Artificial-Intelligence Method for the Derivation of Generic Aggregated Dynamic Equivalent Models

Kontis, Eleftherios O.; Papadopoulos, Theofilos A.; Syed, Mazheruddin H.; Guillo-Sansano, Efren; Burt, Graeme; Papagiannis, Grigoris K.

doi:10.1109/tpwrs.2019.2894185

Cited by 49 publications

(31 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the mode identification and equivalent modeling techniques is assessed using signal responses obtained from the Kundur two-area system topology [36] and a laboratory-scale ADN [39], respectively. In the following Sections the network topologies employed to obtain the dynamic responses are briefly described.…”

Section: Description Of the Systems Under Studymentioning

confidence: 99%

“…The examined topology is interconnected with the main utility grid, which is simulated using a real-time digital simulator (RTDS) and is connected to the ADN by means of a three-phase programmable voltage source (PVS). A detailed description regarding the examined topology can be found in [39]. In order to obtain the measurements for the assessment of the mode identification methods, a disturbance at bus 7 is caused.…”

Section: Laboratory-scale Active Distribution Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

et al. 2019

View full text Add to dashboard Cite

During the last decade the dynamic properties of power systems have been altered drastically, due to the emerge of new non-conventional types of loads as well as to the increasing penetration of distributed generation. To analyze the power system dynamics and develop accurate models, measurement-based techniques are usually employed by academia and power system operators. In this regard, in this paper an identification toolbox is developed for the derivation of measurement-based equivalent models and the analysis of dynamic responses. The toolbox incorporates eight of the most widely used mode identification techniques as well as several static and dynamic network equivalencing models. First, the theoretical background of the mode identification techniques as well as the mathematical formulation of the examined equivalent models is presented and analyzed. Additionally, multi-signal analysis methods are incorporated in the toolbox to facilitate the development of robust equivalent models. Additionally, an iterative procedure is adopted to automatically determine the optimal order of the derived models. The capabilities of the toolbox are demonstrated using simulation responses, acquired from large-scale benchmark power systems, as well as using measurements recorded at a laboratory-scale active distribution network.

show abstract

Section: Description Of the Systems Under Studymentioning

confidence: 99%

Section: Laboratory-scale Active Distribution Networkmentioning

confidence: 99%

A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

et al. 2019

View full text Add to dashboard Cite

show abstract

“…[18]) and artificial neural networks (e.g. [19]) have been proposed to identify and update parameters of black-box equivalents, this aspect has been comparatively little investigated in the context of grey-box models using large-disturbance simulations. It turns out to be impossible for a single set of equivalent parameters to accommodate all possible operating conditions.…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Equivalent of Active Distribution Network: Derivation, Update, Validation and Use Cases

Chaspierre

Denis²,

Panciatici³

et al. 2021

IEEE Open J. Power Energy

View full text Add to dashboard Cite

This paper deals with the derivation of dynamic equivalents of active distribution networks, hosting inverter-based generators as well as static and motor loads. Equivalents are reduced-order models for use in dynamic simulations of the transmission system. They are of the grey-box types and their parameters are identified from large-disturbance Monte-Carlo simulations accounting for model uncertainty. After presenting an overview of the identification method at a single operating point, the paper deals with the update the equivalent when the operating conditions of the distribution network change. A procedure identifies the parameters to update, hence avoiding a complete new identification. Besides illustrative examples, two sets of simulation results are reported. First, the accuracy of the equivalent is validated in a long-term voltage instability scenario. Second, a larger-scale application is presented, with numerous instances of the equivalent attached to the model of the IEEE Nordic transmission test system. This combined model is used to assess the impact on short-and long-term voltage stability of the inverter-based generators with fast and slow controls.

show abstract

“…However, the system model is an indispensable part of centralized control. The complex network information is often hard to obtain in practice, making conventional coherency-based [21] and linearization modeling methods [22] hard to build the system model. Other new modeling methods, such as the data-driven modeling method [23] and the fisher-discriminant-analysis-based method [24], are also difficult to address the high-order and uncertainties caused by DGs [18].…”

Section: Introductionmentioning

confidence: 99%

Unified Modeling and Hierarchical Control Strategy for Different Distributed Generators in Active Distribution Networks

et al. 2021

View full text Add to dashboard Cite

The growing penetration of renewable energy resources (RESs) increases uncertainties in active distribution networks (ADNs), leading the networks to suffer low inertia, bidirectional power flows, and rapid power changes. The frequency overshoots, overvoltage, and unbalance power sharing are prone to happen in ADNs. To address those issues, this paper proposes a novel unified modeling and a hierarchical control strategy for different distributed generators (DGs). First, to unify the control states of different DGs and separate control states of different control layers, a unified modeling method for grid-following and grid-forming DGs is proposed. Then, based on the unified model, a two-layer control strategy is designed. The robust controllers are locally configured to achieve rapid recovery control, avoiding the impact of communication delays. The collaborative power controller is centralized designed to prevent power circulation caused by the interaction of local controllers of DGs. Compared with other hierarchical control strategies, our strategy can make further use of the residual capacities of different DGs and can more rapidly suppress the unpredictable changes of RESs. The effectiveness of the proposed strategy is validated by several cases in MATLAB/Simulink.

show abstract

Artificial-Intelligence Method for the Derivation of Generic Aggregated Dynamic Equivalent Models

Cited by 49 publications

References 35 publications

A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

A Toolbox for Analyzing and Testing Mode Identification Techniques and Network Equivalent Models

A Dynamic Equivalent of Active Distribution Network: Derivation, Update, Validation and Use Cases

Unified Modeling and Hierarchical Control Strategy for Different Distributed Generators in Active Distribution Networks

Contact Info

Product

Resources

About