LBP-HF features and machine learning applied for automated monitoring of insulators for overhead power distribution lines

Prasad, P. Surya; Rao, B. Prabhakara

doi:10.1109/wispnet.2016.7566245

Cited by 20 publications

(12 citation statements)

References 19 publications

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“…The proposed approach classified the condition of the insulator into one of the three states as in [22] compared with one of the two states (good or bad) in other literature [1, 15, 16, 30]. The reported classification accuracy by using LBP‐HF features and SVM classifier [17] is 93.33% but it was the outcome of just one experimental setup hence not taking into account the random effect in the evaluation. Table 2 presents the classification accuracies of the experiments performed by the authors as well as those from the previously proposed approaches.…”

Section: Resultsmentioning

confidence: 99%

“…The insulators are then separated from the poles and applied to subsequent stages of feature extraction. Local binary pattern histogram Fourier (LBP‐HF) features [17] and wavelet transform based features [14, 15] are obtained from the extracted insulators. The feature vectors are given to support vector machine (SVM) for the classification of insulators into various classes, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Condition monitoring of 11 kV overhead power distribution line insulators using combined wavelet and LBP‐HF features

Prasad¹,

Rao

2017

IET Generation, Transmission & Distribution

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Condition monitoring of 11 kV overhead power distribution line insulators using combined wavelet and LBP‐HF features

Prasad¹,

Rao

2017

IET Generation, Transmission & Distribution

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“…Features extracted SVM 36 RGB color features SVM 37 Statistical features HMM 34 Statistical features ANFIS 1 DOST features ANFIS & SVM 35 Mean, Variance SVM 50 Statistical features SVM 50 Statistical features…”

Section: Methods Of Classificationmentioning

confidence: 99%

“…Trinh Thi Phan et al [49] used the features extracted from Local Binary Pattern (LBP) for mobile product image searching. And its extended version, Local binary pattern Histogram Fourier (LBP-HF) is a rotation invariant feature and is used by P.S.Prasad et al [50] for distinguishing between healthy and risky insulators.…”

Section: Spatial Domain Methodsmentioning

confidence: 99%

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Prasad¹,

Rao²,

Ece³

2016

JESTR

Self Cite

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In the present world, there is a great necessity to have reliable and quality power distribution, and so there is great scope for research on automation of distribution system. The main objective of this paper is to analyze and comprehend different machine learning and image processing based algorithms to find a practical solution for automated inspection of overhead power line insulators. This method is a relatively new approach for. This paper also highlights the constraints and limitations that are present in the various existing methodologies to achieve the objective. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. The few techniques like artificial neural networks (ANN), Hidden Markov Model (HMM), k-means clustering, Wavelet transform features, S-transform features, and support vector machines (SVM) applied in the domain of condition monitoring of the insulators were presented.

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“…Then, the connected regions of all categories were calculated to obtain the smallest circumscribed rectangle of each region for positioning. Prasad et al [65] used SVM and local binary pattern histogram Fourier (LBP-HF) to complete health status detection of insulators, with a precision of 93.33%. For fittings, Zhang et al [66] proposed a fitting means based on HOG.…”

Section: Insulators and Fittingsmentioning

confidence: 99%

Environment Perception Technologies for Power Transmission Line Inspection Robots

et al. 2021

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With the fast development of the power system, traditional manual inspection methods of a power transmission line (PTL) cannot supply the demand for high quality and dependability for power grid maintenance. Consequently, the automatic PTL inspection technology becomes one of the key research focuses. For the purpose of summarizing related studies on environment perception and control technologies of PTL inspection, technologies of three-dimensional (3D) reconstruction, object detection, and visual servo of PTL inspection are reviewed, respectively. Firstly, 3D reconstruction of PTL inspection is reviewed and analyzed, especially for the technology of LiDAR-based reconstruction of power lines. Secondly, the technology of typical object detection, including pylons, insulators, and power line accessories, is classified as traditional and deep learning-based methods. After that, their merits and demerits are considered. Thirdly, the progress and issues of visual servo control of inspection robots are also concisely addressed. For improving the automation degree of PTL robots, current problems of key techniques, such as multisensor fusion and the establishment of datasets, are discussed and the prospect of inspection robots is presented.

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LBP-HF features and machine learning applied for automated monitoring of insulators for overhead power distribution lines

Cited by 20 publications

References 19 publications

Condition monitoring of 11 kV overhead power distribution line insulators using combined wavelet and LBP‐HF features

Condition monitoring of 11 kV overhead power distribution line insulators using combined wavelet and LBP‐HF features

Review on Machine Vision based Insulator Inspection Systems for Power Distribution System

Environment Perception Technologies for Power Transmission Line Inspection Robots

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