SolarQuarter December Issue 2020

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DR KEYUR GANDHI Co-founder and CTO Apollo Energy Analytics

Renewable energy companies all over t he world now recogni ze t he importance of investing in data analyt i cs, such as solar di gi t al t wi ns, AI/ML based analytical engines, and IoT plat forms for keepi ng up with ever growing energy demands and t herefore i dent i fyi ng downtime, efficiency losses, which result s i n si gni fi cant energy losses in the power plant. According t o a report by t he Int ernat i onal Energy Agency, since 2018, global i nvest ment i n di gi t al power infrastructure and software has increased by more t han 30% annually. Newer and more efficient t echnologi es i n wi nd, solar on energy storage have played a very cruci al role i n t hi s growt h, most of which come from the solar industry. As the renewable energy sector stri ves t o achi eve t ransparency i n the health and performance of plant asset s, di gi t i zat i on can make better use of existing grid infrastruct ure, reduce t he need for spare capacity, and greatly reduce dependency on renewable energy. It allows the company to make decisions based on reli able evi dence from the analysis of solar data at its plant s, whi le prepari ng for asset downtime and missed/breached SLAs. In addi t i on, i t can more easi ly connect digital assets, provide real-t i me vi si bi li t y of asset performance, and use a solar digital t wi n syst em t o opt i mi ze asset utilization.

analytics, all the above challenges are bei ng addressed. All we need is a highly scalable and smart model coupled wi t h several layers of AI/ML based automation. Using AI/ML based solut i ons, we can easi ly detect reasons behind energy loss, effi ci ency loss, anomaly detection as well as predict failures. Thi s helps i n get t i ng real-t i me O&M and Asset Management decisions for i ncreased effi ci ency and reduced downtime. AI, ML and DL are becoming the cornerst ones of our planned enhancements to our solar digi t al t wi n based performance intelligence and health analytics solut i on. We are det ermi ned t o revolutionize the solar sector by helpi ng t hem opt i mi se t hei r solar assets & portfolio to streamline O&M, benchmark asset ’ s performance, effectively manage SLAs, i ncrease yi eld & i mprove overall plant’ s performance. We have used advanced dat a analyt i cs, AI/ML and Deep Learning models to assi st O&M t eams reduce t hei r cost by digitizing their power plants and provi di ng t hem wi t h ‘ Root Cause Analysis’ and predicting anomali es i n t hei r syst ems. The patented digital twin solution further provi des act i onable i nsi ght s on performance and health of their transformers, i nvert ers, PV modules and more to enable them in predi ct i ng fai lures, pre-empt i ve and predictive maintenance to increase plant ’ s overall effi ci ency.

Asset downtime and under performance have always been a challenge in the renewable industry. O&M and Asset Management teams need predictive maintenance as agai nst scheduled maintenance. For improved efficiency of t he asset s and t he plant , i t is essential to track the component s and t hei r i mpact on overall asset efficiency. Predicting failure i s far more valuable i n t hi s context than a sudden failure that mi ght result i n st oppage i n operations. Similarly, predictive mai nt enance also helps save t he solar power plants a fortune compared t o a sudden fai lure. Usi ng advanced data

SOMASHEKAR TH Managing Director, EnerMAN Technologies Pvt Ltd

If you look into the technicalities of Large-Scale Solar Plant s, i t ’ s not too complex. Except that you are deali ng wi t h ext remely Hi gh voltage & very large currents; Rest i s about t he volume of components used in the plants. Thousands of PV Panels, Ki lomet res of cables, numerous cable joints or termi nat i ons are t he fact ors t hat add to the complexity of operating and managi ng large solar power plants. Technicians can always be used t o i nspect t he panels, check currents at each string, but given the magni t ude of work i nvolved, i t is not viable or may not be effective. That ’ s where dat a analyt i cs comes to play. Data from solar PV plant s can be collect ed from combiner boxes (string current), Invert ers, Swi t chyards (Trafo/HT panels), Weather Stations & several ot her sensors whi ch can gi ve insights on the performance of the plant . The term Advanced Analytics is oft en used for t he aut omat i c exploration and depiction of meaningful pat t erns t hat may be found from the data acquired by PV plant s. The focus of Advanced Analytics is more on forecasting using t he dat a t o fi nd t he t rends t o determine what is likely to happen i n t he fut ure. Basi c Analyt i cs provides a summary of data whereas; Advanced Analyt i cs goes a step ahead in providing a deeper knowledge about dat a and helps in granular data analysis.


Here is a real case study of 50X1MW Invert ers. (50MW-Ut i li t y Plant A.P) The Inverter performance analysi s showed a huge devi at i on between the best & worst performers. When t he si t e t eam root caused the problem, it was found t hat many of t he st ri ngs were down, hence the DC load on the i nvert er was low causi ng t hi s variation. Note: This particular plant di d not have st ri ng moni t ori ng SCADA. When all the strings were rest ored t here was a subst ant i al increase in generation which of course i s huge savi ngs t o t he customer. Imagine, an O&M manager gets a weekly report clearly i ndi cat i ng t he name of a particular Inverter that is underperformi ng. Also, if that report clearly mentions the reason and gives suggestions on what action needs to be taken. The actions to be taken might be module cleaning, String restoration, fuse failure or some ground fault issues. As the PV plants get older, Advanced data analytics tools will be of great help. Ask your SCADA vendor if this feature is available!

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