A Real-Time Adjustment of Conventional Under-Frequency Load Shedding Thresholds

Potel, Barnabé; Debusschere, Vincent; Cadoux, Florent; Rudež, Urban

doi:10.1109/tpwrd.2019.2900594

Cited by 21 publications

(6 citation statements)

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“…It requires enhanced relay technologies allowing frequent updates of the frequency threshold assigned to each relay. They may also use additional 'richer' measurements such as rate of change of frequency [16][17][18], the second-derivative of frequency [19], a forecast of the frequency evolution [20,21] and/or voltage measurements [22,23]. Indeed, improving the adequacy between the amount of load shed on one hand, and the initial power imbalance on the other hand, may require:…”

Section: Impact Of the Dgmentioning

confidence: 99%

“…It requires enhanced relay technologies allowing frequent updates of the frequency threshold assigned to each relay. They may also use additional ‘richer’ measurements such as rate of change of frequency [16–18], the second‐derivative of frequency [19], a forecast of the frequency evolution [20, 21] and/or voltage measurements [22, 23]. Indeed, improving the adequacy between the amount of load shed on one hand, and the initial power imbalance on the other hand, may require: Additional measurements, to better estimate the appropriate UFLS action to apply at any given time (ex ante), and also to find out the appropriate reaction to a given power imbalance in near real‐time when it occurs ( ex post ); A control infrastructure, including a communication system, that will apply and automatically update some new ‘dynamic’ UFLS scheme; A proper distribution of UFLS relays into the transmission system in order to take into account the entire load of the EPS. By comparison with this futuristic view on the topic of UFLS technology, recall that our assumption in the remainder of this paper is that the parameters of existing UFLS relays may currently not realistically be updated more than about once a year.…”

Section: Introductionmentioning

confidence: 99%

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Clustering‐based method for the feeder selection to improve the characteristics of load shedding

Potel

Cadoux

Garcia³

et al. 2019

IET Smart Grid

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Under-frequency load shedding (UFLS) schemes are designed by specifying a given amount of load to shed at various frequency thresholds to prevent the collapse of the electrical power system in the event of a large generation-load imbalance. An UFLS step is constituted of a group of medium-voltage feeders that trip when a given frequency threshold is reached. This study focuses on the method to be used when allocating a given feeder to a given step. First, the authors introduce performance metrics to quantify the accuracy level with which the UFLS target is met. Second, they model: the allocation method currently used in France; a variant of that method; and a new method introduced in this study, based on an automated clustering technique. Third, based on real consumption patterns measured from a vast area in France, and using the introduced performance metrics, they compare the efficiency of the three described methods. This study is conducted for the current state of loading of the considered distribution network and for a hypothetical situation with an increased share of distribution-side photovoltaic generation. For the chosen performance metrics, they demonstrate that the first two methods provide similar results while the clustering-based method performs remarkably better.

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Section: Impact Of the Dgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Clustering‐based method for the feeder selection to improve the characteristics of load shedding

Potel

Cadoux

Garcia³

et al. 2019

IET Smart Grid

Self Cite

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show abstract

“…The authors (Alrajhi et al, 2018;Małkowski, 2020;Alrajhi Alsiraji and El-Shatshat, 2021) exhibit adaptive fuzzy load shedding for small power systems. The authors implemented Potel et al, 2019;Masood et al, 2021) frequency-based multi-topological algorithms. Another study recommended hardening of the electrical system before a cyclone and softening it thereafter to prevent hurricane damage (Sang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Load shedding for frequency and voltage control in the multimachine system using a heuristic knowledge discovery method

et al. 2022

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The voltage profile of different buses and the rotor dynamics of generators are adversely affected by a generator outage. Generator outages can be minimized using a variety of strategies and algorithms. An AI-based knowledge discovery approach has been reported in this article. This article proposes a technique for identifying sensitive loads and the amount of active and reactive power curtailment for rotor speed regulation and voltage management at the terminals. The MATLAB®/Simulink environment verifies and tests the method’s practicality on an IEEE-10-machine-39-bus system. Active power shedding is considered for rotor angle stability, while reactive power is also shedded for maintaining the terminal voltage at the loads. A sequential outage is considered to simulate a scenario where the two generators with the highest active and reactive power are taken out of service. The generator’s rotor speed, terminal voltage, and load are measured with and without load restriction. In all situations, the rotor and center of inertia speed are 1 p.u. The average steady-state load terminal voltage is 0.967 V. The average terminal voltage of all load buses improves from 0.933 to 0.972 and 0.936 to 0.971, case-wise. The reported results confirm and validate the effectiveness and applicability of the proposed technique.

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“…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].…”

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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A Real-Time Adjustment of Conventional Under-Frequency Load Shedding Thresholds

Cited by 21 publications

References 5 publications

Clustering‐based method for the feeder selection to improve the characteristics of load shedding

Clustering‐based method for the feeder selection to improve the characteristics of load shedding

Load shedding for frequency and voltage control in the multimachine system using a heuristic knowledge discovery method

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

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