[:de]Fühler im Netz 2.0[:en]Fühler im Netz 2.0 – The sensors in the distribution network are spreading[:]

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highlight_text=““ after_text=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ class=““ id=““ content_align=“left“ size=“2″ font_size=““ animated_font_size=““ fusion_font_family_title_font=““ fusion_font_subset_title_font=““ fusion_font_variant_title_font=““ line_height=““ letter_spacing=““ margin_top=““ margin_bottom=““ margin_top_mobile=““ margin_bottom_mobile=““ text_color=“#2d4966″ animated_text_color=““ highlight_color=““ style_type=“none“ sep_color=““ animation_type=““ animation_direction=“left“ animation_speed=“0.3″ animation_offset=““]Fühler im Netz 2.0 – Die Fühler im Verteilnetz breiten sich aus[/fusion_title][/fusion_builder_column][/fusion_builder_row][/fusion_builder_container][fusion_builder_container hundred_percent=“no“ hundred_percent_height=“no“ hundred_percent_height_scroll=“no“ hundred_percent_height_center_content=“yes“ equal_height_columns=“no“ menu_anchor=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ 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Was bisher geschah…

Aufbauend auf dem Forschungsvorhaben “Fühler im Netz (FiN)” werden in FiN 2.0 großflächige Lösungen zur Netzzustandsüberwachung mittels Big Data und künstlicher Intelligenz erprobt.

Im Projekt FiN, das 2017 nach einer dreijährigen Laufzeit erfolgreich zu Ende ging, ist es gelungen erste praxistaugliche Lösungen für die drei zentralen Herausforderungen des Netzbetriebs zu demonstrieren:

Systematischer Ausbau der Netzzustandsüberwachung
Zustandserfassung von Netzbetriebsmitteln
Errichtung einer effizienten Kommunikationsinfrastruktur

Der FiN-Ansatz – also die Datenübertragung mittels Breitband-Powerline-Kommunikation selbst zu analysieren und gleichzeitig die lokale Spannung zu messen – stellt dabei eine kostengünstige Methode für das Echtzeit-Netzmonitoring sowie die Zustandserfassung von Kabeln und Anlagen dar.

Aufbauend auf den Ergebnissen aus “Fühler im Netz” wurde das Sensor BPL-Modem 4NQ entwickelt, welches die Erfassung des BPL-Spektrums und eine integrierte Sensorik in sich vereint. Hierdurch wird – ganz ohne zusätzliche Messbox – eine flächendeckende Spannungsmessung ermöglicht, mit der sich Netzzustände kontinuierlich erfassen und analysieren lassen. Es erfolgt eine kontinuierliche, dreiphasige Messung von

Spannungen
Phasenwinkeln (zwischen den Spannungen)
THD (Total Harmonic Distortions)

So geht es weiter…

“Fühler im Netz 2.0” nutzt die gewonnenen Erkenntnisse des Vorgängerprojekts und legt den Fokus darauf, die Möglichkeiten zur Anlagen- und Netzzustandsüberwachung im großen Feldtest auszuweiten. Dabei sollen Optimierungspotentiale gehoben werden, die sich aus der Erfassung und der automatisierten Analyse von Massendaten im großen Maßstab ergeben. Für beide Kernelemente des FiN-Ansatzes, also sowohl für die BPL-Spektrumsanalyse als auch für die Erfassung der lokalen Spannung, kommen hierfür Big Data Analysen und Methoden der Künstlichen Intelligenz zum Einsatz. Zusätzlich findet im Rahmen des Projekts eine Weiterentwicklung der dezentralen Netzautomatisierung auf Basis der Messwerte der Sensor BPL-Modems statt.

Big Data

In einem großen Feldtest werden über 3500 BPL-Sensormodems in den Netzgebieten der Netze BW, der Mainzer Netze und der Energieversorgung Leverkusen (EVL) installiert. Diese werden in einer hohen zeitlichen Auflösung Daten über den lokalen Netzzustand (insb. der Spannung) und den Zustand von Kabeln und Anlagen erfassen. Dadurch fallen innerhalb kürzester Zeit große Datenmengen an, die für eine detaillierte Analyse genutzt werden können.[/fusion_text][/fusion_builder_column][fusion_builder_column type=“1_2″ layout=“1_2″ spacing=““ center_content=“no“ link=““ target=“_self“ min_height=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ class=““ id=““ hover_type=“none“ border_size=“0″ border_color=““ border_style=“solid“ border_position=“all“ border_radius=““ box_shadow=“no“ dimension_box_shadow=““ box_shadow_blur=“0″ box_shadow_spread=“0″ box_shadow_color=““ box_shadow_style=““ padding_top=““ padding_right=““ padding_bottom=““ padding_left=““ margin_top=““ margin_bottom=““ background_type=“single“ gradient_start_color=““ gradient_end_color=““ gradient_start_position=“0″ gradient_end_position=“100″ gradient_type=“linear“ radial_direction=“center center“ linear_angle=“180″ background_color=““ background_image=““ background_image_id=““ background_position=“left top“ background_repeat=“no-repeat“ background_blend_mode=“none“ animation_type=““ animation_direction=“left“ animation_speed=“0.3″ animation_offset=““ filter_type=“regular“ filter_hue=“0″ filter_saturation=“100″ filter_brightness=“100″ filter_contrast=“100″ filter_invert=“0″ filter_sepia=“0″ filter_opacity=“100″ filter_blur=“0″ filter_hue_hover=“0″ filter_saturation_hover=“100″ filter_brightness_hover=“100″ filter_contrast_hover=“100″ filter_invert_hover=“0″ filter_sepia_hover=“0″ filter_opacity_hover=“100″ filter_blur_hover=“0″ last=“no“][fusion_text columns=““ column_min_width=““ column_spacing=““ rule_style=“default“ rule_size=““ rule_color=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ class=““ id=““ animation_type=““ animation_direction=“left“ animation_speed=“0.3″ animation_offset=““]

Künstliche Intelligenz

Die generierten Daten werden mithilfe von Massendatenverarbeitungsmethoden erfasst und mittels KI-Algorithmen auf Muster und Auffälligkeiten untersucht. So kann eine Vorhersage des Spannungsverlaufs und der Asymmetrie zwischen den Phasen ermittelt werden. Diese Erkenntnisse werden in Zukunft insbesondere bei der Integration von E-Mobilität in den Verteilnetzen relevant.

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Projektkonsortium

Die Mitglieder des Projektkonsortiums unter der Leitung von PPC sind:

Netze BW
Mainzer Netze
Energieversorgung Leverkusen (EVL)
Deutsches Forschungszentrum für Künstliche Intelligenz
Bergische Universität Wuppertal
Software AG

Das Projekt wird vom BMBF mit rund 2,5 Millionen Euro gefördert.[/fusion_text][/fusion_builder_column][fusion_builder_column type=“1_4″ layout=“1_2″ spacing=““ center_content=“no“ link=““ target=“_self“ min_height=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ class=““ id=““ hover_type=“none“ border_size=“0″ border_color=““ border_style=“solid“ border_position=“all“ border_radius=““ box_shadow=“no“ dimension_box_shadow=““ box_shadow_blur=“0″ box_shadow_spread=“0″ box_shadow_color=““ box_shadow_style=““ padding_top=““ padding_right=““ padding_bottom=““ padding_left=““ margin_top=““ margin_bottom=““ background_type=“single“ gradient_start_color=““ gradient_end_color=““ gradient_start_position=“0″ gradient_end_position=“100″ gradient_type=“linear“ radial_direction=“center center“ linear_angle=“180″ background_color=““ background_image=““ background_image_id=““ background_position=“left top“ background_repeat=“no-repeat“ background_blend_mode=“none“ 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Alle Innovations- und Forschungsprojekte

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Fühler im Netz 2.0 – The sensors in the distribution network are spreading

[/fusion_title][/fusion_builder_column][/fusion_builder_row][/fusion_builder_container][fusion_builder_container hundred_percent=“no“ hundred_percent_height=“no“ hundred_percent_height_scroll=“no“ hundred_percent_height_center_content=“yes“ equal_height_columns=“no“ menu_anchor=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ status=“published“ publish_date=““ class=““ id=““ link_color=““ link_hover_color=““ border_size=““ border_color=““ border_style=“solid“ margin_top=““ margin_bottom=““ padding_top=““ padding_right=““ padding_bottom=““ padding_left=““ gradient_start_color=““ gradient_end_color=““ gradient_start_position=“0″ gradient_end_position=“100″ gradient_type=“linear“ radial_direction=“center center“ linear_angle=“180″ background_color=““ background_image=““ background_position=“center center“ background_repeat=“no-repeat“ fade=“no“ background_parallax=“none“ enable_mobile=“no“ parallax_speed=“0.3″ background_blend_mode=“none“ video_mp4=““ video_webm=““ video_ogv=““ video_url=““ video_aspect_ratio=“16:9″ video_loop=“yes“ video_mute=“yes“ video_preview_image=““ filter_hue=“0″ filter_saturation=“100″ filter_brightness=“100″ filter_contrast=“100″ filter_invert=“0″ filter_sepia=“0″ filter_opacity=“100″ filter_blur=“0″ filter_hue_hover=“0″ filter_saturation_hover=“100″ filter_brightness_hover=“100″ filter_contrast_hover=“100″ filter_invert_hover=“0″ filter_sepia_hover=“0″ filter_opacity_hover=“100″ filter_blur_hover=“0″][fusion_builder_row][fusion_builder_column type=“3_4″ layout=“1_2″ spacing=““ center_content=“no“ link=““ target=“_self“ min_height=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ class=““ id=““ hover_type=“none“ border_size=“0″ border_color=““ border_style=“solid“ border_position=“all“ border_radius=““ box_shadow=“no“ dimension_box_shadow=““ box_shadow_blur=“0″ box_shadow_spread=“0″ box_shadow_color=““ box_shadow_style=““ padding_top=““ padding_right=““ padding_bottom=““ padding_left=““ margin_top=““ margin_bottom=““ background_type=“single“ gradient_start_color=““ gradient_end_color=““ gradient_start_position=“0″ gradient_end_position=“100″ gradient_type=“linear“ radial_direction=“center center“ linear_angle=“180″ background_color=““ background_image=““ background_image_id=““ background_position=“left top“ background_repeat=“no-repeat“ background_blend_mode=“none“ animation_type=““ animation_direction=“left“ animation_speed=“0.3″ animation_offset=““ filter_type=“regular“ filter_hue=“0″ filter_saturation=“100″ filter_brightness=“100″ filter_contrast=“100″ filter_invert=“0″ filter_sepia=“0″ filter_opacity=“100″ filter_blur=“0″ filter_hue_hover=“0″ filter_saturation_hover=“100″ filter_brightness_hover=“100″ filter_contrast_hover=“100″ filter_invert_hover=“0″ filter_sepia_hover=“0″ filter_opacity_hover=“100″ filter_blur_hover=“0″ last=“no“][fusion_text columns=““ column_min_width=““ column_spacing=““ rule_style=“default“ rule_size=““ rule_color=““ hide_on_mobile=“small-visibility,medium-visibility,large-visibility“ class=““ id=““ animation_type=““ animation_direction=“left“ animation_speed=“0.3″ animation_offset=““]

What has happened so far…

Building on the „Fühler im Netz (FiN) (Sensors in the Grid)“ research project, FiN 2.0 tests large-scale solutions for grid status monitoring using Big Data and artificial intelligence.

The FiN project, which was successfully concluded in 2017 after a three-year term, has succeeded in demonstrating the first practical solutions for the three central challenges of network operation:

Systematic rollout of network condition monitoring
Condition monitoring of network equipment
Establishment of an efficient communication infrastructure

The FiN approach – i.e. to analyse the data transmission itself using broadband powerline communication and at the same time measure the local voltage – is a cost-effective method for real-time network monitoring as well as for recording the condition of cables and Systems.

Based on the results from “ Sensors in the Grid“ the sensor BPL modem 4NQ was developed, which combines the acquisition of the BPL spectrum and an integrated sensor technology. This enables – without the need for an additional measuring box – a comprehensive voltage measurement with which network conditions can be continuously recorded and analysed. A continuous, three-phase measurement of

Tensions
Phase angles (between voltages)
THD (Total Harmonic Distortions)

And so it goes on…

„Sensors in the Grid 2.0″ uses the findings of the previous project and focuses on extending the possibilities for plant and grid condition monitoring in a large-scale field test. In this context, optimisation potentials are to be raised which result from the acquisition and automated evaluation of mass data on a large scale. For both core elements of the FiN approach, i.e. for BPL spectrum analysis as well as for the recording of local voltage, big data analyses and methods of artificial intelligence are used for this purpose. In addition, a further development of the decentralised network automation based on the measured values of the sensor BPL modems will take place within the project.

Big Data

In a large field test, more than 3500 BPL sensor modems will be installed in the network areas of the Netze BW, Mainzer Netze and Energieversorgung Leverkusen (EVL). These will collect data in a high temporal resolution about the local network status (especially voltage) and the condition of cables and installations. As a result, large amounts of data are generated within a very short time, which can be used for detailed Analysis.

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Artificial Intelligence

The generated data is collected using mass data processing methods and examined for patterns and anomalies using AI algorithms. In this way, a prediction of the stress curve and the asymmetry between the phases can be determined. In the future, these findings will be particularly relevant for the integration of e-mobility in the Distribution networks.

Project consortium

The members of the project consortium led by PPC are:

Netze BW
Mainzer Netze
Energieversorgung Leverkusen (EVL)
Deutsches Forschungszentrum für Künstliche Intelligenz
Bergische Universität Wuppertal
Software AG

The project is funded by the BMBF with around 2.5 million euros.

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