Experimentally validated partial least squares model for dynamic line rating

Morrow, D. John; Fu, Jun; Abdelkader, Sobhy M.

doi:10.1049/iet-rpg.2013.0097

Cited by 23 publications

(11 citation statements)

References 16 publications

(17 reference statements)

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“…This assumes negligible dielectric losses, while charging current must be considered carefully as discussed in section VI. The percentages of heat output selected to calculate load current limits according to the number of states are: 4S= [15,50,75, 100] (%), 8S= [15,30,50,60,70,80,90, 100] (%), 17S= [15,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95, 100] (%).…”

Section: B Markov Chain Models (Mcm)mentioning

confidence: 99%

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Cable Thermal Risk Estimation for Overplanted Wind Farms

Colin

Pilgrim

2020

IEEE Trans. Power Delivery

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This paper presents a methodology for the forward estimation of thermal risk in offshore wind farm cables under scenarios where the farm is overplanted for economic purposes. A dynamic thermal rating assessment of the cable is proposed to estimate the thermal effects of probable load current scenarios considering actual temperatures. Calculated future temperatures are used to estimate the probability of the cable being overheated. Due to the ability of the method to estimate forward thermal risk, unnecessary power curtailment can be reduced while avoiding thermal damage to the cable. Simulated results of online thermal risk estimation and curtailment show additional power delivery of 7.26%, 9.16% and 9.67% per year for a 6% 9.9% and 13.7% wind farm overplanting respectively. The additional power is calculated compared to the annual power delivered (W h/year) with the use of the traditional continuous rating limits for the case studied.

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Section: B Markov Chain Models (Mcm)mentioning

confidence: 99%

“…They measure or estimate actual environmental conditions to calculate the cable temperature and assess permissible future loading i.e. [13]- [15]. However, real-time rating calculations do not consider uncertainty of power generation into the future, making them hard to apply to offshore wind power applications.…”

Section: Introductionmentioning

confidence: 99%

Cable Thermal Risk Estimation for Overplanted Wind Farms

Colin

Pilgrim

2020

IEEE Trans. Power Delivery

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“…It performs well in wind power prediction [7,8]. In detail, the partial least squares (PLS) approach is a common multivariate regression algorithm for linear system and could yield the statistical model of prediction issues [9][10][11]. While wind data is inherently nonlinear, PLS regression may not always catch the function of wind power output and historical wind speed input [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Wind Power Prediction Based on Nonlinear Partial Least Square

Lei

Cao

2018

Mathematical Problems in Engineering

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Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical penetration of wind energy. The intermittent and the randomness of wind would affect the accuracy of prediction. According to the sequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction. The proposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the nonlinear partial least square is adopted to map the wind speed under the time series to wind power. The model carries the neural network as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of the original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the real data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation neural network, radial basis function neural network, support vector machine, and partial least square are performed on the real data and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the real power running curves also verify that the proposed method can predict the wind power in short-term effectively.

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“…Such temperature-based transmission capacity constraints are called dynamic line ratings (DLRs) [10,11]. Given recent advances in information and communication technology (ICT) and computational techniques (e.g., those used for meteorological predictions), real-time monitoring and forecasting technologies [12,13] might become available for DLR in the future. Although DLR was proposed several decades ago [10,11], the possibility and practicality of employing DLR [14][15][16] in the real-time operation of power systems have arisen only recently.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method for Real-Time Dynamic Line Ratings Based on Line Current Variation Model for Representing Forecast Error of Intermittent Renewable Generation

2017

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Due the high penetration of intermittent renewable energy sources (IRESs), transmission line currents show large fluctuations and thus significant uncertainty. This makes it difficult to operate a power system without violating transmission capacity constraints. This paper evaluates the dynamic line ratings (DLRs) of overhead lines based on changes in the line current owing to the high penetration of intermittent renewable energy sources. In particular, by focusing on extremely large (but rare) forecasting errors in the intermittent renewable energy source output, which are generally inevitable in most forecasting methods, a model for representing the forecasting error in line with current variation due to intermittent renewable energy source output is developed. The model is based on a shape parameter that represents the equivalent current variation required for the same temperature increase as that due to the extremely large forecasting error. Finally, based on the annual minute-by-minute irradiance data, preventive control of the transmission network with dynamic line ratings is evaluated using worst-case parameter values.

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Experimentally validated partial least squares model for dynamic line rating

Cited by 23 publications

References 16 publications

Cable Thermal Risk Estimation for Overplanted Wind Farms

Cable Thermal Risk Estimation for Overplanted Wind Farms

Wind Power Prediction Based on Nonlinear Partial Least Square

Evaluation Method for Real-Time Dynamic Line Ratings Based on Line Current Variation Model for Representing Forecast Error of Intermittent Renewable Generation

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