An interesting post on Linkedin has caught my attention in recent weeks. It was a contribution from CESI’s Innovation Manager who expressed a concept of enormous impact from my point of view: “High voltage engineering and technology do not scale: you cannot just make things bigger and double the voltage, because that is not the way the physics work. Also, the higher the voltage, the more important small details become “.

This assumption drives to some general thoughts about innovation in power sector. I hope therefore to boost up some interesting exchange.

Scale up against electrical physics laws: an obstacle for the growth of startups?

We often forget that there is something known as a “physical limit” in industrial applications and especially in software. The concept of scale up, for example, often drive investors towards digital startup sector.

At the same time, it is not a surprise that there are almost no unicorn start-ups in the energy sector. In fact, when the replicability limit of a seemingly disruptive product is verified, “purely financial” investments from venture capitals stop.

Hence, startup founders in energy sector often choose, either to invest in a serene but not very ambitious development as SMEs, or to find an exit strategy such as being bought by a major player in the sector, whether OEM or utility, needing that specific solution or portfolio of solutions for their assets.

    A modest alternative proposal: multi scale-up

    If we cannot force the limits of Maxwell’s equations and general relativity, we can perhaps act smarter. The scalability of solutions must no longer be evaluated for a single application but on a portfolio of different devices.

    For example, the Ronin AI dashboard can be replicated for other substation assets viz., switchgears, breakers, and bushings. Similarly, it can be easily adopted for turbines and other rotating machines in a power plant for example. What becomes scalable IS NOT the solution itself but the way in which it was applied. Therefore, Seetalabs is ready to collaborate with key-players, whether OEM, utility or consulting company in this direction.

      Some other small problems from the real world

      Speaking of digital solutions, smart grids and innovation we often poke around “cool” concepts and buzzwords. We are often unaware that they may be largely inconsiderate of their roots based on real, banal and boring things like cables, copper, metal plates.

      So, in the last few months two problems have emerged strongly.

      Firstly, the Feb’ 2020 weather crisis of Texas and, albeit to a lesser extent, that of the summer in British Columbia, with blackouts and human and material losses. ┬áBoth highlighted how essential it is to test the reliability of electrical equipment against extreme climatic events.

      Various reports, such as Siemens position papers, emerged suggesting the necessity to evaluate construction materials with respect to weather assessment and condition monitoring as the key for asset managers.

      Interestingly, our Ronin AI dashboard is ready to adopt and apply such cross functional assessments. For example, Ronin AI dashboard can integrate climate inputs and production efficiency parameters with the asset health for industrial sector.

      Secondly, I was surprised that many companies are experiencing delays in the delivery of hardware purchase or in development of new upgrades due to a global chip shortage, as explained by the experts.

      That would certainly de-accelerate the company growth until new paths are explored.

        Ronin and a new road in condition monitoring

        When Seetalabs first thought of developing a friendly and accessible solution both economically and technically to any user, it was also almost naturally decided to pursue the path of the web solution. Because web platforms are decentralized and can duplicate backup allowing users to bypass the physicality of objects, at least within a certain limit.

        Thus, we can enrich the value given by the artificial intelligence that calculates the health status of an asset from thousands of kilometers afar by relying on parameters easily measured off line, even by technicians belonging to different laboratories, at different times but always returning an accurate, reliable result, validated and almost “chip-free”.