EPE Association: EPE 2020 - LS6d: Energy Digitalization and Management

EPE 2020 - LS6d: Energy Digitalization and Management
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2020 ECCE Europe - Conference > EPE 2020 - Topic 06: Grids, Smart Grids, AC & DC > EPE 2020 - LS6d: Energy Digitalization and Management

   [return to parent folder]

   Enhanced Power System Damping Estimation via Optimal Probing Signal Design
By Sjoerd BOERSMA [View]
[Download]

Abstract: For real-time power system dynamic monitoring, it is important to provide accurate estimations of the network's critical electro-mechanical modes, which are time-varying frequency and damping values. This paper employs a framework for designing a multisine probing signal that, when applied in the control inputs of one of the power electronics-based grid actuators, is able to provide a damping estimation with user specified variance. The employed framework is demonstrated through simulations in a nonlinear simulator using models of varying complexity.

   Fault Diagnosis of HVDC Transmission System Using Wavelet Energy Entropy and the Wavelet Neural Network
By Cuicui LIU [View]
[Download]

Abstract: The failure of the HVDC transmission system is the main factor affecting its reliability. There are many types of faults in the actual project. When a fault occurs, timely and effective identification of the fault type to determine the specific cause of the failure has important research value for improving the reliability of the system. Therefore, this paper focuses on the fault diagnosis method of HVDC transmission system. In this paper, a new fault diagnosis method combining wavelet energy spectrum entropy and wavelet neural network is proposed. In this method, the inverter-side converter bus voltage signal is analyzed as an electrical quantity, and the energy spectrum entropy value of the signal is used to distinguish the normal operating state from each fault state. First, the db10 wavelet is used to decompose and reconstruct the inverter-side converter bus voltage signal collected during the system operation into 10 layers and to obtain the detailed signal of wavelet reconstruction at various scales, and then calculate the wavelet energy spectrum information entropy value of each layer. Use the extracted feature energy spectrum entropy as the input feature vector of wavelet neural network, so as to realize the diagnosis of each fault type of HVDC transmission. The results show that the diagnosis method can accurately diagnose the diagnosis cause of the reduced reliability of the converter valve system.

   Small-signal stability analysis of smart grids considering high penetration of power electronics converters and energy markets
By Patricio MENDOZA-ARAYA [View]
[Download]

Abstract: Future smart grids are expected to have a high amount of renewable energy sources as well as anadvanced metering and communication infrastructure. These communication technologies offerdynamic information that can be sent to the utilities and used by the power system operators. The power system operators drive the scheduling optimization of the generators usually at a different and slower timescale. However, as the exchange of information get closer to real-time, it is possible to turn this into a closed-loop control-based problem, that adjusts the generator and load power outputs constantly as system conditions change. Under this new scenario, it is possible to observe interactions between the markets and the physical power system. This work proposes a methodology to assess the stability of future smart grids, with high penetration of power electronics converters and considering the coupling between the power systems and energy markets. The power system and energy market are modeled to form a feedback system that can be assessed by the Nyquist stability criterion. The simulations are done in the MATLAB/SIMULINK environment using the WSCC 3 machine 9 bus power system.

   State of charge control for a frequency-supporting storage system based on an auto-regressive frequency forecast
By Alberto BOLZONI [View]
[Download]

Abstract: An advanced state-of-charge (SoC) management strategy for grid-connected energy storage units during the provision of fast frequency regulation services is proposed. The approach is based on an adaptive auto-regressive predictor of grid frequency and yields an average reduction between 13\% and 70\% of the energy costs in comparison to alternative standard SoC management approaches.

EPE 2020 - LS6d: Energy Digitalization and Management
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2020 ECCE Europe - Conference > EPE 2020 - Topic 06: Grids, Smart Grids, AC & DC > EPE 2020 - LS6d: Energy Digitalization and Management
	[return to parent folder]

	Enhanced Power System Damping Estimation via Optimal Probing Signal Design By Sjoerd BOERSMA	[View] [Download]
Abstract: For real-time power system dynamic monitoring, it is important to provide accurate estimations of the network's critical electro-mechanical modes, which are time-varying frequency and damping values. This paper employs a framework for designing a multisine probing signal that, when applied in the control inputs of one of the power electronics-based grid actuators, is able to provide a damping estimation with user specified variance. The employed framework is demonstrated through simulations in a nonlinear simulator using models of varying complexity.

	Fault Diagnosis of HVDC Transmission System Using Wavelet Energy Entropy and the Wavelet Neural Network By Cuicui LIU	[View] [Download]
Abstract: The failure of the HVDC transmission system is the main factor affecting its reliability. There are many types of faults in the actual project. When a fault occurs, timely and effective identification of the fault type to determine the specific cause of the failure has important research value for improving the reliability of the system. Therefore, this paper focuses on the fault diagnosis method of HVDC transmission system. In this paper, a new fault diagnosis method combining wavelet energy spectrum entropy and wavelet neural network is proposed. In this method, the inverter-side converter bus voltage signal is analyzed as an electrical quantity, and the energy spectrum entropy value of the signal is used to distinguish the normal operating state from each fault state. First, the db10 wavelet is used to decompose and reconstruct the inverter-side converter bus voltage signal collected during the system operation into 10 layers and to obtain the detailed signal of wavelet reconstruction at various scales, and then calculate the wavelet energy spectrum information entropy value of each layer. Use the extracted feature energy spectrum entropy as the input feature vector of wavelet neural network, so as to realize the diagnosis of each fault type of HVDC transmission. The results show that the diagnosis method can accurately diagnose the diagnosis cause of the reduced reliability of the converter valve system.

	Small-signal stability analysis of smart grids considering high penetration of power electronics converters and energy markets By Patricio MENDOZA-ARAYA	[View] [Download]
Abstract: Future smart grids are expected to have a high amount of renewable energy sources as well as anadvanced metering and communication infrastructure. These communication technologies offerdynamic information that can be sent to the utilities and used by the power system operators. The power system operators drive the scheduling optimization of the generators usually at a different and slower timescale. However, as the exchange of information get closer to real-time, it is possible to turn this into a closed-loop control-based problem, that adjusts the generator and load power outputs constantly as system conditions change. Under this new scenario, it is possible to observe interactions between the markets and the physical power system. This work proposes a methodology to assess the stability of future smart grids, with high penetration of power electronics converters and considering the coupling between the power systems and energy markets. The power system and energy market are modeled to form a feedback system that can be assessed by the Nyquist stability criterion. The simulations are done in the MATLAB/SIMULINK environment using the WSCC 3 machine 9 bus power system.

	State of charge control for a frequency-supporting storage system based on an auto-regressive frequency forecast By Alberto BOLZONI	[View] [Download]
Abstract: An advanced state-of-charge (SoC) management strategy for grid-connected energy storage units during the provision of fast frequency regulation services is proposed. The approach is based on an adaptive auto-regressive predictor of grid frequency and yields an average reduction between 13\% and 70\% of the energy costs in comparison to alternative standard SoC management approaches.