Interpretable Dimensionally-Consistent Feature Extraction from Electrical Network Sensors

Crochepierre, Laure; Boudjeloud-Assala, Lydia; Barbesant, Vincent

doi:10.1007/978-3-030-67667-4_27

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…Implementing a common language between human experts and machines such as ontologies [63] that describe the concepts over a knowledge graph could be one practical foundation to improve trust in AI.…”

Section: B Aimentioning

confidence: 99%

Perspectives on Future Power System Control Centers for Energy Transition

Marot

Kelly²,

Naglič

et al. 2022

Journal of Modern Power Systems and Clean Energy

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Today's power systems are seeing a paradigm shift under the energy transition, sparkled by the electrification of demand, digitalisation of systems, and an increasing share of decarbonated power generation. Most of these changes have a direct impact on their control centers, forcing them to handle weather-based energy resources, new interconnections with neighbouring transmission networks, more markets, active distribution networks, micro-grids, and greater amounts of available data. Unfortunately, these changes have translated during the past decade to small, incremental changes, mostly centered on hardware, software, and human factors. We assert that more transformative changes are needed, especially regarding humancentered design approaches, to enable control room operators to manage the future power system. This paper discusses the evolution of operators towards continuous operation planners, monitoring complex time horizons thanks to adequate real-time automation. Reviewing upcoming challenges as well as emerging technologies for power systems, we present our vision of a new evolutionary architecture for control centers, both at backend and frontend levels. We propose a unified hypervision scheme based on structured decision-making concepts, providing operators with proactive, collaborative, and effective decision support.

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Section: B Aimentioning

confidence: 99%

Perspectives on Future Power System Control Centers for Energy Transition

Marot

Kelly²,

Naglič

et al. 2022

Journal of Modern Power Systems and Clean Energy

Self Cite

View full text Add to dashboard Cite

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“…In Grammar-Guided Genetic Programming (G3P) (Whigham et al, 1995), also called Grammar-Based GP, a Context-Free Grammar (CFG) (Cremers and Ginsburg, 1975) is used to define constraint rules. Grammatical rules allow defining physical units, thanks to which G3P has found a variety of industrial applications (Crochepierre et al, 2021;Cherrier et al, 2019b). CFGs are often written in Backus-Naur form (BNF) (Knuth, 1964), which is made of:…”

Section: Knowledge Insertion By Constraintsmentioning

confidence: 99%

A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic Regression

Crochepierre¹,

Boudjeloud-Assala²,

Barbesant³

2022

Preprint

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In recent years, symbolic regression has been of wide interest to provide an interpretable symbolic representation of potentially large data relationships. Initially circled to genetic algorithms, symbolic regression methods now include a variety of Deep Learning based alternatives. However, these methods still do not generalize well to real-world data, mainly because they hardly include domain knowledge nor consider physical relationships between variables such as known equations and units. Regarding these issues, we propose a Reinforcement-Based Grammar-Guided Symbolic Regression (RBG2-SR) method that constrains the representational space with domain-knowledge using context-free grammar as reinforcement action space. We detail a Partially-Observable Markov Decision Process (POMDP) modeling of the problem and benchmark our approach against state-of-the-art methods. We also analyze the POMDP state definition and propose a physical equation search use case on which we compare our approach to grammar-based and non-grammarbased symbolic regression methods. The experiment results show that our method is competitive against other state-of-the-art methods on the benchmarks and offers the best error-complexity trade-off, highlighting the interest of using a grammar-based method in a real-world scenario.

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“…Further, automatic hierarchical and contextual representations of the grid [95] enable scope services and give greater flexibility to convey the right context and interpret a situation. [96] also lets an AI learn interpretable and physically-consistent contextual indicators associated with a particular operator's task or help build knowledge graphs [97]. Finally, [98,99,100] let operators explore interactively and iteratively historical explainable factors across similar situations and decisions for augmenting and keeping up-to-date the system knowledge and proper labels.…”

Section: Power System Ai Modules For Assistant Functionsmentioning

confidence: 99%

Towards an AI Assistant for Power Grid Operators

Marot¹,

Rozier²,

Dussartre³

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Power grids are becoming more complex to operate in the digital age given the current energy transition to cope with climate change. As a result, real-time decision-making is getting more challenging as the human operator has to deal with more information, more uncertainty, more applications, and more coordination. While supervision has been primarily used to help them make decisions over the last decades, it cannot reasonably scale up anymore. There is a great need for rethinking the human-machine interface under more unified and interactive frameworks. Taking advantage of the latest developments in Human-Machine Interface and Artificial Intelligence, we expose our vision of a new assistant framework relying on an hypervision interface and greater bidirectional interaction. We review the known principles of decision-making driving our assistant design alongside its supporting assistance functions. We finally share some guidelines to make progress towards the development of such an assistant.

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Interpretable Dimensionally-Consistent Feature Extraction from Electrical Network Sensors

Cited by 4 publications

References 43 publications

Perspectives on Future Power System Control Centers for Energy Transition

Perspectives on Future Power System Control Centers for Energy Transition

A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic Regression

Towards an AI Assistant for Power Grid Operators

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