efficiency in the steady-state, the PMU-based state estimator increases complexity by QSS operation and finally, by adopting an anomaly detection, discrimination, and identification module, the PMU-based state estimator is enhanced to co-simulate within the fast refresh rates of PMUs under a fully dynamic grid with abrupt SLC and multiple bad-data events.
Second, with PMU-detectable events addressed, events with complex temporal signatures are systematically identified using data-driven models. Recommendations are developed for a forecast-based event detection model and subsequent real-time data pre-processing, which collect disturbance signatures. A multivariate 1D CNN classification model is designed to identify event types using disturbance signatures in real time. In the first stage, simulations are performed for events which are known and previously trained by the model. In the next stage, the DIL strategy is used to adapt the data-driven model for unforeseen and statistically drifted event types. The classification accuracy, memory consumption, and computational efficiency are used as performance metrics to validate in near-real-time conditions.
Third, in order for data-driven models to meet industrial expectations, an AdInFier expert system is developed, which primarily adds a validation stage to verify the classification results using an unsupervised learning approach. A Soft-DTW technique is used for event representatives that will be compared with incoming disturbance signatures to provide a similarity score. The classifier-validator duo provides a two-stage approach for event identification so that control actions can be actuated in real time in high-stakes environments of control centres. Subsequently, we inculcate a human-in-the-loop approach within an AI environment to deal with complex, contradictory situations where the grid collected data is not mature enough for models to decide on the event type. This step is mainly to add domain expert knowledge in the solutions of over-deterministic data-driven models.
The main purpose of this dissertation is to get a step closer to real-life implementation of state-of-the-art model-driven algorithms and ensure trust in the new cutting-edge data-driven domains with the ultimate goal of meeting industrial requirements. As future recommendations, we propose further enhancements to the AdInFier expert system in terms of control actions and solution fulfilment capabilities, so that we can safely manoeuvre in today's fast-paced technological landscape. ...
♦ A real-time PMU-based distribution state estimation.
♦ A near-real-time dynamic incremental learning-based event classifier.
♦ An adaptive human-in-the-loop event identification methodology.
First, to conduct extensive simulations on variety of model-driven and data-driven algorithms, a close to real-life simulation environment needs to be set-up. Using RTDS a cyber-physical replica of a 50 kV ring network operated by Stedin B.V. in the Zeeland area of the Netherlands is developed. Further, the grid is upgraded in 3 operational stages to meet steady-state, quasi-steady-state and dynamic-state conditions. This forms the benchmark grid for all further studies. Subsequently, as a first step towards real-time grid situational awareness, state-of-the-art EKF- and UKF-based state estimation algorithms are developed, tested and validated to achieve complete grid observability in terms of determined node voltage phasors for the grid. With enough confidence in terms of SE accuracy and computational efficiency in the steady-state, the PMU-based state estimator increases complexity by QSS operation and finally, by adopting an anomaly detection, discrimination, and identification module, the PMU-based state estimator is enhanced to co-simulate within the fast refresh rates of PMUs under a fully dynamic grid with abrupt SLC and multiple bad-data events.
Second, with PMU-detectable events addressed, events with complex temporal signatures are systematically identified using data-driven models. Recommendations are developed for a forecast-based event detection model and subsequent real-time data pre-processing, which collect disturbance signatures. A multivariate 1D CNN classification model is designed to identify event types using disturbance signatures in real time. In the first stage, simulations are performed for events which are known and previously trained by the model. In the next stage, the DIL strategy is used to adapt the data-driven model for unforeseen and statistically drifted event types. The classification accuracy, memory consumption, and computational efficiency are used as performance metrics to validate in near-real-time conditions.
Third, in order for data-driven models to meet industrial expectations, an AdInFier expert system is developed, which primarily adds a validation stage to verify the classification results using an unsupervised learning approach. A Soft-DTW technique is used for event representatives that will be compared with incoming disturbance signatures to provide a similarity score. The classifier-validator duo provides a two-stage approach for event identification so that control actions can be actuated in real time in high-stakes environments of control centres. Subsequently, we inculcate a human-in-the-loop approach within an AI environment to deal with complex, contradictory situations where the grid collected data is not mature enough for models to decide on the event type. This step is mainly to add domain expert knowledge in the solutions of over-deterministic data-driven models.
The main purpose of this dissertation is to get a step closer to real-life implementation of state-of-the-art model-driven algorithms and ensure trust in the new cutting-edge data-driven domains with the ultimate goal of meeting industrial requirements. As future recommendations, we propose further enhancements to the AdInFier expert system in terms of control actions and solution fulfilment capabilities, so that we can safely manoeuvre in today's fast-paced technological landscape. ...
The urgent need to decrease global carbon emissions to meet the Paris Climate Agreement calls for sustainable methods to manage growing electrical demands. This energy transition requires the decommissioning of large fuel-based power plants and simultaneously replacing them with power-electronics interfaced intermittent renewable energy sources and loads. These developments pose high stress on our ageing grid infrastructure, leading to an increased level of unanticipated electrical disturbances that, if left unchecked, might lead to total grid collapse. The thesis presents an expert system that fortifies the ever-evolving grid with advanced event identification and learning architecture engineered to protect against contemporary and evolving grid disturbances. The chosen design perspectives are intended to assure trust in academic algorithms and bridge the expanding gap between the academia-industry. In this context, the dissertation has three main contributions namely:
♦ A real-time PMU-based distribution state estimation.
♦ A near-real-time dynamic incremental learning-based event classifier.
♦ An adaptive human-in-the-loop event identification methodology.
First, to conduct extensive simulations on variety of model-driven and data-driven algorithms, a close to real-life simulation environment needs to be set-up. Using RTDS a cyber-physical replica of a 50 kV ring network operated by Stedin B.V. in the Zeeland area of the Netherlands is developed. Further, the grid is upgraded in 3 operational stages to meet steady-state, quasi-steady-state and dynamic-state conditions. This forms the benchmark grid for all further studies. Subsequently, as a first step towards real-time grid situational awareness, state-of-the-art EKF- and UKF-based state estimation algorithms are developed, tested and validated to achieve complete grid observability in terms of determined node voltage phasors for the grid. With enough confidence in terms of SE accuracy and computational efficiency in the steady-state, the PMU-based state estimator increases complexity by QSS operation and finally, by adopting an anomaly detection, discrimination, and identification module, the PMU-based state estimator is enhanced to co-simulate within the fast refresh rates of PMUs under a fully dynamic grid with abrupt SLC and multiple bad-data events.
Second, with PMU-detectable events addressed, events with complex temporal signatures are systematically identified using data-driven models. Recommendations are developed for a forecast-based event detection model and subsequent real-time data pre-processing, which collect disturbance signatures. A multivariate 1D CNN classification model is designed to identify event types using disturbance signatures in real time. In the first stage, simulations are performed for events which are known and previously trained by the model. In the next stage, the DIL strategy is used to adapt the data-driven model for unforeseen and statistically drifted event types. The classification accuracy, memory consumption, and computational efficiency are used as performance metrics to validate in near-real-time conditions.
Third, in order for data-driven models to meet industrial expectations, an AdInFier expert system is developed, which primarily adds a validation stage to verify the classification results using an unsupervised learning approach. A Soft-DTW technique is used for event representatives that will be compared with incoming disturbance signatures to provide a similarity score. The classifier-validator duo provides a two-stage approach for event identification so that control actions can be actuated in real time in high-stakes environments of control centres. Subsequently, we inculcate a human-in-the-loop approach within an AI environment to deal with complex, contradictory situations where the grid collected data is not mature enough for models to decide on the event type. This step is mainly to add domain expert knowledge in the solutions of over-deterministic data-driven models.
The main purpose of this dissertation is to get a step closer to real-life implementation of state-of-the-art model-driven algorithms and ensure trust in the new cutting-edge data-driven domains with the ultimate goal of meeting industrial requirements. As future recommendations, we propose further enhancements to the AdInFier expert system in terms of control actions and solution fulfilment capabilities, so that we can safely manoeuvre in today's fast-paced technological landscape.
♦ A real-time PMU-based distribution state estimation.
♦ A near-real-time dynamic incremental learning-based event classifier.
♦ An adaptive human-in-the-loop event identification methodology.
First, to conduct extensive simulations on variety of model-driven and data-driven algorithms, a close to real-life simulation environment needs to be set-up. Using RTDS a cyber-physical replica of a 50 kV ring network operated by Stedin B.V. in the Zeeland area of the Netherlands is developed. Further, the grid is upgraded in 3 operational stages to meet steady-state, quasi-steady-state and dynamic-state conditions. This forms the benchmark grid for all further studies. Subsequently, as a first step towards real-time grid situational awareness, state-of-the-art EKF- and UKF-based state estimation algorithms are developed, tested and validated to achieve complete grid observability in terms of determined node voltage phasors for the grid. With enough confidence in terms of SE accuracy and computational efficiency in the steady-state, the PMU-based state estimator increases complexity by QSS operation and finally, by adopting an anomaly detection, discrimination, and identification module, the PMU-based state estimator is enhanced to co-simulate within the fast refresh rates of PMUs under a fully dynamic grid with abrupt SLC and multiple bad-data events.
Second, with PMU-detectable events addressed, events with complex temporal signatures are systematically identified using data-driven models. Recommendations are developed for a forecast-based event detection model and subsequent real-time data pre-processing, which collect disturbance signatures. A multivariate 1D CNN classification model is designed to identify event types using disturbance signatures in real time. In the first stage, simulations are performed for events which are known and previously trained by the model. In the next stage, the DIL strategy is used to adapt the data-driven model for unforeseen and statistically drifted event types. The classification accuracy, memory consumption, and computational efficiency are used as performance metrics to validate in near-real-time conditions.
Third, in order for data-driven models to meet industrial expectations, an AdInFier expert system is developed, which primarily adds a validation stage to verify the classification results using an unsupervised learning approach. A Soft-DTW technique is used for event representatives that will be compared with incoming disturbance signatures to provide a similarity score. The classifier-validator duo provides a two-stage approach for event identification so that control actions can be actuated in real time in high-stakes environments of control centres. Subsequently, we inculcate a human-in-the-loop approach within an AI environment to deal with complex, contradictory situations where the grid collected data is not mature enough for models to decide on the event type. This step is mainly to add domain expert knowledge in the solutions of over-deterministic data-driven models.
The main purpose of this dissertation is to get a step closer to real-life implementation of state-of-the-art model-driven algorithms and ensure trust in the new cutting-edge data-driven domains with the ultimate goal of meeting industrial requirements. As future recommendations, we propose further enhancements to the AdInFier expert system in terms of control actions and solution fulfilment capabilities, so that we can safely manoeuvre in today's fast-paced technological landscape.
Conference paper
(2024)
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Nidarshan Veerakumar, Aleksandar Boričić, Ilya Tyuryukanov, Marko Tealane, Matija Naglič, Maarten van Riet, Danny Klaar, M.A.M.M. van der Meijden, Marjan Popov, More authors...
This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.
...
This paper deals with the essentials of synchrophasor’s applications for future power systems to increase system reliability and resilience, which have been investigated within a four-year research project. The project has several applications, covering real-time disturbance detection and blackout prevention distributed across multiple work-packages. Firstly, an advanced big-data management platform built in a real-time digital simulation (RTDS) environment is described to support measurement data collection, processing, and sharing among stakeholders. This platform further presents and demonstrates a network-splitting methodology to avoid cascading failures. Online generator coherency identification is another synchrophasor application implemented on the platform, the use of which is demonstrated in the context of controlled network splitting. Using synchrophasors, data-analytics techniques can also identify and classify disturbances in real time with minor human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system is outlined to detect and classify critical events. Finally, the paper elaborates on developing advanced system resilience metrics for real-time vulnerability assessment of power systems with a high penetration of renewable energy, focusing on increasingly relevant dynamic interactions and system instability risks.
With recent telemetric advancements, the real-time availability of power grid measurements has opened challenging opportunities for the design of advanced protection and control schemes. Artificial neural networks (ANN) are promising approaches for detecting and classifying disturbance events from measurement data. Numerous offline ANN-based classification algorithms were proposed in the past, which increased the interest for their real-world deployment. However, these algorithms are inadequate due to their conventional offline training procedures, model updating, and large backend computing requirements. Besides, most ANN-based algorithms require disturbance event samples to be collectively available during training. This availability may be uncommon in practice as disturbance events are rare, non-deterministic, and uncertain. Hence, an online training procedure where the model processes the events on-the-fly is required. However, ANNs may also suffer from catastrophic forgetting where the model may unintentionally unlearn an occurred disturbance under the learning of new event types; this means ANN may not detect very similar disturbances of the same type in the future. In this paper, we propose Dynamic Incremental Learning (IL) method for ANN models, which is updated in real-time when a new disturbance is detected. Our proposed method adopts a Replay-based IL strategy for designing long-term IL, balancing the accuracy with catastrophic forgetting of disturbance events. The method is designed in a way to learn efficiently for incoming disturbance data with minimized training time and the highest classification accuracy eliminating catastrophic forgetting. The results describe the methodology’s performance regarding classification accuracy, training time, and storage memory. The findings demonstrate that the Dynamic IL method is promising for efficient learning and event classification.
...
With recent telemetric advancements, the real-time availability of power grid measurements has opened challenging opportunities for the design of advanced protection and control schemes. Artificial neural networks (ANN) are promising approaches for detecting and classifying disturbance events from measurement data. Numerous offline ANN-based classification algorithms were proposed in the past, which increased the interest for their real-world deployment. However, these algorithms are inadequate due to their conventional offline training procedures, model updating, and large backend computing requirements. Besides, most ANN-based algorithms require disturbance event samples to be collectively available during training. This availability may be uncommon in practice as disturbance events are rare, non-deterministic, and uncertain. Hence, an online training procedure where the model processes the events on-the-fly is required. However, ANNs may also suffer from catastrophic forgetting where the model may unintentionally unlearn an occurred disturbance under the learning of new event types; this means ANN may not detect very similar disturbances of the same type in the future. In this paper, we propose Dynamic Incremental Learning (IL) method for ANN models, which is updated in real-time when a new disturbance is detected. Our proposed method adopts a Replay-based IL strategy for designing long-term IL, balancing the accuracy with catastrophic forgetting of disturbance events. The method is designed in a way to learn efficiently for incoming disturbance data with minimized training time and the highest classification accuracy eliminating catastrophic forgetting. The results describe the methodology’s performance regarding classification accuracy, training time, and storage memory. The findings demonstrate that the Dynamic IL method is promising for efficient learning and event classification.
Journal article
(2022)
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Nidarshan Veerakumar, Dragan Ćetenović, K. Kongurai, M. Popov, Arjen Jongepier, Vladimir Terzija
In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.
...
In this paper, a real-time state estimation platform for distribution grids monitored by Phasor Measurement Units (PMUs) is developed, tested, and validated using Real Time Digital Simulator (RTDS). The developed platform serves as a proof-of-concept for potential implementation in an existing 50 kV ring network of the Dutch distribution utility Stedin medium voltage distribution grid located in the southwest (Zeeland area) of the Netherlands. To catch up with the fast sampling rates of PMUs, the platform incorporates computationally efficient techniques for state estimation and detection, discrimination and identification of anomalies like bad data and sudden load changes. Forecasting Aided State Estimation has been utilized to enable measurement innovations needed for fast anomaly detection, discrimination, and identification, whilst the Extended Kalman Filter (EKF) algorithm is selected to provide fast state forecasting and filtering. The platform has been tested under various normal and abnormal operating conditions considering different statistical properties of measurement noise as well as different bad data and sudden load change scenarios. To demonstrate advantages and disadvantages for embedding EKF into the platform, EKF is compared with Unscented Kalman Filter (UKF) in terms of estimation accuracy, computational efficiency, and compatibility with the module for anomaly detection, discrimination, and identification. The results of extensive simulations provide good hints about the feasibility of PMU-based real-time state estimation for the Stedin distribution grid.
Conference paper
(2022)
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J. J. Chavez, N. Veera Kumar, M. Popov, P. Palensky, Sadegh Azizi, Enrique Melgoza, Vladimir Terzija
Fault currents may result in cascading failures and even system collapse if not detected and cleared on time. To account for the possibility of failure of primary protection under stressed system conditions, an extra layer of protection is commonly employed, referred to as backup protection. This paper introduces an effective formulation for realizing remote backup protection using available data from PMUs and Intelligent Electronic Devices (IEDs). The proposed method is split into three main stages. The first stage deals with the zoning detection of the fault. The second stage is aimed at faulted line detection, and finally, the third stage determines the fault distance on the faulted line. The method is designed to take full advantage of measurements provided by PMUs and IEDs. The challenges associated with different reporting rates are resolved thanks to the dynamic decimator employed to this end. The proposed method has been implemented in real-time by applying co-simulation with MATLAB and validated using the New England IEEE 39 bus system with several fault events.
...
Fault currents may result in cascading failures and even system collapse if not detected and cleared on time. To account for the possibility of failure of primary protection under stressed system conditions, an extra layer of protection is commonly employed, referred to as backup protection. This paper introduces an effective formulation for realizing remote backup protection using available data from PMUs and Intelligent Electronic Devices (IEDs). The proposed method is split into three main stages. The first stage deals with the zoning detection of the fault. The second stage is aimed at faulted line detection, and finally, the third stage determines the fault distance on the faulted line. The method is designed to take full advantage of measurements provided by PMUs and IEDs. The challenges associated with different reporting rates are resolved thanks to the dynamic decimator employed to this end. The proposed method has been implemented in real-time by applying co-simulation with MATLAB and validated using the New England IEEE 39 bus system with several fault events.
Conference paper
(2022)
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M. Popov, A. Boricic, Nidarshan Veerakumar, Matija Naglic, I. Tyuryukanov, M. Tealane, José L. Rueda, M.A.M.M. van der Meijden, P. Palensky, More Authors...
This paper deals with the essentials of synchrophasor applications for future power systems aimed at increasing system reliability and resilience. In this work, several applications are presented, covering real-time disturbance detection and blackout prevention. Firstly, an advanced big-data management platform built in real-time digital simulation (RTDS) environment to support measurement data collection, processing and sharing among stakeholders is described. With this platform, a network splitting methodology to avoid cascading failures is presented and demonstrated, which upon the occurrence of a disturbance successfully isolates the affected part to avoid catastrophic cascade system outage. Online generator coherency identification is another synchrophasor application implemented on the platform, whose use is demonstrated in the context of controlled network splitting. By using synchrophasors, data-analytics techniques can also be used for identifying and classifying different disturbances in real-time with the least human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system to detect and classify critical events is outlined. Finally, the paper elaborates on the development of advanced system resilience metrics for real-time vulnerability assessment, with a focus on increasingly relevant dynamic interactions between distribution and transmission systems.
...
This paper deals with the essentials of synchrophasor applications for future power systems aimed at increasing system reliability and resilience. In this work, several applications are presented, covering real-time disturbance detection and blackout prevention. Firstly, an advanced big-data management platform built in real-time digital simulation (RTDS) environment to support measurement data collection, processing and sharing among stakeholders is described. With this platform, a network splitting methodology to avoid cascading failures is presented and demonstrated, which upon the occurrence of a disturbance successfully isolates the affected part to avoid catastrophic cascade system outage. Online generator coherency identification is another synchrophasor application implemented on the platform, whose use is demonstrated in the context of controlled network splitting. By using synchrophasors, data-analytics techniques can also be used for identifying and classifying different disturbances in real-time with the least human intervention. Therefore, a novel centralized artificial intelligence (AI) based expert system to detect and classify critical events is outlined. Finally, the paper elaborates on the development of advanced system resilience metrics for real-time vulnerability assessment, with a focus on increasingly relevant dynamic interactions between distribution and transmission systems.
Journal article
(2021)
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José Rueda Torres, Zameer Ahmad, Nidarshan Veera Kumar, Elyas Rakhshani, Ebrahim Adabi, Peter Palensky, Mart van der Meijden
Future electrical power systems will be dominated by power electronic converters, which are deployed for the integration of renewable power plants, responsive demand, and different types of storage systems. The stability of such systems will strongly depend on the control strategies attached to the converters. In this context, laboratory-scale setups are becoming the key tools for prototyping and evaluating the performance and robustness of different converter technologies and control strategies. The performance evaluation of control strategies for dynamic frequency support using fast active power regulation (FAPR) requires the urgent development of a suitable power hardware-in-the-loop (PHIL) setup. In this paper, the most prominent emerging types of FAPR are selected and studied: droop-based FAPR, droop derivative-based FAPR, and virtual synchronous power (VSP)-based FAPR. A novel setup for PHIL-based performance evaluation of these strategies is proposed. The setup combines the advanced modeling and simulation functions of a real-time digital simulation platform (RTDS), an external programmable unit to implement the studied FAPR control strategies as digital controllers, and actual hardware. The hardware setup consists of a grid emulator to recreate the dynamic response as seen from the interface bus of the grid side converter of a power electronic-interfaced device (e.g., type-IV wind turbines), and a mockup voltage source converter (VSC, i.e., a device under test (DUT)). The DUT is virtually interfaced to one high-voltage bus of the electromagnetic transient (EMT) representation of a variant of the IEEE 9 bus test system, which has been modified to consider an operating condition with 52% of the total supply provided by wind power generation. The selected and programmed FAPR strategies are applied to the DUT, with the ultimate goal of ascertaining its feasibility and effectiveness with respect to the pure software-based EMT representation performed in real time. Particularly, the time-varying response of the active power injection by each FAPR control strategy and the impact on the instantaneous frequency excursions occurring in the frequency containment periods are analyzed. The performed tests show the degree of improvements on both the rate-of-change-of-frequency (RoCoF) and the maximum frequency excursion (e.g., nadir).
...
Future electrical power systems will be dominated by power electronic converters, which are deployed for the integration of renewable power plants, responsive demand, and different types of storage systems. The stability of such systems will strongly depend on the control strategies attached to the converters. In this context, laboratory-scale setups are becoming the key tools for prototyping and evaluating the performance and robustness of different converter technologies and control strategies. The performance evaluation of control strategies for dynamic frequency support using fast active power regulation (FAPR) requires the urgent development of a suitable power hardware-in-the-loop (PHIL) setup. In this paper, the most prominent emerging types of FAPR are selected and studied: droop-based FAPR, droop derivative-based FAPR, and virtual synchronous power (VSP)-based FAPR. A novel setup for PHIL-based performance evaluation of these strategies is proposed. The setup combines the advanced modeling and simulation functions of a real-time digital simulation platform (RTDS), an external programmable unit to implement the studied FAPR control strategies as digital controllers, and actual hardware. The hardware setup consists of a grid emulator to recreate the dynamic response as seen from the interface bus of the grid side converter of a power electronic-interfaced device (e.g., type-IV wind turbines), and a mockup voltage source converter (VSC, i.e., a device under test (DUT)). The DUT is virtually interfaced to one high-voltage bus of the electromagnetic transient (EMT) representation of a variant of the IEEE 9 bus test system, which has been modified to consider an operating condition with 52% of the total supply provided by wind power generation. The selected and programmed FAPR strategies are applied to the DUT, with the ultimate goal of ascertaining its feasibility and effectiveness with respect to the pure software-based EMT representation performed in real time. Particularly, the time-varying response of the active power injection by each FAPR control strategy and the impact on the instantaneous frequency excursions occurring in the frequency containment periods are analyzed. The performed tests show the degree of improvements on both the rate-of-change-of-frequency (RoCoF) and the maximum frequency excursion (e.g., nadir).
Journal article
(2021)
-
José Rueda Torres, Nidarshan Veera Kumar, Elyas Rakhshani, Zameer Ahmad, Ebrahim Adabi, Peter Palensky, Mart van der Meijden
This paper concerns the feasibility of Fast Active Power Regulation (FAPR) in renewable energy hubs. Selected state-of-the-art FAPR strategies are applied to various controllable devices within a hub, such as a solar photovoltaic (PV) farm and an electrolyzer acting as a responsive load. Among the selected strategies are droop-based FAPR, droop derivative-based FAPR, and virtual synchronous power (VSP)-based FAPR. The FAPR-supported hub is interconnected with a test transmission network, modeled and simulated in a real-time simulation electromagnetic transient (EMT) environment to study a futuristic operating condition of the high-voltage infrastructure covering the north of the Netherlands. The real-time EMT simulations show that the FAPR strategies (especially the VSP-based FAPR) can successfully help to significantly and promptly limit undesirable large instantaneous frequency deviations.
...
This paper concerns the feasibility of Fast Active Power Regulation (FAPR) in renewable energy hubs. Selected state-of-the-art FAPR strategies are applied to various controllable devices within a hub, such as a solar photovoltaic (PV) farm and an electrolyzer acting as a responsive load. Among the selected strategies are droop-based FAPR, droop derivative-based FAPR, and virtual synchronous power (VSP)-based FAPR. The FAPR-supported hub is interconnected with a test transmission network, modeled and simulated in a real-time simulation electromagnetic transient (EMT) environment to study a futuristic operating condition of the high-voltage infrastructure covering the north of the Netherlands. The real-time EMT simulations show that the FAPR strategies (especially the VSP-based FAPR) can successfully help to significantly and promptly limit undesirable large instantaneous frequency deviations.
Journal article
(2021)
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Jose J. Chavez, Nidarshan Veera kumar, Sadegh Azizi, Jose L. Guardado, José Rueda, Peter Palensky, Vladimir Terzija, Marjan Popov
Local protection elements such as fuses and relays are the first protective mechanism to clear the fault and isolate the affected part of the power grid. Although the selectivity, speed, and sensitivity of these primary protection devices are relatively high, they cannot be considered flawless. There is a small percentage of events for which relays experience blinding effects. For these scenarios, a redundant arrangement can be made through backup protection. This paper proposes a centralized remote backup protection method based on two techniques, the delta algorithm and the least-squares technique. The proposed method successfully detects the faulted transmission line, fault type, and the distance to the fault. Besides, it makes use of phasor measurement unit data and it is non-iterative. The grid is split in a user-determined number of subareas based on the phasor mesurement unit locations, in order to accurately determine the fault location. Firstly, the faulty area is located and thereafter an in-depth search is carried out on the faulted area to determine the faulted line. Finally, the fault distance is determined based on the distributed parameter model of the transmission line. The method is demonstrated and validated in an RTDS-Matlab co-simulation platform. Extensive simulation studies are carried out on the IEEE 39-bus system to validate the proposed method.
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Local protection elements such as fuses and relays are the first protective mechanism to clear the fault and isolate the affected part of the power grid. Although the selectivity, speed, and sensitivity of these primary protection devices are relatively high, they cannot be considered flawless. There is a small percentage of events for which relays experience blinding effects. For these scenarios, a redundant arrangement can be made through backup protection. This paper proposes a centralized remote backup protection method based on two techniques, the delta algorithm and the least-squares technique. The proposed method successfully detects the faulted transmission line, fault type, and the distance to the fault. Besides, it makes use of phasor measurement unit data and it is non-iterative. The grid is split in a user-determined number of subareas based on the phasor mesurement unit locations, in order to accurately determine the fault location. Firstly, the faulty area is located and thereafter an in-depth search is carried out on the faulted area to determine the faulted line. Finally, the fault distance is determined based on the distributed parameter model of the transmission line. The method is demonstrated and validated in an RTDS-Matlab co-simulation platform. Extensive simulation studies are carried out on the IEEE 39-bus system to validate the proposed method.
Conference paper
(2021)
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C. Zhang, E. Rakhshani, N. Veera Kumar, J. L. Rueda-Torres, P. Palensky, F. Gonzalez-Longatt
The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). Energy storage systems (ESS) are used to supply extra power injection to enhance the frequency stability during a disturbance. This paper presents a novel approach for improving the frequency dynamics by incorporating a designed ultracapacitor (UC) with a fully decoupled wind power generation (FDWG) unit. To this aim, a suitable model implementation of UC for real-time simulations is presented. The model constitutes a parallel RC branch, which is appropriate for illustrating the relevant fast UC dynamics that occur within the first milliseconds of the time period of action for fast active-power frequency control services. The frequency performance achieved by the support of the FDWG equipped with UC is compared against the performance achieved by using electrical batteries. The comparison includes the application of droop-derivative frequency control.
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The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). Energy storage systems (ESS) are used to supply extra power injection to enhance the frequency stability during a disturbance. This paper presents a novel approach for improving the frequency dynamics by incorporating a designed ultracapacitor (UC) with a fully decoupled wind power generation (FDWG) unit. To this aim, a suitable model implementation of UC for real-time simulations is presented. The model constitutes a parallel RC branch, which is appropriate for illustrating the relevant fast UC dynamics that occur within the first milliseconds of the time period of action for fast active-power frequency control services. The frequency performance achieved by the support of the FDWG equipped with UC is compared against the performance achieved by using electrical batteries. The comparison includes the application of droop-derivative frequency control.
Journal article
(2021)
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Chenrui Zhang, Elyas Rakhshani, Nidarshan Veerakumar, José Luis Rueda Torres, Peter Palensky
The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). This paper investigates the improvements of the frequency response of fully decoupled wind power generators (FDWG) by proposing a novel generic model implementation of ultracapacitors (UC) within a hybrid scheme in real-time simulations of wind power plants. UCs are selected as ideal power sources in fast active power-frequency control due to their high power density and fast-reacting speed. Batteries and UCs combined hybrid energy storage systems (HESS) are formed to complement their characteristics. Droop-based and frequency derivative-based control and virtual synchronous power (VSP) are the selected control strategies to support power system frequency stability. The best trade-off between frequency performance and HESS cost is found by solving a proposed optimization problem formulation. The proposed optimization problem is used to define the HESS size and the controller parameters. The optimization results show how the fast active power-frequency response is enhanced by the fast UC power injection. It also shown that VSP leads to faster frequency support than the droop-based control and the frequency derivative control.
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The frequency stability of the power system is challenged by the high penetration of power electronic interfaced renewable energy sources (RES). This paper investigates the improvements of the frequency response of fully decoupled wind power generators (FDWG) by proposing a novel generic model implementation of ultracapacitors (UC) within a hybrid scheme in real-time simulations of wind power plants. UCs are selected as ideal power sources in fast active power-frequency control due to their high power density and fast-reacting speed. Batteries and UCs combined hybrid energy storage systems (HESS) are formed to complement their characteristics. Droop-based and frequency derivative-based control and virtual synchronous power (VSP) are the selected control strategies to support power system frequency stability. The best trade-off between frequency performance and HESS cost is found by solving a proposed optimization problem formulation. The proposed optimization problem is used to define the HESS size and the controller parameters. The optimization results show how the fast active power-frequency response is enhanced by the fast UC power injection. It also shown that VSP leads to faster frequency support than the droop-based control and the frequency derivative control.
Review
(2020)
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Z. Ahmad, José L. Rueda, Nidarshan Veerakumar, E. Rakhshani, P. Palensky, M.A.M.M. van der Meijden
A task for new power generation technologies, interfaced to the electrical grid by power electronic converters, is to stiffen the rate of change of frequency (RoCoF) at the initial few milliseconds (ms) after any variation of active power balance. This task is defined in this article as fast active power regulation (FAPR), a generic definition of the FAPR is also proposed in this study. Converters equipped with FAPR controls should be tested in laboratory conditions before employment in the actual power system. This paper presents a power hardware-in-the-loop (PHIL) based method for FAPR compliance testing of the wind turbine converter controls. The presented PHIL setup is a generic test setup for the testing of all kinds of control strategies of the grid-connected power electronic converters. Firstly, a generic PHIL testing methodology is presented. Later on, a combined droop- anFd derivative-based FAPR control has been implemented and tested on the proposed PHIL setup for FAPR compliance criteria of the wind turbine converters. The compliance criteria for the FAPR of the wind turbine converter controls have been framed based on the literature survey. Improvement in the RoCoF and and maximum underfrequency deviation (NADIR) has been observed if the wind turbine converter controls abide by the FAPR compliance criteria.
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A task for new power generation technologies, interfaced to the electrical grid by power electronic converters, is to stiffen the rate of change of frequency (RoCoF) at the initial few milliseconds (ms) after any variation of active power balance. This task is defined in this article as fast active power regulation (FAPR), a generic definition of the FAPR is also proposed in this study. Converters equipped with FAPR controls should be tested in laboratory conditions before employment in the actual power system. This paper presents a power hardware-in-the-loop (PHIL) based method for FAPR compliance testing of the wind turbine converter controls. The presented PHIL setup is a generic test setup for the testing of all kinds of control strategies of the grid-connected power electronic converters. Firstly, a generic PHIL testing methodology is presented. Later on, a combined droop- anFd derivative-based FAPR control has been implemented and tested on the proposed PHIL setup for FAPR compliance criteria of the wind turbine converters. The compliance criteria for the FAPR of the wind turbine converter controls have been framed based on the literature survey. Improvement in the RoCoF and and maximum underfrequency deviation (NADIR) has been observed if the wind turbine converter controls abide by the FAPR compliance criteria.
Journal article
(2020)
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E. Rakhshani, A.D. Perilla Guerra, Nidarshan Veerakumar, Z. Ahmad, José L. Rueda, M.A.M.M. van der Meijden, P. Palensky
In this study, a novel methodology is proposed for sensitivity-based tuning and analysis of derivative-based fast active power injection (FAPI) controllers in type-4 wind turbine units integrated into a low-inertia power system. The FAPI controller is attached to a power electronic interfaced generation (PEIG) represented by a generic model of wind turbines type 4. It consists of a combination of droop and derivative controllers, which is dependent on the measurement of the frequency. The tuning methodology performs parametric sensitivity to search for the most suitable set of parameters of the attached FAPI that minimises the maximum frequency deviation in the containment period. The FAPI is adjusted to safeguard system stability when increasing the share of PEIG. Since the input signal of the FAPI is the measured frequency, the impact of different values and parameter settings of the phase-locked loop used for the FAPI controller is also investigated. Detailed validation with a full-scaled wind power converter is also provided with a real-time digital simulator testbed. Obtained simulation results using a three-area test system, identify the maximum achievable degree of increase in the share of wind power when a proper combination of wind park locations considering their suggested settings for inertia emulation.
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In this study, a novel methodology is proposed for sensitivity-based tuning and analysis of derivative-based fast active power injection (FAPI) controllers in type-4 wind turbine units integrated into a low-inertia power system. The FAPI controller is attached to a power electronic interfaced generation (PEIG) represented by a generic model of wind turbines type 4. It consists of a combination of droop and derivative controllers, which is dependent on the measurement of the frequency. The tuning methodology performs parametric sensitivity to search for the most suitable set of parameters of the attached FAPI that minimises the maximum frequency deviation in the containment period. The FAPI is adjusted to safeguard system stability when increasing the share of PEIG. Since the input signal of the FAPI is the measured frequency, the impact of different values and parameter settings of the phase-locked loop used for the FAPI controller is also investigated. Detailed validation with a full-scaled wind power converter is also provided with a real-time digital simulator testbed. Obtained simulation results using a three-area test system, identify the maximum achievable degree of increase in the share of wind power when a proper combination of wind park locations considering their suggested settings for inertia emulation.
Conference paper
(2020)
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Nidarshan Veerakumar, Zameer Ahmad, M. Ebrahim Adabi , José Rueda Torres, Peter Palensky, Mart van der Meijden, Francisco Gonzalez-Longatt
This paper presents a comparative assessment of fast active power regulation (FAPR) control strategies implemented on megawatt-scale controllable electrolysers, with the goal of achieving enhanced frequency support during large active power imbalances that lead to major under-frequency deviations. The FAPR control strategies consist of three different types of controllers, namely, droop, derivative and Virtual Synchronous Power (VSP). Each of these controllers has been implemented on a 300 MW electrolyser plant with proton exchange membrane (PEM) electrolysers. The compared FAPR controllers are individually set to perform a fast adjustment of the active power consumption of the plant-based on the dynamic grid conditions. The modelling and comparative assessment is done in a platform for computationally efficient simulations of Electromagnetic Transients (EMT) in real-time. A synthetic model of the Northern Netherlands Network (N3 Network) is prototyped as a test bench to simulate and evaluate the performance of the implemented FAPR controllers. The EMT simulations show the superiority of the VSP based FAPR developed for controlling and exploiting the boundaries for active power adjustment of the Voltage Source Converter (VSC) that interfaces the PEM electrolyser plant with the N3 Network.
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This paper presents a comparative assessment of fast active power regulation (FAPR) control strategies implemented on megawatt-scale controllable electrolysers, with the goal of achieving enhanced frequency support during large active power imbalances that lead to major under-frequency deviations. The FAPR control strategies consist of three different types of controllers, namely, droop, derivative and Virtual Synchronous Power (VSP). Each of these controllers has been implemented on a 300 MW electrolyser plant with proton exchange membrane (PEM) electrolysers. The compared FAPR controllers are individually set to perform a fast adjustment of the active power consumption of the plant-based on the dynamic grid conditions. The modelling and comparative assessment is done in a platform for computationally efficient simulations of Electromagnetic Transients (EMT) in real-time. A synthetic model of the Northern Netherlands Network (N3 Network) is prototyped as a test bench to simulate and evaluate the performance of the implemented FAPR controllers. The EMT simulations show the superiority of the VSP based FAPR developed for controlling and exploiting the boundaries for active power adjustment of the Voltage Source Converter (VSC) that interfaces the PEM electrolyser plant with the N3 Network.
Conference paper
(2019)
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Elyas Rakhshani, Nidarshan Veerakumar, Zameer Ahmad, Jose Rueda Torres, Mart A.M.M. Van Der Meijden, Peter Palensky
This paper deals with the implementation of a Fast Active Power Injection (FAPI) controller in a Type-4 Wind Turbine. Two different FAPI controllers, droop-based and a modified derivative-based controller are proposed and investigated under real-time simulation platform. The implementation is done in a Real-Time Digital Simulator (RTDS) by using the functionalities of RSCAD software. The IEEE 9 bus system is taken as a case study to quantitatively check the suitability of the implemented controller. The response of the wind turbine observed in EMT simulations is compared against the response obtained via numerical simulations with a generic wind turbine model built-in DIg SILENT PowerFactory software. The details of the model implemented in RSCAD provides better insight on capturing the impacts of controller parameters. Obtained results clearly demonstrate how the proposed controller can effectively improve the dynamic frequency performance of the power system.
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This paper deals with the implementation of a Fast Active Power Injection (FAPI) controller in a Type-4 Wind Turbine. Two different FAPI controllers, droop-based and a modified derivative-based controller are proposed and investigated under real-time simulation platform. The implementation is done in a Real-Time Digital Simulator (RTDS) by using the functionalities of RSCAD software. The IEEE 9 bus system is taken as a case study to quantitatively check the suitability of the implemented controller. The response of the wind turbine observed in EMT simulations is compared against the response obtained via numerical simulations with a generic wind turbine model built-in DIg SILENT PowerFactory software. The details of the model implemented in RSCAD provides better insight on capturing the impacts of controller parameters. Obtained results clearly demonstrate how the proposed controller can effectively improve the dynamic frequency performance of the power system.