Health Index Prediction of Overhead Transmission Lines: A Machine Learning Approach

Manninen, Henri; Kilter, Jako; Landsberg, Mart

doi:10.1109/tpwrd.2021.3052721

Cited by 17 publications

(8 citation statements)

References 15 publications

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“…A health index (HI) is utilized to indicate the condition of the transmission tower. 31 Lower HI indicates a lower operational capacity, and in the proposed model, this translates to a lower transmission capacity. The representative value of HI ranges from 0 to 1, and the lower the value the lower the associated capacity of transmission lines.…”

Section: Implication Of Condition Of the Towermentioning

confidence: 96%

Resilient expansion planning of virtual power plant with an integrated energy system considering reliability criteria of lines and towers

Mishra

Bordin

et al. 2022

Intl J of Energy Research

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Section: Implication Of Condition Of the Towermentioning

confidence: 96%

Resilient expansion planning of virtual power plant with an integrated energy system considering reliability criteria of lines and towers

Mishra

Bordin

et al. 2022

Intl J of Energy Research

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“…Authors in [19] propose a joint expansion planning model with ownership sharing of the energy system and transmission grid. Author in [20] present a health index prediction method for overhead transmission lines. Automated utility pole condition monitoring is presented in [21].…”

Section: Reasoning and Challengesmentioning

confidence: 99%

“…The associated power apparatus are part of the tower or line depending on the where the apparatus is installed. A health index as introduced in [20] is used for representing the health of towers. In addition to that, a risk factor associated to a region to which the tower is serving is also applied.…”

Section: The Challenges Of Network Conditionmentioning

confidence: 99%

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Resilient expansion planning of virtual energy plant with an integrated energy system -- reliability criteria of lines and towers

Mishra¹,

Manninen²,

Wu³

et al. 2021

Preprint

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Virtual power plants, while being virtual, rely on a physical network for operations. The portfolio of virtual power plant is flexible in facilitating a wide range of resources including the local heat pumps. The power transmission network has the responsibility to ensure security of supply, reliability of operation, planning and expansion. The power transmission network and apparatus including lines and towers are also ageing with time. Furthermore, the transmission network covers a large geographical area which is expensive to maintain.The objective of this paper is to investigate the effect of power network condition on the power network expansion planning. The condition of power network is determined by maintenance cost of lines and a health index and a risk factor associated to tower. The investigation begins with answering how the inclusion of heat pump impacts the decisions on network interventions. Thereafter, the condition network is factored in into the decision making by better understanding the impact of the network condition on the overall expansion planning. Furthermore many sensitivity analysis are conducted to evaluate the trade-offs between decision variables such as cost of heat pump, coefficient of performance of heat pump, risk factors and line and tower costs.

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“…Common data driven approaches, such as statistical and artificial intelligence (AI) application based models, have been widely used as prediction models for condition deterioration of high voltage assets [7][8][9][10]. Predicting the HI of the transformers based on these approaches is one of many other applications.…”

Section: Introductionmentioning

confidence: 99%

Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers

et al. 2021

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In this study, statistical distribution model (SDM) is used to predict the health index (HI) of transformers by utilizing the condition parameters data from dissolved gas analysis (DGA), oil quality analysis (OQA), and furanic compound analysis (FCA), respectively. First, the individual condition parameters data were categorized based on transformer age from year 1 to 15. Next, the individual condition parameters data for every age were fitted while using a probability plot to find the representative distribution models. The distribution parameters were calculated based on 95% confidence level and extrapolated from year 16 to 25 through representative fitting models. The individual condition parameters data within the period were later calculated based on the estimated distribution parameters through the inverse cumulative distribution function (ICDF) of the selected distribution models. The predicted HI was then determined based on the conventional scoring method. The Chi-square test for statistical hypothesis reveals that the predicted HI for the transformer data is quite close to the calculated HI. The average percentage of absolute error is 2.7%. The HI that is predicted based on SDM yields 97.83% accuracy for the transformer data.

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Health Index Prediction of Overhead Transmission Lines: A Machine Learning Approach

Cited by 17 publications

References 15 publications

Resilient expansion planning of virtual power plant with an integrated energy system considering reliability criteria of lines and towers

Resilient expansion planning of virtual power plant with an integrated energy system considering reliability criteria of lines and towers

Resilient expansion planning of virtual energy plant with an integrated energy system -- reliability criteria of lines and towers

Application of Statistical Distribution Models to Predict Health Index for Condition-Based Management of Transformers

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