Harmonics Detector in Distribution Systems based on Compressive Sensing

Carta, Daniele; Muscas, Carlo; Sulis, Sara

doi:10.1109/amps.2017.8078328

Cited by 5 publications

(14 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e is the overall measurement error vector. While in [14] single harmonic detection was considered, in this paper the estimation of the injecting loads/generators is considered as direct estimation of u…”

Section: A Definition Of the Measurement Modelmentioning

confidence: 99%

“…Several algorithms have been proposed in the literature to face this problem [18]- [20]; in this paper a Block Orthogonal Matching Pursuit (BOMP) algorithm is adopted ( [21], [22]). It is worth highlighting that while in [14] a BOMP algorithm was used at single-harmonic level, the new proposed algorithm is a BOMP algorithm performed at injecting/polluting element (hereinafter referred to as "load" for the sake of brevity) level. Since, in most cases, the harmonic sources are the loads, the new algorithm will be indicated as LBOMP in the following.…”

Section: B Identification and Estimation Algorithmmentioning

confidence: 99%

“…2) Block OMP iterative algorithm. Similarly to [14], the estimation is obtained by a greedy procedure: First, a vector of residuals is initialized to the measurements, that is r 0 = z . Then, the iteration process starts.…”

Section: Lbomp Proceduresmentioning

confidence: 99%

“…Based on this consideration, in this paper the problem of harmonic source identification and estimation is addressed by means of the Compressive Sensing (CS) theory, which is a signal recovery technique suitable to recover sufficiently sparse signals from limited information ( [12], [13]). In particular, starting from the single harmonic identification approach presented in [14], where the main harmonic currents in the network were detected based on an Block Orthogonal Matching Pursuit (BOMP) algorithm, this paper introduces a new global Compressive Sensing Harmonics Detector (CSHD) that allows identifying the main polluting sources regardless of the specific harmonic involved, and estimating the corresponding injected harmonic currents.…”

Section: Introductionmentioning

confidence: 99%

“…It is validated by means of appropriate simulation tests carried out on a small network derived from the 13bus IEEE network and practical aspects of the detection are discussed with reference to international standards. The proposed CSHD proves to be efficient in the identification of the principal polluting loads with respect to both the singleharmonic algorithm in [14] and an identification algorithm based on the classic WLS estimation.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Identification and Estimation of Harmonic Sources Based on Compressive Sensing

Carta

Muscas

Sulis

2019

IEEE Trans. Instrum. Meas.

Self Cite

View full text Add to dashboard Cite

Nodes in electric distribution networks are greatly differentiated and are very often nonlinear and/or unbalanced. They can create significant harmonic pollution, with harmonics that inevitably spread along the grid. Monitoring harmonic propagation and correlated power quality phenomena requires specific measurement devices and methodologies. Nevertheless, because of the unavailability of a rapid diffusion of synchronized and dedicated devices (due to technical and economic reasons) on every node and branch of the network, estimating the harmonic status of the entire grid by means of a complete or even redundant monitoring system can be practically unfeasible. A more feasible, though always meaningful, goal can thus be pursued, that is estimating the main harmonic sources in the network, rather than its complete harmonic status. This approach, of course, can be based on a simpler and cheaper upgrade of the distributed monitoring system. Even more, by considering the common scenario where the number of significant harmonic sources is lower than the number of loads connected to the grid, specific estimation procedures can be defined to further reduce the complexity of the monitoring system. In this scenario, this paper presents an efficient Compressive Sensing Harmonics Detector (CSHD) for the identification and the estimation of the principal pollution sources. The proposed CSHD method is validated by means of appropriate tests performed on an example of distribution grid.

show abstract

Section: A Definition Of the Measurement Modelmentioning

confidence: 99%

Section: B Identification and Estimation Algorithmmentioning

confidence: 99%

Section: Lbomp Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identification and Estimation of Harmonic Sources Based on Compressive Sensing

Carta

Muscas

Sulis

2019

IEEE Trans. Instrum. Meas.

Self Cite

View full text Add to dashboard Cite

show abstract

Impact of Measurement Accuracy on Fault Detection Obtained with Compressive Sensing

Carta

Pegoraro

Sulis

2018

2018 IEEE 9th International Workshop on Applied Measurements for Power Systems (AMPS)

View full text Add to dashboard Cite

Compressive Sensing-Based Harmonic Sources Identification in Smart Grids

Carta

Muscas

Pegoraro

et al. 2021

IEEE Trans. Instrum. Meas.

Self Cite

View full text Add to dashboard Cite

Identifying the prevailing polluting sources would help the distribution system operators in acting directly on the cause of the problem, thus reducing the corresponding negative effects. Due to the limited availability of specific measurement devices, ad-hoc methodologies must be considered. In this regard, Compressive Sensing-based solutions are perfect candidates. This mathematical technique allows recovering sparse signals when a limited number of measurements are available, and thus overcoming the lack of Power Quality meters.In this paper, a new formulation of the 1-minimization algorithm for Compressive Sensing problems, with quadratic constraint, has been designed and investigated in the framework of the identification of the main polluting sources in Smart Grids. A novel whitening transformation is proposed for this context. This specific transformation allows the energy of the measurement errors to be appropriately estimated and thus better identification results are obtained. The validity of the proposal is proved by means of several simulations and tests performed on two distribution networks for which suitable measurement systems are considered along with a realistic quantification of the uncertainty sources.

show abstract

Harmonics Detector in Distribution Systems based on Compressive Sensing

Cited by 5 publications

References 26 publications

Identification and Estimation of Harmonic Sources Based on Compressive Sensing

Identification and Estimation of Harmonic Sources Based on Compressive Sensing

Impact of Measurement Accuracy on Fault Detection Obtained with Compressive Sensing

Compressive Sensing-Based Harmonic Sources Identification in Smart Grids

Contact Info

Product

Resources

About