Event‐driven frequency and voltage stability predictive assessment and unified load shedding

Singh, Ajeet; Fozdar, Manoj

doi:10.1049/iet-gtd.2018.6750

Cited by 20 publications

(27 citation statements)

References 38 publications

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“…Otherwise, further operations need to be performed for the unstable islands (e.g. splitting the unstable islands again, or adopting generator or load tripping actions [37, 38]).…”

Section: Implemention Of Islanding Algorithmmentioning

confidence: 99%

Controlled islanding algorithm for AC/DC hybrid power systems utilising DC modulation

Fan

Crisostomi

Thomopulos

et al. 2020

IET gener. transm. distrib.

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The controlled islanding problem is typically considered only for pure ac power systems, with the ultimate objective of either minimising power imbalance or power-flow among islands. However, as ac/dc hybrid power systems are becoming popular worldwide, current controlled islanding schemes should be redesigned to take into account the presence of high-voltage dc (HVDC) links, possibly connecting different islands. Accordingly, in this study, the authors solve the classic islanding problem combining HVDC power modulation with the conventional ac cutset search. The flexibility of the HVDC allows the authors' strategy to jointly capture the previously mentioned objectives, and provides even lower power imbalances than the minimum imbalance strategy, at a relatively small cost of increasing power-flow impact. Then, the optimisation problem is formulated as a mixed-integer linear programming problem, which can be conveniently solved with existing commercial solvers. Finally, they validate their proposed strategy in two case studies, corresponding to a modified IEEE-118 system and to the China Southern Power Grid system. In both cases, dynamic simulations show that their proposed approach outperforms classic algorithms where the presence of HVDC power modulation is not explicitly taken into account.

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“…Otherwise, further operations need to be performed for the unstable islands (e.g. splitting the unstable islands again, or adopting generator or load tripping actions [37, 38]).…”

Section: Implemention Of Islanding Algorithmmentioning

confidence: 99%

Controlled islanding algorithm for AC/DC hybrid power systems utilising DC modulation

Fan

Crisostomi

Thomopulos

et al. 2020

IET gener. transm. distrib.

View full text Add to dashboard Cite

show abstract

“…Adaptive load shedding methods are mainly studied for load shedding control in recent publications [9]- [11]. These methods first calculate power deficit or estimate the minimum frequency after disturbance, then the actual amount to be shed can be calculated or guided by a pre-defined strategy [12]. However, this type of scheme is usually based on the equivalent system model and is limited by the accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control

Xie

Sun

2021

IEEE Access

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Fast and accurate prediction and control of power system dynamic frequency after disturbance is essential to enhance power system stability. Machine learning methods have great potential in harnessing data for online application with accurate predictions. This paper proposes a two-stage novel transfer and deep learning-based method to predict power system dynamic frequency after disturbance and provide optimal event-based load shedding strategy to maintain system frequency. The proposed deep learning model combines convolutional neural network (CNN) and long short-term memory (LSTM) network to harness both spatial and temporal measurements in the input data, through a four-dimensional (4-D) tensor input construction process including, 1) capture system network topology information and critical measurements from different time intervals; 2) compute a multi-dimensional electric distance matrix and reduce to a 2-D plane which can describe the system nodal distribution; 3) construct 3-D tensors based on state variables at different sample times; and 4) integrate into 4-D tensor inputs. Moreover, a transfer learning process is employed to overcome the challenge of insufficient data and operating condition changes in real power systems for new prediction tasks. Simulation results in IEEE 118-bus system verify that the CNN-LSTM method not only greatly improves the timeliness of online frequency control, but also presents good accuracy and effectiveness. Test cases in the New England 39-bus system and the South Carolina 500-bus system validate that the transfer learning process can provide accurate results even with insufficient training data.

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“…Since ESSs with flexible power control are suitable for RDFC, EDFC is generally not used by ESSs. However, EDFC has been studied in the field of under-frequency load shedding (UFLS) [19][20][21]. A method was proposed for predicting the magnitude of disturbance with the ROCOF according to the generator outage data and performing load shedding depending on the disturbance [19].…”

Section: Introductionmentioning

confidence: 99%

“…In another study, an extreme learning machine trained with operating conditions and contingencies has been used to derive the necessary load shedding when an actual contingency occurred [20]. There has been a load-shedding scheme that is based on an active and reactive power change [21]. In [20,21], a machine-learning algorithm was used to predict proper load shedding.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Storage System Event-Driven Frequency Control Using Neural Networks to Comply with Frequency Grid Code

et al. 2020

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As the penetration of renewable energy sources (RESs) increases, the rate of conventional generators and the power system inertia are reduced accordingly, resulting in frequency-stability concerns. As one of the solutions, the battery-type energy storage system (ESS), which can rapidly charge and discharge energy, is utilized for frequency regulation. Typically, it is based on response-driven frequency control (RDFC), which adjusts its output according to the measured frequency. In contrast, event-driven frequency control (EDFC) involves a determined frequency support scheme corresponding to a particular event. EDFC has the advantage that control action is promptly performed compared to RDFC. This study proposes an ESS EDFC strategy that involves estimating the required operating point of the ESS according to a specific disturbance through neural-network training. When a disturbance occurs, the neural networks can estimate the proper magnitude and duration of the ESS output to comply with the frequency grid code. A simulation to validate the proposed control method was performed for an IEEE 39 bus system. The simulation results indicate that a neural-network estimation offers sufficient accuracy for practical use, and frequency response can be adjusted as intended by the system operator.

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Event‐driven frequency and voltage stability predictive assessment and unified load shedding

Cited by 20 publications

References 38 publications

Controlled islanding algorithm for AC/DC hybrid power systems utilising DC modulation

Controlled islanding algorithm for AC/DC hybrid power systems utilising DC modulation

A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control

Energy Storage System Event-Driven Frequency Control Using Neural Networks to Comply with Frequency Grid Code

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