**4.4 Experimental validation by analyzing a photovoltaic generation system**

Additionally, the proposed method is evaluated under a real scenario in order to highlight the effectiveness and performance during the novelty detection of PQ disturbances. In this regard, experimentation is performed in a 30-MW wind farm located in northwest Spain. A proprietary data acquisition system (DAS) is used for collecting and storage the electrical signals. This DAS is based on field programmable gate array (FPGA) technology and it is able to acquire data from 7 channels simultaneously. Three of these channels are devoted to collect the voltage signals, whereas the four remaining channels are intended to receive current signals. The FPGA-based DAS operates at a sampling rate of 8000 samples per seconds and has a 16-bit analog to digital converter that ensures the proper representation of the acquired data. Finally, the DAS incorporates a 128 GB SD memory that allows performing the uninterrupted data storage for periods up to 11 days. When the memory is full, it can be easily replaced to continue with the acquisition process. The DAS is located at the substation of the windfarm, which means that the production of the complete farm can be monitored. The measurements are taken from a measuring transformer, so the DAS must measure voltages up to 110 Vrms. The commercial current clamps SCT-013-010 from YHDC are used to perform the current measurements in this location.

Therefore, the proposed novelty detection method for detecting the occurrence of PQ disturbances is applied to real data acquired from a real scenario as follows:


In this regard, after evaluating the set of the statistical features through each one of the SOM neuron grid models, *SOM*1, *SOM*2, *SOM*3, and *SOM*4, the *Ēq* value is obtained. Thereby, the *Ēq* value achieved by each SOM model is represented and show from **Figure 10a-d**, respectively. From these obtained results it should be highlighted that the graphical representation of the *Ēq* value of **Figure 10a, c** and **d**, presents an abrupt increase. This increase is produced due to the neuron grid models *SOM*1, *SOM*3 and *SOM*4 detect a novelty; on the other hand, the *Ēq* value achieved by the *SOM*2 neuron grid does not show the increase since it could be considered that the novelty detection belongs to the occurrence of sag.

**35**

**Figure 10.**

*Novelty Detection Methodology Based on Self-Organizing Maps for Power Quality Monitoring*

Afterward, in order to validate the occurrence of the sag, the voltage signal is analyzed by visual inspection to find and detect such PQ disturbance; in this sense, in **Figure 11a** is shown the voltage signal and it may be observed that

*Achieved mean quantization error by each SOM neuron model by evaluating the electric power signal of a* 

*transformer in: (a) SOM1, (b) SOM2, (c) SOM3 and (d) SOM4.*

*DOI: http://dx.doi.org/10.5772/intechopen.96145*

*Novelty Detection Methodology Based on Self-Organizing Maps for Power Quality Monitoring DOI: http://dx.doi.org/10.5772/intechopen.96145*

#### **Figure 10.**

*Artificial Intelligence - Latest Advances, New Paradigms and Novel Applications*

approaches such as OC-SVM.

are used to evaluate the available data to detect novelties during the PQ monitoring. Thus, by evaluating the available data by means of such approach a global classification ratio about 62% is approximately obtained for each evaluated condition. Therefore, it must be mentioned that the consideration SOM neuron grids as a part of a novelty detection structure leads to obtain advantageous results over classical

**4.4 Experimental validation by analyzing a photovoltaic generation system**

Additionally, the proposed method is evaluated under a real scenario in order to highlight the effectiveness and performance during the novelty detection of PQ disturbances. In this regard, experimentation is performed in a 30-MW wind farm located in northwest Spain. A proprietary data acquisition system (DAS) is used for collecting and storage the electrical signals. This DAS is based on field programmable gate array (FPGA) technology and it is able to acquire data from 7 channels simultaneously. Three of these channels are devoted to collect the voltage signals, whereas the four remaining channels are intended to receive current signals. The FPGA-based DAS operates at a sampling rate of 8000 samples per seconds and has a 16-bit analog to digital converter that ensures the proper representation of the acquired data. Finally, the DAS incorporates a 128 GB SD memory that allows performing the uninterrupted data storage for periods up to 11 days. When the memory is full, it can be easily replaced to continue with the acquisition process. The DAS is located at the substation of the windfarm, which means that the production of the complete farm can be monitored. The measurements are taken from a measuring transformer, so the DAS must measure voltages up to 110 Vrms. The commercial current clamps SCT-013-010 from YHDC are used to perform the current measurements in this location. Therefore, the proposed novelty detection method for detecting the occurrence of PQ disturbances is applied to real data acquired from a real scenario as follows:

1.One of the voltage signals that was acquired during the monitoring of the transformed is processed as is described in Section 3, this processing is performed in order to compute the proposed set of 3 statistical features.

2.Subsequently, the set of statistical features that represent the voltage signal is evaluated though all the SOM neuron grids models that were obtained during the training procedure, in which, the synthetic signals were considered. Specifically, such set of statistical features is evaluated through the neuron grid model: *SOM*1, *SOM*2, *SOM*3 and *SOM*4, which represent the normal condition,

3.The mean quantization error, *Ēq*, is analyzed aiming to determine the novelty detection and aiming to determine whether the occurrence of a PQ distur-

In this regard, after evaluating the set of the statistical features through each one of the SOM neuron grid models, *SOM*1, *SOM*2, *SOM*3, and *SOM*4, the *Ēq* value is obtained. Thereby, the *Ēq* value achieved by each SOM model is represented and show from **Figure 10a-d**, respectively. From these obtained results it should be highlighted that the graphical representation of the *Ēq* value of **Figure 10a, c** and **d**, presents an abrupt increase. This increase is produced due to the neuron grid models *SOM*1, *SOM*3 and *SOM*4 detect a novelty; on the other hand, the *Ēq* value achieved by the *SOM*2 neuron grid does not show the increase since it could

be considered that the novelty detection belongs to the occurrence of sag.

the occurrence of sag, swell, and fluctuations, respectively.

bance is detected by one of the SOM models.

**34**

*Achieved mean quantization error by each SOM neuron model by evaluating the electric power signal of a transformer in: (a) SOM1, (b) SOM2, (c) SOM3 and (d) SOM4.*

Afterward, in order to validate the occurrence of the sag, the voltage signal is analyzed by visual inspection to find and detect such PQ disturbance; in this sense, in **Figure 11a** is shown the voltage signal and it may be observed that

**Figure 11.**

*Voltage signal acquired during the monitoring of the electric transformer in which a novelty detection is detected. (a) Complete voltage signal, (b) zoom over the specific area in which the disturbance is detected and identified as sag.*

around the eight-second a disturbance is presented. Also, in **Figure 11b** is shown a zoom of such specific area in which the disturbance is presented and, it can be appreciated that the disturbance has the specific characteristics that belong to the occurrence of sag. In this sense, it should be highlighted that the effectiveness of the proposed novelty detection methodology has been proved by analyzing real data acquired from a real scenario that includes the monitoring of a transformer.

#### **5. Conclusions**

This chapter proposes a novelty detection methodology based on Self-Organizing Maps to perform the monitoring of Power Quality. The obtained result proves the effectiveness of the proposed method for detecting the occurrence of unexpected and undesirable electric power disturbances such as sag, swell, and fluctuations.

Thus, two main important key points must be highlighted from this proposal. First, the characterization of the electric power signals through statistical timedomain based features leads to achieving a high-performance representation of the data distribution. Second, the modeling of the available data by means of SOM's neuron grids allows preserving the topology of the data, which is a key feature that leading the detection of novelty events. Additionally, the consideration of a collaborative SOM neuron structure based on the analysis of the mean quantization error effectively detects all novel electric power disturbances considered.

**37**

**Author details**

San Juan del Rio, Mexico

Juan Jose Saucedo-Dorantes\*, David Alejandro Elvira-Ortiz, Arturo Yosimar Jaen-Cuéllar and Manuel Toledano-Ayala Engineering Faculty, Autonomous University of Queretaro,

\*Address all correspondence to: jsaucedo@hspdigital.org

provided the original work is properly cited.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Novelty Detection Methodology Based on Self-Organizing Maps for Power Quality Monitoring*

Finally, the proposed method is evaluated under a synthetic database of electric power signals that considers the occurrence of four conditions, normal, sag, swell, and fluctuations. In fact, the proposed PQ monitoring structure may be extended to other power disturbances. The obtained results depict that this proposal is a suitable option to be implemented in embedded systems, such as field-programmable gate arrays (FPGA), as a tool for online monitoring with application in industrial

This research work has been partially supported by the FONDEC-UAQ-2019

*DOI: http://dx.doi.org/10.5772/intechopen.96145*

under the registered project FIN202011.

The authors declare no conflict of interest.

processes.

**Acknowledgements**

**Conflict of interest**

*Novelty Detection Methodology Based on Self-Organizing Maps for Power Quality Monitoring DOI: http://dx.doi.org/10.5772/intechopen.96145*

Finally, the proposed method is evaluated under a synthetic database of electric power signals that considers the occurrence of four conditions, normal, sag, swell, and fluctuations. In fact, the proposed PQ monitoring structure may be extended to other power disturbances. The obtained results depict that this proposal is a suitable option to be implemented in embedded systems, such as field-programmable gate arrays (FPGA), as a tool for online monitoring with application in industrial processes.
